Lots of dismissive comments ITT, very few tackling the substance of the article.
> AI Cannot Afford To Slow Down — It Needs $3 Trillion Or More In Revenue By End Of 2030 To Sustain Its Existence
Is this true?
With the total 2024 wages being 11.7 trillion USD [0], and nonfarm payrolls totaling 158,000 in the same year [1], it's an order of magnitude higher than my back of the napkin guesses I've made that AI needs to take or create 1/20 jobs minimum to break even.
There is no substance to the article to tackle. Ed Zitron is a clown that makes seven million dollars a year writing unsubstantiated bullshit on the internet. He has been consistently wrong in every single one of his predictions and will continue predicting the sky is falling, because his content has zero educational value. It is purely a confirmation-bias inducing dopamine hit for the perpetually aggrieved.
Hypothetically speaking, how does one go about being so handsomely rewarded for being wrong about everything? I feel like I am well qualified for this type of job.
You never know if people who are running a skeptic grift are at the same time long the very trends they're criticizing.
I agree with the general thesis that AI is a bubble and there's a lot of over/mal-investment, but that hasn't stopped me from riding the wave and making a considerable amount of money investing in AI-related stocks.
If I was more ambitious, I'd write a $70/year newsletter ranting about how AI is a massive bubble that could collapse at any moment to make money on both sides.
He has said every month for the past three years that there is no technical progress left to be made in LLMs and that there is no more room in the market for inference spending to grow.
Here[0] is a fun selection of excerpts from his July 2024 post "How Does OpenAI Survive?"[1]
"I see no signs that the transformer-based architecture can do significantly more than it currently does."
that's confusing "subscribers" and "paid subscribers".
from Zitron's own website [0]:
> I have 84,000 subscribers and a 55-60% open rate, as well as an 8-11% clickthrough rate.
if the 84k number was all paid subscribers, then the "55-60% open rate" would mean that ~40% of his paying audience doesn't bother to read the thing they're paying for, which does not add up.
also, that's in the "Can I Advertise On Your Newsletter?" section. if there were an even higher number of non-paying subscribers, he'd have an obvious incentive to mention that, because the total number of eyeballs is what a potential advertiser cares about.
I find that completely unbelievable. There’s too much high-quality free information for me to ever consume to even think about paying for it, let alone something of mediocre quality. Are we sure the newsletter subscribership isn’t just a total fabrication? Or that it isn’t just a money laundering scheme?
Also, I would not see "making seven million dollars a year by writing" as a reason to dismiss someone. Like, sounds like that such person is doing something other writers just cant.
The reason is obviously that engagement drives his revenue, and correctness or facts have nothing to do with it. Everyone knows that content creators will maximise engagement, and clearly he has found an audience who are seeking out a certain narrative, and will write to that narrative to generate revenue.
I haven't fact checked anything here, I took the $7 million at face value. If he is generating that amount of money from his content creation, then I think my statement makes sense, right?
I don't wacth/read his content at all because I find AI-doomers really boring and tiresome.
The issue he highlights is purely number driven here.
His predictions timing sucks but he’s right in pointing out the insane numbers involved.
I think AI is a wonderful tool but I also think that there are many wonderful tools that if we bet our entire economy on would result in a catastrophe.
So a writer making money is not okay and means he is incorrect by default, but the CEOs siphoning billions off of circular investment schemes is fine?
Personally, I see both parties making predictions.
Meanwhile, I still must labour. How many more yachts are needed before the earth becomes the utopia they keep selling everyone on? How many more communities must have their rights trampled against their expressed will?
Or do I need to scan my retinas and provide Alt-man a DNA sample for a handful of empty crypto currency first?
lol - the guy who thinks every single subscriber to a newsletter is a paying customer wants to be taken seriously. Zitron's POVs are always deeply researched, well-sourced and clearly formulated - things that, in and of themselves, are extremely valuable in today's world. Oh the AI bubble hasn't burst yet, so he's an idiot? Please tell me how you think the US stock market as a whole, following the Iran war, Trump's tariffs, etc. isn't just pure fraud at this point? The fact it hasn't collapsed /yet/ is not indicative of him being wrong, but rather that the 'economy' is absolutely detached from any real value whatsoever (something it historically had to be tied to, at least tangentially).
The funniest thing is that his articles are constantly pushed to HN frontpage, and every time the top comments (or the comments one level nested below top comments) are pointing out how wrong he's always been.
Either that he's HN mods's favorites or we're experiencing a special case of xkcd 1053[0]: there are always ten thousand people haven't realized how wrong Ed Zitron is.
At this point I don't think even Ed himself believes what he writes. It's just fan service for subscribers. And that is totally reasonable if we view blogging as a business.
One of HN's quirks, for better or worse, is that the lack of ability to downvote submissions encourages "controversial" content to rise to the top.
On Reddit, if 500 people like a submission and 500 people dislike it, then it'll end up with 0 points and fail to reach /r/all
But on HN, the same content will end up at 500 points since only the people who like it can affect its rank (unless it dies after getting mass flagged).
The end result is that HN's system favors "hot takes" while Reddit's system favors "preaching to the choir".
The two systems have their pros and cons, but personally I dislike being unable to downvote articles that are full of nonsense.
People (ab)use the flagging system for that. I've seen plenty of categories of article that get upvoted onto the front page and then instantly vaporized off it by flagging.
I like how he's started preliminarily saying "Now listen, I don't make any trades on this fine thinking. And that's okay by me." So far below the standards of public markets due diligence. Sadly credulous readers will follow his advice. Which could turn out to be accurate in the public markets -- who knows? But if it's accurate it will be an accident, because the quality of analysis is so poor we should not call it analysis.
But we know global GDP per capita (a proxy to wages)- the US represent about 25% of total global GDP (a metric which accounts for US wages being higher than average). I’m not being contrarian, I genuinely think the addressable market is the global market and not just the US (and by a wide margin) and as such thats the real potential of anthropic/openai/et al.
You're partly right. But OTOH, China is (pretty successfully) developing its own AI solutions, and even the (former?) US allies in Europe, America and Asia have become painfully aware that they are dependent on a hostile US administration and tech companies cozying up to that administration, and will be wary of further deepening this dependency, so they will also prefer home-grown solutions. So the addressable market for US companies is much smaller than the global market, even in countries that could theoretically afford Anthropic's and OpenAI's prices.
This is just the offshoring and remote discourse all over again. It turns out that the prestige of having a big office full of workers that the CEO can see is well worth the massive expense of siting it in California and paying California salaries. For whatever reason.
(also I suspect the anti-globalization discourse will get even more pointed)
Currently slightly under 4%. I have a feeling that part of the solution here is going to end up being, what was the euphemism? Quantitative easing - also known as “printing money”. I would not be surprised if we end up seeing near double digit inflation in USD - it has also significantly lost its status as a global reserve currency, which makes this all the more likely.
Tbh I think it’s already happening in real terms, but the CPIs aren’t fully showing it yet.
Oh but it is. The debt that’s funding the AI buildout ends up in the oddest of places - like pension funds. If defaults occur and work their way up the chain, it’s not dissimilar to the subprime affair - and at that point it’s bail out the poor pensioners or get crucified at the ballot box.
There’s lots of precedent for this over the past decades, both in the U.S. and over the pond.
Not readily, as the private companies are private, and the public ones obfuscate their books - and even then the question that remains is how much of that equity is funded by debt.
If I thought there were some actual small cabal of people running the global economy, this is almost like a novel: massive amounts of money entered the economy starting in 2008 and 2020-2023, the rich became insanely wealthy. Their wealth is now all tied up in the 2020s version of the railroad/fiber, we're going to essentially erase trillions of dollars from the global economy and reset.
Every government has complete control on how much of their money exists. If it disappears from the private finance market, it can compensate with any amount it wishes.
It would be better to lean into inflation and just dump a ton of money into the bottom of the market to dilute the top. Of course this only makes sense if the government is willing to regulate market control, which it has demonstrated time and time again it will not.
It's more interesting to ask, "Does AI need to follow the current model of evil megacorps building massive data centres that, collectively, guzzle more energy than most nations on Earth?"
Perhaps LLM's (or something better) will develop to be more efficient and quickly become something most people run on local hardware. Perhaps fad-obsessed management types will move onto the next big thing and AI will start being used more judiciously. Perhaps society will set sane regulatory limits that shape the direction AI is going in, from models that take jobs people want to models that, given the right hardware, can do the jobs few want.
Anthropic and OpenAI don't have to succeed for AI to succeed. If they turn out to be a bubble that bursts and torches a lot of investors, it might actually be a fundamentally good thing for everybody else.
To be clear, nobody WANTS to have to go build all these datacenters. Well, maybe some pure-play hyperscalers do. But there's an immense amount of economic incentive to be able to do this more efficiently, capital and energy. And, what those hyperscalers want will not matter for a second if there isn't demand for the tokens output by those datacenters - they'll go instantly dark and have to seek new forms of valuable compute to offer.
If this current building spree ends in massive solar and other power generation being overbuilt and cutting energy costs, we've had a really good outcome.
This is why there is so much interest in space based AI compute. It's not just SpaceX - Google, Anthropic and Nvidia have openly expressed interest
If you look at SpaceX plans and ambitions, they hope to deploy massive compute to orbit (multiple Terrawatts, hundreds of thousands of sats). If their ambitions even slightly materialise it would make ground based compute pale in comparison.
Whether or not they succeed in their plans is beside the point - the point is they know that terrestrial electric infra can't sustain the growth they need
It really doesn't. You're purely relying on radiation fins to carry heat away, which are incredibly inefficient.
> The radiator surface area problem also scales uncomfortably. At 838 watts per square meter, rejecting 1 megawatt of waste heat requires roughly 1,200 square meters of radiator. Deploying that much surface area on a satellite is a structural engineering challenge that gets harder with every order of magnitude. The ISS solar arrays span about 2,500 square meters total.
So even a 2MW data centre in space requires a cooling array rivalling the international space station.
Starcloud launched a single H100 in November and they were unable to run it 24/7 due to heat buildup.
Even with novel solutions to make heat transfer to the fins more efficient, like phase-change liquids, the limiting factor is that the vacuum of space is a tremendous insulator.
It's literally a deflection mechanism to the fact they want to build data centers all over the land by proposing a fantastical better way that simply won't work.
That has been covered to death since months, there is no good solution to cooling without an atmosphere. But even going into the technical details is a waste of time, think about how complicated maintaining such a system in space would be compared to having it on earth, the whole idea is a complete grift from beginning to end. There is literally no benefit to do it in space other than as a marketing tool.
Nobody who suggests the idea has ever presented a model that is even remotely close to reality
What a surprise that something this implausible could come from the same guy who proposed trains that run in vacuum tubes.
I can't believe the people who actually listen to this guy can tie their own shoelaces. And somehow he became the richest person on earth. Humans are irredeemable.
#3 Trillion, or even more, will be achievable. We are at the very beginning of the monetization of AI. It all started with the free Chat GPT, and a few others. Now the standard is $20 a month if you are not using AI tools too much and you don't need anything fancier. Otherwise you need to pay more, like $200 a month.
Unless you are not company and you don't have some "enterprise deal". And you need an enterprise deal, as 1) it guarantees that your (and your customers) data will not be sold to someone else 2) you are scared that your competitor will have such deal and become much more productive.
This is what we have now. What will be the future?
Well, soon, if you want something like financial advice or medical advice or job search/CV polishing you will be told, that your $20/$200 is not covering that, you need to purchase additional model to have that. Will you do that? It depends how much you are desperate to get medical advice or find a job.
Anthropic Mythos is an example. Soon, if you are programmer and you will ask AI Agent to spot a bugs, AI Agent will tell you that you need to buy extra model for this. Same with performance analysis, same with the design using tool X, Y or Z.
This is pretty scary, as it will put our well-being, productivity in the hands of few corps. It will be event worst that Google Search monopoly we used to have (until AI chats broke this, replacing Google Monopoly with a few other vendors monopoly).
Can this be prevented? Surely. Hopefully we will have capable open models and consumer-level hardware will catch up. But I think this is the place where governments should step in, invest into alternative models which will be at least comparable with flagships.
Chinese models shows that this is doable, DeepSeek is worst than Chat GPT/Claude/Gemini, but not that much and is clearly better than Grok (which is a huge disappointment for me). I guess India would join this game (especially with nationalist like Modi as the leader).
Europe could join this game, the problem is it kills its capabilities with high energy prices and inability to come out with some reasonable, well financed solution. So the only thing EU was able to come up with is some set of regulations that are blocking fast AI development in Europe...
There is French Mistral, but it is French, it is under-financed, it is only-French, as France would not like to lose control over it.
Germany have totally different strategy, they invest into manufacturing oriented AI, what makes a lot of sense, but does not help with the dangers we are facing.
The rest of the Europe is just too poor to spend billions on AI.
There is still time to buckle up for Europe, but given the course of events, stupidity of Brussels elites who does not see the storm coming I am not optimistic.
The only thing you’re neglecting is that personal computers will be able to run “good enough” open source models locally within 3 years. Can already run it today.
Extremely sorry for this unrelated rant. Got triggered by the keyword "Modi".
Quoting Modi is a joke which one cannot even remotely relate w.r.t to AI and don't even feel sorry for saying that since that man blabbers on every stage about non-sensical / non-existent stuff! Watching his videos is the best timepass one can have!
Having said that, the infra and mindset is definitely not there in India to even remotely to innovate or compete in AI race!
Academia is a huge BS where every other person is a backstabber!
A lot of talent is there for sure, but all wants to work for some company or another since there is absolutely zero support for entrepreneurs . No real innovation.
All copy cats as they have proven with mobile and robotics. Just copying or masking Chinese products with the local brand names and reselling. That's all they are good at and that's the irony.
So far, nothing has come from that country which is a real innovation or ground breaking. The day it happens probably one can consider that they are good.
But otherwise, they are good at selling / reselling and scamming the world and nothing else! They cannot produce anything or whatever they produce is taken control by a handful of big corporates from the western region. That's a narcistic corporate monopoly!
Extremely bad tax structure, endless corruption and useless and unqualified ministers occupying worst portfolios, people are really struggling to survive!
Where will they innovate or compete in the global AI race?
Everyone at every level just want to scam and make money to surive that's all!
Ed is an interesting character. His financial analysis of the AI industry makes logical sense to me (though I am not knowledgeable enough to actually know if it is correct.) However, he seems to be so angry at AI in general, that he misses the obvious areas where LLMs are actually changing the State of the Art.
Coding seems to be one of the core use-cases for LLMs (as Simon Willison pointed out recently) and even if that's the only real use-case for LLMs, they're wildly useful. I do understand that useful != profitable and that's where I think Ed has a real point: until inference becomes much cheaper these companies cannot be profitable. Some mega-players will pay the API token price, but most will not.
I don't think whether "LLMs are actually changing the State of the Art" or not matters for anything he wrote.
If the AI companies need $X billion in revenue to stay afloat, it doesn't matter if 0.5% or 5% or 50% of that revenue is from transforming the State of the Art. It's 100% irrelevant: what matters is that, transformation or no, these companies won't have the income to pay their bills. And if they can't pay their bills, a whole lot of other companies can't either.
So again, transformation or no, it's still a house of cards waiting to collapse. The only thing that would change that is not more "transformation" ... it's a feature set that lets them multiply their current user base (or multiply how much they charge them) several times over.
maybe its insane to think this, but if all AI providers turned off free plans tomorrow i think they would easily have enough people willing to pay $20 a month for it to sustain all their spending.
everyone is still fighting for market share so they are giving stuff away, but that doesnt mean people wouldnt be willing to pay for it if it wasnt free.
This proposition boils down to a belief that there are 3 billion people who are interested in AI for free but aren’t currently paying $20, but who would pay $20 if that was the price.
The global median income is around $12k, so this would mean that there’s roughly be a global budget of 0.5% of everyone’s annual income going to chatbots.
If you’re off by half, the price doubles for each person.
I think you’d make a lot of money betting against the existence of 3 billion ghost customers
I do think Ed in intentionally ignorant of the capabilities of LLMs. But I also don't know that I would classify LLMs as 'wildly useful' for coding. Most productivity gains seem to be hallucinated, and while it's too early to make any claims on long term outcomes, there are plenty of studies indicating they might be even more negative.
There are definitely use cases for LLMs in coding. And at times, they can be wildly useful. But I feel like the industry atm wildly overestimates their broader/long term utility.
Anecdotally, I have not seen an explosion in quality/bespoke software since LLMs. In fact I've noticed the opposite to quite the extreme. Not only are new products worse in quality, but the quality of existing products is falling off a cliff.
> Anecdotally, I have not seen an explosion in quality/bespoke software since LLMs. In fact I've noticed the opposite to quite the extreme. Not only are new products worse in quality, but the quality of existing products is falling off a cliff.
This is the big one. It's clear that AI can generate huge volumes of code by KLOC. It is not clear that spending a lot of money tokenmaxxing will eventually result in increased real revenue for software businesses, and eventually even an MBA has to look at a "money in vs money out" chart.
Have been thinking about this a lot recently. AI could be an absolute game changer for a small start-up rushing a product to market – you could quickly build an MVP that would take years and tens of hires before.
But how much ROI is there for large businesses with established products and huge development teams burning through tokens making subtle tweaks that can’t be directly tied to revenue?
Not much ROI, if any. My employer's been making some studies and come up with very modest productivity gains - so of course they want us all to use it, but I'm not sure they're taking the true costs into account. Especially not once token pricing actually reflects reality, and we all get brain rot from using the things instead of thinking. If this thing doesn't collapse before we can run a solid coding assistant model on a developer's machine, maybe it's got some legs.
That doesn't seem very likely.
The legacy of LLMs will live on in various models doing various specialist things (they seem like a really good progression on speech synthesis for example) but the current edifice will come crashing down and if we're very very very lucky they won't take the global economy with it.
I've been thinking about this too. The quality maintenance of large systems isn't something you can just completely automate away with AI. Even if the code is written with AI, you still have to read through and verify it.
Even though that is still faster than regularly writing code, I end up losing that nuanced knowledge that I get from going through documentation and writing it out by hand - actually doing the work. I just don't see it actually replacing developers unless managers are willing to produce MORE code with the SAME level of quality.
> I do think Ed in intentionally ignorant of the capabilities of LLMs.
I think it's more complicated than that too. He's pretty well versed in the stated capabilities of LLMs.
The fact that he isn't a deeply involved technical developer who knows the ins and outs and nuances of using LLM tools is the point, because the stated capabilities of LLMs are that they are trivial to use, extremely powerful, and getting so much better every month that you personally can replace developers without even trying as a completely non-technical person with basic writing skills.
Given the hype and extreme claims being made, the fact that he remains ignorant and gets practically no use out of LLMs immediately disproves those statements. The counterargument boiling down to "you're using it wrong" is actually just a further indictment of Sam Altman and his like, because it shouldn't be possible to use LLMs wrong!
The rest, well, the hype needs to die before anyone can make sane estimates of what LLM tech can do for us in various fields. Right now it's all a complete mess.
I don’t get me wrong, I’m on Ed’s side and get where he’s coming from. I just think his arguments are normally taken to the extreme, making them less defensible, when he could make the same arguments from a more moderate stance and ultimately be more convincing.
His arguments, albeit valid, can often sound like reductos ad absurdums the way he presents them.
One of the worst things about LLM writing is how it makes big promises of what it can prove in some piece of writing, and then never really follows up on that, or has specifics that go all the way towards the original, grandiose statement.
And frankly, Zitron is guilty of that pattern of writing too, or of relying on some unstated "baseline" knowledge which is clear from his other writing but not in the specific piece.
So, basically yeah, agreeing about the ad absurdum thing.
(I will note, the tone, the swearing, etc. really doesn't matter nearly as much as these problems, and everyone instead obsessing about the swearing and personality is really boring)
Personally I only find his swearing and delivery annoying when he is not delivering his point well (ie reducto ad absurdum). I’d be welling to bet a good amount of the complaints about his swearing are really just from a poor delivery, and people don’t know why, so they latch onto his swearing.
His early stuff was just as degenerate and vulgar, but was much less of an issue for me.
Part of the problem is the like - sociomedia factor. Ed’s figured out how to break through the noise. I’m not surprised that Ed Zitron is the kind of counterpoint you get in the Musk-Trump attention economy / CEO’s that sound more like prophets than business executives world.
One person's ignorance of something can never be evidence that it doesn't exist. It's far too easy to be willfully ignorant; no one can force you to abandon ignorance if you don't want to.
On the other hand, the hype of "Sam Altman and his like" being plainly exaggerated doesn't mean there's nothing at all behind it. It's plain to see there's something important about LLM capabilities. I don't even use them myself, as emotionally I find them entirely repugnant, and I can still see that.
We need to wait to get the whole story about LLMs, but we don't need to wait to confidently reject both extremes of opinion about them.
> Anecdotally, I have not seen an explosion in quality/bespoke software since LLMs. In fact I've noticed the opposite to quite the extreme. Not only are new products worse in quality, but the quality of existing products is falling off a cliff.
Very little new or ground-breaking (I struggle to think of things AI has produced that aren't themselves just more AI), but various previously-stable sites and services breaking.
The studies you are talking about are probably outdated, it's difficult to deny the actual productivity boost of coding agents.
I'm not talking about the quantity of code produced, but about actual user needs that are now resolved that would not have been before.
The main productivity gain will not come from existing software engineer, but from people that couldn't code at all before but are now able to do things by themselves. We are still very early.
As a developer who has not been able to get any boost in productivity from coding agents, I find it incredibly easy to deny.
I’m a solopreneur, if I could lighten my load I would. However I have yet to save time using coding agents, with the exception of “I made this change to my model file, update all model to match the new format.” Which is cool, but maybe 0.01% of my job, and took a 1 hour task down to 10 minutes.
We previously had a backlog of 2 years of features planned. Now we have no work and are just working on tech debt and planning to get more involved in the product side because they need help getting the developers work to do.
It still takes the same mindset and skills to use AI productively and effectively as regular programming. The productivity boost only applies if you know what you’re doing and can actually steer the agent carefully.
Vibecoding hits a glass ceiling very quickly and this will not be solved incrementally. Besides, if the agent could work autonomously to that degree then it would no longer need any prompting at all and we’re living in a very different world. On the other hand that would make the debt actually meaningless, so I guess that is 'a' solution.
He's got subscribers. Maybe the attitude is one he's found plays well with them.
I find it quite refreshing in some ways. Lots of people, when they start complaining about this or that aspect of this AI stuff, are wont to add in a little disclaimer that, despite all of the above, they actually really like AI and use it all the time. I assume this is to avoid the scenario of a bunch of pragmatic builders turning up and calmly shipping nuance in the comments (or whatever you call it these days when you get brigaded by a pile of angry keyboard warriors with chips on their shoulder) - and it sure is tiring having to wade through the equivocation.
That's a criticism that'd be hard to level at Zitron! Say what you like about the man, but he's unafraid to appear to take a side.
> Maybe the attitude is one he's found plays well with them.
Kind of a self-fulfilling prophecy.
What's not a problem, by the way. That's why people always recommend content creators to be themselves. If they try to be somebody else, they find their public is already busy following other people.
So here's the thing. I am not generally an angry person. But Ed's writing really resonates with me, because for the last four years these people have been making a strategy of scaring the shit out of us while trying to ruin something I genuinely love (coding), while simultaneously fucking up the economy and multiple industries and turning the internet into slop. I very badly want more people to call these guys "chucklefucks" or whatever innovative ways he comes up with to insult them because they deserve far more public ridicule and disdain than the (captured, useless) media is giving them.
So far the data for productivity in coding is.. sus. The productivity gains outside of toy projects are mostly anecdotal and it's hard to tell if those accounts are even real humans or just astroturfing and bots. Almost every programmer I know personally has a pretty measured opinion on where these things are useful and where they're not. The breathless hype seems mostly from non coders.
> Almost every programmer I know personally has a pretty measured opinion on where these things are useful and where they're not. The breathless hype seems mostly from non coders.
We have polar opposite media bubbles. I see OG programmers all over my timeline either grieving the "end of software engineering" (a la Ryan Dahl) or extolling "automatic programming" (a la antirez).
> We have polar opposite media bubbles. I see OG programmers all over my timeline
The person you’re replying to, in the bit you quoted, said specifically:
> Almost every programmer I know personally
People you know personally are not a “media bubble”. They are, to borrow your expression, polar opposites. It’s people you can speak with candidly and trust versus bits of text without the full context.
Ultimately though it's those people's bosses who set the direction and from my experience those people are telling you to your face that you'll be replaced by AI as soon as they're able to do so while they continuously fail to see it's shortcomings.
I want to be one data point seeing as this goes uncontested (the ones in the know don’t care anymore to be honest).
They are not only useful it is obvious they are. If you don’t see it I really, really don’t know what to tell you. You can tell yourself I am bot or shill or whatever if that helps you sleep but .. just trying to help out another dev here. Wake the F up.
> it is obvious they are. If you don’t see it I really, really don’t know what to tell you. (…) just trying to help out another dev here. Wake the F up.
Your whole post is dismissive and insulting and has zero arguments. No one is helped by that, no one is going to change their mind with that, you’re only making the divide more pronounced. You’re being actively unhelpful.
> You can tell yourself I am bot or shill or whatever
I mean, your account was obviously created specifically to post that comment…
> I do understand that useful != profitable and that's where I think Ed has a real point: until inference becomes much cheaper these companies cannot be profitable.
If inference becomes cheaper, it becomes cheaper for everyone.
It's pretty likely that inference will get substantially cheaper. His argument is that for these companies to be profitable some very major and (pre 2022) unprecedented things have to happen. Which I tend to agree with, except I think they will happen, seeing as how they've been happening for a few years.
Inference has been going down in price on a cost/intelligence basis. If you don't need the smartest model, there are plenty of good Chinese models that are dirt cheap.
It supports his point that they're planning to massively overbuild compute, which was already well supported by the financials. A lot of that planned compute buildout can be walked back though, and the technology is unquestionably useful in moderation, so it's not the catastrophe he suggests, and his hyperbole is part of what makes me dislike him even if there are elements of his foundational argument I agree with.
check out DeepSeek V4 Pro .... this is where the threat vector comes from IMHO. If anything is triggering a rush to IPO imho it's seeing these cheap / free models on the horizon that are "good enough" for 80% of the core use case supporting their valuation.
I would say his overall negative outlook is a well needed counterbalance to the completely delusional hype one is exposed to on a daily basis. The truth will then probably land somewhere in the middle.
It seems that a certain kind of person cannot separate the following things:
1) I dislike AI as a technology
2) I dislike the people and companies that profit from AI
3) I think AI is useless
These are three completely separate positions to have. You can think AI is incredibly useful and also dislike it because it will, for example, reduce your relative status in society. You can love the tech but think that Sam Altman is a dishonest person, etc. But for some reason, most anti-AI commentators feel compelled to present all three arguments.
Which is even sillier when you think about it, because if it's useless, then you really shouldn't care: the markets will eventually find out that it's useless, and everything will go back to normal, and the people you don't like will have lost money, so there's no point in being outraged. Of course, I don't really believe that they think it's useless. I do think they're worried about what it'll do to their prestige, though, and they're just hoping beyond hope that somehow everyone will one day "wake up" and share their belief that LLMs are just "stochastic parrots" with no utility, despite the fact that people are using them every day and can watch in real time as they improve.
> ... the markets will eventually find out that it's useless, and everything will go back to normal, and the people you don't like will have lost money, so there's no point in being outraged...
Except that in the process of the markets finding out, things will not go back to normal if everyone's retirement is tied to the market. And in the process of finding out, things will not go back to normal if the hype cycle disrupts traditional hiring/firing decisions.
If it's as bad as some of us believe, then when it falls apart, a lot of people get hurt as collateral damage.
The market eventually found out about Bear Stearns, but a lot of innocent people lost their homes in the process.
Tetraethyl lead is not useless; in fact, it was hugely useful to the petrol engine economy throughout the 20th century! It just happens to cause nonobvious brain damage throughout societies.
In some ways things that are both useful and harmful are the hardest to deal with. And this isn't just "prestige", it's the already-decaying post-truth infosphere and the already-overheating CO2 levels in the atmosphere.
AI is useful in cases where you can automatically catch errors. Programming is uniquely suitable for this, because we have already got all our machinery of type systems and CI tests to catch human errors. How useful it is in other cases depends on how cheap it is to catch errors and how much they cost - and whether the cost of errors is inflicted on other people.
> Which is even sillier when you think about it, because if it's useless, then you really shouldn't care: the markets will eventually find out that it's useless, and everything will go back to normal
People who are against AI don’t care if it’s useless, they care it’s harmful. And you can’t systematically cause harm then say “oops, our bad” and have everything return to how it was with a snap of the fingers. The consequences of harm don’t go away when the source does.
> I do think they're worried about what it'll do to their prestige
Why must this always be the argument? It was the same with cryptocurrencies and NFTs, there is a specific type of proponent who always accuses critics of secretly being pro the technology but publicly against it due to some ulterior motive. Most people aren’t selfish lying rat bastards who think like that.
> > I do think they're worried about what it'll do to their prestige
> Why must this always be the argument? It was the same with cryptocurrencies and NFTs, there is a specific type of proponent who always accuses critics of secretly being pro the technology but publicly against it due to some ulterior motive. Most people aren’t selfish lying rat bastards who think like that.
Meanwhile, the prestige to be gained/lost from supporting/doubting the big mainstream thing is immense, and the incentives are actually in completely the opposite direction...
Anyway, on that topic The Line Goes Up video covers the arguments about prestige far more extensively and far more elaborately than I ever could: https://www.youtube.com/watch?v=YQ_xWvX1n9g
But it's very much not the doubters who are worrying about prestige in crypto and NFTs, and probably not with AI either.
I see plenty if I care to look outside my usual echo chamber. There’s lots of ‘it’s just the next token prediction’ (in 2026 still!), but there are more sophisticated arguments like ‘it can’t be creative’, ‘it doesn’t think’, ‘it’s just pattern matching’ etc. They might even be true for today’s models, but linearly extrapolating an exponential trend is a classic mistake.
> Which is even sillier when you think about it, because if it's useless, then you really shouldn't care: the markets will eventually find out that it's useless, and everything will go back to normal
And in that period where the markets are irrational people are losing their jobs, hardware is being priced out of consumer markets and the rich are trying to embed themselves so hard that we get to pay for it when the market corrects itself. I think your take is highly indicative that you live in a shrinking bubble unaffected by those things.
> until inference becomes much cheaper these companies cannot be profitable. Some mega-players will pay the API token price, but most will not.
This is often repeated but comes from ignorance mostly. You have * zero * reason to believe inference is costly other than just vibes. If you go by data and intuitions - the margins are high.
This kind of thinking really reinforces my belief that people have no idea and are using this whole [AI is not profitable and too costly] thing as a cathartic way to deal with immense progress.
However, it needs to be said that he received those numbers. I personally have quite a few issues with him, but there's no reason to doubt his journalistic integrity. Because of that, I believe he reports truthfully on data he receives by informants.
Additionally, none of the frontier models actually publicly talks about inference costs in anything but broad, "let's just forget that"-like takes. Which does not exactly spark confidence.
I'm eagerly awaiting anthropic's public disclosure of their financial details. That should be rather interesting in any case and finally put the inference-discussion to rest.
No reason to doubt his journalistic integrity? He's not a journalist for starters. He's a PR flack who does PR for AI startups on the side while blogging on substack. There is every reason to doubt his journalistic integrity.
The PR-thing was always openly communicated by him and is not some secret or gotcha. It's essentially "fleecing the boosters", which I fully approve of and do similarly myself.
I'll gladly tell my customers all the most glorious stuff about AI and big tech while spending a significant chunk of the money they pay me on supporting AI-/tech-counterculture, such as doctorow, zitron and quite a few other writers, journalists and activists.
It's the old "you live in a society" counter-point against anti-capitalist activism. Needing to make ends meet does not imply that your points or principles are meaningless, it just implies that you have no interest in being homeless and that way losing your chance to actually change things.
So that's fine to me. But: I stated it for a reason, because I know others don't agree. I, personally, consider him trustworthy. You do not, and that's fine. I suspect we both await anthropic's Z.1, which will be able to settle a big chunk of the debate.
He was right about the cost changes, which he predicted quite some time ago. People shouted at him that he was making it all up - yet it was correct.
He was also right about AI-video and sora in particular being a fundamentally flawed idea.
He was also right about the dangers and problems with the general inaccuracy of LLMs and people relying on it.
Also about the expected triggering of ROI-checking in companies, such as Uber is doing now. His prediction is, ROI is negative. And I'm awaiting the society's consensus on that.
The general direction seems correct to me.
He's not a technical guy and does not have the knowledge to critique models on a factual basis. I do wish he'd just focus on the stuff he _does_ know about, which is the financial side of things.
He is a much needed counterweight to the unhealthy hype going around, imho.
> He was also right about AI-video and sora in particular being a fundamentally flawed idea.
He specifically predicted that AI videos have plateaued in 2024 which is egregiously wrong.
> He was also right about the dangers and problems with the general inaccuracy of LLMs and people relying on it.
He specifically predicted that accuracy won't increase but accuracy has increased over the time significantly to the point where you can't get it to say anything inaccurate using the reasoning models.
> Also about the expected triggering of ROI-checking in companies, such as Uber is doing now. His prediction is, ROI is negative. And I'm awaiting the society's consensus on that.
The whole Uber skepticism is a good point because all of those people were wrong and Uber is profitable now.
You didn't address my other criticisms - he claimed that revenue would drop in 2024 and it skyrocketed. He claimed that users weren't interested in ChatGPT but now it has a billion users (6x jump).
Remember that OpenAI is subsidized from here to the highway.
A better way to model this, since you seem interested is the following:
How much would it cost you to start such a service for, say, 10k users?
Any other internet service has had virtually Zero cost, $0. Google, Facebook, youtube, Wikipedia, you name it. They all went into the dumpster to pick up a thrown away desktop computer, and they could serve up towards 100k if not a million users.
How much would it cost you to serve, say, 10k simultaneous users with a SOTA model? And if you wanted to go cash positive after a year, how much would each user have to pay?
> How much would it cost you to serve, say, 10k simultaneous users with a SOTA model? And if you wanted to go cash positive after a year, how much would each user have to pay?
My post has this same argument - we have multiple third party companies running open weight models. They are obviously not subsidised. And people are willing to pay for it. And these models are as good as the SOTA models from last year. So this kinda proves my point that SOTA is sustainable.
I'm simply saying this: there are third party hosters of Open Weight models like deepseek and they have been doing this for a while.
Obviously they are not subsidised, do you disagree? If you agree, they have a way to price it at a point that people wanna pay for it and also they aren't losing money.
So there's nothing inherent about inference that makes it too costly or whatever.
> I'm simply saying this: there are third party hosters of Open Weight models like deepseek and they have been doing this for a while.
> Obviously they are not subsidised, do you disagree? If you agree, they have a way to price it at a point that people wanna pay for it and also they aren't losing money.
> So there's nothing inherent about inference that makes it too costly or whatever.
Do we have audited GAAP financial data for any of these companies? If we don't, all these are... vibes, man.
I'll bet 50€ that big AI companies (OpenAI, Anthropic, xAI) have bad regarding their core financials. Excluding one time deals such as renting hardware, external cash infusions, the core AI model business (training models and selling inference) is unprofitable and will be be at the expected scales (50 billion € or more) for the next 5 years at least.
I bet that within 5 years they will be sold for scrap to bigger companies and will become divisions inside them.
1. You can't have a model without training. Training is part of the cost. A model trained 5 years ago is borderline useless. Also models cannot be retrained continuously, we do not have the technology (even if we had it, it would just increase the ongoing operational cost).
2. Rephrasing your statement:
At June 2026 prices, profit margins for all Anthropic models are >10%.
That claim is super defensive. Serving a model can only be done if the model has been built and trained, can't have it any other way. Building it and training it costs lots of money.
Even so, fine, I'll take that bet. Anthropic inference prices are still marginally subsdizided. Once they're public they will hike their API prices several times over the next 24 months. Even that might not save them, because when we take all their expenses into account, they will probably need to raise prices 2-3x compared to their June 2026 prices.
Your whole calculation is also ridiculous - I think you assumed what revenue per month is required to pay off hardware within a year? Why would I use hardware within a year?
I would suggest consulting with ChatGPT and coming up with a better and more coherent argument.
I try to do some napkin math of what it takes to start a company serving an LLM to customers. I thought maybe 10,000 users sounded like a reasonable number.
I'd like also to compare it to traditional Internet companies, e.g. Twitter. I'd guess Twitter with 10k users would cost me literally a dumpster dive, so essentially no cost.
If I were to start an LLM company, what would my initial investment look like?
Turns out around 15+ million dollars assuming I would get 10,000 users willing to pay 200$/month.
Today Apple launched its revamped AI offering. Judging by several reports, Apple pays Google a mere billion dollars a year to operate it. Essentially just licensing the IP. Google are (allegedly) happy to turn over the right to operate and distill their models for only a billion a year.
Consumer revenue is only a smallish share of the puzzle, but still:
If you are a consumer and you have a Mac or an iPhone, what do you need from AI that Apple's new offering won't provide? Why would you pay for ChatGPT, or even tolerate its inevitably increasingly desperate ad placements?
Assume Google will have similar tools in their phones, and Google search will continue to have the offering it does.
In short, where is the evidence that once Apple's tech exists, consumer AI is worth, to Anthropic or OpenAI, anything noticeably more than that $1B a year?
Maybe OpenAI strikes a deal to put something in Samsung phones. Let's say Samsung is ten times as desperate as Apple (which is how it looks, often). Still only $10B a year?
2026 consumer revenue projections from OpenAI are pitched at $14-15 billion, apparently. If they get that, it's the only year they will get that, because by late this year, everyone with an iPhone will have something useful built in.
Ed Zitron is a mouthy British rabble-rouser, but I think he is probably mostly on the money.
>If you are a consumer and you have a Mac or an iPhone, what do you need from AI that Apple's new offering won't provide? Why would you pay for ChatGPT, or even tolerate its inevitably increasingly desperate ad placements?
Probably the same reason the Gemini app is still well behind ChatGPT in consumer usage and adoption despite being preinstalled on android phones worldwide ? Why are people using GPT on Windows. There's even a copilot button on new keyboards!
Or maybe its the same reason Microsoft Edge is not the most popular Windows browser ? Maybe its the same reason Instagram threads did not even dent Tiktok ?
You are asking the question the wrong way around. People use and like what they like and have a strong preference to continue doing so.
This is just human behaviour. You don't need mind blowing moat. You begin to have problems only when:
- Users are constantly using your product unsatisifed.
- There's a competitor(s) with a significantly better offering that people are talking about.
Will Apple's offering be providing any meaningful/significant benefit over just using GPT ? If not, don't expect any miracles.
> Will Apple's offering be providing any meaningful/significant benefit over just using GPT ? If not, don't expect any miracles.
Judging by the announcements today about its integration into the OSes? They are offering useful things ChatGPT cannot offer unless they write an "everything app".
One can (maybe should) make the argument that this is the browser monopoly again, but given that the USA has seemingly no intention of ever litigating that question again even if the EU does, there are clearly features here that OpenAI is effectively locked out of offering.
Just because Apple said they're 'integrating into the OS' (which can really mean a lot of things) doesn't mean they'll offer something users will actually care about that Open AI can't match.
> Apple said they're 'integrating into the OS' (which can really mean a lot of things)
Well, it can mean a lot of things, which is why Apple outlined plenty of specific use-cases and details of what they meant by "integrating into the OS" here.
What did they outline? We had "browser use" for a while now, but it is still way too slow to be usable. Not to mention whatever OS integration they are making.
The other day I wrote up some notes for a presentation. Opened Google Slides, clicked the gemini button, pasted the notes in and asked it to make the slides. Nope; gemini can only modify a single slide at a time in Google Slides.
Pasted the same notes into claude, it wrote a pptx file, I imported that into Slides, job done.
Being integrated into the product doesn't always mean a better result.
Not being integrated can be an advantage because it gives you the freedom to think outside the box.
Meanwhile an AI engineer embedded into an incumbent slide app team has to ask permission and get cross functional alignment for every little feature. And deal with neckbeard tech leads lecturing them on what the right architecture is
Well I sure hope that the gemini developers don't have access to agentic development tools because they might read this comment, build the multi slide editing feature using 450M tokens and ship it yesterday removing that Claude edge forever.
I hate that I am defending apple here but Microslop has been pushing a garbage tacked on integration of AI into Windows.
By all accounts of the last 20 years of Apple, they are much much better at integrating different services into one fluid system.
I admit to not having yet taken a deep dive on Apple's privacy claims (i.e. on device, private cloud etc) but one thing that Apple is going to be providing in this case is an actual privacy commitment that takes into consideration the level of sensitivity of data being uploaded into these AI chat interfaces.
OpenAI's commitment to privacy is absymal relative to the sensitivity of the data people are dumping onto the platform. The CEO also has a reputation for being untrustworthy.
The biggest threat to ChatGPT's moat may be a brilliant marketing campaign by Apple that really gets people thinking about what platform they want to be upload their secrets to.
That's all true... however the point that Google will get just $1 billion per year from Apple for the AI service is still insightful.
If AI use via Apple represents 10% of the total that vaguely implies that the total AI market is worth around $10 billion per year (which admittedly seems a bit low), and if it is just 1% (which also seems low) then we get $100 billion per year upper-end estimate.
Which just is not enough to justify the current valuations of AI companies.
If AI use via Apple represents 10% of the total that vaguely implies that the total AI market is worth around $10 billion per year (which admittedly seems a bit low), and if it is just 1% (which also seems low) then we get $100 billion per year upper-end estimate.
This is a bizarre way to try and estimate market size.
Maybe Apple’s AI represents 100% of the market! Maybe it represents 0.000001%! We can make up any numbers we want!
Anthropic subscriptions alone already dwarf your $10 billion number.
> If you are a consumer and you have a Mac or an iPhone, what do you need from AI that Apple's new offering won't provide
Honestly, I don't think I need anything from AI at all. It's a convenience but it doesn't really enable anything I wasn't doing before. That's probably their biggest problem. The biggest thing is it enables non-coders to write code, but it's very debatable that that's a good thing excluding personal projects
Yep... I've met him and I can confirm that he is in fact, any given person. To that, I might add that I'm in the same situation. AI is a convenience and light entertainment, and as such, I'm willing and ready to pay exactly 0 USD for it privately and in my business. If the price rises beyond this point, they for sure have to move it from convenience to essential and so far, I have not seen any shadow of essential.
> If you are a consumer and you have a Mac or an iPhone, what do you need from AI that Apple's new offering won't provide?
I've been using Kagi Assistant for my AI needs, and have to say, Siri will probably replace it in the fall. The question will be, will I still want to keep Kagi for search, or will this new Siri get me where I need to be on all fronts? I need to start paying more attention to how often I actually use the search results vs just the AI summary.
There are things I didn't see Apple show and I wonder how Siri will handle it. One example would be basic coding. They mentioned LLMs in Xcode and Siri with the Shortcuts app and Safari Extensions, but I just had Kagi write up a webpage as a means to display a bunch of data it gave me. Gemini could also do this, so maybe it's not a problem for Siri, but it remains to be seen. There is also a question of what the experience will be like. ChatGPT, for example, handles writing up this code is a much nicer way than Kagi Assistant. Kagi feels more like the results I would have had from ChatGPT a couple years ago where it just dumps out the code in a block and any change is an entirely new code dump, meanwhile ChatGPT goes into a coding interface with a live editor. Going to Xcode feels like overkill, Siri will probably be not enough... so that's a gap in the market Apple may not serve. I assume there will be several things like this. The prosumer level of AI usage, if you will.
They will, but they won’t realize it’s writing code. It will look like Claude Cowork, which writes code for itself under the hood but is results-oriented for the user.
Co-work is damn near magic. I've been working on a mapping project the past few days, am probably a couple hundred prompts deep in to it (I'm doing some very weird stuff with the data to produce a hybrid map). The processing pipeline is something like 12k lines of python and counting.
I don’t think what I did would be too uncommon. I asked the LLM to design an exercise program and went back and forth with it a bit. Everything was kind of scattered in the chat and hard to read/find. I asked for a web page to consolidate everything so I could just check it each day and see what to use. It made a single file I can just double-click and open in the browser. It’s infinitely better than what the chat itself would have provided, and much better than telling it to give me a bunch of markdown tables.
I could see the same thing being useful for the ultimate output of a lot of chats. For example, they showed Siri comparing specs for few different products. I used an LLM to do this once as well, but it was comparing 12 things with about 50 attributes. The table was fine, but what was better was asking for a webpage that let me click on the attribute rows I cared about so it could total up each column, which allowed me to easily rank them and better make a decision.
Once it can make html files, it’s a small step to have Siri throw it into iCloud, and make it web accessible. This would be more of a feature than something it would just do, but I could see this being used in the same way Google talked about making dynamic widgets to help explain concepts within Google Search. That’s dynamic coding with an LLM as well, even if people don’t know it. Apple wouldn’t even need to show the code, they could just save it directly to a file and open Safari. That’s essentially what their extension builder will do… write some JavaScript and load it into Safari.
those few are the ones shelling out ridiculous money. mom&pop ChatGPT users aren’t their key demographic/users, it is Uber’s with $1.5k/SWE/month budgets
> 2026 consumer revenue projections from OpenAI are pitched at $14-15 billion, apparently. If they get that, it's the only year they will get that
Would you care to wager on that?
Because I would gladly take the other side at even odds.
> consumer AI is worth, to Anthropic
Anthropic does not really care about consumer AI. I expect consumer is where their least profitably customers are.
My primary expectation is that Apple will mostly increase usage of AI by general consumers. To me, this reads like Instagram adding stories. Did it stop Snapchat's growth? Sure. But I would be cautious about claiming it will take too many users away from OpenAI. I think it will be a fairly different product offering.
If you're paying to use ChatGPT right now, you might be using it for hobby coding, projects, or image generation. If you're paying a lot for ChatGPT, you're almost certainly using it for personal programming projects.
The $100/month (and up) subscribers aren't going to churn because of this, and I would be extremely surprised if the $20/month users do in any meaningful way.
When I use these models, I honestly can't tell much of a difference between any of the frontier ones. Admittedly I'm not sitting around benchmarking them during the hype cycles, but as a coder I have zero issues switching to whatever's cheapest. Custom harnesses or whatever are not much of a moat (honestly Claude is so buggy right now that I've been using codex and opencode just so I don't have to deal with a flickery mess that screwed up my arrow keys)
I just don't see how being the "premium" provider really works if much cheaper models are basically good enough.
"OpenAI CEO Sam Altman pitched the idea of turning over shares in his company to Trump in early 2025 and discussed the matter again with senior officials in recent weeks"
People who use ChatGPT have fed so much data about their own lives and interests into it. This includes a lot of information about personal lives, interests, plans, business and even family! Shifting to another AI app is painful as they would need to start from scratch.
I don’t know anyone who uses ChatGPT who cares about that stuff. Most people just use it as a Google replacement.
I actively hate it when it brings in some nonsense it thinks it knows about me. I told it my income once in an attempt to use it to find the perfect rewards credit card mix. Now anytime I try to get it to search for a deal it brings up some nonsense about “as a high income individual you don’t worry about saving $X, you care more about reliability, so you don’t need to look for the lowest cost” or something similar.
there is the option to opt-out of personalization, opt-out of using your conversations for training, and in that process reduce or eliminate any memory the system has of your personal preferences and context. If these actions don't erode your particular use-cases for ChatGPT, and if you think you can trust the model to follow these options this might be of use. I'm not trying to say "you're using it wrong" but that taking a more active control of the instaces facing you as a user might be of some benefit.
I have iterated through different option configurations to reach a level of 'customization' that more or less conforms to my own use case, and this does include opting out of any and all lasting memory between instances and across chat sessions; and adds a selection of single initialization prompts which shape the chatbot's behavior to my requirements for that session's objective. these trim most if not all af the sycophantic interactions, reduce outputs to the specific formats and contours as defined and omits any of the 'explanations of the underlying reasons behind...' which is just noise. This also has enabled some pretty useful results without ever spending a dime on a paid account: the premium behavior presented to 'potential customers' as a lure continues to work for me, and for iteration across instances and accounts is possible with machine-ready yaml context file when a single sessions hits the 90% wall : one emit and ingest cycle rotation across account profiles in firefox and i pick right up with a fresh limit.
Bouncing between ChatGPT and Claude, and between models for discrete subsets of larger tasks has really been impactful for my particular needs; but as i am not working in regions of knowledge that are beyond my own expertise and because I require the model to limit responses to very specific parameters, the logic space for unchecked hallucinations is low (but not zero).
The most useful project results for me have been in developing an air-gapped private menagerie of multi-domain models which uses an operating structure not dissimilar to OpenMythos; but then my background includes HPC environment development for NUMA, unikernels, MPI and bare metal hypervisor design - so getting a design plan and functional code without requiring a team of programmers and months of time in order to even start using models under my control which have zero public facing risk for the projects i'm working on is a much better place to spend limited budget on. Last gen hardware in the V100 class is perfectly capable of running and delivering the physics calculation optimizations as required and I would rather buy and/or install solar+storage to supply the electricity for token generation than rent the same from any of the frontier models AND trust that "don't train and learn from me" preferences are and continue to be followed.
If your use-case is a a 'lifestyle shopping assistant' then just turning off customization might be sufficient to stop it from telling you how to live your best life.
Let's imagine for a second that this a few billion dollars per year to Google is correct. Why do you assume that it covers everything to be done by Google itself - from hosting to running actual servers? Apple may very well pay Google a licensing fee, take a trained LLM and run inference themselves locally or even at a yet another 3rd party for example a datacenter corporation or any mix of these. And then a true real cost of running just the inference on every Apple device would be separated into a completely different org payment flows, very obscured and higher than just a license fee.
I'm not saying that this is what really happens. I'm saying that believing a CEO is as foolish and as grounded in reality as believing Ed Zitron.
> Why do you assume that it covers everything to be done by Google itself - from hosting to running actual servers?
I don't, and that's the point, isn't it?
It's the keys to a substantial chunk of the kingdom for $1B a year. Literally they are getting, for a very small price, the right to distill their own models from Gemini.
Is there money in this for someone with a data centre? Possibly. Is there money in it for NVIDIA? Possibly.
But either way, that's not OpenAI or Anthropic, is it?
> It's the keys to a substantial chunk of the kingdom for $1B a year. Literally they are getting, for a very small price, the right to distill their own models from Gemini.
Here is a different interpretation: Apple bought the rights to distill and use a smaller version of one unspecified model in the Gemini family (there are many such models).
The distillation will be carried out at Google's data centres so that the original weights never leave Google premises.
For this to be keys to be kingdom it would need to cover all current and future models and would need to be very permissive with regards to distillation parameters and allowed uses of the distilled model.
I expect the reality to be somewhere between these two extremes.
Right but OpenAI are for real making that prediction about their consumer revenue, which seems decidedly ambitious (considering that they are making nothing from their current phone placement). And they have said that they expect it to be quite a large share!
"OpenAI projects revenue will be divided nearly equally between its consumer and enterprise business units by 2030"
That it is so absurdly ambitious and so likely to run up against reality strikes me as really indicative of the quality of the envelopes these calculations are being sketched on.
The free Chinese models are always approaching frontier-level power. The cost to Enterprise to run these models is where Anthropic and OpenAI are competing against in the long run.
That's cool, but the enterprise is cheap. If you have any proximity to sales in the space you know that procurement and lawyers exist that have a full time job redlining purchase orders and agreements to the N-th degree. The enterprise will pay for something that will make them money or prevent them from losing it. But the enterprise isn't paying a premium and they know that.
Even within the Fortune 5 of the US if be surprised if any of them are paying more than $1B annually currently in total.
And then you can take the parent context into account. If they can just equip users with a slightly more expensive Mac and call their Dell rep to order a few thousand DGX Spark to handle the rest... Why would they risk their trade secrets and intimate details flowing into models that may or may not be trustworthy long term?
Most large enterprise have been burned by SaaS over the years in some way. I can't imagine there aren't architects in the large organizations that are truly weighing how to effectively use AI. And beyond that we're seeing more and more progress in SLMs and orchestration agents which become easier to run at scale on-prem.
> If you have any proximity to sales in the space you know that procurement and lawyers exist that have a full time job redlining purchase orders and agreements to the N-th degree.
I think so too (Enterprise), but I think its going to look different from "Pay subscription for access to a model from OpenAI/Anthropic/Google."
I don't think people will be doing business with the labs directly. "Enterprise AI" will be distilled down into purpose built products, with the model just basically being a generic commodity, and nearly irrelevant to the enterprises buying whatever these products are much like how I don't care if whatever SaaS was built in React, Vue, or some other framework as long as it works.
Ironically, for as much shit as they get about Copilot, Microsoft I think has the right idea for the long game they just suck at execution. Copilot is the tool, integrated into the rest of their enterprise stack, it doesn't care what the model is behind the scenes (they already offer you the ability to choose between different models).
That doesn't really bode well for the labs and their trillion dollar IPOs, because they are effectively reduced down to being a developer framework.
You are kind of glossing over the B2B market where contract pricing is basically just MBA vibes and the fact that people don't really care necessarily about the performance of the language model once it hits a baseline. They care about how it integrates into their lives. Precisely where first mover advantage comes into play. Having to train a language model all over again is it's own sunk cost.
The trick is in the wording - they probably aren't getting that many subs. They're saying they "expect" to get that many, at some point in the mythical future.
B2B absolutely cares, especially since ZIRP is unlikely to ever come back in our lifetimes. No sane corporation is going to continue to throw billions down the drain with nothing to show for it.
Most people using ChatGPT aren't using it for coding. They are using it for writing emails, working with spreadsheets, doing research, and writing reports. They could not care less about the coding aspect of language models. ZIRP in this context is meaningless. It's just another expense for every law and accounting firm. There's an entire world beyond tech jumping into this stuff right now. Like to give you perspective on this. I called my aunt in Germany who is an almost retired MD and she was the one that brought up ChatGPT and Claude to me.
b2b needs actual ROI and that's no where near. CEOs would be yelling loudly if this were returning them cash instead they're just jettison people to afford the bills.
This would be greate for google, because most people, specially in the apple environment don't much care to install new tools if they have a native tool that works reasonable well. If you have an ai assistant that's minimally competent in your desktop or phone, you will not care to go after chatgpt or alternatives, and google will receive tons of data to improve their models.
Mmhmm and why spend on wrapper SaaS when open or self trained on device models do the job
Web SaaS gonna end up being seen as another failed play at slim clients and entirely centralized sources of pay to play access to eyeballs; more AOL-ification of networks
Zitron is in the business of content creation and not successful predictions. It doesn't matter how many times he (and several others around) will say the end is here, they have to be right only once.
BTW, one thing for sure he is right about are the economics, as of today there is no way these massive investments are gone be paid.
One of the "smells" that gives away a quacky ranter is they speak in impassioned, "Why doesn't everyone understand this?" tones, but in fact their argument just doesn't flow. If Zitron's argument were as solid as he keeps saying it is, you would read it and understand it and see that it is solid. He would begin somewhere–statistics on AI demand, say–and then walk the calculations carefully over to the next step–maybe revenue needed for profitability by AI companies–and you could follow the argument. But no. He jumps. He leaps. He circles back. If the situation were really "Gosh why can't you see it?!"-clear, his explanation of the situation would be clear. It isn't, because it isn't.
I don't read Ed Zitron, aside from when he appears here on Hacker News, and I also find his tone to be over-the-top. I think we might agree on that much.
These articles are lengthy but, to my understanding, Ed's idea is...
* AI companies have committed to purchasing X amount of compute
* Data centers are being constructed to meet this demand, they'll need to charge amount Y
* AI companies do not have sufficient revenue to pay amount Y
IMHO this isn't surprising, personally the only real use-case for AI that I've seen is code generation or automated sales or scam calls. This doesn't seem like a big enough market for the huge dollar amounts I'm seeing thrown around.
I'm curious why you think Ed is so far off the mark on this. To me, it seems like we are headed for a big correction on the whole AI thing.
• He seems to think that the moment Nvidia release new hardware, all existing hardware becomes worthless. It doesn't and there are plenty of tokens being served by old GPUs. This makes all his calculations about how quickly datacenters have to pay off useless.
• All his numbers about costs, revenues etc are guesses or attempts to work backwards from off the cuff and frequently inconsistent comments by tech executives. They could easily be very far off.
• He doesn't seem to understand that datacenters have never been full of hardware on their opening day. A lot of his attacks revolve around this confusion - he learns that an opened datacenter isn't yet at full load or fully equipped with GPUs and thinks that means it's been delayed. I remember when Google first opened their facility in the Dalles, it took years for it to completely fill with machines.
> All his numbers about costs, revenues etc are guesses or attempts to work backwards from off the cuff and frequently inconsistent comments by tech executives. They could easily be very far off.
Agreed, but I'd argue that Ed doesn't have much else to work with. I'd like to see journalists take this tack and start asking these executives to either back up their statements or back down from them. They should be held accountable for their statements.
Even if we dial down these numbers by a magnitude they are still insanely large and the AI companies do not seem to be making enough money to balance things out.
> He seems to think that the moment Nvidia release new hardware, all existing hardware becomes worthless. It doesn't and there are plenty of tokens being served by old GPUs. This makes all his calculations about how quickly datacenters have to pay off useless.
I agree that older hardware from Nvidia doesn't become worthless when Nvidia releases new, more powerful hardware. I have to point out that it certainly loses a great deal of value and that's not nothing.
> He doesn't seem to understand that datacenters have never been full of hardware on their opening day. A lot of his attacks revolve around this confusion - he learns that an opened datacenter isn't yet at full load or fully equipped with GPUs and thinks that means it's been delayed. I remember when Google first opened their facility in the Dalles, it took years for it to completely fill with machines.
Is that really the case? I mean, I read about the build out of these data centers being delayed all of the time. I read this last week and it seems roughly in line with Ed's ravings:
> A JPMorgan analysis last month found that more than 60% of data-center capacity planned for completion in 2027 isn’t yet under construction, and another 7% is delayed.[0]
H100s installed 4 years ago are more expensive to rent now than they were on day 1. It is not at all clear that older hardware is losing its value in a world where the next gen model is smarter and faster due to improved training+inference algorithms (e.g. custom kernels) but runs on the same hardware.
It's either new GPUs make the old ones worthless or old GPUs make the new ones too expensive because they're still useful, it depends which ranter you're reading at the time.
Just like Michael Burry kept comparing NVDA to CSCO and now he doesn't do so anymore now that NVDA's P/E is ~31 and CSCO's is ~41. Funny that.
It helps if you look at Zitron's work history and experience. He's a hype man and a games journalist. His opinions on this are whatever sells, not exactly whatever is correct.
This is alarmingly obvious whenever he talks out of his depth about things like how companies actually use AI and reason about business decisions.
This doesn't match my experience, in academia I saw ~40-45% utilization NVIDIA GPU clusters that went 6 years with <20% failure rate. Might be a TPU thing?
I'm FAR form an expert on this, but I believe that the operating costs such as power + cooling form a big part of the lifecycle. I have no doubt that at some point within the 6 years that are being booked, that replacing entire working racks won't be more cost efficient.
That is current practice, yes. The economics of replacing racks then selling the old ones to people who will salvage and resell working components works out better than trying to repair/retrofit in place.
> He seems to think that the moment Nvidia release new hardware, all existing hardware becomes worthless.
I am the OP and I totally agree with you on this one point. In fact the progress being made by open weights models strongly suggests that some of this hardware has much more of a life.
The overarching point he makes about incomplete data centres is that the current offering is running successfully on that very incomplete capacity, right?
What he is saying is that he cannot believe the demand exists to fill any of the unbuilt stuff, but much of it is still commitments that are going to have to be paid for, unless they can be backed out. He points to Nadella essentially confirming there will be overcapacity.
He also makes an interesting point that people tend to think "I can't get a GPU right now" means "there is intense, live demand for GPUs in data centres" when in fact the reason you can't get one is buy-and-hold. Including much of that new replacement hardware: it is being bought even the old stuff would (let us stipulate will) do the job.
I think he (or someone who interviewed him) recently said it reminded them less of the dot com boom and more of the Chinese real estate bubble.
Future demand is unknowable. He might be directionally correct but wrong in magnitude, or right about everything, or wrong about everything. Unfortunately people who call bubbles never make their claims falsifiable or do anything else to build confidence, like take short positions. Zitron attacks the very notion that he might put skin in the game like that as obviously crazy.
I don't know to what extent we can say the current offering is running successfully. Anthropic have had visible capacity constraints for 18 months now with lots of throttling and quota capping going on. Those are good signs that demand does exceed supply at the current price point.
Additionally, Mythos has not launched publicly and one reason seems to be that it's too slow/expensive to make widely available, i.e. is capacity constrained.
But supply/demand is always in equilibrium, in some sense. So you could argue that it's currently balanced, or would be if priced correctly. That tells you little about future demand though.
All of this is fair, but it's also important to weigh your criticisms of Zitron's claims against the absolutely unsupported claims being made by Altman on a regular basis. They never show their working in even the way he does.
FWIW on capacity constraints, my gut instinct is that like every other startup these AI companies are are really only now beginning to do the serious efficiency work, because they had money and resources to throw at scaling without it; never optimise too early is pretty much a startup mantra.
Sure, I'm not claiming that anyone who isn't Zitron must therefore be completely reliable.
I think all the labs have done a lot of efficiency work for a long time, tbh. You can see the evidence in their papers, open source releases and product design choices like model routers. They know they need to reduce their cost base a lot to become profitable.
> personally the only real use-case for AI that I've seen is code generation or automated sales or scam calls.
That seems like a giant paucity of imagination. I can easily name a lot of areas where AI is already having a large impact and it's not hard to imagine the impact growing:
1. Customer service. Yes, we all like to laugh at the silly chatbot mistakes, linked list reversals and Instagram oopsies, but a lot of companies are putting a lot of effort (and spend) into AI for customer service.
2. The legal profession is already spending a lot on AI, and it will only grow. Again, we all like to read about hallucinated case citations, but those are solvable problems (honestly I felt they were more human problems than tech problems to begin with) and there are so many areas in research and document summarization that AI is really good at.
3. Radiology. There are lots of arguments over whether AI will "replace radiologists", but that's besides the point. The largest radiology groups in the country already use AI software to check for specific missed diagnoses, and the expected spend on AI will grow, a lot.
4. Enterprise knowledge management. Services like Glean are popular and growing.
You annihilated your own argument with the inclusion of radiology. The only successfully deployed "AI" in use by radiologists (that I'm aware of) are bespoke image analysis models, not LLMs. And that space is rapidly fragmenting as there's a frustrating and seemingly irresolvable tension between sensitivity, generalizability, and accuracy.
Everyone I know hates AI customer service. A couple of prominent food delivery apps here in India switched to AI chatbot customer services and it’s been horrible since then. It’s been almost impossible to get refunds since then, even when there’s straight up fraud involved without screaming ok twitter.
Now ofc it can be said that they haven’t implemented it properly but at some point it needs to be considered that why isn’t no one figuring it out?
I don't know if Ed is far off the mark. But this article does nothing to help illuminate it.
He mixes estimated capex spend by like 3 different sources with actually commitments by the LLM providers.
He talks about how crazy it would be for ai providers to double revenue every year. But openai is doubling every 9 months and anthropic is doubling every 3.
It's obvious if AI consumption stops growing today those companies are in trouble, and if AI consumption keeps growing at current rates they'll be more than fine.
Most people expect growth rate to slow, just no one knows by how much. This will determine if there is an over build out or not.
> He would begin somewhere–statistics on AI demand, say–and then walk the calculations carefully over to the next step–maybe revenue needed for profitability by AI companies–and you could follow the argument.
That's exactly what the first (titled) section does?
Haha thought you were referring to the upsell at the start asking to subscribe to the newsletter for $70 / year. But yes it does call out the unprecedented amount of money getting dumped into AI.
What turned me off though was this paragraph:
> This is a hysterical era perpetuated by liars, cowards, imbeciles, craven boosters and the easily-fooled. Those excited about generative AI are either the victim or the perpetrator of a con centered around a technology to ingratiate at the highest cost possible.
That's a very bold claim. Really anyone excited about generative AI dude? That's just an absurd claim, and makes it sound like he hasn't used an LLM since GPT 3.5. It's just the language is so hyperbolic and angry that it's giving me more rant vibes that really hurt the tone and damage the (many valid) claims he's trying to make.
Really tried to read through this all the way, but man I'm just not in love with this guy. I feel like the frustration is clouding his judgement. This line is another one with a fact that isn't really grounded:
> so, you know, they only need to grow by 496% by the end of 2029!
Which isn't wrong, but also Anthropic's revenue increased from $1 billion in Dec. 2024 to $47 billion May of 2026. Which of course doesn't guarantee that it will continue to grow at that scale, but it's clear that there is a strong demand for what they are creating.
Idk, not really sure what my point is here. There are just so many facts and numbers quoted in here... It's a bit exhausting to refute a piece like this, when parts are genuinely correct, and parts are maybe subconciously exaggerated due to some emotional leaking into the argument.
I just woke up and THIS! ... you almost owe me a new keyboard! I love it!
This statement cleanly encapsulates the entire problem with all of the frontier models' companies' pre-IPO numbers.
They have something-something "new technology" and we don't know anything about how the market is going to settle on the ethics, the utility, the human capacity opportunity cost impacts of not training and/or mis-educating an entire cohort of intern-engineers for a few seasons to a generation, the full environmental costs of hardware and operations necessary for the training each new larger model, ... and we cant even quantify the unknown-unknowns - the risks we cannot forsee.
To predict market revenues for the next few years based on the curves, that they self report without external disclosure of the underlying numbers, is just like expecting your 2 yr old to continue growing at the same pace in the future and in the past - laughable. Good thing it was just water not coffee and it didnt quite come "out my nose" :- ) Thank you kind stranger!
Dividing by 12 you still have the same problem. They're projected numbers as opposed to real ones as well as being grossly skewed by any short term fluctuations.
Divide both by 12 and you do not get the projected numbers. You get monthly revenue, a real measured number. It is the number being reported * 12 when they state a new ARR.
E.g. When Anthropic stated $1B ARR (an extrapolated value) what they were actually reporting is $(1/12)B Monthly revenue. If it helps their current monthly revenue is 47 times that, for a grand total of $(47/12)B per month in revenue.
Yes it is the current monthly revenue which is a projected number as far as the other 11 months go. That's fine if the overall economy has low volatility, your sector is well established and predictable, and your company isn't undergoing any significant changes. Absolutely none of that applies to the frontier AI labs.
Well, he dismisses any value whatsoever to GenAI. That's immediate bozo bit criteria to me. And, well, if Anthropic revenue doesn't grow 5x between now and the end of the decade, I'll be pretty surprised. But, sure, if it doesn't, then someone will keep them around anyway. AMD almost died in the 2010s as one example, but they kept getting propped up and now they're back in the game swinging. There are people who can see alpha beyond the next 10Q. Ed Zitron isn't that sort.
> Well, he dismisses any value whatsoever to GenAI.
I didn’t read it that way. I see a lot of value in it.
I just don’t see us justifying the amount of infrastructure being built or current valuations. Or in the unlikely event that we do, the societal upheaval is going to take away the ability to monetize it meaningfully.
OpenAI and Anthropic may make it through. But that is different from saying valuations are justified or that all this infrastructure will pay off.
"Those excited about generative AI are either the victim or the perpetrator of a con centered around a technology to ingratiate at the highest cost possible."
How else would you read the above statement? He's just preaching to his own choir IMO.
My take: like any gold rush, a lot of dumb ideas will get backed and they will all fail. And then we'll keep the ones that worked. SSND. Good luck picking the winners a priori.
I read it in context as being about the market prospects of genai.
The problem is, when there is so much overinvestment, everything gets wrecked. In the aftermath of the dotcom boom there was at least a bedrock of fiber and still useful equipment to build upon amid the rubble. This time we are going so much further; also many of the durable assets are misplaced bets and the depreciating ones will depreciate more steeply.
Someone should do the analysis of a decade and a half of Nvidia datacenter GPUs from Fermi to Kepler to Maxwell to Pascal to Volta to (Turing) to Ampere to Hopper to Blackwell and generate some hard depreciation numbers. Fiddling around a bit, 16-20% annual depreciation (so 5-6 years total and then any further revenue is bonus goods) it would appear, but that's a fiddle number.
But confounding this, K80s and V100s are still offered by cloud providers 13 and 9 years after their releases and academia still loves their GTX 1080 Pascals in their desktops. At companies, the beancounters take a computation and find the best architecture !/$ for that calculation. It does not need to be brand new shiny. It's Nvidia's job to make that case, not them. But anyway, the real data is right there. And those old GPUs demonstrate the dark fiber is already in place (and it's not so dark or they'd pull their racks).
AI is the special case. New GPU generations are the only way to access HW implementations of last year's research on precision modes and matrix math. If that slows down, that would be the first real bellwether of a slowdown. It hasn't happened yet. I'm a little surprised myself, but I also think coding agents are the vanguard of general design agents and that's going to hit a lot of industries at once.
So as long as the next generation of GPU halves the price of tokens and doubles throughput (or better), the demand for tokens will continue to rise IMO.
What I don't think is that AI can come for anyone's job successfully no matter what the C-suite sorts insist.
In summary, if you're a bear, you can point to the depreciation cycle and scream the sky is falling. And if you're a bull you can point to GPUs staying in production for a very long time despite the depreciation. Guess we have to wait for 2030.
Sure, according to an unnamed "GenAI principal at Alphabet" of whom "We could not verify the name of the person who describes themselves as 'GenAI principal architect at Alphabet' and therefore we cannot 100% trust their claims."
But let's run with Deep Layer(tm)'s hot take from 2024, GPUs all die in 2 years, no exceptions*. Poor guy just spent $400,000 on a DGX with 8 B200s, each of those B200s generates a piddly ~$3,000 in profit monthly spewing tokens, netting $576K in 2 years, that's a pathetic 20% annual return. Oh no... Won't someone call Michael Burry!
*Never mind the 3-year warranty or any extended service contract, that GPU is D E D and you're S O L.
Chinese providers realized that LLMs have peaked and have started trying to reduce the price per token. Deepseek pro v4 can easily add tests to my complicated code and costs cents for a million tokens.
I can ask Claude or ChatGPT architecture questions and then use Deepseek for the rest.
How are these businesses going to pay to price of energy and GPU depreciation again?
I love nonsense like this. If using a larger model to plan a chain of thought task for a smaller model works, what makes you think Anthropic can't do the same thing and offer it as one of their effort level settings? It reminds me of all those fallen AI ASIC startups insisting they can crush Nvidia until they found out the hard way that the rules of the game are dynamic.
The real challenge IMO is whether enterprise will want to run the models on-site for 100% security and privacy, but even then, what stops Anthropic from offering such an option on-prem or in the cloud?
He implies $400 billion in revenue by the end of 2029 is unrealistic when in fact it's very doable if you look at the trajectory of this technology since ChatGPT 4.0 launch. Google and Meta bring in around $500 billion in ad revenue between two of them annually. ChatGPT will easily bring 100s of billions in ad revenue if fully monetized given 1. it has billion weekly active users 2. ChatGPT conversation provides even better context for ad targeting vs search or social media. Enterprise AI revenue is going through the roof already, and with computer use companies will literally be able to fire large percentage of white collar workers and replace them with AI agent without updating their software infra.
Does that '100s of billions' come from a big bucket somewhere called 'spare cash', or does it correlate to a commensurate reduction in the 'around $500 billion in ad revenue' that Google and Meta are extracting?
Do your assumptions - " if you look at the trajectory " - factor in a slowing economy, a slowing growth in quality improvements in the tech, and/or the asymptote of market saturation for punters happy to stump up more than $50 a month?
What about a few hundred billion in salary and benefits reductions due to mass layoffs?
Not saying this would be good (qualitatively) or even good business in any sense, but we’ve already seen companies willing to sacrifice headcount to cover CAPEX for these models.
A few hundred billion in salary and benefits reductions equates to millions of layoffs. At minimum, we'd be looking at something about the same magnitude as the 2008 financial crisis. That scale of workforce reduction would have profound implications for the broader economy.
In a consumption-driven economy, businesses need consumers. Any gains from these layoffs would be short term at best.
I mean it almost certainly won't increase unless a major company takes out substantial debt, in which case we just kick the can and have conversations about bigger numbers. I don't quite think you understand, where will these hundreds of billions come from? By 2029 we will be well into a hardware glut and people will run their own models. Anthropic doesn't have the data flywheel to compete with OpenAI or Google. They went all in on special purpose AI and hit a brick wall and had a "do as much evil as possible" strategy which didn't pay off. Hopefully they fail before they get the entire industry regulated.
> Haha thought you were referring to the upsell at the start asking to subscribe to the newsletter for $70 / year.
People like you would be why I put "(titled)" in the reply.
> That's a very bold claim. Really anyone excited about generative AI dude? That's just an absurd claim, and makes it sound like he hasn't used an LLM since GPT 3.5. It's just the language is so hyperbolic and angry that it's giving me more rant vibes that really hurt the tone and damage the (many valid) claims he's trying to make.
The premise is that AI is significantly more expensive than current subscription & token fees. Within that framing, yes basically all AI users are getting conned. Tricked into redesigning their workflow around an unaffordable technology, in the hopes there will be too much sunk cost and they'll just eat a thousands-a-month fee.
> Which isn't wrong, but also Anthropic's revenue increased from $1 billion in Dec. 2024 to $47 billion May of 2026. Which of course doesn't guarantee that it will continue to grow at that scale, but it's clear that there is a strong demand for what they are creating.
"Doesn't guarantee it will continue to grow" is an understatement.
Let's take a generous assumption of the average subscription; $1000/month/seat. This will be quite a bit higher than pretty much everything but hardcore software dev, we'll re-do the math with $200 in a moment. Let's also grab Ed's $60B figure for both Anthropic/OpenAI, as it's more generous.
That's 30 million subscribers for Anthropic, 30 million for OpenAI, 60 million total.
They need to 5x. So 240 million extra subscriptions.
... Are there 240 million people left on the planet who can afford $1000/month?? (Either directly, or their employer) This kind of scaling is already hitting the limits of people on the planet. That sounds ridiculous for "240 million people" against 8 billion, but remember that $1000/month is a lot of money and a lot of jobs just do not benefit from AI. 2/3rds of employment in the US is stuff that happens in the physical world. Claude won't restock shelves, manufacture goods, construct buildings, cook food, or wipe geriatric asses.
Go again with $200/month. While this monthly fee is much more palatable, the sub-count inflates to 300 million subs needing to grow to 1.5 billion. They'd need to sell a sub to everyone in Europe and North America.
(And while there's loads of people in Africa and Asia, most of those are low income. You're not getting expensive AI subscriptions out of them or their employers either. China's obviously not gonna buy US AI, India has a GDP-per-capita of $250/month.)
>They'd need to sell a sub to everyone in Europe and North America.
Yep. Every man, woman and child, and even then provided we include Russia, Mexico, Cuba, Haiti etc, and, out of desperation to get to 1.5 billion, Turkey, which is in Europe a little.
> He would begin somewhere–statistics on AI demand, say–and then walk the calculations carefully over to the next step–maybe revenue needed for profitability by AI companies–and you could follow the argument.
Which of the hyperlinks provided at the beginning sounded like what you wanted, and after you clicked it* how did it disappoint you?
The information you are describing is stuff I would not expect anybody to repeatedly duplicate across periodic blog-posts.
* (Yes, I'm being sardonic, but if you did bother to click them, then I'm legitimately interested in your answer.)
He's right that its all going to pop dramatically and catastrophically for some. But having read a bunch of his stuff, there are two things he's just plain wrong about and they make his martyrdom tone too grating.
- His own objectivity - he consistently throws shade (rightfully) at the pro-AI side being financially 'required' to hold a certain world view, but is completely blind to his own claim to fame effecting him similarly.
- He consistently claims AI can't be made to work, and tries to prove this by calculating with the bubble prices. Its like saying tulips could never be profitable in the middle of the mania because ships were too expensive as proven by their current price to use for shipping tulips.
Add in the semi regular instance downplaying AI's usefulness contradicting my own experience and I mostly dont bother reading him anymore.
Its not like I'll be surprised that shit hits the fan, and he's not going to call the 'when' any better than wallstreetbets or an octopus.
Yeah, to be honest I think his take is a bit nonsense because it's so historically inaccurate.
Most hugely transformational technologies in the past also resulted in giant bubbles that burst, because investors piled into lots of companies in the hope that their particular company would win out. Railroads, automobiles, telecommunications networks, the Internet, etc. etc. were all hugely important, transformational technologies that all caused giant bubbles that burst.
But Ed Zitron seems hellbent on saying AI is a nothing burger, and that's why the bubble will burst. But the latter doesn't necessarily follow from the former, and indeed the examples I gave show that the exact opposite is often true.
I believe that the AI bubble will burst precisely because it is such a transformational technology. AI may not live up to the ways its biggest cultists like to shout ("Feel the AGI flow through you!!!"), but similarly in the .com boom/bust there was tons of nonsense about how we'd do absolutely everything online, we were in a new "eyeball economy", whatever that meant, yada yada, yet I'd argue that in some ways the Internet was actually a bigger impact than originally envisioned, just not necessarily in the way that late 90s boosters envisioned it.
Agreed. Phrases like "journalists are currently gooning over OpenAI and Anthropic" really put me off. It's a poor attempt at modern muckraking; cheeky yet offering little substance.
I didn't say it was. I'm just observing that his muckraking style is part of a very long British pundit tradition. Americans have never liked it — Intel got very upset about The Register's coverage of "the Itanic".
(And he's not Gen Z anyway is he; he's among the older millennials. He's appropriating it for muckraking purposes.)
Sure, but does that vibe invalidate the argument? What an odd time the middle of an argument is to be clutching pearls and worrying about prose quality.
Style and vibes notwithstanding, is there anything in your view that wrong with the argument itself? Could a better or more polite writer have convinced you with the same shape of logic?
> He is not writing his blog to convince people, his primary audience already agrees.
He's selling a paid newsletter, so at least one of his motivations is to make money. His target subscribers are certainly people who lean towards his viewpoint but he still needs to do some convincing because the market of people who are open if not warm to his thesis is much bigger than the market of people who already share his thesis.
> That doesn't make him wrong.
I think it's way too early for anyone pontificating about AI, the economics of AI, etc. to be declared "wrong" or "right". This is going to take years, if not decades, to play out.
Subscription based writing is all about writing for audience that agrees. Yes it creates bubbles, but economic of it is "people paying to read stuff they agree with".
Well, we don't have to speculate as to whether there is some sort of emotional taint on Zitron's thinking; it's shot through. But again, that does nothing to damage or offset _the argument_, which is available for your inspection and consideration, and you, as a thinking person, are handily capable of vetting. :) There is no need to use a heuristic; you have the thing itself.
The square of the hypotenuse — you multiply it by its bleedin' self — is LITERALLY equal to the sum of the squares of the other two sides. Again. Multiply each of the other sides BY THEMSELVES, and then add them together. It's that bleedin' simple Jim Cramer could do it. That number is the same as the square of the hypotenuse, no matter what idiot CEOs think.
> He is preaching to the choir, if you already hate AI you will love the article, if you don't hate AI already you will find the article insufferable.
I'm neither (or both, if you want - I can hate the direction its taking humanity while not hating my usage of it or opportunities it brings), and I definitely did not find his writing to be either lovable or insufferable.
I enjoyed reading it in a "smells-like-BOFH-but-in-finance" type of way.
It's fully in the BOFH/The Register tradition, yeah.
Some of his manner reminds me also of Max Keiser's "The Truth About Markets" which I credit with telling me about the subprime crisis a couple of years before it happened, even though people thought he was insane… because he kind of is.
I'm not a Brit, but I do enjoy British culture, including writing. I haven't been able to read any of Ed's rants to the end despite generally being on the cautious side towards LLMs
I particularly enjoy reading big banners asking me to pay for a newsletter subscription if I "liked" the content. Not if I found it interesting. Not if it actually provided any value whatsoever to me. No, you just have to "like" it. In other words, it is meant to be written in an engaging way and perhaps reinforce your believes like an echo chamber or even stir up certain strong emotions. Not to convey information. So, thanks, but no. I'm sure this opinion blog is very well written, but I don't think it is more well founded than anything else in this sea of opinions that sports a bigger garbage patch than the Pacific Ocean.
I know the HN guidelines discourage commenting on "tangential annoyances" on a website, but I think this issue is more than just tangential and more than just an annoyance.
When an author is this relentless in pushing you to sign up, there is good reason to suspect that financial motives are unduly driving an agenda.
I counted 8 such instances:
1. In the sidebar
2. At the top of the article
3. Popup in the middle of the screen after just a couple of scrolls into the body
4. Several paragraphs into the article
5. At the bottom of the article
6. At the bottom of the page under the comments section
7. Popup at the bottom of the screen after scrolling to the end of the body
8. (My personal favorite) Click the "user" icon in the bottom-right corner, which you'd normally expect to open an AI chat bot these days, and (surprise) you're prompted to sign up for a paid subscription
This sort of behavior just completely tanks any and all credibility this person may have.
Of things to be upset about, an independent journalist asking readers to pay for access ranks very low. Especially compared to LLM companies that are exacerbating the climate crisis, increasing cancer rates among residents, or increasing utilities for residents.
This sort of behavior completely tanks any and all credibility this commentator may have.
Is the OP article “journalism” or more of a rant with self-aggrandizement about how they’re so smart and such a good person that it makes lots of people angry?
What are you talking about? Why is "liking" something mutually exclusive with conveying information? I like lots of things precisely because they convey information!
>I like lots of things precisely because they convey information!
Correction: You may like them because you think they convey information. But without any sort of vetting process, the internet has become a cesspool of "news" or "general knowledge" places that ended up quite successful, but which are essentially just a contest of who is most confident when talking about topics and who can present bullshit in the most engaging way. You can see the peak of this on the JRE podcast. Anyone with actual expertise in a subject would be able to call out many of the guests, but since the host knows nothing about most of their fields he just gives them a platform to spread their opinions as facts. And millions of people who also don't know better will accept them without question.
I like Ed's sense of humor, I also like that he can distill down a lot of messy details into something more cogent, especially with the money side.
But, I also think he has missed the mark on a fair few things in terms of out comes. He may be proven right yet in terms of the general shape of things for some parts of the industry but also will have some big misses.
My general take away usually comes down to, places like OpenAI, Anthropic and Oracle have gone in a little to hard to fast and it may hurt them long term as they struggle to make it work in terms of economics. not that they can't just it will be difficult. But places like Microsoft, Google, Meta, Apple, Amazon; they have a very long runway to endure the growing pains and make it through to a long term business that no longer burns cash.
Oddly suspicious how this comment which was not one of the first comments which does not address the content at all but the tone skyrocketed to the top.
The tone is written as abrasive to anyone who doesn't already agree, which shows this is more of an emotional opinion piece than open minded objective research.
Hype cycles never last forever, but that doesn't mean all the value has been tapped by any means. The fact that modern GPUs can solve ridiculously complex high dimensional functions is a superpower in every possible field of research.
It's not entirely clear to me that the opposing argument is well-formed either. You constantly see numbers and statistics being wildly mis-used or overextrapolated.
Arguments have smells but rigour demands you investigate further. Zitron's smelly prose is, ironically, just the kind of stylistic distraction that AI can help condition; the further irony is that he will one day seem to have been right, for a year or two.
The money is indeed losing its mind over AI, and Zitron is a stopped clock. A correction is coming but the tool isn't going anywhere.
I kinda get it - he's been attacked for his negative views a lot, and that tends to produce a more passionate writing style. It's a little immature, sure, but also authentically human.
> One of the "smells" that gives away a quacky ranter is they speak in impassioned, "Why doesn't everyone understand this?" tones, but in fact their argument just doesn't flow.
It always seemed very natural to me that AI will move “down the stack”, where Open AI and Anthropic don’t really have a foot in the door.
Who makes consumer devices? Google
Who makes operating systems? Google
Who makes browsers? Google
Who makes the world’s most popular websites? Google
By the time 90% of average internet users get to chatgpt.com or whatever, they already went through several Google chokepoints, each layer is one more place Google can answer their questions.
And that’s not even getting into the chips, the data centers, the data, the talent, the consumer apps, the enterprise apps, the cloud platform, the brand, and of course the biggest cash printing machine in human history.
You would honestly have to be insane to bet against G.
Part of the pitch of AI companies is that they mediate and provide a new surface for ads, for taking an affiliate cut of sales, etc.
But it isn't like this hasn't been the long-running strategy for Google as well - provide more results on search so that people don't go to the site with ads, provide paid product results for shopping, to offer more services to keep people providing personal/behavioral queues to Google and more opportunities for ad placement.
If anything, AI turned up the heat such that the frog noticed what temperature the pot was. But that doesn't really put them in a better position to execute than Google.
There was a point in time when this would have been untrue of Google. They didn't manifest into this market position, they rose there over the years after starting with a relatively modest product offering. Nothing saying someone else can't do the same.
> You would honestly have to be insane to bet against G.
Unless you are confident that Google will be antitrust forced to weakening their hold in some of those markets. A crack is all you need for the water to rush in and widen the crack.
I think most of Google's deep research projects were done in the pursuit of pure science, not monetization or productization. In hindsight sure, it looks like they missed an opportunity. But not everything needs to be about money.
Zitron is begging for a collapse at this point. Yes, his macro analysis correctly identifies a massive financial risk but his incessant pessimism completely misses the incredible ground-level utility that many of us on HN celebrate every day through undeniable, massive productivity gains.
At this point I'm trying to believe there's a middle ground where the level of individual capability this unlocks, leads to major discoveries.
Writing about AI, destroying the planet for data centers, there's a lot of money to be made.
That being said, AI seems kind of miraculous sometimes.
Similar to cars. So enticing that we make everything else in the world worse in order to maximize the profit, make it indispensable, subsidize it, and make the dependency on it irreversible.
And it's not even something to blame individual people for.
Driving away from all the other cars to spend a weekend feels like freedom.
Using AI to answer a question feels like a "bicycle for the mind".
But in fact it's more like a car. It requires massive resources and creates perverse incentives, and the result is ineffective and corrupt.
Both cars and AI are amazing technology and extremely useful, but using them is not an individual responsibility. It requires societal subsidy.
The environmental impact of answering a question on an obscure topic with ai model is less than an the impact of answering the question with an hour-long google search hunting for references or a drive to the public library.
That's true, and I am not anti-AI. I was not only thinking about the environmental effects of some single prompt or a certain amount of tokens.
Neither did I want to say that a car is always more wasteful than some alternative.
But defaulting to the behemoth is inefficient, unless everyone is driven to do it: then it's in some way reasonable.
By adding "corrupt" and "dependent", as well as the economic terms, I wanted to offer a broader critique and create an analogy, not just talk about energy usage on its own.
What I had in mind was: it's easier to go many places that are a mile or less from me, by car. Because everything is obstructed by cars. And I'm atrophied by lack of movement.
Best would be to drive somewhere to move/walk.
People already do that in masses.
And doing shopping by car, because everything else seems unbearable, also takes away your time, apart from wasting energy compared to more, smaller shops that would be reachable by foot, bycicle etc.
I guess you know the argument.
Today, people's thinking atrophies because their LLM is probably right in their summarization of some Wikipedia article, plus 2-3 other random sources.
Or so.
Using the Wikipedia search function is not expensive.
But, I mostly had a bigger picture in mind than what is the cost of inference.
I think it's a good analogy in many ways, and personally I think car-centric society has a lot of flaws. I think the ease that AI brings to tasks may erode mental capabilities in the same way cars have eroded our collective physical health.* That said, it doesn't seem to me that we would be better off without cars altogether, despite all the related issues.
I am concerned about the environmental impacts that AI poses, but they don't seem to me to be so catastrophic. Solar and battery tech has made enormous leaps in the past couple decades, and we will need to pivot to clean energy future irrespective of AI.
*This said, I have become gradually more alarmed over the past decade at the lack of epistemological rigor in the general public, as made apparent through the rise of social media. I don't know that AI becoming a truth-seeking crutch for people wouldn't be more good than bad.
> it doesn't seem to me that we would be better off without cars altogether, despite all the related issues
Oh my god, no. I also want the benefits of automobiles! They are strictly more capable than, say, trains. That's where I would derail the discussion completely when going into details, but no, I am not against cars as a technology.
Apart from all the ethical and social arguments (logistics, ambulances, the elderly, etc etc). But that's not where I wanted to go.
I was making a leap here simply because of the whole complex around prisoner's, dilemma, the commons, state economy, and so forth.
Since at least ~100yrs ago, I guess cars and streets as the primary mode of transportation have also "won the vote" / are what the majority wants, so it's also an interesting analogy for diminishing returns maybe.
Building out more car infrastructure is certainly not controversial where there is absolutely none but there are commercial or residential buildings.
Anyway, lots of associations are worth considering here IMO. The ultimate limiting capacity here, when disregarding all environmental or health concerns, is simply space and the positive externalities (cities etc) around existing infrastructure.
The problem with grandiose statements like the GGP's are that they compare a realized outcome with an entirely idealized outcome they have in their brains as an alternative.
> I was not only thinking about the environmental effects of some single prompt or a certain amount of tokens.
Hand wringing about AI datacenter's environmental impact is well and good. We should keep the data centers accountable for their consumption and waste.
I just wish the same people had been upset the last 20 years with poor water resource management in a lot of areas (the west US especially) with urban, ranching and farming development.
The past may be past, but it's important that even now we point out the relative scale of resource usage, pollution, etc going forward of everything from cars to AI to golf courses to beef.
It's like saying if we didn't have cheap commercial flights people would travel by foot anyways and would consume more resources for food &co. than the plane would consume in fuel...
80% of generative AI queries wouldn't even exist as google searches.
To be clear, your position here is that insurmountable barriers to information is the preferable state of the world?
One claim of the parent comment was that AI is ineffective. For the purpose of finding answers to questions, it is more resource-efficient than the alternatives, and, to your point, capable of answering questions that were impossible to answer via other means before. In what way is that ineffective?
No, they're saying that 80% of genai queries (aka anything sent to an LLM; I won't speak on the validity of the percentage) are not things someone would search on Google. It's things like trial-and-error vibecoding, openclaw-like agentic loops, talking to chatgpt like it's a person, etc. In other words, most genai queries are not for getting "obscure information" or even getting direct information at all. It's about either getting it to do something you don't want to do yourself, or using it as a replacement for someone else (junior dev, therapist, friend, significant other, etc).
That's just what some people generally use to refer to LLM input string/prompt/message/etc. The only thing the LLM can do is return information...in the form of text, so every request is one for information.
If we want to get really pedantic, every generated token is the answer to the query: what's the next most probable token in this sequence of tokens?
If "query" doesn't imply intent by the user, it ceases to be a useful word. You can acrobat your way to imagine a digital system has agency to ask a question before it receives bits, but then any transfer of data could be called a query.
When I post this http request containing this reply, you could say my machine is querying the server to ask "what did you do with the message I just gave you", but then query stops having any useful semantic value to distinguish it from "request"
Regardless, this is tangential. I don't disagree that a lot of LLM use is not in pursuit of knowledge, but enough of it is for me to think that preferring LLMs not to exist is a hard position to defend, at least without making the case for existential doom.
I do plenty of AI queries, both pragmatic ones and some for entertainment: witnessing talktotransformer was mind-bending already at the time! And since then, I've tried frontier models, local, coding agents, and use plenty of them on the regular.
I awe at the capabilites of generative AI.
I also enjoy sitting in or driving a car.
I did not want to make a moral argument, unless you consider each and every form of utilitarianism as moralism.
There's nothing the selfish hate more than hearing their "choices" have a net negative effect. They hate the "doomers", because it puts a damper on their little side-effect free party. Doomers are more often correct because it only requires bare minimum pattern recognition. It requires more effort to defend an action that will certainly be different this time.
AI, Crypto, car-centric urban planning, cardboard suburbs in a barren field with bright green grass.
You tell someone that their selfish choice to drive a pickup truck only to haul something maybe one time a year, they shit their pants. They can't stand it, they start personal attacks and ask "what about you??". They seem to know it's wasteful, damaging, etc but they deserve it for some reason.
I agree with your message but not sure about the conclusion. Cars themselves are commodified luxury available (in the US pretty much required) to everyone, and they do need to be subsidized, both in terms of infrastructure and the lifestyle they require.
But with AI what is the exact price? My understanding is that R&D is extremely expensive, but running non-SOTA models is not that bad. We are getting pretty close to models which can be useful locally in many applications.
Or do you mean that at scale running them locally is not possible and hence the infrastructure price is in data centers, which will be expensive to maintain and scale for demand?
Thanks for asking an open question about my point.
First, because I initially failed to answer your more closed questions (this paragraph is edited in):
> We are getting pretty close to models which can be useful locally in many applications.
Or do you mean that at scale running them locally is not possible and hence the infrastructure price is in data centers, which will be expensive to maintain and scale for demand?
I don't think there's a way around making the best of AI capabilities with minimum price and maximum control, and I'd agree this is met by on-prem data centers, just not in a rationally targeted way.
Back to my original comment:
Because it (my conclusion) was not so clear, and maybe I just wanted to highlight some observations without delivering a real argument for or against things [, I thank you for your open question].
The utility/leverage aspect for AI seems more esoteric than the one for cars because, apart from Chatbots, it's more hidden.
And also, similar to cars (or many other phenomena of industrialization), yes, my first vague point was the subsidization of infrastructure. But also, the power gap: that's something not only associated with AI or cars, but with a lot of technologies we all hold dear: sewage, powerline, logistics, etc etc.
What reminds me of cars in the current AI frenzy is the fixation on cementing infrastructure. And also, I think, a lot more people agree on, for example, some kind of universal right to, for example, clean water.
But all of industrialization confronts people with questions of efficiency, inequality, and collective support.
Most people would, for example, support a right to get a minimum amount of clean water when you are living and working in a tradionally inhabited space (if you're on the social-darwinist side) or at least not harming society (if you're more of a social democrat).
And, similar to the buildup of car infrastructure, and the procurement of resources, space etc for maximum building, giant data centers can obstruct people in buying drinking water. Or walking outside (AI obstructs traditional methods of online collaboration).
The original point of the stock market was to fund gigantic society-level projects (like railroads). Modern VC has replaced some of that at smaller scales but not all of it at the largest scales. So this could just be the stock market performing the function it was designed to perform -- helping fund something transformative on a societal level.
> So where is all the productivity going? Where is the value?
Infrastructure doesn't produce value overnight. How long did it take the Interstate System to provide measurable value? I asked Gemini. Supposedly increased national productivity by 25% over 39 years[1]. But if you drove on a newly finished interstate in 1959, you saw the same cars just moving a lot faster.
That's what we're seeing right now. People can produce an incredible amount of stuff really quickly with AI. Is it directly connected to measurable productivity across the entire economy? No, because, realizing a mass productivity increase from infrastructure takes time.
Not sure what your point is. Stock markets are based on money going into securities based on estimated future value. Even if AI were doubling productivity at a non-AI company, there is more leverage to that money going into an AI company.
The question is, is AI leading to massive productivity gains in companies that implement it? AI productivity gains take time to diffuse, but so far companies in the S&P 500 are seeing very high growth. YOY earnings growth rate for the S&P 500 is 21.7%
https://advantage.factset.com/hubfs/Website/Resources%20Sect...
My point is that they're selling us Skynet and the end of employment as we now it, things that we shouldn't even have to measure to perceive the results of, yet no one is able to measure any of it
Pointing a finger at nvidia, google, and the other few companies stuck in circular investment schemes that shouldn't even be legal and saying "OOGA BOOGA line go UP, UP GOOD!" doesn't count in my book
Charitably the lag time for this technology to have noticeable effects could just be ~5 years away. Similarly to how computers didn't have a big impact for a decade after they were introduced as people got used to using them.
Those kind of returns would generally be considered at least ‘good’ even if they weren't excluding some of the best performing stocks such as AI as was done here. Some periods truly have no returns or even negative returns, even without excluding some of the best performing stocks like was done here.
And even more so since inflation was 2-3%, not considered high, during most of that period.
> AI-related stocks have accounted for 75% of S&P 500 returns, 80% of earnings growth and 90% of capital spending growth since ChatGPT launched in November 2022.
has it occurred to you that AI companies may be making huge returns because AI is genuinely increasing productivity and driving actual economic growth via their products?
If all these false practices can pull revenue out of nothing, why doesn’t every company do it? How come AI companies seem to be able to pull off financial magic that no other company can match?
> has it occurred to you that AI companies may be making huge returns because AI is genuinely increasing productivity and driving actual economic growth via their products?
Then why can't anyone point at actual numbers? The best we get is "look: line go up" while pointing at either the companies selling the AI shovels or the companies selling $1 of tokens for 50ct.
When cars replaced horses we didn't have to twist the numbers to understand the benefits. When emails replaced mails we didn't have to do 6 hours of mental gymnastics to see the increased productivity. Heck my grandma could tell the benefits of computers when the town hall she worked for finally discontinued typewriters
They're spending hundreds of billions if not trillions, and have nothing to show for it besides like 5 stocks pumping like shit coins. On top of the the drawbacks are massive and very visible...
Literally right here. eComm business turned around from losing money to profitable in less than 12 months after vibecoding a bunch of solutions to variousn problems we were having.
> Take any stock index, remove AI stocks, what do you see? That's right! Nothing...
I mean, do you know what the value of those stocks would be if AI didn't exist. Maybe they would be much more negative. Maybe we would be in a recession. Without a control this type of analysis is meaningless.
And that is even assuming that AI productivity gains are happening now instead of 5-10 years from now.
He has also consistently demonstrated, at least to me, that he doesn't really understand how inference works from a technical perspective, which weakens much of his core thesis for why there should be a collapse.
I do value having some naysayers in the mix generally, because we do need balanced critique in what is otherwise a very frothy hype cycle. I just don't think he's making sound arguments, and that's even assuming you even agree with his premises in the first place.
My biggest gripe with his napkin math is that he treats inference gross margins as something novel that you can't compare to normal SaaS margins. He's right in part: the constant carousel of R&D costs from model training, related infrastructure buildout, and other adjacent costs required to stay competitive do change the analysis a bit.
But he takes this way too far when he says this is structurally different from normal SaaS margins. The business model definitely doesn't look like Dropbox, but it absolutely looks a lot like AWS, especially early AWS, CDNs, telecom, etc. I can speak to the telecom bit personally, since it's been over half of my professional career as an engineer and, in this specific case, also as a founder. You can have a brutally capital-intensive infra business where profitability depends on utilization, oversubscription, peak-capacity planning, segmentation, and recovering capex over time.
The math he presents gets even more questionable as we see explicit segmentation happening for cost-saving reasons. Many forward-thinking orgs are waking up to the fact that they don't need to use the best, most expensive model for every task. They can route easier tasks to cheaper models, use caching, batch non-urgent workloads, and reserve frontier models for the subset of work that actually needs frontier intelligence. That directly undermines his claim that providers always need to chase frontier intelligence in order to maintain current demand, utilization, and pricing curves.
I think he doesn't need to understand the technology to point out the books are cooked. a business can sink in either way: the technology flops or the finances flop. he's arguing the /finances/ would flop. he doesn't argue that the /technology/ would flop, only that they can't come up with the money to pay their debters.
There is a piece of this I agree with. That you do not need to be a deep technical expert to notice that a company is burning cash by overcommitting to capex, or relying on heroic revenue projections that may or may not come to pass.
But that is not the full argument he is making. If the claim is that the labs will not be able to pay their creditors because inference is structurally incapable of becoming profitable, then he absolutely needs to be right about the technical economics of inference.
One part of that is the balance-sheet argument (which already shows insanely good margins). But it also depends on how inference-time compute actually works: routing, batching, kv cache reuse, model segmentation, different latency tiers, etc. Much of those details he's just been straight up wrong about in his writing, so as a result I have to call into question the rest of his reasoning as well (in part to avoid Gell-Mann amnesia).
Doesn't this kinda imply its own smoke and mirrors though? Like if the name of the game with inference is already routing things around and caching so you can make money, why is the newest biggest model always the most important critical thing? How does this square with any of their press about it? Also wouldn't that just add more inference? Because you need to pre-judge every prompt to know where to route it?
Also, if there is significant gains from caching, then like.. what are even doing here? Inputting something and then reading cached pieces of text based on their similarity to the input? Kinda like a search engine?
I don't think its smoke and mirrors, though I do have plenty of gripes with how the labs market this product landscape generally speaking.
The newest biggest model can still matter even if you do not run every prompt through it. You'll always have some task where even small amounts of loss are unacceptable and thus you need to make sure frontier intelligence is used for it.
On the router point, yes, routing has some overhead. But the router does not need to run the biggest model to decide which model to use. We've been using tiny classifiers for recommendation engines for ages now, usually on CPU. If routing saves you from sending a large fraction of traffic to the expensive reasoning model, the routing overhead can easily be worth it.
> Also, if there is significant gains from caching, then like.. what are even doing here? Inputting something and then reading cached pieces of text based on their similarity to the input? Kinda like a search engine?
The caching I'm talking about is explicitly the attention/kv cache, so its not input similarity retrieval (that would be more like what you'd use in a RAG/IR system). Prompt caching is generally about reusing already-computed attention scores for repeated prompt prefixes. The idea being you don't recompute the same static system prompt, tool definitions, schemas, long shared context, or repeated boilerplate every time. In more sophisticated systems, you usually store multiple checkpoints so that a small prompt change doesn't result in all-or-nothing hit/miss scenario.
> That directly undermines his claim that providers always need to chase frontier intelligence in order to maintain current demand, utilization, and pricing curves.
But does it also not mean that they will make less money given that there is already brutal competition for that lower tier from openrouter, Deepseek, Amazon, etc.?
You can't on the one hand say "customers are beginning to understand they can spend less" and on the other hand suggest that this is good for forecasts of revenue.
> You can't on the one hand say "customers are beginning to understand they can spend less" and on the other hand suggest that this is good for forecasts of revenue.
Sure you can. Just because there is a non-zero amount of margin pressure from the lower tier inference providers does not imply that revenue forecasts ought to be poor. Jevon's Paradox gets oversold in this current cycle, but I do think it's a relevant lens to view this through given how much demand has outpaced capacity.
The argument is that customers learning to spend less per task can be good for the viability of the market (really the total demand) even if it is bad for naive revenue-per-token assumptions. If a workflow goes from economically stupid to economically viable because you route 80% of it to cheaper models and reserve frontier models for the hard cases, that can expand total usage and improve cost per useful outcome.
There's examples both in his writing and also in his appearances on podcasts, interviews, etc.
I'll cherry pick a couple:
“When these new models ‘reason,’ they break a user’s input and break into component parts, then run inference on each one of those parts.” [1]
This is not at all how test-time compute works. At best, this is a very loose metaphor that he may have used out of convenience. This might sound a bit pedantic to point out, but this is a very basic thing that he's getting wrong (presumably at least, again it could be that he just used a poor metaphor).
A less pedantic example would be his claims related to gpt-5/chatgpt auto-routing. He argued that having a router means OpenAI can no longer cache static prompts, because the user prompt has to come before the hidden instructions [2]. This is just not at all how this works at inference-time. There is no evidence that the standard approach of system>developer>user instruction hierarchy has changed, the public API and caching docs maintain this.
But even more broadly, it suggests he is reasoning about kv/prefix caching at the wrong level of abstraction. It's true that conventional prefix caching does require a stable prefix, so yes, if you literally put variable user content before the static prompt, you would destroy the cacheability of that static prompt.
But that is exactly why inference systems are designed to preserve reusable prefixes where possible (via checkpointing or similar), and why serving systems care so much about prefix caching. This is also a big part of how disaggregated prefill/decode infra works where cache-aware routing is critical. His argument treats a bad prompt layout as if it were a necessary consequence of routing, rather than an avoidable implementation choice.
A router can read the user request, decide which model path to use, and then construct a normal downstream model call with stable static instructions first and user content later. Treating that as impossible implies a fundamental architectural misunderstanding.
Productivity is not value. It's quite possible for you to experience productivity improvements, and actual value to not be created. That is what I think the most robust data is showing.
Also, supposed productivity gains are dubious. I personally experience at best no productivity gains when using LLMs to write code, and sometimes it's an active drain on my productivity. There was that one study a year or so ago showing similar results. People are trying to say the productivity gains are there and undeniable, but that is not true. It is very much a subject of controversy whether AI helps productivity.
I can see an argument that the productivity gains are illusory / don’t translate to economic productivity. I’m not denying the possibility.
However, most of the engineers I respect have gone from being skeptics a year ago to convinced today. I don’t personally know any true holdouts any more. If there are studies that disprove productivity gains more than six months ago, I’m happy to believe that it was true of the AIs that were available at the time. But I’m going to need something much more recent before I disbelieve my lyin’ eyes where it pertains to the AIs available today.
There is an observational study that was published in March 2026 that followed 4000 teams over 2 years. It shows, in my view, exactly that the productivity gains don't translate into economic value.
If it was published in March 2026, even if the data was collected up to the day the study was published, 7/8ths of it would fail my “within the last six months” test. But I am looking forward to the results of future studies on this topic!
I get wanting to wait for more data. And thinking that LLMs have improved enough that this will change.
My view is that it's not really about how good the models are - it's about how we're using them. Understanding what you've built is an important part of value creation, and LLMs eliminate that.
Its funny, I've noticed the same thing, but did not come to the same conclusion.
I currently don't have work access to Claude Code, but most of my teammates do. Watching from the outside, the cycle seems to look like this:
1. Experience some success, which hooks you into relying on AI.
2. The AI keeps failing at some task, but you don't want to stop. Keep trying over and over again.
3. Run out of tokens and take a break.
Now, sometimes 1 doesn't happen. Sometimes 2 doesn't happen. 3 is a certainty though.
Now, if you told me that the productivity gain from 1 is enough to offset the loss from 2 and 3, I could believe you. But I also wouldn't be surprised if it didn't.
As I work with Claude more and gain a feel for its capabilities, I tend to run into 2 far less often, as I'll decompose my messages more for the current model limitations. The threshold also changes each release.
I’m going back to being a holdout, but it’s nuanced - My theory into why LLMs don’t lead to the colloquial definition of productivity would be something like - if code was never the bottleneck than generating code faster doesn’t result in more meaningful output.
Even if you take for granted that AI is as good as the best people say in writing code. And Ive spent a lot of time generating codes, I won’t disagree - Then the question becomes - does this change your daily incentives such that you reach for code as the solution to your problems rather than something else (coordinating with your colleagues? Product management? Planning and Design?
So from a holistic perspective, I think intentionally limiting your own AI usage is the best approach for maximum long-term productivity.
>So from a holistic perspective, I think intentionally limiting your own AI usage is the best approach for maximum long-term productivity.
I think this is right. They are much better applied as editors than authors, IMO.
The key thing is stay in control of your output. i.e. understand it thoroguhly. I think you let the LLM make decisions you don't really understand, you're increasing the likelihood of introducing defects that are expensive to address.
I’m not completely closed to your idea but if code was never the bottleneck why did so many organizations always feel so chronically low on coders? And of course this requires the AI to be no help at all with what is actually the bottleneck.
So, one is, it probably does depend on workflow. Some folks probably are doing things that can be accelerated by AI, and I think if you’re a small team with a good product head and know what you’re doing then AI probably helps a lot.
But what if the problem you’re trying to solve is the altogether too often problem of like getting teams that are dependent on you to upgrade the library they use. And what if the library is a breaking change, and last year they upgraded to the library on your advice and it broke production and now they’re suss and want to accept all changes, and integrating that library change isn’t in their critical path so they’re just not going to spend time on it, even if you submit the MR them. Even if you show them their tests pass after the change.
Importantly to the above, you probably need more devs to do more of the above in parallel. You don’t hire devs to write more code, you hire more devs to carry on the mental load of a broader scope of work. Even in the before times, so much code got stuck at the integration step.
But because all that is hard, instead you go and codegen to fix an obscure bug that sure makes a few customers happy, but no one thought was a limiting factor for paying your company more money.
It’s not that I don’t think AI can help, I think it’s a prerequisite for the job and everyone should use it. It’s more that I think in the grand scheme of things, people will bias towards using it for tasks that aren’t in the critical path - refactors, tech debt, bug smashing, tool building; and I think it could really help devex and that’s good.
But I think people are bad at knowing the difference between “my job feels a bit easier” or “I’m more productive” and “this task had an impact on the bottom line” and when you extrapolate that out to a whole engineering org, that’s where the productivity statistics get lost.
I’ll addd one data point to this is like this thread itself. So many people on AI skepticism threads point to their own subjective experience as evidence we’re not in a bubble, and sort of ignore the entire concept of economics. I’m not saying we’re in 100% in a bubble, but subjective experience isn’t great evidence of it.
And this is just sort of one of the factors, what about the increased cost and mental load of supporting more software? What about junior engineers who feel pressured to ship work but don’t actually learn the software engineering? What about lost context from not intimately understanding your software?
All good questions. I am not a big believer in claiming I know whether we are in a financial bubble or not. I just put it all in VT and we will see what happens. I know that the AI allows me to write code I couldn’t write before much more quickly than before but I admit that this may not help with organizational friction.
Although if this theory is true — that AI helps with coding but coding is not the friction point in organizations with multiple humans, even that should allow faster iteration by allowing one human to do more coding therefore reducing the size of teams required to make some programs. You should see good acceleration in solo shops too.
Yeah, again the answer is definitely not no AI. And I’ve seen the - you know - I can one shot whole applications that would have taken me weeks or months before. But it’s also much easier to one shot apps that aren’t in the critical path. I’m building all sorts of tools to make my job easier, but I still have to do the job.
I’m a platform engineer. The primary failure mode for platform engineers is building tools people don’t want. AI doesn’t really make that easier. Or it can but it can also make it harder by making it easier to chase down ideas that you don’t get traction on. And I think that - net balanced across the organization is probably why productivity gains get sort of averaged out.
For sure I think solo devs who have a system are seeing gains, as long as you can I think have the discipline to have a process that includes feedback and learning and your not just feeding off of dopamine hits of one shotting features but yeah. I mean for solo devs the code was never really the limiting factor, it was product-market fit and marketing.
So solo devs who have a system may be laughing themselves all the way to the bank, but we may not see a lot of net new solo devs.
But if code is cheap now then it’s sort of inherently devalued. 2 8 person startups can probably relatively easily find a dev with AI experience to rocket ship their code generation, which means the basic skills of talking to customers, change management, and building the right thing become even more valuable.
Even solo devs I wonder - almost every post-mortem of a failed company goes “I wish we had spent more time talking to customers and less time writing code”
Again if you can get the discipline right, maybe as a solo devs you can get more work done faster and spend more time with your family. That’s incredibly valuable!
But if you go and add a big new feature, or a second product - unless your community is primed for constant growth(no man’s sky is one community where more more more seems good) you’re just growing the surface area where all the other skills are more necessary.
From an economic perspective productivity is defined as the creation of value isn't it? Then if you "improve productivity" and does not create value in the end you're no improving productivity at all.
economists define productivity as gdp per hour worked. Like a lot of other economic measurements, its mostly a bogus number people use as an argument on why their politics are better than someone elses politics. You can have an efficient business located in a poor country making the same product and same quality as that same business in a rich country, the rich country will be more "productive" because local cost of goods is higher there (i.e. a restaurant in NYC is more "productive" than a restaurant in bangladesh).
Sure. But that's not, in my view, how most people use the word productivity when describing LLM use.
In my field - operations - productivity is usually described as some rate of production for a specific asset. 100 widgets / machine / hour - for example.
"My productivity is 3 PRs / day with the LLM as opposed to 1 PR per every three days". That's how I think people are thinking about it.
My point is that's not the same thing as value. I.e. what people will pay for.
You're correct, I just wanted to add that there is another definition that you may see used online, and it is very specific, and it's important to be aware it's NOT exactly the same thing most normal people mean when they say "productivity".
The report was not paywalled for me. It just required a work email. Which is totally fair from my perspective. Faros is providing a ton of value with the report. People do deserve to get paid - even if in collected emails.
You're right my analysis is at variance to what Faros.ai says. I think they interpret their data trying to rescue utility for the dominant patterns of LLM use.
But I think to anyone who is experienced with process improvement or queuing theory, their interpretation is clearly weak. Rework is a huge problem in queue systems, and they mostly just elide the throughput impact of an 860% increase in code churn coupled to a massive spike in bugs.
Obviously draw your own conclusions. But I don't think because I disagree with the interpretation of the people who originated the data makes me wrong.
That's possible, sure. But I think the answer is more likely in the numbers, not in just qualitatively saying AI isn't worth anything. Like if I pay $30k for an ounce of gold, I got value. Gold is worth something. But that amount of gold wasn't worth what I spent.
EDIT: In fact, parent comment has a link to some numbers.
[EDIT: Most] people don't want to go through the numbers. Ok. But there's a history here. When people don't want to see the numbers, certain kinds of things tend to happen.
I've posted numbers that indicate that productivity is becoming decoupled from value delivery. If you follow the link in my comment it reviews a pretty robust study of 4000 teams over 2 years. There is no product throughput increase.
Code acceleration is great, but.... something precedes that. Vision and strategy re. expansion of offerings and businesses. Once a firm reaches maturity in what it offers and is only touching the edges - this code acceleration is literally useless when you factor in all of the trade-offs.
This is a good thing - it means fat and slow incumbents are sitting ducks to be out-witted by creative and imaginative founders, which is healthy for a well-functioning economy.
Now the economics of existing frontier models are not sustainable - its looking like a mix of the airline (supersonic vs subsonic) and EV industry with China in the background providing decent offerings at much lower prices.
I admit that if a small team or an individual uses an LLM, it's likely they can create value faster.
I think as soon as you don't own the responsibility for the defects you generate with an LLM, their use starts to destroy value. Regardless of product maturity.
Yeah this part scares me a little. I imagine it scares everyone who is more than a couple of years out of school. I hear that "the solution to LLM tech debt is more LLM." That might be true, but it might not be.
I actually think this is precisely the reason LLMs can't be the basis for a technological revolution. Because it's only one way.
Like, if you have a compiler, and it has a bug. You can discover if that bug is influencing your code execution and patch it. You can go both up and down the stack.
With LLMs, there is no way to patch it's translation function. You have to rely on it to forward process.
I don't think there is any way to avoid us understanding our tech stacks.
If you are producing something that delivers a far better experience, irrespective of what's under the hood (see Claude Code et al), you will decimate an incumbent who is trying to use LLMs in the context of incrementally improving a mature product.
LLMs are suited for the development of revolutionary innovation, not incremental.
He’s been continuously predicting that the collapse was just around the corner, that progress was slowing, and that there was no market for inference, since 2024.
The fact he’s never reflected on the glaring failures in his analysis tells what we need to know about his intellectual integrity. There’s truth in some of his words about financial risk, but if you can’t acknowledge that there’s upside too, you can’t evaluate risk properly either.
Do you think it's not slowing? Do I miss anything really important?
My understanding is that we have now is incremental improvement on thinking models which appeared more than a year ago. Of course, a breakthrough might happen, but I don't see one yet.
The most important thing I would point to is Mythos et al and the wave of vulnerabilities that have been discovered in the past couple months. It’s a completely unprecedented event, brought forth almost entirely by improvements in the models themselves.
That said. keep in mind, I’m talking about over the past two years. With Claude code and the capabilities gained since December of last year, there have been incredible gains in the capabilities that are now available. Demand for inference is higher now than it was a year ago, because capability has improved. A specific criticism that I would hold is that claiming that progress with LLMs is slowing, prior to that point, is embarrassingly wrong in my view.
One could argue that the model capability improvements are slowing, and all the improvements were in harnesses. I think that’s a stronger argument, but I have a few problems with it.
1. Utility is utility. Whether that comes from the model or the harness is irrelevant when making claims about utility. I don’t think that’s a useful distinction most of the time, but especially when talking about the technology as a whole.
2. Marginal intelligence gain is different than marginal utility gain. It’s estimated that intelligence grows logarithmically relative to investment. However, the utility of a marginally more intelligent model may grow exponentially, because once behavior crosses a reliability threshold, it unlocks new capabilities.
3. Even on those terms, it’s not clear to me that frontier capabilities are slowing down. With Mythos and its contemporaries, we have been seeing a vast change in the security industry as vulnerabilities are discovered at an unprecedented rate. OpenBSD vulnerabilities, more Firefox vulnerabilities found in a single month than the past two years, critical Linux vulnerabilities. It’s hard for me to look at the effects there, a radical new capabilities baked into the model itself, and see stagnation. A part of the reason it might feel like it’s slowing down is because we plebs don’t have access to the top models.
The article says in the second section that the author did not have access to Mythos. I think it’s dangerous to rely on claims made by others without even bothering to read them first, let alone check.
It found hundreds of vulnerabilities in Firefox, according to Mozilla: how does Mozilla benefit? It found a 27 year old vulnerability in OpenBSD. How do they benefit from that? Is that made up? Are the maintainers of those codebases lying for the benefit of Anthropic’s IPO? Is copy fail a fabrication by big AI? The 12 OpenSSL vulnerabilities found in January?
Im not sure whose claims you think I’m relying on. I trust Firefox that they’re not overstating the number of CVES they’ve found. Same for OpenSSL. The OpenBSD folks definitely don’t seem like the types. I’ve not known Linux to fabricate CVEs either. I think my sources are fine.
Have a muck about with what Qwen 3.6 or Gemma 4 can do and you'll see. I mean this as an illustration but Qwen just isn't as far behind as I expected, and compared to the data centre hardware it will run on a potato.
The frontier models are losing their undeniable edge over that which is unmetered.
And even putting aside my optimism for the smaller open weights models, there's a huge amount of scope for the larger, hosted open weights models that are only just behind the cutting edge and which cost, what, 1/25th of the price on opencode go, openrouter etc.
Commodification is coming, and with it slimmer profit margins; it's hard to see them making anywhere near the kind of money they need to in a commodified market.
> He’s been continuously predicting that the collapse was just around the corner, that progress was slowing, and that there was no market for inference, since 2024.
Old WSB saying: The market can remain irrational for (far) longer than one can remain solvent.
And unfortunately, a lot of the market on the "buyer" side has been acting irrationally. When you see CEOs telling their employees that they don't care about token cost, only about "how much AI do you use" because that is what the stock market wants to hear - that's when you know we're all getting cooked, the question is how long it takes until the bubble bursts.
It’s a very hard experiment to run. You have a population that’s already “treated”. You can’t blind them to the fact that they’re using AI tools. It’s hard to imagine a study that wouldn’t have serious flaws that people would then use to dismiss and form their own conclusions. Sure you have METR but that was very low n with a very old model.
I think the surest sign of productivity gains is the sheer volume of adoption. If you look beyond headlines, adoption is just incredible. Of course adoption does not necessarily point to productivity gains, but if this was some sort of FOMO or smoke and mirrors you would not see this much retention and this feverish a pace of adoption. You would not see a large segment of the profession using coding agents exclusively. All of these companies track productivity, again with imperfect proxies, yet everything points to a pretty consistent picture. Same with benchmarks, again a lot of crappy benchmarks but a lot of high quality ones too and a very diverse collection of tasks and capabilities they probe.
Your second paragraph appears to be 3 different instances of saying "X does not necessarily point to productivity gains... but in the case of AI, X definitely means productivity" without really saying why that is true or why other explanations do not fit.
Adoption meaning productivity supposes there are no other dominant factors for the AI push nor AI retention. It is possible for practices to be picked up or continued in spite of causing productivity DROPS. What studies have suggested are factors that make for productive work environments and what is actually enforced in the workplace are different things.
It’s 3 different weak but complimentary proxies. We form beliefs from imperfect evidence and I find these fairly convincing when it’s hard to find any hard evidence of no productivity and exactly the scenario you would expect under the hypothesis that we do see productivity gains. None of this is supposed to be unassailable. I would challenge then if you disagree what the evidence you have for this is?
Adoption implying at least some significant productivity gains doesn’t contradict there being other factors. You’re seeing entire companies reshaped. The argument is this is all for show or CEOs are in some sort of idiot class?
“It is possible for practices to be picked up or continued in spite of causing productivity drops” well of course. I just find that incredibly far away from Occam’s razor.
My point is: we have lots of evidence that’s highly consistent with real productivity gains, and I don’t see many pieces of evidence to the contrary.
Sheer volume of adoption is fairly forced though - "use it or you're fired, and tokenmaxx the hell out of it". Most the people I know outside of tech don't seem to be particularly captured by it, if they use it at all.
Yes since frontier perf is too expensive. Consider though frontier perf costs roughly 40x less per year. So you see super adoption for mega costs by the companies with the scale and budget for it, and then folks who can’t afford it that have to wait until maybe fall until they have the game changing perf we saw for opus 4.5 that spurred the enormous adoption by the major players.
I agree that there are incentives to waste tokens but this is strategic: they spend now for people to build and explore, then they keep the things that work and drop the things that don’t.
I do not disagree with what you are saying, but I honestly still believe that most of the utility we experience are honestly gonna become very boring very soon that we can just run local... Even if it's a bit more slow who cares, can just run in background while you work on other stuff yourself, read up on things, review other work...
It's not that the utility of it put in question. What is however a giant question mark is how the heck any of the big AI companies are ever gonna get that ROI? Given how many of us are becoming more and more fine with local models that run just fine especially on a good enough computer which most developers have anyway...
Even more dangerous to the big 2 AI companies is the fact that the 20 different Chinese companies are catching up fast and for a lot lower cost.
Why should someone pick Opus 4.8 when Qwen3.7 Plus produces similar results for about 1/20th the cost.
That sort of pricing disparity is across the board. But further it's becoming more and more apparent that they are doing more with less parameters. That's what's giving the local models their super powers.
Because it doesn't. Not for the tasks where using Opus instead of a lower tier model is appropriate, at any rate. Benchmarks show this, as do revealed preferences of actual users. To believe that Qwen is as capable as Opus at 1/20 the cost you have to believe that every person who does not make the choice to use Qwen over Opus for a given task is some mix of ignorant or delusional. This is certainly an opinion you can hold about other engineers, but it's definitely a questionable one at best.
The benchmarks between the two are close and the engineers that have used both (like myself) can attest that the differences aren't so wide as you might believe.
I'd say that yes, ignorance plays a role here because a decent number of engineers are looking strictly at the benchmarks and choosing Opus just for that reason.
But I'd also say that a major factor for Opus use is because Opus is being purchased for the engineers by their employers. They don't get to pick which models they are using.
I find myself rarely reaching for Opus nowadays, it's just too slow. I assume there are tricky use-cases where it's really useful though, just not super relevant for my day to day. I much prefer a faster, "weaker" model.
Every day people here debate whether or not there are any actual productivity gains from LLM, and it's only in the limited context of software development. While I understand that this place obviously skews heavily towards the software industry, the notion that LLMs are anywhere near as useful in other industries is hubristic (at best).
And where are those? They seem particularly hard to actually observe and only appear in anecdotes.
> I'm trying to believe
For every exponential increase in compute capacity you see linear gains in output accuracy. This is a death spiral. Anyways, you see "massive productivity gains" so why is "belief" a function of your viewpoint?
Even if we assume that everything you said holds true, how is that we as a crowd can make viable a service that eats some $300bn annually in infrastructure costs? Where would that money come from? Most tech companies these days are cutting their AI budgets because the per token pricing is killing them.
Cite a real source for that last bit, I don’t think that is true. Also the budgets should be cut the spend at some places goes beyond any reasonable amount. The strategy there is to hook everything in and find the right processes, then cut the rest. Things then get better and better with each model release.
The way you make a viable service that eats 300bn annually is to have enough demand to service that. Anthropic underbought compute. That tells you something.
I think over the years local models have fairly consistently been ~7 months behind frontier performance. Local models are hugely important but I don’t see the calculus changing. I can imagine it’s certainly the case for many tasks that there will be diminishing gains for performance improvements or reliability pass some threshold, in which case you don’t need frontier performance and you can certainly use local models or at least cheaper tiers of proprietary models if local is too much of a hassle. Plus of course use cases where local is necessary or the pros of having local models or on device models outweighs that of frontier.
Maybe things will change though, I would assume through basically government subsidies from China etc, to undercut existing frontier labs, but you can always spend more (better data more compute etc) for better performance and that I can imagine will always have a selling point.
Yeah they're very much deniable. Raw LOC/hr is much higher, and putting together a MVP, but I've yet to see any evidence that an LLM is capable of doing anything unsupervised, and if you need a human supervising everything it does... why bother having an LLM in the first place?
Because it can perform much faster? Monitoring allows you to multitask more effectively. I would also disagree that you can’t one shot anything…claims like this are weak and I have enough counter examples in my own life that it’s trivially false. The question is more: can it one shot the right things with a low enough failure rate for it to be a good replacement. It’s hard to figure that out a priori.
He has recently made the very good point that actually, the FAANG companies are struggling to put any ROI numbers on that incredible ground-level utility.
Uber, for example, is so unclear there is any ROI, they are cutting their exposure pretty radically.
He points out that one single Anthropic customer — a payments provider — accidentally had to pay Anthropic $500M for one month of token spend.
That is half what Apple is reportedly paying Google for the supply side of their entire consumer AI strategy.
It doesn't matter under Capitalist Realism, the banks were bailed out, the AI companies will be bailed out, and you will pay for it. There is no alternative.
I'm not sure if they would be bailed out. The government tends to help with bank bailouts as they are essentially the hemoglobin of the economy, I see this being more like the dot-com bubble were they will just let it fall and have the bigger more entrenched player pickup the scraps for cents on the dollar.
According to NOTUS, Altman himself seems to have sold Trump on the idea that the USA should own a chunk of OpenAI, and despite their potential IPO, OpenAI have recently followed up on the idea.
Owning a chunk is pretty directly how more than one country injected confidence into their at-risk banks; it’s certainly how it was done in the UK.
Agreed that he has an extreme POV (or more accurately that he trolls for views/subscriptions). But his central argument is valid: if AI underdelivers financially, this bubble will burst and this bubble is magnitudes larger than what we've seen before, so there could be very rough seas ahead.
The question is: what does "underdeliver" mean here? the pro-AI arguments I am seeing in this thread are equating mass adoption to agentic coding. Er, I dont know of any trillion dollar cap companies that sell dev tools. The point is Zitron doesn't have to be 100% right for his central prediction to come true.
I don’t get this. We already have an insane demand. And yes exactly, this is primarily just with coding agents, but are you aware of what’s coming down the pipeline? It’s not hard to be you just have to find a decent way to keep up with literature.
* robotics (need to close data gap and release first viable product to get a data flywheel)
* conversational ai (no one is ready for this and we’re getting closer and closer to natural speech. The quality still isn’t good enough but it’ll be soon).
* other agentic use cases, openclaw adoption was crazy and that had a ton of barriers to entry
* ai products, like the one OpenAI is working on with Johnny Ive
Anyone thinking it’s unreasonable to hit whatever revenue requirements is just not that aware of what’s happening. Not to mention were capacity constrained already!! This is barely speculation at this point.
I don't think the issue with robotics is a data gap. maybe somewhat, but the real issues are that:
- RL is extraordinarily sample-inefficient.
- distribution shift/catastrophic forgetting aren't solved. only off-policy learning with giant decorrelated batches works.
- the breakout success of transformers as an architecture doesn't neatly translate to robot motion policy models.
the field is missing fundamental breakthroughs.
I also find it very interesting that conversational AI has taken this long. where are the models with good turn-taking? passive listening? the ability not to respond in paragraphs? has Anthropic simply not gotten around to it?
All of these points are great. The first one motivates world models which lots of labs work on. Not many people tend to understand the strategic value of those “open world” or interactive generation models: its robotics and planning. But also like you say you’re right, there are complicated problems to solve and it’s not totally clear the timeline. But where there’s data and compute, there’s a way.
For conversational AI these labs do have lots of things to do lol but you’re right; it likely also requires some architectural improvements but you see the infancy: look at the llama4 speech duplex model. Very unimpressive yet all of the components are there. Just a matter of pushing on them, licensing and commissioning better data, etc. takes time and compute is stretched thin.
Yes. Zitron has been predicting and begging for collapse since 2024. It's not just his brand at this point. It's his entire identity. As such, he cannot back down, he cannot question himself, and he cannot accept any other viewpoint. And he will keep moving his goal posts until something happens that can make him go "aha! I told you guys!!"
This, combined with his extreme ignorance, makes him unreadable. The only reason people read his stuff is because it validates and confirms their own anti-AI beliefs. It's why every time he publishes an article, it reaches the front page in an hour or less.
I can see how folks find Zitron shrill, but I wouldn't dare say he is ignorant. The man seems to be bending over backwards to do actual journalism when most outlets take CEOs at their word and promote a market that goes irrationally up despite evidence. Case and point, the situation in Iran is such a mess, yet every time Trump says there is a deal of some sort, the market rallies. It has been months now of this game.
As a listener, I have not heard him predict collapse once. Maybe I got to him late, but so far as I know, he claims that the industry as it exists is unsustainable. You may think that is him dodging the logical progression of "collapse" but I think so far he has been pretty correct on the unsustainable part, the signs are there. If anyone could predict such things they'd be a billionaire many times over. In the end I think he will be more or less correct, the timing and how long investors can prop it up is the much harder part to predict.
How are they undeniable? They're very deniable. One example is the (seemingly) increasing maintenance costs for AI-generated code[1]. Another is the cost incurred by everybody reading AI slop instead of actual communication.
I don't have hard data as to whether these cancel out the benefits, but it's not as rosy as some seem to think.
[1] After years of people understanding that LOC is not only a poor productivity metric but also a negative indicator of code quality (shorter code for the same thing is better), we now have people touting how many LOC their LLM agent is generating. It's like everyone forgot what LOC actually represents and what it means for long term maintenance costs.
No, he's not, he's making tons of money every month from his Substack subscriptions. In fact, the AI bubble popping would be the worse thing ever for him, he would be out of a job.
Just like the who have predicated the US dollar will collapse any-moment-now and which pushed gold for decades.
Funny how people always say "oh, you are an AI lab, of course you are going to hype AI", but never "oh, you make sooo much money from predicting the collapse of the AI bubble..."
I really like some good drama slop that reads like a thriller, it is entertaining.
I don't take any of it THAT serious, but lately with the IPOs that are about to hit the indizes, he has gained a lot of attention.
If you look around the internet, most people publish a negative angle on something and then extrapolate it into some grand conspiracy, which is really captivating.
Its crazy when you enter some echo chamber you never engage with (movies, gaming, art/comics) and they have their own head cannon for why the world is bad and collapsing. It puts your echo chamber into perspective to see the same patterns of argumentation and presentation spin out in a different way
Ed's argument for why "AI is slowing down" rests on company spending caps, in particular the Uber $1,500/engineer/tool cap.
I interpret the exact same evidence in the opposite direction. A year ago the idea that a company would spend $1,500/month/employee on AI tooling felt absurd, what could people possible want to do with AI that would cost that much?
Then coding agents (and, increasingly, general purpose agents) happened and suddenly companies are having to set limits because otherwise the demand from their employees is too high.
The TAM of these AI companies just leapt up to $1,500/knowledge-worker/month, how is that "slowing down"?
Maybe in USA in big tech where companies give absurd wages to engineers anyway in some states, that might be acceptable. But to make their ROI they need that (and more) to be spend world wide... no way that is gonna be a budget that is gonna fly in the long term...
Companies love to cut costs, and just like they axe employee numbers at will, they will just as well make that kind of budget quickly dissapear the moment they realize they can go a different path for same or better value... Or simply because share holder short-term value demands it...
The Uber $1,500/engineer/month thing is just the first signal we have had of the price companies may be willing to accept. This price will clearly vary wildly across professions, industries and geographies.
I think it's a poor number to build an "AI is slowing down" narrative around.
The problem is that $1500/engineer/month would be a pretty modest amount of demand for labs. OpenAI/Anthropic are basing their $1T valuations on the explosive uncapped growth of unlimited agentic token spending. On so many levels of the industry this growth is now priced in. You don't think so?
>OpenAI/Anthropic are basing their $1T valuations on the explosive uncapped growth of unlimited agentic token spending.
No they're not. In reality, actual 'explosive uncapped growth of unlimited agentic token spending' will result in valuations several times more than a 'mere' $1T.
Uber is not the only company that's putting a per-developer limit on AI spending. I know this because I work for another one (and we have a significantly lower limit). You just heard about Uber first because they're high profile.
(Sorry I'm being vague, but I'm not sure I'm not sure what's public knowledge)
The cap is moderately above the high subscription tiers, and managements/the executives were clearly extremely concerned about how expensive it would be if we all or even mostly came close to hitting it. I heard that they originally wanted to go lower but the developers in the pilot program blew past their planned limit very quickly.
As for the company, its almost entirely B2B SaaS (I think it has some offerings that are used by consumers, but they're mostly/entirely paid for by another business on behalf of their customers), and they have developers all over the world, although the headquarters and biggest group of developers is in silicon valley (my office is in the midwest).
It's also not $1,500 per month per engineer. It's that per month per engineer per tool. Which means it could easily be at least $3,000 (Claude Code and Cursor) or $4,500 if Codex was also an option on top of those two.
And as you have written on your blog it's a soft cap that can be exceeded with justification.
I don't know that Uber is generalizable. I also think specific company dynamics matter (are your execs encouraging tokenmaxxing, have you IPO'd or are you playing w/ VC money all of whom are encouraging tokenmaxxing, etc).
Another way you could take it is, avg Uber salary is what $300k/yr? Does Uber think LLMs make their engineers at most .5% more productive?
I don't really understand how engineers at Uber are hitting $1500/month. Are they forced to pay API costs?
My company provides employees with API keys and soft limits, but as soon as you approach ~$400/month they ask that you get a Claude/Codex Max subscription instead. Curious if it's not the same case at Uber.
>but as soon as you approach ~$400/month they ask that you get a Claude/Codex Max subscription instead
While this seems to be allowed because the current ToS don't seem to explicitly forbid it, I'd be surprised if this loophole stayed open for long... Why would they even distinguish between business and (much cheaper) individual plans if companies can work around it by telling employees to just pay for the latter themselves?
Enterprise agreements are billed at the API rate (albeit sometimes with committed spend discounts). There is no equivalent of the Max subscription in this context.
Saying its going to be 1500 a month across the board is highly speculative. How many companies can even demonstrate that they're getting more than 18000 dollars a year in surplus value per employee by using this tech?
I'm sort of an AI skeptic but I have been seeing this guy's essays for years now and he has always been super pessimistic on AI progress.
I think a much more reasoned critique of AI is that of Tyler Cowen, whose argument is basically that most processes aren't constrained by lack of intelligence but by organizational and social factors which mean for AI to be useful you have to redesign organizations and work to take advantage of what AI is good at. Since most organizations are fairly bureaucratic that takes a while, especially in the large industries that are the most economically important.
Ed's criticism of the large AI companies seems particularly misguided to me since they are the ones actually advancing the technology and seem to have real moats given their access to large amounts of training data from their users. I don't see any possible future in which 5 or 10 years from now there is less AI than we have now and I would expect usage to be much higher.
Completely agree. AI is here to stay, it's going to garner more and more use overtime. However, I'm skeptical that the investments being made right now are at of the right scale & at the right time. I completely agree that over time we'll rework more and more of our society around LLMs or their successors. However, like you say it's a slow process: we have to learn how to do it effectively, organizations need to change, people's attitudes and behaviors have to adapt. I just don't see is "getting there" fast enough to justify current spending levels.
Before you spend 20 minutes reading this article, it's worth understanding that the writer has been posting popular but consistently wrong takes for 2+ years (e.g. https://www.wheresyoured.at/peakai/ from March 2024) arguing that AI is failing, is a waste of money, is bad, will never work, etc.
Not sure where I heard this, but I'm reminded of a story about someone predicting the dotcom crash early, circa 1998. For 2 years they were demonstrably crazy, and missed out on massive stock market gains. Then they were right. (And yes, tech slowly bounced back after that.)
Predicting the timing of such a thing is notoriously difficult. I don't think being wrong about timing 2 years ago means there won't be a correction.
I'm also reminded of all the HN posts from 2007-2009 that predicted that the adoption of social networking would be a terrible thing for privacy, that it would destroy society, that people would lose their jobs over crazy shit they said on the Internet, that it would lead to the decline of trust and in-person interactions, that people would forget how to socialize, etc.
They were right about all of that but it took 15-20 years and the companies involved grew 100x in that timefold, eventually reaching trillion-dollar valuations that would've seemed insane in 2007.
There is a tremendous amount of money to be made in destroying society.
Eh, you can find HN posts predicting that literally everything will destroy privacy/society/trust/etc. Predicting doom is a popular pasttime.
What I remember from that time period is people predicting that we were in a tech bubble driven by social media, that obviously Facebook and LinkedIn were overvalued because social media was a trivial fad, and so on. Example article pulled at random:
They weren't wrong. We were in a tech bubble driven by social media. Digg, StumbleUpon, Kongregate, MySpace, Orkut, Slide, Meebo, Mahalo, Bebo, Justin.TV, etc. aren't exactly around anymore. Facebook and YouTube are the winners.
I was definitely around when that video was current, but I don't remember it. It's pretty amusing.
Ironically I feel like it captures the spirit of the then-coming 2010s boom more than the climate in 2007, though some of the language it's using is decidedly pre-mobile and more "web 2.0"-ish.
Not related to AI but, I recently rewatched "The Big Short" and your comment reminded me of it. I can't testify the accuracy of the movie, but for over year, Michael Burry was viewed as in the same manner for shorting the market, while the economy was was in a hype cycle.
I'm open-minded to arguments about AI being a financial bubble and a bad business.
I'm not open-minded to arguments about utility, given that I personally witnessed LLMs evolve from interesting but useless toys to insanely helpful tools I use every day.
I guess one of Zitron's arguments is that the utility you see today is based on subsidized costs, that if you had to pay more it might not be worth the tradeoff to you.
So the claim is the cost isn't coming down enough to make it make sense for a lot of uses in the long term. When I hear that next to the most wild claims, some by influential people, that the entire white collar workforce is going to be replaced very shortly, it's a bit of a useful reality check.
Exactly. The question is not "are people using it to do stuff?" because we know right now they are. Given free or heavily-subsidised access to powerful tools, people will use them.
If I had someone giving me free access to cranes and excavators, I'd be raving about how easy it was to build houses now. But tomorrow when I have to pay full price for them, I'm going to be making very different calculations about return on investment.
The question we need to be asking is "what is the likely full-price cost we'll have to pay for these tools, and is that cost likely to be worth paying?"
What Ed's pointing to is that the full-price cost will have to cover the capital expenditures that have been invested, or the companies which risked that capital will go bust. That gives us a floor for what the full price cost will be, and that floor seems higher than the value being offered by the tools.
Can you point to anything specific from the article that you'd describe as consistently wrong? Not disagreeing with you, but nothing popped out to me after skimming the article.
I didn't read the posted article (I don't read this author anymore because I think it's basically anti-AI ideological propaganda).
But from the article I linked back in March 2024:
"Generative AI models are expensive and compute-intensive without providing obvious, tangible mass-market use cases. Murati and Altman's futures depend heavily on keeping the world believing that development and improvement of their models' capabilities will continue a rapacious pace of progress that has unquestionably slowed, with OpenAI admitting that GPT-4 may be worse on some tasks.
As I've written before, hallucinations are a feature not a bug. These models do not "know" anything. They are mathematical behemoths generating a best guess based on training data and labeling, and thus do not "know" what you are asking it to do. You simply cannot fix them. Hallucinations are not going away."
Since then:
- hallucinations are dramatically less of a problem
- several mass market use cases have emerged, most notably coding
I think the points you raise are reasonable signals to consider, but I don't think they show the author being "consistently wrong". The overall thesis still remains plausible even though we have seen LLMs continue to improve.
> - hallucinations are dramatically less of a problem
Sure, but it remains a big enough problem that human intervention and review is still necessary for any serious work across all use cases and industries.
> - several mass market use cases have emerged, most notably coding
Coding seems to be the only one, but there are still a lot of open questions about how the market can sustain the costs, and that's without considering the market dynamics that could emerge once costs are lowered enough that open source models start to become an attractive option.
> Sure, but it remains a big enough problem that human intervention and review is still necessary for any serious work across all use cases and industries.
Another important consideration: Hallucinations getting less common/severe but not (as-good-as) solved makes them worse.
LLMs used to very obviously get things wrong. And people wouldn't trust them. Now they're good enough that people blindly trust them.
Now people just directly PR AI output with little to no manual review. We even have clowns calling for the complete abolition of directly human-authored code.
Whatever gains were had in better AI code output over the past two years I lose in having to review much more thoroughly.
> several mass market use cases have emerged, most notably coding
Most notably? This is not a mass market use case in the way the author is describing. They are asserting that the amount of spend they need to get this off the ground necessitates the entire world coming in on it, and I would say that opinion has aged pretty well. There are a lot of coders, but there are more people scratching their heads as AI is shoved into every part of their lives.
Has rate of progress increased? How does one measure that? Genuinely curious - would be very interesting to map out the "effectiveness" of each AI model vs how long it took to train/release.
From my perspective, the model gains are mostly incremental now and a lot of the gains are just from things like improving the agent harnesses. I could be wrong though.
Hallucinations are still a problem. I recently asked one to give me a quote from a book, figuring that since these AI companies have pirated all books in existence surely it can just recite a specific passage no? It hallucinated the quote, I had even told it what chapter it was in. Had I not read the book recently maybe I would've believed the hallucinated quote.
And it got me thinking, they sell these AIs as assistants, but it couldn't even look up a passage from a book. This is basic, elementary stuff, it should get it right. I would have fired this assistant right away if it were a person. Not only did it get it totally wrong, it came to me with utmost confidence that this is the quote from the book. Unreliable assistants? That's the product they're trying to sell? Get out of here with that trash. I can't trust it.
> hallucinations are dramatically less of a problem
No they aren't. The models still hallucinate just like they always did. You cannot trust them, ever, to get something right.
> several mass market use cases have emerged, most notably coding
They aren't really useful for coding based upon the above. Since you can't trust them, you have to carefully review everything they make, which in turn destroys any productivity they could've given you.
> rate of progress has increased
I have yet to see any progress. Opus 4.8 that you get today is no more effective than GPT-3.5 was. Much less would I agree that the rate of progress has increased. Only hype has increased, but there has yet to be a drop of substance.
His point is that coding is only a “market” because it is being sold at a loss. Businesses have to pay per-token prices and are saying that the cost is not justified.
Nevertheless, it all misses the point if we get to AI post-scarcity utopia. But thats a big if.
It doesn't miss the point because if we get to some AI post-scarcity utopia then the companies pouring trillions into it now are never going to make their money back on that investment.
The only way they make their money back is if everyone pays them tons of money for it.
> I believe that artificial intelligence has three quarters to prove itself before the apocalypse comes, and when it does, it will be that much worse, savaging the revenues of the biggest companies in tech. Once usage drops, so will the remarkable amounts of revenue that have flowed into big tech, and so will acres of data centers sit unused, the cloud equivalent of the massive overhiring we saw in post-lockdown Silicon Valley.
We have seen 8 quarters since. Has any of that come to pass?
if you can't predict when it will pop then you should really not predict anything. I can also predict that Google will pop. I won't tell you when but I'll tell you that it will. I'll remain thoroughly unfalsifiable and I'll keep pushing the dates.
The quality of AI doomerism takes is matched only by the quality of AI boosterism takes. Ed's kind of interesting as a temperature sensor but I don't feel like you can really take anything he writes seriously.
When asked whether it will be possible to fix Sora's videos after they've been generated, Murati said "eventually," and then couched that by saying "that's what we're trying to figure out...how to use this technology as a tool that people can edit and create with." She promised that there would "eventually" be "more steerability, control and accuracy...and reflecting of intent of what you want."
You'll "eventually" be able to add audio to Sora videos, and when asked when Sora's generative videos will be available to the public, she once again said "eventually," and when pushed said that Sora's launch would "definitely be this year, but could be a few months."
Murati, living in a world of "eventuallies," provided no technical insights, no specifics, and very few details.
What if you phrase the question from "will AI ever be useful" (a term as utterly vague as "IT") to "will it ever be able to promise the financial gains these companies are hoping? Especially with local models eating their lunch :shrug:
Yeah they seem clickable because anything Anti-AI is a bit soothing right now, but he is constantly wrong and usually is pushing the angle of "these businesses aren't even profitable!"
Instantly close the tab as soon as the popup to subscribe to his newsletter pops up.
They ain’t profitable yet. Most of the model maker’s will be gone soon. It’s unsustainable unless you’re Google who has other income coming in to support their hobby, and the Chinese model makers are spending a fraction to be six months behind and many of them will be there for the long-term because they have backup support (government) who is in the race for the long-term.
One other thing that’s working against the model makers is the hardware is getting better and the models are getting smaller and more capable. I don’t think we’re going back to the mainframe days. Local will be the endgame.
Is Ed right? Probably because in the end it’s unsustainable the companies left will be the companies that have income coming from somewhere else and there’s one large tech company that isn’t even participating in the boondoggle unless you count $1 billion dollars a year as participating ultimately there is no moat in AI model making.
Nvidia and Microsoft trying to introduce another Arm processor in a laptop of all things won’t change the tide either.
> Culture is destroyed, truth is destroyed, democracy is destroyed.
Just because those things happened in Dune doesn't mean they'll come true. It should be obvious what massive potential the thinking machines have for humanity as a whole.
> which is an extremely rationale judgement to make.
So it's "rational" to take bias into reading? Why even read? If you know what you think and refuse to accept new information then what purpose is there in consuming anything?
You should just read the comments and get a warm fuzzy that the crowd, for the time being, agrees with your intentionally static ideology.
Comments like these obviously hope they can sway the crowd before they can take an unbiased reading of the article. If the author is that wrong then the crowd here should be able to discover that on their own. If the author convinces the crowd then I'd think you'd want to present a better argument than "well, he was wrong _before_." Post hoc, ergo propter hoc, in action.
That's the exact opposite of rational. It is, in fact, a formal logical fallacy (ad hominem). His argument can be correct even if he himself is not typically correct.
On the surface, that's quite fair. However, there's one problem: it is much easier to make statements than to verify them, and that asymmetry is part of why the internet has been slowly eroding society.
It's useful/necessary to use past writing/arguments from an author to say whether they should actually receive any further critical evaluation, or be dismissed. We shouldn't say definitively "they're always wrong, so they're wrong now". However, it's reasonable to say: the author has a demonstrated lack of credibility, so we can probably assume they're wrong here, particularly if they have been wrong in this domain so many times before. Or if they happen to be correct, it's probably not strongly demonstrated by their work.
What's the point of reading someone's writings on a subject where you know they're not typically correct? How would we know what we 'learn' from Ed is right?
The way I see it, AI is going to change the world radically. It could be for the worse, the better, or a mix of both, but in my mind there's no doubt.
We are only five or six years into the leap LLMs represent. For reference, radio waves were discovered in 1886, Marconi used them for communications in 1895, and while telephone and radio coexisted for many decades, it wasn't until the 1995 that mobile phones and wireless technologies started picking up. It took so long not because of the physics of radio waves required time to mature and improve, but because everything else needed to profit from it did require time.
To me, LLMs are not so much AI as it is a building block. Radiowaves maybe, or the equivalent of transistors. We are already seeing that it's possible to chain LLMs into agents. Currently, price is a strict limiting factor for coding and agents.It's probably fine-ish if all you want is Claude Code or Codex, but there are many other possible compositions of LLMs that most people don't dare to experiment with. For example, LLMs to drive NPC dialog and world mechanics in games is not a thing due to cost. Were prices of inference hardware go down and inference algorithms keep improving, I'm convinced (and afraid) we would see things very difficult to imagine today.
> For example, LLMs to drive NPC dialog and world mechanics in games is not a thing due to cost.
Hah, I'm actually working on just this problem.
Cost isn't the issue. There are only so many coherent (in context) responses and scenarios, that you don't need an LLM to generate text in the game, in real time. Instead, you can have LLMs build a vast corpus of "atoms" (dialog messages, fragments, cues, etc.) that can be stringed together in a deterministic way in response to player input. These can also be pre-screened and subjected to various tests prior to implementation.
To a player interacting in the game, a system like this would seem functionally indistinguishable from generated text within the game's designed interaction envelope. And it has huge advantages: Although it can expose seams if the player breaks character and decides to probe it, it won't be exploitable the way an LLM would be.
>Far more interested in dialog and characters developed by a writer - simulation is boring
It entirely depends on the situation. Background NPCs that just have conversations among themselves would be a great use of LLMs to make the world feel more immersive. Obviously you never want to directly engage the player with LLM generated writing.
> The way I see it, AI is going to change the world radically. It could be for the worse, the better, or a mix of both, but in my mind there's no doubt.
Worthless statement. Wow, you suspect something can make things better, worse, or both? That's a keen insight there.
> For reference, radio waves were discovered in 1886, Marconi used them for communications in 1895, and while telephone and radio coexisted for many decades, it wasn't until the 1995 that mobile phones and wireless technologies started picking up.
We are still so early.
I mean, we have advertised them in multiple super bowls, have companies that basically own tech news (incredulous journalists will repeat any stupid insane shit a CEO wants to say), that say they're valued at over a trillion dollars and nobody with the power to argue those finances seems willing to do anything but agree. We have built hundreds and hundreds of acres of data centers (and made deals for data centers that are never going to happen) that demand *billions* per month. They are devouring all the silicon to where people are visibly seeing the price of hardware double, triple, more in price. Work places insist on employees using AI (then pulled back because it turns out this stuff costs money and it's not fun anymore when it's not subsidized).
But we just need more time, more eyes, more people looking at it.
Already many comments saying this but Ed Zitron is not a person I trust. He's been so biased and wrong on stuff that I consider very obvious and trivial that his complicated analysis with numbers and trends I can't just take at face value.
As an example of obvious wrong things, I remember a tweet of his where he was mocking people talking about agents and agentic coding. He was kind of saying that he was going crazy as agents weren't a thing really and people talking about them like they were real. Something like "agents?! what agents?! these guys hear themselves?!". The answers were full of hundreds of people patiently explaining how they were actually _using_ agents. This wasn't in 2023, it was a couple of months ago.
He just has an audience and an engagement target. His objective is clicks, not informing.
His numbers are based on sources that he says he doesn’t trust, which is quite interesting. While he may be directionally accurate (eg. The revenue needed to match the spend seems lofty at best) he seems to be mixing and matching numbers to create a worst case scenario that doesn’t necessarily line up with reality. Combined with his complete unwillingness to be open minded about anything even tangentially related to AI, and I can’t take him that seriously.
Publications love a doom and gloom rant, which is why he seems to have built an entire career on hysterical anti-ai screeds. It doesn’t mean that he’s right though.
It's disappointing that this article got mindshare when a more neutral author could better argue the bearish case for AI valuations. I want the steelman argument from a more respected individual.
The problem is when untrusted authors take positions, then it circulates widely, then people discredit the author and by proxy the position, when the position could be correct.
The article has a number of emotional appeals in it. Something more focused on raw numbers would foster more curious discussion.
He provided a lot of quantitative analysis in this article. Perhaps an example or two you think these numbers are off-base could help bolster this point?
I think the most compelling part of the article is that these numbers point to a situation where the level of investment required seems unsustainably high by plain dollars.
You don’t really have to agree with the author to see how it plays out. OpenAI and SpaceX and Anthropic need to go public this year to avoid running out of money. There’s no more private money, not enough to fund them. The IPO is the last funding round.
They can continue growing extremely quickly and AI can still be highly useful and maybe be transformative, but still not have the money to fund that growth.
That part he wrote about an AI company gone bust canceling their Oracle contract made Oracle feel like a Nortel analogy to me. If they have a sudden lapse with a big chunk of their customers they are writing down triple digit billions of dollars.
I guess what I'm saying is that I won't look at his numbers since he's an unreliable source from my point of view and there's a chance that he's going to try to deceive me and it's a waste of time for me to listen to him.
I do have other sources of information and I probably agree in general that AI companies are doing pretty shady financial shenanigans. I even think it's possible that openai is in real trouble. But I don't extrapolate that into "AI is useless", which is what he does.
> I guess what I'm saying is that I won't look at his numbers since he's an unreliable source from my point of view and there's a chance that he's going to try to deceive me and it's a waste of time for me to listen to him.
I would agree with you that extrapolating to “AI is useless” is definitely a giant step too far, and that part of the article ruins a lot of the other interesting bits of it.
It’s great that he cites a lot of sources but some of them aren’t great, like the Microsoft story about canceling their Claude spend. I think that particular story isn’t much of an indicator of anything, and it might not even be true.
But the financial part…this guy isn’t the only person out there sounding the alarm about the math not mathing.
FWIW I agree the financials are a bit crazy and OpenAI went a bit nuts with the circular deals. That said, honestly, I don't think it's the end of the world. I think there will probably be some correction/crash and it will probably be healthy. A lot of these circular deals will get canceled, but it's at the end of the day people changing imaginary numbers with each other. The underlying tech I still think it's revolutionary regardless, the same way that the internet was and the tech boom crash at the end of the day was a distraction from the fact that these companies did end up "ruling the world"
This is what critical reading is for. It requires you examining your own assumptions as much as the text's. If you don't engage with something or someone because of your own bias or assumptions, that is also willful ignorance; you also might end up never updating your prior stance when new information emerges.
There is a financial argument and capability argument.
In this case, he doesn't make the claim one follows from the other.
There's no shortage of sources of information. I'll exercise "critical reading" with sources I consider trustworthy to begin with. I've no time to engage with difficult analysis from people who are not worth the effort. You wouldn't engage with every lunacy you read on a tabloid, right? similar principle
Fair enough. I won't debate preferences or how you choose to spend your time. I think one of the merits of his articles is that he surveys and gathers sources that others can engage with. Even if we admit he is biased, that exercise (his writing) alone is valuable because one can contradict or reassess his claims.
Grandparent comment’s primary claim was that Ed has frequently claimed untrue things in the past and so questioned why people would continue going to such a source, but your reply didn't seem to address that at all?
B. AI's output accuracy was already about as high as it would ever be
C. AI users were already declining or was as high as it would ever be.
So we have 3 2024 claims about whether AI was already the biggest/best it would ever be, and whether AI usage was even already shrinking. All ended up being the opposite of true.
Have you looked at whether Ed’s previous claims that went against popular AI views and are testable ended up being true or not?
Does it matter whether an author’s claims like that are true or not for whether you will continue consuming them?
If straightforward claims like the above are so easily disprovable, what makes you believe that he isn't cherry-picking other stats in order to spread misinformation or disinformation, as the individual stats he points to might even be completely true, but if they are cherry-picked, they may be more misleading than elucidative?
If someone has a multi-year history of frequently spreading false claims, should we trust their predictions about future events more than other sources?
He also calls everyone a grifter, when he seems like one himself. Im deeply skeptical of our AI overlords, but its disingenuous to keep pretwnding theres nothing there.
Always a bit eyebrow-raising to me how much people focus on Ed’s style rather than his message, which is, broadly, that the tech industry is deeply morally corrupted. He struggles to speak about it without becoming impassioned, but I read it as incredulousness rather than baseless hyperbole: “How are you still investing in and working for companies like Meta, despite the overwhelming evidence that they are terrible company that does terrible things to people?”
Style is relevant to how humans communicate and it's not always about the message, and it can sometimes work against it. AP Style is an editorial standard for a reason.
IF I WROTE AN ENTIRE BLOG POST IN ALL CAPS ABOUT HOW AI IS LITERALLY SATAN PEOPLE WOULD JUST THINK I AM A CRAZY PERSON
> his message, which is, broadly, that the tech industry is deeply morally corrupted
If that's his message, why is he going on about ecconomic sustainability? Whether or not you have a coherent business model has nothing to do with how morally corrupt you are.
Ultimately i agree with the GP post, the article reads like something preaching to the choir. If you already agree it seems natural. If you don't agree it looks like an incoherent rant that is not particularly convincing.
Arguing against the business model is a method of exposing the tactics of AI businesses as a short term grift rather than a principled venture. The entire economy suffers when grifters profit - there’s not infinite money to spread around.
Is it really a grift when everyone knows? Its not like they are keeping their financials secret. Heck, the main AI companies aren't even public yet, so its largely sophisticated investors getting "grifted".
Normally a grift involves tricking someone. The AI situation seems more like a bunch of investors knowingly investing in something very speculative. If they lose their money, while that is the nature of speculative investments.
What’s the line that separates grift from speculative investment? Does it only require true believers? If Elizabeth Holmes truly believed in her product, would that have made it not a grift?
> The AI situation seems more like a bunch of investors knowingly investing in something very speculative. If they lose their money, while that is the nature of speculative investments.
This is a bit reductive, especially as the government’s role in shaping American life is reduced and the influence of extremely high wealth individuals impacts every citizen. We’re somewhat losing our democratic rights when Congress does not answer to our interests, and the extremely high wealth individuals directly (FoxNews) or indirectly (X, Meta incentives) manufacture consent.
The whole theory of low taxes and light regulations is that high wealth individuals will be incentivized to “create jobs”, like modern Lords of the Manor. When those who essentially steer the direction of the economy take actions that are highly speculative seemingly only to enrich themselves and concentrate wealth further despite public outcry … well, maybe there isn’t a word that elegantly describes that situation, but “grift” is as close as I can muster.
> What’s the line that separates grift from speculative investment?
Are they actively lying to people in concrete ways? Not just, we think this is going to be big, when that is questionable, but do they say AI can do X right now, when objectively it cannot even remotely do X under any circumstance? Lying is what makes the difference.
> well, maybe there isn’t a word that elegantly describes that situation, but “grift” is as close as I can muster.
I think the word you are looking for is capitalism.
Rich people put their money where they think it will generate more money. The economy uses that to determine what to focus on. Sometimes rich people are wrong and are punished by losing a bunch of money. That is how the system is supposed to work. It is far from perfect but traditionally has done better than the alternatives.
To your first point about lying, surely that’s not the bar you’d like to set? Misleading investors is pretty much the greatest talent of all of history’s greatest capitalists - were they all grifters? Is it the extent of the lying that matters? Because pure honesty is nowhere to be found in SV.
> I think the word you are looking for is capitalism.
“It” may be a consequence of some countries’ and cultures’ attempt at implementing capitalist policies, but I don’t think that’s anyone’s working definition of capitalism.
“It” being a runaway concentration of wealth being held by few individuals who are not as well-informed and smart as they think they are, exploiting economies of scale and highly financialized systems.
> That is how the system is supposed to work.
The system is supposed to work in the way that we design it to work. Massive speculative bubbles where every rich yahoo is simply looking for an “exit strategy” is far from ideal.
You’re far too forgiving, covering your eyes while the industrialists rob us blind. There’s a culture of grift and exploitation running rampant in the US at the moment, and this sort of apologia only serves to exacerbate.
> To your first point about lying, surely that’s not the bar you’d like to set?
Its literally the dictionary definition.
> Misleading investors is pretty much the greatest talent of all of history’s greatest capitalists - were they all grifters? Is it the extent of the lying that matters?
Some are. Plenty are in the grey.
But there is a big difference between objectively lying like in theranos, and hyping things up questionably. There is a reason why Theranos people are in jail, while many a hype man walk free.
> anyone’s working definition of capitalism.
How about the dictionary definition of capitalism. Private individuals owning and controlling the means of production and subjecting to their whims is as fundamental to capitalisim as you can get.
Maybe there is an exception for monopolies, but i dont think any of the AI companies are monopolies.
> held by few individuals who are not as well-informed and smart as they think they are
I mean, seems to be working out pretty well for them. Can't argue with success.
> The system is supposed to work in the way that we design it to work
Key word being "we". As in society. Not you personally.
> There’s a culture of grift and exploitation running rampant in the US at the moment, and this sort of apologia only serves to exacerbate.
People making heated diatribes instead of cold logical arguments serve to exacerbate it.
Elon Musk just got the rules of the NASDAQ changed so he can more or less force index funds to buy his shell company and take money from people's 401k. Feels very grifty to me.
This is the kind of thing that the people in power really don't want you to know, but I'll say it anyway because if we don't get the message out, it's just another free win for the Nazis: a bad presentation is a poor information medium.
Buried lede (if the title is the actual promise), the sources don't seem to back the title either. Someone with more patience can correct me if I accidentally missed a bombshell anyway.
Edit:
> If you’re wondering what the story is, [...] I expect it to be out in the next two weeks [...] I can guarantee you it’ll be worth it, and you’ll be stunned by what I report.
Ok, this takes clickbait to new lows. The headline is trying to sell the teaser here, with very limited meat in the middle of the sandwich.
> the sources don't seem to back the title either.
This is nothing new for Zitron. The last article of his I saw:
> I also severely doubt that Anthropic managed to make the cost of running its services profitable in the space of six months.
> [Per The Information in January], Anthropic missed on its gross margin projections, saying that its inference costs were 23% higher than the company had anticipated.
> How did Anthropic, which faced a massive influx of new business to the point that Anthropic was forced to buy more compute from Elon Musk, magically become profitable? Other than that discount, of course.
If you follow the link to The Information, you’ll see that it’s a paid article with the headline “Anthropic Lowers Gross Margin Projection as Revenue Skyrockets”. But what happens when you actually read the article?
> Anthropic last month projected it would generate a 40% gross profit margin from selling AI to businesses and application developers in 2025, according to two people with knowledge of its financials. That margin was 10 percentage points lower than its earlier optimistic expectations, though it’s still a big improvement from the year before.
So, according to Zitron’s own source, Anthropic are actually earning 40% gross profit margin on inference, and that is a dramatic jump upwards! This totally contradicts his position that it’s an implausible “swindle” for Anthropic to claim profitability. He’s counting on the fact that most of his readers don’t subscribe to The Information and will only see the headline, or that they will just see a citation and trust that it backs him up without checking.
I find it difficult to separate this piece’s tone from its content. The tone puts me off and makes it hard for me to judge it on its merits, despite some of the arguments seeming sound and well supported.
"I believe that artificial intelligence has three quarters to prove itself before the apocalypse comes, and when it does, it will be that much worse, savaging the revenues of the biggest companies in tech. Once usage drops, so will the remarkable amounts of revenue that have flowed into big tech, and so will acres of data centers sit unused, the cloud equivalent of the massive overhiring we saw in post-lockdown Silicon Valley."
You can disagree. Sarcastically, or otherwise. But I think you may be reading more into my comment than I put there.
I’m not attacking the piece. I’m not saying it’s right. I’m not saying it’s wrong.
What I’m saying is, the tone made it hard for me to judge the arguments fairly, despite finding some of them convincing. And as much as I dislike it, persuasion does partly depend on how an argument is made.
Ed's posts are peak preaching to the choir, they're usually factually correct but he is really bad at convincing anyone who doesn't already strongly agree with him.
Have you seen his recent Bloomberg appearance? He's calm, collected, and matter-of-fact -- the complete opposite of how he presents himself on his newsletter and podcasts, but with the same argument. You wouldn't know from listening to him how spicy he usually is.
It's tuned to the audience. Bloomberg was traditionally for people who actually wanted information. People who were fallible and had limited knowledge.
Of course that mentality is obsolete. Now we all have infinite access to perfectly correct information via the internet.
Perhaps that’s it. I would tend to agree with his position, I think, but don’t appreciate being preached to. Even less so when I agree with what’s being said.
Agreed. I am open to the possibility of the bubble bursting or whatever, but this piece is like 3,000 words and cites everything as evidence the sky is falling. It's just as bad as the pro-AI grifters, just in the other direction.
Does the truth normally lie somewhere in the middle of it all?
Probably. Although I feel more inclined to forgive Ed in this case because it's sort of fighting fire with fire, the insanely hyperbolic and obscenely misleading drivel that's coming out of the most ardent AI boosters is continually unchallenged in the public eye. In a world where we had a more realistic view of AI/ML/LLMs, the limits to its capabilities, and the negative externalities of its widespread adoption in places where it quite frankly does not belong, then I'd be more critical of the Chicken Little sort of writing style
The obvious answer to where the AI Labs get customers is Cloud GPUs. Most users (globally) have cheap phones with poor CPUs and small amounts of RAM. They can't run usable models locally, and it's not clear from the Google-Apple deal if G is selling access to their cloud compute as part of that $1B, or just sharing the weights/IP.
Apple themselves have said there is usage limits, with a subscription upgrade for more usage. So clearly AI Labs are directly competing on that front, it's just a normal default/chosen decision. Considering there are defaults and still successful competitors (eg. safari v chrome), there's no reason to think that competition can't handle this too.
Edit: I want to add that Google is also probably willing to give the model away at a discount to its true value in exchange for guaranteeing that their primary competition (who has tons of cash) won’t have an economic incentive to enter the foundation model training arms race.
Most users who actually want these features for anything more serious than summarization and style updates will probably find value in a modest subscription or ad-supported tier of higher quality models, even if just for occasional usage. Apple can provide this, but once you're comparing features, for many Gemini/Claude/ChatGPT may be a better fit.
Oh, and I think there is an unfortunate but real risk that once again, apple totally over-promises here, and their AI models that they ship end up being pretty poor, and that drives users further into subscriptions.
> "Some features, including image generation, have daily usage limits because they rely on powerful server models. Increased access is available with most iCloud plus subscription plans".
Start at 1:07:00 in their announcement video. Craig is absolutely talking about "Apple Intelligence" as a whole in this segment.
Pragmatically, of course they'd need to add metering to any cloud available APIs that rely on large models. There's no way they will eat the cost of serving unlimited access to a cloud LLM to end users if they won't eat the cost of an image generation model.
> Oh, and I think there is an unfortunate but real risk that once again, apple totally over-promises here, and their AI models that they ship end up being pretty poor, and that drives users further into subscriptions.
OK, that I would concede is a possibility. Though Gemini is clearly capable, and the (alleged) story is that they have licensed a one-trillion parameter form of Gemini. I don't think they are making the same mistake.
ETA: I also concede they could make a different mistake ;-)
The AI labs are racing to create a moat out of trillion-parameter models and the GPUs that can run them. The problem is this is the wrong architecture for most AI inference use cases. On-device inference is where this is going, clearly Apple believes this too. So Zitron is entirely correct about this AI datacenter build out being a boondoggle with no ROI.
Ed Zitron with his subpar takes again.
You could kill all the major AI labs today, and even just today's open-weight models are enough to absolutely transform society, with video-game GPU stuck together at homes.
From what I've seen lately, Ed isn't saying the technology isn't significant. He criticizes the fact that AI companies are dumping this kind of money into it with circular financing deals. If it goes like you say, (GPUs at home) the AI labs will be destroyed, because no one will need the inference capacity they're dumping money into when they can do the decent part at home. From what I've seen he says it's more "not worth the investment" rather than not a breakthrough. It just sucks compared to the trillions they're spending which they'll never recover, is the argument. But I guess it's easier to strawman the argument to "this stuff isn't going to change anything".
He is saying technology isnt significant. Again and again and he puts a lot of emphasis on it. He is also focusing the circular financing deals, on it being a bubble etc.
Obviously, you can agree with one thing and not with other. And he can be right in one thing and not the other. But, if you listen to what he is saying, he is absolutely saying the technology is not significant.
I think you're misinterpreting a strongly worded "it isn't as great as the hype" with "there's nothing there". Because "nothing there that matches the hype" is both 100% true and completely different than "nothing there".
I don't think I am misinterpreting him. I did read some of his articles and did listened to some of his podcasts.
He is very very clear and open on what he thinks about usefulness of ai. He is not saying "it isn't as great as the hype". He is saying "it is useless". He is simply not the centrist kind of guy when it comes to AI usefulness.
Although I see huge utility in AI, I think he is right in terms of overspending and overenthusiastic build out. Because of for example what Apple is doing by putting an extremely efficient model with task adapters right onto phones.
Also because we now have a massive demonstration that vastly more efficient hardware is desperately needed.
Similarly other effective efforts towards on-device AI like Nvidia RTX Spark PCs and 2bit quants of strong models like DS4.
So inevitably, significant investment will be going into vastly more efficient CIM efforts like Mythic AI and new FeFET devices etc. in order to make human-level and beyond AI at scale feasible. There is so much demand for this and the power requirements of current hardware are so excessive, it seems unlikely that the data center build-outs will be able to recoup their costs before the more efficient paradigms make it out of the lab and start scaling.
I have been a pretty consistent user of AI since 2022 (Instruct-GPT), so I don't have a bad opinion about the topic. However, I think the real problem now has become pretty obvious. We are hitting a reality wall, where we simply don't have enough ressources to feed the AI industry. We don't generate enough electrical power nor enough GPU or TPU. For the first time in computer science, the real issue here is the finitude of the physical world. Unless, we start digging asteroids, we are already facing a shortage of raw material and industrial output. In my opinion, the only way to go is small models running on regular hardwares.
Aren't small local models worse efficiency-wise? It means that every person must have a powerful enough machine to power a small model, and we are very, very far away from that.
The best solution, from an efficiency point of view, is to use smaller models on datacenters, requiring much less of them.
There's an efficiency sweet spot where hardware that people have anyway gets a higher percentage of load.
MacBooks have a lot of memory and a lot of FLOPs. They mostly sit unused all day. Yes, the excess energy use will be higher than a GPU in a datacenter doing the same work, but you have to generate an absurd amount of tokens before the dollar-efficiency catches up with the MacBook.
The future is clearly that, model inference running on consumer hardware not a network datacenter. We are getting there, local models gets better and better, it's only a matter of time. Of course this would be bad news for big AI companies (and whoever invested in them).
I find it nuts that I can use Claude Code for $20pm - I imagine that won't last forever but have to say it is great value for money.
So when I see monthly budgets in the thousands for developers at some larger companies, I'm curious to learn how they are managing to spend that kind of figure: how much code/documentation are they feeding into their prompts, are they using agent orchestration systems to make the code factory run 24/7, and how much value is coming out the other end versus before?
And, if they are pouring thousands into LLMs per developer, have they considered looking at alternatives like having LLMs running locally on own hardware with their own agent harness?
Those are the kind of questions I'd love to ask - I just wonder how much stuff is truly cutting edge and how much might be wasteful?
Developers at big companies need to pay per token, they don't have subscription available. So in case you use that, you likely spend way more than $20 in tokens.
As for how to spend that much -- not that hard, to be honest. Just give it a lot of context and some relatively open-ended problem and it will easily eat through tons of tokens.
I have $200 subscription for Codex and it is crazy what it can do in terms of debugging. I have a pretty complex Electron setup with some native code linked via Node addons, a few App Extensions and it can easily read the source code to see how the builder works internally (e.g. if your end Info.plist is not correct), debug the xcodebuild output to see at which step something is not linked correctly (like after XCode major version bump), etc.
It is not a silver bullet but if you are not the one paying for it, there is no downside to throw a problem at it and see if it can come up with a fix.
> And, if they are pouring thousands into LLMs per developer, have they considered looking at alternatives like having LLMs running locally on own hardware with their own agent harness?
I am curious about that myself. I have a good machine now (Macbook Pro M5 Pro with 48GB memory), so I'll give it a try; I don't have high expectations so if it is actually helpful would be very neat.
I just looked at ccusage for a personal project. In 5 days (doing it as a hobby) I've managed to spend $250 in API tokens on a $200 subscription. 5 days, and thats on one computer (I split time using 3 of them). If I had to pay $2000 a month -- no fricking way, not worth it.
This is wishful thinking. AI is still getting better rapidly. Anthropic's revenue is still growing at an unprecedented rate and they haven't even released their best model (Mythos) for 4 months now.
I asked around my network recently - in the last month or two basically every large company has put in spending limits per engineer. Curious what their S1 will look like when they go public.
Whats a bit wild to me is Google's only selling point for their Pixel phones are increasingly Gemini.
Now that you can get Gemini, operated by Apple (with the Apple privacy features that come along with that), why would you ever consider going Android/Pixel (outside of running GrapheneOS, but I'm talking regular consumers here)?
Google isn't even making anything on the deal with Apple. They pay $20B/year to be the default search engine. This is Apple just giving a $1B a year discount to that to be able to license Gemini.
I switched from iPhone to Pixel after I couldn't stand Liquid Glass and found myself using Gemini more than I expected.
If you're in the Google ecosystem like Gmail and Calendar, it is exceptionally refreshing to be able to use an assistant that uses that ecosystem, instead of iOS requiring you to use Mail or its own Calendar app.
I don't think there's any real gap between Pixel and iPhone on the things that matter: UX jank, battery life, camera. Even the messaging issue in the US has closed with encrytped RCS support between them launching. So now it's just an ecosystem question, which might be why Gemini is mentioned so much with Pixel.
Android phones are also quite a bit more capable than iPhones in a number of ways due to being more open. Plenty of people just straight up prefer the experience (and plenty of others prefer the cheaper prices).
Yes. And there is only one other major phone brand in the West with this kind of clout: Samsung. Who I think will want their own thing that isn't Google's, and who do have some connections to OpenAI.
But given how dependent OpenAI are on Samsung, it's hard to believe they will see a radically better deal in material terms.
I don’t think the idea is that these companies are going to make their $ selling their services, that’s just step one. they’re betting that they’ll have their own “country of geniuses in a data center” to put toward whatever thing they think will make them the most money.
It doesn't matter if it's slowing down, pretty much no one has implemented it to its full extent yet. It could stop right now and we'll be finding new implementations a decade from now.
Anthropic and Open AI could evaporate tomorrow and we'll still be using the models.
The market may collapse, but the people who think AI is going to disappear as a result don't understand what it is.
Ed's posts always sing the same tune - AI spend is unsustainable. But why are the investors pouring into these megacorps allowing them to burn cash? What calculations are the investors making? I would like to see those projections, assumptions and backout plans.
Why bubbles happen? Because investors, out of greed, pour into corporations that burn their cash.
The internet was a bubble: you could make a web page and sell it for millions because next year it was going to be worth billions. And then internet grew up.
AI is technology that's still beginning to find its place to settle. It's far from mature and that's perfectly fine. We'll have reached a reasonable plateau once the technology and the related stack stops changing every month and instead develops incrementally and boringly over the span of few years. That's like internet in the 2008-2010, and many investors will have a collection of new burn marks by that time.
Not only financially there's an unsustainable push for AI by the zealots du jour who are more often than not managers rather than engineers. AI is championed most ruthlessly as a silver bullet revolution by people who least grasp the limitations of AI. It'll take some time to figure out the dreamed-up proceeds won't be there, and "then what?".
I predict that the real bottlenecks of development will re-emerge as soon as the limitations of AI will manifest out of the hype. They bottlenecks are human-based, in development processes and in human interactions. A large part of development is trying to understand what we want and what we need and you can't offload that to AI.
I suspect that it's merely a long-shot bet that AI/LLMs will drastically change the nature of the world's economy and workforce. If there's only a 0.1% chance of that happening, but promises a chance of giving out 10,000% the return, then it kind of makes sense.
investors are driven by herding behavior. for people with enormous amounts of capital to invest, the worst thing would be for them to miss out on the next big thing. If that means losing on a bunch of bets that did not turn out to be the next big thing, that's fine, so long as you don't miss out on whatever that next big thing is. We're promised AI is the next big thing, and everyone else is throwing money at it. So that means I have to throw money at it, because otherwise I would miss out.
Some people seem to see the world only through bubbles. But if you look at human history, despite the ups and downs, we have a trajectory; generally speaking, human-created systems evolve toward ever-increasing complexity, impact, and efficiency.
The current wave of AI unlocked language - the tools are now speaking and understanding. This, on its own, is astonishing progress. Language is the foundation of our culture and society; it is the very technology that got us, as a species, to where we are today. To have tools that can understand, manipulate, and produce it is a massive leap forward.
Once you see things that way, it is clear that we are not in a bubble; we are in a transition. Yes, there is tons of hype and over-investment, but the demand is real, and so is the impact. Unless you are deep in the tech and have that structural depth, it is easy to dismiss. This is like the invention of the personal computer, but with 100x the impact and speed.
The only "bubble" with AI is that the initial build out is cyclical, and many of the high flying chip stocks with no software arms (ala Nvidia's CUDA) will come back to Earth. I think anyone that thinks AI is going away or won't have massive impact (though maybe not in the doomsday scenario) are in complete denial.
RTFA; it's not about AI's massive impact or lack thereof ... it's about these businesses not having a viable business model that will sustain them (beyond the next couple years).
I think Zitron's problem is he's equating AI to OpenAI and Anthropic. I'd agree with him that both those businesses are in a dangerous position given how fast they've burnt through cash. However, that's not the entirety of the industry and there are a lot smaller labs doing more for a lot less capital.
The business model does appear to be viable for these labs. But that viability comes because they aren't wasting a bunch of R&D money developing worthless products like AI video production.
I admit, I didn't read the whole article; I read a few paragraphs and extrapolated the mindset from which the author operates.
Regarding your comment about the business model—the people in Silicon Valley are not stupid. They know the playbook; we've seen it with social networks. The issue isn't the business model itself; it's that these companies need to dominate the market, and the big players are competing for that on a global scale. It's the exact same playbook that played out in financial systems and social networks, and now it's happening with AI. Once these technologies are deeply integrated into enterprises and the global economy, these players will dominate the market for decades to come.
I can assure you, the people running those companies are smarter than you, me, and the author of this article."
What I suspect isn't that AI goes somewhere, but I do think that the cutting edge companies like Anthropic and OpenAI are in a very precarious position. They don't have very much of a moat and the competition has been catching up quick while spending a lot less doing so. IMO, the main thing keeping them alive right now is name recognition.
If I were to make a prediction, it's that ultimately these cheaper models are going end up eating their lunch. I don't think they'll make back the money they've invested and once that reality hits investors, those two companies are sunk.
That, however, is not the end of AI. Nor will it be the end of Nvidia/micron/etc. It will more just be a localized bubble pop that doesn't eliminate the product from the market.
It is not just about cheaper models; it is about integration with the economy.
These models are building deep integrations into companies and the entire economy. Once that stabilizes, it will be like the electricity grid—pumping tokens to fuel decision-making across the entire global society. Good luck unplugging from that.
Furthermore, there is a massive geopolitical aspect to it: those who are already on the Western financial and technical stack will get integrated even deeper now.
> These models are building deep integrations into companies and the entire economy. Once that stabilizes, it will be like the electricity grid—pumping tokens to fuel decision-making across the entire global society. Good luck unplugging from that.
Much like the electric grid, what we are seeing is a convergence on standard APIs. For example, most of these cheaper models are hosted using APIs compatible with OpenAI. It's not a matter of rewiring your electric plug to work with a different socket standard, instead it's just the process of plugging it into a new socket.
> Furthermore, there is a massive geopolitical aspect to it: those who are already on the Western financial and technical stack will get integrated even deeper now.
Certainly the Chinese models appear to be some of the best when it comes to competition, but they aren't the only ones. There are European models and other US based models which all run for cheaper.
I see your point, but having worked as a consultant for a few years, I think most companies will opt to stay once things are stable. Once these systems are functional, nobody wants to touch them.
I remember one government project where we wanted to migrate a system from COBOL to a modern stack. The requirement was for the UI to stay exactly the same as the old green terminal; the evaluation criterion was pixel-perfect proximity to the original. We literally had to build terminals using web tech.
These models are not the same as each other. Once they are integrated and working, the incentive to change them is incredibly low. So really, the race is about who can integrate deeper, wider, and faster over the next couple of years—that is what will determine the long-term winners.
This is the exact same playbook we saw with social networks. There is a reason why we have only a handful of them dominating globally, and guess what? It's not because of the tech.
There is no incentive to rewrite working software in COBOL to something else. You don't really change the people cost of maintaining that code all that much and you incur a huge rewrite cost.
AI is different, it's an ongoing cost to the company. If that cost raises aggressively, you can bet companies will race to eliminate it, no matter how integrated it is. Companies can and do do this all the time.
And the models are close, not the same, but close. That's what matters in LLM stuff in general. If a model is capable of doing the same work for less, it will be chosen. Especially since the switch over cost is often on the level of "point the tool at this URL instead of that URL".
I get what you are saying if this were a more sticky concrete tech that is harder to move away from. But that's simply not the case for these LLMs. A big selling point they have is that they are super flexible.
I don't think the transition will be as simple as just flipping a URL. There is an entire legal and technical infrastructure being built around these models and their integration. I think you underestimate an organization's resistance to change once things actually work, as well as the sheer complexity of making that shift.
I also expect pressure will eventually drive the cost of running these models down. Power plants are being built, more capable chips are being produced, and a big chunk of the capital right now is being used to scale the physical infrastructure—the data centers and energy grid. Once that stabilizes, these companies will have positive cash flows. Again, it's highly similar to what we saw with the expansion of social networks, just with more aggressive and widespread adoption.
Ultimately, a handful of companies are going to provide these core capabilities, just like we have a handful of major cloud providers right now. Why do you think this would change? If anything, the trend toward deep vendor lock-in is even stronger now.
The moat is the infrastructure and lock-in. Similar to AWS or anything else. Small data centers can't compete, and similarly people without massive compute won't be able to either (at least not on the enterprise level.) You might get a few edge models, but for huge businesses they will be using OpenAI and Anthropic (and Google/Microsoft/Amazon, etc).
The biggest competitors aren't small models, they are just the traditional players that already have an "in" with enterprises. That I think will start to show its face once this initial round of buildout is complete, which may not be for another 5+ years.
I disagree. Mainly because those small models are exactly what erode away the moat of needing a giant data center. Those smaller models have been proving themselves to not be far of from the SOTA models.
As OpenAI and Anthropic look to raise their prices, businesses will be much more compelled to looking at cheaper models. And if the narrative is "do the same as you did with OpenAI at 1/20th the cost" that's going to sell to a lot of businesses.
It certainly cuts into what exactly these companies can sell in general. For example, if I wanted to integrate AI into a product I'd almost certainly not chose OpenAI or Anthropic. That's because they are simply way too expensive and what they'd give me is a lot less. We've actually ran into just this. We needed a classifier for a lot of records, we picked a free model because, as you can imagine, we didn't need something as good as what OpenAI and Anthopic offered and free works.
> The current wave of AI unlocked language - the tools are now speaking and understanding. This, on its own, is astonishing progress. Language is the foundation of our culture and society; it is the very technology that got us, as a species, to where we are today.
I hadn't heard of the TMobile and Brex spend caps, only knew about Uber's because it went viral last week. I expect we'll see more of that now that everyone is paying per token, and it sort of feels like you cannot both have spending caps and require extensive AI usage for performance reviews -- I wonder that will shake out in the end?
Anecdotally, $dayJob consumes Anthropic models via Azure subscriptions which lend themselves pretty neatly to the spending dashboards Ed mentions are missing from Anthropic themselves, and finance seems ok with the current usage, but there's no real hard incentives internally for AI usage either.
I guess Q3-4 are going to be interesting to see where this all goes.
> The AI Companies Are Going To Start Getting Desperate
This is the part that concerns me the most, AI companies will start bidding on increasingly contentious contracts out of desperation. In practice, this means building services that facilitate killing to anyone they're not legally prevented from doing business with... and even then, they may be able to do business with them still through intermediaries.
As a tangent, I don’t understand where and why meta fits into the AI race. They did not get any mind share (consumers) from the llms so far, granted they started the open source side to this but the Chinese companies produce far better models and have essentially become the default for on device set up.
They have ai glasses and integration into instagram and facebook as the other avenues. I don’t see ai glasses as compelling yet, and don’t know how much more ad revenue or user engagement they can squeeze out with llms baked into the IG of FB flows. They are spending a lot and not seeing any returns. Am I wrong in being pessimistic about meta with AI?
You should probably be more pessimistic about Meta. Look at their last major venture, the Metaverse, which was basically embarassing. Their AI strategy is incoherent.
>LLM-type AI exacts huge costs because it is terrible at reporting "I don't know". When it doesn't know, it generates noise and polishes it.
>If a "confidence too low for output" signal could be extracted, this whole technology would be a lot more useful
Anthropic's interpretability research explored this topic a bit in 2025. Apparently, the signal is extractable:
It turns out that, in Claude, refusal to answer is the default behavior: we find a circuit that is "on" by default and that causes the model to state that it has insufficient information to answer any given question. However, when the model is asked about something it knows well—say, the basketball player Michael Jordan—a competing feature representing "known entities" activates and inhibits this default circuit (see also this recent paper for related findings). This allows Claude to answer the question when it knows the answer. In contrast, when asked about an unknown entity ("Michael Batkin"), it declines to answer.
By intervening in the model and activating the "known answer" features (or inhibiting the "unknown name" or "can’t answer" features), we’re able to cause the model to hallucinate (quite consistently!) that Michael Batkin plays chess.
Sometimes, this sort of “misfire” of the “known answer” circuit happens naturally, without us intervening, resulting in a hallucination. In our paper, we show that such misfires can occur when Claude recognizes a name but doesn't know anything else about that person. In cases like this, the “known entity” feature might still activate, and then suppress the default "don't know" feature—in this case incorrectly. Once the model has decided that it needs to answer the question, it proceeds to confabulate: to generate a plausible—but unfortunately untrue—response.
That's real progress. The paper behind it is [1] They try to extract "attribution graphs" to understand why the LLM produced some result. It's encouraging to see more work on what's going on inside. They obtained insight into a specific kind of hallucination: not finding a specific fact. "We uncover circuit mechanisms that allow the model to distinguish between familiar and unfamiliar entities, which determine whether it elects to answer a factual question or profess ignorance. “Misfires” of this circuit can cause hallucinations." That should be tested on queries which resulted in making up legal citations.
Isn’t the thing that an LLM never knows (at all)? It just guesses words based on the context and previous words and often gets lucky.
It isn’t thinking or knowing and then expressing the resulting understanding but just spitting out contextual words and hoping it reaches a conclusion or ending of some sort.
This is apparently news to all the hardware retailers who are continuing to maintain the insanely overinflated prices on NVME storage, DDR5, and even DDR4 memory.
Some are still steadily increasing prices.
A 1TB NVME drive - a good one - cost about $70. Now it costs anywhere from $150 for shit-tier drives to $300+ for the higher end stuff that used to cost $100-120.
The chart seems logical to me. Most problems are solved in the app space. New apps don't have to be the new facebook. They just need to be useful for the right audience (even a small one). It's like you have meat and bread in the supermarket, and add all other stuff you dont really need. Will be bought, but not as much as meat and bread, right?
The best outcome of the AI hype for was to be the investment into next generation nuclear power plants, ushering the world into a post energy scarcity era.
Maybe we'll get there, maybe not. These days I only hear of datacenter investments.
> This is a hysterical era perpetuated by liars, cowards, imbeciles, craven boosters and the easily-fooled. Those excited about generative AI are either the victim or the perpetrator of a con centered around a technology to ingratiate at the highest cost possible.
Who writes like this? When you lead with "everyone who doesn't agree with me is a lying cheat coward imbecile" I think we should just turn the volume down on you to zero.
This is breakdown in dialog. If it leads like this then I I don't care how accurate the critical analysis to follow is. I didn't read the rest of the article and don't think anyone else should either out of sheer disdain for this argumentation style.
He may be bombastic but Zitron is right about the AI problem. These companies do hemorrhage cash, and have no viable plan to even become solvent. It may not be a scam but it sure looks like one. The problem it poses for the economy... is just as he says.
This rests on a lot of assumptions that the published figures for "planned" datacentres, "committed" AI spend, etc. are irreversible. I suspect that at least some of it is possible to back out of.
Given how I can manage and develop a huge production code base with an incredibly small team - and the rest of the industry apparently is not able to do it - I deem that we are still in the very early days.
AI companies are racing to win the future of computing.
They are possibly in a winner take all death race against each other.
The stakes are so high that these cash rich companies cannot afford not to throw everything they have into this.
The sunk costs are irrelevant when it’s a question of survival.
Whether you hate or love AI computing is being completely reinvented - at the absolute core of this is computers programming computers.
Anthropic is winning this race by a country mile right now.
This is such an important future bet for these companies that the trillions must be spent because there’s no future or a greatly diminished future for some of them unless they have ownership of the technology.
As WIRED reported[0], despite constantly writing about how an AI collapse is just about to come, Zitron privately does PR for AI firms on the side. The man is an obvious hack, and it's disappointing that he has become one of the mainstream faces of AI skepticism.
Anthropic has made $330 billion in compute and chip commitments between Google, Amazon, and Microsoft, another $30 billion with CoreWeave and another $15 billion with SpaceX. To pay for this compute, Anthropic must meet its projected revenue of $174 billion a year by 2029.
Anthropic has raised $95 billion across rounds in February, April (from Google and Amazon), and May. These funds will be insufficient to cover Anthropic’s costs, as will Anthropic’s cash flow, meaning that it will have to raise at least another $200 billion in the next year.
How people take this seriously?
Anthropic is at 45B ARR
S-1 shows inference margin climbed to 70% (obviously could drop)
So where that 200B number is coming from ?
I see this kind of arguments made all the time by people I know in finance etc, who seem willing to look at AI only from a finance/sociopolitical lens, disregarding tech altogether. The very fact that they are still writing about this for me is impact enough.
Am I missing something or does the author not count the existing revenue to cover the buildout? I think they have it covered even in the worst case scenario (and requiring every piece of cash flow they have).
Also, if Anthropic or OpenAI fail in their revenue requirements I believe they would be absorbed by the guys with the money printing machines (Google, Microsoft and Meta).
His rhetoric is a bit obsessive and frankly biased against AI.
That said, I think his voice is useful as a counter to the mainstream opinion.
Given the amount of investments, approaching AI from the angle of economics seems correct.
We all have some level of personal experience using AI/LLMs, both chatbots and coding tools, and I personally enjoy using them, but I am sure this experience is relevant in this discussion.
I also enjoy luxury hotels, gourmet food, jet skis and helicopters, but this is not something I indulge in often because of the cost-utility ratio.
The real cost of AI may or may not be lower than its utility. The bet is that utility is increasing while cost is falling.
I guess my ears kind of turn off when you say "it's all slop, none of the apps are good, and it's a failure because no one has used AI to make the next Salesforce".
I have found agentic coding to be extremely useful for a bunch of small, middleware, very focused bits of software for small businesses:
* A company had a very specific scheduling need, they needed to move about 8-15 staff around with a bunch of different shifts, and have custom reports on who was working how many hours, and have the employees get a nice clean email summarizing their schedule
* A manager wanted a very simple "let me send a text to add a to-do to the group list" need
* A sales team of 3 wanted to be able to type pricing of raw goods into their phone, have it compared to other market sources, and have it text the other 2 salespeople and their manager when they were out in the field
All of these were coded with Codex in about 4 hours with further refinements over the next week of back-and-forth with the people using the tools.
I suppose yes we could have found some custom middleware solutions that did similar things, but it's nice to be able to make a web page or tiny mobile app that just does EXACTLY what the person wants.
It's hard to do that and then listen to someone who says it's all just garbage.
Ed Zitron speaks to a particular type of angry tech conservative. He’s not speaking truth or exposing anything. He’s the soothing voice the tech nerds of yesterday year are yearning for.
The angry polemic that goes on and on and on with cuss words used liberally is just meant to evoke emotion and cathartic resolution to the type of people mentioned above. Not truth.
The thing is, there are a lot of people that find comfort in what he’s writing - primarily because it’s a coping mechanism against how quickly things are moving and a way to deal with being left behind. When you spend time, years, building institutional knowledge and making a whole identity out of it, you obviously will feel bad with the threat of it being commoditised.
I would write against the content of the article but I find it easier and more illuminating to write what he has said before instead. Then it shows how incorrect the guy has been and with what confidence he keeps speaking with.
I'm collecting many kinds of predictions Ed Zitron made so that you can see for yourself whether he has a good track record.
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> While complex, generative AI is a technology that probabilistically generates answers, and has no "intelligence." It is inherently limited by its architecture, and in turn can only get "better" in a linear fashion. I see no signs that the transformer-based architecture can do significantly more than it currently does.
He wrote this in 2024 before reasoning models came out. Remember how ChatGPT was in 2024? Do you think this person is someone who gets predictions right?
> Furthermore, I hypothesize a race to the bottom in generative AI will significantly hamper OpenAI's ability to expand revenue, compounded by the fact that we're approaching the limits of transformer-based architecture.
He wrote this in 2024 and since then Anthropic's revenue increased by 160x to $40 B dollars a year and OpenAI's increased by 6x. Do you think this person gets predictions right still?
> I believe we're reaching the upper limits about what generative AI can do and how accurate its outputs can be,
He wrote this in 2024, do you really think we have reached upper limits? Huh?? What I'm using today is significantly more accurate and 2 tiers above what we had.
> And if there are true industry-changing possibilities waiting for us on the other side, I am yet to hear them outside of the fan fiction of Silicon Valley hucksters.
He says this about AI when we have with all honesty have had industry changing possibilities like agentic coding.
> There are indications that consumers have also lost interest. As pointed out by Alex Kantrowitz’ Big Technology newsletter, traffic to ChatGPT on both mobile and web has started to stagnate, if not decline. In January 2024, ChatGPT had 1.6 billion visits — 11% below the all-time peak of 1.8 billion. This makes it only modestly more popular than Bing, which had 1.3 billion unique visits during that period. On the mobile front, ChatGPT has an estimated 6.3 million US users — or 1.7 times less than the total of new Snapchat users added during Q4 2023.
He agrees with the claim that the consumer interest has declined. Since he said this, there was a 9x growth in active users.
"A.I bubble is bursting with Ed Zitron" (1 year back)
He's been constantly crying bubble for years now.
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> AI video won’t get truly fixed just by waiting a year.
This is what he had said in 2024, and you just need to compare video from then and now to check whether the predictions came true. Why would anyone trust what this guy has to say?
How’s that meme go? "We are 2/3 years into being 6 months away from AI taking all white collar jobs".
The criticism goes both ways. The word "fixed", in Ed terms, can be translated to "become a viable business that justifies the spend".
In regards to AI video, I think the fact that Sora is no long around is an indicator. And there is seemingly no real appetite for AI video outside of memes, jokes, and misinformation, probably indicates that the prediction around AI video has come true.
> He wrote this in 2024 before reasoning models came out. Remember how ChatGPT was in 2024? Do you think this person is someone who gets predictions right?
Any benchmark on AI shows that it gets better linearly. So his prediction was correct. There is no exponential AI improvement anywhere.
Ok, I guess there are some weird benchmarks where they managed to squeeze out an exponential. But even with Google's, OpenAI's or Anthropic's own benchmark it's a linear improvement between models.
Whenever I read these kind of articles about AI financials, I'm reminded of identical screeds I read about Uber a few years ago. They were angrily insistent that Uber was a scam company run by criminals and charlatans and could never, ever become profitable or make money for its investors. It was a house of cards that would come crashing down sooner or later, and take everyone's money with it. Now it's 2026. Uber still exists, has revenues of $50bn and is apparently a highly profitable business. I don't know if the original investors have made their money back yet, but Uber certainly hasn't collapsed.
Maybe AI is different. Certainly, the level scale of investment is on a different order of magnitude. But I'm wary of believing anything about the financial impossibility of AI being sustainable when I've seen such similarly confident arguments proved wrong in the past.
Uber used the classic triple-E philosophy of Microsoft and entered a market that was ripe for disruption -- many cities lacked reliable taxi service entirely, others were cartels that fixed prices. They undercut prices to an extreme degree, subsidized fares, and when it either drove local taxi companies out of business and spurred widespread adoption as the default, it had a captive market and duopoly with Lyft which allowed them to raise fares without losing any market share whatsoever.
It's a pretty classic business strategy, and not directly comparable to any of the AI companies. There's a reason people compare the current situation to the dotcom era and not Uber. Also, don't take Uber as an example of a slam-dunk VC success story and leave it at that -- plenty of dumb ideas get pitched and funded and go bankrupt for every Uber.
Yeah, people forget the risk to Uber was real in the early days. If municipalities had enforced their taxi laws, the company would have died and all those millions invested would have been lost (or pivoted into something else).
It was only because Uber successfully bulldozed over all regulations that it was able to succeed ... and that was hard to predict before it happened.
Absolutely. Even these days, Uber really only has one or two viable competitors. With any 3rd one in a far distant 3rd. Meanwhile, swapping which AI I’m using is as easy as clicking a dropdown. Hardly comparable to a physical car ride.
Funny thing, the uber's investor results from last year only mentions "profit" once, in a motivating paragraph where they say they will be great.
But it's famous for having collapsed after their IPO. It took 4 years to get back at the same nominal valuation (not inflation corrected), and after all the 2020s inflation it is still at 2x the initial price.
I don't think anybody actually believes that the current investment is going to yield returns that they are projecting. Neither did people back in Dotcom or Railways or any other hype/bubbles. Yet these technology did transform and the returns came to fruition.
Internet continued to thrive and grow even after the stock market came and went, it took 13 years to roughly nasdaq to recover but the explosion of GDP from internet has been largely decoupled from the previous bubble boom and bust.
If you use the stock market as a yard stick to project new revolutionary technology we shouldn't have had trains, internet. In fact internet should've stopped with the bust of Nasdaq and everybody would've moved back to using paper but we didn't it gave rise to the next wave of economic output powered by this new tech.
> that the infrastructure being built and compute commitments being made are being done so at a level that demands that generative AI and AI compute generate over $2 trillion in annual revenue by 2030
That seems doable. Next generation architectures and the models they produce are accelerating progress. More capable with less data and compute, which ironically will drive more demand, aka Jevon's paradox.
> If you are someone in the executive team of any major tech company, know that your employees are, for the most part, completely and utterly miserable.
I agree this is a problem. Adopting too eagerly and too early, and not listening to feedback from the people who are using these tools is a recipe for disaster.
The handwringing tone of the article is off-putting.
Ed is confused between whether AI is useful, and whether the current level of funding and valuations are sustainable. The following statements can both be true:
1. AI is already quite useful and will continue to be so. This is true even if AGI doesn’t happen.
2. The funding and valuations of many AI companies are too far ahead of their skis, and will probably roll back. Some may fail entirely.
About the “where’s the productivity in AI?” question: I think it’s entirely possible that the primary benefit of AI will not be top-line growth but reduced costs (through reduced human labor). Companies will need to reduce prices to prevent losing market share to existing or new competitors, meaning that GDP may not increase, but costs will.
I think it's time to distinguish between what frontier AI companies need regarding AI, and what will happen with AI if these companies don't get everything they need. Probably there's a bit more to this. Much of the technology is available via open source already and there's a growing ecosystem of AI tech that isn't really dependent on anything else than the hardware infrastructure needed to run it.
A good analogy might be networking companies and infrastructure companies during the dot com bubble. It devalued a lot of companies but the internet stayed. A lot of dot com companies didn't make it. Much of the infrastructure investment did not go to waste, however. Nor did a the technology go away.
I think it will be the same with data centers, related infrastructure, GPU hardware, algorithms, OSS components, etc. for AI companies. More companies need that stuff than is currently available. The ones that don't make it will have a lot of assets that they can pass on to the one that still have a chance. I don't think a lot of that stuff will get decommissioned or will be underutilized. It might get a little hair cut in value though. And like during the dot com bubble, some companies actually survived and did quite well. Especially those in the business of selling shovels during a gold rush.
After the inevitable consolidation that follows the next logical stages in the hype cycle, I don't think AI will go away. It might be a bit of a bloodbath for some silicon valley investors that placed the wrong bets in the last few years. But that's the price of doing business over there. That doesn't mean it's all bad. And the smarter ones probably spread their risk enough that they still might come out looking alright.
And like with the dot com bubble, many financial types have no clue what is happening and are running around like headless chickens. Which is why they ended up sinking a lot of money in exactly the wrong things. You'd hope they would have learned something.
But articles like this suggest that that might be too much to hope. They still don't really get how technology tends to not stagnate and might continue to deliver potential for performance and cost optimization. The current level of investment is only unsustainable if that doesn't happen and nothing else changes. I don't think those kind of closed world assumptions are a safe bet at all.
There's a reason why everyone must use agents and LLMs exclusively or be left behind. The entire Silicon valley livelihood is now a stack of cards built on a promise of infinite productivity gains based on LLMs. If that falls we're likely seeing a repeat of the 2008 financial crisis given the insane commitments made.
The gulf between reality and this tech-illiterate grifter continues to widen. I strongly suspect his shtick will fall apart before this year is over. And I hope at least some have been keeping close track of what he's been saying.
Missing from Zitron's calculations is government ownership/bailout of American AI in national security interest and winning the race to AGI with China. Trump has been making noise lately of owning 50% of these companies. Taxpayers will prop them up in other words.
It's possible that AI is the greatest technological leap forward since the Industrial Revolution, and simultaneously a bubble that will pop in the near future.
I don't know much about the economics side; TFA gives a barrage of stats that seem to make a compelling case for bubblehood. OTOH, the claims about the utility of LLMs being unmeasurable are weak (the same criticism applies to hiring programmers, or indeed most office workers) and the metal spider straw man is frankly embarrassing to anybody who has actually used recent frontier agents for programming and seen what they can do.
"Last week I went on Bloomberg and discussed the state of the AI bubble with a clarity that rattled even the sweatiest boosters, mostly because I spoke with clarity about an investment frenzy whipped up through hype, deceit and mythology."
Well there are a lot of commenters so presumably some interest. I just had a look at the Bloomberg bit https://youtu.be/zbKDmkJPVvI and didn't see sweaty boosters rattled, just Ed doing his usual spiel - they are loss making and so it's all a big con. Which is kind of unproven on the big con bit.
Yes! Email me at ed@ezpr.com. I have an extremely high bar both for advertisers and the cost of advertising on here - I have 84,000 subscribers and a 55-60% open rate, as well as an 8-11% clickthrough rate.
I do not do any kind of outcome-based advertisement (IE: X number of people click through and you pay me Y), so any kind of agreement would effectively be a sponsorship. I have an engaged reader base and you will have to pay to get in front of them, as I also do not need advertising to support this newsletter."
Maybe the "AI" companies could pay for sponsorship
It's like someone arguing that cheese isn't real. Yes I can go to the grocery store and take a picture of cheese and show it, but what's the point? They can live in their own world. It doesn't change any of our lives. The world is what it is.
Lol... in this case, cheese imports from China are much cheaper, just not quite as good.
And for those who are all "but dur CCP get all ur data" you can use things like AWS Bedrock (at least for earlier versions of Deepseek and Qwen for now) and have more familiar people get all your data. Or buy (at obnoxiously inflated prices) your own HW and not send your data to anyone.
The funniest part of this is that people are often talking about how LLMs are now writing 100% of their code, then also saying that they don't want to expose their code to foreign government exfiltration by using foreign models.
But, uh, if an LLM is writing 100% of your code you have no actual secret sauce to hide from anyone, so why worry about it.
Perfect for idea people. All the value is in the prompt. Ideas are important, not execution. A decade or two ago, they would have been looking for a technical co-founder.
Yeah, so true. There is no moat to your competitors using the exact same tools and prompts to generate their apps and services. Companies should be hiring/retaining creative thinkers that give them that human edge rather than laying people off under the guise of "improved efficiency"
I think we're going to see a lot of craziness in the future in this regard. Not just "secrets", but hypocrites trying to copyright and patent all the AI outputs. All kinds of rabid attempts at constructing monopolies for every half-baked idea they have tried to utter as a prompt.
Meanwhile, like I think you suggest, I would assume everyone can generate similar outputs themselves. The idea that you can claim priority on your dream prompt and lock up the market on prompt responses sounds delusional to me. It's not novel invention when you're spit-balling at the same level of abstraction as every fantasy/scifi writer who ever was.
So I also have doubts about the sustainable business model. How long will it take for this fantasy to unravel, as people discover they cannot monetize their AI outputs as much as they dreamed, and in turn cannot afford to pay the AI services they use?
My absolute nightmare is that this becomes a "too big to fail" thing and oppressive/fascist governments decide to back full regulatory capture. That instead of letting it unwind, they grant and support enforcement of an increasingly absurd and arbitrary copyright/patent regime to support this monetization scheme.
AI has been slowing down relatively, considering its trajectory over the past 20-30 years. For one, even if LLM may have plateaud in terms of intelligence-parameters ratio, research is on-going on new frontiers for ML, including (but not limited to) world models. Other research directions are studying backpropagation and its physical analogies, such as equilibrium of chaotic states.
In addition, there's a lot of research on the hardware angle and actual prototypes are already being built such as AI-on-chip Cerebra and Taalas for one.
Lots of dismissive comments ITT, very few tackling the substance of the article.
> AI Cannot Afford To Slow Down — It Needs $3 Trillion Or More In Revenue By End Of 2030 To Sustain Its Existence
Is this true? With the total 2024 wages being 11.7 trillion USD [0], and nonfarm payrolls totaling 158,000 in the same year [1], it's an order of magnitude higher than my back of the napkin guesses I've made that AI needs to take or create 1/20 jobs minimum to break even.
[0] https://fred.stlouisfed.org/series/BA06RC1A027NBEA [1] https://fred.stlouisfed.org/series/PAYEMS
There is no substance to the article to tackle. Ed Zitron is a clown that makes seven million dollars a year writing unsubstantiated bullshit on the internet. He has been consistently wrong in every single one of his predictions and will continue predicting the sky is falling, because his content has zero educational value. It is purely a confirmation-bias inducing dopamine hit for the perpetually aggrieved.
To name some of his "predictions", in 2024 he was saying that "things are beginning to collapse":
https://www.wheresyoured.at/subprimeai/
He is extremely vague in his predictions and timelines, and yet still manages to miss.
Hypothetically speaking, how does one go about being so handsomely rewarded for being wrong about everything? I feel like I am well qualified for this type of job.
There are three things to make money:
- tell lies to ones who want to hear lies -> riches
- tell truth to ones who want to hear truth -> modesty
- tell truth to ones who want to hear lies -> bankruptcy
p.s. read it on a blog name I dont remember :(
Ask Alex Jones before he got sued for being wrong, or just any politician in general.
Not in the divine secrets, never heard of that person before, but my wild guess would be have the relevant people in your acquaintances?
You never know if people who are running a skeptic grift are at the same time long the very trends they're criticizing.
I agree with the general thesis that AI is a bubble and there's a lot of over/mal-investment, but that hasn't stopped me from riding the wave and making a considerable amount of money investing in AI-related stocks.
If I was more ambitious, I'd write a $70/year newsletter ranting about how AI is a massive bubble that could collapse at any moment to make money on both sides.
I'd love to read about the predictions that have been wrong (genuinely)
> AI's biggest critic has lost the plot
— https://www.theargumentmag.com/p/ais-biggest-critic-has-lost...
He has said every month for the past three years that there is no technical progress left to be made in LLMs and that there is no more room in the market for inference spending to grow.
Here[0] is a fun selection of excerpts from his July 2024 post "How Does OpenAI Survive?"[1]
"I see no signs that the transformer-based architecture can do significantly more than it currently does."
[0]: https://xcancel.com/pathsnotchosen/status/206360940100129633...
[1]: https://www.wheresyoured.at/to-serve-altman/
He’s started a cult around anti AI basically.
That statement isn't proof of anything.
If I start a climate crisis cult, doesn't mean my predictions are wrong
I don’t have much time to compile his predictions. I’ll let someone else do that.
That said, I just wanted to point out the grift he is doing. He’s making money by telling some people what they want to hear.
If AI invents a cure for cancer, he will tell you it’s still useless because it didn’t invent immortality.
Where is the source for that "makes seven million dollars a year" claim?
Probably this: [1]
80k * $7/month * 12 months/year = $6.7M/year
[1] https://www.theguardian.com/technology/2026/jan/19/ed-zitron...
that's confusing "subscribers" and "paid subscribers".
from Zitron's own website [0]:
> I have 84,000 subscribers and a 55-60% open rate, as well as an 8-11% clickthrough rate.
if the 84k number was all paid subscribers, then the "55-60% open rate" would mean that ~40% of his paying audience doesn't bother to read the thing they're paying for, which does not add up.
also, that's in the "Can I Advertise On Your Newsletter?" section. if there were an even higher number of non-paying subscribers, he'd have an obvious incentive to mention that, because the total number of eyeballs is what a potential advertiser cares about.
0: https://www.wheresyoured.at/about/
wrong math: usually 1-5% of subscribers are paying customers.
I find that completely unbelievable. There’s too much high-quality free information for me to ever consume to even think about paying for it, let alone something of mediocre quality. Are we sure the newsletter subscribership isn’t just a total fabrication? Or that it isn’t just a money laundering scheme?
Also, I would not see "making seven million dollars a year by writing" as a reason to dismiss someone. Like, sounds like that such person is doing something other writers just cant.
The reason is obviously that engagement drives his revenue, and correctness or facts have nothing to do with it. Everyone knows that content creators will maximise engagement, and clearly he has found an audience who are seeking out a certain narrative, and will write to that narrative to generate revenue.
"clearly he has found an audience"
What do you mean "clearly"? Do you know his engagement numbers? Or you just find the content engaging?
Youtube view/subscribe/like numbers are public. His are .. definitely in the area one can make a living off, but still below popular game streamers.
I haven't fact checked anything here, I took the $7 million at face value. If he is generating that amount of money from his content creation, then I think my statement makes sense, right?
I don't wacth/read his content at all because I find AI-doomers really boring and tiresome.
The issue he highlights is purely number driven here.
His predictions timing sucks but he’s right in pointing out the insane numbers involved.
I think AI is a wonderful tool but I also think that there are many wonderful tools that if we bet our entire economy on would result in a catastrophe.
So a writer making money is not okay and means he is incorrect by default, but the CEOs siphoning billions off of circular investment schemes is fine?
Personally, I see both parties making predictions.
Meanwhile, I still must labour. How many more yachts are needed before the earth becomes the utopia they keep selling everyone on? How many more communities must have their rights trampled against their expressed will?
Or do I need to scan my retinas and provide Alt-man a DNA sample for a handful of empty crypto currency first?
Go ahead. Spend all your money on the SpaceX IPO. I always love to see newcomers in Financial Darwin Awards Prize.
Ok, so how many jobs does AI have to take (replacing existing or filling new "jobs") to pay off, then?
Any proof of this?
I am unfamiliar with his work, can you provide sources for his past incorrect predictions?
lol - the guy who thinks every single subscriber to a newsletter is a paying customer wants to be taken seriously. Zitron's POVs are always deeply researched, well-sourced and clearly formulated - things that, in and of themselves, are extremely valuable in today's world. Oh the AI bubble hasn't burst yet, so he's an idiot? Please tell me how you think the US stock market as a whole, following the Iran war, Trump's tariffs, etc. isn't just pure fraud at this point? The fact it hasn't collapsed /yet/ is not indicative of him being wrong, but rather that the 'economy' is absolutely detached from any real value whatsoever (something it historically had to be tied to, at least tangentially).
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The funniest thing is that his articles are constantly pushed to HN frontpage, and every time the top comments (or the comments one level nested below top comments) are pointing out how wrong he's always been.
Either that he's HN mods's favorites or we're experiencing a special case of xkcd 1053[0]: there are always ten thousand people haven't realized how wrong Ed Zitron is.
At this point I don't think even Ed himself believes what he writes. It's just fan service for subscribers. And that is totally reasonable if we view blogging as a business.
[0]: https://xkcd.com/1053/
One of HN's quirks, for better or worse, is that the lack of ability to downvote submissions encourages "controversial" content to rise to the top.
On Reddit, if 500 people like a submission and 500 people dislike it, then it'll end up with 0 points and fail to reach /r/all
But on HN, the same content will end up at 500 points since only the people who like it can affect its rank (unless it dies after getting mass flagged).
The end result is that HN's system favors "hot takes" while Reddit's system favors "preaching to the choir".
The two systems have their pros and cons, but personally I dislike being unable to downvote articles that are full of nonsense.
People (ab)use the flagging system for that. I've seen plenty of categories of article that get upvoted onto the front page and then instantly vaporized off it by flagging.
I use a pushbullet channel for HN500. I get a notification whenever an HN submission hits 500 up votes.
It's not common, but every so often one of these submissions will be flagged by the time I get around to clicking the notification.
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I like how he's started preliminarily saying "Now listen, I don't make any trades on this fine thinking. And that's okay by me." So far below the standards of public markets due diligence. Sadly credulous readers will follow his advice. Which could turn out to be accurate in the public markets -- who knows? But if it's accurate it will be an accident, because the quality of analysis is so poor we should not call it analysis.
Not sure what you're getting at here. Would you be happier if he was trading on the arguments he's making, introducing an incentive to lie?
Yes, vastly. It’s not hard to do it ethically: “I’m short. Here’s why.”
Why only US wages rather than global wages? The US is the biggest economy but it’s still only a minority of the global economy.
Just consider the math there for a second, when you factor in the average US wage.
But we know global GDP per capita (a proxy to wages)- the US represent about 25% of total global GDP (a metric which accounts for US wages being higher than average). I’m not being contrarian, I genuinely think the addressable market is the global market and not just the US (and by a wide margin) and as such thats the real potential of anthropic/openai/et al.
You're partly right. But OTOH, China is (pretty successfully) developing its own AI solutions, and even the (former?) US allies in Europe, America and Asia have become painfully aware that they are dependent on a hostile US administration and tech companies cozying up to that administration, and will be wary of further deepening this dependency, so they will also prefer home-grown solutions. So the addressable market for US companies is much smaller than the global market, even in countries that could theoretically afford Anthropic's and OpenAI's prices.
Globally available AI means cheap LLM pilots offshore means huge wage deflation in white collar jobs in the US.
…assuming the AI pace of progress slows down enough that there are still meaningful white collar jobs to have.
This is just the offshoring and remote discourse all over again. It turns out that the prestige of having a big office full of workers that the CEO can see is well worth the massive expense of siting it in California and paying California salaries. For whatever reason.
(also I suspect the anti-globalization discourse will get even more pointed)
The other question nobody seems to ask - what will $3tn in 2030 be worth? I would bet quite a bit less than it is today.
US inflation is slightly under 4%, so by my maths receiving $3tn in 2030 would be worth about $2.5tn in 2026 dollars.
Currently slightly under 4%. I have a feeling that part of the solution here is going to end up being, what was the euphemism? Quantitative easing - also known as “printing money”. I would not be surprised if we end up seeing near double digit inflation in USD - it has also significantly lost its status as a global reserve currency, which makes this all the more likely.
Tbh I think it’s already happening in real terms, but the CPIs aren’t fully showing it yet.
> Quantitative easing
Why though? This isn't a bank run situation. Sure, it's unwise to bet against "US President does something stupid", but the rationale is missing.
The oil situation remains very concerning, and you can't print oil.
Oh but it is. The debt that’s funding the AI buildout ends up in the oddest of places - like pension funds. If defaults occur and work their way up the chain, it’s not dissimilar to the subprime affair - and at that point it’s bail out the poor pensioners or get crucified at the ballot box.
There’s lots of precedent for this over the past decades, both in the U.S. and over the pond.
How much of this is debt as opposed to equity, though? Can we have a balance sheet?
Not readily, as the private companies are private, and the public ones obfuscate their books - and even then the question that remains is how much of that equity is funded by debt.
If I thought there were some actual small cabal of people running the global economy, this is almost like a novel: massive amounts of money entered the economy starting in 2008 and 2020-2023, the rich became insanely wealthy. Their wealth is now all tied up in the 2020s version of the railroad/fiber, we're going to essentially erase trillions of dollars from the global economy and reset.
We sure do need a reset.
I really doubt the US will erase any money any time soon.
The reset is prone to happen by other means.
Rapid inflation/devaluation is certainly a way to do it.
I consider myself of average intelligence, and I see this coming. Maybe I assume too much.
Every government has complete control on how much of their money exists. If it disappears from the private finance market, it can compensate with any amount it wishes.
It would be better to lean into inflation and just dump a ton of money into the bottom of the market to dilute the top. Of course this only makes sense if the government is willing to regulate market control, which it has demonstrated time and time again it will not.
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It's more interesting to ask, "Does AI need to follow the current model of evil megacorps building massive data centres that, collectively, guzzle more energy than most nations on Earth?"
Perhaps LLM's (or something better) will develop to be more efficient and quickly become something most people run on local hardware. Perhaps fad-obsessed management types will move onto the next big thing and AI will start being used more judiciously. Perhaps society will set sane regulatory limits that shape the direction AI is going in, from models that take jobs people want to models that, given the right hardware, can do the jobs few want.
Anthropic and OpenAI don't have to succeed for AI to succeed. If they turn out to be a bubble that bursts and torches a lot of investors, it might actually be a fundamentally good thing for everybody else.
To be clear, nobody WANTS to have to go build all these datacenters. Well, maybe some pure-play hyperscalers do. But there's an immense amount of economic incentive to be able to do this more efficiently, capital and energy. And, what those hyperscalers want will not matter for a second if there isn't demand for the tokens output by those datacenters - they'll go instantly dark and have to seek new forms of valuable compute to offer.
If this current building spree ends in massive solar and other power generation being overbuilt and cutting energy costs, we've had a really good outcome.
This is why there is so much interest in space based AI compute. It's not just SpaceX - Google, Anthropic and Nvidia have openly expressed interest
If you look at SpaceX plans and ambitions, they hope to deploy massive compute to orbit (multiple Terrawatts, hundreds of thousands of sats). If their ambitions even slightly materialise it would make ground based compute pale in comparison.
Whether or not they succeed in their plans is beside the point - the point is they know that terrestrial electric infra can't sustain the growth they need
What is the multiplier in cost for a teraflop of compute in space vs on the ground? 100x? 1,000x?
> Whether or not they succeed in their plans is beside the point
No, I think that does matter eventually? Maybe for the IPO value?
How would cooling work in space based computing? To a layman like me it seems like a significant hurdle to overcome.
It really doesn't. You're purely relying on radiation fins to carry heat away, which are incredibly inefficient.
> The radiator surface area problem also scales uncomfortably. At 838 watts per square meter, rejecting 1 megawatt of waste heat requires roughly 1,200 square meters of radiator. Deploying that much surface area on a satellite is a structural engineering challenge that gets harder with every order of magnitude. The ISS solar arrays span about 2,500 square meters total.
So even a 2MW data centre in space requires a cooling array rivalling the international space station. Starcloud launched a single H100 in November and they were unable to run it 24/7 due to heat buildup.
Even with novel solutions to make heat transfer to the fins more efficient, like phase-change liquids, the limiting factor is that the vacuum of space is a tremendous insulator.
https://thecoolingreport.com/intel/starcloud-orbital-data-ce...
https://satnews.com/2026/03/17/the-physics-wall-orbiting-dat...
Can we stop spreading the obvious bullshit that is space compute?
Care to at least refer to some sources why?
It's literally a deflection mechanism to the fact they want to build data centers all over the land by proposing a fantastical better way that simply won't work.
That has been covered to death since months, there is no good solution to cooling without an atmosphere. But even going into the technical details is a waste of time, think about how complicated maintaining such a system in space would be compared to having it on earth, the whole idea is a complete grift from beginning to end. There is literally no benefit to do it in space other than as a marketing tool.
Nobody who suggests the idea has ever presented a model that is even remotely close to reality
What a surprise that something this implausible could come from the same guy who proposed trains that run in vacuum tubes.
I can't believe the people who actually listen to this guy can tie their own shoelaces. And somehow he became the richest person on earth. Humans are irredeemable.
Ah yes. Just need to displace 16 million people’s livelihoods. No big deal.
#3 Trillion, or even more, will be achievable. We are at the very beginning of the monetization of AI. It all started with the free Chat GPT, and a few others. Now the standard is $20 a month if you are not using AI tools too much and you don't need anything fancier. Otherwise you need to pay more, like $200 a month.
Unless you are not company and you don't have some "enterprise deal". And you need an enterprise deal, as 1) it guarantees that your (and your customers) data will not be sold to someone else 2) you are scared that your competitor will have such deal and become much more productive.
This is what we have now. What will be the future?
Well, soon, if you want something like financial advice or medical advice or job search/CV polishing you will be told, that your $20/$200 is not covering that, you need to purchase additional model to have that. Will you do that? It depends how much you are desperate to get medical advice or find a job.
Anthropic Mythos is an example. Soon, if you are programmer and you will ask AI Agent to spot a bugs, AI Agent will tell you that you need to buy extra model for this. Same with performance analysis, same with the design using tool X, Y or Z.
This is pretty scary, as it will put our well-being, productivity in the hands of few corps. It will be event worst that Google Search monopoly we used to have (until AI chats broke this, replacing Google Monopoly with a few other vendors monopoly).
Can this be prevented? Surely. Hopefully we will have capable open models and consumer-level hardware will catch up. But I think this is the place where governments should step in, invest into alternative models which will be at least comparable with flagships.
Chinese models shows that this is doable, DeepSeek is worst than Chat GPT/Claude/Gemini, but not that much and is clearly better than Grok (which is a huge disappointment for me). I guess India would join this game (especially with nationalist like Modi as the leader).
Europe could join this game, the problem is it kills its capabilities with high energy prices and inability to come out with some reasonable, well financed solution. So the only thing EU was able to come up with is some set of regulations that are blocking fast AI development in Europe...
There is French Mistral, but it is French, it is under-financed, it is only-French, as France would not like to lose control over it.
Germany have totally different strategy, they invest into manufacturing oriented AI, what makes a lot of sense, but does not help with the dangers we are facing.
The rest of the Europe is just too poor to spend billions on AI.
There is still time to buckle up for Europe, but given the course of events, stupidity of Brussels elites who does not see the storm coming I am not optimistic.
The only thing you’re neglecting is that personal computers will be able to run “good enough” open source models locally within 3 years. Can already run it today.
Extremely sorry for this unrelated rant. Got triggered by the keyword "Modi".
Quoting Modi is a joke which one cannot even remotely relate w.r.t to AI and don't even feel sorry for saying that since that man blabbers on every stage about non-sensical / non-existent stuff! Watching his videos is the best timepass one can have!
Having said that, the infra and mindset is definitely not there in India to even remotely to innovate or compete in AI race!
Academia is a huge BS where every other person is a backstabber!
A lot of talent is there for sure, but all wants to work for some company or another since there is absolutely zero support for entrepreneurs . No real innovation.
All copy cats as they have proven with mobile and robotics. Just copying or masking Chinese products with the local brand names and reselling. That's all they are good at and that's the irony.
So far, nothing has come from that country which is a real innovation or ground breaking. The day it happens probably one can consider that they are good.
But otherwise, they are good at selling / reselling and scamming the world and nothing else! They cannot produce anything or whatever they produce is taken control by a handful of big corporates from the western region. That's a narcistic corporate monopoly!
Extremely bad tax structure, endless corruption and useless and unqualified ministers occupying worst portfolios, people are really struggling to survive!
Where will they innovate or compete in the global AI race?
Everyone at every level just want to scam and make money to surive that's all!
Period.
Ed is an interesting character. His financial analysis of the AI industry makes logical sense to me (though I am not knowledgeable enough to actually know if it is correct.) However, he seems to be so angry at AI in general, that he misses the obvious areas where LLMs are actually changing the State of the Art.
Coding seems to be one of the core use-cases for LLMs (as Simon Willison pointed out recently) and even if that's the only real use-case for LLMs, they're wildly useful. I do understand that useful != profitable and that's where I think Ed has a real point: until inference becomes much cheaper these companies cannot be profitable. Some mega-players will pay the API token price, but most will not.
I don't think whether "LLMs are actually changing the State of the Art" or not matters for anything he wrote.
If the AI companies need $X billion in revenue to stay afloat, it doesn't matter if 0.5% or 5% or 50% of that revenue is from transforming the State of the Art. It's 100% irrelevant: what matters is that, transformation or no, these companies won't have the income to pay their bills. And if they can't pay their bills, a whole lot of other companies can't either.
So again, transformation or no, it's still a house of cards waiting to collapse. The only thing that would change that is not more "transformation" ... it's a feature set that lets them multiply their current user base (or multiply how much they charge them) several times over.
maybe its insane to think this, but if all AI providers turned off free plans tomorrow i think they would easily have enough people willing to pay $20 a month for it to sustain all their spending.
everyone is still fighting for market share so they are giving stuff away, but that doesnt mean people wouldnt be willing to pay for it if it wasnt free.
This proposition boils down to a belief that there are 3 billion people who are interested in AI for free but aren’t currently paying $20, but who would pay $20 if that was the price. The global median income is around $12k, so this would mean that there’s roughly be a global budget of 0.5% of everyone’s annual income going to chatbots. If you’re off by half, the price doubles for each person. I think you’d make a lot of money betting against the existence of 3 billion ghost customers
I wouldn't.
I do think Ed in intentionally ignorant of the capabilities of LLMs. But I also don't know that I would classify LLMs as 'wildly useful' for coding. Most productivity gains seem to be hallucinated, and while it's too early to make any claims on long term outcomes, there are plenty of studies indicating they might be even more negative.
There are definitely use cases for LLMs in coding. And at times, they can be wildly useful. But I feel like the industry atm wildly overestimates their broader/long term utility.
Anecdotally, I have not seen an explosion in quality/bespoke software since LLMs. In fact I've noticed the opposite to quite the extreme. Not only are new products worse in quality, but the quality of existing products is falling off a cliff.
> Anecdotally, I have not seen an explosion in quality/bespoke software since LLMs. In fact I've noticed the opposite to quite the extreme. Not only are new products worse in quality, but the quality of existing products is falling off a cliff.
This is the big one. It's clear that AI can generate huge volumes of code by KLOC. It is not clear that spending a lot of money tokenmaxxing will eventually result in increased real revenue for software businesses, and eventually even an MBA has to look at a "money in vs money out" chart.
Have been thinking about this a lot recently. AI could be an absolute game changer for a small start-up rushing a product to market – you could quickly build an MVP that would take years and tens of hires before.
But how much ROI is there for large businesses with established products and huge development teams burning through tokens making subtle tweaks that can’t be directly tied to revenue?
Not much ROI, if any. My employer's been making some studies and come up with very modest productivity gains - so of course they want us all to use it, but I'm not sure they're taking the true costs into account. Especially not once token pricing actually reflects reality, and we all get brain rot from using the things instead of thinking. If this thing doesn't collapse before we can run a solid coding assistant model on a developer's machine, maybe it's got some legs.
That doesn't seem very likely.
The legacy of LLMs will live on in various models doing various specialist things (they seem like a really good progression on speech synthesis for example) but the current edifice will come crashing down and if we're very very very lucky they won't take the global economy with it.
I've been thinking about this too. The quality maintenance of large systems isn't something you can just completely automate away with AI. Even if the code is written with AI, you still have to read through and verify it.
Even though that is still faster than regularly writing code, I end up losing that nuanced knowledge that I get from going through documentation and writing it out by hand - actually doing the work. I just don't see it actually replacing developers unless managers are willing to produce MORE code with the SAME level of quality.
> I do think Ed in intentionally ignorant of the capabilities of LLMs. I think it's more complicated than that too. He's pretty well versed in the stated capabilities of LLMs.
The fact that he isn't a deeply involved technical developer who knows the ins and outs and nuances of using LLM tools is the point, because the stated capabilities of LLMs are that they are trivial to use, extremely powerful, and getting so much better every month that you personally can replace developers without even trying as a completely non-technical person with basic writing skills.
Given the hype and extreme claims being made, the fact that he remains ignorant and gets practically no use out of LLMs immediately disproves those statements. The counterargument boiling down to "you're using it wrong" is actually just a further indictment of Sam Altman and his like, because it shouldn't be possible to use LLMs wrong!
The rest, well, the hype needs to die before anyone can make sane estimates of what LLM tech can do for us in various fields. Right now it's all a complete mess.
I don’t get me wrong, I’m on Ed’s side and get where he’s coming from. I just think his arguments are normally taken to the extreme, making them less defensible, when he could make the same arguments from a more moderate stance and ultimately be more convincing.
His arguments, albeit valid, can often sound like reductos ad absurdums the way he presents them.
Yeah, probably.
One of the worst things about LLM writing is how it makes big promises of what it can prove in some piece of writing, and then never really follows up on that, or has specifics that go all the way towards the original, grandiose statement.
And frankly, Zitron is guilty of that pattern of writing too, or of relying on some unstated "baseline" knowledge which is clear from his other writing but not in the specific piece.
So, basically yeah, agreeing about the ad absurdum thing.
(I will note, the tone, the swearing, etc. really doesn't matter nearly as much as these problems, and everyone instead obsessing about the swearing and personality is really boring)
Personally I only find his swearing and delivery annoying when he is not delivering his point well (ie reducto ad absurdum). I’d be welling to bet a good amount of the complaints about his swearing are really just from a poor delivery, and people don’t know why, so they latch onto his swearing.
His early stuff was just as degenerate and vulgar, but was much less of an issue for me.
Part of the problem is the like - sociomedia factor. Ed’s figured out how to break through the noise. I’m not surprised that Ed Zitron is the kind of counterpoint you get in the Musk-Trump attention economy / CEO’s that sound more like prophets than business executives world.
One person's ignorance of something can never be evidence that it doesn't exist. It's far too easy to be willfully ignorant; no one can force you to abandon ignorance if you don't want to.
On the other hand, the hype of "Sam Altman and his like" being plainly exaggerated doesn't mean there's nothing at all behind it. It's plain to see there's something important about LLM capabilities. I don't even use them myself, as emotionally I find them entirely repugnant, and I can still see that.
We need to wait to get the whole story about LLMs, but we don't need to wait to confidently reject both extremes of opinion about them.
> Anecdotally, I have not seen an explosion in quality/bespoke software since LLMs. In fact I've noticed the opposite to quite the extreme. Not only are new products worse in quality, but the quality of existing products is falling off a cliff.
Very little new or ground-breaking (I struggle to think of things AI has produced that aren't themselves just more AI), but various previously-stable sites and services breaking.
The studies you are talking about are probably outdated, it's difficult to deny the actual productivity boost of coding agents.
I'm not talking about the quantity of code produced, but about actual user needs that are now resolved that would not have been before.
The main productivity gain will not come from existing software engineer, but from people that couldn't code at all before but are now able to do things by themselves. We are still very early.
As a developer who has not been able to get any boost in productivity from coding agents, I find it incredibly easy to deny.
I’m a solopreneur, if I could lighten my load I would. However I have yet to save time using coding agents, with the exception of “I made this change to my model file, update all model to match the new format.” Which is cool, but maybe 0.01% of my job, and took a 1 hour task down to 10 minutes.
> The studies you are talking about are probably outdated, it's difficult to deny the actual productivity boost of coding agents.
Is it? Can you produce any evidence for such a productivity boost?
We previously had a backlog of 2 years of features planned. Now we have no work and are just working on tech debt and planning to get more involved in the product side because they need help getting the developers work to do.
I'm sure all your users would totally agree and will be head over heels about the slop you've pushed on them.
It still takes the same mindset and skills to use AI productively and effectively as regular programming. The productivity boost only applies if you know what you’re doing and can actually steer the agent carefully.
Vibecoding hits a glass ceiling very quickly and this will not be solved incrementally. Besides, if the agent could work autonomously to that degree then it would no longer need any prompting at all and we’re living in a very different world. On the other hand that would make the debt actually meaningless, so I guess that is 'a' solution.
He's got subscribers. Maybe the attitude is one he's found plays well with them.
I find it quite refreshing in some ways. Lots of people, when they start complaining about this or that aspect of this AI stuff, are wont to add in a little disclaimer that, despite all of the above, they actually really like AI and use it all the time. I assume this is to avoid the scenario of a bunch of pragmatic builders turning up and calmly shipping nuance in the comments (or whatever you call it these days when you get brigaded by a pile of angry keyboard warriors with chips on their shoulder) - and it sure is tiring having to wade through the equivocation.
That's a criticism that'd be hard to level at Zitron! Say what you like about the man, but he's unafraid to appear to take a side.
> Maybe the attitude is one he's found plays well with them.
Kind of a self-fulfilling prophecy.
What's not a problem, by the way. That's why people always recommend content creators to be themselves. If they try to be somebody else, they find their public is already busy following other people.
So here's the thing. I am not generally an angry person. But Ed's writing really resonates with me, because for the last four years these people have been making a strategy of scaring the shit out of us while trying to ruin something I genuinely love (coding), while simultaneously fucking up the economy and multiple industries and turning the internet into slop. I very badly want more people to call these guys "chucklefucks" or whatever innovative ways he comes up with to insult them because they deserve far more public ridicule and disdain than the (captured, useless) media is giving them.
So far the data for productivity in coding is.. sus. The productivity gains outside of toy projects are mostly anecdotal and it's hard to tell if those accounts are even real humans or just astroturfing and bots. Almost every programmer I know personally has a pretty measured opinion on where these things are useful and where they're not. The breathless hype seems mostly from non coders.
> Almost every programmer I know personally has a pretty measured opinion on where these things are useful and where they're not. The breathless hype seems mostly from non coders.
We have polar opposite media bubbles. I see OG programmers all over my timeline either grieving the "end of software engineering" (a la Ryan Dahl) or extolling "automatic programming" (a la antirez).
> We have polar opposite media bubbles. I see OG programmers all over my timeline
The person you’re replying to, in the bit you quoted, said specifically:
> Almost every programmer I know personally
People you know personally are not a “media bubble”. They are, to borrow your expression, polar opposites. It’s people you can speak with candidly and trust versus bits of text without the full context.
I disagree, people you know are much more likely to have the same exact opinions as you on most things. It's the definition of a bubble.
Ultimately though it's those people's bosses who set the direction and from my experience those people are telling you to your face that you'll be replaced by AI as soon as they're able to do so while they continuously fail to see it's shortcomings.
I want to be one data point seeing as this goes uncontested (the ones in the know don’t care anymore to be honest).
They are not only useful it is obvious they are. If you don’t see it I really, really don’t know what to tell you. You can tell yourself I am bot or shill or whatever if that helps you sleep but .. just trying to help out another dev here. Wake the F up.
> it is obvious they are. If you don’t see it I really, really don’t know what to tell you. (…) just trying to help out another dev here. Wake the F up.
Your whole post is dismissive and insulting and has zero arguments. No one is helped by that, no one is going to change their mind with that, you’re only making the divide more pronounced. You’re being actively unhelpful.
> You can tell yourself I am bot or shill or whatever
I mean, your account was obviously created specifically to post that comment…
I'm not sure you understand what "obvious" means.
> I do understand that useful != profitable and that's where I think Ed has a real point: until inference becomes much cheaper these companies cannot be profitable.
If inference becomes cheaper, it becomes cheaper for everyone.
It's pretty likely that inference will get substantially cheaper. His argument is that for these companies to be profitable some very major and (pre 2022) unprecedented things have to happen. Which I tend to agree with, except I think they will happen, seeing as how they've been happening for a few years.
Except inference has been getting more expensive, not less
Inference has been going down in price on a cost/intelligence basis. If you don't need the smartest model, there are plenty of good Chinese models that are dirt cheap.
Doesn't that sort-of make one of Zitron's core points?
"Chinese models are dirt cheap" isn't going to do anything good for the return the investments in OpenAI and Anthropic demand.
It supports his point that they're planning to massively overbuild compute, which was already well supported by the financials. A lot of that planned compute buildout can be walked back though, and the technology is unquestionably useful in moderation, so it's not the catastrophe he suggests, and his hyperbole is part of what makes me dislike him even if there are elements of his foundational argument I agree with.
Aren't these Chinese models also heavily subsidized at the moment ? They are running off govt money which also has a cut-off date by 2028-30.
It’s been steadily getting cheaper per token per problems solved even if you don’t define ‘unsolvable => infinity token cost’
American AI companies are charging more, that doesnt mean inference isnt getting cheaper. idk why this is so hard for people to understand.
check out DeepSeek V4 Pro .... this is where the threat vector comes from IMHO. If anything is triggering a rush to IPO imho it's seeing these cheap / free models on the horizon that are "good enough" for 80% of the core use case supporting their valuation.
I would say his overall negative outlook is a well needed counterbalance to the completely delusional hype one is exposed to on a daily basis. The truth will then probably land somewhere in the middle.
It seems that a certain kind of person cannot separate the following things: 1) I dislike AI as a technology 2) I dislike the people and companies that profit from AI 3) I think AI is useless
These are three completely separate positions to have. You can think AI is incredibly useful and also dislike it because it will, for example, reduce your relative status in society. You can love the tech but think that Sam Altman is a dishonest person, etc. But for some reason, most anti-AI commentators feel compelled to present all three arguments.
Which is even sillier when you think about it, because if it's useless, then you really shouldn't care: the markets will eventually find out that it's useless, and everything will go back to normal, and the people you don't like will have lost money, so there's no point in being outraged. Of course, I don't really believe that they think it's useless. I do think they're worried about what it'll do to their prestige, though, and they're just hoping beyond hope that somehow everyone will one day "wake up" and share their belief that LLMs are just "stochastic parrots" with no utility, despite the fact that people are using them every day and can watch in real time as they improve.
> ... the markets will eventually find out that it's useless, and everything will go back to normal, and the people you don't like will have lost money, so there's no point in being outraged...
Except that in the process of the markets finding out, things will not go back to normal if everyone's retirement is tied to the market. And in the process of finding out, things will not go back to normal if the hype cycle disrupts traditional hiring/firing decisions.
If it's as bad as some of us believe, then when it falls apart, a lot of people get hurt as collateral damage.
The market eventually found out about Bear Stearns, but a lot of innocent people lost their homes in the process.
They'll be fine
Tetraethyl lead is not useless; in fact, it was hugely useful to the petrol engine economy throughout the 20th century! It just happens to cause nonobvious brain damage throughout societies.
In some ways things that are both useful and harmful are the hardest to deal with. And this isn't just "prestige", it's the already-decaying post-truth infosphere and the already-overheating CO2 levels in the atmosphere.
AI is useful in cases where you can automatically catch errors. Programming is uniquely suitable for this, because we have already got all our machinery of type systems and CI tests to catch human errors. How useful it is in other cases depends on how cheap it is to catch errors and how much they cost - and whether the cost of errors is inflicted on other people.
> Which is even sillier when you think about it, because if it's useless, then you really shouldn't care: the markets will eventually find out that it's useless, and everything will go back to normal
People who are against AI don’t care if it’s useless, they care it’s harmful. And you can’t systematically cause harm then say “oops, our bad” and have everything return to how it was with a snap of the fingers. The consequences of harm don’t go away when the source does.
> I do think they're worried about what it'll do to their prestige
Why must this always be the argument? It was the same with cryptocurrencies and NFTs, there is a specific type of proponent who always accuses critics of secretly being pro the technology but publicly against it due to some ulterior motive. Most people aren’t selfish lying rat bastards who think like that.
> > I do think they're worried about what it'll do to their prestige
> Why must this always be the argument? It was the same with cryptocurrencies and NFTs, there is a specific type of proponent who always accuses critics of secretly being pro the technology but publicly against it due to some ulterior motive. Most people aren’t selfish lying rat bastards who think like that.
Meanwhile, the prestige to be gained/lost from supporting/doubting the big mainstream thing is immense, and the incentives are actually in completely the opposite direction...
Anyway, on that topic The Line Goes Up video covers the arguments about prestige far more extensively and far more elaborately than I ever could: https://www.youtube.com/watch?v=YQ_xWvX1n9g
But it's very much not the doubters who are worrying about prestige in crypto and NFTs, and probably not with AI either.
been involved with several crypto projects as a dev
i think you are wrong
I see little evidence that people combine all three positions together. You're making a broad generalization based on personal vibes.
I see plenty if I care to look outside my usual echo chamber. There’s lots of ‘it’s just the next token prediction’ (in 2026 still!), but there are more sophisticated arguments like ‘it can’t be creative’, ‘it doesn’t think’, ‘it’s just pattern matching’ etc. They might even be true for today’s models, but linearly extrapolating an exponential trend is a classic mistake.
Confusing a logarithmic trend with an exponential one is also a classic mistake.
accurate; your point being...?
> Which is even sillier when you think about it, because if it's useless, then you really shouldn't care: the markets will eventually find out that it's useless, and everything will go back to normal
And in that period where the markets are irrational people are losing their jobs, hardware is being priced out of consumer markets and the rich are trying to embed themselves so hard that we get to pay for it when the market corrects itself. I think your take is highly indicative that you live in a shrinking bubble unaffected by those things.
> until inference becomes much cheaper these companies cannot be profitable. Some mega-players will pay the API token price, but most will not.
This is often repeated but comes from ignorance mostly. You have * zero * reason to believe inference is costly other than just vibes. If you go by data and intuitions - the margins are high.
This kind of thinking really reinforces my belief that people have no idea and are using this whole [AI is not profitable and too costly] thing as a cathartic way to deal with immense progress.
We know that inference cost is very significant, as he shows for example in this piece.
https://www.wheresyoured.at/oai_docs/
However, it needs to be said that he received those numbers. I personally have quite a few issues with him, but there's no reason to doubt his journalistic integrity. Because of that, I believe he reports truthfully on data he receives by informants.
Additionally, none of the frontier models actually publicly talks about inference costs in anything but broad, "let's just forget that"-like takes. Which does not exactly spark confidence.
I'm eagerly awaiting anthropic's public disclosure of their financial details. That should be rather interesting in any case and finally put the inference-discussion to rest.
No reason to doubt his journalistic integrity? He's not a journalist for starters. He's a PR flack who does PR for AI startups on the side while blogging on substack. There is every reason to doubt his journalistic integrity.
The PR-thing was always openly communicated by him and is not some secret or gotcha. It's essentially "fleecing the boosters", which I fully approve of and do similarly myself.
I'll gladly tell my customers all the most glorious stuff about AI and big tech while spending a significant chunk of the money they pay me on supporting AI-/tech-counterculture, such as doctorow, zitron and quite a few other writers, journalists and activists.
It's the old "you live in a society" counter-point against anti-capitalist activism. Needing to make ends meet does not imply that your points or principles are meaningless, it just implies that you have no interest in being homeless and that way losing your chance to actually change things.
So that's fine to me. But: I stated it for a reason, because I know others don't agree. I, personally, consider him trustworthy. You do not, and that's fine. I suspect we both await anthropic's Z.1, which will be able to settle a big chunk of the debate.
If he is right, the numbers will show it.
Why do you consider him trust worthy when sooo many of his predictions are false?
https://news.ycombinator.com/item?id=48447549
He was right about the cost changes, which he predicted quite some time ago. People shouted at him that he was making it all up - yet it was correct.
He was also right about AI-video and sora in particular being a fundamentally flawed idea.
He was also right about the dangers and problems with the general inaccuracy of LLMs and people relying on it.
Also about the expected triggering of ROI-checking in companies, such as Uber is doing now. His prediction is, ROI is negative. And I'm awaiting the society's consensus on that.
The general direction seems correct to me. He's not a technical guy and does not have the knowledge to critique models on a factual basis. I do wish he'd just focus on the stuff he _does_ know about, which is the financial side of things.
He is a much needed counterweight to the unhealthy hype going around, imho.
> He was also right about AI-video and sora in particular being a fundamentally flawed idea.
He specifically predicted that AI videos have plateaued in 2024 which is egregiously wrong.
> He was also right about the dangers and problems with the general inaccuracy of LLMs and people relying on it.
He specifically predicted that accuracy won't increase but accuracy has increased over the time significantly to the point where you can't get it to say anything inaccurate using the reasoning models.
> Also about the expected triggering of ROI-checking in companies, such as Uber is doing now. His prediction is, ROI is negative. And I'm awaiting the society's consensus on that.
The whole Uber skepticism is a good point because all of those people were wrong and Uber is profitable now.
You didn't address my other criticisms - he claimed that revenue would drop in 2024 and it skyrocketed. He claimed that users weren't interested in ChatGPT but now it has a billion users (6x jump).
> You have * zero * reason to believe inference is costly other than just vibes. If you go by data and intuitions - the margins are high.
1. What data?
2. Intuitions = vibes.
Vibes are bad when used against you, but good when used in your favor.
Come on :-)))
I have the data here and intuition https://simianwords.bearblog.dev/conclusive-proofs-that-llm-...
But if you don't believe me, lets have a bet based on what the IPO filings show?
Remember that OpenAI is subsidized from here to the highway.
A better way to model this, since you seem interested is the following:
How much would it cost you to start such a service for, say, 10k users?
Any other internet service has had virtually Zero cost, $0. Google, Facebook, youtube, Wikipedia, you name it. They all went into the dumpster to pick up a thrown away desktop computer, and they could serve up towards 100k if not a million users.
How much would it cost you to serve, say, 10k simultaneous users with a SOTA model? And if you wanted to go cash positive after a year, how much would each user have to pay?
> How much would it cost you to serve, say, 10k simultaneous users with a SOTA model? And if you wanted to go cash positive after a year, how much would each user have to pay?
My post has this same argument - we have multiple third party companies running open weight models. They are obviously not subsidised. And people are willing to pay for it. And these models are as good as the SOTA models from last year. So this kinda proves my point that SOTA is sustainable.
I didn't find the answer there, that's why I asked.
What hardware is needed, how much of it, cooling, and what does it all cost you?
Or are you saying I can take my old desktop and serve Deepseek v3.2 to 10k users simultaneously and it would cost me about $1 per megatoken?
I'm simply saying this: there are third party hosters of Open Weight models like deepseek and they have been doing this for a while.
Obviously they are not subsidised, do you disagree? If you agree, they have a way to price it at a point that people wanna pay for it and also they aren't losing money.
So there's nothing inherent about inference that makes it too costly or whatever.
> I'm simply saying this: there are third party hosters of Open Weight models like deepseek and they have been doing this for a while.
> Obviously they are not subsidised, do you disagree? If you agree, they have a way to price it at a point that people wanna pay for it and also they aren't losing money.
> So there's nothing inherent about inference that makes it too costly or whatever.
Do we have audited GAAP financial data for any of these companies? If we don't, all these are... vibes, man.
Good questions. The post you are replying to is also based on vibes. So how about we make a bet and come back after IPO filings?
But isn't it okay to suggest that random 3rd party hosting companies are ... not losing money? Why?
I'll bet 50€ that big AI companies (OpenAI, Anthropic, xAI) have bad regarding their core financials. Excluding one time deals such as renting hardware, external cash infusions, the core AI model business (training models and selling inference) is unprofitable and will be be at the expected scales (50 billion € or more) for the next 5 years at least.
I bet that within 5 years they will be sold for scrap to bigger companies and will become divisions inside them.
This is not fair, lets just speak about Inference margins. You are bringing 100 other things to make it more ambgigous.
Feel free to either agree to what I'm saying or bet otherwise.1. You can't have a model without training. Training is part of the cost. A model trained 5 years ago is borderline useless. Also models cannot be retrained continuously, we do not have the technology (even if we had it, it would just increase the ongoing operational cost).
2. Rephrasing your statement:
That claim is super defensive. Serving a model can only be done if the model has been built and trained, can't have it any other way. Building it and training it costs lots of money.Even so, fine, I'll take that bet. Anthropic inference prices are still marginally subsdizided. Once they're public they will hike their API prices several times over the next 24 months. Even that might not save them, because when we take all their expenses into account, they will probably need to raise prices 2-3x compared to their June 2026 prices.
Since you couldn't answer, I asked ChatGPT.
It said: upfront investment: $3M to $6M.
Customers should pay $25k per month.
Checks out
I pasted everything you wrote and ChatGPT said
>The "$25k per month" figure is almost certainly the result of ChatGPT making assumptions, not a fact derived from any known business model.
https://chatgpt.com/share/6a28193b-6ec0-8333-a1af-d07e8d89ef...
Your whole calculation is also ridiculous - I think you assumed what revenue per month is required to pay off hardware within a year? Why would I use hardware within a year?
I would suggest consulting with ChatGPT and coming up with a better and more coherent argument.
I reran the numbers, break even after four years. 10k users.
Minimum upfront: about $15M
Comfortable upfront: $20M–$25M
Monthly revenue needed: $900k–$1.5M
Required price per 10k individual customers: $99–$149/month
API-equivalent output price: usually $8–$20/M output tokens, unless utilization is very high.
dawg -- what is your point? please write your statement clearly and we can discuss.
My apologies for not being clear about my point.
I try to do some napkin math of what it takes to start a company serving an LLM to customers. I thought maybe 10,000 users sounded like a reasonable number.
I'd like also to compare it to traditional Internet companies, e.g. Twitter. I'd guess Twitter with 10k users would cost me literally a dumpster dive, so essentially no cost.
If I were to start an LLM company, what would my initial investment look like?
Turns out around 15+ million dollars assuming I would get 10,000 users willing to pay 200$/month.
I just don't see how the numbers you show add up.
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Today Apple launched its revamped AI offering. Judging by several reports, Apple pays Google a mere billion dollars a year to operate it. Essentially just licensing the IP. Google are (allegedly) happy to turn over the right to operate and distill their models for only a billion a year.
Consumer revenue is only a smallish share of the puzzle, but still:
If you are a consumer and you have a Mac or an iPhone, what do you need from AI that Apple's new offering won't provide? Why would you pay for ChatGPT, or even tolerate its inevitably increasingly desperate ad placements?
Assume Google will have similar tools in their phones, and Google search will continue to have the offering it does.
In short, where is the evidence that once Apple's tech exists, consumer AI is worth, to Anthropic or OpenAI, anything noticeably more than that $1B a year?
Maybe OpenAI strikes a deal to put something in Samsung phones. Let's say Samsung is ten times as desperate as Apple (which is how it looks, often). Still only $10B a year?
2026 consumer revenue projections from OpenAI are pitched at $14-15 billion, apparently. If they get that, it's the only year they will get that, because by late this year, everyone with an iPhone will have something useful built in.
Ed Zitron is a mouthy British rabble-rouser, but I think he is probably mostly on the money.
>If you are a consumer and you have a Mac or an iPhone, what do you need from AI that Apple's new offering won't provide? Why would you pay for ChatGPT, or even tolerate its inevitably increasingly desperate ad placements?
Probably the same reason the Gemini app is still well behind ChatGPT in consumer usage and adoption despite being preinstalled on android phones worldwide ? Why are people using GPT on Windows. There's even a copilot button on new keyboards!
Or maybe its the same reason Microsoft Edge is not the most popular Windows browser ? Maybe its the same reason Instagram threads did not even dent Tiktok ?
You are asking the question the wrong way around. People use and like what they like and have a strong preference to continue doing so.
This is just human behaviour. You don't need mind blowing moat. You begin to have problems only when:
- Users are constantly using your product unsatisifed.
- There's a competitor(s) with a significantly better offering that people are talking about.
Will Apple's offering be providing any meaningful/significant benefit over just using GPT ? If not, don't expect any miracles.
> Will Apple's offering be providing any meaningful/significant benefit over just using GPT ? If not, don't expect any miracles.
Judging by the announcements today about its integration into the OSes? They are offering useful things ChatGPT cannot offer unless they write an "everything app".
One can (maybe should) make the argument that this is the browser monopoly again, but given that the USA has seemingly no intention of ever litigating that question again even if the EU does, there are clearly features here that OpenAI is effectively locked out of offering.
Just because Apple said they're 'integrating into the OS' (which can really mean a lot of things) doesn't mean they'll offer something users will actually care about that Open AI can't match.
> Apple said they're 'integrating into the OS' (which can really mean a lot of things)
Well, it can mean a lot of things, which is why Apple outlined plenty of specific use-cases and details of what they meant by "integrating into the OS" here.
What did they outline? We had "browser use" for a while now, but it is still way too slow to be usable. Not to mention whatever OS integration they are making.
> that Open AI can't match.
Open AI can match it but at what price?
I don't understand what you are saying here
likely cheaper than buying an apple product
Windows has been pushing the same thing hard without much success.
Why do I care if AI is integrated into my OS when I can choose my preferred AI and it can use the OS directly?
This.
The other day I wrote up some notes for a presentation. Opened Google Slides, clicked the gemini button, pasted the notes in and asked it to make the slides. Nope; gemini can only modify a single slide at a time in Google Slides.
Pasted the same notes into claude, it wrote a pptx file, I imported that into Slides, job done.
Being integrated into the product doesn't always mean a better result.
Yeah!
Not being integrated can be an advantage because it gives you the freedom to think outside the box.
Meanwhile an AI engineer embedded into an incumbent slide app team has to ask permission and get cross functional alignment for every little feature. And deal with neckbeard tech leads lecturing them on what the right architecture is
That's progress, last time I tried that a month or something ago gemini (the web app) crashed when trying to generate slides.
Well I sure hope that the gemini developers don't have access to agentic development tools because they might read this comment, build the multi slide editing feature using 450M tokens and ship it yesterday removing that Claude edge forever.
I hate that I am defending apple here but Microslop has been pushing a garbage tacked on integration of AI into Windows. By all accounts of the last 20 years of Apple, they are much much better at integrating different services into one fluid system.
I guess, but I haven’t been thrilled by anything apple has released in a long time either. The status quo has just remained unchanged.
I admit to not having yet taken a deep dive on Apple's privacy claims (i.e. on device, private cloud etc) but one thing that Apple is going to be providing in this case is an actual privacy commitment that takes into consideration the level of sensitivity of data being uploaded into these AI chat interfaces.
OpenAI's commitment to privacy is absymal relative to the sensitivity of the data people are dumping onto the platform. The CEO also has a reputation for being untrustworthy.
The biggest threat to ChatGPT's moat may be a brilliant marketing campaign by Apple that really gets people thinking about what platform they want to be upload their secrets to.
That's all true... however the point that Google will get just $1 billion per year from Apple for the AI service is still insightful.
If AI use via Apple represents 10% of the total that vaguely implies that the total AI market is worth around $10 billion per year (which admittedly seems a bit low), and if it is just 1% (which also seems low) then we get $100 billion per year upper-end estimate.
Which just is not enough to justify the current valuations of AI companies.
If AI use via Apple represents 10% of the total that vaguely implies that the total AI market is worth around $10 billion per year (which admittedly seems a bit low), and if it is just 1% (which also seems low) then we get $100 billion per year upper-end estimate.
This is a bizarre way to try and estimate market size.
Maybe Apple’s AI represents 100% of the market! Maybe it represents 0.000001%! We can make up any numbers we want!
Anthropic subscriptions alone already dwarf your $10 billion number.
Uber was recently said to have a $1500 per head per month AI budget (1). Business use dwarfs consumer spend.
(1) https://news.ycombinator.com/item?id=48383056
> There's even a copilot button on new keyboards!
But not because anybody asked for that. It is part of the force feeding so execs can make this exact argument, and pretend that demand is there.
> If you are a consumer and you have a Mac or an iPhone, what do you need from AI that Apple's new offering won't provide
Honestly, I don't think I need anything from AI at all. It's a convenience but it doesn't really enable anything I wasn't doing before. That's probably their biggest problem. The biggest thing is it enables non-coders to write code, but it's very debatable that that's a good thing excluding personal projects
They were not asking for your individual opinion. They were asking why any given person would need it.
It turns out I am any given person
Yep... I've met him and I can confirm that he is in fact, any given person. To that, I might add that I'm in the same situation. AI is a convenience and light entertainment, and as such, I'm willing and ready to pay exactly 0 USD for it privately and in my business. If the price rises beyond this point, they for sure have to move it from convenience to essential and so far, I have not seen any shadow of essential.
You are missing the forest for the trees.
> If you are a consumer and you have a Mac or an iPhone, what do you need from AI that Apple's new offering won't provide?
I've been using Kagi Assistant for my AI needs, and have to say, Siri will probably replace it in the fall. The question will be, will I still want to keep Kagi for search, or will this new Siri get me where I need to be on all fronts? I need to start paying more attention to how often I actually use the search results vs just the AI summary.
There are things I didn't see Apple show and I wonder how Siri will handle it. One example would be basic coding. They mentioned LLMs in Xcode and Siri with the Shortcuts app and Safari Extensions, but I just had Kagi write up a webpage as a means to display a bunch of data it gave me. Gemini could also do this, so maybe it's not a problem for Siri, but it remains to be seen. There is also a question of what the experience will be like. ChatGPT, for example, handles writing up this code is a much nicer way than Kagi Assistant. Kagi feels more like the results I would have had from ChatGPT a couple years ago where it just dumps out the code in a block and any change is an entirely new code dump, meanwhile ChatGPT goes into a coding interface with a live editor. Going to Xcode feels like overkill, Siri will probably be not enough... so that's a gap in the market Apple may not serve. I assume there will be several things like this. The prosumer level of AI usage, if you will.
Very very few consumers will be looking to use an AI to write code for them.
They will, but they won’t realize it’s writing code. It will look like Claude Cowork, which writes code for itself under the hood but is results-oriented for the user.
Co-work is damn near magic. I've been working on a mapping project the past few days, am probably a couple hundred prompts deep in to it (I'm doing some very weird stuff with the data to produce a hybrid map). The processing pipeline is something like 12k lines of python and counting.
I don’t think what I did would be too uncommon. I asked the LLM to design an exercise program and went back and forth with it a bit. Everything was kind of scattered in the chat and hard to read/find. I asked for a web page to consolidate everything so I could just check it each day and see what to use. It made a single file I can just double-click and open in the browser. It’s infinitely better than what the chat itself would have provided, and much better than telling it to give me a bunch of markdown tables.
I could see the same thing being useful for the ultimate output of a lot of chats. For example, they showed Siri comparing specs for few different products. I used an LLM to do this once as well, but it was comparing 12 things with about 50 attributes. The table was fine, but what was better was asking for a webpage that let me click on the attribute rows I cared about so it could total up each column, which allowed me to easily rank them and better make a decision.
Once it can make html files, it’s a small step to have Siri throw it into iCloud, and make it web accessible. This would be more of a feature than something it would just do, but I could see this being used in the same way Google talked about making dynamic widgets to help explain concepts within Google Search. That’s dynamic coding with an LLM as well, even if people don’t know it. Apple wouldn’t even need to show the code, they could just save it directly to a file and open Safari. That’s essentially what their extension builder will do… write some JavaScript and load it into Safari.
those few are the ones shelling out ridiculous money. mom&pop ChatGPT users aren’t their key demographic/users, it is Uber’s with $1.5k/SWE/month budgets
> 2026 consumer revenue projections from OpenAI are pitched at $14-15 billion, apparently. If they get that, it's the only year they will get that
Would you care to wager on that?
Because I would gladly take the other side at even odds.
> consumer AI is worth, to Anthropic
Anthropic does not really care about consumer AI. I expect consumer is where their least profitably customers are.
My primary expectation is that Apple will mostly increase usage of AI by general consumers. To me, this reads like Instagram adding stories. Did it stop Snapchat's growth? Sure. But I would be cautious about claiming it will take too many users away from OpenAI. I think it will be a fairly different product offering.
If you're paying to use ChatGPT right now, you might be using it for hobby coding, projects, or image generation. If you're paying a lot for ChatGPT, you're almost certainly using it for personal programming projects.
The $100/month (and up) subscribers aren't going to churn because of this, and I would be extremely surprised if the $20/month users do in any meaningful way.
> Because I would gladly take the other side at even odds.
If you’re only giving even odds you’re not very confident in openAI at all. $15 billion is peanuts.
I don't have to give them better odds then they'll accept
It's a steal at even odds
It's still positive EV at much worse odds but not worth my time
1. They didn’t accept those odds.
2. You can’t know whether it’s worth your time or not since you don’t know how much the wager was for.
If you’re 99% confident, 10:1 odds with a 10x larger bet is still a steal. My guess is your confidence is somewhere just north of 50%.
When I use these models, I honestly can't tell much of a difference between any of the frontier ones. Admittedly I'm not sitting around benchmarking them during the hype cycles, but as a coder I have zero issues switching to whatever's cheapest. Custom harnesses or whatever are not much of a moat (honestly Claude is so buggy right now that I've been using codex and opencode just so I don't have to deal with a flickery mess that screwed up my arrow keys)
I just don't see how being the "premium" provider really works if much cheaper models are basically good enough.
> Would you care to wager on that?
I don't gamble. Though you might not be alone taking the bet:
https://www.notus.org/technology/trump-blindsided-ai-compani...
"OpenAI CEO Sam Altman pitched the idea of turning over shares in his company to Trump in early 2025 and discussed the matter again with senior officials in recent weeks"
People who use ChatGPT have fed so much data about their own lives and interests into it. This includes a lot of information about personal lives, interests, plans, business and even family! Shifting to another AI app is painful as they would need to start from scratch.
I don’t know anyone who uses ChatGPT who cares about that stuff. Most people just use it as a Google replacement.
I actively hate it when it brings in some nonsense it thinks it knows about me. I told it my income once in an attempt to use it to find the perfect rewards credit card mix. Now anytime I try to get it to search for a deal it brings up some nonsense about “as a high income individual you don’t worry about saving $X, you care more about reliability, so you don’t need to look for the lowest cost” or something similar.
Turn off the "memory" feature in the settings. It just rots the context anyways.
there is the option to opt-out of personalization, opt-out of using your conversations for training, and in that process reduce or eliminate any memory the system has of your personal preferences and context. If these actions don't erode your particular use-cases for ChatGPT, and if you think you can trust the model to follow these options this might be of use. I'm not trying to say "you're using it wrong" but that taking a more active control of the instaces facing you as a user might be of some benefit.
I have iterated through different option configurations to reach a level of 'customization' that more or less conforms to my own use case, and this does include opting out of any and all lasting memory between instances and across chat sessions; and adds a selection of single initialization prompts which shape the chatbot's behavior to my requirements for that session's objective. these trim most if not all af the sycophantic interactions, reduce outputs to the specific formats and contours as defined and omits any of the 'explanations of the underlying reasons behind...' which is just noise. This also has enabled some pretty useful results without ever spending a dime on a paid account: the premium behavior presented to 'potential customers' as a lure continues to work for me, and for iteration across instances and accounts is possible with machine-ready yaml context file when a single sessions hits the 90% wall : one emit and ingest cycle rotation across account profiles in firefox and i pick right up with a fresh limit.
Bouncing between ChatGPT and Claude, and between models for discrete subsets of larger tasks has really been impactful for my particular needs; but as i am not working in regions of knowledge that are beyond my own expertise and because I require the model to limit responses to very specific parameters, the logic space for unchecked hallucinations is low (but not zero).
The most useful project results for me have been in developing an air-gapped private menagerie of multi-domain models which uses an operating structure not dissimilar to OpenMythos; but then my background includes HPC environment development for NUMA, unikernels, MPI and bare metal hypervisor design - so getting a design plan and functional code without requiring a team of programmers and months of time in order to even start using models under my control which have zero public facing risk for the projects i'm working on is a much better place to spend limited budget on. Last gen hardware in the V100 class is perfectly capable of running and delivering the physics calculation optimizations as required and I would rather buy and/or install solar+storage to supply the electricity for token generation than rent the same from any of the frontier models AND trust that "don't train and learn from me" preferences are and continue to be followed.
If your use-case is a a 'lifestyle shopping assistant' then just turning off customization might be sufficient to stop it from telling you how to live your best life.
making me glad I always use ChatGPT in an incognito window, guy.
Let's imagine for a second that this a few billion dollars per year to Google is correct. Why do you assume that it covers everything to be done by Google itself - from hosting to running actual servers? Apple may very well pay Google a licensing fee, take a trained LLM and run inference themselves locally or even at a yet another 3rd party for example a datacenter corporation or any mix of these. And then a true real cost of running just the inference on every Apple device would be separated into a completely different org payment flows, very obscured and higher than just a license fee.
I'm not saying that this is what really happens. I'm saying that believing a CEO is as foolish and as grounded in reality as believing Ed Zitron.
> Why do you assume that it covers everything to be done by Google itself - from hosting to running actual servers?
I don't, and that's the point, isn't it?
It's the keys to a substantial chunk of the kingdom for $1B a year. Literally they are getting, for a very small price, the right to distill their own models from Gemini.
Is there money in this for someone with a data centre? Possibly. Is there money in it for NVIDIA? Possibly.
But either way, that's not OpenAI or Anthropic, is it?
> It's the keys to a substantial chunk of the kingdom for $1B a year. Literally they are getting, for a very small price, the right to distill their own models from Gemini.
Here is a different interpretation: Apple bought the rights to distill and use a smaller version of one unspecified model in the Gemini family (there are many such models).
The distillation will be carried out at Google's data centres so that the original weights never leave Google premises.
For this to be keys to be kingdom it would need to cover all current and future models and would need to be very permissive with regards to distillation parameters and allowed uses of the distilled model.
I expect the reality to be somewhere between these two extremes.
I expect that a lot of the money will be in Enterprise AI.
Right but OpenAI are for real making that prediction about their consumer revenue, which seems decidedly ambitious (considering that they are making nothing from their current phone placement). And they have said that they expect it to be quite a large share!
https://mlq.ai/news/openai-projects-over-280-billion-revenue...
"OpenAI projects revenue will be divided nearly equally between its consumer and enterprise business units by 2030"
That it is so absurdly ambitious and so likely to run up against reality strikes me as really indicative of the quality of the envelopes these calculations are being sketched on.
I think OpenAI is just getting beaten so badly in the Enterprise space that they have to make rosy predictions about the consumer space.
Fidji Simo had to take medical leave so they're behind on their advertising platform. But in principle that could make a ton of money.
The free Chinese models are always approaching frontier-level power. The cost to Enterprise to run these models is where Anthropic and OpenAI are competing against in the long run.
That's cool, but the enterprise is cheap. If you have any proximity to sales in the space you know that procurement and lawyers exist that have a full time job redlining purchase orders and agreements to the N-th degree. The enterprise will pay for something that will make them money or prevent them from losing it. But the enterprise isn't paying a premium and they know that.
Even within the Fortune 5 of the US if be surprised if any of them are paying more than $1B annually currently in total.
And then you can take the parent context into account. If they can just equip users with a slightly more expensive Mac and call their Dell rep to order a few thousand DGX Spark to handle the rest... Why would they risk their trade secrets and intimate details flowing into models that may or may not be trustworthy long term?
Most large enterprise have been burned by SaaS over the years in some way. I can't imagine there aren't architects in the large organizations that are truly weighing how to effectively use AI. And beyond that we're seeing more and more progress in SLMs and orchestration agents which become easier to run at scale on-prem.
> If you have any proximity to sales in the space you know that procurement and lawyers exist that have a full time job redlining purchase orders and agreements to the N-th degree.
AWS billing.
I think so too (Enterprise), but I think its going to look different from "Pay subscription for access to a model from OpenAI/Anthropic/Google."
I don't think people will be doing business with the labs directly. "Enterprise AI" will be distilled down into purpose built products, with the model just basically being a generic commodity, and nearly irrelevant to the enterprises buying whatever these products are much like how I don't care if whatever SaaS was built in React, Vue, or some other framework as long as it works.
Ironically, for as much shit as they get about Copilot, Microsoft I think has the right idea for the long game they just suck at execution. Copilot is the tool, integrated into the rest of their enterprise stack, it doesn't care what the model is behind the scenes (they already offer you the ability to choose between different models).
That doesn't really bode well for the labs and their trillion dollar IPOs, because they are effectively reduced down to being a developer framework.
This this this 1000 times. It’s not that the tech ain’t great, but it’s just not marketable to the levels these scammers are screaming about.
You are kind of glossing over the B2B market where contract pricing is basically just MBA vibes and the fact that people don't really care necessarily about the performance of the language model once it hits a baseline. They care about how it integrates into their lives. Precisely where first mover advantage comes into play. Having to train a language model all over again is it's own sunk cost.
OpenAI themselves said, in their revenue projections, that they expect the consumer vs enterprise revenue split to be 50:50, though — see:
https://news.ycombinator.com/item?id=48451053
That's actually a really good sign if they are getting that many consumer subs. I was expecting it to be more like 1:4.
The trick is in the wording - they probably aren't getting that many subs. They're saying they "expect" to get that many, at some point in the mythical future.
B2B absolutely cares, especially since ZIRP is unlikely to ever come back in our lifetimes. No sane corporation is going to continue to throw billions down the drain with nothing to show for it.
Most people using ChatGPT aren't using it for coding. They are using it for writing emails, working with spreadsheets, doing research, and writing reports. They could not care less about the coding aspect of language models. ZIRP in this context is meaningless. It's just another expense for every law and accounting firm. There's an entire world beyond tech jumping into this stuff right now. Like to give you perspective on this. I called my aunt in Germany who is an almost retired MD and she was the one that brought up ChatGPT and Claude to me.
b2b needs actual ROI and that's no where near. CEOs would be yelling loudly if this were returning them cash instead they're just jettison people to afford the bills.
This would be greate for google, because most people, specially in the apple environment don't much care to install new tools if they have a native tool that works reasonable well. If you have an ai assistant that's minimally competent in your desktop or phone, you will not care to go after chatgpt or alternatives, and google will receive tons of data to improve their models.
Mmhmm and why spend on wrapper SaaS when open or self trained on device models do the job
Web SaaS gonna end up being seen as another failed play at slim clients and entirely centralized sources of pay to play access to eyeballs; more AOL-ification of networks
ChatGPT has >1B users globally a mere 3 years in. iPhone is at 1.5B mostly concentrated in rich areas.
Only maybe fifty million of them are paying, though.
It does not look nearly as good when you compare paying customers.
[dead]
"Ed Zitron is a"
gobby ... British rabble-rouser. "Gob" is the Dick van Dyke approved word for mouth.
Zitron is in the business of content creation and not successful predictions. It doesn't matter how many times he (and several others around) will say the end is here, they have to be right only once.
BTW, one thing for sure he is right about are the economics, as of today there is no way these massive investments are gone be paid.
For the purposes of content creation they don't even have to be right once
Now that you mention it, has Ed ever made a single testable prediction that came true?
That prices would increase for the frontier models, and that usage limits would go down.
They don't have to be right even once. Why do they?
One of the "smells" that gives away a quacky ranter is they speak in impassioned, "Why doesn't everyone understand this?" tones, but in fact their argument just doesn't flow. If Zitron's argument were as solid as he keeps saying it is, you would read it and understand it and see that it is solid. He would begin somewhere–statistics on AI demand, say–and then walk the calculations carefully over to the next step–maybe revenue needed for profitability by AI companies–and you could follow the argument. But no. He jumps. He leaps. He circles back. If the situation were really "Gosh why can't you see it?!"-clear, his explanation of the situation would be clear. It isn't, because it isn't.
I don't read Ed Zitron, aside from when he appears here on Hacker News, and I also find his tone to be over-the-top. I think we might agree on that much.
These articles are lengthy but, to my understanding, Ed's idea is...
* AI companies have committed to purchasing X amount of compute
* Data centers are being constructed to meet this demand, they'll need to charge amount Y
* AI companies do not have sufficient revenue to pay amount Y
IMHO this isn't surprising, personally the only real use-case for AI that I've seen is code generation or automated sales or scam calls. This doesn't seem like a big enough market for the huge dollar amounts I'm seeing thrown around.
I'm curious why you think Ed is so far off the mark on this. To me, it seems like we are headed for a big correction on the whole AI thing.
Not the OP but Zitron makes clear errors:
• He seems to think that the moment Nvidia release new hardware, all existing hardware becomes worthless. It doesn't and there are plenty of tokens being served by old GPUs. This makes all his calculations about how quickly datacenters have to pay off useless.
• All his numbers about costs, revenues etc are guesses or attempts to work backwards from off the cuff and frequently inconsistent comments by tech executives. They could easily be very far off.
• He doesn't seem to understand that datacenters have never been full of hardware on their opening day. A lot of his attacks revolve around this confusion - he learns that an opened datacenter isn't yet at full load or fully equipped with GPUs and thinks that means it's been delayed. I remember when Google first opened their facility in the Dalles, it took years for it to completely fill with machines.
> All his numbers about costs, revenues etc are guesses or attempts to work backwards from off the cuff and frequently inconsistent comments by tech executives. They could easily be very far off.
Agreed, but I'd argue that Ed doesn't have much else to work with. I'd like to see journalists take this tack and start asking these executives to either back up their statements or back down from them. They should be held accountable for their statements.
Even if we dial down these numbers by a magnitude they are still insanely large and the AI companies do not seem to be making enough money to balance things out.
> He seems to think that the moment Nvidia release new hardware, all existing hardware becomes worthless. It doesn't and there are plenty of tokens being served by old GPUs. This makes all his calculations about how quickly datacenters have to pay off useless.
I agree that older hardware from Nvidia doesn't become worthless when Nvidia releases new, more powerful hardware. I have to point out that it certainly loses a great deal of value and that's not nothing.
> He doesn't seem to understand that datacenters have never been full of hardware on their opening day. A lot of his attacks revolve around this confusion - he learns that an opened datacenter isn't yet at full load or fully equipped with GPUs and thinks that means it's been delayed. I remember when Google first opened their facility in the Dalles, it took years for it to completely fill with machines.
Is that really the case? I mean, I read about the build out of these data centers being delayed all of the time. I read this last week and it seems roughly in line with Ed's ravings:
> A JPMorgan analysis last month found that more than 60% of data-center capacity planned for completion in 2027 isn’t yet under construction, and another 7% is delayed.[0]
[0]: https://www.msn.com/en-us/news/technology/america-s-data-cen...
H100s installed 4 years ago are more expensive to rent now than they were on day 1. It is not at all clear that older hardware is losing its value in a world where the next gen model is smarter and faster due to improved training+inference algorithms (e.g. custom kernels) but runs on the same hardware.
It's either new GPUs make the old ones worthless or old GPUs make the new ones too expensive because they're still useful, it depends which ranter you're reading at the time.
Just like Michael Burry kept comparing NVDA to CSCO and now he doesn't do so anymore now that NVDA's P/E is ~31 and CSCO's is ~41. Funny that.
It helps if you look at Zitron's work history and experience. He's a hype man and a games journalist. His opinions on this are whatever sells, not exactly whatever is correct.
This is alarmingly obvious whenever he talks out of his depth about things like how companies actually use AI and reason about business decisions.
accuracy and precision are not the same thing. he's delivering one, you're asking for the other. no?
To put it more bluntly: he provides neither in his pursuit of rage views.
They don't immediately become worthless, but they don't last all that long either
https://www.tomshardware.com/pc-components/gpus/datacenter-g...
This doesn't match my experience, in academia I saw ~40-45% utilization NVIDIA GPU clusters that went 6 years with <20% failure rate. Might be a TPU thing?
I'm FAR form an expert on this, but I believe that the operating costs such as power + cooling form a big part of the lifecycle. I have no doubt that at some point within the 6 years that are being booked, that replacing entire working racks won't be more cost efficient.
That is current practice, yes. The economics of replacing racks then selling the old ones to people who will salvage and resell working components works out better than trying to repair/retrofit in place.
> He seems to think that the moment Nvidia release new hardware, all existing hardware becomes worthless.
I am the OP and I totally agree with you on this one point. In fact the progress being made by open weights models strongly suggests that some of this hardware has much more of a life.
The overarching point he makes about incomplete data centres is that the current offering is running successfully on that very incomplete capacity, right?
What he is saying is that he cannot believe the demand exists to fill any of the unbuilt stuff, but much of it is still commitments that are going to have to be paid for, unless they can be backed out. He points to Nadella essentially confirming there will be overcapacity.
He also makes an interesting point that people tend to think "I can't get a GPU right now" means "there is intense, live demand for GPUs in data centres" when in fact the reason you can't get one is buy-and-hold. Including much of that new replacement hardware: it is being bought even the old stuff would (let us stipulate will) do the job.
I think he (or someone who interviewed him) recently said it reminded them less of the dot com boom and more of the Chinese real estate bubble.
Future demand is unknowable. He might be directionally correct but wrong in magnitude, or right about everything, or wrong about everything. Unfortunately people who call bubbles never make their claims falsifiable or do anything else to build confidence, like take short positions. Zitron attacks the very notion that he might put skin in the game like that as obviously crazy.
I don't know to what extent we can say the current offering is running successfully. Anthropic have had visible capacity constraints for 18 months now with lots of throttling and quota capping going on. Those are good signs that demand does exceed supply at the current price point.
Additionally, Mythos has not launched publicly and one reason seems to be that it's too slow/expensive to make widely available, i.e. is capacity constrained.
But supply/demand is always in equilibrium, in some sense. So you could argue that it's currently balanced, or would be if priced correctly. That tells you little about future demand though.
All of this is fair, but it's also important to weigh your criticisms of Zitron's claims against the absolutely unsupported claims being made by Altman on a regular basis. They never show their working in even the way he does.
FWIW on capacity constraints, my gut instinct is that like every other startup these AI companies are are really only now beginning to do the serious efficiency work, because they had money and resources to throw at scaling without it; never optimise too early is pretty much a startup mantra.
Sure, I'm not claiming that anyone who isn't Zitron must therefore be completely reliable.
I think all the labs have done a lot of efficiency work for a long time, tbh. You can see the evidence in their papers, open source releases and product design choices like model routers. They know they need to reduce their cost base a lot to become profitable.
> personally the only real use-case for AI that I've seen is code generation or automated sales or scam calls.
That seems like a giant paucity of imagination. I can easily name a lot of areas where AI is already having a large impact and it's not hard to imagine the impact growing:
1. Customer service. Yes, we all like to laugh at the silly chatbot mistakes, linked list reversals and Instagram oopsies, but a lot of companies are putting a lot of effort (and spend) into AI for customer service.
2. The legal profession is already spending a lot on AI, and it will only grow. Again, we all like to read about hallucinated case citations, but those are solvable problems (honestly I felt they were more human problems than tech problems to begin with) and there are so many areas in research and document summarization that AI is really good at.
3. Radiology. There are lots of arguments over whether AI will "replace radiologists", but that's besides the point. The largest radiology groups in the country already use AI software to check for specific missed diagnoses, and the expected spend on AI will grow, a lot.
4. Enterprise knowledge management. Services like Glean are popular and growing.
I can easily go on.
You annihilated your own argument with the inclusion of radiology. The only successfully deployed "AI" in use by radiologists (that I'm aware of) are bespoke image analysis models, not LLMs. And that space is rapidly fragmenting as there's a frustrating and seemingly irresolvable tension between sensitivity, generalizability, and accuracy.
Everyone I know hates AI customer service. A couple of prominent food delivery apps here in India switched to AI chatbot customer services and it’s been horrible since then. It’s been almost impossible to get refunds since then, even when there’s straight up fraud involved without screaming ok twitter.
Now ofc it can be said that they haven’t implemented it properly but at some point it needs to be considered that why isn’t no one figuring it out?
I would argue that all 4 of these that you have mentioned can be handled with relatively small models very well.
The real question is what situations are the flagship, larger models useful in and will that produce enough demand.
Radiology isn't using chat bots
I don't know if Ed is far off the mark. But this article does nothing to help illuminate it.
He mixes estimated capex spend by like 3 different sources with actually commitments by the LLM providers.
He talks about how crazy it would be for ai providers to double revenue every year. But openai is doubling every 9 months and anthropic is doubling every 3.
It's obvious if AI consumption stops growing today those companies are in trouble, and if AI consumption keeps growing at current rates they'll be more than fine.
Most people expect growth rate to slow, just no one knows by how much. This will determine if there is an over build out or not.
Code generation isn't big enough of a market?
> He would begin somewhere–statistics on AI demand, say–and then walk the calculations carefully over to the next step–maybe revenue needed for profitability by AI companies–and you could follow the argument.
That's exactly what the first (titled) section does?
Haha thought you were referring to the upsell at the start asking to subscribe to the newsletter for $70 / year. But yes it does call out the unprecedented amount of money getting dumped into AI.
What turned me off though was this paragraph:
> This is a hysterical era perpetuated by liars, cowards, imbeciles, craven boosters and the easily-fooled. Those excited about generative AI are either the victim or the perpetrator of a con centered around a technology to ingratiate at the highest cost possible.
That's a very bold claim. Really anyone excited about generative AI dude? That's just an absurd claim, and makes it sound like he hasn't used an LLM since GPT 3.5. It's just the language is so hyperbolic and angry that it's giving me more rant vibes that really hurt the tone and damage the (many valid) claims he's trying to make.
Really tried to read through this all the way, but man I'm just not in love with this guy. I feel like the frustration is clouding his judgement. This line is another one with a fact that isn't really grounded:
> so, you know, they only need to grow by 496% by the end of 2029!
Which isn't wrong, but also Anthropic's revenue increased from $1 billion in Dec. 2024 to $47 billion May of 2026. Which of course doesn't guarantee that it will continue to grow at that scale, but it's clear that there is a strong demand for what they are creating.
Idk, not really sure what my point is here. There are just so many facts and numbers quoted in here... It's a bit exhausting to refute a piece like this, when parts are genuinely correct, and parts are maybe subconciously exaggerated due to some emotional leaking into the argument.
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I just woke up and THIS! ... you almost owe me a new keyboard! I love it!
This statement cleanly encapsulates the entire problem with all of the frontier models' companies' pre-IPO numbers.
They have something-something "new technology" and we don't know anything about how the market is going to settle on the ethics, the utility, the human capacity opportunity cost impacts of not training and/or mis-educating an entire cohort of intern-engineers for a few seasons to a generation, the full environmental costs of hardware and operations necessary for the training each new larger model, ... and we cant even quantify the unknown-unknowns - the risks we cannot forsee.
To predict market revenues for the next few years based on the curves, that they self report without external disclosure of the underlying numbers, is just like expecting your 2 yr old to continue growing at the same pace in the future and in the past - laughable. Good thing it was just water not coffee and it didnt quite come "out my nose" :- ) Thank you kind stranger!
Glad to be of service. I can't take credit for the idea, it was stolen from a meme I saw long ago, but it was one which sticks with you.
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well if he takes after you i 'd say he tops out at 100m
> Anthropic's revenue increased from $1 billion in Dec. 2024 to $47 billion May of 2026.
That's the kind of claim that requires and asterix, and things like this are what feeds into the AI propaganda machine.
That is an anualized revenue, which are projected numbers and not "real numbers".
Divide both by 12 then and you have monthly revenues. The ratio between them remains the same and remains rather astonishing.
Dividing by 12 you still have the same problem. They're projected numbers as opposed to real ones as well as being grossly skewed by any short term fluctuations.
Divide both by 12 and you do not get the projected numbers. You get monthly revenue, a real measured number. It is the number being reported * 12 when they state a new ARR.
E.g. When Anthropic stated $1B ARR (an extrapolated value) what they were actually reporting is $(1/12)B Monthly revenue. If it helps their current monthly revenue is 47 times that, for a grand total of $(47/12)B per month in revenue.
Yes it is the current monthly revenue which is a projected number as far as the other 11 months go. That's fine if the overall economy has low volatility, your sector is well established and predictable, and your company isn't undergoing any significant changes. Absolutely none of that applies to the frontier AI labs.
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So basically you can't find fault with the numbers but you find the tone annoying?
Well, he dismisses any value whatsoever to GenAI. That's immediate bozo bit criteria to me. And, well, if Anthropic revenue doesn't grow 5x between now and the end of the decade, I'll be pretty surprised. But, sure, if it doesn't, then someone will keep them around anyway. AMD almost died in the 2010s as one example, but they kept getting propped up and now they're back in the game swinging. There are people who can see alpha beyond the next 10Q. Ed Zitron isn't that sort.
> Well, he dismisses any value whatsoever to GenAI.
I didn’t read it that way. I see a lot of value in it.
I just don’t see us justifying the amount of infrastructure being built or current valuations. Or in the unlikely event that we do, the societal upheaval is going to take away the ability to monetize it meaningfully.
OpenAI and Anthropic may make it through. But that is different from saying valuations are justified or that all this infrastructure will pay off.
"Those excited about generative AI are either the victim or the perpetrator of a con centered around a technology to ingratiate at the highest cost possible."
How else would you read the above statement? He's just preaching to his own choir IMO.
My take: like any gold rush, a lot of dumb ideas will get backed and they will all fail. And then we'll keep the ones that worked. SSND. Good luck picking the winners a priori.
I read it in context as being about the market prospects of genai.
The problem is, when there is so much overinvestment, everything gets wrecked. In the aftermath of the dotcom boom there was at least a bedrock of fiber and still useful equipment to build upon amid the rubble. This time we are going so much further; also many of the durable assets are misplaced bets and the depreciating ones will depreciate more steeply.
Someone should do the analysis of a decade and a half of Nvidia datacenter GPUs from Fermi to Kepler to Maxwell to Pascal to Volta to (Turing) to Ampere to Hopper to Blackwell and generate some hard depreciation numbers. Fiddling around a bit, 16-20% annual depreciation (so 5-6 years total and then any further revenue is bonus goods) it would appear, but that's a fiddle number.
But confounding this, K80s and V100s are still offered by cloud providers 13 and 9 years after their releases and academia still loves their GTX 1080 Pascals in their desktops. At companies, the beancounters take a computation and find the best architecture !/$ for that calculation. It does not need to be brand new shiny. It's Nvidia's job to make that case, not them. But anyway, the real data is right there. And those old GPUs demonstrate the dark fiber is already in place (and it's not so dark or they'd pull their racks).
AI is the special case. New GPU generations are the only way to access HW implementations of last year's research on precision modes and matrix math. If that slows down, that would be the first real bellwether of a slowdown. It hasn't happened yet. I'm a little surprised myself, but I also think coding agents are the vanguard of general design agents and that's going to hit a lot of industries at once. So as long as the next generation of GPU halves the price of tokens and doubles throughput (or better), the demand for tokens will continue to rise IMO.
What I don't think is that AI can come for anyone's job successfully no matter what the C-suite sorts insist.
In summary, if you're a bear, you can point to the depreciation cycle and scream the sky is falling. And if you're a bull you can point to GPUs staying in production for a very long time despite the depreciation. Guess we have to wait for 2030.
5-6 years is wildly optimistic for GPUs in an AI data center
Try 1-2: https://www.tomshardware.com/pc-components/gpus/datacenter-g...
Sure, according to an unnamed "GenAI principal at Alphabet" of whom "We could not verify the name of the person who describes themselves as 'GenAI principal architect at Alphabet' and therefore we cannot 100% trust their claims."
But let's run with Deep Layer(tm)'s hot take from 2024, GPUs all die in 2 years, no exceptions*. Poor guy just spent $400,000 on a DGX with 8 B200s, each of those B200s generates a piddly ~$3,000 in profit monthly spewing tokens, netting $576K in 2 years, that's a pathetic 20% annual return. Oh no... Won't someone call Michael Burry!
*Never mind the 3-year warranty or any extended service contract, that GPU is D E D and you're S O L.
SSND?
same shit new day, I'm guessing
Thank you
Alright, let me explain what's happening this Q
Chinese providers realized that LLMs have peaked and have started trying to reduce the price per token. Deepseek pro v4 can easily add tests to my complicated code and costs cents for a million tokens.
I can ask Claude or ChatGPT architecture questions and then use Deepseek for the rest.
How are these businesses going to pay to price of energy and GPU depreciation again?
I love nonsense like this. If using a larger model to plan a chain of thought task for a smaller model works, what makes you think Anthropic can't do the same thing and offer it as one of their effort level settings? It reminds me of all those fallen AI ASIC startups insisting they can crush Nvidia until they found out the hard way that the rules of the game are dynamic.
The real challenge IMO is whether enterprise will want to run the models on-site for 100% security and privacy, but even then, what stops Anthropic from offering such an option on-prem or in the cloud?
China's available AI coding agent subscription slots are apparently gone by 9:30 every morning: https://hellochinatech.com/p/china-ai-coding-boom-economics-...
What stops Anthropic of American energy and infrastructure costs. And those AI ASIC companies just got bought by OpenAI and you guessed it... NVIDIA
Tell that to Esperanto, Graphcore, Untether, Mythic, Wave Computing, Cornami, Copia Automation, Kneron, Lightmatter, the list goes on...
But you run with this Anthropic will die because it will run out of electrons narrative. At least it's creative.
He implies $400 billion in revenue by the end of 2029 is unrealistic when in fact it's very doable if you look at the trajectory of this technology since ChatGPT 4.0 launch. Google and Meta bring in around $500 billion in ad revenue between two of them annually. ChatGPT will easily bring 100s of billions in ad revenue if fully monetized given 1. it has billion weekly active users 2. ChatGPT conversation provides even better context for ad targeting vs search or social media. Enterprise AI revenue is going through the roof already, and with computer use companies will literally be able to fire large percentage of white collar workers and replace them with AI agent without updating their software infra.
Does that '100s of billions' come from a big bucket somewhere called 'spare cash', or does it correlate to a commensurate reduction in the 'around $500 billion in ad revenue' that Google and Meta are extracting?
Do your assumptions - " if you look at the trajectory " - factor in a slowing economy, a slowing growth in quality improvements in the tech, and/or the asymptote of market saturation for punters happy to stump up more than $50 a month?
What about a few hundred billion in salary and benefits reductions due to mass layoffs?
Not saying this would be good (qualitatively) or even good business in any sense, but we’ve already seen companies willing to sacrifice headcount to cover CAPEX for these models.
A few hundred billion in salary and benefits reductions equates to millions of layoffs. At minimum, we'd be looking at something about the same magnitude as the 2008 financial crisis. That scale of workforce reduction would have profound implications for the broader economy.
In a consumption-driven economy, businesses need consumers. Any gains from these layoffs would be short term at best.
And if a pig had wings it could fly
> Anthropic's revenue increased from $1 billion in Dec. 2024 to $47 billion May of 2026.
Where are those numbers from?
I mean it almost certainly won't increase unless a major company takes out substantial debt, in which case we just kick the can and have conversations about bigger numbers. I don't quite think you understand, where will these hundreds of billions come from? By 2029 we will be well into a hardware glut and people will run their own models. Anthropic doesn't have the data flywheel to compete with OpenAI or Google. They went all in on special purpose AI and hit a brick wall and had a "do as much evil as possible" strategy which didn't pay off. Hopefully they fail before they get the entire industry regulated.
> Haha thought you were referring to the upsell at the start asking to subscribe to the newsletter for $70 / year.
People like you would be why I put "(titled)" in the reply.
> That's a very bold claim. Really anyone excited about generative AI dude? That's just an absurd claim, and makes it sound like he hasn't used an LLM since GPT 3.5. It's just the language is so hyperbolic and angry that it's giving me more rant vibes that really hurt the tone and damage the (many valid) claims he's trying to make.
The premise is that AI is significantly more expensive than current subscription & token fees. Within that framing, yes basically all AI users are getting conned. Tricked into redesigning their workflow around an unaffordable technology, in the hopes there will be too much sunk cost and they'll just eat a thousands-a-month fee.
> Which isn't wrong, but also Anthropic's revenue increased from $1 billion in Dec. 2024 to $47 billion May of 2026. Which of course doesn't guarantee that it will continue to grow at that scale, but it's clear that there is a strong demand for what they are creating.
"Doesn't guarantee it will continue to grow" is an understatement.
Let's take a generous assumption of the average subscription; $1000/month/seat. This will be quite a bit higher than pretty much everything but hardcore software dev, we'll re-do the math with $200 in a moment. Let's also grab Ed's $60B figure for both Anthropic/OpenAI, as it's more generous.
That's 30 million subscribers for Anthropic, 30 million for OpenAI, 60 million total.
They need to 5x. So 240 million extra subscriptions.
... Are there 240 million people left on the planet who can afford $1000/month?? (Either directly, or their employer) This kind of scaling is already hitting the limits of people on the planet. That sounds ridiculous for "240 million people" against 8 billion, but remember that $1000/month is a lot of money and a lot of jobs just do not benefit from AI. 2/3rds of employment in the US is stuff that happens in the physical world. Claude won't restock shelves, manufacture goods, construct buildings, cook food, or wipe geriatric asses.
Go again with $200/month. While this monthly fee is much more palatable, the sub-count inflates to 300 million subs needing to grow to 1.5 billion. They'd need to sell a sub to everyone in Europe and North America.
(And while there's loads of people in Africa and Asia, most of those are low income. You're not getting expensive AI subscriptions out of them or their employers either. China's obviously not gonna buy US AI, India has a GDP-per-capita of $250/month.)
>They'd need to sell a sub to everyone in Europe and North America.
Yep. Every man, woman and child, and even then provided we include Russia, Mexico, Cuba, Haiti etc, and, out of desperation to get to 1.5 billion, Turkey, which is in Europe a little.
> He would begin somewhere–statistics on AI demand, say–and then walk the calculations carefully over to the next step–maybe revenue needed for profitability by AI companies–and you could follow the argument.
Which of the hyperlinks provided at the beginning sounded like what you wanted, and after you clicked it* how did it disappoint you?
The information you are describing is stuff I would not expect anybody to repeatedly duplicate across periodic blog-posts.
* (Yes, I'm being sardonic, but if you did bother to click them, then I'm legitimately interested in your answer.)
He's right that its all going to pop dramatically and catastrophically for some. But having read a bunch of his stuff, there are two things he's just plain wrong about and they make his martyrdom tone too grating.
- His own objectivity - he consistently throws shade (rightfully) at the pro-AI side being financially 'required' to hold a certain world view, but is completely blind to his own claim to fame effecting him similarly.
- He consistently claims AI can't be made to work, and tries to prove this by calculating with the bubble prices. Its like saying tulips could never be profitable in the middle of the mania because ships were too expensive as proven by their current price to use for shipping tulips.
Add in the semi regular instance downplaying AI's usefulness contradicting my own experience and I mostly dont bother reading him anymore.
Its not like I'll be surprised that shit hits the fan, and he's not going to call the 'when' any better than wallstreetbets or an octopus.
Yeah, to be honest I think his take is a bit nonsense because it's so historically inaccurate.
Most hugely transformational technologies in the past also resulted in giant bubbles that burst, because investors piled into lots of companies in the hope that their particular company would win out. Railroads, automobiles, telecommunications networks, the Internet, etc. etc. were all hugely important, transformational technologies that all caused giant bubbles that burst.
But Ed Zitron seems hellbent on saying AI is a nothing burger, and that's why the bubble will burst. But the latter doesn't necessarily follow from the former, and indeed the examples I gave show that the exact opposite is often true.
I believe that the AI bubble will burst precisely because it is such a transformational technology. AI may not live up to the ways its biggest cultists like to shout ("Feel the AGI flow through you!!!"), but similarly in the .com boom/bust there was tons of nonsense about how we'd do absolutely everything online, we were in a new "eyeball economy", whatever that meant, yada yada, yet I'd argue that in some ways the Internet was actually a bigger impact than originally envisioned, just not necessarily in the way that late 90s boosters envisioned it.
Agreed. Phrases like "journalists are currently gooning over OpenAI and Anthropic" really put me off. It's a poor attempt at modern muckraking; cheeky yet offering little substance.
He's just a Brit, writing in a style we write in. Sweary, comical, red-top. The Register did it for years.
I don't think you know what "gooning" means. It's edgy Gen Z slang and has nothing to do with being British.
I didn't say it was. I'm just observing that his muckraking style is part of a very long British pundit tradition. Americans have never liked it — Intel got very upset about The Register's coverage of "the Itanic".
(And he's not Gen Z anyway is he; he's among the older millennials. He's appropriating it for muckraking purposes.)
Sure, but does that vibe invalidate the argument? What an odd time the middle of an argument is to be clutching pearls and worrying about prose quality.
Style and vibes notwithstanding, is there anything in your view that wrong with the argument itself? Could a better or more polite writer have convinced you with the same shape of logic?
I responded to a comment about the prose. Why are you not calling out that one instead?
> Could a better or more polite writer have convinced you with the same shape of logic?
If you're writing in an attempt to convince people of something, isn't how you deliver the message of critical importance?
This is basic Sales 101. The way you sell (products, services, ideas, etc.) is directly related to how successful you are.
He is not writing his blog to convince people, his primary audience already agrees.
That doesn't make him wrong.
> He is not writing his blog to convince people, his primary audience already agrees.
He's selling a paid newsletter, so at least one of his motivations is to make money. His target subscribers are certainly people who lean towards his viewpoint but he still needs to do some convincing because the market of people who are open if not warm to his thesis is much bigger than the market of people who already share his thesis.
> That doesn't make him wrong.
I think it's way too early for anyone pontificating about AI, the economics of AI, etc. to be declared "wrong" or "right". This is going to take years, if not decades, to play out.
Subscription based writing is all about writing for audience that agrees. Yes it creates bubbles, but economic of it is "people paying to read stuff they agree with".
I think this author makes a number of good points. So I'm part of that audience "that agrees" to a large extent.
But I would never consider subscribing to this newsletter and the biggest reason is the writing style/tone. I find it unpleasant.
So even when people agree with you, you can lose them by being abrasive, unpleasant, unlikable.
It shows that the author has a strong negative emotional reaction towards AI which likely influences his opinions and impartiality.
He is preaching to the choir, if you already hate AI you will love the article, if you don't hate AI already you will find the article insufferable.
Well, we don't have to speculate as to whether there is some sort of emotional taint on Zitron's thinking; it's shot through. But again, that does nothing to damage or offset _the argument_, which is available for your inspection and consideration, and you, as a thinking person, are handily capable of vetting. :) There is no need to use a heuristic; you have the thing itself.
It absolutely damages the argument. Not sure why you think your question is a gotcha.
Think back. Has there ever been a time when you were both correct and angry?
Would an angry Pythagoras' theorem be wrong, simply by virtue of his anger?
He was a bit bonkers anyway. But maybe:
The square of the hypotenuse — you multiply it by its bleedin' self — is LITERALLY equal to the sum of the squares of the other two sides. Again. Multiply each of the other sides BY THEMSELVES, and then add them together. It's that bleedin' simple Jim Cramer could do it. That number is the same as the square of the hypotenuse, no matter what idiot CEOs think.
...and as horrible as that writing is, Pythagoras would have still been telling you a true thing.
So too, Zitron?
> He is preaching to the choir, if you already hate AI you will love the article, if you don't hate AI already you will find the article insufferable.
I'm neither (or both, if you want - I can hate the direction its taking humanity while not hating my usage of it or opportunities it brings), and I definitely did not find his writing to be either lovable or insufferable.
I enjoyed reading it in a "smells-like-BOFH-but-in-finance" type of way.
It's fully in the BOFH/The Register tradition, yeah.
Some of his manner reminds me also of Max Keiser's "The Truth About Markets" which I credit with telling me about the subprime crisis a couple of years before it happened, even though people thought he was insane… because he kind of is.
I'm not a Brit, but I do enjoy British culture, including writing. I haven't been able to read any of Ed's rants to the end despite generally being on the cautious side towards LLMs
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I particularly enjoy reading big banners asking me to pay for a newsletter subscription if I "liked" the content. Not if I found it interesting. Not if it actually provided any value whatsoever to me. No, you just have to "like" it. In other words, it is meant to be written in an engaging way and perhaps reinforce your believes like an echo chamber or even stir up certain strong emotions. Not to convey information. So, thanks, but no. I'm sure this opinion blog is very well written, but I don't think it is more well founded than anything else in this sea of opinions that sports a bigger garbage patch than the Pacific Ocean.
A big chunk of text asked for support on the basis of the article. I hadn’t read the article.
I scrolled down a bit to read. A popup took up my screen, asking me to subscribe, having read essentially nothing at this point.
I just left. Life is too short.
I know the HN guidelines discourage commenting on "tangential annoyances" on a website, but I think this issue is more than just tangential and more than just an annoyance.
When an author is this relentless in pushing you to sign up, there is good reason to suspect that financial motives are unduly driving an agenda.
I counted 8 such instances:
1. In the sidebar
2. At the top of the article
3. Popup in the middle of the screen after just a couple of scrolls into the body
4. Several paragraphs into the article
5. At the bottom of the article
6. At the bottom of the page under the comments section
7. Popup at the bottom of the screen after scrolling to the end of the body
8. (My personal favorite) Click the "user" icon in the bottom-right corner, which you'd normally expect to open an AI chat bot these days, and (surprise) you're prompted to sign up for a paid subscription
This sort of behavior just completely tanks any and all credibility this person may have.
Of things to be upset about, an independent journalist asking readers to pay for access ranks very low. Especially compared to LLM companies that are exacerbating the climate crisis, increasing cancer rates among residents, or increasing utilities for residents.
This sort of behavior completely tanks any and all credibility this commentator may have.
Is the OP article “journalism” or more of a rant with self-aggrandizement about how they’re so smart and such a good person that it makes lots of people angry?
What are you talking about? Why is "liking" something mutually exclusive with conveying information? I like lots of things precisely because they convey information!
>I like lots of things precisely because they convey information!
Correction: You may like them because you think they convey information. But without any sort of vetting process, the internet has become a cesspool of "news" or "general knowledge" places that ended up quite successful, but which are essentially just a contest of who is most confident when talking about topics and who can present bullshit in the most engaging way. You can see the peak of this on the JRE podcast. Anyone with actual expertise in a subject would be able to call out many of the guests, but since the host knows nothing about most of their fields he just gives them a platform to spread their opinions as facts. And millions of people who also don't know better will accept them without question.
I like Ed's sense of humor, I also like that he can distill down a lot of messy details into something more cogent, especially with the money side.
But, I also think he has missed the mark on a fair few things in terms of out comes. He may be proven right yet in terms of the general shape of things for some parts of the industry but also will have some big misses.
My general take away usually comes down to, places like OpenAI, Anthropic and Oracle have gone in a little to hard to fast and it may hurt them long term as they struggle to make it work in terms of economics. not that they can't just it will be difficult. But places like Microsoft, Google, Meta, Apple, Amazon; they have a very long runway to endure the growing pains and make it through to a long term business that no longer burns cash.
Oddly suspicious how this comment which was not one of the first comments which does not address the content at all but the tone skyrocketed to the top.
The tone is written as abrasive to anyone who doesn't already agree, which shows this is more of an emotional opinion piece than open minded objective research.
Hype cycles never last forever, but that doesn't mean all the value has been tapped by any means. The fact that modern GPUs can solve ridiculously complex high dimensional functions is a superpower in every possible field of research.
HN does this with every Zitron article.
Right, because markets are always rational and nobody gets greedy and ignores the skeptics until things are out of hand.
https://en.wikipedia.org/wiki/Tulip_mania
It's not entirely clear to me that the opposing argument is well-formed either. You constantly see numbers and statistics being wildly mis-used or overextrapolated.
Arguments have smells but rigour demands you investigate further. Zitron's smelly prose is, ironically, just the kind of stylistic distraction that AI can help condition; the further irony is that he will one day seem to have been right, for a year or two.
The money is indeed losing its mind over AI, and Zitron is a stopped clock. A correction is coming but the tool isn't going anywhere.
I kinda get it - he's been attacked for his negative views a lot, and that tends to produce a more passionate writing style. It's a little immature, sure, but also authentically human.
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He does this on his podcast on a regular basis.
Your own iamverysmart retort also fails to point out even a single issue with his actual argumentation.
His arguments on the numbers of AI are actually pretty solid.
I am still to see a solid counter to what he brings up there.
> One of the "smells" that gives away a quacky ranter is they speak in impassioned, "Why doesn't everyone understand this?" tones, but in fact their argument just doesn't flow.
Exactly what the AI evangelists are doing.
It always seemed very natural to me that AI will move “down the stack”, where Open AI and Anthropic don’t really have a foot in the door.
Who makes consumer devices? Google
Who makes operating systems? Google
Who makes browsers? Google
Who makes the world’s most popular websites? Google
By the time 90% of average internet users get to chatgpt.com or whatever, they already went through several Google chokepoints, each layer is one more place Google can answer their questions.
And that’s not even getting into the chips, the data centers, the data, the talent, the consumer apps, the enterprise apps, the cloud platform, the brand, and of course the biggest cash printing machine in human history.
You would honestly have to be insane to bet against G.
Part of the pitch of AI companies is that they mediate and provide a new surface for ads, for taking an affiliate cut of sales, etc.
But it isn't like this hasn't been the long-running strategy for Google as well - provide more results on search so that people don't go to the site with ads, provide paid product results for shopping, to offer more services to keep people providing personal/behavioral queues to Google and more opportunities for ad placement.
If anything, AI turned up the heat such that the frog noticed what temperature the pot was. But that doesn't really put them in a better position to execute than Google.
There was a point in time when this would have been untrue of Google. They didn't manifest into this market position, they rose there over the years after starting with a relatively modest product offering. Nothing saying someone else can't do the same.
> You would honestly have to be insane to bet against G.
Unless you are confident that Google will be antitrust forced to weakening their hold in some of those markets. A crack is all you need for the water to rush in and widen the crack.
> You would honestly have to be insane to bet against G.
Nah this is just Googler cope
Google missed the AI boat. Period.
Google literally invented the boat (transformers) to be fair.
> Google literally invented the boat (transformers) to be fair.
Isn’t that more damming for Google?
Invent the boat, don’t know how to use it, abandon it, then someone else comes along and steals your boat.
I think most of Google's deep research projects were done in the pursuit of pure science, not monetization or productization. In hindsight sure, it looks like they missed an opportunity. But not everything needs to be about money.
Zitron is begging for a collapse at this point. Yes, his macro analysis correctly identifies a massive financial risk but his incessant pessimism completely misses the incredible ground-level utility that many of us on HN celebrate every day through undeniable, massive productivity gains.
At this point I'm trying to believe there's a middle ground where the level of individual capability this unlocks, leads to major discoveries.
> undeniable, massive productivity gains.
Take any stock index, remove AI stocks, what do you see? That's right! Nothing...
So where is all the productivity going? Where is the value? Where are the massive unemployment stats or the millions of new startups making big $$$?
Writing about AI, destroying the planet for data centers, there's a lot of money to be made.
That being said, AI seems kind of miraculous sometimes.
Similar to cars. So enticing that we make everything else in the world worse in order to maximize the profit, make it indispensable, subsidize it, and make the dependency on it irreversible.
And it's not even something to blame individual people for.
Driving away from all the other cars to spend a weekend feels like freedom.
Using AI to answer a question feels like a "bicycle for the mind".
But in fact it's more like a car. It requires massive resources and creates perverse incentives, and the result is ineffective and corrupt.
Both cars and AI are amazing technology and extremely useful, but using them is not an individual responsibility. It requires societal subsidy.
The environmental impact of answering a question on an obscure topic with ai model is less than an the impact of answering the question with an hour-long google search hunting for references or a drive to the public library.
That's true, and I am not anti-AI. I was not only thinking about the environmental effects of some single prompt or a certain amount of tokens.
Neither did I want to say that a car is always more wasteful than some alternative.
But defaulting to the behemoth is inefficient, unless everyone is driven to do it: then it's in some way reasonable.
By adding "corrupt" and "dependent", as well as the economic terms, I wanted to offer a broader critique and create an analogy, not just talk about energy usage on its own.
What I had in mind was: it's easier to go many places that are a mile or less from me, by car. Because everything is obstructed by cars. And I'm atrophied by lack of movement. Best would be to drive somewhere to move/walk.
People already do that in masses.
And doing shopping by car, because everything else seems unbearable, also takes away your time, apart from wasting energy compared to more, smaller shops that would be reachable by foot, bycicle etc.
I guess you know the argument.
Today, people's thinking atrophies because their LLM is probably right in their summarization of some Wikipedia article, plus 2-3 other random sources.
Or so.
Using the Wikipedia search function is not expensive.
But, I mostly had a bigger picture in mind than what is the cost of inference.
I think it's a good analogy in many ways, and personally I think car-centric society has a lot of flaws. I think the ease that AI brings to tasks may erode mental capabilities in the same way cars have eroded our collective physical health.* That said, it doesn't seem to me that we would be better off without cars altogether, despite all the related issues.
I am concerned about the environmental impacts that AI poses, but they don't seem to me to be so catastrophic. Solar and battery tech has made enormous leaps in the past couple decades, and we will need to pivot to clean energy future irrespective of AI.
*This said, I have become gradually more alarmed over the past decade at the lack of epistemological rigor in the general public, as made apparent through the rise of social media. I don't know that AI becoming a truth-seeking crutch for people wouldn't be more good than bad.
> it doesn't seem to me that we would be better off without cars altogether, despite all the related issues
Oh my god, no. I also want the benefits of automobiles! They are strictly more capable than, say, trains. That's where I would derail the discussion completely when going into details, but no, I am not against cars as a technology.
Apart from all the ethical and social arguments (logistics, ambulances, the elderly, etc etc). But that's not where I wanted to go.
I was making a leap here simply because of the whole complex around prisoner's, dilemma, the commons, state economy, and so forth.
Since at least ~100yrs ago, I guess cars and streets as the primary mode of transportation have also "won the vote" / are what the majority wants, so it's also an interesting analogy for diminishing returns maybe.
Building out more car infrastructure is certainly not controversial where there is absolutely none but there are commercial or residential buildings.
Anyway, lots of associations are worth considering here IMO. The ultimate limiting capacity here, when disregarding all environmental or health concerns, is simply space and the positive externalities (cities etc) around existing infrastructure.
The problem with grandiose statements like the GGP's are that they compare a realized outcome with an entirely idealized outcome they have in their brains as an alternative.
The thread is full of statements more grandiose and idealized than mine.
> Take any stock index, remove AI stocks, what do you see? That's right! Nothing...
> we make everything else in the world worse in order to maximize the profit
> destroying the planet for data centers
> I was not only thinking about the environmental effects of some single prompt or a certain amount of tokens.
Hand wringing about AI datacenter's environmental impact is well and good. We should keep the data centers accountable for their consumption and waste.
I just wish the same people had been upset the last 20 years with poor water resource management in a lot of areas (the west US especially) with urban, ranching and farming development.
> That's true, and I am not anti-AI.
Me neither!
The past may be past, but it's important that even now we point out the relative scale of resource usage, pollution, etc going forward of everything from cars to AI to golf courses to beef.
It's like saying if we didn't have cheap commercial flights people would travel by foot anyways and would consume more resources for food &co. than the plane would consume in fuel...
80% of generative AI queries wouldn't even exist as google searches.
To be clear, your position here is that insurmountable barriers to information is the preferable state of the world?
One claim of the parent comment was that AI is ineffective. For the purpose of finding answers to questions, it is more resource-efficient than the alternatives, and, to your point, capable of answering questions that were impossible to answer via other means before. In what way is that ineffective?
No, they're saying that 80% of genai queries (aka anything sent to an LLM; I won't speak on the validity of the percentage) are not things someone would search on Google. It's things like trial-and-error vibecoding, openclaw-like agentic loops, talking to chatgpt like it's a person, etc. In other words, most genai queries are not for getting "obscure information" or even getting direct information at all. It's about either getting it to do something you don't want to do yourself, or using it as a replacement for someone else (junior dev, therapist, friend, significant other, etc).
A request that isn't asking for information isn't a query
That's just what some people generally use to refer to LLM input string/prompt/message/etc. The only thing the LLM can do is return information...in the form of text, so every request is one for information.
If we want to get really pedantic, every generated token is the answer to the query: what's the next most probable token in this sequence of tokens?
If "query" doesn't imply intent by the user, it ceases to be a useful word. You can acrobat your way to imagine a digital system has agency to ask a question before it receives bits, but then any transfer of data could be called a query.
When I post this http request containing this reply, you could say my machine is querying the server to ask "what did you do with the message I just gave you", but then query stops having any useful semantic value to distinguish it from "request"
Regardless, this is tangential. I don't disagree that a lot of LLM use is not in pursuit of knowledge, but enough of it is for me to think that preferring LLMs not to exist is a hard position to defend, at least without making the case for existential doom.
I do plenty of AI queries, both pragmatic ones and some for entertainment: witnessing talktotransformer was mind-bending already at the time! And since then, I've tried frontier models, local, coding agents, and use plenty of them on the regular.
I awe at the capabilites of generative AI.
I also enjoy sitting in or driving a car.
I did not want to make a moral argument, unless you consider each and every form of utilitarianism as moralism.
That might be true, but at least I started asking way more questions since we’ve had competent LLMs.
Vonnegut said in his last living work that the greatest addiction modern people face is the drug of cheap oil.
We got addicted to the convenience and overuse, and have started a mass extinction event because of it.
The perverse incentives will come for us all.
It is exactly this thought, in the form of this sentence, that could replace almost all of my comments in this thread.
It feels depressing, but I think the same. When thinking about the larger world, it becomes increasingly hard to ignore. And of course it is not new.
There were "doomers" already in the midst of the 20th century, but it doesn't mean that they were wrong.
There's nothing the selfish hate more than hearing their "choices" have a net negative effect. They hate the "doomers", because it puts a damper on their little side-effect free party. Doomers are more often correct because it only requires bare minimum pattern recognition. It requires more effort to defend an action that will certainly be different this time.
AI, Crypto, car-centric urban planning, cardboard suburbs in a barren field with bright green grass.
You tell someone that their selfish choice to drive a pickup truck only to haul something maybe one time a year, they shit their pants. They can't stand it, they start personal attacks and ask "what about you??". They seem to know it's wasteful, damaging, etc but they deserve it for some reason.
I agree with your message but not sure about the conclusion. Cars themselves are commodified luxury available (in the US pretty much required) to everyone, and they do need to be subsidized, both in terms of infrastructure and the lifestyle they require.
But with AI what is the exact price? My understanding is that R&D is extremely expensive, but running non-SOTA models is not that bad. We are getting pretty close to models which can be useful locally in many applications.
Or do you mean that at scale running them locally is not possible and hence the infrastructure price is in data centers, which will be expensive to maintain and scale for demand?
Thanks for asking an open question about my point.
First, because I initially failed to answer your more closed questions (this paragraph is edited in):
> We are getting pretty close to models which can be useful locally in many applications. Or do you mean that at scale running them locally is not possible and hence the infrastructure price is in data centers, which will be expensive to maintain and scale for demand?
I don't think there's a way around making the best of AI capabilities with minimum price and maximum control, and I'd agree this is met by on-prem data centers, just not in a rationally targeted way.
Back to my original comment:
Because it (my conclusion) was not so clear, and maybe I just wanted to highlight some observations without delivering a real argument for or against things [, I thank you for your open question].
The utility/leverage aspect for AI seems more esoteric than the one for cars because, apart from Chatbots, it's more hidden.
And also, similar to cars (or many other phenomena of industrialization), yes, my first vague point was the subsidization of infrastructure. But also, the power gap: that's something not only associated with AI or cars, but with a lot of technologies we all hold dear: sewage, powerline, logistics, etc etc.
What reminds me of cars in the current AI frenzy is the fixation on cementing infrastructure. And also, I think, a lot more people agree on, for example, some kind of universal right to, for example, clean water.
But all of industrialization confronts people with questions of efficiency, inequality, and collective support.
Most people would, for example, support a right to get a minimum amount of clean water when you are living and working in a tradionally inhabited space (if you're on the social-darwinist side) or at least not harming society (if you're more of a social democrat).
And, similar to the buildup of car infrastructure, and the procurement of resources, space etc for maximum building, giant data centers can obstruct people in buying drinking water. Or walking outside (AI obstructs traditional methods of online collaboration).
The original point of the stock market was to fund gigantic society-level projects (like railroads). Modern VC has replaced some of that at smaller scales but not all of it at the largest scales. So this could just be the stock market performing the function it was designed to perform -- helping fund something transformative on a societal level.
> Take any stock index, remove AI stocks, what do you see? That's right! Nothing...
Where did all the stock gains go before AI?
FAANG / MAG-7.
Was everything from 2012-2020 fake, too?
They went from ~9% of the sp500 to ~35% over your timeframe...
> So where is all the productivity going? Where is the value?
Infrastructure doesn't produce value overnight. How long did it take the Interstate System to provide measurable value? I asked Gemini. Supposedly increased national productivity by 25% over 39 years[1]. But if you drove on a newly finished interstate in 1959, you saw the same cars just moving a lot faster.
That's what we're seeing right now. People can produce an incredible amount of stuff really quickly with AI. Is it directly connected to measurable productivity across the entire economy? No, because, realizing a mass productivity increase from infrastructure takes time.
[1] - https://www.richmondfed.org/publications/research/econ_focus...
Not sure what your point is. Stock markets are based on money going into securities based on estimated future value. Even if AI were doubling productivity at a non-AI company, there is more leverage to that money going into an AI company.
The question is, is AI leading to massive productivity gains in companies that implement it? AI productivity gains take time to diffuse, but so far companies in the S&P 500 are seeing very high growth. YOY earnings growth rate for the S&P 500 is 21.7% https://advantage.factset.com/hubfs/Website/Resources%20Sect...
> YOY earnings growth rate for the S&P 500 is 21.7%
Now remove the companies selling the AI shovels: https://pbs.twimg.com/media/HIAjbZxacAARHwD.png
> Not sure what your point is.
My point is that they're selling us Skynet and the end of employment as we now it, things that we shouldn't even have to measure to perceive the results of, yet no one is able to measure any of it
Pointing a finger at nvidia, google, and the other few companies stuck in circular investment schemes that shouldn't even be legal and saying "OOGA BOOGA line go UP, UP GOOD!" doesn't count in my book
Charitably the lag time for this technology to have noticeable effects could just be ~5 years away. Similarly to how computers didn't have a big impact for a decade after they were introduced as people got used to using them.
Your grandparent comment:
> Take any stock index, remove AI stocks, what do you see? That's right! Nothing...
Parent comment:
> Now remove the companies selling the AI shovels: https://pbs.twimg....
From your linked image, "excluding AI stocks" is "+16%" (the figure with AI stocks is far higher).
Your sole source says +16% excluding AI - in what kind of market is +16% “nothing”?
> in what kind of market is +16% “nothing”?
It's nothing because it happens all the time, it's not statically relevant, like not at all: https://www.macrotrends.net/2526/sp-500-historical-annual-re...
This forum is full of techies with very strong opinions about their toys but 0 economical, political or historical education, and it shows
Those kind of returns would generally be considered at least ‘good’ even if they weren't excluding some of the best performing stocks such as AI as was done here. Some periods truly have no returns or even negative returns, even without excluding some of the best performing stocks like was done here.
And even more so since inflation was 2-3%, not considered high, during most of that period.
Is the image you provided depicting revenue, or stock value? My point is about revenue.
Revenues don't matter when you sell a dollar for 50ct and half of the deals are circular anyways
So you're claiming that the revenue growth of the S&P 500 over the last few years is largely due to "selling dollars for 50ct" and circular deals?
Yes.
https://insights.som.yale.edu/insights/this-is-how-the-ai-bu...
> AI-related stocks have accounted for 75% of S&P 500 returns, 80% of earnings growth and 90% of capital spending growth since ChatGPT launched in November 2022.
has it occurred to you that AI companies may be making huge returns because AI is genuinely increasing productivity and driving actual economic growth via their products?
If all these false practices can pull revenue out of nothing, why doesn’t every company do it? How come AI companies seem to be able to pull off financial magic that no other company can match?
All your analyses still ignore the revenue point.
But then why don’t we see this productivity growth in any other statistics? In layoffs or in faster GDP growth or in new software products?
> has it occurred to you that AI companies may be making huge returns because AI is genuinely increasing productivity and driving actual economic growth via their products?
Then why can't anyone point at actual numbers? The best we get is "look: line go up" while pointing at either the companies selling the AI shovels or the companies selling $1 of tokens for 50ct.
When cars replaced horses we didn't have to twist the numbers to understand the benefits. When emails replaced mails we didn't have to do 6 hours of mental gymnastics to see the increased productivity. Heck my grandma could tell the benefits of computers when the town hall she worked for finally discontinued typewriters
They're spending hundreds of billions if not trillions, and have nothing to show for it besides like 5 stocks pumping like shit coins. On top of the the drawbacks are massive and very visible...
Literally right here. eComm business turned around from losing money to profitable in less than 12 months after vibecoding a bunch of solutions to variousn problems we were having.
> Take any stock index, remove AI stocks, what do you see? That's right! Nothing...
I mean, do you know what the value of those stocks would be if AI didn't exist. Maybe they would be much more negative. Maybe we would be in a recession. Without a control this type of analysis is meaningless.
And that is even assuming that AI productivity gains are happening now instead of 5-10 years from now.
He has also consistently demonstrated, at least to me, that he doesn't really understand how inference works from a technical perspective, which weakens much of his core thesis for why there should be a collapse.
I do value having some naysayers in the mix generally, because we do need balanced critique in what is otherwise a very frothy hype cycle. I just don't think he's making sound arguments, and that's even assuming you even agree with his premises in the first place.
My biggest gripe with his napkin math is that he treats inference gross margins as something novel that you can't compare to normal SaaS margins. He's right in part: the constant carousel of R&D costs from model training, related infrastructure buildout, and other adjacent costs required to stay competitive do change the analysis a bit.
But he takes this way too far when he says this is structurally different from normal SaaS margins. The business model definitely doesn't look like Dropbox, but it absolutely looks a lot like AWS, especially early AWS, CDNs, telecom, etc. I can speak to the telecom bit personally, since it's been over half of my professional career as an engineer and, in this specific case, also as a founder. You can have a brutally capital-intensive infra business where profitability depends on utilization, oversubscription, peak-capacity planning, segmentation, and recovering capex over time.
The math he presents gets even more questionable as we see explicit segmentation happening for cost-saving reasons. Many forward-thinking orgs are waking up to the fact that they don't need to use the best, most expensive model for every task. They can route easier tasks to cheaper models, use caching, batch non-urgent workloads, and reserve frontier models for the subset of work that actually needs frontier intelligence. That directly undermines his claim that providers always need to chase frontier intelligence in order to maintain current demand, utilization, and pricing curves.
I think he doesn't need to understand the technology to point out the books are cooked. a business can sink in either way: the technology flops or the finances flop. he's arguing the /finances/ would flop. he doesn't argue that the /technology/ would flop, only that they can't come up with the money to pay their debters.
There is a piece of this I agree with. That you do not need to be a deep technical expert to notice that a company is burning cash by overcommitting to capex, or relying on heroic revenue projections that may or may not come to pass.
But that is not the full argument he is making. If the claim is that the labs will not be able to pay their creditors because inference is structurally incapable of becoming profitable, then he absolutely needs to be right about the technical economics of inference.
One part of that is the balance-sheet argument (which already shows insanely good margins). But it also depends on how inference-time compute actually works: routing, batching, kv cache reuse, model segmentation, different latency tiers, etc. Much of those details he's just been straight up wrong about in his writing, so as a result I have to call into question the rest of his reasoning as well (in part to avoid Gell-Mann amnesia).
Doesn't this kinda imply its own smoke and mirrors though? Like if the name of the game with inference is already routing things around and caching so you can make money, why is the newest biggest model always the most important critical thing? How does this square with any of their press about it? Also wouldn't that just add more inference? Because you need to pre-judge every prompt to know where to route it?
Also, if there is significant gains from caching, then like.. what are even doing here? Inputting something and then reading cached pieces of text based on their similarity to the input? Kinda like a search engine?
I don't think its smoke and mirrors, though I do have plenty of gripes with how the labs market this product landscape generally speaking.
The newest biggest model can still matter even if you do not run every prompt through it. You'll always have some task where even small amounts of loss are unacceptable and thus you need to make sure frontier intelligence is used for it.
On the router point, yes, routing has some overhead. But the router does not need to run the biggest model to decide which model to use. We've been using tiny classifiers for recommendation engines for ages now, usually on CPU. If routing saves you from sending a large fraction of traffic to the expensive reasoning model, the routing overhead can easily be worth it.
> Also, if there is significant gains from caching, then like.. what are even doing here? Inputting something and then reading cached pieces of text based on their similarity to the input? Kinda like a search engine?
The caching I'm talking about is explicitly the attention/kv cache, so its not input similarity retrieval (that would be more like what you'd use in a RAG/IR system). Prompt caching is generally about reusing already-computed attention scores for repeated prompt prefixes. The idea being you don't recompute the same static system prompt, tool definitions, schemas, long shared context, or repeated boilerplate every time. In more sophisticated systems, you usually store multiple checkpoints so that a small prompt change doesn't result in all-or-nothing hit/miss scenario.
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> That directly undermines his claim that providers always need to chase frontier intelligence in order to maintain current demand, utilization, and pricing curves.
But does it also not mean that they will make less money given that there is already brutal competition for that lower tier from openrouter, Deepseek, Amazon, etc.?
You can't on the one hand say "customers are beginning to understand they can spend less" and on the other hand suggest that this is good for forecasts of revenue.
> You can't on the one hand say "customers are beginning to understand they can spend less" and on the other hand suggest that this is good for forecasts of revenue.
Sure you can. Just because there is a non-zero amount of margin pressure from the lower tier inference providers does not imply that revenue forecasts ought to be poor. Jevon's Paradox gets oversold in this current cycle, but I do think it's a relevant lens to view this through given how much demand has outpaced capacity.
The argument is that customers learning to spend less per task can be good for the viability of the market (really the total demand) even if it is bad for naive revenue-per-token assumptions. If a workflow goes from economically stupid to economically viable because you route 80% of it to cheaper models and reserve frontier models for the hard cases, that can expand total usage and improve cost per useful outcome.
> that he doesn't really understand how inference works from a technical perspective
Could you share what tells about it? I.e. where he was wrong about it?
There's examples both in his writing and also in his appearances on podcasts, interviews, etc.
I'll cherry pick a couple:
“When these new models ‘reason,’ they break a user’s input and break into component parts, then run inference on each one of those parts.” [1]
This is not at all how test-time compute works. At best, this is a very loose metaphor that he may have used out of convenience. This might sound a bit pedantic to point out, but this is a very basic thing that he's getting wrong (presumably at least, again it could be that he just used a poor metaphor).
A less pedantic example would be his claims related to gpt-5/chatgpt auto-routing. He argued that having a router means OpenAI can no longer cache static prompts, because the user prompt has to come before the hidden instructions [2]. This is just not at all how this works at inference-time. There is no evidence that the standard approach of system>developer>user instruction hierarchy has changed, the public API and caching docs maintain this.
But even more broadly, it suggests he is reasoning about kv/prefix caching at the wrong level of abstraction. It's true that conventional prefix caching does require a stable prefix, so yes, if you literally put variable user content before the static prompt, you would destroy the cacheability of that static prompt.
But that is exactly why inference systems are designed to preserve reusable prefixes where possible (via checkpointing or similar), and why serving systems care so much about prefix caching. This is also a big part of how disaggregated prefill/decode infra works where cache-aware routing is critical. His argument treats a bad prompt layout as if it were a necessary consequence of routing, rather than an avoidable implementation choice.
A router can read the user request, decide which model path to use, and then construct a normal downstream model call with stable static instructions first and user content later. Treating that as impossible implies a fundamental architectural misunderstanding.
[1] https://www.wheresyoured.at/how-to-argue-with-an-ai-booster/
[2] https://www.wheresyoured.at/how-does-gpt-5-work/
Productivity is not value. It's quite possible for you to experience productivity improvements, and actual value to not be created. That is what I think the most robust data is showing.
https://unessays.substack.com/p/talk-is-cheap
Also, supposed productivity gains are dubious. I personally experience at best no productivity gains when using LLMs to write code, and sometimes it's an active drain on my productivity. There was that one study a year or so ago showing similar results. People are trying to say the productivity gains are there and undeniable, but that is not true. It is very much a subject of controversy whether AI helps productivity.
I can see an argument that the productivity gains are illusory / don’t translate to economic productivity. I’m not denying the possibility.
However, most of the engineers I respect have gone from being skeptics a year ago to convinced today. I don’t personally know any true holdouts any more. If there are studies that disprove productivity gains more than six months ago, I’m happy to believe that it was true of the AIs that were available at the time. But I’m going to need something much more recent before I disbelieve my lyin’ eyes where it pertains to the AIs available today.
There is an observational study that was published in March 2026 that followed 4000 teams over 2 years. It shows, in my view, exactly that the productivity gains don't translate into economic value.
Here is the report:
https://www.faros.ai/blog/ai-acceleration-whiplash-takeaways
And my commentary:
https://unessays.substack.com/p/talk-is-cheap
If it was published in March 2026, even if the data was collected up to the day the study was published, 7/8ths of it would fail my “within the last six months” test. But I am looking forward to the results of future studies on this topic!
I get wanting to wait for more data. And thinking that LLMs have improved enough that this will change.
My view is that it's not really about how good the models are - it's about how we're using them. Understanding what you've built is an important part of value creation, and LLMs eliminate that.
Its funny, I've noticed the same thing, but did not come to the same conclusion.
I currently don't have work access to Claude Code, but most of my teammates do. Watching from the outside, the cycle seems to look like this:
1. Experience some success, which hooks you into relying on AI.
2. The AI keeps failing at some task, but you don't want to stop. Keep trying over and over again.
3. Run out of tokens and take a break.
Now, sometimes 1 doesn't happen. Sometimes 2 doesn't happen. 3 is a certainty though.
Now, if you told me that the productivity gain from 1 is enough to offset the loss from 2 and 3, I could believe you. But I also wouldn't be surprised if it didn't.
As I work with Claude more and gain a feel for its capabilities, I tend to run into 2 far less often, as I'll decompose my messages more for the current model limitations. The threshold also changes each release.
I’m going back to being a holdout, but it’s nuanced - My theory into why LLMs don’t lead to the colloquial definition of productivity would be something like - if code was never the bottleneck than generating code faster doesn’t result in more meaningful output.
Even if you take for granted that AI is as good as the best people say in writing code. And Ive spent a lot of time generating codes, I won’t disagree - Then the question becomes - does this change your daily incentives such that you reach for code as the solution to your problems rather than something else (coordinating with your colleagues? Product management? Planning and Design?
So from a holistic perspective, I think intentionally limiting your own AI usage is the best approach for maximum long-term productivity.
>So from a holistic perspective, I think intentionally limiting your own AI usage is the best approach for maximum long-term productivity.
I think this is right. They are much better applied as editors than authors, IMO.
The key thing is stay in control of your output. i.e. understand it thoroguhly. I think you let the LLM make decisions you don't really understand, you're increasing the likelihood of introducing defects that are expensive to address.
I’m not completely closed to your idea but if code was never the bottleneck why did so many organizations always feel so chronically low on coders? And of course this requires the AI to be no help at all with what is actually the bottleneck.
So, one is, it probably does depend on workflow. Some folks probably are doing things that can be accelerated by AI, and I think if you’re a small team with a good product head and know what you’re doing then AI probably helps a lot.
But what if the problem you’re trying to solve is the altogether too often problem of like getting teams that are dependent on you to upgrade the library they use. And what if the library is a breaking change, and last year they upgraded to the library on your advice and it broke production and now they’re suss and want to accept all changes, and integrating that library change isn’t in their critical path so they’re just not going to spend time on it, even if you submit the MR them. Even if you show them their tests pass after the change.
Importantly to the above, you probably need more devs to do more of the above in parallel. You don’t hire devs to write more code, you hire more devs to carry on the mental load of a broader scope of work. Even in the before times, so much code got stuck at the integration step.
But because all that is hard, instead you go and codegen to fix an obscure bug that sure makes a few customers happy, but no one thought was a limiting factor for paying your company more money.
It’s not that I don’t think AI can help, I think it’s a prerequisite for the job and everyone should use it. It’s more that I think in the grand scheme of things, people will bias towards using it for tasks that aren’t in the critical path - refactors, tech debt, bug smashing, tool building; and I think it could really help devex and that’s good.
But I think people are bad at knowing the difference between “my job feels a bit easier” or “I’m more productive” and “this task had an impact on the bottom line” and when you extrapolate that out to a whole engineering org, that’s where the productivity statistics get lost.
I’ll addd one data point to this is like this thread itself. So many people on AI skepticism threads point to their own subjective experience as evidence we’re not in a bubble, and sort of ignore the entire concept of economics. I’m not saying we’re in 100% in a bubble, but subjective experience isn’t great evidence of it.
And this is just sort of one of the factors, what about the increased cost and mental load of supporting more software? What about junior engineers who feel pressured to ship work but don’t actually learn the software engineering? What about lost context from not intimately understanding your software?
All good questions. I am not a big believer in claiming I know whether we are in a financial bubble or not. I just put it all in VT and we will see what happens. I know that the AI allows me to write code I couldn’t write before much more quickly than before but I admit that this may not help with organizational friction.
Although if this theory is true — that AI helps with coding but coding is not the friction point in organizations with multiple humans, even that should allow faster iteration by allowing one human to do more coding therefore reducing the size of teams required to make some programs. You should see good acceleration in solo shops too.
Yeah, again the answer is definitely not no AI. And I’ve seen the - you know - I can one shot whole applications that would have taken me weeks or months before. But it’s also much easier to one shot apps that aren’t in the critical path. I’m building all sorts of tools to make my job easier, but I still have to do the job.
I’m a platform engineer. The primary failure mode for platform engineers is building tools people don’t want. AI doesn’t really make that easier. Or it can but it can also make it harder by making it easier to chase down ideas that you don’t get traction on. And I think that - net balanced across the organization is probably why productivity gains get sort of averaged out.
For sure I think solo devs who have a system are seeing gains, as long as you can I think have the discipline to have a process that includes feedback and learning and your not just feeding off of dopamine hits of one shotting features but yeah. I mean for solo devs the code was never really the limiting factor, it was product-market fit and marketing.
So solo devs who have a system may be laughing themselves all the way to the bank, but we may not see a lot of net new solo devs.
But if code is cheap now then it’s sort of inherently devalued. 2 8 person startups can probably relatively easily find a dev with AI experience to rocket ship their code generation, which means the basic skills of talking to customers, change management, and building the right thing become even more valuable.
Even solo devs I wonder - almost every post-mortem of a failed company goes “I wish we had spent more time talking to customers and less time writing code”
Again if you can get the discipline right, maybe as a solo devs you can get more work done faster and spend more time with your family. That’s incredibly valuable!
But if you go and add a big new feature, or a second product - unless your community is primed for constant growth(no man’s sky is one community where more more more seems good) you’re just growing the surface area where all the other skills are more necessary.
From an economic perspective productivity is defined as the creation of value isn't it? Then if you "improve productivity" and does not create value in the end you're no improving productivity at all.
It does depend on how you define productivity. But the way it's commonly used is "I'm going faster, personally, with these tools."
The thing people I think have a hard time seeing is that "I go faster" does not mean "more features get finished".
It's a scale issue, and one scale is better than the other. People only pay for finished features, they do not pay for how much code you emit.
economists define productivity as gdp per hour worked. Like a lot of other economic measurements, its mostly a bogus number people use as an argument on why their politics are better than someone elses politics. You can have an efficient business located in a poor country making the same product and same quality as that same business in a rich country, the rich country will be more "productive" because local cost of goods is higher there (i.e. a restaurant in NYC is more "productive" than a restaurant in bangladesh).
Sure. But that's not, in my view, how most people use the word productivity when describing LLM use.
In my field - operations - productivity is usually described as some rate of production for a specific asset. 100 widgets / machine / hour - for example.
"My productivity is 3 PRs / day with the LLM as opposed to 1 PR per every three days". That's how I think people are thinking about it.
My point is that's not the same thing as value. I.e. what people will pay for.
You're correct, I just wanted to add that there is another definition that you may see used online, and it is very specific, and it's important to be aware it's NOT exactly the same thing most normal people mean when they say "productivity".
I appreciate you looking out. Thank you.
I’ve noticed more gold-plating.
“This random part of the code is slow, I used an LLM to generate a PR that speeds it up.”
Okay, you optimized the part that’s not a bottleneck, sped up nothing and cost the company $100 in tokens. Good job?
I'm quite fond of this play on "if a tree falls in the woods":
"If an LLM builds a feature, and no one uses it, did it make value?"
Productivity is defined revenue per worker hour. And we know worker hours are going down as there are fewer workers with the layoffs.
Which is why modern management thinks firing everyone in your factory and selling the inventory in your warehouse is amazingly productive.
That report doesn't match what faros.ai conclude which is mostly a paywalled report.
The report was not paywalled for me. It just required a work email. Which is totally fair from my perspective. Faros is providing a ton of value with the report. People do deserve to get paid - even if in collected emails.
You're right my analysis is at variance to what Faros.ai says. I think they interpret their data trying to rescue utility for the dominant patterns of LLM use.
But I think to anyone who is experienced with process improvement or queuing theory, their interpretation is clearly weak. Rework is a huge problem in queue systems, and they mostly just elide the throughput impact of an 860% increase in code churn coupled to a massive spike in bugs.
Obviously draw your own conclusions. But I don't think because I disagree with the interpretation of the people who originated the data makes me wrong.
That's possible, sure. But I think the answer is more likely in the numbers, not in just qualitatively saying AI isn't worth anything. Like if I pay $30k for an ounce of gold, I got value. Gold is worth something. But that amount of gold wasn't worth what I spent.
EDIT: In fact, parent comment has a link to some numbers.
[EDIT: Most] people don't want to go through the numbers. Ok. But there's a history here. When people don't want to see the numbers, certain kinds of things tend to happen.
I've posted numbers that indicate that productivity is becoming decoupled from value delivery. If you follow the link in my comment it reviews a pretty robust study of 4000 teams over 2 years. There is no product throughput increase.
Yep.
Code acceleration is great, but.... something precedes that. Vision and strategy re. expansion of offerings and businesses. Once a firm reaches maturity in what it offers and is only touching the edges - this code acceleration is literally useless when you factor in all of the trade-offs.
This is a good thing - it means fat and slow incumbents are sitting ducks to be out-witted by creative and imaginative founders, which is healthy for a well-functioning economy.
Now the economics of existing frontier models are not sustainable - its looking like a mix of the airline (supersonic vs subsonic) and EV industry with China in the background providing decent offerings at much lower prices.
I think its worse than that.
I admit that if a small team or an individual uses an LLM, it's likely they can create value faster.
I think as soon as you don't own the responsibility for the defects you generate with an LLM, their use starts to destroy value. Regardless of product maturity.
This is what I think the data says.
https://unessays.substack.com/p/talk-is-cheap
Yeah this part scares me a little. I imagine it scares everyone who is more than a couple of years out of school. I hear that "the solution to LLM tech debt is more LLM." That might be true, but it might not be.
It scares me too.
I actually think this is precisely the reason LLMs can't be the basis for a technological revolution. Because it's only one way.
Like, if you have a compiler, and it has a bug. You can discover if that bug is influencing your code execution and patch it. You can go both up and down the stack.
With LLMs, there is no way to patch it's translation function. You have to rely on it to forward process.
I don't think there is any way to avoid us understanding our tech stacks.
You're not really getting it.
If you are producing something that delivers a far better experience, irrespective of what's under the hood (see Claude Code et al), you will decimate an incumbent who is trying to use LLMs in the context of incrementally improving a mature product.
LLMs are suited for the development of revolutionary innovation, not incremental.
I think we mostly agree.
I think I just disagree about the power of the LLM to deliver revolutionary innovation. That's something you do. Not the machine.
And, pretty soon on your journey to scale, the LLM becomes a hinderance rather than a help.
Interesting data, thanks.
He’s been continuously predicting that the collapse was just around the corner, that progress was slowing, and that there was no market for inference, since 2024.
The fact he’s never reflected on the glaring failures in his analysis tells what we need to know about his intellectual integrity. There’s truth in some of his words about financial risk, but if you can’t acknowledge that there’s upside too, you can’t evaluate risk properly either.
I find it difficult to take him seriously.
> progress was slowing
Do you think it's not slowing? Do I miss anything really important?
My understanding is that we have now is incremental improvement on thinking models which appeared more than a year ago. Of course, a breakthrough might happen, but I don't see one yet.
The most important thing I would point to is Mythos et al and the wave of vulnerabilities that have been discovered in the past couple months. It’s a completely unprecedented event, brought forth almost entirely by improvements in the models themselves. That said. keep in mind, I’m talking about over the past two years. With Claude code and the capabilities gained since December of last year, there have been incredible gains in the capabilities that are now available. Demand for inference is higher now than it was a year ago, because capability has improved. A specific criticism that I would hold is that claiming that progress with LLMs is slowing, prior to that point, is embarrassingly wrong in my view. One could argue that the model capability improvements are slowing, and all the improvements were in harnesses. I think that’s a stronger argument, but I have a few problems with it. 1. Utility is utility. Whether that comes from the model or the harness is irrelevant when making claims about utility. I don’t think that’s a useful distinction most of the time, but especially when talking about the technology as a whole. 2. Marginal intelligence gain is different than marginal utility gain. It’s estimated that intelligence grows logarithmically relative to investment. However, the utility of a marginally more intelligent model may grow exponentially, because once behavior crosses a reliability threshold, it unlocks new capabilities. 3. Even on those terms, it’s not clear to me that frontier capabilities are slowing down. With Mythos and its contemporaries, we have been seeing a vast change in the security industry as vulnerabilities are discovered at an unprecedented rate. OpenBSD vulnerabilities, more Firefox vulnerabilities found in a single month than the past two years, critical Linux vulnerabilities. It’s hard for me to look at the effects there, a radical new capabilities baked into the model itself, and see stagnation. A part of the reason it might feel like it’s slowing down is because we plebs don’t have access to the top models.
The maintainer of curl - who has access to mythos - disagrees [0].
I think it's dangerous to rely on claims made by people who financially profit from you believing them without checking.
[0]: https://daniel.haxx.se/blog/2026/05/11/mythos-finds-a-curl-v...
The article says in the second section that the author did not have access to Mythos. I think it’s dangerous to rely on claims made by others without even bothering to read them first, let alone check.
It found hundreds of vulnerabilities in Firefox, according to Mozilla: how does Mozilla benefit? It found a 27 year old vulnerability in OpenBSD. How do they benefit from that? Is that made up? Are the maintainers of those codebases lying for the benefit of Anthropic’s IPO? Is copy fail a fabrication by big AI? The 12 OpenSSL vulnerabilities found in January?
https://venturebeat.com/security/mythos-detection-ceiling-se... https://www.wired.com/story/mozilla-used-anthropics-mythos-t... https://cyberscoop.com/copy-fail-linux-vulnerability-artific... https://www.schneier.com/blog/archives/2026/02/ai-found-twel...
Im not sure whose claims you think I’m relying on. I trust Firefox that they’re not overstating the number of CVES they’ve found. Same for OpenSSL. The OpenBSD folks definitely don’t seem like the types. I’ve not known Linux to fabricate CVEs either. I think my sources are fine.
That blog post is very clear about the maintainer having no access to Mythos.
Does that matter that somebody else ran it for him?
When it is explicitly an appeal to authority, and the basis for the authority is incorrect? Feels like it matters.
And presumably the GP thought that saying the maintainer had access to Mythos made it a more compelling argument. Otherwise why even mention it?
Do you have access to Mythos?
Nope. Just watching the volume and severity of CVEs coming through since it’s been running. It’s been a busy few months.
Progress is slowing, in an important way.
Have a muck about with what Qwen 3.6 or Gemma 4 can do and you'll see. I mean this as an illustration but Qwen just isn't as far behind as I expected, and compared to the data centre hardware it will run on a potato.
The frontier models are losing their undeniable edge over that which is unmetered.
And even putting aside my optimism for the smaller open weights models, there's a huge amount of scope for the larger, hosted open weights models that are only just behind the cutting edge and which cost, what, 1/25th of the price on opencode go, openrouter etc.
Commodification is coming, and with it slimmer profit margins; it's hard to see them making anywhere near the kind of money they need to in a commodified market.
> He’s been continuously predicting that the collapse was just around the corner, that progress was slowing, and that there was no market for inference, since 2024.
Old WSB saying: The market can remain irrational for (far) longer than one can remain solvent.
And unfortunately, a lot of the market on the "buyer" side has been acting irrationally. When you see CEOs telling their employees that they don't care about token cost, only about "how much AI do you use" because that is what the stock market wants to hear - that's when you know we're all getting cooked, the question is how long it takes until the bubble bursts.
anyone that takes him seriously at this point... I don't want to say very bad words here...
>undeniable, massive productivity gains.
How can something so undeniable have zero scientific evidence? Are there any large peer reviewed or meta studies confirming your claim?
It’s a very hard experiment to run. You have a population that’s already “treated”. You can’t blind them to the fact that they’re using AI tools. It’s hard to imagine a study that wouldn’t have serious flaws that people would then use to dismiss and form their own conclusions. Sure you have METR but that was very low n with a very old model.
I think the surest sign of productivity gains is the sheer volume of adoption. If you look beyond headlines, adoption is just incredible. Of course adoption does not necessarily point to productivity gains, but if this was some sort of FOMO or smoke and mirrors you would not see this much retention and this feverish a pace of adoption. You would not see a large segment of the profession using coding agents exclusively. All of these companies track productivity, again with imperfect proxies, yet everything points to a pretty consistent picture. Same with benchmarks, again a lot of crappy benchmarks but a lot of high quality ones too and a very diverse collection of tasks and capabilities they probe.
Your second paragraph appears to be 3 different instances of saying "X does not necessarily point to productivity gains... but in the case of AI, X definitely means productivity" without really saying why that is true or why other explanations do not fit.
Adoption meaning productivity supposes there are no other dominant factors for the AI push nor AI retention. It is possible for practices to be picked up or continued in spite of causing productivity DROPS. What studies have suggested are factors that make for productive work environments and what is actually enforced in the workplace are different things.
It’s 3 different weak but complimentary proxies. We form beliefs from imperfect evidence and I find these fairly convincing when it’s hard to find any hard evidence of no productivity and exactly the scenario you would expect under the hypothesis that we do see productivity gains. None of this is supposed to be unassailable. I would challenge then if you disagree what the evidence you have for this is?
Adoption implying at least some significant productivity gains doesn’t contradict there being other factors. You’re seeing entire companies reshaped. The argument is this is all for show or CEOs are in some sort of idiot class?
“It is possible for practices to be picked up or continued in spite of causing productivity drops” well of course. I just find that incredibly far away from Occam’s razor.
My point is: we have lots of evidence that’s highly consistent with real productivity gains, and I don’t see many pieces of evidence to the contrary.
Sheer volume of adoption is fairly forced though - "use it or you're fired, and tokenmaxx the hell out of it". Most the people I know outside of tech don't seem to be particularly captured by it, if they use it at all.
Yes since frontier perf is too expensive. Consider though frontier perf costs roughly 40x less per year. So you see super adoption for mega costs by the companies with the scale and budget for it, and then folks who can’t afford it that have to wait until maybe fall until they have the game changing perf we saw for opus 4.5 that spurred the enormous adoption by the major players.
I agree that there are incentives to waste tokens but this is strategic: they spend now for people to build and explore, then they keep the things that work and drop the things that don’t.
Because even in a field like software engineering where the output of our work is save in version control, measuring baseline productivity is hard.
LoC: people argue it’s not what’s important
PRs/day: same as LoC
Getting projects done faster: oh but what about the quality.
Solve the technical problems and actually be more productive, the social systems build around the old way of doing things will hole you back.
Finish a PR in 10 minutes doesn’t matter if you’re waiting days for a human review.
I do not disagree with what you are saying, but I honestly still believe that most of the utility we experience are honestly gonna become very boring very soon that we can just run local... Even if it's a bit more slow who cares, can just run in background while you work on other stuff yourself, read up on things, review other work...
It's not that the utility of it put in question. What is however a giant question mark is how the heck any of the big AI companies are ever gonna get that ROI? Given how many of us are becoming more and more fine with local models that run just fine especially on a good enough computer which most developers have anyway...
Even more dangerous to the big 2 AI companies is the fact that the 20 different Chinese companies are catching up fast and for a lot lower cost.
Why should someone pick Opus 4.8 when Qwen3.7 Plus produces similar results for about 1/20th the cost.
That sort of pricing disparity is across the board. But further it's becoming more and more apparent that they are doing more with less parameters. That's what's giving the local models their super powers.
Because it doesn't. Not for the tasks where using Opus instead of a lower tier model is appropriate, at any rate. Benchmarks show this, as do revealed preferences of actual users. To believe that Qwen is as capable as Opus at 1/20 the cost you have to believe that every person who does not make the choice to use Qwen over Opus for a given task is some mix of ignorant or delusional. This is certainly an opinion you can hold about other engineers, but it's definitely a questionable one at best.
The benchmarks between the two are close and the engineers that have used both (like myself) can attest that the differences aren't so wide as you might believe.
I'd say that yes, ignorance plays a role here because a decent number of engineers are looking strictly at the benchmarks and choosing Opus just for that reason.
But I'd also say that a major factor for Opus use is because Opus is being purchased for the engineers by their employers. They don't get to pick which models they are using.
I find myself rarely reaching for Opus nowadays, it's just too slow. I assume there are tricky use-cases where it's really useful though, just not super relevant for my day to day. I much prefer a faster, "weaker" model.
Every day people here debate whether or not there are any actual productivity gains from LLM, and it's only in the limited context of software development. While I understand that this place obviously skews heavily towards the software industry, the notion that LLMs are anywhere near as useful in other industries is hubristic (at best).
Perhaps they aren't, but not currently viable !== always unviable.
Is it really worth it to cause a global economical collapse and harm society well-being to an unimaginable degree just to find out if it is viable?
Why cant it naturally grow and prove it's worth?
Just 5 more years and $500 billion more, bro. We're still so early.
And?
> through undeniable, massive productivity gains.
And where are those? They seem particularly hard to actually observe and only appear in anecdotes.
> I'm trying to believe
For every exponential increase in compute capacity you see linear gains in output accuracy. This is a death spiral. Anyways, you see "massive productivity gains" so why is "belief" a function of your viewpoint?
Even if we assume that everything you said holds true, how is that we as a crowd can make viable a service that eats some $300bn annually in infrastructure costs? Where would that money come from? Most tech companies these days are cutting their AI budgets because the per token pricing is killing them.
Cite a real source for that last bit, I don’t think that is true. Also the budgets should be cut the spend at some places goes beyond any reasonable amount. The strategy there is to hook everything in and find the right processes, then cut the rest. Things then get better and better with each model release.
The way you make a viable service that eats 300bn annually is to have enough demand to service that. Anthropic underbought compute. That tells you something.
https://www.theverge.com/tech/930447/microsoft-claude-code-d...
https://finance.yahoo.com/sectors/technology/articles/ai-bin...
https://blog.pragmaticengineer.com/the-pulse-token-spend-bre...
When you say "Things then get better and better with each model release."
How far behind are models that can be run locally, and do you expect that this will be widespread?
https://epoch.ai/data-insights/consumer-gpu-model-gap
I think over the years local models have fairly consistently been ~7 months behind frontier performance. Local models are hugely important but I don’t see the calculus changing. I can imagine it’s certainly the case for many tasks that there will be diminishing gains for performance improvements or reliability pass some threshold, in which case you don’t need frontier performance and you can certainly use local models or at least cheaper tiers of proprietary models if local is too much of a hassle. Plus of course use cases where local is necessary or the pros of having local models or on device models outweighs that of frontier.
Maybe things will change though, I would assume through basically government subsidies from China etc, to undercut existing frontier labs, but you can always spend more (better data more compute etc) for better performance and that I can imagine will always have a selling point.
> undeniable, massive productivity gains.
The jury is still out on that.
Yeah they're very much deniable. Raw LOC/hr is much higher, and putting together a MVP, but I've yet to see any evidence that an LLM is capable of doing anything unsupervised, and if you need a human supervising everything it does... why bother having an LLM in the first place?
Because it can perform much faster? Monitoring allows you to multitask more effectively. I would also disagree that you can’t one shot anything…claims like this are weak and I have enough counter examples in my own life that it’s trivially false. The question is more: can it one shot the right things with a low enough failure rate for it to be a good replacement. It’s hard to figure that out a priori.
They are absolutely deniable. Huge swathes of people deny them.
He has recently made the very good point that actually, the FAANG companies are struggling to put any ROI numbers on that incredible ground-level utility.
Uber, for example, is so unclear there is any ROI, they are cutting their exposure pretty radically.
He points out that one single Anthropic customer — a payments provider — accidentally had to pay Anthropic $500M for one month of token spend.
That is half what Apple is reportedly paying Google for the supply side of their entire consumer AI strategy.
It doesn't matter under Capitalist Realism, the banks were bailed out, the AI companies will be bailed out, and you will pay for it. There is no alternative.
I'm not sure if they would be bailed out. The government tends to help with bank bailouts as they are essentially the hemoglobin of the economy, I see this being more like the dot-com bubble were they will just let it fall and have the bigger more entrenched player pickup the scraps for cents on the dollar.
According to NOTUS, Altman himself seems to have sold Trump on the idea that the USA should own a chunk of OpenAI, and despite their potential IPO, OpenAI have recently followed up on the idea.
Owning a chunk is pretty directly how more than one country injected confidence into their at-risk banks; it’s certainly how it was done in the UK.
Agreed that he has an extreme POV (or more accurately that he trolls for views/subscriptions). But his central argument is valid: if AI underdelivers financially, this bubble will burst and this bubble is magnitudes larger than what we've seen before, so there could be very rough seas ahead.
The question is: what does "underdeliver" mean here? the pro-AI arguments I am seeing in this thread are equating mass adoption to agentic coding. Er, I dont know of any trillion dollar cap companies that sell dev tools. The point is Zitron doesn't have to be 100% right for his central prediction to come true.
I don’t get this. We already have an insane demand. And yes exactly, this is primarily just with coding agents, but are you aware of what’s coming down the pipeline? It’s not hard to be you just have to find a decent way to keep up with literature.
* robotics (need to close data gap and release first viable product to get a data flywheel)
* conversational ai (no one is ready for this and we’re getting closer and closer to natural speech. The quality still isn’t good enough but it’ll be soon).
* other agentic use cases, openclaw adoption was crazy and that had a ton of barriers to entry
* ai products, like the one OpenAI is working on with Johnny Ive
Anyone thinking it’s unreasonable to hit whatever revenue requirements is just not that aware of what’s happening. Not to mention were capacity constrained already!! This is barely speculation at this point.
I don't think the issue with robotics is a data gap. maybe somewhat, but the real issues are that:
- RL is extraordinarily sample-inefficient.
- distribution shift/catastrophic forgetting aren't solved. only off-policy learning with giant decorrelated batches works.
- the breakout success of transformers as an architecture doesn't neatly translate to robot motion policy models.
the field is missing fundamental breakthroughs.
I also find it very interesting that conversational AI has taken this long. where are the models with good turn-taking? passive listening? the ability not to respond in paragraphs? has Anthropic simply not gotten around to it?
All of these points are great. The first one motivates world models which lots of labs work on. Not many people tend to understand the strategic value of those “open world” or interactive generation models: its robotics and planning. But also like you say you’re right, there are complicated problems to solve and it’s not totally clear the timeline. But where there’s data and compute, there’s a way.
For conversational AI these labs do have lots of things to do lol but you’re right; it likely also requires some architectural improvements but you see the infancy: look at the llama4 speech duplex model. Very unimpressive yet all of the components are there. Just a matter of pushing on them, licensing and commissioning better data, etc. takes time and compute is stretched thin.
Yes. Zitron has been predicting and begging for collapse since 2024. It's not just his brand at this point. It's his entire identity. As such, he cannot back down, he cannot question himself, and he cannot accept any other viewpoint. And he will keep moving his goal posts until something happens that can make him go "aha! I told you guys!!"
This, combined with his extreme ignorance, makes him unreadable. The only reason people read his stuff is because it validates and confirms their own anti-AI beliefs. It's why every time he publishes an article, it reaches the front page in an hour or less.
> This, combined with his extreme ignorance,
Extreme ignorance?
I can see how folks find Zitron shrill, but I wouldn't dare say he is ignorant. The man seems to be bending over backwards to do actual journalism when most outlets take CEOs at their word and promote a market that goes irrationally up despite evidence. Case and point, the situation in Iran is such a mess, yet every time Trump says there is a deal of some sort, the market rallies. It has been months now of this game.
As a listener, I have not heard him predict collapse once. Maybe I got to him late, but so far as I know, he claims that the industry as it exists is unsustainable. You may think that is him dodging the logical progression of "collapse" but I think so far he has been pretty correct on the unsustainable part, the signs are there. If anyone could predict such things they'd be a billionaire many times over. In the end I think he will be more or less correct, the timing and how long investors can prop it up is the much harder part to predict.
> undeniable, massive productivity gains
How are they undeniable? They're very deniable. One example is the (seemingly) increasing maintenance costs for AI-generated code[1]. Another is the cost incurred by everybody reading AI slop instead of actual communication.
I don't have hard data as to whether these cancel out the benefits, but it's not as rosy as some seem to think.
[1] After years of people understanding that LOC is not only a poor productivity metric but also a negative indicator of code quality (shorter code for the same thing is better), we now have people touting how many LOC their LLM agent is generating. It's like everyone forgot what LOC actually represents and what it means for long term maintenance costs.
I quite like my mechanical spider from Wild Wild West and the coffee it makes with a 50% success rate
> Zitron is begging for a collapse at this point
No, he's not, he's making tons of money every month from his Substack subscriptions. In fact, the AI bubble popping would be the worse thing ever for him, he would be out of a job.
Just like the who have predicated the US dollar will collapse any-moment-now and which pushed gold for decades.
Funny how people always say "oh, you are an AI lab, of course you are going to hype AI", but never "oh, you make sooo much money from predicting the collapse of the AI bubble..."
I really like some good drama slop that reads like a thriller, it is entertaining. I don't take any of it THAT serious, but lately with the IPOs that are about to hit the indizes, he has gained a lot of attention. If you look around the internet, most people publish a negative angle on something and then extrapolate it into some grand conspiracy, which is really captivating. Its crazy when you enter some echo chamber you never engage with (movies, gaming, art/comics) and they have their own head cannon for why the world is bad and collapsing. It puts your echo chamber into perspective to see the same patterns of argumentation and presentation spin out in a different way
> undeniable, massive productivity gains.
Just because you keep repeating something doesn't make it an undeniable truth.
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i don't think this comment contributes much to the discussion. can you elaborate more than saying "no"?
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Please don't comment like this on HN. We can disagree with people whilst still affording people basic decency; it's a condition of participating here.
If you wouldn't mind reviewing https://news.ycombinator.com/newsguidelines.html and taking the intended spirit of the site more to heart, we'd be grateful.
Ed's argument for why "AI is slowing down" rests on company spending caps, in particular the Uber $1,500/engineer/tool cap.
I interpret the exact same evidence in the opposite direction. A year ago the idea that a company would spend $1,500/month/employee on AI tooling felt absurd, what could people possible want to do with AI that would cost that much?
Then coding agents (and, increasingly, general purpose agents) happened and suddenly companies are having to set limits because otherwise the demand from their employees is too high.
The TAM of these AI companies just leapt up to $1,500/knowledge-worker/month, how is that "slowing down"?
Maybe in USA in big tech where companies give absurd wages to engineers anyway in some states, that might be acceptable. But to make their ROI they need that (and more) to be spend world wide... no way that is gonna be a budget that is gonna fly in the long term...
Companies love to cut costs, and just like they axe employee numbers at will, they will just as well make that kind of budget quickly dissapear the moment they realize they can go a different path for same or better value... Or simply because share holder short-term value demands it...
The Uber $1,500/engineer/month thing is just the first signal we have had of the price companies may be willing to accept. This price will clearly vary wildly across professions, industries and geographies.
I think it's a poor number to build an "AI is slowing down" narrative around.
The problem is that $1500/engineer/month would be a pretty modest amount of demand for labs. OpenAI/Anthropic are basing their $1T valuations on the explosive uncapped growth of unlimited agentic token spending. On so many levels of the industry this growth is now priced in. You don't think so?
I don't have a particularly great answer to that question - I'm not enough of a financial analysis to have confidence in an opinion.
I do however think that shouting "look, Uber capped pricing at $1500/engineer/month hence AI is slowing down" is a questionable position to take.
> I don't have a particularly great answer to that question
Maybe you could use your $1,500 quota to ask AI for better arguments?
I mean, I did that already for my own curiosity... but I'm not about to share those on a public forum and pretend I have expertise that I don't.
>OpenAI/Anthropic are basing their $1T valuations on the explosive uncapped growth of unlimited agentic token spending.
No they're not. In reality, actual 'explosive uncapped growth of unlimited agentic token spending' will result in valuations several times more than a 'mere' $1T.
Uber is not the only company that's putting a per-developer limit on AI spending. I know this because I work for another one (and we have a significantly lower limit). You just heard about Uber first because they're high profile.
I didn't say they were the only company, I said they were the "first signal".
The more signals the better! What cap did your company pick, and what geography / kind of industry are you in?
(Sorry I'm being vague, but I'm not sure I'm not sure what's public knowledge)
The cap is moderately above the high subscription tiers, and managements/the executives were clearly extremely concerned about how expensive it would be if we all or even mostly came close to hitting it. I heard that they originally wanted to go lower but the developers in the pilot program blew past their planned limit very quickly.
As for the company, its almost entirely B2B SaaS (I think it has some offerings that are used by consumers, but they're mostly/entirely paid for by another business on behalf of their customers), and they have developers all over the world, although the headquarters and biggest group of developers is in silicon valley (my office is in the midwest).
It's also not $1,500 per month per engineer. It's that per month per engineer per tool. Which means it could easily be at least $3,000 (Claude Code and Cursor) or $4,500 if Codex was also an option on top of those two.
And as you have written on your blog it's a soft cap that can be exceeded with justification.
I don't know that Uber is generalizable. I also think specific company dynamics matter (are your execs encouraging tokenmaxxing, have you IPO'd or are you playing w/ VC money all of whom are encouraging tokenmaxxing, etc).
Another way you could take it is, avg Uber salary is what $300k/yr? Does Uber think LLMs make their engineers at most .5% more productive?
I don't really understand how engineers at Uber are hitting $1500/month. Are they forced to pay API costs?
My company provides employees with API keys and soft limits, but as soon as you approach ~$400/month they ask that you get a Claude/Codex Max subscription instead. Curious if it's not the same case at Uber.
Yes, companies like Uber are forced to pay API costs now - by OpenAI and Anthropic (and GitHub for copilot): https://simonwillison.net/2026/May/27/product-market-fit/#en...
>but as soon as you approach ~$400/month they ask that you get a Claude/Codex Max subscription instead
While this seems to be allowed because the current ToS don't seem to explicitly forbid it, I'd be surprised if this loophole stayed open for long... Why would they even distinguish between business and (much cheaper) individual plans if companies can work around it by telling employees to just pay for the latter themselves?
Enterprise agreements are billed at the API rate (albeit sometimes with committed spend discounts). There is no equivalent of the Max subscription in this context.
Saying its going to be 1500 a month across the board is highly speculative. How many companies can even demonstrate that they're getting more than 18000 dollars a year in surplus value per employee by using this tech?
How speculative would it have been to say that it was anything more than $20/mo back in November?
I'm sort of an AI skeptic but I have been seeing this guy's essays for years now and he has always been super pessimistic on AI progress.
I think a much more reasoned critique of AI is that of Tyler Cowen, whose argument is basically that most processes aren't constrained by lack of intelligence but by organizational and social factors which mean for AI to be useful you have to redesign organizations and work to take advantage of what AI is good at. Since most organizations are fairly bureaucratic that takes a while, especially in the large industries that are the most economically important.
Ed's criticism of the large AI companies seems particularly misguided to me since they are the ones actually advancing the technology and seem to have real moats given their access to large amounts of training data from their users. I don't see any possible future in which 5 or 10 years from now there is less AI than we have now and I would expect usage to be much higher.
Completely agree. AI is here to stay, it's going to garner more and more use overtime. However, I'm skeptical that the investments being made right now are at of the right scale & at the right time. I completely agree that over time we'll rework more and more of our society around LLMs or their successors. However, like you say it's a slow process: we have to learn how to do it effectively, organizations need to change, people's attitudes and behaviors have to adapt. I just don't see is "getting there" fast enough to justify current spending levels.
Before you spend 20 minutes reading this article, it's worth understanding that the writer has been posting popular but consistently wrong takes for 2+ years (e.g. https://www.wheresyoured.at/peakai/ from March 2024) arguing that AI is failing, is a waste of money, is bad, will never work, etc.
Not sure where I heard this, but I'm reminded of a story about someone predicting the dotcom crash early, circa 1998. For 2 years they were demonstrably crazy, and missed out on massive stock market gains. Then they were right. (And yes, tech slowly bounced back after that.)
Predicting the timing of such a thing is notoriously difficult. I don't think being wrong about timing 2 years ago means there won't be a correction.
I'm also reminded of all the HN posts from 2007-2009 that predicted that the adoption of social networking would be a terrible thing for privacy, that it would destroy society, that people would lose their jobs over crazy shit they said on the Internet, that it would lead to the decline of trust and in-person interactions, that people would forget how to socialize, etc.
They were right about all of that but it took 15-20 years and the companies involved grew 100x in that timefold, eventually reaching trillion-dollar valuations that would've seemed insane in 2007.
There is a tremendous amount of money to be made in destroying society.
Eh, you can find HN posts predicting that literally everything will destroy privacy/society/trust/etc. Predicting doom is a popular pasttime.
What I remember from that time period is people predicting that we were in a tech bubble driven by social media, that obviously Facebook and LinkedIn were overvalued because social media was a trivial fad, and so on. Example article pulled at random:
https://theconversation.com/linkedin-is-floating-on-air-or-i...
And yet there was no bubble, these companies did fine and Meta became a financial Godzilla.
They weren't wrong. We were in a tech bubble driven by social media. Digg, StumbleUpon, Kongregate, MySpace, Orkut, Slide, Meebo, Mahalo, Bebo, Justin.TV, etc. aren't exactly around anymore. Facebook and YouTube are the winners.
Anyone remember this video?
https://www.youtube.com/watch?v=I6IQ_FOCE6I
How many of the logos that scroll by there still exist?
I was definitely around when that video was current, but I don't remember it. It's pretty amusing.
Ironically I feel like it captures the spirit of the then-coming 2010s boom more than the climate in 2007, though some of the language it's using is decidedly pre-mobile and more "web 2.0"-ish.
The fact that some companies fail isn't evidence there was a bubble. Companies are always failing.
Not related to AI but, I recently rewatched "The Big Short" and your comment reminded me of it. I can't testify the accuracy of the movie, but for over year, Michael Burry was viewed as in the same manner for shorting the market, while the economy was was in a hype cycle.
Burry of course has famously predicted 40 of the last 5 crashes, so maybe not the best example.
And lost his shirt doing it. (Or, well, his investors' money. I'm sure he's fine.) https://www.reuters.com/sustainability/sustainable-finance-r...
This is like the old quote, "the market can stay irrational longer than you can stay solvent"
> Predicting the timing of such a thing is notoriously difficult.
So, it stands to reason that it wasn't a prediction, but a lucky guess (unless the alleged predictor has a history of correct predictions).
That actually doesn’t stand to reason at all!
If I just continually predict that it will rain tomorrow I'll be right eventually. That doesn't mean that I know anything about the weather.
I'm open-minded to arguments about AI being a financial bubble and a bad business.
I'm not open-minded to arguments about utility, given that I personally witnessed LLMs evolve from interesting but useless toys to insanely helpful tools I use every day.
I guess one of Zitron's arguments is that the utility you see today is based on subsidized costs, that if you had to pay more it might not be worth the tradeoff to you.
So the claim is the cost isn't coming down enough to make it make sense for a lot of uses in the long term. When I hear that next to the most wild claims, some by influential people, that the entire white collar workforce is going to be replaced very shortly, it's a bit of a useful reality check.
Exactly. The question is not "are people using it to do stuff?" because we know right now they are. Given free or heavily-subsidised access to powerful tools, people will use them.
If I had someone giving me free access to cranes and excavators, I'd be raving about how easy it was to build houses now. But tomorrow when I have to pay full price for them, I'm going to be making very different calculations about return on investment.
The question we need to be asking is "what is the likely full-price cost we'll have to pay for these tools, and is that cost likely to be worth paying?"
What Ed's pointing to is that the full-price cost will have to cover the capital expenditures that have been invested, or the companies which risked that capital will go bust. That gives us a floor for what the full price cost will be, and that floor seems higher than the value being offered by the tools.
you cant operate setup cranes or excavators alone
What does that have to do with anything?
Is reading comprehension really this bad nowadays?
it was an analogy
Can you point to anything specific from the article that you'd describe as consistently wrong? Not disagreeing with you, but nothing popped out to me after skimming the article.
I didn't read the posted article (I don't read this author anymore because I think it's basically anti-AI ideological propaganda).
But from the article I linked back in March 2024:
"Generative AI models are expensive and compute-intensive without providing obvious, tangible mass-market use cases. Murati and Altman's futures depend heavily on keeping the world believing that development and improvement of their models' capabilities will continue a rapacious pace of progress that has unquestionably slowed, with OpenAI admitting that GPT-4 may be worse on some tasks.
As I've written before, hallucinations are a feature not a bug. These models do not "know" anything. They are mathematical behemoths generating a best guess based on training data and labeling, and thus do not "know" what you are asking it to do. You simply cannot fix them. Hallucinations are not going away."
Since then:
- hallucinations are dramatically less of a problem
- several mass market use cases have emerged, most notably coding
- rate of progress has increased
I think the points you raise are reasonable signals to consider, but I don't think they show the author being "consistently wrong". The overall thesis still remains plausible even though we have seen LLMs continue to improve.
> - hallucinations are dramatically less of a problem
Sure, but it remains a big enough problem that human intervention and review is still necessary for any serious work across all use cases and industries.
> - several mass market use cases have emerged, most notably coding
Coding seems to be the only one, but there are still a lot of open questions about how the market can sustain the costs, and that's without considering the market dynamics that could emerge once costs are lowered enough that open source models start to become an attractive option.
> - rate of progress has increased
Debatable.
> Sure, but it remains a big enough problem that human intervention and review is still necessary for any serious work across all use cases and industries.
Another important consideration: Hallucinations getting less common/severe but not (as-good-as) solved makes them worse.
LLMs used to very obviously get things wrong. And people wouldn't trust them. Now they're good enough that people blindly trust them.
Now people just directly PR AI output with little to no manual review. We even have clowns calling for the complete abolition of directly human-authored code.
Whatever gains were had in better AI code output over the past two years I lose in having to review much more thoroughly.
> several mass market use cases have emerged, most notably coding
Most notably? This is not a mass market use case in the way the author is describing. They are asserting that the amount of spend they need to get this off the ground necessitates the entire world coming in on it, and I would say that opinion has aged pretty well. There are a lot of coders, but there are more people scratching their heads as AI is shoved into every part of their lives.
Has rate of progress increased? How does one measure that? Genuinely curious - would be very interesting to map out the "effectiveness" of each AI model vs how long it took to train/release.
From my perspective, the model gains are mostly incremental now and a lot of the gains are just from things like improving the agent harnesses. I could be wrong though.
On the front page right now is the newest announcement from Xiaomi serving large model at over 1,000 tok/s on standard server gpus.
Every facet of the field is being pushed on and advanced at the same time.
Hallucinations are still a problem. I recently asked one to give me a quote from a book, figuring that since these AI companies have pirated all books in existence surely it can just recite a specific passage no? It hallucinated the quote, I had even told it what chapter it was in. Had I not read the book recently maybe I would've believed the hallucinated quote.
And it got me thinking, they sell these AIs as assistants, but it couldn't even look up a passage from a book. This is basic, elementary stuff, it should get it right. I would have fired this assistant right away if it were a person. Not only did it get it totally wrong, it came to me with utmost confidence that this is the quote from the book. Unreliable assistants? That's the product they're trying to sell? Get out of here with that trash. I can't trust it.
> hallucinations are dramatically less of a problem
No they aren't. The models still hallucinate just like they always did. You cannot trust them, ever, to get something right.
> several mass market use cases have emerged, most notably coding
They aren't really useful for coding based upon the above. Since you can't trust them, you have to carefully review everything they make, which in turn destroys any productivity they could've given you.
> rate of progress has increased
I have yet to see any progress. Opus 4.8 that you get today is no more effective than GPT-3.5 was. Much less would I agree that the rate of progress has increased. Only hype has increased, but there has yet to be a drop of substance.
His point is that coding is only a “market” because it is being sold at a loss. Businesses have to pay per-token prices and are saying that the cost is not justified.
Nevertheless, it all misses the point if we get to AI post-scarcity utopia. But thats a big if.
It doesn't miss the point because if we get to some AI post-scarcity utopia then the companies pouring trillions into it now are never going to make their money back on that investment.
The only way they make their money back is if everyone pays them tons of money for it.
https://news.ycombinator.com/item?id=48447549
Not the person you are responding to, but here:
> I believe that artificial intelligence has three quarters to prove itself before the apocalypse comes, and when it does, it will be that much worse, savaging the revenues of the biggest companies in tech. Once usage drops, so will the remarkable amounts of revenue that have flowed into big tech, and so will acres of data centers sit unused, the cloud equivalent of the massive overhiring we saw in post-lockdown Silicon Valley.
We have seen 8 quarters since. Has any of that come to pass?
Even if you see a real bubble or catastrophy in the making, predicting when it will pop is a fools game.
if you can't predict when it will pop then you should really not predict anything. I can also predict that Google will pop. I won't tell you when but I'll tell you that it will. I'll remain thoroughly unfalsifiable and I'll keep pushing the dates.
The quality of AI doomerism takes is matched only by the quality of AI boosterism takes. Ed's kind of interesting as a temperature sensor but I don't feel like you can really take anything he writes seriously.
And its been 3 years of AI boosters telling me that my job as a litigating attorney will not exist in 2 months. Yet here I am, gainfully employed.
From your link:
And, take a look at https://openai.com/index/sora-is-here/Please speculate on why OpenAI wouldn't just leave it up (whether or not they were able to improve it).
I highly recommend folks read Wired's profile on him: https://www.wired.com/story/ai-pr-ed-zitron-profile/
Tim Lee also pointed out that when Ed has posted details on some of his analysis, they have had some....oddities: https://x.com/binarybits/status/2034377838883700953
What if you phrase the question from "will AI ever be useful" (a term as utterly vague as "IT") to "will it ever be able to promise the financial gains these companies are hoping? Especially with local models eating their lunch :shrug:
Yeah they seem clickable because anything Anti-AI is a bit soothing right now, but he is constantly wrong and usually is pushing the angle of "these businesses aren't even profitable!"
Instantly close the tab as soon as the popup to subscribe to his newsletter pops up.
They ain’t profitable yet. Most of the model maker’s will be gone soon. It’s unsustainable unless you’re Google who has other income coming in to support their hobby, and the Chinese model makers are spending a fraction to be six months behind and many of them will be there for the long-term because they have backup support (government) who is in the race for the long-term.
One other thing that’s working against the model makers is the hardware is getting better and the models are getting smaller and more capable. I don’t think we’re going back to the mainframe days. Local will be the endgame.
Is Ed right? Probably because in the end it’s unsustainable the companies left will be the companies that have income coming from somewhere else and there’s one large tech company that isn’t even participating in the boondoggle unless you count $1 billion dollars a year as participating ultimately there is no moat in AI model making.
Nvidia and Microsoft trying to introduce another Arm processor in a laptop of all things won’t change the tide either.
Why is anti-AI soothing?
Because there are still a huge number of people who would be very relieved if the whole AI thing just went away.
For some of us it is, I suppose as an alternate view to AI booster-ism, particularly if you think the long term effects would be mostly negative.
It's seen as an existential threat to young people. If you can't get a job you starve.
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Gen AI is strictly bad for society.
Can't really agree. It's improved my life more than any other single innovation made in my lifetime.
The positives: Some tech nerds got a cool new toy
The negatives: Culture is destroyed, truth is destroyed, democracy is destroyed.
I guess its a wash at the end of the day.
> Culture is destroyed, truth is destroyed, democracy is destroyed.
Just because those things happened in Dune doesn't mean they'll come true. It should be obvious what massive potential the thinking machines have for humanity as a whole.
> Can't really agree. It's improved my life more than any other single innovation made in my lifetime.
So? Something can be bad for society while improving your individual lot in life.
From the linked article, "Sora is not going to generate movies." How's Sora doing these days?
> Before you spend 20 minutes reading this article, it's worth understanding that the writer has been posting popular but consistently wrong
So, judge the book by it's cover?
> arguing that AI is failing, is a waste of money, is bad, will never work, etc.
Then the opposite should be easy to prove. AI is succeeding, is efficient, is universally good, and is working everywhere it's tried. Are those true?
> So, judge the book by it's cover?
It is literally judging the book by it's author, which is an extremely rationale judgement to make.
> It is literally judging the book by it's author
How is that better?
> which is an extremely rationale judgement to make.
So it's "rational" to take bias into reading? Why even read? If you know what you think and refuse to accept new information then what purpose is there in consuming anything?
You should just read the comments and get a warm fuzzy that the crowd, for the time being, agrees with your intentionally static ideology.
Comments like these obviously hope they can sway the crowd before they can take an unbiased reading of the article. If the author is that wrong then the crowd here should be able to discover that on their own. If the author convinces the crowd then I'd think you'd want to present a better argument than "well, he was wrong _before_." Post hoc, ergo propter hoc, in action.
That's the exact opposite of rational. It is, in fact, a formal logical fallacy (ad hominem). His argument can be correct even if he himself is not typically correct.
On the surface, that's quite fair. However, there's one problem: it is much easier to make statements than to verify them, and that asymmetry is part of why the internet has been slowly eroding society.
It's useful/necessary to use past writing/arguments from an author to say whether they should actually receive any further critical evaluation, or be dismissed. We shouldn't say definitively "they're always wrong, so they're wrong now". However, it's reasonable to say: the author has a demonstrated lack of credibility, so we can probably assume they're wrong here, particularly if they have been wrong in this domain so many times before. Or if they happen to be correct, it's probably not strongly demonstrated by their work.
What's the point of reading someone's writings on a subject where you know they're not typically correct? How would we know what we 'learn' from Ed is right?
He also does PR for AI companies and only really acknowledges this in interviews. As far as I know he never discloses it in his rants.
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Please don't comment like this on HN. We can disagree with people whilst still affording people basic decency; it's a condition of participating here.
If you wouldn't mind reviewing https://news.ycombinator.com/newsguidelines.html and taking the intended spirit of the site more to heart, we'd be grateful.
The way I see it, AI is going to change the world radically. It could be for the worse, the better, or a mix of both, but in my mind there's no doubt.
We are only five or six years into the leap LLMs represent. For reference, radio waves were discovered in 1886, Marconi used them for communications in 1895, and while telephone and radio coexisted for many decades, it wasn't until the 1995 that mobile phones and wireless technologies started picking up. It took so long not because of the physics of radio waves required time to mature and improve, but because everything else needed to profit from it did require time.
To me, LLMs are not so much AI as it is a building block. Radiowaves maybe, or the equivalent of transistors. We are already seeing that it's possible to chain LLMs into agents. Currently, price is a strict limiting factor for coding and agents.It's probably fine-ish if all you want is Claude Code or Codex, but there are many other possible compositions of LLMs that most people don't dare to experiment with. For example, LLMs to drive NPC dialog and world mechanics in games is not a thing due to cost. Were prices of inference hardware go down and inference algorithms keep improving, I'm convinced (and afraid) we would see things very difficult to imagine today.
> For example, LLMs to drive NPC dialog and world mechanics in games is not a thing due to cost.
Hah, I'm actually working on just this problem.
Cost isn't the issue. There are only so many coherent (in context) responses and scenarios, that you don't need an LLM to generate text in the game, in real time. Instead, you can have LLMs build a vast corpus of "atoms" (dialog messages, fragments, cues, etc.) that can be stringed together in a deterministic way in response to player input. These can also be pre-screened and subjected to various tests prior to implementation.
To a player interacting in the game, a system like this would seem functionally indistinguishable from generated text within the game's designed interaction envelope. And it has huge advantages: Although it can expose seams if the player breaks character and decides to probe it, it won't be exploitable the way an LLM would be.
> LLMs to drive NPC dialog
Far more interested in dialog and characters developed by a writer - simulation is boring
>Far more interested in dialog and characters developed by a writer - simulation is boring
It entirely depends on the situation. Background NPCs that just have conversations among themselves would be a great use of LLMs to make the world feel more immersive. Obviously you never want to directly engage the player with LLM generated writing.
> For example, LLMs to drive NPC dialog and world mechanics in games is not a thing due to cost.
I have deployed LLM-based NPCs in production for a reasonably popular game (100k DAU), so this is news to me.
I count the start of this AI era as when ChatGPT was released, so we’re actually only 3.5 years in.
And we’re only about a year into the agentic coding era portion of that era.
> The way I see it, AI is going to change the world radically. It could be for the worse, the better, or a mix of both, but in my mind there's no doubt.
Worthless statement. Wow, you suspect something can make things better, worse, or both? That's a keen insight there.
> For reference, radio waves were discovered in 1886, Marconi used them for communications in 1895, and while telephone and radio coexisted for many decades, it wasn't until the 1995 that mobile phones and wireless technologies started picking up.
We are still so early.
I mean, we have advertised them in multiple super bowls, have companies that basically own tech news (incredulous journalists will repeat any stupid insane shit a CEO wants to say), that say they're valued at over a trillion dollars and nobody with the power to argue those finances seems willing to do anything but agree. We have built hundreds and hundreds of acres of data centers (and made deals for data centers that are never going to happen) that demand *billions* per month. They are devouring all the silicon to where people are visibly seeing the price of hardware double, triple, more in price. Work places insist on employees using AI (then pulled back because it turns out this stuff costs money and it's not fun anymore when it's not subsidized).
But we just need more time, more eyes, more people looking at it.
Where in the radio wave timeline did this happen?
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Already many comments saying this but Ed Zitron is not a person I trust. He's been so biased and wrong on stuff that I consider very obvious and trivial that his complicated analysis with numbers and trends I can't just take at face value.
As an example of obvious wrong things, I remember a tweet of his where he was mocking people talking about agents and agentic coding. He was kind of saying that he was going crazy as agents weren't a thing really and people talking about them like they were real. Something like "agents?! what agents?! these guys hear themselves?!". The answers were full of hundreds of people patiently explaining how they were actually _using_ agents. This wasn't in 2023, it was a couple of months ago.
He just has an audience and an engagement target. His objective is clicks, not informing.
His numbers are based on sources that he says he doesn’t trust, which is quite interesting. While he may be directionally accurate (eg. The revenue needed to match the spend seems lofty at best) he seems to be mixing and matching numbers to create a worst case scenario that doesn’t necessarily line up with reality. Combined with his complete unwillingness to be open minded about anything even tangentially related to AI, and I can’t take him that seriously.
Publications love a doom and gloom rant, which is why he seems to have built an entire career on hysterical anti-ai screeds. It doesn’t mean that he’s right though.
It's disappointing that this article got mindshare when a more neutral author could better argue the bearish case for AI valuations. I want the steelman argument from a more respected individual.
The problem is when untrusted authors take positions, then it circulates widely, then people discredit the author and by proxy the position, when the position could be correct.
The article has a number of emotional appeals in it. Something more focused on raw numbers would foster more curious discussion.
He provided a lot of quantitative analysis in this article. Perhaps an example or two you think these numbers are off-base could help bolster this point?
I think the most compelling part of the article is that these numbers point to a situation where the level of investment required seems unsustainably high by plain dollars.
You don’t really have to agree with the author to see how it plays out. OpenAI and SpaceX and Anthropic need to go public this year to avoid running out of money. There’s no more private money, not enough to fund them. The IPO is the last funding round.
They can continue growing extremely quickly and AI can still be highly useful and maybe be transformative, but still not have the money to fund that growth.
That part he wrote about an AI company gone bust canceling their Oracle contract made Oracle feel like a Nortel analogy to me. If they have a sudden lapse with a big chunk of their customers they are writing down triple digit billions of dollars.
I guess what I'm saying is that I won't look at his numbers since he's an unreliable source from my point of view and there's a chance that he's going to try to deceive me and it's a waste of time for me to listen to him.
I do have other sources of information and I probably agree in general that AI companies are doing pretty shady financial shenanigans. I even think it's possible that openai is in real trouble. But I don't extrapolate that into "AI is useless", which is what he does.
> I guess what I'm saying is that I won't look at his numbers since he's an unreliable source from my point of view and there's a chance that he's going to try to deceive me and it's a waste of time for me to listen to him.
But he's linking out to sources
I would agree with you that extrapolating to “AI is useless” is definitely a giant step too far, and that part of the article ruins a lot of the other interesting bits of it.
It’s great that he cites a lot of sources but some of them aren’t great, like the Microsoft story about canceling their Claude spend. I think that particular story isn’t much of an indicator of anything, and it might not even be true.
But the financial part…this guy isn’t the only person out there sounding the alarm about the math not mathing.
FWIW I agree the financials are a bit crazy and OpenAI went a bit nuts with the circular deals. That said, honestly, I don't think it's the end of the world. I think there will probably be some correction/crash and it will probably be healthy. A lot of these circular deals will get canceled, but it's at the end of the day people changing imaginary numbers with each other. The underlying tech I still think it's revolutionary regardless, the same way that the internet was and the tech boom crash at the end of the day was a distraction from the fact that these companies did end up "ruling the world"
This is what critical reading is for. It requires you examining your own assumptions as much as the text's. If you don't engage with something or someone because of your own bias or assumptions, that is also willful ignorance; you also might end up never updating your prior stance when new information emerges.
There is a financial argument and capability argument.
In this case, he doesn't make the claim one follows from the other.
There's no shortage of sources of information. I'll exercise "critical reading" with sources I consider trustworthy to begin with. I've no time to engage with difficult analysis from people who are not worth the effort. You wouldn't engage with every lunacy you read on a tabloid, right? similar principle
Fair enough. I won't debate preferences or how you choose to spend your time. I think one of the merits of his articles is that he surveys and gathers sources that others can engage with. Even if we admit he is biased, that exercise (his writing) alone is valuable because one can contradict or reassess his claims.
Grandparent comment’s primary claim was that Ed has frequently claimed untrue things in the past and so questioned why people would continue going to such a source, but your reply didn't seem to address that at all?
Someone else separately linked Ed’s 2024 claims [1] that:
A. AI revenue had about already maxed out.
B. AI's output accuracy was already about as high as it would ever be
C. AI users were already declining or was as high as it would ever be.
So we have 3 2024 claims about whether AI was already the biggest/best it would ever be, and whether AI usage was even already shrinking. All ended up being the opposite of true.
Have you looked at whether Ed’s previous claims that went against popular AI views and are testable ended up being true or not?
Does it matter whether an author’s claims like that are true or not for whether you will continue consuming them?
If straightforward claims like the above are so easily disprovable, what makes you believe that he isn't cherry-picking other stats in order to spread misinformation or disinformation, as the individual stats he points to might even be completely true, but if they are cherry-picked, they may be more misleading than elucidative?
If someone has a multi-year history of frequently spreading false claims, should we trust their predictions about future events more than other sources?
[1] https://news.ycombinator.com/item?id=48447549
He also calls everyone a grifter, when he seems like one himself. Im deeply skeptical of our AI overlords, but its disingenuous to keep pretwnding theres nothing there.
Always a bit eyebrow-raising to me how much people focus on Ed’s style rather than his message, which is, broadly, that the tech industry is deeply morally corrupted. He struggles to speak about it without becoming impassioned, but I read it as incredulousness rather than baseless hyperbole: “How are you still investing in and working for companies like Meta, despite the overwhelming evidence that they are terrible company that does terrible things to people?”
Style is relevant to how humans communicate and it's not always about the message, and it can sometimes work against it. AP Style is an editorial standard for a reason.
IF I WROTE AN ENTIRE BLOG POST IN ALL CAPS ABOUT HOW AI IS LITERALLY SATAN PEOPLE WOULD JUST THINK I AM A CRAZY PERSON
You're right style does matter, and the flat tone of AP is basically extinct now because it was not a meaningful or widely desired style.
> his message, which is, broadly, that the tech industry is deeply morally corrupted
If that's his message, why is he going on about ecconomic sustainability? Whether or not you have a coherent business model has nothing to do with how morally corrupt you are.
Ultimately i agree with the GP post, the article reads like something preaching to the choir. If you already agree it seems natural. If you don't agree it looks like an incoherent rant that is not particularly convincing.
Arguing against the business model is a method of exposing the tactics of AI businesses as a short term grift rather than a principled venture. The entire economy suffers when grifters profit - there’s not infinite money to spread around.
Is it really a grift when everyone knows? Its not like they are keeping their financials secret. Heck, the main AI companies aren't even public yet, so its largely sophisticated investors getting "grifted".
Normally a grift involves tricking someone. The AI situation seems more like a bunch of investors knowingly investing in something very speculative. If they lose their money, while that is the nature of speculative investments.
What’s the line that separates grift from speculative investment? Does it only require true believers? If Elizabeth Holmes truly believed in her product, would that have made it not a grift?
> The AI situation seems more like a bunch of investors knowingly investing in something very speculative. If they lose their money, while that is the nature of speculative investments.
This is a bit reductive, especially as the government’s role in shaping American life is reduced and the influence of extremely high wealth individuals impacts every citizen. We’re somewhat losing our democratic rights when Congress does not answer to our interests, and the extremely high wealth individuals directly (FoxNews) or indirectly (X, Meta incentives) manufacture consent.
The whole theory of low taxes and light regulations is that high wealth individuals will be incentivized to “create jobs”, like modern Lords of the Manor. When those who essentially steer the direction of the economy take actions that are highly speculative seemingly only to enrich themselves and concentrate wealth further despite public outcry … well, maybe there isn’t a word that elegantly describes that situation, but “grift” is as close as I can muster.
> What’s the line that separates grift from speculative investment?
Are they actively lying to people in concrete ways? Not just, we think this is going to be big, when that is questionable, but do they say AI can do X right now, when objectively it cannot even remotely do X under any circumstance? Lying is what makes the difference.
> well, maybe there isn’t a word that elegantly describes that situation, but “grift” is as close as I can muster.
I think the word you are looking for is capitalism.
Rich people put their money where they think it will generate more money. The economy uses that to determine what to focus on. Sometimes rich people are wrong and are punished by losing a bunch of money. That is how the system is supposed to work. It is far from perfect but traditionally has done better than the alternatives.
To your first point about lying, surely that’s not the bar you’d like to set? Misleading investors is pretty much the greatest talent of all of history’s greatest capitalists - were they all grifters? Is it the extent of the lying that matters? Because pure honesty is nowhere to be found in SV.
> I think the word you are looking for is capitalism.
“It” may be a consequence of some countries’ and cultures’ attempt at implementing capitalist policies, but I don’t think that’s anyone’s working definition of capitalism.
“It” being a runaway concentration of wealth being held by few individuals who are not as well-informed and smart as they think they are, exploiting economies of scale and highly financialized systems.
> That is how the system is supposed to work.
The system is supposed to work in the way that we design it to work. Massive speculative bubbles where every rich yahoo is simply looking for an “exit strategy” is far from ideal.
You’re far too forgiving, covering your eyes while the industrialists rob us blind. There’s a culture of grift and exploitation running rampant in the US at the moment, and this sort of apologia only serves to exacerbate.
> To your first point about lying, surely that’s not the bar you’d like to set?
Its literally the dictionary definition.
> Misleading investors is pretty much the greatest talent of all of history’s greatest capitalists - were they all grifters? Is it the extent of the lying that matters?
Some are. Plenty are in the grey.
But there is a big difference between objectively lying like in theranos, and hyping things up questionably. There is a reason why Theranos people are in jail, while many a hype man walk free.
> anyone’s working definition of capitalism.
How about the dictionary definition of capitalism. Private individuals owning and controlling the means of production and subjecting to their whims is as fundamental to capitalisim as you can get.
Maybe there is an exception for monopolies, but i dont think any of the AI companies are monopolies.
> held by few individuals who are not as well-informed and smart as they think they are
I mean, seems to be working out pretty well for them. Can't argue with success.
> The system is supposed to work in the way that we design it to work
Key word being "we". As in society. Not you personally.
> There’s a culture of grift and exploitation running rampant in the US at the moment, and this sort of apologia only serves to exacerbate.
People making heated diatribes instead of cold logical arguments serve to exacerbate it.
> People making heated diatribes instead of cold logical arguments serve to exacerbate it.
Your arguments aren’t nearly as logical as you think they are, they merely tell the rest of us the blind spots your world view is plagued by.
Elon Musk just got the rules of the NASDAQ changed so he can more or less force index funds to buy his shell company and take money from people's 401k. Feels very grifty to me.
That is a very misleading summary of what happened.
because ad hominems are easier for the intellectually lazy.
This is the kind of thing that the people in power really don't want you to know, but I'll say it anyway because if we don't get the message out, it's just another free win for the Nazis: a bad presentation is a poor information medium.
Buried lede (if the title is the actual promise), the sources don't seem to back the title either. Someone with more patience can correct me if I accidentally missed a bombshell anyway.
Edit:
> If you’re wondering what the story is, [...] I expect it to be out in the next two weeks [...] I can guarantee you it’ll be worth it, and you’ll be stunned by what I report.
Ok, this takes clickbait to new lows. The headline is trying to sell the teaser here, with very limited meat in the middle of the sandwich.
> the sources don't seem to back the title either.
This is nothing new for Zitron. The last article of his I saw:
> I also severely doubt that Anthropic managed to make the cost of running its services profitable in the space of six months.
> [Per The Information in January], Anthropic missed on its gross margin projections, saying that its inference costs were 23% higher than the company had anticipated.
> How did Anthropic, which faced a massive influx of new business to the point that Anthropic was forced to buy more compute from Elon Musk, magically become profitable? Other than that discount, of course.
If you follow the link to The Information, you’ll see that it’s a paid article with the headline “Anthropic Lowers Gross Margin Projection as Revenue Skyrockets”. But what happens when you actually read the article?
> Anthropic last month projected it would generate a 40% gross profit margin from selling AI to businesses and application developers in 2025, according to two people with knowledge of its financials. That margin was 10 percentage points lower than its earlier optimistic expectations, though it’s still a big improvement from the year before.
— https://archive.is/aKFYZ
So, according to Zitron’s own source, Anthropic are actually earning 40% gross profit margin on inference, and that is a dramatic jump upwards! This totally contradicts his position that it’s an implausible “swindle” for Anthropic to claim profitability. He’s counting on the fact that most of his readers don’t subscribe to The Information and will only see the headline, or that they will just see a citation and trust that it backs him up without checking.
Given this, his righteous anger towards craven boosters and grifters is pretty funny. Pot calling the kettle black.
I think we need to see Open AI's and/or Anthropic's S1's to really know the state of it all.
Totally agree, remember WeWork's S1 and the fall that followed. Don't think it's the same case, but it'll clarify a lot of things
I find it difficult to separate this piece’s tone from its content. The tone puts me off and makes it hard for me to judge it on its merits, despite some of the arguments seeming sound and well supported.
Given the way tone has been intentionally abused, particularly in this industry, I’ll take a few f bombs and the truth.
https://www.wheresyoured.at/peakai/
"I believe that artificial intelligence has three quarters to prove itself before the apocalypse comes, and when it does, it will be that much worse, savaging the revenues of the biggest companies in tech. Once usage drops, so will the remarkable amounts of revenue that have flowed into big tech, and so will acres of data centers sit unused, the cloud equivalent of the massive overhiring we saw in post-lockdown Silicon Valley."
Ed Zitron. Mar 18, 2024
Prescient
>I’ll take a few f bombs and the truth.
Don't want to ruin it but go read some old posts from the author about AI, the tone is the same and he is very much wrong.
What about all his other articles that had f-bombs and the predictive utility of used toilet paper?
Agreed. If the arguments seem sound and well supported, then all we can do is attack the tone.
You can disagree. Sarcastically, or otherwise. But I think you may be reading more into my comment than I put there.
I’m not attacking the piece. I’m not saying it’s right. I’m not saying it’s wrong.
What I’m saying is, the tone made it hard for me to judge the arguments fairly, despite finding some of them convincing. And as much as I dislike it, persuasion does partly depend on how an argument is made.
Thanks, it's very clear what you're saying.
Ed's posts are peak preaching to the choir, they're usually factually correct but he is really bad at convincing anyone who doesn't already strongly agree with him.
Have you seen his recent Bloomberg appearance? He's calm, collected, and matter-of-fact -- the complete opposite of how he presents himself on his newsletter and podcasts, but with the same argument. You wouldn't know from listening to him how spicy he usually is.
I dont really understand the criticism either way.
He's in the media business... its in his interest to amp things up.
Yes, of course.
It's tuned to the audience. Bloomberg was traditionally for people who actually wanted information. People who were fallible and had limited knowledge.
Of course that mentality is obsolete. Now we all have infinite access to perfectly correct information via the internet.
wow someone tell the philosophers this guy has figured out the knowledge problem!
Perhaps that’s it. I would tend to agree with his position, I think, but don’t appreciate being preached to. Even less so when I agree with what’s being said.
Agreed. I am open to the possibility of the bubble bursting or whatever, but this piece is like 3,000 words and cites everything as evidence the sky is falling. It's just as bad as the pro-AI grifters, just in the other direction.
Does the truth normally lie somewhere in the middle of it all?
At the /. times, there was somebody there with the best signature line. It was something like:
"Some people say the Sun sets at East, other people say it sets at West. The truth, of course, is certainly on the middle."
>Does the truth normally lie somewhere in the middle of it all?
Usually does when you decide what constitutes extreme.
Probably. Although I feel more inclined to forgive Ed in this case because it's sort of fighting fire with fire, the insanely hyperbolic and obscenely misleading drivel that's coming out of the most ardent AI boosters is continually unchallenged in the public eye. In a world where we had a more realistic view of AI/ML/LLMs, the limits to its capabilities, and the negative externalities of its widespread adoption in places where it quite frankly does not belong, then I'd be more critical of the Chicken Little sort of writing style
The obvious answer to where the AI Labs get customers is Cloud GPUs. Most users (globally) have cheap phones with poor CPUs and small amounts of RAM. They can't run usable models locally, and it's not clear from the Google-Apple deal if G is selling access to their cloud compute as part of that $1B, or just sharing the weights/IP.
Apple themselves have said there is usage limits, with a subscription upgrade for more usage. So clearly AI Labs are directly competing on that front, it's just a normal default/chosen decision. Considering there are defaults and still successful competitors (eg. safari v chrome), there's no reason to think that competition can't handle this too.
Edit: I want to add that Google is also probably willing to give the model away at a discount to its true value in exchange for guaranteeing that their primary competition (who has tons of cash) won’t have an economic incentive to enter the foundation model training arms race.
Most users who actually want these features for anything more serious than summarization and style updates will probably find value in a modest subscription or ad-supported tier of higher quality models, even if just for occasional usage. Apple can provide this, but once you're comparing features, for many Gemini/Claude/ChatGPT may be a better fit.
Oh, and I think there is an unfortunate but real risk that once again, apple totally over-promises here, and their AI models that they ship end up being pretty poor, and that drives users further into subscriptions.
> Apple themselves have said there is usage limits, with a subscription upgrade for more usage.
Specifically for image generation. They haven't indicated you have limits for Siri interactions.
> "Some features, including image generation, have daily usage limits because they rely on powerful server models. Increased access is available with most iCloud plus subscription plans".
Start at 1:07:00 in their announcement video. Craig is absolutely talking about "Apple Intelligence" as a whole in this segment.
Pragmatically, of course they'd need to add metering to any cloud available APIs that rely on large models. There's no way they will eat the cost of serving unlimited access to a cloud LLM to end users if they won't eat the cost of an image generation model.
> Oh, and I think there is an unfortunate but real risk that once again, apple totally over-promises here, and their AI models that they ship end up being pretty poor, and that drives users further into subscriptions.
OK, that I would concede is a possibility. Though Gemini is clearly capable, and the (alleged) story is that they have licensed a one-trillion parameter form of Gemini. I don't think they are making the same mistake.
ETA: I also concede they could make a different mistake ;-)
The AI labs are racing to create a moat out of trillion-parameter models and the GPUs that can run them. The problem is this is the wrong architecture for most AI inference use cases. On-device inference is where this is going, clearly Apple believes this too. So Zitron is entirely correct about this AI datacenter build out being a boondoggle with no ROI.
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Ed Zitron with his subpar takes again. You could kill all the major AI labs today, and even just today's open-weight models are enough to absolutely transform society, with video-game GPU stuck together at homes.
From what I've seen lately, Ed isn't saying the technology isn't significant. He criticizes the fact that AI companies are dumping this kind of money into it with circular financing deals. If it goes like you say, (GPUs at home) the AI labs will be destroyed, because no one will need the inference capacity they're dumping money into when they can do the decent part at home. From what I've seen he says it's more "not worth the investment" rather than not a breakthrough. It just sucks compared to the trillions they're spending which they'll never recover, is the argument. But I guess it's easier to strawman the argument to "this stuff isn't going to change anything".
He is saying technology isnt significant. Again and again and he puts a lot of emphasis on it. He is also focusing the circular financing deals, on it being a bubble etc.
Obviously, you can agree with one thing and not with other. And he can be right in one thing and not the other. But, if you listen to what he is saying, he is absolutely saying the technology is not significant.
I think you're misinterpreting a strongly worded "it isn't as great as the hype" with "there's nothing there". Because "nothing there that matches the hype" is both 100% true and completely different than "nothing there".
I don't think I am misinterpreting him. I did read some of his articles and did listened to some of his podcasts.
He is very very clear and open on what he thinks about usefulness of ai. He is not saying "it isn't as great as the hype". He is saying "it is useless". He is simply not the centrist kind of guy when it comes to AI usefulness.
Zitron is actually making both of those arguments. And both are equally wrong.
How would they transform society and why haven't they yet?
because theyve been out for 6 months? i mean what the hell are you people expecting?
The internet transformed society. That didn't save us from the .com bubble bursting.
Although I see huge utility in AI, I think he is right in terms of overspending and overenthusiastic build out. Because of for example what Apple is doing by putting an extremely efficient model with task adapters right onto phones.
Also because we now have a massive demonstration that vastly more efficient hardware is desperately needed.
Similarly other effective efforts towards on-device AI like Nvidia RTX Spark PCs and 2bit quants of strong models like DS4.
So inevitably, significant investment will be going into vastly more efficient CIM efforts like Mythic AI and new FeFET devices etc. in order to make human-level and beyond AI at scale feasible. There is so much demand for this and the power requirements of current hardware are so excessive, it seems unlikely that the data center build-outs will be able to recoup their costs before the more efficient paradigms make it out of the lab and start scaling.
So I open this page and the first line of the article should be the last, right?
> If you liked this piece, you should subscribe to my premium newsletter. It’s $70 a year
Ok let me read the thing so I can make up my mind… start scrolling down and get slapped by some subscribe pop up.
That’s where I decided to just cut my losses and go do something else.
I have been a pretty consistent user of AI since 2022 (Instruct-GPT), so I don't have a bad opinion about the topic. However, I think the real problem now has become pretty obvious. We are hitting a reality wall, where we simply don't have enough ressources to feed the AI industry. We don't generate enough electrical power nor enough GPU or TPU. For the first time in computer science, the real issue here is the finitude of the physical world. Unless, we start digging asteroids, we are already facing a shortage of raw material and industrial output. In my opinion, the only way to go is small models running on regular hardwares.
Aren't small local models worse efficiency-wise? It means that every person must have a powerful enough machine to power a small model, and we are very, very far away from that.
The best solution, from an efficiency point of view, is to use smaller models on datacenters, requiring much less of them.
There's an efficiency sweet spot where hardware that people have anyway gets a higher percentage of load.
MacBooks have a lot of memory and a lot of FLOPs. They mostly sit unused all day. Yes, the excess energy use will be higher than a GPU in a datacenter doing the same work, but you have to generate an absurd amount of tokens before the dollar-efficiency catches up with the MacBook.
You need to have a 3k dollar machine available though, I think you are overestimating how many people have access to it
The future is clearly that, model inference running on consumer hardware not a network datacenter. We are getting there, local models gets better and better, it's only a matter of time. Of course this would be bad news for big AI companies (and whoever invested in them).
All the top comments are commenting on the author. And now I add this metacommentary. Probably good it was flagged.
I find it nuts that I can use Claude Code for $20pm - I imagine that won't last forever but have to say it is great value for money.
So when I see monthly budgets in the thousands for developers at some larger companies, I'm curious to learn how they are managing to spend that kind of figure: how much code/documentation are they feeding into their prompts, are they using agent orchestration systems to make the code factory run 24/7, and how much value is coming out the other end versus before?
And, if they are pouring thousands into LLMs per developer, have they considered looking at alternatives like having LLMs running locally on own hardware with their own agent harness?
Those are the kind of questions I'd love to ask - I just wonder how much stuff is truly cutting edge and how much might be wasteful?
Developers at big companies need to pay per token, they don't have subscription available. So in case you use that, you likely spend way more than $20 in tokens.
As for how to spend that much -- not that hard, to be honest. Just give it a lot of context and some relatively open-ended problem and it will easily eat through tons of tokens.
I have $200 subscription for Codex and it is crazy what it can do in terms of debugging. I have a pretty complex Electron setup with some native code linked via Node addons, a few App Extensions and it can easily read the source code to see how the builder works internally (e.g. if your end Info.plist is not correct), debug the xcodebuild output to see at which step something is not linked correctly (like after XCode major version bump), etc.
It is not a silver bullet but if you are not the one paying for it, there is no downside to throw a problem at it and see if it can come up with a fix.
> And, if they are pouring thousands into LLMs per developer, have they considered looking at alternatives like having LLMs running locally on own hardware with their own agent harness?
I am curious about that myself. I have a good machine now (Macbook Pro M5 Pro with 48GB memory), so I'll give it a try; I don't have high expectations so if it is actually helpful would be very neat.
I just looked at ccusage for a personal project. In 5 days (doing it as a hobby) I've managed to spend $250 in API tokens on a $200 subscription. 5 days, and thats on one computer (I split time using 3 of them). If I had to pay $2000 a month -- no fricking way, not worth it.
This is wishful thinking. AI is still getting better rapidly. Anthropic's revenue is still growing at an unprecedented rate and they haven't even released their best model (Mythos) for 4 months now.
>have to be roughly twice the size they are today, and then double again basically every year until 2029 or 2030.
Anthropic is growing way faster than doubling yearly so don't think this is entirely implausible
I asked around my network recently - in the last month or two basically every large company has put in spending limits per engineer. Curious what their S1 will look like when they go public.
In the long run I’d say the real question is how much of the labour market you can displace rather than looking at tool spend
That’s really the only way the math works - outright displacement of labour
Whats a bit wild to me is Google's only selling point for their Pixel phones are increasingly Gemini.
Now that you can get Gemini, operated by Apple (with the Apple privacy features that come along with that), why would you ever consider going Android/Pixel (outside of running GrapheneOS, but I'm talking regular consumers here)?
Google isn't even making anything on the deal with Apple. They pay $20B/year to be the default search engine. This is Apple just giving a $1B a year discount to that to be able to license Gemini.
I switched from iPhone to Pixel after I couldn't stand Liquid Glass and found myself using Gemini more than I expected.
If you're in the Google ecosystem like Gmail and Calendar, it is exceptionally refreshing to be able to use an assistant that uses that ecosystem, instead of iOS requiring you to use Mail or its own Calendar app.
I don't think there's any real gap between Pixel and iPhone on the things that matter: UX jank, battery life, camera. Even the messaging issue in the US has closed with encrytped RCS support between them launching. So now it's just an ecosystem question, which might be why Gemini is mentioned so much with Pixel.
Android phones are also quite a bit more capable than iPhones in a number of ways due to being more open. Plenty of people just straight up prefer the experience (and plenty of others prefer the cheaper prices).
Yes. And there is only one other major phone brand in the West with this kind of clout: Samsung. Who I think will want their own thing that isn't Google's, and who do have some connections to OpenAI.
But given how dependent OpenAI are on Samsung, it's hard to believe they will see a radically better deal in material terms.
I don’t think the idea is that these companies are going to make their $ selling their services, that’s just step one. they’re betting that they’ll have their own “country of geniuses in a data center” to put toward whatever thing they think will make them the most money.
It doesn't matter if it's slowing down, pretty much no one has implemented it to its full extent yet. It could stop right now and we'll be finding new implementations a decade from now.
Anthropic and Open AI could evaporate tomorrow and we'll still be using the models.
The market may collapse, but the people who think AI is going to disappear as a result don't understand what it is.
Ed's posts always sing the same tune - AI spend is unsustainable. But why are the investors pouring into these megacorps allowing them to burn cash? What calculations are the investors making? I would like to see those projections, assumptions and backout plans.
Why bubbles happen? Because investors, out of greed, pour into corporations that burn their cash.
The internet was a bubble: you could make a web page and sell it for millions because next year it was going to be worth billions. And then internet grew up.
AI is technology that's still beginning to find its place to settle. It's far from mature and that's perfectly fine. We'll have reached a reasonable plateau once the technology and the related stack stops changing every month and instead develops incrementally and boringly over the span of few years. That's like internet in the 2008-2010, and many investors will have a collection of new burn marks by that time.
Not only financially there's an unsustainable push for AI by the zealots du jour who are more often than not managers rather than engineers. AI is championed most ruthlessly as a silver bullet revolution by people who least grasp the limitations of AI. It'll take some time to figure out the dreamed-up proceeds won't be there, and "then what?".
I predict that the real bottlenecks of development will re-emerge as soon as the limitations of AI will manifest out of the hype. They bottlenecks are human-based, in development processes and in human interactions. A large part of development is trying to understand what we want and what we need and you can't offload that to AI.
The idea is the same as in all hype cycles: Ride the wave and let the small investors hold the bag in the end.
I suspect that it's merely a long-shot bet that AI/LLMs will drastically change the nature of the world's economy and workforce. If there's only a 0.1% chance of that happening, but promises a chance of giving out 10,000% the return, then it kind of makes sense.
investors are driven by herding behavior. for people with enormous amounts of capital to invest, the worst thing would be for them to miss out on the next big thing. If that means losing on a bunch of bets that did not turn out to be the next big thing, that's fine, so long as you don't miss out on whatever that next big thing is. We're promised AI is the next big thing, and everyone else is throwing money at it. So that means I have to throw money at it, because otherwise I would miss out.
Greater fool theory, obviously :-/
Some people seem to see the world only through bubbles. But if you look at human history, despite the ups and downs, we have a trajectory; generally speaking, human-created systems evolve toward ever-increasing complexity, impact, and efficiency.
The current wave of AI unlocked language - the tools are now speaking and understanding. This, on its own, is astonishing progress. Language is the foundation of our culture and society; it is the very technology that got us, as a species, to where we are today. To have tools that can understand, manipulate, and produce it is a massive leap forward.
Once you see things that way, it is clear that we are not in a bubble; we are in a transition. Yes, there is tons of hype and over-investment, but the demand is real, and so is the impact. Unless you are deep in the tech and have that structural depth, it is easy to dismiss. This is like the invention of the personal computer, but with 100x the impact and speed.
The only "bubble" with AI is that the initial build out is cyclical, and many of the high flying chip stocks with no software arms (ala Nvidia's CUDA) will come back to Earth. I think anyone that thinks AI is going away or won't have massive impact (though maybe not in the doomsday scenario) are in complete denial.
RTFA; it's not about AI's massive impact or lack thereof ... it's about these businesses not having a viable business model that will sustain them (beyond the next couple years).
I think Zitron's problem is he's equating AI to OpenAI and Anthropic. I'd agree with him that both those businesses are in a dangerous position given how fast they've burnt through cash. However, that's not the entirety of the industry and there are a lot smaller labs doing more for a lot less capital.
The business model does appear to be viable for these labs. But that viability comes because they aren't wasting a bunch of R&D money developing worthless products like AI video production.
I did. So, I'm confused how does that negate my comment exactly? Your second complete sentence totally is in conflict with your first btw.
I admit, I didn't read the whole article; I read a few paragraphs and extrapolated the mindset from which the author operates.
Regarding your comment about the business model—the people in Silicon Valley are not stupid. They know the playbook; we've seen it with social networks. The issue isn't the business model itself; it's that these companies need to dominate the market, and the big players are competing for that on a global scale. It's the exact same playbook that played out in financial systems and social networks, and now it's happening with AI. Once these technologies are deeply integrated into enterprises and the global economy, these players will dominate the market for decades to come.
I can assure you, the people running those companies are smarter than you, me, and the author of this article."
What I suspect isn't that AI goes somewhere, but I do think that the cutting edge companies like Anthropic and OpenAI are in a very precarious position. They don't have very much of a moat and the competition has been catching up quick while spending a lot less doing so. IMO, the main thing keeping them alive right now is name recognition.
If I were to make a prediction, it's that ultimately these cheaper models are going end up eating their lunch. I don't think they'll make back the money they've invested and once that reality hits investors, those two companies are sunk.
That, however, is not the end of AI. Nor will it be the end of Nvidia/micron/etc. It will more just be a localized bubble pop that doesn't eliminate the product from the market.
It is not just about cheaper models; it is about integration with the economy.
These models are building deep integrations into companies and the entire economy. Once that stabilizes, it will be like the electricity grid—pumping tokens to fuel decision-making across the entire global society. Good luck unplugging from that.
Furthermore, there is a massive geopolitical aspect to it: those who are already on the Western financial and technical stack will get integrated even deeper now.
> These models are building deep integrations into companies and the entire economy. Once that stabilizes, it will be like the electricity grid—pumping tokens to fuel decision-making across the entire global society. Good luck unplugging from that.
Much like the electric grid, what we are seeing is a convergence on standard APIs. For example, most of these cheaper models are hosted using APIs compatible with OpenAI. It's not a matter of rewiring your electric plug to work with a different socket standard, instead it's just the process of plugging it into a new socket.
> Furthermore, there is a massive geopolitical aspect to it: those who are already on the Western financial and technical stack will get integrated even deeper now.
Certainly the Chinese models appear to be some of the best when it comes to competition, but they aren't the only ones. There are European models and other US based models which all run for cheaper.
I see your point, but having worked as a consultant for a few years, I think most companies will opt to stay once things are stable. Once these systems are functional, nobody wants to touch them.
I remember one government project where we wanted to migrate a system from COBOL to a modern stack. The requirement was for the UI to stay exactly the same as the old green terminal; the evaluation criterion was pixel-perfect proximity to the original. We literally had to build terminals using web tech.
These models are not the same as each other. Once they are integrated and working, the incentive to change them is incredibly low. So really, the race is about who can integrate deeper, wider, and faster over the next couple of years—that is what will determine the long-term winners.
This is the exact same playbook we saw with social networks. There is a reason why we have only a handful of them dominating globally, and guess what? It's not because of the tech.
> the incentive to change them is incredibly low
There is no incentive to rewrite working software in COBOL to something else. You don't really change the people cost of maintaining that code all that much and you incur a huge rewrite cost.
AI is different, it's an ongoing cost to the company. If that cost raises aggressively, you can bet companies will race to eliminate it, no matter how integrated it is. Companies can and do do this all the time.
And the models are close, not the same, but close. That's what matters in LLM stuff in general. If a model is capable of doing the same work for less, it will be chosen. Especially since the switch over cost is often on the level of "point the tool at this URL instead of that URL".
I get what you are saying if this were a more sticky concrete tech that is harder to move away from. But that's simply not the case for these LLMs. A big selling point they have is that they are super flexible.
We might need to agree to disagree on this one.
I don't think the transition will be as simple as just flipping a URL. There is an entire legal and technical infrastructure being built around these models and their integration. I think you underestimate an organization's resistance to change once things actually work, as well as the sheer complexity of making that shift.
I also expect pressure will eventually drive the cost of running these models down. Power plants are being built, more capable chips are being produced, and a big chunk of the capital right now is being used to scale the physical infrastructure—the data centers and energy grid. Once that stabilizes, these companies will have positive cash flows. Again, it's highly similar to what we saw with the expansion of social networks, just with more aggressive and widespread adoption.
Ultimately, a handful of companies are going to provide these core capabilities, just like we have a handful of major cloud providers right now. Why do you think this would change? If anything, the trend toward deep vendor lock-in is even stronger now.
The moat is the infrastructure and lock-in. Similar to AWS or anything else. Small data centers can't compete, and similarly people without massive compute won't be able to either (at least not on the enterprise level.) You might get a few edge models, but for huge businesses they will be using OpenAI and Anthropic (and Google/Microsoft/Amazon, etc).
The biggest competitors aren't small models, they are just the traditional players that already have an "in" with enterprises. That I think will start to show its face once this initial round of buildout is complete, which may not be for another 5+ years.
> The biggest competitors aren't small models
I disagree. Mainly because those small models are exactly what erode away the moat of needing a giant data center. Those smaller models have been proving themselves to not be far of from the SOTA models.
As OpenAI and Anthropic look to raise their prices, businesses will be much more compelled to looking at cheaper models. And if the narrative is "do the same as you did with OpenAI at 1/20th the cost" that's going to sell to a lot of businesses.
It certainly cuts into what exactly these companies can sell in general. For example, if I wanted to integrate AI into a product I'd almost certainly not chose OpenAI or Anthropic. That's because they are simply way too expensive and what they'd give me is a lot less. We've actually ran into just this. We needed a classifier for a lot of records, we picked a free model because, as you can imagine, we didn't need something as good as what OpenAI and Anthopic offered and free works.
I share the same perspective.
Uhh, citations for all of these claims please.
Download the tools and use them along with your head? I mean a lot of what I stated there is the obvious.
You need citations for humanity shared history?
> The current wave of AI unlocked language - the tools are now speaking and understanding. This, on its own, is astonishing progress. Language is the foundation of our culture and society; it is the very technology that got us, as a species, to where we are today.
This is fire erasure
/s
Agreed haha! our beloved fire.
I hadn't heard of the TMobile and Brex spend caps, only knew about Uber's because it went viral last week. I expect we'll see more of that now that everyone is paying per token, and it sort of feels like you cannot both have spending caps and require extensive AI usage for performance reviews -- I wonder that will shake out in the end?
Anecdotally, $dayJob consumes Anthropic models via Azure subscriptions which lend themselves pretty neatly to the spending dashboards Ed mentions are missing from Anthropic themselves, and finance seems ok with the current usage, but there's no real hard incentives internally for AI usage either.
I guess Q3-4 are going to be interesting to see where this all goes.
> The AI Companies Are Going To Start Getting Desperate
This is the part that concerns me the most, AI companies will start bidding on increasingly contentious contracts out of desperation. In practice, this means building services that facilitate killing to anyone they're not legally prevented from doing business with... and even then, they may be able to do business with them still through intermediaries.
As a tangent, I don’t understand where and why meta fits into the AI race. They did not get any mind share (consumers) from the llms so far, granted they started the open source side to this but the Chinese companies produce far better models and have essentially become the default for on device set up.
They have ai glasses and integration into instagram and facebook as the other avenues. I don’t see ai glasses as compelling yet, and don’t know how much more ad revenue or user engagement they can squeeze out with llms baked into the IG of FB flows. They are spending a lot and not seeing any returns. Am I wrong in being pessimistic about meta with AI?
You should probably be more pessimistic about Meta. Look at their last major venture, the Metaverse, which was basically embarassing. Their AI strategy is incoherent.
How are the author's missed predictions different from the missed predictions of "AI" believers
For example,
https://news.ycombinator.com/item?id=48464333
* * *
>LLM-type AI exacts huge costs because it is terrible at reporting "I don't know". When it doesn't know, it generates noise and polishes it.
>If a "confidence too low for output" signal could be extracted, this whole technology would be a lot more useful
Anthropic's interpretability research explored this topic a bit in 2025. Apparently, the signal is extractable:
https://www.anthropic.com/research/tracing-thoughts-language...That's real progress. The paper behind it is [1] They try to extract "attribution graphs" to understand why the LLM produced some result. It's encouraging to see more work on what's going on inside. They obtained insight into a specific kind of hallucination: not finding a specific fact. "We uncover circuit mechanisms that allow the model to distinguish between familiar and unfamiliar entities, which determine whether it elects to answer a factual question or profess ignorance. “Misfires” of this circuit can cause hallucinations." That should be tested on queries which resulted in making up legal citations.
[1] https://transformer-circuits.pub/2025/attribution-graphs/met...
Isn’t the thing that an LLM never knows (at all)? It just guesses words based on the context and previous words and often gets lucky.
It isn’t thinking or knowing and then expressing the resulting understanding but just spitting out contextual words and hoping it reaches a conclusion or ending of some sort.
Funny I just read an article on how it was actually speeding up.
This article would be improved if the author passed it through an AI to tone it down. The writing style is too much for me.
This is apparently news to all the hardware retailers who are continuing to maintain the insanely overinflated prices on NVME storage, DDR5, and even DDR4 memory.
Some are still steadily increasing prices.
A 1TB NVME drive - a good one - cost about $70. Now it costs anywhere from $150 for shit-tier drives to $300+ for the higher end stuff that used to cost $100-120.
The chart seems logical to me. Most problems are solved in the app space. New apps don't have to be the new facebook. They just need to be useful for the right audience (even a small one). It's like you have meat and bread in the supermarket, and add all other stuff you dont really need. Will be bought, but not as much as meat and bread, right?
Hank Green also explains 400 billions are really very much cash: https://www.youtube.com/watch?v=AcjnLc4TH4M
The best outcome of the AI hype for was to be the investment into next generation nuclear power plants, ushering the world into a post energy scarcity era.
Maybe we'll get there, maybe not. These days I only hear of datacenter investments.
> This is a hysterical era perpetuated by liars, cowards, imbeciles, craven boosters and the easily-fooled. Those excited about generative AI are either the victim or the perpetrator of a con centered around a technology to ingratiate at the highest cost possible.
Who writes like this? When you lead with "everyone who doesn't agree with me is a lying cheat coward imbecile" I think we should just turn the volume down on you to zero.
This is breakdown in dialog. If it leads like this then I I don't care how accurate the critical analysis to follow is. I didn't read the rest of the article and don't think anyone else should either out of sheer disdain for this argumentation style.
He may be bombastic but Zitron is right about the AI problem. These companies do hemorrhage cash, and have no viable plan to even become solvent. It may not be a scam but it sure looks like one. The problem it poses for the economy... is just as he says.
if you think AI is slowing down, you may not be smart enough to tell the difference anymore.
Number of active users on ChatGPT is at an all-time high. Number of tokens consumed on OpenRouter is at an all-time high. I'm not seeing the plateau.
This rests on a lot of assumptions that the published figures for "planned" datacentres, "committed" AI spend, etc. are irreversible. I suspect that at least some of it is possible to back out of.
That's true but then that's basically the end of Nvidia if that happens
Given how I can manage and develop a huge production code base with an incredibly small team - and the rest of the industry apparently is not able to do it - I deem that we are still in the very early days.
Yeah. Models haven't really improved much from last year to this year.
I really love LLMs for debugging and rubber ducking, but I kinda want to write all my code.
LLMs tend to have a hard time understanding composition.
every week I see this guy on HN. only forum where ppl still buy this c**
The top twenty comments are negative about Ed. I think maybe HN just likes being skeptical.
AI companies are racing to win the future of computing.
They are possibly in a winner take all death race against each other.
The stakes are so high that these cash rich companies cannot afford not to throw everything they have into this.
The sunk costs are irrelevant when it’s a question of survival.
Whether you hate or love AI computing is being completely reinvented - at the absolute core of this is computers programming computers.
Anthropic is winning this race by a country mile right now.
This is such an important future bet for these companies that the trillions must be spent because there’s no future or a greatly diminished future for some of them unless they have ownership of the technology.
> Anthropic is winning this race by a country mile right now.
How's that?
I think the frontier labs are gonna launch new models under a new tier, but they're still figuring out how to announce it.
Let us see if Mythos breaks the trend :) or it is another overhyped model (once it is supposedly released)
It has been a day, and HN is still angry at Ed Zitron for insulting its current gods.
Impressive.
As WIRED reported[0], despite constantly writing about how an AI collapse is just about to come, Zitron privately does PR for AI firms on the side. The man is an obvious hack, and it's disappointing that he has become one of the mainstream faces of AI skepticism.
[0]: https://www.wired.com/story/ai-pr-ed-zitron-profile/
Anthropic has made $330 billion in compute and chip commitments between Google, Amazon, and Microsoft, another $30 billion with CoreWeave and another $15 billion with SpaceX. To pay for this compute, Anthropic must meet its projected revenue of $174 billion a year by 2029. Anthropic has raised $95 billion across rounds in February, April (from Google and Amazon), and May. These funds will be insufficient to cover Anthropic’s costs, as will Anthropic’s cash flow, meaning that it will have to raise at least another $200 billion in the next year.
How people take this seriously? Anthropic is at 45B ARR S-1 shows inference margin climbed to 70% (obviously could drop) So where that 200B number is coming from ?
Anthropic's S-1 is not public yet
That wasn't an S1 and we have zero idea what GAAP compliant numbers for Anthropic or OpenAI would look like
I see this kind of arguments made all the time by people I know in finance etc, who seem willing to look at AI only from a finance/sociopolitical lens, disregarding tech altogether. The very fact that they are still writing about this for me is impact enough.
I stopped as soon as the popup hit.
Am I missing something or does the author not count the existing revenue to cover the buildout? I think they have it covered even in the worst case scenario (and requiring every piece of cash flow they have).
Also, if Anthropic or OpenAI fail in their revenue requirements I believe they would be absorbed by the guys with the money printing machines (Google, Microsoft and Meta).
His rhetoric is a bit obsessive and frankly biased against AI.
That said, I think his voice is useful as a counter to the mainstream opinion.
Given the amount of investments, approaching AI from the angle of economics seems correct.
We all have some level of personal experience using AI/LLMs, both chatbots and coding tools, and I personally enjoy using them, but I am sure this experience is relevant in this discussion.
I also enjoy luxury hotels, gourmet food, jet skis and helicopters, but this is not something I indulge in often because of the cost-utility ratio.
The real cost of AI may or may not be lower than its utility. The bet is that utility is increasing while cost is falling.
I guess my ears kind of turn off when you say "it's all slop, none of the apps are good, and it's a failure because no one has used AI to make the next Salesforce".
I have found agentic coding to be extremely useful for a bunch of small, middleware, very focused bits of software for small businesses:
* A company had a very specific scheduling need, they needed to move about 8-15 staff around with a bunch of different shifts, and have custom reports on who was working how many hours, and have the employees get a nice clean email summarizing their schedule
* A manager wanted a very simple "let me send a text to add a to-do to the group list" need
* A sales team of 3 wanted to be able to type pricing of raw goods into their phone, have it compared to other market sources, and have it text the other 2 salespeople and their manager when they were out in the field
All of these were coded with Codex in about 4 hours with further refinements over the next week of back-and-forth with the people using the tools.
I suppose yes we could have found some custom middleware solutions that did similar things, but it's nice to be able to make a web page or tiny mobile app that just does EXACTLY what the person wants.
It's hard to do that and then listen to someone who says it's all just garbage.
> currently gooning
> No matter how horny or flaccid you are
These analogies are great.
Ed Zitron speaks to a particular type of angry tech conservative. He’s not speaking truth or exposing anything. He’s the soothing voice the tech nerds of yesterday year are yearning for.
The angry polemic that goes on and on and on with cuss words used liberally is just meant to evoke emotion and cathartic resolution to the type of people mentioned above. Not truth.
The thing is, there are a lot of people that find comfort in what he’s writing - primarily because it’s a coping mechanism against how quickly things are moving and a way to deal with being left behind. When you spend time, years, building institutional knowledge and making a whole identity out of it, you obviously will feel bad with the threat of it being commoditised.
I would write against the content of the article but I find it easier and more illuminating to write what he has said before instead. Then it shows how incorrect the guy has been and with what confidence he keeps speaking with.
I'm collecting many kinds of predictions Ed Zitron made so that you can see for yourself whether he has a good track record.
-------
> While complex, generative AI is a technology that probabilistically generates answers, and has no "intelligence." It is inherently limited by its architecture, and in turn can only get "better" in a linear fashion. I see no signs that the transformer-based architecture can do significantly more than it currently does.
He wrote this in 2024 before reasoning models came out. Remember how ChatGPT was in 2024? Do you think this person is someone who gets predictions right?
> Furthermore, I hypothesize a race to the bottom in generative AI will significantly hamper OpenAI's ability to expand revenue, compounded by the fact that we're approaching the limits of transformer-based architecture.
He wrote this in 2024 and since then Anthropic's revenue increased by 160x to $40 B dollars a year and OpenAI's increased by 6x. Do you think this person gets predictions right still?
> I believe we're reaching the upper limits about what generative AI can do and how accurate its outputs can be,
He wrote this in 2024, do you really think we have reached upper limits? Huh?? What I'm using today is significantly more accurate and 2 tiers above what we had.
> And if there are true industry-changing possibilities waiting for us on the other side, I am yet to hear them outside of the fan fiction of Silicon Valley hucksters.
He says this about AI when we have with all honesty have had industry changing possibilities like agentic coding.
> There are indications that consumers have also lost interest. As pointed out by Alex Kantrowitz’ Big Technology newsletter, traffic to ChatGPT on both mobile and web has started to stagnate, if not decline. In January 2024, ChatGPT had 1.6 billion visits — 11% below the all-time peak of 1.8 billion. This makes it only modestly more popular than Bing, which had 1.3 billion unique visits during that period. On the mobile front, ChatGPT has an estimated 6.3 million US users — or 1.7 times less than the total of new Snapchat users added during Q4 2023.
He agrees with the claim that the consumer interest has declined. Since he said this, there was a 9x growth in active users.
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https://www.youtube.com/watch?v=_wStScmT748&t=1s
"AI Bubble Already Bursting?" (8 months back)
https://www.youtube.com/watch?v=T8ByoAt5gCA&t=1s
"A.I bubble is bursting with Ed Zitron" (1 year back)
He's been constantly crying bubble for years now.
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> AI video won’t get truly fixed just by waiting a year.
This is what he had said in 2024, and you just need to compare video from then and now to check whether the predictions came true. Why would anyone trust what this guy has to say?
How’s that meme go? "We are 2/3 years into being 6 months away from AI taking all white collar jobs".
The criticism goes both ways. The word "fixed", in Ed terms, can be translated to "become a viable business that justifies the spend".
In regards to AI video, I think the fact that Sora is no long around is an indicator. And there is seemingly no real appetite for AI video outside of memes, jokes, and misinformation, probably indicates that the prediction around AI video has come true.
[flagged]
If the performance and accuracy was there, don't you think there would be a market other than memes?
I'm not speaking about that, he made a specific claim that AI videos wont get better and that was completely wrong.
> He wrote this in 2024 before reasoning models came out. Remember how ChatGPT was in 2024? Do you think this person is someone who gets predictions right?
Any benchmark on AI shows that it gets better linearly. So his prediction was correct. There is no exponential AI improvement anywhere.
>Any benchmark on AI shows that it gets better linearly. So his prediction was correct. There is no exponential AI improvement anywhere.
?? https://metr.org/blog/2025-03-19-measuring-ai-ability-to-com...
Ok, I guess there are some weird benchmarks where they managed to squeeze out an exponential. But even with Google's, OpenAI's or Anthropic's own benchmark it's a linear improvement between models.
I predict the bubble is going to pop right after the midterm election.
Concur.
Whenever I read these kind of articles about AI financials, I'm reminded of identical screeds I read about Uber a few years ago. They were angrily insistent that Uber was a scam company run by criminals and charlatans and could never, ever become profitable or make money for its investors. It was a house of cards that would come crashing down sooner or later, and take everyone's money with it. Now it's 2026. Uber still exists, has revenues of $50bn and is apparently a highly profitable business. I don't know if the original investors have made their money back yet, but Uber certainly hasn't collapsed.
Maybe AI is different. Certainly, the level scale of investment is on a different order of magnitude. But I'm wary of believing anything about the financial impossibility of AI being sustainable when I've seen such similarly confident arguments proved wrong in the past.
Uber used the classic triple-E philosophy of Microsoft and entered a market that was ripe for disruption -- many cities lacked reliable taxi service entirely, others were cartels that fixed prices. They undercut prices to an extreme degree, subsidized fares, and when it either drove local taxi companies out of business and spurred widespread adoption as the default, it had a captive market and duopoly with Lyft which allowed them to raise fares without losing any market share whatsoever.
It's a pretty classic business strategy, and not directly comparable to any of the AI companies. There's a reason people compare the current situation to the dotcom era and not Uber. Also, don't take Uber as an example of a slam-dunk VC success story and leave it at that -- plenty of dumb ideas get pitched and funded and go bankrupt for every Uber.
Yeah, people forget the risk to Uber was real in the early days. If municipalities had enforced their taxi laws, the company would have died and all those millions invested would have been lost (or pivoted into something else).
It was only because Uber successfully bulldozed over all regulations that it was able to succeed ... and that was hard to predict before it happened.
Absolutely. Even these days, Uber really only has one or two viable competitors. With any 3rd one in a far distant 3rd. Meanwhile, swapping which AI I’m using is as easy as clicking a dropdown. Hardly comparable to a physical car ride.
Funny thing, the uber's investor results from last year only mentions "profit" once, in a motivating paragraph where they say they will be great.
But it's famous for having collapsed after their IPO. It took 4 years to get back at the same nominal valuation (not inflation corrected), and after all the 2020s inflation it is still at 2x the initial price.
I mean, do you really want to compare AI to the "do crimes hard and fast enough we become a monopoly before anyone can properly respond" model.
I don't think anybody actually believes that the current investment is going to yield returns that they are projecting. Neither did people back in Dotcom or Railways or any other hype/bubbles. Yet these technology did transform and the returns came to fruition.
Internet continued to thrive and grow even after the stock market came and went, it took 13 years to roughly nasdaq to recover but the explosion of GDP from internet has been largely decoupled from the previous bubble boom and bust.
If you use the stock market as a yard stick to project new revolutionary technology we shouldn't have had trains, internet. In fact internet should've stopped with the bust of Nasdaq and everybody would've moved back to using paper but we didn't it gave rise to the next wave of economic output powered by this new tech.
I don't see AI to be any different.
> it took 13 years to roughly nasdaq to recover
So it's okay for everyone's who's due to retire in the next 13 years to have their 401k or equivalent wiped out when the correction happens?
do you think so ?
> that the infrastructure being built and compute commitments being made are being done so at a level that demands that generative AI and AI compute generate over $2 trillion in annual revenue by 2030
That seems doable. Next generation architectures and the models they produce are accelerating progress. More capable with less data and compute, which ironically will drive more demand, aka Jevon's paradox.
> If you are someone in the executive team of any major tech company, know that your employees are, for the most part, completely and utterly miserable.
I agree this is a problem. Adopting too eagerly and too early, and not listening to feedback from the people who are using these tools is a recipe for disaster.
The handwringing tone of the article is off-putting.
Ed is confused between whether AI is useful, and whether the current level of funding and valuations are sustainable. The following statements can both be true:
1. AI is already quite useful and will continue to be so. This is true even if AGI doesn’t happen.
2. The funding and valuations of many AI companies are too far ahead of their skis, and will probably roll back. Some may fail entirely.
About the “where’s the productivity in AI?” question: I think it’s entirely possible that the primary benefit of AI will not be top-line growth but reduced costs (through reduced human labor). Companies will need to reduce prices to prevent losing market share to existing or new competitors, meaning that GDP may not increase, but costs will.
Xiaomi just announced a super fast 1t model, doesn't look like it's slowing down
I think it's time to distinguish between what frontier AI companies need regarding AI, and what will happen with AI if these companies don't get everything they need. Probably there's a bit more to this. Much of the technology is available via open source already and there's a growing ecosystem of AI tech that isn't really dependent on anything else than the hardware infrastructure needed to run it.
A good analogy might be networking companies and infrastructure companies during the dot com bubble. It devalued a lot of companies but the internet stayed. A lot of dot com companies didn't make it. Much of the infrastructure investment did not go to waste, however. Nor did a the technology go away.
I think it will be the same with data centers, related infrastructure, GPU hardware, algorithms, OSS components, etc. for AI companies. More companies need that stuff than is currently available. The ones that don't make it will have a lot of assets that they can pass on to the one that still have a chance. I don't think a lot of that stuff will get decommissioned or will be underutilized. It might get a little hair cut in value though. And like during the dot com bubble, some companies actually survived and did quite well. Especially those in the business of selling shovels during a gold rush.
After the inevitable consolidation that follows the next logical stages in the hype cycle, I don't think AI will go away. It might be a bit of a bloodbath for some silicon valley investors that placed the wrong bets in the last few years. But that's the price of doing business over there. That doesn't mean it's all bad. And the smarter ones probably spread their risk enough that they still might come out looking alright.
And like with the dot com bubble, many financial types have no clue what is happening and are running around like headless chickens. Which is why they ended up sinking a lot of money in exactly the wrong things. You'd hope they would have learned something.
But articles like this suggest that that might be too much to hope. They still don't really get how technology tends to not stagnate and might continue to deliver potential for performance and cost optimization. The current level of investment is only unsustainable if that doesn't happen and nothing else changes. I don't think those kind of closed world assumptions are a safe bet at all.
There's a reason why everyone must use agents and LLMs exclusively or be left behind. The entire Silicon valley livelihood is now a stack of cards built on a promise of infinite productivity gains based on LLMs. If that falls we're likely seeing a repeat of the 2008 financial crisis given the insane commitments made.
The gulf between reality and this tech-illiterate grifter continues to widen. I strongly suspect his shtick will fall apart before this year is over. And I hope at least some have been keeping close track of what he's been saying.
Missing from Zitron's calculations is government ownership/bailout of American AI in national security interest and winning the race to AGI with China. Trump has been making noise lately of owning 50% of these companies. Taxpayers will prop them up in other words.
It's possible that AI is the greatest technological leap forward since the Industrial Revolution, and simultaneously a bubble that will pop in the near future.
I don't know much about the economics side; TFA gives a barrage of stats that seem to make a compelling case for bubblehood. OTOH, the claims about the utility of LLMs being unmeasurable are weak (the same criticism applies to hiring programmers, or indeed most office workers) and the metal spider straw man is frankly embarrassing to anybody who has actually used recent frontier agents for programming and seen what they can do.
Bbut.. Elon said we are all going to be billionaires
"Last week I went on Bloomberg and discussed the state of the AI bubble with a clarity that rattled even the sweatiest boosters, mostly because I spoke with clarity about an investment frenzy whipped up through hype, deceit and mythology."
Bloomberg is interested in what he has to say
But not HN commenters
Well there are a lot of commenters so presumably some interest. I just had a look at the Bloomberg bit https://youtu.be/zbKDmkJPVvI and didn't see sweaty boosters rattled, just Ed doing his usual spiel - they are loss making and so it's all a big con. Which is kind of unproven on the big con bit.
"Can I Advertise On Your Newsletter?
Yes! Email me at ed@ezpr.com. I have an extremely high bar both for advertisers and the cost of advertising on here - I have 84,000 subscribers and a 55-60% open rate, as well as an 8-11% clickthrough rate.
I do not do any kind of outcome-based advertisement (IE: X number of people click through and you pay me Y), so any kind of agreement would effectively be a sponsorship. I have an engaged reader base and you will have to pay to get in front of them, as I also do not need advertising to support this newsletter."
Maybe the "AI" companies could pay for sponsorship
Would he take the money and run their ads
I'm so sick of people who peddle outrage for a living.
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What's the point of arguing with any of this.
It's like someone arguing that cheese isn't real. Yes I can go to the grocery store and take a picture of cheese and show it, but what's the point? They can live in their own world. It doesn't change any of our lives. The world is what it is.
Lol... in this case, cheese imports from China are much cheaper, just not quite as good.
And for those who are all "but dur CCP get all ur data" you can use things like AWS Bedrock (at least for earlier versions of Deepseek and Qwen for now) and have more familiar people get all your data. Or buy (at obnoxiously inflated prices) your own HW and not send your data to anyone.
> "but dur CCP get all ur data"
The funniest part of this is that people are often talking about how LLMs are now writing 100% of their code, then also saying that they don't want to expose their code to foreign government exfiltration by using foreign models.
But, uh, if an LLM is writing 100% of your code you have no actual secret sauce to hide from anyone, so why worry about it.
Perfect for idea people. All the value is in the prompt. Ideas are important, not execution. A decade or two ago, they would have been looking for a technical co-founder.
Yeah, so true. There is no moat to your competitors using the exact same tools and prompts to generate their apps and services. Companies should be hiring/retaining creative thinkers that give them that human edge rather than laying people off under the guise of "improved efficiency"
I think we're going to see a lot of craziness in the future in this regard. Not just "secrets", but hypocrites trying to copyright and patent all the AI outputs. All kinds of rabid attempts at constructing monopolies for every half-baked idea they have tried to utter as a prompt.
Meanwhile, like I think you suggest, I would assume everyone can generate similar outputs themselves. The idea that you can claim priority on your dream prompt and lock up the market on prompt responses sounds delusional to me. It's not novel invention when you're spit-balling at the same level of abstraction as every fantasy/scifi writer who ever was.
So I also have doubts about the sustainable business model. How long will it take for this fantasy to unravel, as people discover they cannot monetize their AI outputs as much as they dreamed, and in turn cannot afford to pay the AI services they use?
My absolute nightmare is that this becomes a "too big to fail" thing and oppressive/fascist governments decide to back full regulatory capture. That instead of letting it unwind, they grant and support enforcement of an increasingly absurd and arbitrary copyright/patent regime to support this monetization scheme.
> What's the point of arguing with any of this.
> It's like someone arguing that cheese isn't real
I agree with your first statement (any being you) because of your second statement.
AI has been slowing down relatively, considering its trajectory over the past 20-30 years. For one, even if LLM may have plateaud in terms of intelligence-parameters ratio, research is on-going on new frontiers for ML, including (but not limited to) world models. Other research directions are studying backpropagation and its physical analogies, such as equilibrium of chaotic states.
In addition, there's a lot of research on the hardware angle and actual prototypes are already being built such as AI-on-chip Cerebra and Taalas for one.