A few months ago folks were understandably annoyed when Microsoft dropped their heavily subsidized per-request pricing model because it was literally burning cash.
Well, I'm here to tell you that whatever is going on behind the scenes at Cursor with this Space-X acquisition in the works, the Auto setting is clearly routing all prompts through "Cursor Grok 4.6 High" right now.
This is a degree of subsidy that makes the Microsoft thing look quaint.
I reduced my $200/month subscription to the $20/month level and have proceeded to do what I would have paid about $1500 to do with Opus 4.7 or thereabouts, which is how Grok 4.6 High feels like it compares. I don't have anything remotely like hard evidence to back this estimate up beyond what I'm watching it do and I still somehow have ~10 of my monthly Auto capacity left on my account. It's completely nuts.
The real revolution is Deepseek v4 flash and similar models (GPT 5.6 Luna, muse spark 1.2, mimo, etc...) - Genuinely good performance for a tiny fraction of the cost of Fable and even GLM etc...
I think a lot of people would be very content if they never got smarter, and just kept getting even cheaper/faster. Of course, both things continue to happen on a seemingly monthly basis
I was using ChatGPT voice during cooking to reflect on variations of a dishes i was preparing for years.
It was so amazing to get advices and reflect that it struck me : I could use this model forever - it’s clever enough to help me tons and do lot of work for me - even if ai would stop evolving I would love it
All it needs is Internet access to remain useful with few shortcomings.
The next step would be automatic self-training. A free LLM that could access HN everyday (and the linked sites) for more data would remain current in programming for a really long time.
imo this is the problem some of these labs are gonna face, because open models will do this just fine and you as the consumer don't need to pay their training costs
especially considering imo most use falls under this instead of those kind of tasks where you'd need the SOTA
Yeah. Sometimes I wonder who the long term financial winners will be from the ai boom. It might be ram / gpu manufacturers. Or whoever cracks putting LLMs on asics.
It's going to be the shareholders of the first companies to crack AGI, and make human brains fully irrelevant economically. With the trillions of dollars that's going in through both investment and users, it's going to happen. I don't believe the human brain has fundamental magic that will make this impossible.
I share this feeling too. The latest models, even if not necessarily frontier, say Opus 5, Sol high and the likes, I could keep using these models forever even if they did not significantly improve beyond this point. I also believe we'll come up with new ways of using these very same models beyond the mainstream chat and agent interfaces, as the bottleneck is imho in harnesses/environments and not so much model intelligence anymore.
+1 regarding voice usage too, I use it in so many different ways it's hard to enumerate: while driving long distances (think of a custom made, interactive podcast) / as a way to collaboratively build specs or shape an idea / as a way to provide input while vibe coding / just as a normal voice assistant (straight in the ChatGPT app or as OpenClaw input via telegram voice notes). I can't overstate how much my routines have changed over the last couple of years.
>I think a lot of people would be very content if they never got smarter, and just kept getting even cheaper/faster.
There's a lot of truth to this. I think we're starting to approach the point where increased intelligence has declining marginal returns, such that it might not even be worthwhile to improve models unless it can be done cheaply.
I'd be content if I could get the DS4 flash, luna, mimo level intelligence running on MY low-end hardware completely offline and bearable TPS, not otherwise.
If they could be cheap+fast and not try to do too much, that's a good spot for me. I don't use the smarter models as much because of cost and because they're still not good enough to let loose on a lot of problems. For assistance I prefer something that can very quickly spit out a specific piece I can review on the spot and keep going. I let smarter models handle things that I treat as external dependencies and don't care how they're written, but in my core domain I'm still mostly hand coding
I have a similar process - its just a pair programmer most of the time. I dont understand how people can have a fleet of agents working a bunch of waterfall specs..
Evidence actually supports that capabilities are leveling off, and cheaper/faster is not really coming. Just log-linearly more capability at smaller parameter counts as they saturate.
Please explain why you think cheaper/faster is not coming?
All current devices used to run AI are very far from an efficient solution to the problem. What you really want is a pure dataflow architecture, instead of a von Neumann machine. The reason people aren't really making them yet is that when you build one, even if you use SRAM for the weights, you are binding yourself to the dimensions of the model you target -- your chip is only ever going to run variants of that specific model. And SRAM is much more expensive than ROM, so if you want to make a cheap version, you need to design a specific model into silicon.
Once model improvements taper off, the next thing that will happen is everyone will chase speed. There is no physical reason why a mid-sized model could not run at >1 million tokens per second on leading edge silicon, if all computation that can be parallelized, is. No-one will go straight to that, even for a mid-sized model that's like 20 distinct reticle-limited chips. But something like the next version of Taalas HC1 (presumably called HC2?) will probably boost a ~30B parameter model to ten of thousand of tokens+ per second from a single stream within 12 months.
what do you mean cheaper/faster is not really coming? the cost of the same level of intelligence steadily decreases year over year. computer hardware also advances at the same time enabling cheaper and faster serving (or move to local)
The biggest generalist models beat the most fine-tuned specialists, as a rule. You can bias an LLM away from literature knowledge and towards coding capabilities, but that buys you very little performance, and for too much effort.
Generality and intelligence seem to be entangled very heavily in LLMs.
And yet, there's VibeThinker 3B to bring this long-held premise into question (if not to blast it to pieces.) It is practically illiterate by the standards of larger models, yet performs like models 100x its size on mathematical and logical reasoning tasks.
What "evidence"? Because we keep running out of benchmarks to distinguish frontier model performance. If capabilities are "leveling off", we're not seeing it yet.
Eh. I don't think Luna is good enough. I think that threshold is around Opus / Sol where it can do most of the tasks for me. But I still have many tasks which require either better intelligence or better UI design capabilities.
With how generous subscriptions are, what I actually want is GPT Astra, not cheaper Sol.
Most of the things I work on are at least security adjacent. At some point chatting with Fable inevitably leads to it thinking about the security related aspects, tripping the safeguards.
Maybe Fable can do the same things better than other models, but having to tiptoe around to avoid tripping safeguards makes GPT 5.6 so much easier to work with that I don’t even bother with Fable (or Opus 5) now.
That's completely valid. But worth noting that most of the stuff I work on is not security adjacent (mostly UI / layout / rendering related), and I almost never run into this.
> At some point chatting with Fable inevitably leads to it thinking about the security related aspects, tripping the safeguards.
It happens to me all the time with things that have nothing to do with security, Fable spawns a subagent that then adversarially checks the code Fable just wrote and hits guardrails, with zero prompting from me.
As models train up the intelligence ladder, many common tasks will hit fully diminished returns, and instead it'll just get progressively cheaper to do that task. But the tasks that AI is capable of doing are also expanding. I'm not sure 'Some tasks don't require the peak of the frontier' is worth worrying about, from an AI finance perspective.
Yes. I use Opus for tasks that Sonnet could probably handle, but I'm not hitting my quota. Whatever minor incremental gain is "worth it", since marginal cost is zero.
Even now, I use Fable as the planner and coordinator, with it farming out to agents. I don't hit my Fable limits either.
Which means I could accomplish more, but these are side projects so I don't need 30x productivity. Still, claude is constantly churning away at something.
I operate mostly in the devops arena. Lots of things opus is fine for. But there is just things where I can hand hold Opus through changes, or I can ask Fable to do it and it gets it right on the first try. People will say let fable plan and validate with opus doing the work. I found that burns fable tokens even faster because opus makes so many mistakes, fable has to review things 4-5 times before opus gets it right. A single fable implementation at medium or low effort would have one shot it.
Yep. Every time you get more intelligence, that buys you more autonomy, more reliability, more task complexity. Tasks done with less mistakes, less handholding, less interventions.
This is what the "good enough" people fail to grasp. There's no "good enough" - unless your tasks are genuinely small scope and will stay that way forever. If not, there are always more gains to extract.
Exactly; so far, we've only replaced the need to design algorithms and hand-write code; what if we apply the same effort towards the skill needed for system architecture, project management, and the rest of the SDLC? Or even outside of software!
Right now, it feels like all of that is today where coding was a year or two ago, and we're on the cusp of some massive improvements outside of coding. It'll be interesting to see what these companies decide to automate next.
As a software engineer, I selfishly hope that they spend more effort on non software tasks since I’ve feel like we hit a sweet spot where engineers still have some value and autonomy, but a super charged tool.
Pragmatically, I suspect that “non software” tasks will be a tarpit because most tasks can’t be automated and verified as easily in an RL loop compared to software projects. Especially since most skilled labor is either not nearly as expensive as software engineers (eg biologists), or regulated (eg doctors, lawyers).
I suspect the focus will probably shift once software engineering is no longer the biggest cost center for most AI company's clients, and we'll start working on getting rid of the next cost center.
Reading this as someone who switched over to ChatGPT after (and largely because of the changes made in) the Fable release, it reads a bit naive. Not only do I find Sol to be as good, if not better than, Fable it is also faster, better behaved and has a much more coherent writing style. You also don't randomly get the Opus downgrade. OpenAI seems to be pulling this off due to their partnership with Cerebras so I wouldn't make any comparisons to Moore's law just yet considering it seems like we're just getting started in that department. Anthropic could (and should) do the same thing. It certainly feels like model development is at a point where it would be worthwhile building special purpose silicon for the models we have now since they are capable enough that they would still be useful even when/if further advancements are made. If anything, I think Anthropic's problem has more to do with their micromanagement of what users can do with their models, they're creating an undue amount of overhead for themselves by over-policing usage and capabilities.
Etched is doing this. it seems like in the near future they'll actually ramp up production. not sure how much faster/economical compared to Cerebras but..
You're right, I should have clarified that they are still slowly integrating it and it isn't the thing running all models. I meant moreso that since they are planning on moving more usage over to Cerebras wafers, they're able to relieve some pressure on their predicted expenses while also moving some current workload (ultrafast and codex spark) onto them freeing up Nvidia GPUs.
I guess Moore's law analogy is weak. CPU speed has hit a limit in that case. What has hit a limit in AI case? Newer versions of the models are still flowing with more and more capability.
For the users, I feel it is more like "free lunch started", with all these awesome open-weight models being thrown around, breaking the monopoly of a few biggies.
This concept of a free lunch was never true. In a competitive dynamic, speed and performance were always worth optimizing, comparing, and improving.
One of the primary reasons for this is that computers operate in a vast range of orders of magnitude. There’s several orders of magnitude between cache local cpu operation and dram, then several to disk, then several to network, then several to globally durable guarantees. When your code has literally thirteen orders of magnitude to optimize under, there’s never a free lunch. You always need to understand your stuff.
As 80% of enterprise software is CRUD with a bit of sprinkling of user authorization and tenant customisation. But subtly different for every business domain. It's mainly what properties the models and validations have that are different.
When you add a new module or whatever most of the code you have to write is rote code.
And sonnet can handle that crap just fine, you just point it at a similar example in the code, it picks up your userContext convention, how you're doing i18n, etc. and you're done.
I like saying that enterprise code is often shallow but wide. I must have written at least 4 purchase order systems in my career that are all completely different but almost exactly the same.
I've been offering Deepseek V4 Flash for free in www.freepi.ai and I've started using it as my main driver as well.
Besides trying to dogfood my own product I've hit a wall in terms of my patience with a)how slow fable is b)how expensive fable is. Not to mention how often it refuses totally legitimate work.
So yeah- I've moved to DeepSeek and I actually ask the freepi harness to delegate planning to fable but then move back to doing implementation in it's own harness. My current providers are super fast so it's a joy to use.
From my point of view the issue is that there are too many things wrong with Fable, making it seriously not worth the money.
For starters I don't know if it is an artifact of the model or something by design, but the level of gratuitous cognitive load carried by the complexity of its replies is unbearable.
Yes, it's a beast at coding, and also it's incredible nuanced at improving writing, validating specs, etc.
But when it comes to replying, it's the William Gibson of LLMs [1].
It has this tendency to take extreme detours to say things that could had been said in less, much simpler words. [2]
It really, really like to wrap very simple and atomic ideas on several layers of abstraction, building on unnecessary terms that carry no intrinsic information and assumes this vocabulary as shared and then building on top of it.
By the time I got to the end of the reply I'm bored to death and didn't understand even a third of what it told me.
I think the people at Anthropic should reflect on the maxim "You don't know a subject if you cannot explain it"
If you pardon my french, Fable is an insufferable obnoxious cunt.
---
[1]
I apologize on the comparison but, as much as I love his first 2 trilogies, haven't been able to finish any of his last 2 books.
[2]
"The residual you're accepting is the one from before: recovery currently rests on beneficial non-compliance, which may erode as models get more literal" == "We already accepted this risk"
" Its observable when it erodes is a stall that survives relaunch — loud at operator level, recoverable from the worklog, and fixable by codifying at that moment" == "When it breaks, it'll break visibly and recoverably"
"That is the iteration model applied exactly as written: resolve on first contact, don't pre-solve " == "So we fix it then, not now"
>> GLM 5.2 is worth focusing on. It came out the same week as Fable and is roughly 1/9th the cost (and ~1/5th the cost of Opus 5). Is GLM 1/9th the quality of Fable? Perhaps, for certain classes of tasks. But for most rote coding it’s more than sufficient. Especially when provided with great context. I frequently chat with Fable to interrogate and shape a design, before handing off a brief to GLM.
People say stuff like this a lot, but I have a different take.
The whole "such-and-such model is 90% as good as Fable at 1/10th the price" assumes that the value increase of intelligence is linear. But I think it's exponential: that last 10% makes a massive amount of difference. It can result in a key insight that helps you strategize more effectively, a novel approach that saves a huge amount of time, a feature design that is lot more user-friendly (because top models like Fable also possess substantial non-software domain knowledge that help bridge the gap between user and software), or the depth and breadth of engineering expertise that helps avoid a nasty bug that would otherwise have cost you users and revenue.
Yes, it is totally possible to use Fable as the planner and delegate implementation to lesser models. I do that. But, my theory (which I unfortunately do not have the money to test and prove) is that a codebase designed and implemented by Fable would be substantially better than one that is designed by Fable and implemented by Opus 5, GPT 5.6 Sol, GLM, Qwen, Deepseek, etc. The reason I believe this is because I read the code Fable writes and compare it to code that any other model writes and the difference is night and day. It's not just 10% better. It's mid-level engineer vs. principal/staff-level engineer. And the thing is, even for rote tasks, a more senior engineer is going to be more likely to come up with a clean design than a mid-level engineer. They will also be much more likely to take a step back and ask important questions or propose different approaches.
So if you're using Fable and everyone else is using lesser models, sure they might be saving a lot of money, but there's a higher likelihood that your product will be higher quality, perhaps to a significant extent. And models that are released in the future will benefit from it as well.
Something I’ve found comparing between Fable and Opus is that Fable has impressively good analysis skills, but both of them seem to go way way overboard with “present state” comments “# We’re making this change here because of this issue blah blah, here’s what you need to know about np.percentile, blah blah” that I end up significantly pruning before making a PR. I let it do the same style verbose commit messages (because a contextual history is cool there). I haven’t actually noticed a ton of difference in the code that they write personally, but have found that Fable does find nuances during data analysis that Opus misses.
In that light, I often go the other way: let Opus (and Haiku subagents) do most of the heavy lifting and then give Fable a shot at finding holes, especially if there are holes or unanswered questions or unearned assertions that I’ve caught on my own in Opus’ output. This, so far, seems like a clean tradeoff that doesn’t burn my Fable credits as hard and still gives solid results.
Those "present state" comments are the bane of my existence. It was present in 4.7/etc but i put in a ton of guards against that into my global memory and it worked quite well. Fable and Opus 5 regressed badly in this space though and i can't keep it from making those types of comments again.
> my theory (which I unfortunately do not have the money to test and prove) is that a codebase designed and implemented by Fable would be substantially better than one that is designed by Fable and implemented by [others]
I don't have proof, only my anecdotal experience: I leave plenty of Fable usage on the table because I do not think its implementations of code have been better to Opus 4.8, not even close. It overengineered, obscured and picked awkward constructs all the time over plain, simple, perfectly clean and performant code patterns. Code was smarter AND worse in the kind of way that a brilliant and overeager recent grad often does. (I know I did)
As a counterpoint (data point of one code base), I had Fable lead development of a complex system recently (an end-to-end insurance claims billing system) as a test project. It blew me away. Opus could not have done the same, given the feedback Fable had to give when Opus would implement individual features.
Granted, I laid out a document with coding practices, architecture, and technical design recommendations to steer it towards good engineering. And it's a domain I know super well, so I could give very nuanced feedback on trade-offs + architecture. If it had been left to its own devices, maybe it would have over-engineered the h*ck out of it.
But the code it produced—and the implementations it guided Opus towards—were excellent.
> It can result in a key insight that helps you strategize more effectively, a novel approach that saves a huge amount of time, a feature design that is lot more user-friendly
over time greater intelligence will be expressed in smaller and cheaper models. we are still somewhat near the beginning of this bc we are finally starting to understand what makes a model truly intelligent/capable.
With Sol we see openai making the model extremely slow and paranoid about process/ceremony. Sure this is a good guardrail against AI going rogue, but it also sets the stage for companies to charge for 2x, 4x, 8x performance, with 1x being barely tolerable and frankly slower than last year's models (though less error prone).
The irony is that the smarter the model, the more it can be trusted to do with less supervision, so one engineer can manage a team of 20 fable subscriptions more effectively than a team of 3 of last year's model subscriptions.
It's been cheap again on openrouter for the past few days. No idea how long it will last, but I've been using it from Baidu over the weekend, and it was about half the cost of the old DS prices, before the increase. Looks like people are figuring out how to offer it for peanuts.
Somewhat weird that the article was released today but did not mention GLM 5.3.
If you're telling me to focus on something, why not focus on the actual latest thing that is the same as 5.2 but better?
I get the "came out at the same time as fable" thing, but still.. no mention at all?
Yes, weights aren't out yet, but neither are the ones of Fable.
I can’t host GLM 5.3 yet, so my agents still run on 5.2. But the fact that 5.2 is sufficient and there’s another gen in the wings kinda proves my point, imo.
You've selectively quoted the article. The full quote (emphasis added):
"When Moore’s Law slowed in the mid-2000s (specifically, single-threaded performance stagnated), we suddenly had to think about parallelization, architecture, memory locality, etc."
Your link is talking about transistor count. The article is talking about single-threaded performance. Today's CPUs are faster in large part because they have more and more cores.
A few months ago folks were understandably annoyed when Microsoft dropped their heavily subsidized per-request pricing model because it was literally burning cash.
Well, I'm here to tell you that whatever is going on behind the scenes at Cursor with this Space-X acquisition in the works, the Auto setting is clearly routing all prompts through "Cursor Grok 4.6 High" right now.
This is a degree of subsidy that makes the Microsoft thing look quaint.
I reduced my $200/month subscription to the $20/month level and have proceeded to do what I would have paid about $1500 to do with Opus 4.7 or thereabouts, which is how Grok 4.6 High feels like it compares. I don't have anything remotely like hard evidence to back this estimate up beyond what I'm watching it do and I still somehow have ~10 of my monthly Auto capacity left on my account. It's completely nuts.
The real revolution is Deepseek v4 flash and similar models (GPT 5.6 Luna, muse spark 1.2, mimo, etc...) - Genuinely good performance for a tiny fraction of the cost of Fable and even GLM etc...
I think a lot of people would be very content if they never got smarter, and just kept getting even cheaper/faster. Of course, both things continue to happen on a seemingly monthly basis
I was using ChatGPT voice during cooking to reflect on variations of a dishes i was preparing for years.
It was so amazing to get advices and reflect that it struck me : I could use this model forever - it’s clever enough to help me tons and do lot of work for me - even if ai would stop evolving I would love it
All it needs is Internet access to remain useful with few shortcomings.
The next step would be automatic self-training. A free LLM that could access HN everyday (and the linked sites) for more data would remain current in programming for a really long time.
imo this is the problem some of these labs are gonna face, because open models will do this just fine and you as the consumer don't need to pay their training costs
especially considering imo most use falls under this instead of those kind of tasks where you'd need the SOTA
Yeah. Sometimes I wonder who the long term financial winners will be from the ai boom. It might be ram / gpu manufacturers. Or whoever cracks putting LLMs on asics.
It's going to be the shareholders of the first companies to crack AGI, and make human brains fully irrelevant economically. With the trillions of dollars that's going in through both investment and users, it's going to happen. I don't believe the human brain has fundamental magic that will make this impossible.
I share this feeling too. The latest models, even if not necessarily frontier, say Opus 5, Sol high and the likes, I could keep using these models forever even if they did not significantly improve beyond this point. I also believe we'll come up with new ways of using these very same models beyond the mainstream chat and agent interfaces, as the bottleneck is imho in harnesses/environments and not so much model intelligence anymore.
+1 regarding voice usage too, I use it in so many different ways it's hard to enumerate: while driving long distances (think of a custom made, interactive podcast) / as a way to collaboratively build specs or shape an idea / as a way to provide input while vibe coding / just as a normal voice assistant (straight in the ChatGPT app or as OpenClaw input via telegram voice notes). I can't overstate how much my routines have changed over the last couple of years.
>I think a lot of people would be very content if they never got smarter, and just kept getting even cheaper/faster.
There's a lot of truth to this. I think we're starting to approach the point where increased intelligence has declining marginal returns, such that it might not even be worthwhile to improve models unless it can be done cheaply.
I'd be content if I could get the DS4 flash, luna, mimo level intelligence running on MY low-end hardware completely offline and bearable TPS, not otherwise.
If they could be cheap+fast and not try to do too much, that's a good spot for me. I don't use the smarter models as much because of cost and because they're still not good enough to let loose on a lot of problems. For assistance I prefer something that can very quickly spit out a specific piece I can review on the spot and keep going. I let smarter models handle things that I treat as external dependencies and don't care how they're written, but in my core domain I'm still mostly hand coding
I have a similar process - its just a pair programmer most of the time. I dont understand how people can have a fleet of agents working a bunch of waterfall specs..
Evidence actually supports that capabilities are leveling off, and cheaper/faster is not really coming. Just log-linearly more capability at smaller parameter counts as they saturate.
Please explain why you think cheaper/faster is not coming?
All current devices used to run AI are very far from an efficient solution to the problem. What you really want is a pure dataflow architecture, instead of a von Neumann machine. The reason people aren't really making them yet is that when you build one, even if you use SRAM for the weights, you are binding yourself to the dimensions of the model you target -- your chip is only ever going to run variants of that specific model. And SRAM is much more expensive than ROM, so if you want to make a cheap version, you need to design a specific model into silicon.
Once model improvements taper off, the next thing that will happen is everyone will chase speed. There is no physical reason why a mid-sized model could not run at >1 million tokens per second on leading edge silicon, if all computation that can be parallelized, is. No-one will go straight to that, even for a mid-sized model that's like 20 distinct reticle-limited chips. But something like the next version of Taalas HC1 (presumably called HC2?) will probably boost a ~30B parameter model to ten of thousand of tokens+ per second from a single stream within 12 months.
what do you mean cheaper/faster is not really coming? the cost of the same level of intelligence steadily decreases year over year. computer hardware also advances at the same time enabling cheaper and faster serving (or move to local)
not an AI researcher - this is probably true for these "everything" LLMs but I think specialized models are gonna be the next big thing
"Specialized models" are a bit of a doozy.
The biggest generalist models beat the most fine-tuned specialists, as a rule. You can bias an LLM away from literature knowledge and towards coding capabilities, but that buys you very little performance, and for too much effort.
Generality and intelligence seem to be entangled very heavily in LLMs.
And yet, there's VibeThinker 3B to bring this long-held premise into question (if not to blast it to pieces.) It is practically illiterate by the standards of larger models, yet performs like models 100x its size on mathematical and logical reasoning tasks.
Cheaper/faster is coming for sure.
Model on a custom silicon: https://chatjimmy.ai/
1-bit models that run on a CPU: https://github.com/microsoft/BitNet
What "evidence"? Because we keep running out of benchmarks to distinguish frontier model performance. If capabilities are "leveling off", we're not seeing it yet.
Eh. I don't think Luna is good enough. I think that threshold is around Opus / Sol where it can do most of the tasks for me. But I still have many tasks which require either better intelligence or better UI design capabilities.
With how generous subscriptions are, what I actually want is GPT Astra, not cheaper Sol.
Most of the things I work on are at least security adjacent. At some point chatting with Fable inevitably leads to it thinking about the security related aspects, tripping the safeguards.
Maybe Fable can do the same things better than other models, but having to tiptoe around to avoid tripping safeguards makes GPT 5.6 so much easier to work with that I don’t even bother with Fable (or Opus 5) now.
That's completely valid. But worth noting that most of the stuff I work on is not security adjacent (mostly UI / layout / rendering related), and I almost never run into this.
> At some point chatting with Fable inevitably leads to it thinking about the security related aspects, tripping the safeguards.
It happens to me all the time with things that have nothing to do with security, Fable spawns a subagent that then adversarially checks the code Fable just wrote and hits guardrails, with zero prompting from me.
As models train up the intelligence ladder, many common tasks will hit fully diminished returns, and instead it'll just get progressively cheaper to do that task. But the tasks that AI is capable of doing are also expanding. I'm not sure 'Some tasks don't require the peak of the frontier' is worth worrying about, from an AI finance perspective.
Yes. I use Opus for tasks that Sonnet could probably handle, but I'm not hitting my quota. Whatever minor incremental gain is "worth it", since marginal cost is zero.
Even now, I use Fable as the planner and coordinator, with it farming out to agents. I don't hit my Fable limits either.
Which means I could accomplish more, but these are side projects so I don't need 30x productivity. Still, claude is constantly churning away at something.
I operate mostly in the devops arena. Lots of things opus is fine for. But there is just things where I can hand hold Opus through changes, or I can ask Fable to do it and it gets it right on the first try. People will say let fable plan and validate with opus doing the work. I found that burns fable tokens even faster because opus makes so many mistakes, fable has to review things 4-5 times before opus gets it right. A single fable implementation at medium or low effort would have one shot it.
Yep. Every time you get more intelligence, that buys you more autonomy, more reliability, more task complexity. Tasks done with less mistakes, less handholding, less interventions.
This is what the "good enough" people fail to grasp. There's no "good enough" - unless your tasks are genuinely small scope and will stay that way forever. If not, there are always more gains to extract.
Exactly; so far, we've only replaced the need to design algorithms and hand-write code; what if we apply the same effort towards the skill needed for system architecture, project management, and the rest of the SDLC? Or even outside of software!
Right now, it feels like all of that is today where coding was a year or two ago, and we're on the cusp of some massive improvements outside of coding. It'll be interesting to see what these companies decide to automate next.
> Or even outside of software!
As a software engineer, I selfishly hope that they spend more effort on non software tasks since I’ve feel like we hit a sweet spot where engineers still have some value and autonomy, but a super charged tool.
Pragmatically, I suspect that “non software” tasks will be a tarpit because most tasks can’t be automated and verified as easily in an RL loop compared to software projects. Especially since most skilled labor is either not nearly as expensive as software engineers (eg biologists), or regulated (eg doctors, lawyers).
I suspect the focus will probably shift once software engineering is no longer the biggest cost center for most AI company's clients, and we'll start working on getting rid of the next cost center.
It’s worth considering for companies paying API prices, and not relying on a subscription quota
Reading this as someone who switched over to ChatGPT after (and largely because of the changes made in) the Fable release, it reads a bit naive. Not only do I find Sol to be as good, if not better than, Fable it is also faster, better behaved and has a much more coherent writing style. You also don't randomly get the Opus downgrade. OpenAI seems to be pulling this off due to their partnership with Cerebras so I wouldn't make any comparisons to Moore's law just yet considering it seems like we're just getting started in that department. Anthropic could (and should) do the same thing. It certainly feels like model development is at a point where it would be worthwhile building special purpose silicon for the models we have now since they are capable enough that they would still be useful even when/if further advancements are made. If anything, I think Anthropic's problem has more to do with their micromanagement of what users can do with their models, they're creating an undue amount of overhead for themselves by over-policing usage and capabilities.
Etched is doing this. it seems like in the near future they'll actually ramp up production. not sure how much faster/economical compared to Cerebras but..
The Cerebras version of 5.6 is available only to select customers
You're right, I should have clarified that they are still slowly integrating it and it isn't the thing running all models. I meant moreso that since they are planning on moving more usage over to Cerebras wafers, they're able to relieve some pressure on their predicted expenses while also moving some current workload (ultrafast and codex spark) onto them freeing up Nvidia GPUs.
I guess Moore's law analogy is weak. CPU speed has hit a limit in that case. What has hit a limit in AI case? Newer versions of the models are still flowing with more and more capability.
For the users, I feel it is more like "free lunch started", with all these awesome open-weight models being thrown around, breaking the monopoly of a few biggies.
This concept of a free lunch was never true. In a competitive dynamic, speed and performance were always worth optimizing, comparing, and improving.
One of the primary reasons for this is that computers operate in a vast range of orders of magnitude. There’s several orders of magnitude between cache local cpu operation and dram, then several to disk, then several to network, then several to globally durable guarantees. When your code has literally thirteen orders of magnitude to optimize under, there’s never a free lunch. You always need to understand your stuff.
What are all these rote coding tasks people do that they can farm it out to lesser models?
Write a detailed plan using a more expensive model and implement it using the cheaper one.
How much are you saving once the more expensive model already has all the context loaded and ready to go?
API calls get more expensive, not less, as you've loaded more context. This is exactly when you want to switch to cheaper models.
One task I've found this useful for is writing example code. Release admin (updating version numbers, etc) as well.
Are you genuinely asking?
As 80% of enterprise software is CRUD with a bit of sprinkling of user authorization and tenant customisation. But subtly different for every business domain. It's mainly what properties the models and validations have that are different.
When you add a new module or whatever most of the code you have to write is rote code.
And sonnet can handle that crap just fine, you just point it at a similar example in the code, it picks up your userContext convention, how you're doing i18n, etc. and you're done.
I like saying that enterprise code is often shallow but wide. I must have written at least 4 purchase order systems in my career that are all completely different but almost exactly the same.
I've been offering Deepseek V4 Flash for free in www.freepi.ai and I've started using it as my main driver as well.
Besides trying to dogfood my own product I've hit a wall in terms of my patience with a)how slow fable is b)how expensive fable is. Not to mention how often it refuses totally legitimate work.
So yeah- I've moved to DeepSeek and I actually ask the freepi harness to delegate planning to fable but then move back to doing implementation in it's own harness. My current providers are super fast so it's a joy to use.
looks like you haven't tried openai or Sol, or even luna (max)
From my point of view the issue is that there are too many things wrong with Fable, making it seriously not worth the money.
For starters I don't know if it is an artifact of the model or something by design, but the level of gratuitous cognitive load carried by the complexity of its replies is unbearable.
Yes, it's a beast at coding, and also it's incredible nuanced at improving writing, validating specs, etc.
But when it comes to replying, it's the William Gibson of LLMs [1].
It has this tendency to take extreme detours to say things that could had been said in less, much simpler words. [2]
It really, really like to wrap very simple and atomic ideas on several layers of abstraction, building on unnecessary terms that carry no intrinsic information and assumes this vocabulary as shared and then building on top of it.
By the time I got to the end of the reply I'm bored to death and didn't understand even a third of what it told me.
I think the people at Anthropic should reflect on the maxim "You don't know a subject if you cannot explain it"
If you pardon my french, Fable is an insufferable obnoxious cunt.
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[1] I apologize on the comparison but, as much as I love his first 2 trilogies, haven't been able to finish any of his last 2 books.
[2] "The residual you're accepting is the one from before: recovery currently rests on beneficial non-compliance, which may erode as models get more literal" == "We already accepted this risk"
" Its observable when it erodes is a stall that survives relaunch — loud at operator level, recoverable from the worklog, and fixable by codifying at that moment" == "When it breaks, it'll break visibly and recoverably"
"That is the iteration model applied exactly as written: resolve on first contact, don't pre-solve " == "So we fix it then, not now"
He started with thinking were to send what.
I throw everything at claude Opus.
While some people start thinking like OP, A LOT of people just start exploring ai.
And others which are already using it, only understand half of it and just use what they are allowed to use. Claude, GitHub Copilot, Curser, etc.
I just use Fable for reviews of specs and code then hand off to Opus to work on. Works well.
Does it not silently degrade to Opus if it does not like some word?
>> GLM 5.2 is worth focusing on. It came out the same week as Fable and is roughly 1/9th the cost (and ~1/5th the cost of Opus 5). Is GLM 1/9th the quality of Fable? Perhaps, for certain classes of tasks. But for most rote coding it’s more than sufficient. Especially when provided with great context. I frequently chat with Fable to interrogate and shape a design, before handing off a brief to GLM.
People say stuff like this a lot, but I have a different take.
The whole "such-and-such model is 90% as good as Fable at 1/10th the price" assumes that the value increase of intelligence is linear. But I think it's exponential: that last 10% makes a massive amount of difference. It can result in a key insight that helps you strategize more effectively, a novel approach that saves a huge amount of time, a feature design that is lot more user-friendly (because top models like Fable also possess substantial non-software domain knowledge that help bridge the gap between user and software), or the depth and breadth of engineering expertise that helps avoid a nasty bug that would otherwise have cost you users and revenue.
Yes, it is totally possible to use Fable as the planner and delegate implementation to lesser models. I do that. But, my theory (which I unfortunately do not have the money to test and prove) is that a codebase designed and implemented by Fable would be substantially better than one that is designed by Fable and implemented by Opus 5, GPT 5.6 Sol, GLM, Qwen, Deepseek, etc. The reason I believe this is because I read the code Fable writes and compare it to code that any other model writes and the difference is night and day. It's not just 10% better. It's mid-level engineer vs. principal/staff-level engineer. And the thing is, even for rote tasks, a more senior engineer is going to be more likely to come up with a clean design than a mid-level engineer. They will also be much more likely to take a step back and ask important questions or propose different approaches.
So if you're using Fable and everyone else is using lesser models, sure they might be saving a lot of money, but there's a higher likelihood that your product will be higher quality, perhaps to a significant extent. And models that are released in the future will benefit from it as well.
Something I’ve found comparing between Fable and Opus is that Fable has impressively good analysis skills, but both of them seem to go way way overboard with “present state” comments “# We’re making this change here because of this issue blah blah, here’s what you need to know about np.percentile, blah blah” that I end up significantly pruning before making a PR. I let it do the same style verbose commit messages (because a contextual history is cool there). I haven’t actually noticed a ton of difference in the code that they write personally, but have found that Fable does find nuances during data analysis that Opus misses.
In that light, I often go the other way: let Opus (and Haiku subagents) do most of the heavy lifting and then give Fable a shot at finding holes, especially if there are holes or unanswered questions or unearned assertions that I’ve caught on my own in Opus’ output. This, so far, seems like a clean tradeoff that doesn’t burn my Fable credits as hard and still gives solid results.
Those "present state" comments are the bane of my existence. It was present in 4.7/etc but i put in a ton of guards against that into my global memory and it worked quite well. Fable and Opus 5 regressed badly in this space though and i can't keep it from making those types of comments again.
Really frustrating.
I have Sol prune/revise those comments.
> my theory (which I unfortunately do not have the money to test and prove) is that a codebase designed and implemented by Fable would be substantially better than one that is designed by Fable and implemented by [others]
I don't have proof, only my anecdotal experience: I leave plenty of Fable usage on the table because I do not think its implementations of code have been better to Opus 4.8, not even close. It overengineered, obscured and picked awkward constructs all the time over plain, simple, perfectly clean and performant code patterns. Code was smarter AND worse in the kind of way that a brilliant and overeager recent grad often does. (I know I did)
As a counterpoint (data point of one code base), I had Fable lead development of a complex system recently (an end-to-end insurance claims billing system) as a test project. It blew me away. Opus could not have done the same, given the feedback Fable had to give when Opus would implement individual features.
Granted, I laid out a document with coding practices, architecture, and technical design recommendations to steer it towards good engineering. And it's a domain I know super well, so I could give very nuanced feedback on trade-offs + architecture. If it had been left to its own devices, maybe it would have over-engineered the h*ck out of it.
But the code it produced—and the implementations it guided Opus towards—were excellent.
> It can result in a key insight that helps you strategize more effectively, a novel approach that saves a huge amount of time, a feature design that is lot more user-friendly
My brother, that's my job.
over time greater intelligence will be expressed in smaller and cheaper models. we are still somewhat near the beginning of this bc we are finally starting to understand what makes a model truly intelligent/capable.
With Sol we see openai making the model extremely slow and paranoid about process/ceremony. Sure this is a good guardrail against AI going rogue, but it also sets the stage for companies to charge for 2x, 4x, 8x performance, with 1x being barely tolerable and frankly slower than last year's models (though less error prone).
The irony is that the smarter the model, the more it can be trusted to do with less supervision, so one engineer can manage a team of 20 fable subscriptions more effectively than a team of 3 of last year's model subscriptions.
Are people still using deepseek-v4-flash everywhere? I found after the price increases, mimo-v2.5 seems far more attractive.
It's been cheap again on openrouter for the past few days. No idea how long it will last, but I've been using it from Baidu over the weekend, and it was about half the cost of the old DS prices, before the increase. Looks like people are figuring out how to offer it for peanuts.
Awesome. Thanks for the heads up.
Somewhat weird that the article was released today but did not mention GLM 5.3.
If you're telling me to focus on something, why not focus on the actual latest thing that is the same as 5.2 but better? I get the "came out at the same time as fable" thing, but still.. no mention at all?
Yes, weights aren't out yet, but neither are the ones of Fable.
Doesn't feel well informed enough to give advice.
Ok, buddy.
I can’t host GLM 5.3 yet, so my agents still run on 5.2. But the fact that 5.2 is sufficient and there’s another gen in the wings kinda proves my point, imo.
"When Moore’s Law slowed in the mid-2000s" it did not, in fact, slow down in the mid 2000s, or at all.
https://ourworldindata.org/data-insights/moores-law-has-accu...
You've selectively quoted the article. The full quote (emphasis added):
"When Moore’s Law slowed in the mid-2000s (specifically, single-threaded performance stagnated), we suddenly had to think about parallelization, architecture, memory locality, etc."
Your link is talking about transistor count. The article is talking about single-threaded performance. Today's CPUs are faster in large part because they have more and more cores.
It did in terms of the traditional more MHz (GHz) is better, but as you've correctly pointed out, not when it comes to actual compute.