A lot of people here are responding to the message but not to the meaning.
It would be a good idea for young people to deeply know how these programs work. Not so that they can spend their career building them, but so that they can approach the next class of problems we'll all start trying to solve, with intuition all the way down to the weights and underlying mathematics. And also, to develop a healthy intuition of when "Just LLM it" will not be the right choice.
"Build an OS" wasn't a common university project because we were all expected to go out and work on Windows, but because understanding the bare-metal firmware for a computer helps you deeply understand how to intuit building for a whole class of problems.
The funny thing about LLM is, you can build one from scratch and yet you still won't understand how it works. You would understand what kind of matrix multiplications the neural network performs (in fact that's not that hard. An OS is orders of magnitudes more complex), but you would still have no idea why it does what it does.
This is a great point. The complexity sources are entirely different. An OS is complex because it’s solving a difficult problem and we’ve seen decades of evolution both in knowledge and-how and, at the frontier, in performance standards.
The abilities of LLMs are emergent. You can experiment with LLMs and know as much about their observable behavior as the experts. But there’s no way to “crack open” an LLM and see precisely where each skill or tendency lives; as far as we currently understand, it’s all mashed together.
Learning how to build emergent systems is also a skill kids should learn these days. The closest I got was coding up game of life for CSE 142 (intro programming).
If stochastic gradient descent isn't taught in whatever CS Theory 101 is now, it really should be these days.
The spirit of your point stands, though. This kind of research is interesting to read about, but it's very hard, more like neuroscience or biology than computer science ("LLMs are grown, not made"). You're dealing with a lot of extremely _messy_ complexity, for which organic life is really the only good point of comparison. Most of us here are't really equipped for that kind of work; it's not at all like, say, reverse-engineering a piece of software written by humans. And of course the only people who can do it on frontier models from Anthropic and OpenAI are people within the labs themselves. (But I'm optimistic we'll see more of this work on open weights models...)
Yah, "building" it is not sufficient. But a lot of times when I build I want to know the why. "Why does gradient Descent have some clever tricks that easily translate to matrix math"?
I'm not sure it's possible to have intuition about systems that work in thousands of orthogonal dimensions. In fact I'm pretty sure most of the research is people trying fairly arbitrary things and testing them and then post rationalising implied understanding of what is really happening on top of good outcomes.
I think it’s reasonable to have a shallow understanding of most parts and a deep understanding of a small number of parts. That’s how most engineers are.
Most software engineers do not have a deep understanding of CPU architectures. In fact they probably don’t even have a shallow understanding and get around just fine. How many of them are looking up the instruction set for the CPUs they deploy their CRUD app to in EC2?
But in the case of CPU architecture there are SOME people who understand how things work 100%, and they've built and vetted abstractions/mental models that enable other engineers and scientists to have that kind of mixed shallow/deep understanding in a way that works. On the side of LLMs we're still lacking an expertise which could flawlessly explain how these things operate; the abstractions that we're using are instead derived inductively and are totally unvetted.
You are right that the field doesn’t have a theoretically sound explanation for the architectural choices aside from “A works better than B”. However, I would argue this is an ideal opportunity for the “gentleman scientist” or eager 17 year old.
Basically every part of the original transformer was replaced with something more efficient or better:
LayerNorm -> RMSNorm
Sinusoidal position encoding -> RoPE
MHA -> GQA
ReLU -> GELU
What this means is that there is ample opportunity to improve on what we’ve done thus far.
The original RoPE paper is a great example of the authors not actually understanding how their own discovery was functioning fully which is a fun example of what I’m talking about I guess…
Yes you might progress the field, but will you really understand why? You can make up an explanation and anthropomorphise it with a few contrived diagrams and everyone will cheer!
Oh yeah I absolutely agree that there's lots of greenfield for relative newcomers to find some big improvements! I'm just saying that reasoning about 'best practices' in terms of what works for something like a CPU is missing some important and relevant differences.
In fact, one of the jobs of an engineer is to make sure that other engineers who don't work in his or her area do not need to understand that area deeply, yet build something reliable with it. They need just the summary that he or she writes up into the datasheet for the part. Ensure these conditions are met for safe/reliable operation, give it these inputs, expect these outputs, these timings, this energy consumption, this heat generation, frequency response, tensile strength, whatever.
I disagree. It's absolutely possible to develop an intuition about extremely complex mathematical ideas, including llms or high dimensional systems. Learning to build an llm is a great way to start building that intuition.
Developing an intuition about high dimensional systems is pretty different from understanding the character of some specific point on a 1e9+ dimensional manifold of parameters, in my professional opinion (setting aside all the degrees of freedom that come from the structure of the thing). Sure one can understand generic principles like the curse of dimensionality, but truly groking how an LLM works is basically an open problem as far as I'm aware. I'm not saying there's no benefit for amateurs to study how LLMs work, but let's be realistic about how far mere intuition can truly take anyone in this space.
What provable conclusions have you intuited around how LLMs work? Give me some examples to prove your point? I'm absolutely happy to change my view with enough data.
I think provable conclusions and building an intuition are different things to be fair. In my opinion it is entirely possible to build intuition about seemingly impossible to understand topics like infinitesimal areas, infinite limits, function mappings, and high dimensional spaces. For example, 3D spaces are easy to intuit if you can visualize vectors in your head. Some people claim it is impossible to understand dimensions higher than 3 but that's obviously not true because you could assign a color to each vector to visualize the next dimension. You could then assign a width to visualize a 5th dimension, and an arrow shape to signify the next, and so on and so on. That's more of a creative act that can start to build at least a visual understanding in you head of these higher dimension spaces. That's a way to start to intuit about those things.
Now with llms we need way more that 6 dimensions so we can start thinking of assigning matrices to each 3d point for example. That allows us to increase the dimension from 3 to 3 + whatever the matrix dimension is.
We can visualize the matrices instead of having numbers as having colors for each entry, so they can be a sort of cube with each vowel being a different color.
Now you can start to visually intuit about how these massively high dimensional spaces can be formed of these colored matrices that can react to some input training data.
That's a start of an idea for intuiting things that might seem impossible to have an intuition about. I think visualization is a great way to start.
Yes, if it cannot be measured it doesn’t exist no matter how much it would make everyone feel more comfortable if we could intuitively understand these systems.
Visualizing large dimensional matrices is a way of starting to develop intuition about llms. If you can't visualize high dimensional spaces, and you haven't studied llms, you're not going to have any intuition about how they work.
Depending on your knowledge of math, I recommend starting with linear algebra, building an understanding of the equations and try to visualize more and more complex systems, then study llms to see how you can apply your linear algebra intuition to your understanding of llms.
VTK is a great toolkit for visualizing complex systems. 3 blue one brown on YouTube has other visuals that might help you.
I'm not looking to change your view. But for other readers who are curious, here is a link to an interesting task to gain intuition. Ahmad is a good data point for someone who tinkered, built intuition, then started his own ai company.
https://twitter.com/TheAhmadOsman/status/2087742080793620593...
I think we are talking about different things to be honest. Understanding LLMs is a fine thing to do but to say you understand what the network is actually doing, even in toy examples like MNIST is extremely tricky. Figuring out what a ~3 trillion parameter network is doing in thousands of dimensions seems intractable to me.
Linking to a Twitter thread about training a small LLM to be good at Wordle is not an example of what I'm talking about. It might well be a useful task but it doesn't allow us to understand deeply what's happening.
You may be right. Nonetheless, it's possible that the act of trying to get your brain to understand a system that works in more than a few orthogonal dimensions will help develop useful intuitions about how LLMs, and the many systems/products/robots that will be built on top of them, work.
I'd have to push back, though not on the part you'd expect. Your description of human researchers is roughly right: a lot of the field is try-things-and-narrativize-after.
But the load-bearing assumption is that intuition has to be human-shaped intuition. Humans can't intuit thousands of orthogonal directions because we project everything down into a 3D metaphor and hope it holds. That's a fact about our hardware, not about the systems.
And the reason why is the most interesting part: nothing requires the compression step. A model or an agent can operate over the actual objects, holding thousands of runs and ablations in context and noticing regularities in the native dimensionality, without translating them into a picture of a ball rolling down a hill. No bottleneck at "can you visualize it."
So the narrower claim: it's not that intuition here is impossible full-stop, it's that human intuition is unreliable. Your post-hoc rationalization point is evidence for that, not against it. The story exists because a person needs something to hold in their head. Drop that requirement and the failure mode goes with it.
What corpus would one be advised to use for training? Should one limit oneself to legally downloadable documents or should one follow the examples of Altman, Amodei, and Zuckerberg and use LibGen and Anna's Archive?
> understanding the bare-metal firmware for a computer
IMO this is still relevant, everything surrounding the LLMs needs such a vast infrastructure that I don't know if I would find it more useful to learn the maths behind ML than CS
As a 17 year old, I agree with this. Ofc I'm against all the hate directed at PG, I believe that all knowledge has value regardless of its economic utility, but I understand where the hate is coming from. Personally, I find LLMs boring for now, and I'm more focused on CS and electrical engineering.
Agreed. During comp sci we got to re-implement various algos of networks, OS, database, firmware.. and it all gave complimentary intuitions that were useful when tackling practical implementations and bottlenecks.
The next class of problems to solve for future generations is how to survive on a hot planet and how to recover from the damage done by reckless lunatic profiteering assholes.
Maybe people want to take it as cynicism but I think you are factually correct that personal cooling, infectious disease, atypical weather and fire breaks are all more realistic topics for people too young to already have patents, connections or their own oil well.
Depends, the assumption things are predictable always negatively affects both Market Bears and Bulls alike.
Indeed, if credulous folks look to the world expecting people to bestow success upon them... than the disillusionment with reality will hit their savings harder.
The Shrek movie market correction correlations are undeniably funny, and a new film is due July 2027. OpenAI may be going public in the next few months while still losing $2.25 for every $1 of customer revenue, and with 6 other firms sharing over $4Tn in debt disclosed to investors in a footnote.
There is only one direction things can go at the Peak of inflated expectations. Popcorn ready. =3
> "Build an OS" wasn't a common university project because we were all expected to go […] helps you deeply understand how to intuit building for a whole class of problems.
Sure, but it was for a specific degree with a syllabus that taught you the foundational knowledge. It was not expected from the law students to learn how to build one.
> It would be a good idea for young people to deeply know how these programs work.
It would be a good idea for _everyone in the industry_ to deeply know how LLM training, inference and "agents" work, not least because it removes the ability of shysters to bamboozle with bullshit.
But, as much as a good idea it is for the young to understand this, it's the elderly who will be really taken advantage of if they do not keep up - just look at Facebook for good examples of why.
There's this dilemma where in theory there's a ton of demand for engineers that can do real LLM machine-learning, but in practice there are very few available positions and entrepreneurship opportunities.
The reality is that an incredibly small minority of companies in the world do any real training or optimisation. It's unnecessary and inefficient for most purposes unless you are fully dedicated to being an LLM company, and still then it's a struggle. Those few that do train, they spend most of their budget on compute and have relatively small teams.
Getting experience in this field requires having access to very expensive hardware to begin with. And the skills will be quite hard to convert into any real value for someone, leading to a decent income, unless you have a ton of funding from patient investors, or you have decent contacts in Bay Area networks to get hired at the right place.
With all due respect, paulg is in somewhat of a bubble, this is not congruent with the global situation.
In that regard, it's not too different from mobile telephony. Mobile phones drove the electronics industry 20 years ago, but there is limited demand for people who really know how to build a phone (ie. build the hardware and write all the signal processing from scratch), as there aren't that many companies that do phones at the lowest level. A few of the engineers got rich (eg. Viterbi), but most 'just' made a good living. Most people who got rich off phones didn't do it by knowing how phones work.
Incidentally, the skills for the lowest levels of LLMs aren't that far removed from those needed for mobile telephony, in that both are based on maths, computation and information theory.
I was there at the start of the smartphone boom. I built a demo Android device that was capable of telephony/data, 3D rendering, etc. all before Google open-sourced the OS, for a SoC vendor that wasn't in Google's inner circle. Yet the industry was not interested in my junior profile during the subprime crisis.
I wonder if the modern incarnation of Android flagships would have taken off at all if not for Apple. When Android first showed up, the dominant "smartphones" were running Symbian/Windows Mobile and they had a very different view of the world when it comes to UX and hardware.
But it was generally seen as a gimmick instead of desired before Apple made it look good. Even when the iPhone came out, one of the jokes was how the grid of icons looks like how a Windows user's desktop would look like when they didn't understand the filesystem.
This is super interesting because I moved from mobile telephony into ML and data science, and information theory and working with data in statistically correct way was what helped me! This was 10 years ago though.
Good points, but I think we can expect the AI space to be more tumultuous.
What most people want from mobile technology is for it to work, not too expensively, and for it to get out of their way.
What most people want out of AI is for no leader to emerge and wield supremacy against the rest of us. People are afraid of it in ways they weren't afraid of mobile, so they're more willing to work together against whoever is in the lead.
Its more like an arms race and less like a utility. The disadvantage I face when my competition has better mobile coverage and bandwidth is minor. The disadvantage I face when my competition has better intelligence on tap is much more significant.
>* What most people want out of AI is for no leader to emerge and wield supremacy against the rest of us. People are afraid of it in ways they weren't afraid of mobile, so they're more willing to work together against whoever is in the lead.*
No.
That’s what people like us on HN want. The people out in “Greater Userland” just want the black box to answer their questions. They could care less who is behind it. They don’t yet attach their black box to Amazon or Microsoft etc. And most won’t care enough to be inconvenienced even when they do make the connection. (As your competition argument implies.)
Heck, a lot haven’t even made the connection between the black box that gives them answers and data centers. They think, “ ChatGPT good” and at the same time think “data centers bad”.
We don't really have the demand for as many telecom companies as actually exist in the world. There's a reason we just have one Whatsapp and one Instagram, not three or four almost-but-not-quite clones in every single country that mostly differ in branding. The reason for the current situation has mostly to do with regulation and traditional, enterprise, "obviously every country needs a separate local branch, because that's what mcDonalds does" thinking. Technology has very little to do with it.
This is why the telecom world now consist of equipment manufacturers, who do most of the hard tech stuff, and actual telecom companies, who operate the equipment, rig towers in their local country, and maybe write some glue code to integrate a core from vendor A, a billing system from vendor B and a CRM / corporate invoicing system from government-approved local vendor C.
Banking also works similarly, though modern Neobanks / Fintechs and bank consolidation are slowly dissolving the concept of national bank branches.
Yes. It's like looking at the (Apollo) moon rocket launch and then suggesting teenagers should learn to build rockets in their garages for the coming space age.
It is viable as a toy project, but there are vanishingly few career opportunities.
They'll learn so much more that won't transfer to as many job opportunities. For ex, say more about C++ and less about cutting edge CSS (because modern browser tech is an ocean). I suppose they might luck into other adjacent or unrelated roles with the same skills.
I have a hard time imagining anything where writing a browser wouldn't be an excellent transferable foundation. Even your example: writing css will never teach you as much as writing a css engine.
Sorry if I was unclear. I mean they'll never have enough time to learn a significant amount of modern web tech because they'll be working up to it from lower layers and basics.
Browsers from scratch are multi-year projects for multiple people. Even just skinning and minor tweaks to modern browsers is a deep well for one person.
Building a browser in the early web was actually a very achievable goal for exactly the reasons it isn’t now. There was not JS. No CSS. No SVG. In fact very few widely supported image formats (and graphical browsers weren’t around in the earliest days of the web anyway). TLS didn’t exist. HTML only had a subset of methods. And even POST was usually just managed by CGI/BIN calling an external process, often written in C++ or Perl.
They will also know the fundamental underpinnings of the web, you know - the thing that those html devs are actually using.
That’s like saying someone who fabricates cars doesn’t have the skills to drive them. Perhaps not, but they’re very well placed to pick it up quickly. They’ve also shown they can do something far more challenging, which is actually better than hiring for narrow immediate skills.
In other words: I’d hire that candidate in a heartbeat.
You'd hire an auto designer to be a limo driver? I think it's just two different skill sets. The auto designer is clearly smart but a limo driver also needs to have customer service skills, etc.
I think he's saying that building a browser is not a transferrable skill, like making a generic web page is. Employers don't like specialists. I used to build display drivers for graphics cards. Wonderful learning opportunity but other employers not in the graphics card manufacturing business didn't give a shit--those three years were essentially treated as an employment gap. "Well that's nice, but we really wish you had general experience writing CRUD apps..."
> I think he's saying that building a browser is not a transferrable skill, like making a generic web page is. Employers don't like specialists.
I recently switched roles, and among the seven places I interviewed, none of them seemed to see my then-current browser job as a problem, even though they were not related to browsers. (The closest one was a company implementing a HTTP reverse proxy, and I did not work on the browser's HTTP stack.)
I would argue that in a post-LLM world, becoming as specialized as possible is the only way to survive. If you have the serious systems programming skills required to write graphics drivers, your talent would be wasted writing CRUD apps anyway. I hope you eventually found/will find something more appropriate for your skillset!
I'd argue the exact opposite. If I want a graphics driver, I'll ask the clanker for one. But if I don't know enough to even know what I want, there's no path forward.
People keep talking about a browser in the modern context but the GP specifically said “early days of the internet” (which, in fairness, would mean pre-web. But I think it’s safe to assume they meant “web” not Internet).
In those days, it was actually a much simpler exercise to write a browser than it is today. I even wrote one! And writing a browser absolutely teaches you how HTTP and HTML worked. Plus a lot of backed development was forms data sent to CGI and thus written in languages we wouldn’t even dream of using for web development nowadays, including C++.
So in the early web, writing a browser absolutely was a transferable skill. It might not be now, but in the context defined by the GP, it was.
This is like saying writing a compiler doesn't give you transferable skills. The surrounding competencies required to do this grant a pretty large amount of broad domain awareness
[Blake Ross] worked as an intern at Netscape at the age of 16 ... Ross became disenchanted with the browser he was working on and the direction given to it by America Online, which had recently purchased Netscape. Ross and Hyatt envisioned a smaller, easy-to-use browser that could have mass appeal, and Firefox was born from that idea ... in 2003 all of Mozilla's resources were devoted to the Firefox and Thunderbird projects. Released in November 2004, when Ross was 19, Firefox quickly grabbed market share ... with 100 million downloads in less than a year
In the meantime Mozilla was mocked on slashdot.org and elsewhere relentlessly after the first two years of the project when nobody believed there would be any value in the effort. Hats off to the team that took around five years to get to Firefox 1.0 (and released Mozilla browser in the interim). It took a lot of conviction to see it through.
That may have worked at the time, but no companies are interested in learning projects today. If you didn’t do the reqs list for the last 5-10 years with the same title, forget about it. Because there are dozens of folks who have, lined up. No one is indulging career changers (and most fresh grads) for the time being.
For this example, if you built a browser in 2000 it would put you in a great position to launch android in 2008. I think this is pg’s point. You spend all this time learning the interesting bits and when an inflection comes along that makes a new product or service possible you could be the one to likely launch it
That’s like saying if you can dunk in 8th grade you could be the next LeBron James. A LOT more things have to fall perfectly in place at the perfect time for that end result to materialize.
FWIW I built browsers from 1999 for a long time. (But I was never a wunderkind, just somewhat tenaciously curious).
And I guess I am doing fine, but not amazingly rich or so.
Browsers were always a project closer to research/charity. I think Marc Andreessen said something similar - that he would never do that again. B2B is where you can make money.
I am talking from the perspective of the individual developer.
You don't need your impressive product to succeed to land a good career.
If Alice is doing LLM-from-scratch work today, and Bob is doing agent harness work today, Bob's project is far more likely than Alice's to become useful/popular/profitable.
But if neither project survives, in 5 years, Alice will be more employable/at a higher market rate than Bob.
Neither one has even gone to college yet. I’d expect Alice’s project to be more likely to interest professors, get some undergrad research experience, maybe that can turn into something.
There is a confounding variable, however -- the person is most likely a good dev. It stands to reason that they've had a decent career at least since then.
Yup like you go far enought you will pick lots of transferable skills like data cleaning in the case of LLM, DOM parsing case of web browser or welding case of rocket.
Really? The latter was immediately useful to lots of people which is motivating, and it had a nice smooth learning curve (html -> js -> php -> databases -> apps -> backend). Learning HTML is the first step to learning how to make full blown apps. Making a browser at 17 is like trying to climb Everest as your first hike. The expected outcome is burnout and demotivating failure. At best you'll learn some C++ or Rust.
17 is an interesting age. There are way too many comments here saying things like, 17 year olds should just do whatever seems interesting or bum around the world or focus on getting into university. But historically most kids were expected to be productive adults at 16 or 18. 17 is about the right time to be thinking seriously about what kind of work you'll do, how you'll make a living. University won't help and will just delay this decision.
I contributed to a browser engine around that age (KHTML, which later became WebKit and Blink), and while I don't work in browsers right now, much of that knowledge, mindset and of course the professional network have done much to shape my life. And a fairly successful career, for that matter.
Contributing to an open source project is fine, but the original analogy was "it's like telling teenagers to build browsers".
If teenagers could make small contributions to LLMs via open source then sure, go for it. Optimizing llama.cpp or similar would be a good learning project that might later get you good work via social networks. Contributing to open source is how I got started too.
Unfortunately, training LLMs isn't something that fits well to open source open collaboration. Inferencing codebases are better.
Excellent point, and I agree. Much of the benefit I saw was from working within a like-minded, smart team, not going it alone. And also specifically working on software with a real user audience to learn what providing value to them actually constitutes.
In that sense it's more a "seek out the open source community and real projects when young" rather than "do web browsers", with a bit of "look for ambitious types of projects few get to work on".
Early web didn’t have JS. Nor PHP. In fact a lot of early web pages were written using static HTML with C++ invoked via CGI/bin for processing form data. So writing a browser would teach you the HTML plus C++ too.
The early Internet (which the GP mentioned) didn’t even have the web. But that’s nitpicking.
17 is a weird age but ymmv. I left home at 16 alone to study abroad. I had tons of free time due to dorm curfews and such. Unfortunately, I was too poor to have a computer and the computers we had access to were completely locked up. (Naturally we waltzed past the locks to play some games but it was also under surveillance)
Paradoxically I coded way more between ages 12-14, I regret my wasted late teens.
I mean... that's actually amazing advice. Not because they would grow up to create browser startups. But because they would grow up to create web startups that succeed because of very fundamental of how the web is rendered.
This site exists because paulg created a webshop in a niche language (Lisp) and got a prototype bought out (and discarded) in the gold rush. No browser internals needed to make a fortune on the platform. Just rapid development of an application with natural monetization, and being in a place to do so way at the beginning.
Jeff Bezos did similar, but he did his own fulfillment and hired out the coding.
Early internet let me create the best personal web page in my city that I knew of with 2 weeks experience as a 12 year old. I imagine that same 12 year old could be more knowledgeable about LLMs than 99% of people in the same period.
I doubt it. Creating a website can be done by copy pasting a few snippets together and checking if it visually looks like expected.
Good luck with that approach when trying to toy around with models and their training/inference.
There is also a lot of math basics missing that a 12 year old may be able to grasp, but I would bet they are at least 13 by the time the knowledge is deep enough to understand what operations are happening.
That seems like a perfectly good use of time for a kid in the 70’s! You’d learn a lot of engineering skills and demonstrate a tenaciousness that most people don’t have. I don’t think the goal is to predict what will be the important technology in 10 years. The goal is to challenge oneself with hard tasks and learn interesting stuff. A lot of the “AI” people today were compilers people yesterday
There are more career opportunities building rockets than designing rockets. Lots of welders, machinists, electrical engineers etc build rockets, and those skills transfer.
The same is not so different for AI. A few people design novel AI, but there are a lot of people training AI (especially if you include fine tuning) and implementing AI, even as a hobby.
But he’s not saying that it’s a career opportunity.
It seems to me like he’s saying that doing this thing would be 1) fun and 2) a great way to become employable in the future. I don’t believe he’s saying that this project would be some kind of job training exercise.
I "guess" you could put all your effort into moving up to the next level in computer science, or you could put the same progressive effort into reaching new levels in video games on the same hardware.
Alternatively you could even put all your effort into social activities and leave the technology to other people entirely.
It might even be possible to find a balance between things that are widely "understood" "socially", and those that are not ;)
When I was 17 and still witnessing Apollo Moon launches, I wanted to build an AI that would handily outperform LLMs as we know them today.
But that was way back in the early 1970's and all I had to work with was a mainframe.
Well the mainframe itself wasn't bad, the real show-stopper was that I didn't own the computer outright, no strings attached, no debt, etc.
>I'd probably try to make an LLM that I could use on some specific problem.
I thought so too back then, still do so I guess this is one of those things that could stand the test of time. I always wanted to start with something a lot simpler than a Moon mission myself. At 17 I already had a significant breakthrough in the chem labs and it was from alternatives to a single processing step plus everything that descended from that, rather than trying to tackle a much more complex detailed multi-step synthesis. I was only 17 but I was not trying to be a slouch, I don't think pg was either at that age but his advice is not for just anybody. I couldn't have done it if I hadn't made major progress since being 16, and it really emphasized at the time how much maturity can make a difference. My imagination ran wild as I extrapolated :)
In a reply from LeCun to pg:
>>I'll figure out a set of methods and architectures beyond LLMs that can quickly learn to perform physical tasks as efficiently as humans and animals.
That last item is also what I would if I were 30, 40, 50, or 66 years old
I see no reason to stop at 66 either ;)
But I figured that people owning more computer power than I could ever afford were going to be doing something like this as soon as they could, without having to wait for something like an LLM to arrive before getting peoples' attention.
It did seem like things were going to take longer than you expect, so it's pretty good to have a lifetime of concentrating on the specialized natural science domain expertise, focused now for 50 full years on how it would combine if AI ever got good enough.
Both the natural science and the AI need to be a major cut above, I still see dramatic room for improvement in my own work. If I'm going to have to rely on "other peoples' AI" then that natural science component is going to have to pull a lot of weight to keep up with the kind of computers that only rich-as-hell high-rollers have access to.
I agree with mostly all of this, but personally I wrote a toy LLM almost 5 years ago and while it never saw much use outside of boring my wife with a shitty command line demo with glee it did help me understand how they worked and how to apply them, played a lot with JAX and pytorch, ended up building a ghetto version of MCP and an LLM-Pool to proxy requests to my baby local models and so I didn't struggle to see the evolution of openrouter and MCP agentic workflows. The same way i'm really glad when I was younger I built a bad webserver by myself, a really painful SQLx type database, etc etc etc - none of these things led me to developing for Nginx or Oracle nor will knowing JAX get me a job at an AI research lab, but I do have a lot of depth in understanding how the technology works so that the flavors on top of them are easy to digest and make more use of immediately, and I think the same can be said for engineers coming into the field - if it's a spooky LLM box you aren't going to be squeezing the same amount of juice as the guy that knows how they work inside and out so having at least the understanding of a _babys first LLM_ is going to get you miles ahead of people who don't.
For anyone who wants to dork around there is https://github.com/rasbt/LLMs-from-scratch which is something amazing that I think anyone who wants to engineer things around LLMs should at least blast through and read.
Game cheating and reverse engineering MMO backends taught me a lot: databases, networking, securing a backend (and frontend), limitations of simpler languages when comparing them to more native options for building backends.
Agreed, I was very late to the game and was forced to learn VBA for excel sheets and that is how I finally broke into programming.
When I was a pre-teen I stumbled upon CD-rom hacking guide to bypass disc requirements on games, I remember opening up the file and the screen being filled with HEX code. I was so overwhelmed I just closed it and never touched programming after that for 15 years. My life would have been totally different if I had embraced the unknown instead of retreating.
> The reality is that an incredibly small minority of companies in the world do any real training or optimisation. It's unnecessary and inefficient for most purposes unless you are fully dedicated to being an LLM company, and still then it's a struggle. Those few that do train, they spend most of their budget on compute and have relatively small teams.
And the job postings are often ridiculous. I recently was an AMD job advert in Germany for an ML Kernel Engineer, not Senior mind you. The requirements went something like
> Masters Degree required with strong preference for a PhD with peer reviewed articles in {journals_list}
> 10+ years of experience in C/C++
> GPU programming experience required
> 10 more ridiculous lines
No idea how a teenager self teaching himself LLMs is supposed to even get a shot...
I have always been pro fundamentals. It caused me trouble early in my career with bosses that didn’t understand why I would spend time trying to understand how something worked at a low level if I was a high level user. But then knowing the fundamentals gave me an edge as a designer and developer by understanding capabilities and limitations of the tools I was using. For example understanding how indexes work internally in a relational database. So I see the value in this type of work, not to land a job as a LLM researcher, but as an informed user of the tool.
There are high school students competing in contests that cover parts of the (Math) theory behind AI. A lot of high school research programs are integrating AI with other things and complex mathematical models…
To me, this is bizarre as Calculus is barely taught in high schools (and likely poorly).
Don’t get me wrong, these kids certainly aren’t the usual lot.
Yet, I really wonder if they know the fundamentals. Do they even understand derivatives or just memorized the rule for polynomials? Can they even explain what a transistor is?
Normal curriculum takes 5 years to go from Algebra I to Calculus. Real Analysis, Linear Systems, etc. are fundamentals taught only in college…
Feels like too many are trying sprint before even learning to walk.
That's like saying the only way to do real engineering is with Google-scale Borg deployments. You can do quite a lot on very little hardware, r/StableDiffusion is a prime example.
You can do plenty of "real engineering" under normal conditions. But specifically when it comes to LLM engineering, no there's really not much you can do, they are called "large" for a reason. You can play around at small scale, but those lessons you learn will not be very relevant to the real problems in the market.
Sure you can gradually climb the ladder by demonstrating your skills bit by bit and getting access to more resources. It has very good prospects if you do manage to push through. But it's a hard and risky path, and you will not be able to get any interesting results for the longest time.
For a young middle-class student, it just doesn't make much sense. You can do much more impressive and impactful things with your time without getting into that black hole.
I know how to build an LLM, I know plenty of fellow young engineers that do too. It's really not that complex. But they can't do much with it without capital or access.
Good engineering has never been a bottleneck in this field, it's been all about having access to capital and taking smart but dangerous risks burning it on compute, without much idea of how long you need to keep burning for. There's still no end in sight, some are still managing to convince investors and keep burning, and we are seeing progress, but the business case is still unclear. If you want to get in that game, go ahead, but it's not something I would advice the average young engineer.
> I know how to build an LLM, I know plenty of fellow young engineers that do too. It's really not that complex.
I’m 40, and I don’t.I took that abstraction for granted and “left it to the big labs”. However I want to build my own LLM for learning purposes.
On needing big expensive hardware.. necessity is the mother of great innovation. Perhaps 18year olds trying to build their own LLMs in constrained resources environments will result in ground breaking ideas of achieving better intelligence than the one we currently have….
The world needs pragmatic folks who work at a higher abstraction and make LLMs useful, AND also folks who think why not “this other way”? And build newer ways to do fundamental things.
Given the usefulness of current LLMs, I would certainly encourage anybody to try and build their own LLMs, and see what they come up with…
Heck if they build a rack full of old laptops and run something with it that could be done “better” with modern servers, I’d still appreciate the learning running things on those little machines bring.
Well, that's not how it works. You don't just put some old laptops into a rack.
Maybe with a decent consumer GPU like a 4090, you could do experiments like distilling and fine tuning a small image model for edge deployment for specific tasks.
Even there, many use cases might require renting compute for $10/hour and investing a few hundred.
A LLM from scratch? Forget it. You can do theoretical experiments, but not build anything remotely useful with that kind of budget.
If you're talented enough to come up with revolutionary methods, maybe an university or AI lab would be the place to be.
To me the bottleneck is not even the compute, which is an issue for sure, but the data. All these large companies got their hands into petabytes of data, a lot of which of illegally acquired, but now they are large enough to pay the fines.
> On needing big expensive hardware.. necessity is the mother of great innovation. Perhaps 18year olds trying to build their own LLMs in constrained resources environments will result in ground breaking ideas of achieving better intelligence than the one we currently have….
One of the things to think about when it comes to many kinds of "expensive" technology, is that from so many well-funded ventures, from the capitalists on down almost every decision-maker involved has not spent the majority of their life making every dollar count in some way or another.
Even more so when things are not just expensive by nature, but truly overpriced beyond that point.
>10000%
Once in a while you do get somebody who only spends a dollar and gets more out of it than a seasoned high-roller spending $10000. Most of the time the waste is borne by those who can afford to throw away $10000 more easily than an economizer can afford to lose one dollar, so nobody is crying about it.
With how ridiculously large the language models have gotten though, a 10000x improvement in actual intelligence does seem like it could be lurking unrecognized at a different point on the compass.
Agreed. It's hard to learn unless you have access to quite high end hardware, and even paying by the hour is expensive. There's a low ceiling on what you can learn without doing training runs.
You can however learn everything you need to know to get on the career ladder as a software engineer on a regular home PC.
While the topic here is narrow, the concept is broader.
Do you take the first step or rule it out because you don’t yet see the complete picture.
As a teenager I never hesitated to try things out. As a young adult I wanted the whole picture. Now I’m back to playing / trying things out. I kinda wish I’d not given it up. PG being a bit older and reminiscing - I bet he’s in that bucket too, whereas someone trying to establish themselves professionally probably (aka me early 20s) wants to see the path.
> . But specifically when it comes to LLM engineering, no there's really not much you can do, they are called "large"
The “large” qualifier dates back to pre-transformer language models, where even training a multi-million model was hard due to how poorly it scaled. GPT-2 was a large language model, despite being only 124 millions parameters.
Due to how much high quality data is readily available, anyone can now train a sub-billion (L?)LM on commodity hardware.
And I'm personally convinced that pretty much any enterprise use-case of an LLM (except coding) is better served by a fine-tuned small (<2B) model that is trained specifically on the task, rather than a generalist frontier model, so learning the engineering around fine-tuning is a key skill that companies will realize they need sooner than later.
Why? Oersted is correct, for any size class you can find an LLM that is free and well trained at this point. They are highly adaptable even without fine tuning, in-context learning is still superior to fine tuning in most cases also. And real world fine tuning is mostly about data gathering and cleaning. The actual adapter training is automated and put behind simple APIs.
I saw my first language model in action in 2014, I was writing blog posts about them back in 2016. In recent years, like many of us, I spent some time learning ML frameworks to see if it'd be a fun career pivot.
But:
1. It doesn't seem especially creative. All the frontier labs have nearly identical model personalities, capabilities and even app designs. We're not seeing them differentiate from each other, implying that the design space might not be that large. In which case the opinions and unique approaches of specific engineers aren't that important, they are interchangeable at the right level of skill, and what to do next is usually obvious to everyone.
2. It doesn't seem like a big job market. A lot of ML jobs were wiped out in recent years by the rise of LLMs. Lots of NLP specialists etc were suddenly replaceable with a cheap API. The jobs that remain have compacted into a small number of companies. It's a small community which greatly increases career risk, especially as so many are unprofitable and/or have strong ideological requirements.
3. It's unclear how much demand for better models there actually is. Do we actually need smarter models? In robotics clearly yes and robotics is interesting and high potential, but for pure LLMs/image models, most users are already incapable of setting tasks that stress the best models and are happy with the cheaper smaller ones.
Using the models on the other hand is a very large design space, and has a lot of scope for creativity. I see use cases for AI everywhere, but most companies seem to stop at putting a chatbot on their website or asking Copilot to rewrite an email before they send it. A lot of companies have hollowed out their IT departments over the past twenty years. It feels like a new golden age of consulting work could be upon us.
I really don't think I'd tell a 17 year old to learn how to train LLMs. Learn how they work and how to use them, sure, absolutely.
> We're not seeing them differentiate from each other, implying that the design space might not be that large.
There is the more likely reason they are not differentiating. They use almost exactly the same class of model. Everything is linear, parallelizable. It's incredible path dependence that's now invisible enough we think it's a natural law. Nature is not linear.
Exactly. They had the capital to gather massive amounts of data and compute and took a path that went well and pushed it to it's max. Are still pushing it. Are there better ways that don't need as much data and compute? Well, that's for the 17-year olds who don't have trillions to spend to figure out. I'm hoping they do so in a way that brings back affordable gaming gpus and memory sticks as a thing again.
> They are highly adaptable even without fine tuning, in-context learning is still superior to fine tuning in most cases also.
Good luck relying on in-context learning for a 600M LLM.
> The actual adapter training is automated and put behind simple APIs.
That's like saying it's worthless to learn infra because you can use serverless instead…
> All the frontier labs have nearly identical model personalities, capabilities and even app designs. We're not seeing them differentiate from each other, implying that the design space might not be that large
The design space for a generalist model isn't large, by definition. But the design space for specialized smaller models is much larger. If you can train a 200M model that, for your use-case, is competitive with a frontier one, then you'll make your company save a lot of money in tokens.
> 2. It doesn't seem like a big job market. A lot of ML jobs were wiped out in recent years by the rise of LLMs. Lots of NLP specialists etc were suddenly replaceable with a cheap API. The jobs that remain have compacted into a small number of companies.
We are in a strange place where a few companies are collectively burning a hundreds of billions a year to sell things a few pennies for the dollar. Of course it's going to be cheap and concentrated. How is it supposed to end though?
> 3. It's unclear how much demand for better models there actually is. Do we actually need smarter models?
That's the thing actually: I don't think we need better models this much, and if we don't need better models we need the cheapest possible model for a given use-case.
There is no reason to believe that generalist models need to be transformer (or transformer hybrids) it's just that people are risk averse and not spending their resources on the many scaled risky bets needed to explore the possibilities. There is more there.
That looks like a really nice and well put together library.
But it feels like it sort of backs up my point about there being good models at every size class. Fine tuning Needle looks automated. Yes, you need to know basics like what validation loss means and how to use Python, but otherwise it's all about creating the dataset.
Someone had to come up with needle in the first place though. And it's the kind of thing that's going to come from a big lab with an AGI monopoly ambition.
No, but the skills Google needed (back then) were just normal programming and sysadmin skills scaled up. I had eight interviews that covered Linux sysadmin, programming, debugging, networking, maths and more. If Google hadn't wanted me there'd have been plenty of other companies who needed those skills.
If you look at model training jobs a lot of the work at this point is creating RL gyms (normal programming work), but most people still think the work is all neural architecture research. Doing the former is fine but won't teach you much about how to build LLMs, whatever that means now. Doing the latter is a very hard market to get into: not many jobs and requirements are often like, "you must have published at one of the following conferences". Prior experience is assumed. Most of them seem to treat Google as ML university and source of new recruits. It's understandable given the cost of training runs.
> requirements are often like, "you must have published at one of the following conferences".
I'm not sure why it's like this. If you look at the real world, you have stuff like ggml, which is about as hardcore as it gets in the LLM space, and it was made buy just a guy. Same for this like ComfyUI
If you get enough academics in a place, they tend to close rank, and not let anyone in without the same credentials. Data science used to be like this, they were constantly on about how you need a Math Phd to even apply, yet when I met these guys IRL, most of them were just running Python math libraries.
These previous examples show that if you understand at least a part of the problem space, you can 100% contribute without academic credentials.
You can do it at home, for sure. I've done some NanoGPT training runs and modified the architecture, it wasn't that hard. Came up with some potential research ideas too. It does take money for GPU rental so for a 17 year old, it's not so easy unless their parents give them a budget. For an adult with income you can do it.
The question is more one of opportunity cost. At 17 you need to start finding your way in the world. It's best to learn skills lots of people need.
Interesting/capable diffusion models are much smaller than similarly interesting language models. But yes you could always scale things down to learn the fundamentals.
Learning should not be done only as a direct path to getting paid.
Learn to create pattern matching and intuition to solve future problems.
When you are 17 it is a good time to understand how the world works so you can build on top of it in the future. If we assume most tech is going to have an LLM as part the stack, a solid basis in how LLMs work is likely to help you in future endeavors the same way a solid basis in how the web works helps you today.
Maybe a 17 year old should learn both. As a small anecdote when I was 17 I learned a lot about load balancers, failover, and building self-healing systems running small hosting company that had to be fault tolerant when I was attending high school. This wasn't at state of the art levels (e.g. I wasn't configuring gigabit routers or global CDNs -- but it was useful pattern matching for future problems)
I currently don't touch any of that tech, but I have working knowledge that still serves me today.
I am not the parent but I took it as them saying the premise was wrong to begin with, which I very much agree with. Learning should not be primarily directed by job availability.
A lot of startup companies are not training frontier models but help solve and optimize pain points of LLMs: cyber security, token usage, harnesses etc. These jobs don't require a PHD in machine learning but it does help if you understand LLMs at a deeper level.
But you can train a small LLM with a gaming graphics card -- I managed one on a GTX 1660. I don't think pg is suggesting that you try to chase the frontier. It's more like building your own OS in the 80s, or web server in the 90s -- sure, you'll never match the commercial offerings or the big OS projects, but building something from scratch within the limits of the hardware you can afford is amazing educationally.
In my experience, having a solid understanding of the next level down in the stack -- the foundation you're building your startup on -- is really helpful. We built a PaaS, and knowing enough about Linux internals to be able to work out what would be easy and what would be hard meant that we could focus our efforts on high bang-for-buck features.
So I'd say that understanding LLMs to the level that you get to by training your own baby one would be a solid foundation for pretty much anything built on top of the "real" ones.
Y'all are missing the point: It's probably less than 100 hours to learn the foundations of one of humanity's most-current breakthroughs. It's a disservice to any young hacker to not learn it. Here's your curriculum. Watch these:
when you think about all of the advancements since Attention / GPT a lot of it has been somewhat more obvious than in other fields, as is typical with the massive flood of innovation that follows a big breakthrough.
Paul likely assumes there will be a sequence of additional papers with the same impact as Attention is all you need, which will spawn a lot of opportunity for a larger group of experts who are conversant enough to advance the field even if they do not themselves create such a major innovation. Not only is this deeply exciting, it is also highly meritocratic as there is still scarcity of the kind of intellect and creativity necessary to swim there.
Machine intelligence might soon surpass it, though, and Deepseek is 100% Chinese mainland educated. Paul's description of building an LLM from scratch is meant as a vague starting point for being an innovator of the highest value aspect of modern AI innovation, not as a specific prescription.
And so I think the idea is more to understand tomorrow ... from first principles.
In the late 80's, as a teenager, I learned x86 assembly and C because that was the only way to squeeze out enough juice from my shitty CGA (and later VGA) card to programm the games/graphics that interested me.
I haven't written assembly in years.
But whatever I did in my career: it helped me and gave me an edge over my peers to have a foundation that is very close to the metal.
We don't know this. So many people are simply claiming this confidently, and a lot of them are betting their careers on it, but nobody has a crystal ball. Whenever someone tells you confidently, and without any doubt or qualifications, that something "is the future," be skeptical.
I remember when the Segway was definitely going to change urban planning worldwide.
Current AI can automate significant amounts of grunt work in programming and math. It's good at running web searches and writing summaries. There are a few other niches where it is currently successful. But other than that, many corporate AI projects are spectacular failures.
So just given what we have in hand, assuming no further breakthroughs, then we're maybe looking at AI being somewhat bigger than the Internet. Which would make it a revolutionary technology, sure.
But to get from "a revolutionary technology" to "the substrate the future runs on", then you need to assume more breakthroughs: long-context operation over weeks or months, displacing human workers 100% instead of 75%, and the ability to directly economically compete with actual humans. And people are investing literal trillions of dollars to make that future come true, without really thinking through what truly competitive-with-human AI would actually mean. We might be looking at massive job loss, centralization of power, fully automated "companies" with no humans dominating markets, and other dystopian scenarios.
And in those worlds, it's unclear that being good at CUDA and matrix math will be all that helpful, careerwise. The AIs are already pretty good at that stuff. Data scientists get paid OK when they actually get hired, but it's not everything college students were promised in the 2010s, either.
We can't yet build a fully-general competitor for the human mind. But we're getting closer. And if we ever do build one, the consequences will be really weird in any number of ways. So I worry about visions of the future that assume AI keeps improving significantly, but that also assume it still somehow remains a "normal" technology that doesn't, for example, render most humans fundamentally uncompetitive.
> But to get from "a revolutionary technology" to "the substrate the future runs on", then you need to assume more breakthroughs: long-context operation over weeks or months, displacing human workers 100% instead of 75%, and the ability to directly economically compete with actual humans ... without really thinking through what truly competitive-with-human AI would actually mean. We might be looking at massive job loss, centralization of power, fully automated "companies" with no humans dominating markets, and other dystopian scenarios.
At 75% replacement of a worker we would already have huge job losses as each individual would be doing what several before did.
The only alleviation would be the creation of new equivalently paid jobs, which is no better than a hypothesis right now.
A single 3090 will train qwen 0.8B just fine. While it’s not a very capable model any training technique you would want to master can be used to make real progress. And all the skills you need to learn how to do this can be learned watching Andrej Karpathy’s zero to hero series (shame he quit educational content and went to anthropic)
This is a wildly incorrect and myopic view on the world.
Finetuning model is cheap and incredibly useful for deployment. You don't need to pre-train a frontier llm from scratch to make useful models.
There is tons of domains where you and fine-tune llms and deploy them for value in companies and for your own entrepreneurship ambitions. I have made this a big part of my career for the last few years and now I'm working on finetuning models for starting my own companies.
I find the fine tune approach more interesting than straight to RAG and MCP.
End of the day they're all customized data stores and protocols to interact with them. May as well stick to a uniform toolkit with fine-tunes.
Not that other tools aren't useful. But reaching straight for a bunch of infrastructure reliant services is like jumping in with k8s when you're still at a stage where basic mocks in code are sufficient.
I won't roll my own encryption or UI lib but want to stay focused on the incompleteness of the project I have to ship not all the buttons and knobs of some dependency or framework. Same old manage context switch problem.
Both of you are right. There is demand for tailored (fine-tuned) models; almost every enterprise would theoretically benefit from them.
But there are also a lot of prerequisites, namely does the enterprise have its sh*t together on a technical level. Does it have the processes and data pipelines available to train and benefit from these models? Probably not!
Applied ML is at the crown of a tech pyramid whereas most enterprises are still struggling at ground level. Being able to build from be ground is likely a safer skillset than only knowing how to work at the (non-existent) apex.
Yes and no. I believe the point he is making is simply that there is no substitute for fundamentals and first-principles thinking.
We had scores of students study how microprocessors work and compilers work over decades, yet we have 3 or 4 major processor companies and a handful of programming languages. Yet, what they learned was probably crucial in their development as engineers.
We are also so early right now that even 2-3 years from now who knows how many LLMs and model firms survive (esp. given the "snake eating its tail" venture/investor funding situation)
This is kind of different though isn't it? Doing an assembly or compiler class has pretty clear benefits in this regard.
But LLMs are tools. Does a great engineer need to know how vscode works? Might be helpful to understand how extensions work, LSPs, and project configurations.
Usually when working with any tools, you need to understand how to get the most out of your tool for your needs and that's about it. Core fundamentals about how software and hardware works in general seems like it would be MUCH more useful than LLM core knowledge.
"Necessity is the mother of invention" - limited hardware has always forced people to find cleverer ways of doing more with less. Current models are clearly nowhere near the efficiency limit (the brain does vastly more with far less power).
This is roughly how GPUs for neural networks got started: after Andrew Ng left Google Brain, he no longer had access to a 10,000-CPU cluster used to train the original DistBelief system. But his Stanford students could buy a GPU...
> Current models are clearly nowhere near the efficiency limit (the brain does vastly more with far less power).
I think this is disingenuous. One could say that drones are nowhere the efficiency limit either: a bee can fly for hours on the energy contained in just a few milligrams of honey, while our best battery-powered drones can't stay airborne for more than 30 minutes. But comparing energy efficiency of electric/mechanical devices to their biological counterparts is not an apples-to-apples comparison. There's a world of difference between the energy storage and delivery mechanisms.
And as many have pointed out already in the siblings, it's not just about the compute but the access to petabytes of training data.
I think his point is to do this to understand deeply what they can do, what they can’t do, and what they can almost do. And then find the highest value ‘almost’ use case and push there. Which doesn’t necessarily mean improve the llm, could be applying it in just the right way for the use case. Of course, the bitter lesson makes this hard and risky. But no more risky than investing your time in learning anything else these days.
I bet this will get less true over time though as the rate of change slows down, allowing specialized models/training for specific use cases that aren't TAM heavy enough for the big labs to go after them. It's just now any general model is the best thing to use for everything and you're wasting money to build something on what will certainly be obsolete by the time you can get it to market
I don't think pg is giving advice on what will lead most directly to a job, but rather what is the best learning for a 17yo.
A 17yo who trains their own LLM will have a much richer understanding of what AI is, how it works, what its potential capabilities and pitfalls are, versus someone who spends the same time doing something else.
You can train and run small models on an old gpu. That’s what I’m doing now at, well, much older than 17. Does it produce a useful model? No. Not even remotely.
However, I do learn stuff about models that takes it from “magic” to “useful tool I understand the limitations of.”
Do I do it for that reason? No not really, I’ve never had luck learning something because it would be good for my career. I do it because at my core I’m a bored teenager who wants to make the computer do cool shit.
Not that expect to make it big as a LLM researcher but building something from scratch gives a much deeper understanding than what you can get from simply using something.
Much in the same way as implementing and designing your own programming language makes you a much better programmer.
It is not a skill that you will use in your day to day life, but I think it is part of the fundamentals now. Sure, LLMs are in a bubble, just like the web during the dotcom bubble, but web didn't disappear, and I don't expect LLMs to, even after the bubble bursts.
I didn't write a LLM from scratch but it is on my "wishlist" so to speak. From what it seems, a GPT-1 class LLM can be done from scratch in a few days and tens of dollars of cloud compute or a high-end gaming GPU.
It is an exercise not unlike building a compiler, a school classic. You are unlikely to ever work on a compiler, but at least, now, you know your tools a little better. It is not about becoming an expert, that takes years, it is about knowing what you are doing.
If you intend to make software engineering your career, you will want more than surface knowledge. And that part is entirely on you, or on your school if you are a student. Companies will not pay for you to learn the fundamentals, they want short term returns, because you may leave at any time. But you as a software engineer may have 40+ years left, so it is worth thinking long term. Claude code may become obsolete a few years, but linear algebra is not going anywhere.
What about other machine learning related skills? Does this wave of LLM mean less need for that kind of work?
I would think not, but when I started to look into OCR options recently - assuming that obviously a dedicated tool would do a better job than an LLM - I was wrong (apparently).
Of course, because it's not the LLM that's special but the training data. Nowadays, your favourite AI service to generate code for an LLM whenever you ask for it.
I don’t think that’s correct, the data is not that special either, and getting a similar dataset is significantly easier than getting the compute capacity to use it, even if they are both relatively hard.
Probably this also is too cynical and simplistic, but: really what’s special is the ability to get this kind of capital, with the freedom to burn it on mad moonshots, with long enough leeway to actually get to see a few of the moonshots come true.
No wonder that the head of YC made this happen, this is exactly what they are world-leading at.
> +1, I have a friend, math PhD that's been working on ML research 5+ years in London yet he has not been able to find any position.
Maybe people simply don't want math PhDs but something else? Since 1-2 years ago I started doing consulting/freelancing in the ML space, but more on the infrastructure, deployments and similar stuff, as a general purpose developer, and I have a waiting list of clients interested in more work, some of them even trying to recruit me to work for them full-time as well. I'm based in continental Europe, fwiw.
How've you gone about getting into this btw? I have extensive experience in infra and pipeline rollout but have struggled to find freelance clients for this kind of thing. Would be great to tie it into ML as a learning opportunity there
Spent a year of freetime catching up on everything and learning as much as possible, started sharing what I've found works or not, write a bunch of comments on HN and elsewhere, and have a email in your profile, eventually people will find you if you put out good stuff :)
Also bunch of past workplaces who've adopted AI in various ways who reach out once they find out what my current focus lies, but that's harder for others to replicate unless you've already had a career as a developer.
This only proves the original point which is that there is not much demand for actual machine learning expertise because that is only carried out in a small number of places and what demands there is is for the more basic software carpentry like infrastructure and operations rather than the actual technology and Engineering side of things
What parent says about "there are very few available positions" for "engineers that can do real LLM machine-learning" is fair, yeah, I'd agree with this.
I don't think the "incredibly small minority of companies in the world do any real training or optimisation" part is necessarily as true, as some parts of the work I do get is about helping them optimize training and infrastructure around training. Mind you, none of this is for building LLMs from scratch, it's 99% fine-tuning existing checkpoints.
I'd also agree with "paulg is in somewhat of a bubble" regardless of this, which is worth remembering whenever you read his content. Same goes for any person living in SF, and dare I say the US. But also, YMMV, I live and work in Europe, probably why I have this perspective.
I think he does, but it seems he still considers himself a remote SV native rather than someone working in the UK's economy.
Worst of both worlds - no casual gossip feed in the Bay Area, no big dog meetings in the UK. (Which mostly has no idea he exists.)
As for the question - what are the odds LLMs will be anywhere near the top of the tech tree five years from now?
The trend seems pretty clear to me - local/offshore models are snapping at the heels of the big names in the US, and the current investment arc is insane.
I wouldn't bet on Anthropic or OpenAI being leaders five years from now. Longer term, I especially wouldn't bet on the US build-yourself-a-monopoly corporate model surviving AI at all.
I mean it makes sense right? Anthropic for instance has like, a couple hundred staff in London with plans to expand to somewhere just shy of a thousand. There are far far more ML/Maths/CS/Stats PhD's than there are openings. Especially in London there is no shortage of suitable candidates given Cambridge/Oxford/Imperial/UCL are surrounding it. 2% of the UK population has a PhD alone...
I think a lot of this is based on preconceptions. A lot of apps were made with Electron, because it was common wisdom that native is 'too hard'.
Now with LLMs, people write native apps in Rust, and I'd like to think some of them found that there isn't such a huge jump in difficulty they assumed there would be.
It never was native "too hard" it was always "too expensive".
That's the same case finding companies that will actually pay for hand made LLM instead of using something from big providers will be hard because most companies won't be able to afford it.
Yeah if you find a company that will do that stuff directly, good for you, but you will have to be very lucky and you will have to compete with other people who followed PG advice.
So I would rather learn all there is about properly using LLMs and integrating them with existing systems, that will most likely by useful for 90% of companies out there.
Building business niche harnesses is in my opinion much better direction. Knowing what will work best in specific cases is it FTS or vector search, optimisation of usage, getting best results while using cheaper models, knowing how to use tools to run models on the servers, and all the tooling around that like various MCP or just tooling that will be provided to models.
That is what I am currently busy with and I already have customers for that knowledge.
Oh yes I agree, LLMs are not that complex in principle, most engineers could build a toy version completely from scratch without too much difficulty.
But that’s the tip of the iceberg. If you have any ambitions of doing this professionally, it quickly becomes clear that all it’s all about knowing how to deal with problems that are only present at massive scale, when an LLM is actually L and becomes AI.
The mundane details about how to build a tiny autocomplete model and the maths behind it you can learn in a couple weeks easily. It’s not black magic, there are much harder areas of computers science.
This is a silly take. You can learn to build an LLM, there are great resources to do so (there are books about building them from scratch), you can use older model GPUs or rent them by the hour. The value of understanding them is really high for anyone building any application that uses an LLM at any point.
It similar to understanding how a very basic CPU works. Just because I'm not going to work at intel or nvidia or whatever optimizing the hell out of a chip, it doesn't mean I just throw my hands up and think "magic" - the basic architecture isn't that difficult, and the value of knowing it is astronomical for anyone writing software.
FWIW, at least 20 Y Combinator startups have published ML research recently at ICLR, NeurIPS, ICML, and so on.
I think a lot of people assume that only the big AI labs can do cutting edge research, but there's a strong argument you can do it as part of little tech as well.
I just finished fine tuning Gemma e2b for local code completion on my local machine.
This comment just reinforces what the post actually means. We need people that are LLM natives, computing solves itself with time and with scale adjustments
1. I very much still write code myself without an LLM when I need top quality
2. That's why I have an agentic agent as well installed, Qwen 27B, outrageously good, better than sonnet 2 years ago. And it's mine, I can give it confidential info to work with since I own the whole chain. See where I'm going with this?
The other thing to add as well is that the research teams who do the actual research work are relatively small and very specifically qualified which naturally keeps the barrier to entry high.
I think companies of all sizes will want their own models, or at least customised ones, for their own specific use cases or competition and security issues.
1. Both training and optimisation will get significantly cheaper and easier quickly.
2. Politics will probably get even more insane before a potential reprieve on the 20th of Jan 2029.
3. The big AI firms will become part of the surveillance capitalism network, if they're not already.
So I think for self-protection a lot of companies will be looking near to medium term AI independence.
The argument is sound, but the maths don't math for now, and it's unclear when/if they will.
For the time being, unless you truly have millions, the outcome from training will be very net negative, while focusing on building on top of existing AI will yield amazing things if you apply the same talent and effort.
When it does get cheaper, then it will be easier to acquire the skills and experience too, and the struggle you went through by trying to do it now will be somewhat wasted.
Besides, I am well versed in this field, and it is not rocket science. There are plenty of software engineering domains that are a lot more challenging, like high-end graphics, large-scale data engineering or kernel programming. People will learn to train LLMs when people want them to.
In reality, enterprises are happy to offload even risky tasks to others as long as they get some contractual guarantees about their data. Would they like more choice in who to buy from? Yes, but not enough to in-house such a specific discipline.
The cost of training a model from scratch is going to be cost prohibitive for the vast majority of companies (even if renting the hardware needed for the 1-2 month training time). It's an interesting learning exercise, and some of the things learned can be applied to other parts of the process. There's also the issue of needing a huge amount of data needed to get decent weights.
Fine-tuning a model or LoRA based on the companies data set is more feasible but you're likely going to need several runs as you test/try out different base models, parameters, etc. This is why there are a lot of fine-tuned models on huggingface based on base or instruction-trained models from the larger AI companies that have released open weight models (Microsoft, Google, IBM, Mistral, DeepSeek, Qwen, etc.).
Training is limited on memory first (storing training data and weights) and computation second. Realistically you need to own or rent 2-8 H100/B100 devices or Google's TPUs.
The majority of workflows for a company providing AI capabilities are likely best solved by tailoring a system prompt for the chosen model, evaluating the prompt and model with tools like promptfoo, and then running it on a compute cloud provider (including AWS Bedrock). If the company is big/financially well off enough they could look at buying the hardware needed to run it on their own servers.
For other uses like agentic software development you'd need to spin up a suitable model on a compute cloud provider (or local hardware if the model is small enough) and then tell your IDE/editor to use that model. You would need some way of benchmarking and evaluating the models to see if they are capable of doing the tasks you need. -- There have been some tests done by people on YouTube that suggests that Qwen 3.8 27B is a decent model, but your needs may vary.
Most companies that build physical goods don't care for one second about their IT department other than how much money they can save per month, starting by outsourcing whole of it, thus they have little use for internal LLMs.
And it's across the industry, thinking banks, private banks, insurance, pharamcy etc don't outsource their IT, including development...
I believe US outsource even more than Europe on this matter.
> With all due respect, paulg is in somewhat of a bubble, this is not congruent with the global situation.
Paul, I think, is talking about achieving outsized outcomes in relatively shorter timeframes (as the timing is just right to be investing in learning this tech) for high agency folks who can also afford the ordeal in wanting to maximize for impact & ambition. Of course, there's real risk one may get no where, but even in failure, given you were building the LLM yourself, you might end up with other adjacent, high reward opportunities.
> The reality is that an incredibly small minority of companies in the world do any real training or optimisation.
At the scale you are probably imagining, this is true - but take the hype out of the OP and what you have is just someone saying the field of data science exists and is growing.
I think his point is, if LLMs are the future (like computing is the future in the 90s), you should be an LLM-expert (equivalent of becoming a software developer).
I can see the point. It's unlikely that a 2.4T LLM will be integrated into, say, a pesticide drone. You'll still need some kind of LLMs to achieve maneuvers that "normal" programmings can't achieve.
But what if everything basically turn into that? Essentially, instead of build me a web app to solve X and do Y, build me an LLM to serve X and do Y. (unless the current LLMs are able to do it end-to-end but then they can hardly write coherent software/personal opinion).
Except knowing how LLMs work don’t actually provide much understanding for using them. People don’t use LLMs the way we’ve built on most other tons or platforms. It’s more learning Unity hoping to be a better gamer.
That's not true, because everyone, everyone, everyone seems to want to do training. Which results in a 50 person company training, say, a voice model that then fails, because it's just not good enough.
In reality the problem is that it gets blasted out of the water by a much worse architecture trained on 10000x the infrastructure. And while I'm sure the freshly brought in ML student came up with a 10%, even 30% better architecture, it just doesn't matter. (and never mind that even OpenAI hasn't really solved a voice model yet. Try it. It can probably match 2026-quality call centers, but it's no substitute for an actually empowered human)
... and yet, if you look at what hyperscalers are getting paid for ... comfortably more than half the income is training. Which makes no sense on so many levels.
The big question is whether companies hold enough proprietary data to do useful things that for e.g. Anthropic, etc. can't easily replicate.
For some very niche cases I think this is probably the case but for the vast majority, the company's data isn't as useful as they think it is or anywhere near the size needed.
Everyone says they want to do training, because it's sexy and an easy way to justify raising mad funding rounds. Some manage, most don't.
I don't know where you are located, but in EU, in China, and yes even in Silicon Valley, the vast majority of companies do not do any real AI engineering. There's nothing wrong with it, it's just not a smart path for most purposes. You can do amazing things without training, and if you try to train, you cannot get anything amazing unless you burn millions.
Very few people can afford to play the long game and cross that dessert. And, sure, you will not get far without good engineering, but good engineering is definitely not sufficient and is not the primary bottleneck.
It took a long time to cross that desert, and no sane company would want to get stuck in a desert, unless it's specifically an R+D "desert crossing" company.
I'm (more than) twice that age, but I've spent time learning this exactly this from videos by Andrej Karparthy and from books by Sebastian Raschka.
I didn't do it because it was useful to me in a practical sense. It's because LLMs are fascinating and I want to know how they work. From that perspective it's been a great experience. I have afirm grasp of the basics. This makes it much easier to understand frontier concepts like compressed latent attention. I can follow the field and understand it.
Not sure I would have got as much out of it at seventeen. I have a lot of background and experience which made it much easier to learn. I wasn't struggling with the linear algebra or with python. I already knew pytorch and neural networks. That helped a lot and I covered these tutorials fast and could skip over large sections. A few evenings and the odd weekend day over a couple of months was enough for me.
For seventeen year olds the tutorials are good enough to make it possible to learn this but it would have taken a lot longer to understand. On the other hand I would have learned a lot more. I think I would have learned a lot of valuable stuff.
However I also think 17 year old me was studying for his A levels and probably this was right choice in terms of maximising future opportunities. I'm not sure I think learning about LLMs instead is sensible. Indeed it might be bad advice. But I can absolutely agree with the sentiment.I think 17 year old me would have wanted to do this too.
This video is excellent. I'm a huge fan. Also the video is zero commitment and instantly available which makes it a good way to check you are interested.
The book by Sebastian Raschka is slightly less accessible but very reasonably priced and the experience of working through a book is a lot nicer than skipping back and forth in a video (for me). Sebastian's blog posts on recent architectures are absolutely great too.
I've read Raschka's "Build Your Own LLM from Scratch" book and really enjoyed it. I haven't tried the code yet, but the code from his previous Python ML book worked great.
actually just use chatgpt. There is a new mode of learning thats now avaiable that doesnt require you to read about things that are already discovered leaving you with a shallow knowledge.
you can now play the inventor and start with question "i want to build next token prediction software" and go as far as you can with your current knowledge while brainstroming with chatgpt as a rubber duck.
dont not start with a course on probabality , linear algebra or calculus . do not watch 3 hr videos or 3blue animations .
I think ChatGPT is a great way to get depth on a topic. You can Q&A for a while until you get it, and it won’t get impatient with you.
That said, I think traditional resources are a better way to get breadth and to frame the topic. Just chatting about something can be a little disorienting imo.
While knowledge is always great, I would encourage people not to seek advice from successful people like this (survivorship bias).
Moreover I am not sure it is even good advice? Would you advise a 17 y.o. to learn how transistors work or how to code (i.e. is LLM training the right level in the stack)? LLM training, a discipline where relevant work is already out of reach for 99.999% of budgets really as essential as this post implies?
> I would encourage people not to seek advice from successful people like this (survivorship bias).
Personally I don't see the problem, as long as you're aware there is survivorship bias involved here.
What's the alternative really, seek advice from unsuccessful people? That seems worse :)
Personally I do both, read about what worked for people, also read about what didn't work for people, then ignore both and do whatever the fuck I want.
It's best not to take advice on direction of careers from anyone. It's better to find and work on things that interest you, and then take advice from people that are amazing in that specific field. In mid 2000s in Australia all the "top people" were telling me not to get into a software engineering career because it was dead. It's certainly challenged right now, but it took off during those 15+ years.
“It's best not to take advice on direction of careers from anyone. It's better to find and work on things that interest you, and then take advice from people that are amazing in that specific field.”
Understandable irony. The way I see it, there are no buckets, only context surrounding the advice and your awareness thereof. If the context is unknown, best to take it with a big chunk of salt... Or spend time familiarizing yourself with the context if you think it's worth the time/effort; working on things that interest you is a nice way of doing that.
But some advice is less specific than other advice though. Some things are always stupid, and some things are always smart, if you look at the context of our world and society. I find myself pursuing these "fundamental truths" with great interest lately, especially now that the world is changing so quickly.
> It's best not to take advice on direction of careers from anyone. It's better to find and work on things that interest you,
Agreed, my previous stated "ignore both and do whatever the fuck I want" approach has worked out very well for me in life, people should probably focus on identifying better what their gut tells them, rather than what randoms on the internet thinks and writes.
I don't know; I think people in general are just not great at this. Successful people tend to underrate luck and overrate the brilliance of their own decisions, but the rest of us are prone to either reversing that and blaming everyone but ourselves, or being so determined to take accountability (or just depressed) that we become overly self-critical, or simply not understanding why things happened the way they did and reaching for any explanation that resolves the chaos into something narratively satisfying.
Intuitively, that'd make sense if that unsuccessful person eventually found success, otherwise who knows if they actually picked up what made them unsuccessful in the first place?
I think it's reasonably common for people to understand what makes them unsuccessful but be unable or unwilling to resolve the problem.
For me, I know that one major reason for putting an upper limit on my success is my inability to form effective professional relationships. I know I should go to events and talk to people and use these relationships to my advantage, but that's just not something I've ever been good at, and I find it so incredibly unpleasant that I also just don't want to do it.
Yep, many successful people greatly underappreciate the effect of simple dumb luck in their lives. And often they just make up complex reasoning chains, even wholly believing them, that more of it was in their control/talent, etc.
Nonetheless, there are many successful people I would gladly listen to for advice, though they are often successful in a different meaning than what venture capitalists would use (e.g. parents with great kids, managing to keep a healthy work-life balance, happiness, and maybe even having time to spend on some cool hobby project -- you are heros!)
same do unsuccesful ones. They usually overappreciate luck of succesful people from what I see, as a way of coping, many say "that guy was just lucky, i was unlucky". If you dig deeper, that person wasn't just "unlucky", they lacked the persistence, work ethics and other qualities compared to other succesful ones.
Of course, there are degrees and exceptions on every side, but the coping mechanism is very strong among people.
If there's a single unifying fault to the unsuccessful people I know (for any definition of unsuccesful, including my own failures) is that they're precisely bad at working out why they failed - if they even think to ask the question. The successful people are generally much clearer that it was studying hard or networking or natural talent that contributed to their success. The latter group may have people who don't realise luck played a part, but they massively increased their chances of good luck by the aforementioned behaviours.
Luck is a huge factor in success, so that logic leads nowhere. If one of the quacks selling how-to-succeed recipes had got it right, everyone would do it by now.
Kids don’t even know what it is, even less actually feel what it is. At 17 you think that it won’t hit you, that you will be the one to survive until you don’t.
I would seek advice from people who have a theory of why or why not they were successful.
A lot of those results were happening in very specific contexts and usually should not be regarded as a blueprint, but as inspiration to whatever I do.
The unsuccessful person is much more likely to be actually connected to the daily needs and struggles of a normal person. Paul Graham hasn't seen the inside of a grocery store in the past twenty years.
That’s what you get from listening to “successful people”. You get to learn about all the things they tried that failed, then the things that did work on that 24th try, which was successful.
The “survivorship bias” people always seem to assume that the “survivor” lucked into his fortune on his first try ever, so he can’t have learned anything, so we don’t have to listen to him. But that’s seldom the case.
very good read. I get into the same discussion sometimes with people. If you're successful you're automatically labelled as "lucky".
There are successful people that failed many times before being successful. Somehow these days, previous failures and persistence seems to be ignored and they focus just on the luck you got on the 20th try.
> seek advice from unsuccessful people? That seems worse :)
Not sure why would you think so.
Inverse reasoning is very powerful, and unsuccessful people can give you plenty of "don't do this mistake", which the survivors would not even think about.
> unsuccessful people can tell you teach you plenty of "don't do this mistake",
But how can I know for sure that that particular mistake is actually why they were unsuccessful? Has exactly the same issue as listening only to successful people as they hardly know what actually made them successful most of the time, but they still compose large blog posts with their reasoning for why.
Again, I still think my approach of reading both but then regardless go my own way is the preferable approach, at least for me, ymmv.
I would say you should seek advice from someone has lucky and successful as Paul Graham but I wouldn’t take his public Twitter as advice it’s entertainment period.
An advantage that is not "advisable", like being born in january, in a rich country, in an above average family, or just having luck, might have more influence on the outcome than any conscious action. It is almost sure that one-in-a-million level people only edge over the other 999,999 they competed with is just "have more luck".
Nobody said otherwise, even the video linked talks about talent (which might be also seen as luck!) and hard working. Say you have 100 people, and you pre-select the 10 hardworkers, that "hardwork" between 98 and 100% capacity: even if luck only accounts for 5% of the success, it doubles the 2% you can still extract from hardwork. Of course, someone that only works at 30% capacity has to be immensenly lucky to overcome the 70% they have until the 100%.
Of the 10 most talented and hardworking people above, I bet they have wildy different advise other than "do hardwork": use more LLM, use less LLM, keep away from computers, etc. And we are talking 1 in 100, not 1 in 1,000,000!
It comes to mind someone like Kary Mullis. He was no dumb, but I wont put him either at the peak of talented or even hard working people in the field. Yet, he was "lucky" enough to invent the PCR, which earned him deservedly the Nobel Price. Now that he was made to believe that he is some kind of illuminated and well above the top human, he started giving (at the bare minimum wrong, probably even dangerous) "advise" left, right and center: take LSD, astrology works, extraterrestial glowing racoons exist, etc. Downtune the guy a notch or two, and you can find thousands of Kary Mullis around giving useless advise as they were messiahs.
As someone who teaches AI at a university, understanding the basics of how the LLM works is really invaluable to understanding where and how they'll be applied.
You're right that it's probably a little too in the weeds, but it's also a nice clear and fun objective that teaches you the basics. Like building a TODO list in JavaScript to learn webdev or a Gameboy emulator in C++ to learn how a CPU works.
I would encourage young people to be born rich. It's the best time in 100 years to be advantaged. Why waste your potential by having your labor stolen?
I think it is. He isn't saying to learn how to train a LLM so that you can go on to train LLMs. He's saying to learn it so that you gain a deep understanding of how LLMs work. Ordinary startups can still benefit from things like training or fine tuning highly specialised smaller models, knowing how to select and configure an appropriate model for the task at hand, knowing what software to use and why, understanding what's going on behind the scenes instead of treating everything like a black box, having a higher level of intuition about LLMs generally, etc.
Most computer science courses do in fact teach things which are lower level than coding, such as how transistors work.
IMO these kind of advices never matters. Any individual still needs to make tens or hundreds of little decisions (every day) themselves, and that's what really makes all the difference.
> Would you advise a 17 y.o. to learn how transistors work or how to code
how many of us out here are doing work directly in what we got a degree in? I majored in economics and now I'm a CTO.
I would absolutely advise a 17 yo to learn how to code, understand how transitors work and how to code an llm. even if he never works on llms, you basically end up with a kid with applied knowlege of statistics, math, physics hardware, logic and a whole lot of practice in critical thinking.
As a 17 y.o (way back in the last century) I didn’t need to be advised to learn about transistors. I just had a thirst for the knowledge.
I would encourage everyone to learn something about transistors. They are one of mankind’s most useful discoveries.
Hey, I'm 19 CS undergrad but i don't really know what to do but I really wanna build something that can shape the world. Can you tell me more about why you would advice someone to learn about transistors? what are the possible career paths?
Not the original commenter, but in my view as a crusty old software engineer, the reason to learn how transistors is because they're interesting and the key to how all computation is implemented. Once you learn how transistors work, you can learn how logic gates work. And then how CPUs and memory work. Bit by bit, computers stop being magic boxes and start being something built from technologies you understand.
Now, for most software careers (including my own), that knowledge isn't directly practical. BUT, the most important things for a successful, fulfilling career are curiosity and a willingness to dive into subjects _without_ necessarily knowing how (or even if) that knowledge will yield practical applications. That drive is what's going to lead to insights and breakthroughs over your career that wouldn't happen if you just let abstractions be abstractions.
Right - like advising people to learn nuclear power back in 1992. Perhaps a good idea, but super specific already. My gut is ML/AI is even more complex in 2026… I can’t even remember all the abbreviations and the new ones emerging. And every sub component, such as attention or embeddings, are actually a discipline of its own already.
I just don't think he realizes how saturated it got over the years. Or maybe he knows at a conscious level, but not subconsciously.
In 2000 (his era), it would have been really smart to study the source of Linux or Apache. Would have paid dividends over decades. Cuz that knowledge was so rare. The number of people hacking on LLMs now dwarfs the number of people hacking on web servers 30 years ago, by several orders of magnitude.
And if you turn back the clock even more, I mean just even having access to a computer, let alone owning one, would have put you at a massive advantage.
I don't know what to call it. The pioneers should be respected obviously, but at the same time you need to understand that for them, the game wasn't nearly as played out as it is now.
I just don't think you can afford to be dicking around with LLMs like you could afford to dick around with random Linux distros 20 years ago. Too many people willing to do it for free these days.
You don't wanna end up being the 2030 equivalent of a certain SNES emulator developer, or maintainer of a package manager for jailbroken iPhones, I mean the list goes on and on. Being a hacker doesn't automatically give you a path to being rich, or even making a decent living. It hasn't been that way for a while.
I agree more with Yann LeCunn's salty reply. Over long run, knowing how autoregressive language models work from scratch will be just one step in having foundational understanding, and they might become dated... like knowing how a CRT monitor work. Something of historical interest and good for learning, but not crucial to being well-rounded.
There are other types of models like diffusion models right now that are showing more efficiency and have a higher ceiling for improvement. Understanding math and fundamentals are more important.
I'll use this post as a shameless opportunity to tell more people about a little side project, I made:
http://languagemodelbuilder.com teaches you (in a few hours to days) how to build an LLM from scratch. It's entirely free, without accounts, and without data collection.
I wish this was available for young folk in Romania: As it happens: macOS: 2.70% of desktop operating-system usage in Romania; OS X: 2.55%; Combined Apple desktop share: approximately 5.25%; Windows: 90.65%; Linux: 4.02% or so AI tells me.
I am kind of amazed how negative the comments are here, especially on HN.
Learning to hack something together in high school using the latest technology (vacuum tubes, radios, microprocessors, web/javascript) has been a common theme in the tech world for generations. With LLMs and online tutorials, this isn't even a difficult suggestion. Do people think learning new tech is somehow wasted effort?
> I am kind of amazed how negative the comments are here, especially on HN.
I can't recall or point out exactly when, but there is a stark before/after moment where the opinions of anything pg went from "Interesting and maybe true in some ways" to what we see today, lots of knee-jerk reactions and hardly any comments about the actual content.
Hazarding a guess, I think the moment Altman became the CEO and later during COVID, the sentiment seemed to have been shifting towards what we see today. But this is all based on hazy memory, rather than looking at the data. I'm sure there is a blog post waiting to be written about analyzing the sentiment of comments to PGs articles on HN, and you'll see a shift somewhere.
Imo it's a breakdown of trust of the startup ecosystem as a whole. Repeatedly startups have enshittified and it's become undeniable that the investment apparatus around startups is partly responsible. We have seen a great driver of uncreative destruction, industries undermined, small businesses undermined just to drive masses of money into few pockets - less fairness for the people working in what is now the gig economy and ultimately prices and other costs that end up as high or higher than they were before for consumers. Not to mention the whole AI/OpenAI situation which many perceive as threatening their skillset per se, essentially tearing up the social contract that existed on this site.
The sycophancy on HN is starting to break down because there is a higher proportion of users sceptical towards the outputs of the VC and wider investment world than ones who believe they're potential beneficiaries of it.
Tech industry people are becoming less interested in HN as a warm handshake into the startup world because, frequently, they're disgusted by it. And this reflects on the sentiments people post on PG's articles.
Increasingly if those at YC want the same kind of low-bar praise they got before, they will need to get it from machines.
Because at some point in life everyone gets tired of fairytales. He started mending the anecdotes to his content which always rubs people the wrong way.
So, this comment of yours obviously isn't in the "knee-jerk reaction" category of comments, I suppose? What exactly from the linked tweet(s) are fairytales here? There is hardly any text at all, so strikes me as a comment about previous pg content, but then this would be one of those comments I talk about? Very confusing.
Your hand waving doesn't make it knee-jerk. It's just what happened to his writing since COVID. He goes for more of a shock and awe style and not everybody likes it. He's been writing for over 20 years now, hasn't he? His style has clearly changed, and an changing style attracts a different audience so it's no surprise his original readers might not connect with his newer work...
If you did a data analysis, I expect you would see an uptick with the 2023 Reddit API controversy. HN got a big influx of redditors with different sociopolitical perspectives and discussion norms.
This might be hard to disentangle from the tech correction layoffs of 2023 and 2024, but my perception is that most of the spite comes from Tech Outsiders opposed to jaded Developers.
> I can't recall or point out exactly when, but there is a stark before/after moment where the opinions of anything pg went from "Interesting and maybe true in some ways" to what we see today, lots of knee-jerk reactions and hardly any comments about the actual content.
Hard disagree. This submission is still being highly upvoted, while another recent post[1] on the harms caused by Graham’s fellows[2], with a fairly tame comment section, has been flagged. That is a constant on HN. It’s not a fluke, it’s as predictable as the sunrise and getting more pronounced.
I’m sure we’re both biased in our perceptions. Mine is that HN in general (certainly more than any other website) used to worship[3] everything he wrote, together with others like Musk, until things started to really go to shit and many eyes have been opened to the effects of the unfettered greed of rich tech guys out of touch with reality.[4]
[3]: That word I choose hyperbolically but deliberately. It definitely was not “interesting and maybe true in some ways”, it was much more hardcore than that.
[4]: That is not “knee-jerk” but a slow realisation still ongoing.
> Mine is that HN in general (certainly more than any other website) used to worship[3] everything he wrote [...] It definitely was not “interesting and maybe true in some ways”, it was much more hardcore than that.
Maybe you need to enable "Show Dead" or something? Pgs articles on HN definitely never was free of any critique in the HN comments, just like any article. Although I do agree with you that it used to be different than it is today, and same with Musk too, and Altman, and probably more individuals, where they were lauded before but now pretty much just mentioning them poisons the conversation.
That post has barely any points and comments. It’s not a good indicator of general sentiment, it’s just an indicator of people who were on HN at that time.
> Maybe you need to enable "Show Dead" or something?
I have it enabled.
> Pgs articles on HN definitely never was free of any critique in the HN comments, just like any article.
Of course. I very explicitly wrote “in general (certainly more than any other website)”. That does not mean “always”, or “never”. HN is not a hive mind, there’s never going to be 100% agreement. The general trend is what’s being discussed, and we both agree that in general the sentiment on Graham used to be higher. We’re just disagreeing (we may be able to find ourselves agreeing through tough thorough thought, though[1]) on where exactly it is now and how to interpret it.
[1]: Sorry, can’t believe there was a real organic opportunity to use that sentence, had to take it.
Indeed, no hard disagree, merely details :) Overall you're right though, general/overall tone definitely shifted hard for a bunch of individuals over the years.
Completely agreed. The point is the knowledge, the learning and the journey. If a kid has a passion for building or toying with LLMs, then of course, by all means, please start tearing them apart or even build and train your own model. You'll learn a ton, even if you won't necessarily end up using it here and now. The learning experience will compound and of course that will be useful.
The above is, after all, the whole genesis of the word 'hacker'. We should celebrate that.
How exactly does one go about "tinkering" with an LLM? Any architectural change you introduce needs fine tuning. That needs data and compute
I tried to modify the embedding output of bert to make it generate box embeddings instead of point ones. At the time I had access to university provided A100 gpus but even with all that a training run took half a day. Models these days I don't think I can train it in any reasonable time with that much compute.
(As a TML person, I'm obviously biased, but I couldn't resist because of "tinkering").
TBF it's hard to imagine a real architecture change that wouldn't require a ton of compute, but you could certainly fine tune and play with different recipes, loss functions, etc. And Claude can carry you a lot of the way through doing this.
One fun task is to invent a tool and then train a small model to use it. You could export that small model and run it locally for free forever to do your thing. I think this is what a lot of Software Engineering will look like later.
There are a lot of other high level abstractions here to look at. Prime Intellect has one.
The other thing to play with is self-hosting small models, but IMO most of the interesting stuff is actually related to multi-gpu or multi-node inference so there's not necessarily a ton to learn here.
Now I'm curious, do people actually tried to hack vacuum tubes or other big servers that's barely 1MB RAM? It seems like another thing that needs big investment to work properly, unlike those other techs where results can be shown even with little materials.
Yeah, there are retrocomputing hobbyists who mess around with sometimes-physically-large computers that were important many decades ago. I don't know if anyone is hacking on vacuum tubes of the kind that you could in principle build a computer with - there's a reason they became obsolete for digital computation almost as soon as the transistor was invented. On the other hand, I personally think it would be neat to try to build a CRT in a garage, which is of course a type of vacuum tube. I don't think this would be an easy garage project, but it does seem like might be tractable for someone who understand physical manufacturing and electronics well, has access to glassblowing equipment, etc.
> Now I'm curious, do people actually tried to hack vacuum tubes or other big servers that's barely 1MB RAM?
"Barely?"
It's insane to lump vacuum tubes together with servers with 1 MB RAM. My first PC, which I used for a decade, had 640KB RAM. And that was an upgrade from 512 KB RAM. My other PC had only 128KB RAM. None of these were considered the equivalent of (by then long dead) vacuum tubes in their day.
> With LLMs and online tutorials, this isn't even a difficult suggestion.
Don't many of the commercial ones prevent you from using them to build LLMs?
I would say the reason for the negativity is not because it's a bad idea for a project, or that doing projects in general is a bad idea (it's not!), it's because it's a very specific thing that is not for everyone. The best thing about computing is the low barriers to entry. You can basically work on anything that takes your fancy. So those who are interested in ML will be drawn to learn about LLMs. They don't need anyone to tell them to do it. Telling everyone to do it reminds me of the "just learn to code" stuff of a decade ago. No, please don't, please find something you enjoy.
> Do people think learning new tech is somehow wasted effort?
No. But funnily enough that is a promise by some of the AI cretins and their boosters. Oh yeah best case scenario you learn how to build LLMs for us. We’ll employ you. And then ultimately that just becomes training data for the LLMs to do it themselves.
I'd say, if you were 17, you'd probably be better off learning how to socialize or at least gracefully approach and relate to people your own age. That's probably the greatest challenge of the near future, sadly.
I appreciate the sentiment and I'm pretty curious how I could train an LLM, even are really basic one from 5yrs ago without nuts hardware.
That said, I a good starting point for a 17yo is reading about perceptrons[0], then the basics of neural networks[1] (ex. 3-layer perceptron) then writing a program to train a 3-layer perceptron and classifying the MNIST dataset[2] - a dataset of characters.
This can anywhere between a day and a week and you will demystify the basics of neural networks and work your way forward with more advanced contemporary concepts.
Fun fact: any multi-layer perceptron neural net can be reduced to a 3-layer perceptron network.
Horrible advice. This may have been good advice 10 years ago, but not today. There are no positions for people who "kind of understand how toy LLMs work" because so many engineers do these days. Most of the real LLM optimization work is at the edge of research and highly proprietary and not something you could ever do without infra that costs millions.
I think the answer then is to get a Phd? I mean, one should learn it to satisfy their curiosity and to build things but I don't think it's going to help you career in any significant way.
Throw the computer and the smartphone out of the window.
Or more reasonably, the same old thing : use Linux, hack a little, why not learn programming basics. But learn to own your technology, fight against centralization of technology. The same old RMS story.
IDK where the tech industry is going, if there will be jobs anymore or not, but what I'm sure (and what have been the case for the last 10-15 years anyway) is that for most tech jobs, having good technical knowledge beyond the basics is pretty useless and will probably not be recognized.
If you can, stay a computer geek if that's your thing, but don't make it your career choice, the Eldorado is behind us.
"it doesn't matter what you choose to study because no job is safe from being outsourced overseas, handed over to an indentured servant, or made redundant by a machine. you will spend your whole life surviving while the cannibalistic pedophiles who own everything invent new ways to make you own nothing. be frugal, don't get married, don't have children, do everything you can to stay healthy and independent, and you just might live a reasonably comfortable life."
Bad advice. You’re going to need family connections and loyal people you can bet your life on to survive if the rest of what you’re saying is remotely true.
Most reasonable advice I've seen under this post. Having graduated in 2025 I've experienced the horror of competing with infinitely many third worlders in my small country, who will gladly take 1/3 my pay and be serfs.
I really hope we get another hiring boom like in 2020 when I decided to study CS. Otherwise my career will be very rough. I love it and can't imagine doing anything else.
who would have guessed when first world were enjoying their heyday as they were colonizing the world, and teaching everyone and their mother to learn English.
Kinda sad now you have to compete with serfs from thirls world. chu chu chu. so cute.
shouldn’t have robbed others then. a richer world for you comes at poorer world for someone. we are playing infinite game in finite world.
Yeah he's a moron with a lot of money, that's about it. I'm sure he's said the same thing about various other bags he had bets on throughout the years.
As someone who's at a similar age and was interested in learning how to do this, there just aren't enough resources to do so. Most LLM research is in the form of academic papers, and there isn't any 'popular' way to learn these things, and besides that all said research assumes you have a B200 cluster ready to go. If you have weaker hardware (say an 8GB nVidia GPU, which is what I have) you're going to be limited to fine tuning small models or torturing yourself working the GPU for days per iteration trying to run things like https://github.com/karpathy/nanochat, which is hardly an educational experience. Renting cloud GPUs is expensive, and at this age the most I could muster up for experimentation is probably $100 or so, which only gets me 25 or so hours on a B200 which just isn't enough. So why would I bother myself with this if I'm already at a disadvantage because of not having access to the right hardware and when surely there are better ways to spend my time? I concluded the only way to learn and be competitive is by finding work at an AI lab somehow (not happening at 17), or studying ML at the right university.
Putting this in contrast with programming, I learned coding when I was 8, and it was incredibly stimulating to learn because you can quickly iterate and there were thousands of books and YouTube tutorials that dumb everything down and teach you fundamentals. All you needed was a $300 computer, and you can learn nearly anything you want, without being gatekept from this or that because you don't have enough vRAM / an sm_100 GPU.
I think a problem a lot of people are grappling with here is that due to LLMs and AI generally, it’s basically impossible to predict what the future will look like or what jobs will still be around.
I’d probably say something like: do something you enjoy and seems like it might be useful, but accept that the pace of change may mean that whatever you study ends up being irrelevant.
Whatever solution there ends up being to this, it’s not going to be one that an individual 17 year old can implement. We’re past the point where individual good and bad choices matter that much to economic outcomes.
I would advise any somewhat ambitious 17 year old to avoid tech and get into healthcare if they can stomach human interactions and bodily fluids. Sure, it is not all sunshine and rainbows, but there will still be plenty of work helping people who are ill or elderly. Even in the worst-case economic scenario, medicine will be a more socially rewarding and stable life path.
This seems like a much more interesting question to me.
Telling other people's children what to do is easy and basically doesn't have any downside to being wrong. With your own children things are a bit different.
So: what are people here with school age children telling their own kids about the future? If their kids ask, what kind of careers would they encourage them to pursue, assuming they have the skills and interest?
When I was last in the Bay Area, maybe about a decade ago the bookshops were full of titles like "Python for Preschoolers" (I exaggerate, but only slightly). Clearly at the time a lot of people working in tech thought that cultivating an interest in programming was going to be the path to being a successful (by some metric) adult. Is that still the case?
The real reason to recommend this is that they have an excellent chance of avoiding the "nerd-to-incel" pipeline that tech all-but-guarantees for its best nerds. Post GenAI boom, the social cost of working in tech combined with the coming collapse in high paying jobs, means that unironically coders should be learning a bit about coal mines.
In this regard, Healthcare is a polar opposite. It's pretty hard as a male nurse to not "accidentally" become a home wrecker.
Don't really understand why people are saying this is terrible advice. If you're 17, you should be learning everything. At that age, your brain is a sponge and your energy levels are the highest they will ever be.
None of us know what the future of work, education, or AI is going to look like. But your best bet is to become a life-long learner. Be it LLMs, musical instruments, physics, or business.
I get this is basically advice for young founders and entrepreneurs, but i would ignore that request and encourage 17 year olds to spend time trying to find a happy medium between work and life.
Being a super rich and an unhappy workaholic, or a super-impressive engineer who wakes up one day at 45 and realizes they regret wasting half their life (I ran into way too many of these) is a much worse fate than "not being rich from your startup" and working a relatively regular job while feeling fulfilled and happy by more than just work.
Especially in the US, which is uniquely bad at this and encourages people to work themselves to death, mental health and work life balance are much more valuable things for 17 year olds to focus on than finding good startup ideas.
In case you think i'm being a bit dramatic, let's look at the state of 17 year old mental health in the heart of Silicon Valley:
"The City of Palo Alto and the Palo Alto Unified School District approved a funded contract to place 24/7 human security guards and monitors at all four local Caltrain grade crossings, including the Churchill Avenue crossing directly adjacent to Palo Alto High School."
(in case it's not obvious, it's because of suicides by high school students)
The 17 year olds do not need advice on better startups, and this situation will never get better if we focus our advice on how to be better at work instead of how to be better at life.
This will require redirecting the conversations.
Thank you. They need human contact, not more "sit in a room alone and get stressed as fuck for little ROI" tech bullshit. Unless the kid has a genuine, self-motivated interest in learning these things (a great, positive thing that should be nurtured), they should file pg's advice under "ok boomer."
I'd move to the middle of nowhere and work multiple jobs on a farm and in construction. Learn how to grow food, and build things. Meet the farmer's daughter, and marry her. Then, buy my own land, grow my own food, and build my own things.
I already do this. I live in a small village where there isn't even a wired Internet connection (wireless only). I work full-time with LLMs, on own product ideas and client projects (all LLM led).
I started investing in farms, have 50 pigs and 100+ chickens now. We are planning to grow to 100 pigs and 2000 chickens in a year. We will start growing Shiitake mushrooms in a few months too.
Most 17 year olds I know don’t want anything to do with AI and see the entire industry as an existential threat.
Nothing wrong with learning the theory and understanding the papers. Getting to that point you’ll have to get your fundamentals down. Might be an interesting exercise.
But as a future? I guess we’ll see. I suspect the next financial apocalypse will determine if there is one. Another AI Winter that may outlast all others so far.
If I were 17 again I'd prepare to go for volunteering overseas after high school for 1-2 years (plenty of free options in the EU where you might only need to cover the plane ticket). See the world, you learn a new language, help others and then think about what you want to do.
I built from scratch a sparse text embedding model trained on a 13T token corpus. Not the same as an LLM because there’s no transformer in the mix, but still I learned a shit load of things in order to solve all sorts of problems that emerge when you try to access big datasets and daily update tables with billions of rows. But if it wasn’t for a specific use case that I tried to solve I don’t think that whatever knowledge I gained could be utilized in the market. Sparse models are a very small niche and most people I’ve come across with similar knowledge are in academic circles, not business related ones. So even if LLMs are all the rage these days I doubt the demand for people who know how to build them is that high. Someone who knows how to setup an open weight model and expose an API might be more valuable to a company these days.
I'm not sure what 17 year old me would have done with YouTube tutorials for everything under the sun available.
I'm much older and less wise now, but I still afforded myself the opportunity to follow karpathy's tutorials to build a LLM from scratch. Got to play with a few ideas. Seen similar ideas turn up in frontier model work, which is quite gratifying.
There are so many ideas to try.
Currently playing with autoencoders that takes A and B and produce latents A', B', and C'. Reconstruction of A is from A' and C', B is from B' and C'
The idea is if C' can be made to improve both outputs, it must store as much information as it can about what is common to both inputs.
Mr. McGuire: I just want to say one word to you. Just one word.
Benjamin: Yes, sir.
Mr. McGuire: Are you listening?
Benjamin: Yes, I am.
Mr. McGuire: Plastics.
Benjamin: Exactly how do you mean?
Mr. McGuire: There's a great future in plastics. Think about it. Will you think about it?
---
I love this scene because it so perfectly captures what it's like to be young and given advice, however well-meaning, by an older generation living in a world that no longer exists for the young. And it's ambiguous and trite enough to be essentially useless even if the underlying idea isn't terrible.
The only realistic approach is to train on a limited data set which is probably less usable than the comibnation of a custom RAG + one of the many available LLMs.
Also many people/kids don't have access to proper "productive" systems anymore, since the whole computing and electronics industry shifted to make "consumer"-devices like smartphones or laptops made for netflix, gaming and spotify.
Breaking the barrier to build a custom system, install linux (or developer tools for Windows, MacOS) is already a complex AND costly task. It was just way simpler in the late 90s and 00s to get something working.
Oddly, I just remembered I did the nearest possible thing to this when I was 17... back in 1991.
On an Amiga, I took various public domain text documents from cover disks and counted the probability of the next word given the previous word. Then spat out random sequences of words from it and printed them out. It was called "Splurge". Basically a very very simple single layer statistical language model.
Some of the sentences were randomly not bad sentences, which seemed amazing at the time!
That kind of thing (and Core Wars and Tierra etc) did lead me to getting a job at an artificial life startup at the end of the decade. But that was in turn about 10/15 years too early (no GPUs).
There's some lesson from this about timing, but honestly I've gained the most as a person when I did something that was fun, ethical and gained an audience. A tricky combination.
What about doing abliteration, weight pruning, representation engineering, etc, directly to open LLMs instead?
Building an LLM from scratch has a hard split between a tutorial project you can complete in a weekend (that's useless for actual usage) and then a solid 1km high brick wall if you want to create anything actually useful from scratch.
Modified open models have a very active community around them, without the need to look much further than Hugging Face.
I really don't understand Paul's reasoning here. Does he predict more scarcity on the model-level? That layer seems to be almost a commodity now + training is damn expensive.
If you are really 17, my advice is to identify use-case for AI (ideally relevant for businesses) that work most of the time and find ways to make them work pretty much every time. AI reliability is the scarcity right now.
With the hindsight of experience, the remnants of my 18-year old energy go “woah, that’s cool!” at plenty of engineering feats… and my decades-older second brain goes “well d’oh, I could’ve just learned a trade to work on that!”
I think the last one was seeing a skilled electronics repairman do surgery on a CT machine controller.
This only changes whether you are naive enough to believe “honest” business means anything in today’s age. If anything, I worry being honest is holding back smart people who try to compete in a rigged game.
There's two ways to read my comment. I believe you read it along the lines of "you need to be rich to start a business".
What I meant was that if you're rich you don't need any trade school or to run a business, you can just sit on your ass living off interest from capital. Only "poor" people need to work for a living.
> you are naive enough to believe “honest” business means anything in today’s age
Might be that these people are from outside the US as well, where things like "honest business" is very much possible today, probably most businesses I interact with AFK on a daily business are "honest businesses".
> Someone asked what I'd do if I were 17. I'd learn how to build LLMs from scratch, and then train ones as powerful as I could with whatever hardware I could get access to.
He's seeing a future for models running on everyday hardware just capable enough to do what the use case requires.
When I was not 17 at the times of GPT2, I decided to not bother with learning how to build LLMs because it’s too expensive for an individual. This escalated quickly.
telling a 17 year old to get into tech right now is horrible advice, literally telling them to get at the back of a line with a better part of a million more experienced people in it.
Telling them not to get into tech is also terribly reactionary advice. The truth of the matter is that we don’t yet know whether tech roles will be eliminated or if they’re just going to follow previous innovation breakthroughs where “one person producing way more work” makes software even more of a desirable industry to be involved in.
There really isn’t a very strong correlation between tech industry hiring strength and AI as of yet. Various studies that are out there haven’t even witnessed AI workflows contributing more than modest gains in software engineering efficiency. I.e., being able to write code 20-40% faster isn’t a seismic shift in the industry where everyone is getting laid off tomorrow and we’re all replaced by software.
Even with the questions surrounding the current job market, it’s still an incredibly good ROI career compared to so many other jobs out there.
For example, in my local area you can get a job as a registered nurse working nights in the emergency room and only make ~$115k.
I make almost double that telling an LLM what to do from my house in my pajamas during the day with less time spent in university.
Even if tech roles lose half their salary to automation pressure it’s still a really good gig.
I just want to make it clear that I’m not assigning some kind of moral superiority to my financial situation and the amount that the economy values my labor per hour.
I don’t make the rules for how much each profession is able to make in compensation.
The world will always need nurses, but that doesn’t mean that it’s a fantastic career to get into if you have a neutral career preference and your primary consideration is university tuition ROI, expected compensation, work schedule, and day-to-day physical exertion.
My point isn’t to debate the virtues of each career, I am intending to stick to objective aspects of them.
In that sense, telling today’s kids that there’s no future in tech careers just because there’s a short term hiring slump is extremely premature. I certainly wouldn’t tell a kid who is passionate about tech to avoid the field just because the unemployment rate is currently 7%.
Perhaps I made a mistake in bringing my personal situation into this discussion. Delete that one sentence about my situation and you'll see my underlying argument is still there.
If you have anything to add to the topic at hand rather than critiquing my personality, I would love to hear it.
Is it really wise to tell kids to avoid tech when the #1 ROI bachelors degree is Computer Science and the #1 salary after 10 years degree is Computer Engineering, while the top 9 salaried bachelors degrees after 10 years are engineering degrees? Are we just telling kids to avoid tech/engineering on vibes or real labor force data?
I learned HTML when I was 17 in about 1995 and it's certainly taken me on a pretty fun career path. Less technical than LLMs for sure, but 'figure out where the industry is going and move what you're learning to there' is solid advice.
I think learning how to build your own agent harness framework from scratch and really understand each part of it, what are the modern components that a good agent harness are using these days, is more valuable than learn how to build LLMs, but that depends on what you want to do with your career.
Yeah sure, learning the internals of a technology that’s being actively developed will probably teach you things that remain useful for awhile even if your learning outcomes could end up being different than what he’s implying.
Whether that makes a good long-term investment is quite debatable.
IMO, LeCun’s take (at the end) is far more forward-looking since it aims to gain more insight into what could come next based on what we know about the current state of the art.
All that being said, the extent to which these people capitalize on our tendency to be blinded by the halo effect is incredible. A constant stream of bite-sized aphorisms…
This is especially at a different level for early startup figures who happened to be in the right place at the right time and usually did little more than digitizing mundane, traditional day-to-day processes. Yet they’re treated as geniuses and prophets, with people hanging on their every word as though everything they say contains some deeper wisdom. PG and the like often strike me as broken clocks and they’re still profiting from having been very early players in the game, who had good instincts for commercialization and capitalism.
————
LeCun’s reply:
> I would try to figure out why LLMs can write my essays but not clean my bedroom.
Then I would study topics in college and grad school that could help solve that problem.
I'll figure out a set of methods and architectures beyond LLMs that can quickly learn to perform physical tasks as efficiently as humans and animals.
That last item is also what I would if I were 30, 40, 50, or 66 years old
If i were 17, I'd try to distinguish who to take advice from, and would definitely learn that VCs have interest to spread a specific agenda in their message. Also, I would get drunk and have as much fun as could, as the misery of working under the treat of being replaced by AI, would simply kill any desire to live past 25.
Writing, supervising and training LLMs are now the purview of... even larger LLMs. Optimising CUDA kernels; hand-writing SIMD assembly to speed up data loading; tinkering with your particular brand of DRAM to see if there's anything to gain from optimising for its memory topology and NUMA --- these are now the job of AI.
There is very little reason for humans to get all too engrossed in this type of work now, today, with the hope of being good enough at it to command a high salary in 3-5 years. AI can already do it incredibly well, and they can do it persistently and doggedly 24 hours a day.
I'm making an assumption here, but I think paulg is implying that "learning LLMs" today is like the equivalent of "learning computers" in the earlier days. We could even divide civilization into two eras: Before Transformers (BT) and After Transformers (AT).
This essentially comes down to choosing the search for substance (here in the form of technical depth) over short term gratification and quick wins in life.
I think this kind of mindset should be taught way more in school so that people really appreciate learning a subject deeply.
When I was 17, I made bad cartoons and was in a band. If I were 17, I'd spend more time learning music and design theory. I also learned PHP at this time, but that was low on the list, friendships came first.
I think it's more about the scar tissue (a.k.a. intuition) when building LLMs from scratch than whether it's transferable to job search. Maybe the person will decide they don't like LLMs and not develop that into a career, or maybe they become a researcher in another field because that's a better way to solve problems inherent in LLMs.
What corpus would one be advised to use for training? Should one limit oneself to legally downloadable documents or should one follow the examples of Altman, Amodei, and Zuckerberg and use LibGen and Anna's Archive?
My kids CS teacher asked me what they should do after AP CS.
Previously they had a class where they'd build apps for other teachers. Like tracking when clubs are, etc. But now that's become easy for teachers to vibe code themselves.
I suggested they shouldn't prereq this class on CS. Heck invite anyone in interested in "building things" and they can get practice at building apps for other people / themselves. Maybe that becomes a gateway TO CS - people who want to learn how things work under the hood.
I suggested post CS class for the CS people should probably be building an LLM or something :)
i was trying to build llms from scratch at 17. failed miserably because i did not know linear algebra.
ended up in a different but adjacent field. when chatgtp got big suddenly there were so many people doing llms and i didnt want to compete like that. so much of what was happening was hype, and that really turned me off.
am back to building llms from scratch, but like, its a journey teaching myself all the theory on top of my job. i have decent fundamentals, but i need a better grasp of all the advancements in the field in the past 5 years before i would feel comfortable designing anything. baby steps, essentially. am working on better understanding all the layers of an ai while implementing a rag on my local model as an experiment.
17 year olds should learn whatever theyre interested in but need fundamentals in order to do anything advanced.
As a platform engineer being based mainly out of Australia/Hong Kong, opportunities seem to be getting less unless targeting high frequency trading or banking.
It seems like building a startup with the help of some AI tools might be the best bet.
As someone who doesn't work in this field (I'm in boring old Accounting/Finance), but would like to learn this field (both to future-proof my skillset, but also because I have an interest in it), would it be wise for me to learn math?
Is it something that's going to be fundamental for future technologies?
I always plan to learn, but end up in a death spiral feeling like I'll invest so much time and energy only for the puck to have moved somewhere completely different.
I'm usually a fan of pg, but this post is ignorant of modern AI technologies. Building an LLM from scratch is both a trivial and a useless exercise. There's probably in the range of 5000 github repos doing exactly that. What makes LLMs work is scale, and what makes engineering and training LLMs hard is also scale. And scale is not something you can achieve in your garage.
If the goal is to understand LLMs deeply, one would be better served by either joining one of the big AI companies or doing a PhD.
And to be honest, I think this journey should have been started 5 years ago, because right now there's too much competition.
I wonder at the “worlds” Mr Graham envisions, and what is their cardinality. Is this the only 17 yro reimagining, or is there an army of 17yro, of which this LLM curious persona is but one?
Why is it important to train LLMs or even fine-tune them? LLMs have proven their point, costs are crashing, and there are more of them than most companies need.
The real value is to unlock meaningful insights and directions from existing data that is there inside companies.
I live far outside any tech city, so maybe I do not understand. But working with LLMs full-time, building for clients and tons of own experiments, I see no value in building on LLMs.
I would (and am) going into MLOps. Not just the general infrastructure/systems administration but how to do inference optimization, caching, quantization, memory pinning, vfio passthrough of gpus etc.
If I was 17 again I would sack off work and study and focus on chasing the opposite sex, without the angst I had at the time. I don't know a single person who regrets having had too much sex when they were young. I would not build an llm, too hard, too expensive to run. Learn how easy life is if your morals allow you to grift money of vcs into your own funds and retire.
The core point here is that AI is a massive thing (at the moment) so it's probably a good idea to understand it deeply. Not sure why people are so worked up about it.
That's why. That future is uncertain. So why gamble your future on something that's popular at the moment for something that could change completely a year later?
If you're 17, and seriously curious about how modern day AI works, you might as well just sit down and look at a couple of courses on linear algebra + calculus, machine learning, deep learning, and more LLM specific deep learning. Those courses will teach you how to go from writing your first perceptron to a MVP language model. But also so much more.
When I was 17, I wrote poetry and learned how to play guitar. I’m really happy that I did. That’s what I would do now, were I 17. I feel slightly bad for people who didn’t.
Sure, but isn't this a bit of a sneer? “I feel slightly bad for people who didn’t” has an air of superiority about it, and seems like a veiled swipe at those who lacked whatever qualities your 17-year-old self had. The guidelines have several different ways of asking us to avoid commenting like this.
As to the substance of your comment, there's nothing stopping a 17-year-old from writing poetry, learning guitar, and also learning about how LLMs work, if they're so inspired. Indeed I'm sure pg would encourage it, and that he did the equivalent of all those things himself when he was young. There's plenty of evidence of that in his essays: he short stories, published a scandalous school newspaper, played soccer, and studied fine art and philosophy. See:
As an aside, for someone interested and who's an absolute beginner, can someone please recommend good resources on how to build LLMs from scratch? Thank you in advance.
I second 1. I'm a newbie in neural networks and I think it's an excellent book! One of my barriers in ML is the resources, I find them overcomplicated or too simplistic without a mid term. It's not the case of this book, everything is well-explained. Neural Networks aren't fun for me, but this book makes it very interesting.
I think this is the best structured class out there that teaches how to scale LLMs . Hope the 17 year old knows linear algebra. Building an intuition for the shape of the matrices is important. A lot of understanding the 'building from scratch' means understanding choices like why RoPE instead of the original frequency based positional encoding. Start with Karpathy and then go to CS 336
I mean first that is already what plenty of 17yo are actually doing, because that is what they do at school or in parascholar activities. There are already countless of such tutorials where you can do that in an afternoon.
The pointless part though is precisely why Amazon and others are hunting for rare books, all the low hanging fruits have been picked already so just training a bigger model will simply mean burning more energy and money. Sure training a small one for the basic principle is a great pedagogical thing, training another one, medium, then maybe a large one, is also good in term of learning the process and architecture, but one should not expect it to be useful out of that context.
Pure players are precisely doing everything they can to corner the market by making their own scale unreachable by others. Smaller players with access to lesser infrastructure are thus betting on different market, e.g. embedded systems.
17yos should definitely build their (L)LMs from scratch and whatever bigger model they can train for free, or for cheap, but they should not expect that to bring them any riches.
Why would 17 year old do something that only brings them money? I do not think Mr Graham here is advocating for the path that makes most money as a result of learning how to train a model. I assume that tinkering and learning about LLMs is what enterprising 17 year olds will do to discover ways they can get a competitive edge or further the SotA with their insights further down the line.
No wonder the state of everything when 17 year olds are getting pressured to be “enterprising” and “get a competitive edge”. How about learning to be empathetic, respecting your fellow humans, caring for the place you live in, enhancing the lives of others? We shouldn’t be teaching 17 year olds to be greedy, selfish, self-aggrandising blowhards like Zuckerberg, Musk, and Graham. They are not good role models for the future of humanity.
I don't think you can really call yourself a developer unless you at least have an idea how to build a more complex software project like a compiler, and maybe have built a toy one either at uni or for fun.
It's not clear how long this LLM age of AI will last (to be replaced by something better), but nowadays any developer should at least understand the basics of ANNs, and more than just the "hello world" of a cat vs dog CNN. An LLM/Transformer is maybe the equivalent of a compiler in that regard - something that we all use and is complex enough to present a bit of a challenge. You should at least understand the basics of how an LLM is built, and maybe building a toy LLM will/should become the new Comp. Sci. degree toy compiler replacement.
If you’re 17, you might learn hands on knowledge, like tacit knowledge in areas such as lathes, precision engineering, metrology, and other very niche fields. You can also try climbing and explore arts like music, painting, and drawing. And, of course, spend some time in nature.
It’s probably wise to learn how to build one to understand what you’re dealing with. However, if I were 17 I would lean how to apply an LLM to a problem instead of strictly building one.
certainly better than wasting time with harness and agent workflows that will become irrelevant at the next evolution, same thing happened with 'prompt engineering'
i am a small fan of pg, nevertheless i find this to be an exceptionally good take and it is strange to me to see so much piling on to this one in particular here.
learning about llms is not useful so that you can make llms later, you want to learn about it so that you can work on next generation architectures. llms before long i imagine will be left in the dust by ebm / physics oriented models especially that can have an embodied understanding of the world. but a lot of things you learn about them are transferable by doing something like this
Paul G is not writing this for a general audience of your run of the mill “engineer” hoping to be employed by someone. He is writing it for future founders. What knowledge / skills you need to develop today to be well positioned to have a startup worthy insight when you are 24.
Why are people so negative about this? It feels like a fun project and at 17 the stakes are not really high. Something one could easily do on summer break in a couple of weeks.
I read a bit about how LLM works, but as a hobbyist it is pretty frustrating that I won’t be building anything useful without throwing a lot of money at it
I think pg answered the question as “what I’d do as a project” and not “what I’d do as a career.” So the critical comments are kind of missing the point, IMO.
I don’t see why learning how LLMs work is a bad project for a 17 year old.
Optimizing your entire career and the next decade+ of your life on LLMs? Yeah, probably not ideal. It’s almost always a bad idea to make long term decisions based on current trendy things.
And since everyone is using this topic to give their ideal advice to 17 year olds, my advice as a mid-30s guy: seriously consider becoming highly skilled at a specific thing, and don’t be scared off by the idea that it’ll take 5-10-15 years to get there.
When you’re 17-25, the timescale of a decade seems infinite. But it’s really not, and a decade spent “exploring and keeping your options open” sometimes just ends up with you being pretty decent but not amazing at a lot of random things.
Sometimes I wish I had just become a carpenter, chef, electrician, etc. – a specific skill set that leads to mastery over time, rather than the endless exciting-new-thing hamster wheel of working in tech.
> And since everyone is using this topic to give their ideal advice to 17 year olds, my advice as a mid-30s guy: seriously consider becoming highly skilled at a specific thing, and don’t be scared off by the idea that it’ll take 5-10-15 years to get there.
And this is a problem with modern society today: expecting 17 year-olds to know what they want to do professionally for the rest of their lives, and to focus heavily on professional development aligned to that.
By that age, I think it's not uncommon for individuals to have interests and perhaps even dreams, but a well-defined career focus that serves as the foundation of an actionable skills development plan? Nah. That just isn't common and I'd argue not desirable. 17 year-olds should be exploring their interests, enjoying early adulthood, learning valuable lessons in the social realm, etc. Not training themselves to become compliant little worker bees.
Have you been living under a rock? We might not get "super-intelligences" (if such a thing even exists) from LLMs but they've proven incredibly useful in basically anything relating to text, including code.
The point is that they’re going to be surpassed by a much better technology that doesn’t have the inherent and unavoidable downsides of LLMs.
Like, right now especially in the US you’d probably sound crazy saying that one day nobody’s going to buy a gasoline car, and that EVs are going to completely replace them. But that’s basically an inevitability based on the direction we know technology is going to go, it’s only a matter of when.
Maybe this is a dumb analogy but is that kind of like saying “well EVs will still use lubricant! Cars that use oil are here to stay!”?
The point being that the way we heavily rely on LLMs specifically right now is likely to cease to be the case, and that in the future they’ll be almost like an irrelevant component.
There's far more shared concepts between them than you think.
You should check the code of both world models (JEPA class for example) and compare to GPT. Many of the tricks stay the same, representation is still embeddings, there is a loss function, etc.
The exact architecture will change, but unless there's a new discovery in that area, we've cracked the text component already. We're hitting the limits of LLMs because of the intrinsic limits of text as a medium. But the way we work with text is pretty much settled, fundamentally.
2 years before everyone was doing custom training. What happened to all those today when frontier models itself become more powerful than custom trained ones?
Andrej Karpathy has a great Youtube series on how to build LLMs from scratch. Perfect for somebody who just learned a lot of high school math. Would start there and get busy with some handson python coding.
If I were 17, I’d learn how to invest and build financial literacy, and plot potential growth of my networth throughout my life, before even thinking about a career. Then smoke a bowl.
17 is such a fantastic age to be free and experience the world, you won't get that much of an advantage as these FOMO groomers are selling you into if you start now versus later.
If you are 17, go be yourself, whatever that is, in whatever way you want that to be, but do it so authentically and fully. Be unapologetic about what you love and what motivates you, and pursue that with passion and commitment.
I've come to accept that some people, when faced with impressive technology, simply want to use it. They genuinely have no interest in understanding how it works. Lately, its even become fashionable to shame people for trying to understand ("you still read code? gross, you know AI can do that for you"..)
I will never understand this mentality, to let yourself be so dependent on something you don't understand at all is to live like a child. But it is very common. I doubt too many 17 year olds will bother even trying to understand what an LLM is, let alone build one from scratch.
I also did, and had written code for/on other mobile devices before, and did write a large number of even very ambitious software for other mobile devices later.
I do not see any past constructive experience as a waste of time.
Incredible counterexample, but oddly relatable.
I'd probably have achieved techbro 'post-economic' status earlier if I focused on Android dev instead of the shiny (and new at that time) Xamarin for Windows phones.
God I almost invested in Xamarin after Windows Phone got aborted, I did spend a lil time on UWP, but thank god Flutter came out not so long after that. After all these years I learned to stay away from Microsoft tech stack.
Paul has the same problem just about every tech obsessed engineer has (including myself), he thinks everyone else loves computers too. They don't. At all. I''m a self-taught ex-bartender and I can't tell you how many grown adults in the service industry I've tried to get into computer stuff and they had 0 interest.
And younger people I meet don't even own laptops. I had a genz/millennial cusp friend who wrote all her college papers on her iphone.
Learn how to make language models from scratch, yes. But learn how to use them, in the context of other machine learning tools, on very small hardware.
When the bubble bursts (and I still tend towards thinking it could burst rather than be deflated in a manageable way), the focus will be on uses of AI that are not like the hyperscalars' products.
People will still be interested in useful AI being added to small things — assistive technologies, home security, garden monitoring, their phones and smartwatches, robotics.
Instead of reductive, reactive make-an-anthropic-competitor advice like this, what about advising 17 year olds to focus on broad, integrated, helpful AI — or on going back through eighty years of history to look at AI projects that failed and reassess them?
This is about as intelligent as say "If I were 17, I'd learn digital electroinics". You would waste your time. Sure, in theory it's useful, in reality it's not that useful.
All the negative comments here are really missing the point. At 17, you should be building your foundation. Kids that can make a custom CPU, or retrofit an old car with an electric motor, or screw around with nuclear energy... these are kids that are doing it just to see if they can. It's not about jobs, it's about curiosity and stretching limits and seeing what you can do, who you can be.
I remember when I caught PG on reddit arguing with some guy who'd said something mean about him. He didn't reveal who he was. But you could tell from his history - his first post was from before reddit opened to the public.
Good times.
I wonder if you can dig that out of the historical reddit database. I'd like to see that again. I love how everything is recorded now.
I mean, except when it's censored. That part is a shame. HEY! Is there a tool that tracks the censored comments? I bet there is. And if there's not... that will be a delicious new project for my agents.
When you see anthropic job postings that say the job may not be available in a year... it really doesn't inspire much confidence. I say if you're 17, learn to weld, solder, and fit pipe...
Really? because I feel like LLMs are already pretty much a commodity, not to say their won't be advances in LLMs but I don't see the models themselves being all that ripe for disruption the way that the web was and such. I'm guessing chip design and manufacturing processes will be more important than models in the future.
It seems this is poor advice in that it’s suggesting young people should focus on the current problem as opposed to future problems. Focus on the current problem can result in making some money but it will result in making the incumbents more money, which is not disruptive. Isn’t the goal of radical software startups to maximize disruption?
If the two current bottlenecks, for this LLM madness that could very well be a bubble, are processing capacity and accuracy (a second processing problem) then what comes next? Isn’t that where young people should be looking or are we just giving up on innovation?
I get the sense things have changed a bit since I graduated and there are lot more jobs in AI outside of academia these days, but it's still a very different field from other SWE pursuits, and it's not really accessible to hacker-minded people.
Learning AI isn't like learning HTML in the 90s then expecting to get a job at a tech company building websites. You can't just "learn how to build LLMs" and expect a frontier lab to hire you so I'd argue this is rather bad advise.
Additionally, unlike web development in the 90s you cant really do anything interesting yourself... All of the interesting/useful stuff will require huge amounts of compute and data so there isn't even much point in learning to start your own thing either.
As someone whose built many of NNs from scratch (hand written code, long before the days of LLMs), it's more or less useless knowledge if I wanted to work in a frontier lab or do anything interesting in the field.
I also think anyone thinking about going into a field which is basically a crossover of CompSci and Maths is absolutely insane right now. Even if you think there is a place for CompSci and Maths post LLMs, there's almost no chance anything you learn today will be relevant to the skills required in say 5-10 years.
He said, if he is 17 _now_, with no family, no commitments, no pressure to start a career, he'd invest his time to learn to build LLMs from scratch instead of trying to start a company.
Okay sure it’s not a “time machine” in that sense. He wouldn’t go back forty years or whatever. And he’s still saying the same thing that I was alluding to.
I don't think individuals have the resources to build an interesting llm. The l stands for large. You need a dataset too. Llms are only interesting because theyre large
And it's basically a weekend project to put transformers together in a ML library and train it.
The follow up comment,train it to play a game also doesn't make sense? Llms Sony really play games and there are better ml approaches to do that?
Terrible advice. If I were 17, genetics and bio tech at the next frontier, with opportunities to be more than another corporate drone. AI is a lot of bureaucracy and nepotistic who ya know already.
On first glance, this is good advice and in general I'd give the same for this age group. With age, you'll see more of these scenarios come up and if you have experience and foresight, you can provide direction that may prove fruitful to young people. My son and his friend asked "what should we look into and learn?", about 15 years ago, I told them Python and Java; Python because of versatility and cryptocoins; Java for the long term stability in the job market. I despise Python personally, but I could see its potential and still do; especially for AI. At the end of the day, kids have more time than money and its great experience for them to get their hands dirty and find out what they might be interested in; its a long life.
Somewhere in rural America is a 17 year old that doesn't even have working plumbing in their house still.
I'm sure they'll get right on powering up their computer from the hamster wheel, Paul.
My heart goes to all the kids out there that didn't get the fair shake let alone fair access to tech that gets these condescending "learn to code/learn to LLM" bootstrappy talks from rich pricks that don't know what life really can be like for a lot of American kids out there.
Is going to university really that good of advice nowadays?
Everybody goes to college nowadays and the average white collar has lots of debt and relatively minor financial benefits over a skilled trade worker.
edit: woah, so many people insulted by that. In my bubble and friends, me and another friend are the only people that make very good money compared to non-graduates. Plenty of others opened their shops, went into trades, one learned to tattoo fake eyelashes, one became a (successful) farmer and most make significantly more than the average law/chemist/mathematician/physics/architecture/languages graduates. Sure, the lowest salaries are to be found among the non-graduates too, but I don't see any evidence that graduates make that much more, and that graduating is worth it.
Some answers talking about how "formative college is", but my 25 years old friend with her own shop knows more about real life, business and economy than ivy league MBAs.
Depends on where you live (lots of countries have no tuition fees or far lower than the US), what funding you have, how good a university you go it, what you want to do (some careers require a degree), whether you will enjoy it, and whether you are there just for financial benefits or more than that.
Its not good advice for everybody, but it is good advice for a lot of people. What if you want to be a doctor? What if you want to work in R & D? Not everyone enjoys working in a shop or a farm. Also, how old is your friend group? If they are mid twenties you are ignoring the greater scope for advancement in a lot of white collar careers.
> my 25 years old friend with her own shop knows more about real life, business and economy than ivy league MBAs.
Within the narrow limits relevant to her business. How much does she know about macro-economics or financial economics, or scaling up a business? I also suspect you are comparing her to people who went straight on from bachelors to MBA (which is a bad path - study business after having some experience IMO) and lack experience. How will she compare in 10 years time when those people also have real world experience?
Going to college is worth it for basically everyone other than those that are choosing between a mediocre college / mediocre degree and a blue collar route. AND if college is going to put you into debt.
Financially, intellectually, socially, it’s a good idea - college is a formative period of life in American culture. That is more and more true the better the college gets. At the upper tier (Ivy League, etc.) you don’t really have to pay anything if your family isn’t already wealthy, and the connections and degree you’ll make more than pay for themselves.
Geberal college degree is good for mediocre white collar jobs but higher education is required for anything more. The richest drop out of college win a business lottery.
It depends on the university and your goals, I suppose.
Many people are surprised to learn how affordable elite colleges are if you genuinely need financial aid. I had no idea -- was pleasantly surprised when my alma mater took over 80% off of my tuition.
Indeed, would the advice be good if university was free?
Where I live it's basically free and I still sometimes regret not going into the trades. But I suspect this feeling might mostly be a "grass is greener" thing.
> Is going to university really that good of advice nowadays?
Just some anecdata, but every single one of my university professors was quite bad, but they think that since they are the professor, that means they are smart and the expert.
As opposed to what, neglecting the human experience to grind yourself to the bone for those who own capital, and then voting to uphold that capital? Makes no sense.
> Then vote for someone who will make the debts go away?
As opposed to vote for someone who gives even more power and money to the oligarchs? You bet your last dollar that people will choose the former over the latter.
I mean, have you seen the options for people graduating right now? How people are behaving?
Or forget the data, look at how the story of the new future technology is being told. The people making it recognize that it has the potential to put swathes of white collar workers out of jobs, and they are openly talking/warning/PR-ing about it.
People in tech and SV, the places which have a underlying culture of near delusional optimism, are talking about trying to avoid being part of "the permanent underclass".
Gambling is up, and prediction markets are being treated as financial investments. Wall street bets is a thing, and outright speculative investments are the hope people have to get ahead.
This is happening in the USA, forget the weaker or smaller economies.
When people see the future as one massive zero sum game, with no way to win by building, then they are going to change how they plan their future.
The times are different. When I was 17, we had to buy records; a 17yo today can listen to the whole of the available produced music, plus interviews and all other uncommon and related material, for free from the comfort of "here and now".
Possibilities exist now that did not exist before. Those who do not exploit this are fools.
I genuinely don't think telling young people to do anything tech-related is good career advice. We don't even know if entry level roles will ever come back. The situation couldn't be worse for these roles. PG thinks that some random teenager will build a startup and get rich from it, or some shit. Like get real, man. Completely out of touch, tech bro who hasn't worked a real tech job for the last like 20 years... Remind me how many startups succeed again, Paul? What about the market dynamics for LLMs and how one might "secure compute"?
These guys (VCs) entire livelihoods rely on tens of thousands of young people throwing away their early years of lives trying to get super rich. Only .01% even see moderate success but PG and co don't care and continue to pump the dream and urge folks to waste their lives to try and make them rich.
What the fuck does this guy know about? I'm sure if we went back through similar statements he's said over the years he's said the same thing about various technologies that are no longer relevant. The guy is a talentless hack who larps as a blogger and his only "redeeming" quality is having lots of money.
Owner of Golf Club Company says I should dedicate my life to golf lmfao.
The amount of people who missed the point here is absurd. He's advocating for learning about how LLMs work. For the sake of learning. Because no one's going to invent the next thing without at least some understanding of the current thing.
I think collectively we should all stop listening to Mr Graham..
He capitalizes on greed and hype but with a soft, sober and thoughtful voice so as to lull you with rationalism and now 20 years of his “disruption” has mostly ruined modern society and a whole generation of techies have been led astray into trying to “change the world” is the world of today (minus the magic technology really any better than 20 years ago?)
- Good for him and his Tech Bros, bad for the rest of society
So I am 15. Is it worth trying to build my own archive (s-1.site) of strategy ideas? Do I have any real differentiation? Or am I just wasting time? I figure that I can use it as proof that I have some know-how?
hackers and painters and kids and ROI and startups and capitalism and the destruction of nature and old guys with money talking like they know better in fascist social networks
I do not think it is a proper thing to do for 17 y.o., unless they are exceptionally mathematically gifted, as proper understanding of how LLMs are trained requires a good grasp of calculus, understanding modern OS and SDE tools for proper implementation of pipeline etc.
I'd rather simply write another mnist implementation and check if I really like all that AI stuff at first place. Even then, before going into mature-on-the-way-to-dying tech (LLMs) I'd rather focus on fundamentals - good ols linear models, regressions, stat etc.
> I do not think it is a proper thing to do for 17 y.o
If I'd get a buck every time someone said something like this to me when I was in the 13-18 range, I wouldn't have a ton of money, but it's so very annoying when people tell you this.
Regardless if they're "gifted" or not, regardless if you believe in myths like that or not, let children explore what they want to explore, even if you don't understand what it is or why they want to explore that, just let people explore, regardless of age.
It was such a terrible experience being a young kid growing up, with so many adults spending hours trying to convince me to stop sitting in front of the computer so much doing whatever; "why are you even trying to learn that stuff, you have to go to school to understand anything of this" and so much other similar trash.
Sorry, not your fault and I'm borderline trauma-dumping now, but really sad to see this sort of gatekeeping on HN of all places, age is irrelevant to learning ANYTHING, in my humble opinion at least.
Kids, find anything interesting? Jump into it, ignore what adults tell you, and do whatever you feel like, you'll find your place eventually.
I just voiced my opinion. I just think buiding an LLM from the scratch for 17 y.o. is pointless exercise, advising a teenager to do so is borderline irresponsible, and frankly PG is simply virtue signalling here, as LLMs are still trendy, esp. in his circles.
There still will be varyy small number of outliers among youngsters who'd be able to extract tremensous value from such an excercise, but for most that'd be _IMO_ waste of of time, with illusion of understanding w/o actually having any.
Same! I just happened to disagree with your opinion, and frankly, I'd say trying to gatekeep what people learn is closer to "borderline irresponsible" compared to asking people to build/learn/do X.
> youngsters who'd be able to extract tremensous value from such an excercise
But they're youngsters, who are about "extracting value"? Life is about fun, not extraction, not value, not avoiding waste of time but literally enjoy what you do, nothing is more important (IMO).
Then who knows, doing fun stuff sometimes lead to useful stuff, like in my life. But if you only think about "extracting most value for time spent" or similar "optimization strategies", then you'd never discover this part of life.
> Life is about fun, not extraction, not value, not avoiding waste of time but literally enjoy what you do, nothing is more important (IMO).
This is, pardon, demagoguery. There is always "future fun" and "present fun" which a normal person would assign different nonzero weights (https://en.wikipedia.org/wiki/Discounted_utility). Besides, building a LLM _truly_ from the scratch, just using the famous 2017 paper and numpy manuals is not fun at all, esp. for a high schooler.
> Besides, building a LLM _truly_ from the scratch, just using the famous 2017 paper and numpy manuals is not fun at all, esp. for a high schooler.
To you it isn't, is my entire point here. But why extrapolate what you think is fun, to others? Sure, I don't find that fun either (although useful), but who am I to say it isn't fun for others?
We can continue this pointless conversation, in the tone "who you are to tell what is fun to others and whst is not". You'd be impervious to any argument stating that dealing with far beyound someone understanding and requiring countless hours of digging into difficult math is not fun even for those who thinks it should be fun, as they presumably, loves everything STEM.
Agree with this - started programming through learning scripting in ROBLOX when I was like 12 (this was back like 17 or 18 years ago) and it developed into a life-long passion for software engineering. I am thankful I had people around me (my parents), who were aware enough to realize I wasn't just playing video games and gave me the time I needed on the computer to learn and experiment with programming...
This also meant that by the time I was actually offered to take a programming class in school (junior year of HS), I had already been able to self-teach myself well beyond what that class was covering, thanks to just working on random projects that scratched an itch I had at the time, looking up anything I didn't know or understand, and internalizing those concepts over time.
In short though, I definitely agree, young kids and teens (and also, frankly, adults too!) should be encouraged to explore things that they have a passion for, without being told 'you need to go to school for this' or 'you cant understand this at your age'
Why would you tell people that the correct order is to build foundational knowledge before exploring a subject? For some (many?) people, a 'proper' understanding develops _after_ the exploration.
> Why would you tell people that the correct order
Because I can?. JK. Because that was my experience, of someone who is 2.5 older than 17?
> For some (many?) people, a 'proper' understanding develops _after_ the exploration.
I am afraid you have a too confrontational attitude here, but I'll answer anyway: because I do not believe you can simply "explore" such complex topics like building an LLMs. You'd simply be unable to build LLM drom scratch, unless you'd call cargo-cult chaining magic numpy incantations you've taken from Karpathy's tutorials "exploring".
If I were in "exploratory" state of mins, I'd rather go from entirely different side - I'd try playing with LoRA-ing existing small LLMs, such as venerable 2 y.o. Mistral Nemo, to get "feeling" for what training is and how hyperameters influence the process.
Then it is not "building llm from scratch" in my book. Just mindlees following instructions. Could be educational yes, but only trivially useful, if you have no bloody idea what you are doing.
> Fooling around and getting results is what made it fun. Understanding came later.
> No it's not useful, no you don't know what you're doing, and no it is not 'proper'. But you're learning.
It is sold by PG as something special though.
> Maybe just accept that not everyone starts from fundamental theory, and there are lots of people who start learning by fooling around.
Even then LLMs are strange thing to advice to play with, when there are so much more interesting and theoretically accesible for a 17 y.o. so I wonder why would you'd particularly single it out.
> I do not think it is a proper thing to do for 17 y.o., unless they are exceptionally mathematically gifted
I attempted many projects at a young age that I was absolutely not equipped for. The result of the attempts more often than not left me equipped, every time it left me better off. This is terrible advice.
That'd would be a terrible advice if there weren't a plenty of other things "you are not equipped for", but far less daunting both theoretically and practically. Such as, say, convolutional neural networks, or some older ML tech. Or even something totally unrelated to ML.
Transformers are difficult to understand even to people with strong ML background, let alone a teenager.
You are assuming the 17yo in question as an untrained underdeveloped savage. If I were 17 in 2026, I would certainly have exploited all the availabilities from 2010 on - including YouTube, OpenCourseware, the Web simply (Sebastian Raschka etc.) and LLMs.
That 17yo would have already built many uncommon bases, and would build further.
How would you filter out garbage from your training data, for example? If you are trying to use someone elses corpus, would it be "from the scratch" then?
I quoted the wrong thing. I meant to quote that Calculus is needed for building LLMs when all the equations have been figured out already of what is needed.
If I were 17, I'd be going to parties, music festivals, chasing girls, and enjoying my youth.
But sure, make the kids even more depressed by telling them they need to learn how to build an LLM so they can get a job working themselves to death to make Paul and friends rich.
This community is filled with people who were obsessed with microcomputers or (depending on which generation) websites during their teenage years. HN was created by and is managed by people of this type. No doubt we're a minority, and the type you describe is more common, but if you're implying there's something wrong with teenagers being intellectually curious about technology, I can't help but think of the way "nerds" used to get put down and shamed in the past.
He bases this decision on all of the experience he has amassed, as a 61 year old man in the tech industry. An actual 17 year old, with 17 years of experience, would not think like this, nor should they.
Naturally 17 year olds don't think long-term like this which is why PG's advice is so useful. It gives them a pathway to follow that they likely wouldn't have reasoned otherwise.
I told my much younger brother when he was 12 what programming was and it'd be a great career. He looked into it and within months was writing CLI games. Eventually releasing his own unity 3d game on steam as a teen.
Eventually he got into CS and did really well because none of it was scary and new. He parlayed that into role at Meta out of university.
My point being, 17 year olds have time to learn new skills and guidance can go a long way.
Yes. And they almost certainly have a better understanding of their own situation that him. This is not a dig at Paul Graham, the closer anyone is in age, the better they understand what they have to deal with. I'm roughly in the middle between Paul G and the 17 year old, and even though I'm really quite fascinated with zoomer culture and probably come more in touch with it than most (due to relatives in the age range etc.) I realize I have very little idea what it's like to grow up in the world they grow up in.
>Whoa. I’m 19 and I trained a 100M language model from scratch. Did a v2 now with a new SFT experiment to see if I can get better results on same size.
This is the ultimate builder’s mindset. Tinkering with the hardest technical problems—even for frivolous things like games—always yields the highest return on curiosity. Time to go back to the fundamentals.
A lot of people here are responding to the message but not to the meaning.
It would be a good idea for young people to deeply know how these programs work. Not so that they can spend their career building them, but so that they can approach the next class of problems we'll all start trying to solve, with intuition all the way down to the weights and underlying mathematics. And also, to develop a healthy intuition of when "Just LLM it" will not be the right choice.
"Build an OS" wasn't a common university project because we were all expected to go out and work on Windows, but because understanding the bare-metal firmware for a computer helps you deeply understand how to intuit building for a whole class of problems.
The funny thing about LLM is, you can build one from scratch and yet you still won't understand how it works. You would understand what kind of matrix multiplications the neural network performs (in fact that's not that hard. An OS is orders of magnitudes more complex), but you would still have no idea why it does what it does.
This is a great point. The complexity sources are entirely different. An OS is complex because it’s solving a difficult problem and we’ve seen decades of evolution both in knowledge and-how and, at the frontier, in performance standards.
The abilities of LLMs are emergent. You can experiment with LLMs and know as much about their observable behavior as the experts. But there’s no way to “crack open” an LLM and see precisely where each skill or tendency lives; as far as we currently understand, it’s all mashed together.
Learning how to build emergent systems is also a skill kids should learn these days. The closest I got was coding up game of life for CSE 142 (intro programming).
If stochastic gradient descent isn't taught in whatever CS Theory 101 is now, it really should be these days.
> there’s no way to “crack open” an LLM and see precisely where each skill or tendency lives
Mechanistic Interpretability has entered the chat.
For a classic example, see https://www.anthropic.com/research/tracing-thoughts-language...
The spirit of your point stands, though. This kind of research is interesting to read about, but it's very hard, more like neuroscience or biology than computer science ("LLMs are grown, not made"). You're dealing with a lot of extremely _messy_ complexity, for which organic life is really the only good point of comparison. Most of us here are't really equipped for that kind of work; it's not at all like, say, reverse-engineering a piece of software written by humans. And of course the only people who can do it on frontier models from Anthropic and OpenAI are people within the labs themselves. (But I'm optimistic we'll see more of this work on open weights models...)
Yah, "building" it is not sufficient. But a lot of times when I build I want to know the why. "Why does gradient Descent have some clever tricks that easily translate to matrix math"?
Lot's of neat stuff to learn.
I'm not sure it's possible to have intuition about systems that work in thousands of orthogonal dimensions. In fact I'm pretty sure most of the research is people trying fairly arbitrary things and testing them and then post rationalising implied understanding of what is really happening on top of good outcomes.
I think it’s reasonable to have a shallow understanding of most parts and a deep understanding of a small number of parts. That’s how most engineers are.
Most software engineers do not have a deep understanding of CPU architectures. In fact they probably don’t even have a shallow understanding and get around just fine. How many of them are looking up the instruction set for the CPUs they deploy their CRUD app to in EC2?
But in the case of CPU architecture there are SOME people who understand how things work 100%, and they've built and vetted abstractions/mental models that enable other engineers and scientists to have that kind of mixed shallow/deep understanding in a way that works. On the side of LLMs we're still lacking an expertise which could flawlessly explain how these things operate; the abstractions that we're using are instead derived inductively and are totally unvetted.
You are right that the field doesn’t have a theoretically sound explanation for the architectural choices aside from “A works better than B”. However, I would argue this is an ideal opportunity for the “gentleman scientist” or eager 17 year old.
Basically every part of the original transformer was replaced with something more efficient or better:
LayerNorm -> RMSNorm
Sinusoidal position encoding -> RoPE
MHA -> GQA
ReLU -> GELU
What this means is that there is ample opportunity to improve on what we’ve done thus far.
The original RoPE paper is a great example of the authors not actually understanding how their own discovery was functioning fully which is a fun example of what I’m talking about I guess…
Yes you might progress the field, but will you really understand why? You can make up an explanation and anthropomorphise it with a few contrived diagrams and everyone will cheer!
Oh yeah I absolutely agree that there's lots of greenfield for relative newcomers to find some big improvements! I'm just saying that reasoning about 'best practices' in terms of what works for something like a CPU is missing some important and relevant differences.
In fact, one of the jobs of an engineer is to make sure that other engineers who don't work in his or her area do not need to understand that area deeply, yet build something reliable with it. They need just the summary that he or she writes up into the datasheet for the part. Ensure these conditions are met for safe/reliable operation, give it these inputs, expect these outputs, these timings, this energy consumption, this heat generation, frequency response, tensile strength, whatever.
I disagree. It's absolutely possible to develop an intuition about extremely complex mathematical ideas, including llms or high dimensional systems. Learning to build an llm is a great way to start building that intuition.
Developing an intuition about high dimensional systems is pretty different from understanding the character of some specific point on a 1e9+ dimensional manifold of parameters, in my professional opinion (setting aside all the degrees of freedom that come from the structure of the thing). Sure one can understand generic principles like the curse of dimensionality, but truly groking how an LLM works is basically an open problem as far as I'm aware. I'm not saying there's no benefit for amateurs to study how LLMs work, but let's be realistic about how far mere intuition can truly take anyone in this space.
Seems like a silly goalpost.
As you say, there's a lot of value to unlock with understanding the generic principles, and I would add specific application.
Lots of people are building fantastic Tools or pulling down million dollar salaries without groking the precise representation of a single weight.
What provable conclusions have you intuited around how LLMs work? Give me some examples to prove your point? I'm absolutely happy to change my view with enough data.
I think provable conclusions and building an intuition are different things to be fair. In my opinion it is entirely possible to build intuition about seemingly impossible to understand topics like infinitesimal areas, infinite limits, function mappings, and high dimensional spaces. For example, 3D spaces are easy to intuit if you can visualize vectors in your head. Some people claim it is impossible to understand dimensions higher than 3 but that's obviously not true because you could assign a color to each vector to visualize the next dimension. You could then assign a width to visualize a 5th dimension, and an arrow shape to signify the next, and so on and so on. That's more of a creative act that can start to build at least a visual understanding in you head of these higher dimension spaces. That's a way to start to intuit about those things.
Now with llms we need way more that 6 dimensions so we can start thinking of assigning matrices to each 3d point for example. That allows us to increase the dimension from 3 to 3 + whatever the matrix dimension is.
We can visualize the matrices instead of having numbers as having colors for each entry, so they can be a sort of cube with each vowel being a different color.
Now you can start to visually intuit about how these massively high dimensional spaces can be formed of these colored matrices that can react to some input training data.
That's a start of an idea for intuiting things that might seem impossible to have an intuition about. I think visualization is a great way to start.
i read all that i still dont see what your intuition is about how llm work.
visualizign 100 dim matrix will not tell you how llm work. so what you even talking about.
Yes, if it cannot be measured it doesn’t exist no matter how much it would make everyone feel more comfortable if we could intuitively understand these systems.
Visualizing large dimensional matrices is a way of starting to develop intuition about llms. If you can't visualize high dimensional spaces, and you haven't studied llms, you're not going to have any intuition about how they work.
Depending on your knowledge of math, I recommend starting with linear algebra, building an understanding of the equations and try to visualize more and more complex systems, then study llms to see how you can apply your linear algebra intuition to your understanding of llms.
VTK is a great toolkit for visualizing complex systems. 3 blue one brown on YouTube has other visuals that might help you.
It takes time but it's possible. Good luck.
I'm not looking to change your view. But for other readers who are curious, here is a link to an interesting task to gain intuition. Ahmad is a good data point for someone who tinkered, built intuition, then started his own ai company. https://twitter.com/TheAhmadOsman/status/2087742080793620593...
I think we are talking about different things to be honest. Understanding LLMs is a fine thing to do but to say you understand what the network is actually doing, even in toy examples like MNIST is extremely tricky. Figuring out what a ~3 trillion parameter network is doing in thousands of dimensions seems intractable to me.
Linking to a Twitter thread about training a small LLM to be good at Wordle is not an example of what I'm talking about. It might well be a useful task but it doesn't allow us to understand deeply what's happening.
You may be right. Nonetheless, it's possible that the act of trying to get your brain to understand a system that works in more than a few orthogonal dimensions will help develop useful intuitions about how LLMs, and the many systems/products/robots that will be built on top of them, work.
So computer science actually involves science now.
It is sometimes the opposite - a large number of things makes the system easier to predict and reason about (statistics, behavior of gases etc).
I'd have to push back, though not on the part you'd expect. Your description of human researchers is roughly right: a lot of the field is try-things-and-narrativize-after.
But the load-bearing assumption is that intuition has to be human-shaped intuition. Humans can't intuit thousands of orthogonal directions because we project everything down into a 3D metaphor and hope it holds. That's a fact about our hardware, not about the systems.
And the reason why is the most interesting part: nothing requires the compression step. A model or an agent can operate over the actual objects, holding thousands of runs and ablations in context and noticing regularities in the native dimensionality, without translating them into a picture of a ball rolling down a hill. No bottleneck at "can you visualize it."
So the narrower claim: it's not that intuition here is impossible full-stop, it's that human intuition is unreliable. Your post-hoc rationalization point is evidence for that, not against it. The story exists because a person needs something to hold in their head. Drop that requirement and the failure mode goes with it.
You apparently have no intuition on what the intuition is
This is no longer funny.
> A lot of people here are responding to the message but not to the meaning.
Well it is framed as quite specific advice.
(I'm done with mining PG tweets for meaning)
What corpus would one be advised to use for training? Should one limit oneself to legally downloadable documents or should one follow the examples of Altman, Amodei, and Zuckerberg and use LibGen and Anna's Archive?
> understanding the bare-metal firmware for a computer
IMO this is still relevant, everything surrounding the LLMs needs such a vast infrastructure that I don't know if I would find it more useful to learn the maths behind ML than CS
As a 17 year old, I agree with this. Ofc I'm against all the hate directed at PG, I believe that all knowledge has value regardless of its economic utility, but I understand where the hate is coming from. Personally, I find LLMs boring for now, and I'm more focused on CS and electrical engineering.
I think you're conflating complex and complicated. An OS is very complicated but is predictable. An LLM is complex and not necessarily understandable.
Agreed. During comp sci we got to re-implement various algos of networks, OS, database, firmware.. and it all gave complimentary intuitions that were useful when tackling practical implementations and bottlenecks.
That depth requires math and traditional computing depth. Where is the time to be young and have that depth?
The next class of problems to solve for future generations is how to survive on a hot planet and how to recover from the damage done by reckless lunatic profiteering assholes.
Maybe people want to take it as cynicism but I think you are factually correct that personal cooling, infectious disease, atypical weather and fire breaks are all more realistic topics for people too young to already have patents, connections or their own oil well.
Couldn't have said it better
Depends, the assumption things are predictable always negatively affects both Market Bears and Bulls alike.
Indeed, if credulous folks look to the world expecting people to bestow success upon them... than the disillusionment with reality will hit their savings harder.
The Shrek movie market correction correlations are undeniably funny, and a new film is due July 2027. OpenAI may be going public in the next few months while still losing $2.25 for every $1 of customer revenue, and with 6 other firms sharing over $4Tn in debt disclosed to investors in a footnote.
There is only one direction things can go at the Peak of inflated expectations. Popcorn ready. =3
https://en.wikipedia.org/wiki/Gartner_hype_cycle
> "Build an OS" wasn't a common university project because we were all expected to go […] helps you deeply understand how to intuit building for a whole class of problems.
Sure, but it was for a specific degree with a syllabus that taught you the foundational knowledge. It was not expected from the law students to learn how to build one.
> It would be a good idea for young people to deeply know how these programs work.
It would be a good idea for _everyone in the industry_ to deeply know how LLM training, inference and "agents" work, not least because it removes the ability of shysters to bamboozle with bullshit.
But, as much as a good idea it is for the young to understand this, it's the elderly who will be really taken advantage of if they do not keep up - just look at Facebook for good examples of why.
There's this dilemma where in theory there's a ton of demand for engineers that can do real LLM machine-learning, but in practice there are very few available positions and entrepreneurship opportunities.
The reality is that an incredibly small minority of companies in the world do any real training or optimisation. It's unnecessary and inefficient for most purposes unless you are fully dedicated to being an LLM company, and still then it's a struggle. Those few that do train, they spend most of their budget on compute and have relatively small teams.
Getting experience in this field requires having access to very expensive hardware to begin with. And the skills will be quite hard to convert into any real value for someone, leading to a decent income, unless you have a ton of funding from patient investors, or you have decent contacts in Bay Area networks to get hired at the right place.
With all due respect, paulg is in somewhat of a bubble, this is not congruent with the global situation.
In that regard, it's not too different from mobile telephony. Mobile phones drove the electronics industry 20 years ago, but there is limited demand for people who really know how to build a phone (ie. build the hardware and write all the signal processing from scratch), as there aren't that many companies that do phones at the lowest level. A few of the engineers got rich (eg. Viterbi), but most 'just' made a good living. Most people who got rich off phones didn't do it by knowing how phones work.
Incidentally, the skills for the lowest levels of LLMs aren't that far removed from those needed for mobile telephony, in that both are based on maths, computation and information theory.
I was there at the start of the smartphone boom. I built a demo Android device that was capable of telephony/data, 3D rendering, etc. all before Google open-sourced the OS, for a SoC vendor that wasn't in Google's inner circle. Yet the industry was not interested in my junior profile during the subprime crisis.
I wonder if the modern incarnation of Android flagships would have taken off at all if not for Apple. When Android first showed up, the dominant "smartphones" were running Symbian/Windows Mobile and they had a very different view of the world when it comes to UX and hardware.
Phones were already going in that direction before the iPhone was released: https://phys.org/news/2007-02-samsung-unveils-revolutionary-...
But it was generally seen as a gimmick instead of desired before Apple made it look good. Even when the iPhone came out, one of the jokes was how the grid of icons looks like how a Windows user's desktop would look like when they didn't understand the filesystem.
Ridiculous frankly.
More than sad...
This is super interesting because I moved from mobile telephony into ML and data science, and information theory and working with data in statistically correct way was what helped me! This was 10 years ago though.
Good points, but I think we can expect the AI space to be more tumultuous.
What most people want from mobile technology is for it to work, not too expensively, and for it to get out of their way.
What most people want out of AI is for no leader to emerge and wield supremacy against the rest of us. People are afraid of it in ways they weren't afraid of mobile, so they're more willing to work together against whoever is in the lead.
Its more like an arms race and less like a utility. The disadvantage I face when my competition has better mobile coverage and bandwidth is minor. The disadvantage I face when my competition has better intelligence on tap is much more significant.
>* What most people want out of AI is for no leader to emerge and wield supremacy against the rest of us. People are afraid of it in ways they weren't afraid of mobile, so they're more willing to work together against whoever is in the lead.*
No.
That’s what people like us on HN want. The people out in “Greater Userland” just want the black box to answer their questions. They could care less who is behind it. They don’t yet attach their black box to Amazon or Microsoft etc. And most won’t care enough to be inconvenienced even when they do make the connection. (As your competition argument implies.)
Heck, a lot haven’t even made the connection between the black box that gives them answers and data centers. They think, “ ChatGPT good” and at the same time think “data centers bad”.
Same with telecom companies.
We don't really have the demand for as many telecom companies as actually exist in the world. There's a reason we just have one Whatsapp and one Instagram, not three or four almost-but-not-quite clones in every single country that mostly differ in branding. The reason for the current situation has mostly to do with regulation and traditional, enterprise, "obviously every country needs a separate local branch, because that's what mcDonalds does" thinking. Technology has very little to do with it.
This is why the telecom world now consist of equipment manufacturers, who do most of the hard tech stuff, and actual telecom companies, who operate the equipment, rig towers in their local country, and maybe write some glue code to integrate a core from vendor A, a billing system from vendor B and a CRM / corporate invoicing system from government-approved local vendor C.
Banking also works similarly, though modern Neobanks / Fintechs and bank consolidation are slowly dissolving the concept of national bank branches.
Also, in a few years, LLMs will be building the next generation of LLMs anyway, probably autonomously to a high degree.
Yes. It's like looking at the (Apollo) moon rocket launch and then suggesting teenagers should learn to build rockets in their garages for the coming space age.
It is viable as a toy project, but there are vanishingly few career opportunities.
It's like looking at the early internet and then suggesting teenagers should write browsers as their projects instead of webpages.
If someone writes a browser in their teens, they will probably learn more about the web than if they were just writing web pages
They'll learn so much more that won't transfer to as many job opportunities. For ex, say more about C++ and less about cutting edge CSS (because modern browser tech is an ocean). I suppose they might luck into other adjacent or unrelated roles with the same skills.
I have a hard time imagining anything where writing a browser wouldn't be an excellent transferable foundation. Even your example: writing css will never teach you as much as writing a css engine.
Sorry if I was unclear. I mean they'll never have enough time to learn a significant amount of modern web tech because they'll be working up to it from lower layers and basics.
Browsers from scratch are multi-year projects for multiple people. Even just skinning and minor tweaks to modern browsers is a deep well for one person.
CSS wasn’t around in the early web.
Building a browser in the early web was actually a very achievable goal for exactly the reasons it isn’t now. There was not JS. No CSS. No SVG. In fact very few widely supported image formats (and graphical browsers weren’t around in the earliest days of the web anyway). TLS didn’t exist. HTML only had a subset of methods. And even POST was usually just managed by CGI/BIN calling an external process, often written in C++ or Perl.
It was a simpler time.
They will also know the fundamental underpinnings of the web, you know - the thing that those html devs are actually using.
That’s like saying someone who fabricates cars doesn’t have the skills to drive them. Perhaps not, but they’re very well placed to pick it up quickly. They’ve also shown they can do something far more challenging, which is actually better than hiring for narrow immediate skills.
In other words: I’d hire that candidate in a heartbeat.
You'd hire an auto designer to be a limo driver? I think it's just two different skill sets. The auto designer is clearly smart but a limo driver also needs to have customer service skills, etc.
Are you asserting that C++ is not a marketable skill?
If anyone reading this has this un-marketable skill, Carnegie robotics in Pittsburgh is hiring
carnegie-robotics.breezy.hr/p/2d85f5321cc7-software-engineer
I think he's saying that building a browser is not a transferrable skill, like making a generic web page is. Employers don't like specialists. I used to build display drivers for graphics cards. Wonderful learning opportunity but other employers not in the graphics card manufacturing business didn't give a shit--those three years were essentially treated as an employment gap. "Well that's nice, but we really wish you had general experience writing CRUD apps..."
> I think he's saying that building a browser is not a transferrable skill, like making a generic web page is. Employers don't like specialists.
I recently switched roles, and among the seven places I interviewed, none of them seemed to see my then-current browser job as a problem, even though they were not related to browsers. (The closest one was a company implementing a HTTP reverse proxy, and I did not work on the browser's HTTP stack.)
I would argue that in a post-LLM world, becoming as specialized as possible is the only way to survive. If you have the serious systems programming skills required to write graphics drivers, your talent would be wasted writing CRUD apps anyway. I hope you eventually found/will find something more appropriate for your skillset!
I'd argue the exact opposite. If I want a graphics driver, I'll ask the clanker for one. But if I don't know enough to even know what I want, there's no path forward.
CRUD didn’t exist in the early days of the web.
People keep talking about a browser in the modern context but the GP specifically said “early days of the internet” (which, in fairness, would mean pre-web. But I think it’s safe to assume they meant “web” not Internet).
In those days, it was actually a much simpler exercise to write a browser than it is today. I even wrote one! And writing a browser absolutely teaches you how HTTP and HTML worked. Plus a lot of backed development was forms data sent to CGI and thus written in languages we wouldn’t even dream of using for web development nowadays, including C++.
So in the early web, writing a browser absolutely was a transferable skill. It might not be now, but in the context defined by the GP, it was.
But building a web browser isn't a niche skill - it requires a whole bunch of them. You need to be a generalist to write a web browser.
This is like saying writing a compiler doesn't give you transferable skills. The surrounding competencies required to do this grant a pretty large amount of broad domain awareness
A few did.
https://en.wikipedia.org/wiki/Blake_RossIn the meantime Mozilla was mocked on slashdot.org and elsewhere relentlessly after the first two years of the project when nobody believed there would be any value in the effort. Hats off to the team that took around five years to get to Firefox 1.0 (and released Mozilla browser in the interim). It took a lot of conviction to see it through.
Sure, you had to pick the browser I despise worse than Netscape 4.
That may have worked at the time, but no companies are interested in learning projects today. If you didn’t do the reqs list for the last 5-10 years with the same title, forget about it. Because there are dozens of folks who have, lined up. No one is indulging career changers (and most fresh grads) for the time being.
> No one is indulging career changers (and most fresh grads) for the time being.
Oh hey thank you for that. It really helps. Hope you have your rug pulled from under you today too.
— signed, a career changer trying his best.
it's just a fact of the market, it's not an attack on you.
Indeed, I speak from experience. Generalists like myself are unfavored as well, it seems.
If you learnt to build a browser in 2000 you're probably doing well for yourself lol.
Not a lot of demand, but also probably not a lot of supply.
For this example, if you built a browser in 2000 it would put you in a great position to launch android in 2008. I think this is pg’s point. You spend all this time learning the interesting bits and when an inflection comes along that makes a new product or service possible you could be the one to likely launch it
That’s like saying if you can dunk in 8th grade you could be the next LeBron James. A LOT more things have to fall perfectly in place at the perfect time for that end result to materialize.
Each new bet will be expensive and depend less on technical merit than luck and having a backup so your family doesn't starve.
Agree.
FWIW I built browsers from 1999 for a long time. (But I was never a wunderkind, just somewhat tenaciously curious).
And I guess I am doing fine, but not amazingly rich or so.
Browsers were always a project closer to research/charity. I think Marc Andreessen said something similar - that he would never do that again. B2B is where you can make money.
I am talking from the perspective of the individual developer.
You don't need your impressive product to succeed to land a good career.
If Alice is doing LLM-from-scratch work today, and Bob is doing agent harness work today, Bob's project is far more likely than Alice's to become useful/popular/profitable.
But if neither project survives, in 5 years, Alice will be more employable/at a higher market rate than Bob.
Neither one has even gone to college yet. I’d expect Alice’s project to be more likely to interest professors, get some undergrad research experience, maybe that can turn into something.
This was his point. Being prepare to launch a produce when the opportunity comes.
There is an entire graveyward of browsers, almost all that didn't die are now forgotten.
Sure, but I'm sure most of their lead devs are well paid now.
In general C++ work and similar, if not in Chrome development.
cause != effect
There is a confounding variable, however -- the person is most likely a good dev. It stands to reason that they've had a decent career at least since then.
Yes but as a dev you are shaped by the projects you work on.
I think the point isn't that you'll necessarily build a great browser (or LLM), but the experience will benefit you in other ways.
Yup like you go far enought you will pick lots of transferable skills like data cleaning in the case of LLM, DOM parsing case of web browser or welding case of rocket.
If any were built by teenagers, I’d imagine those teenagers ended up with pretty good careers in technology?
For every wunderkind that has a long career there's also plenty who are overlooked or peak early.
Citation desperately needed.
https://en.wikipedia.org/wiki/Survivorship_bias
Ken Silverman
I think he's doing fine? Sure, he got out of the video game industry, but that's for young people to burn themselves out.
I don't mean he's destitute. Just that he's no longer exceptional, which is fine.
But you were trying to refute my point of:
> I’d imagine those teenagers ended up with pretty good careers in technology?
The guy you mentioned doesn't seem to have ended up with a bad career in technology?
Thanks for clarifying. I read it as exceptional careers
https://en.wikipedia.org/wiki/Ken_Silverman
Is this the guy you’re talking about?
Source: Sour Grapes
Are you fingerpointing Marc Andreessen?
Well, there's a lot more to learn from the former than the latter ...
Really? The latter was immediately useful to lots of people which is motivating, and it had a nice smooth learning curve (html -> js -> php -> databases -> apps -> backend). Learning HTML is the first step to learning how to make full blown apps. Making a browser at 17 is like trying to climb Everest as your first hike. The expected outcome is burnout and demotivating failure. At best you'll learn some C++ or Rust.
17 is an interesting age. There are way too many comments here saying things like, 17 year olds should just do whatever seems interesting or bum around the world or focus on getting into university. But historically most kids were expected to be productive adults at 16 or 18. 17 is about the right time to be thinking seriously about what kind of work you'll do, how you'll make a living. University won't help and will just delay this decision.
Anecdotally, I think it's great advice.
I contributed to a browser engine around that age (KHTML, which later became WebKit and Blink), and while I don't work in browsers right now, much of that knowledge, mindset and of course the professional network have done much to shape my life. And a fairly successful career, for that matter.
Contributing to an open source project is fine, but the original analogy was "it's like telling teenagers to build browsers".
If teenagers could make small contributions to LLMs via open source then sure, go for it. Optimizing llama.cpp or similar would be a good learning project that might later get you good work via social networks. Contributing to open source is how I got started too.
Unfortunately, training LLMs isn't something that fits well to open source open collaboration. Inferencing codebases are better.
Excellent point, and I agree. Much of the benefit I saw was from working within a like-minded, smart team, not going it alone. And also specifically working on software with a real user audience to learn what providing value to them actually constitutes.
In that sense it's more a "seek out the open source community and real projects when young" rather than "do web browsers", with a bit of "look for ambitious types of projects few get to work on".
Historically people lived very different lives, required different skills, were poorer, had different opportunities etc.
A lot of people will make better decisions with a few yeas more maturity, and spending a few years developing themselves.
University will help a lot of people, and for some it will help.
There is a lot more to life than making a living.
Early web didn’t have JS. Nor PHP. In fact a lot of early web pages were written using static HTML with C++ invoked via CGI/bin for processing form data. So writing a browser would teach you the HTML plus C++ too.
The early Internet (which the GP mentioned) didn’t even have the web. But that’s nitpicking.
17 is a weird age but ymmv. I left home at 16 alone to study abroad. I had tons of free time due to dorm curfews and such. Unfortunately, I was too poor to have a computer and the computers we had access to were completely locked up. (Naturally we waltzed past the locks to play some games but it was also under surveillance)
Paradoxically I coded way more between ages 12-14, I regret my wasted late teens.
Me too:
ages 8-15: lots of great 8-bit computer fun.
ages 15-20: girls, booze, motorcycles.
20 onwards: get a PC, back to computers, realize how much I've been missing.
To be honest, judging by my own kids and their friends, late teens seem to generally be an era of hard to avoid stupidity.
If your focus on girls, booze, and motorcycles ends at 20 years old that's a pretty good sign.
I mean... that's actually amazing advice. Not because they would grow up to create browser startups. But because they would grow up to create web startups that succeed because of very fundamental of how the web is rendered.
Which is paulg's point really
This site exists because paulg created a webshop in a niche language (Lisp) and got a prototype bought out (and discarded) in the gold rush. No browser internals needed to make a fortune on the platform. Just rapid development of an application with natural monetization, and being in a place to do so way at the beginning.
Jeff Bezos did similar, but he did his own fulfillment and hired out the coding.
Early internet let me create the best personal web page in my city that I knew of with 2 weeks experience as a 12 year old. I imagine that same 12 year old could be more knowledgeable about LLMs than 99% of people in the same period.
You're vastly over-estimating the difficulty of making a web page and even more vastly under-estimating the difficulty of LLMs.
I doubt it. Creating a website can be done by copy pasting a few snippets together and checking if it visually looks like expected.
Good luck with that approach when trying to toy around with models and their training/inference.
There is also a lot of math basics missing that a 12 year old may be able to grasp, but I would bet they are at least 13 by the time the knowledge is deep enough to understand what operations are happening.
That seems like a perfectly good use of time for a kid in the 70’s! You’d learn a lot of engineering skills and demonstrate a tenaciousness that most people don’t have. I don’t think the goal is to predict what will be the important technology in 10 years. The goal is to challenge oneself with hard tasks and learn interesting stuff. A lot of the “AI” people today were compilers people yesterday
There are more career opportunities building rockets than designing rockets. Lots of welders, machinists, electrical engineers etc build rockets, and those skills transfer.
The same is not so different for AI. A few people design novel AI, but there are a lot of people training AI (especially if you include fine tuning) and implementing AI, even as a hobby.
Building an LLM covers a lot of CS fundamentals and forces you to do lots of research in order to implement one, especially on more modest hardware.
So do a lot of things, that isn't what Paul is implying when he says "I would build a LLM if I was 17".
How far into the future can you see?
But he’s not saying that it’s a career opportunity.
It seems to me like he’s saying that doing this thing would be 1) fun and 2) a great way to become employable in the future. I don’t believe he’s saying that this project would be some kind of job training exercise.
sounds like a great way to not establish social skills
It’s not either/or. You can do 2 things.
I never thought of that :)
I "guess" you could put all your effort into moving up to the next level in computer science, or you could put the same progressive effort into reaching new levels in video games on the same hardware.
Alternatively you could even put all your effort into social activities and leave the technology to other people entirely.
It might even be possible to find a balance between things that are widely "understood" "socially", and those that are not ;)
When I was 17 and still witnessing Apollo Moon launches, I wanted to build an AI that would handily outperform LLMs as we know them today.
But that was way back in the early 1970's and all I had to work with was a mainframe.
Well the mainframe itself wasn't bad, the real show-stopper was that I didn't own the computer outright, no strings attached, no debt, etc.
>I'd probably try to make an LLM that I could use on some specific problem.
I thought so too back then, still do so I guess this is one of those things that could stand the test of time. I always wanted to start with something a lot simpler than a Moon mission myself. At 17 I already had a significant breakthrough in the chem labs and it was from alternatives to a single processing step plus everything that descended from that, rather than trying to tackle a much more complex detailed multi-step synthesis. I was only 17 but I was not trying to be a slouch, I don't think pg was either at that age but his advice is not for just anybody. I couldn't have done it if I hadn't made major progress since being 16, and it really emphasized at the time how much maturity can make a difference. My imagination ran wild as I extrapolated :)
In a reply from LeCun to pg:
>>I'll figure out a set of methods and architectures beyond LLMs that can quickly learn to perform physical tasks as efficiently as humans and animals. That last item is also what I would if I were 30, 40, 50, or 66 years old
I see no reason to stop at 66 either ;)
But I figured that people owning more computer power than I could ever afford were going to be doing something like this as soon as they could, without having to wait for something like an LLM to arrive before getting peoples' attention.
It did seem like things were going to take longer than you expect, so it's pretty good to have a lifetime of concentrating on the specialized natural science domain expertise, focused now for 50 full years on how it would combine if AI ever got good enough.
Both the natural science and the AI need to be a major cut above, I still see dramatic room for improvement in my own work. If I'm going to have to rely on "other peoples' AI" then that natural science component is going to have to pull a lot of weight to keep up with the kind of computers that only rich-as-hell high-rollers have access to.
I agree with mostly all of this, but personally I wrote a toy LLM almost 5 years ago and while it never saw much use outside of boring my wife with a shitty command line demo with glee it did help me understand how they worked and how to apply them, played a lot with JAX and pytorch, ended up building a ghetto version of MCP and an LLM-Pool to proxy requests to my baby local models and so I didn't struggle to see the evolution of openrouter and MCP agentic workflows. The same way i'm really glad when I was younger I built a bad webserver by myself, a really painful SQLx type database, etc etc etc - none of these things led me to developing for Nginx or Oracle nor will knowing JAX get me a job at an AI research lab, but I do have a lot of depth in understanding how the technology works so that the flavors on top of them are easy to digest and make more use of immediately, and I think the same can be said for engineers coming into the field - if it's a spooky LLM box you aren't going to be squeezing the same amount of juice as the guy that knows how they work inside and out so having at least the understanding of a _babys first LLM_ is going to get you miles ahead of people who don't.
For anyone who wants to dork around there is https://github.com/rasbt/LLMs-from-scratch which is something amazing that I think anyone who wants to engineer things around LLMs should at least blast through and read.
Game cheating and reverse engineering MMO backends taught me a lot: databases, networking, securing a backend (and frontend), limitations of simpler languages when comparing them to more native options for building backends.
Agreed, I was very late to the game and was forced to learn VBA for excel sheets and that is how I finally broke into programming.
When I was a pre-teen I stumbled upon CD-rom hacking guide to bypass disc requirements on games, I remember opening up the file and the screen being filled with HEX code. I was so overwhelmed I just closed it and never touched programming after that for 15 years. My life would have been totally different if I had embraced the unknown instead of retreating.
This was my introduction, too, but with Counter-Strike cheats.
A curse upon you and your descendants
It was Quake World for me
> The reality is that an incredibly small minority of companies in the world do any real training or optimisation. It's unnecessary and inefficient for most purposes unless you are fully dedicated to being an LLM company, and still then it's a struggle. Those few that do train, they spend most of their budget on compute and have relatively small teams.
And the job postings are often ridiculous. I recently was an AMD job advert in Germany for an ML Kernel Engineer, not Senior mind you. The requirements went something like
> Masters Degree required with strong preference for a PhD with peer reviewed articles in {journals_list} > 10+ years of experience in C/C++ > GPU programming experience required > 10 more ridiculous lines
No idea how a teenager self teaching himself LLMs is supposed to even get a shot...
It reminds me of that "stone soup" story.
1. I can make turn a stone and water into a delicious soup
"17 year olds, learn to build an LLM from scratch"
2. This soup would be more delicious if we add a few carrots. Does anyone have carrots
"Increase your chance of success by getting a Masters degree"
3. How about potatoes?
"And get a PHD"
4. What about some salt?
"And publish some peer reviewed articles in {journals_list}
5. We should also add beef
"Now work in the industry for 10 years"
6. See, this soup is delicious, and I made it all with a stone
"See, you're rich, and it's all because you learned LLMs as a 17 year old"
I have always been pro fundamentals. It caused me trouble early in my career with bosses that didn’t understand why I would spend time trying to understand how something worked at a low level if I was a high level user. But then knowing the fundamentals gave me an edge as a designer and developer by understanding capabilities and limitations of the tools I was using. For example understanding how indexes work internally in a relational database. So I see the value in this type of work, not to land a job as a LLM researcher, but as an informed user of the tool.
I’ve been wondering about this.
There are high school students competing in contests that cover parts of the (Math) theory behind AI. A lot of high school research programs are integrating AI with other things and complex mathematical models…
To me, this is bizarre as Calculus is barely taught in high schools (and likely poorly).
Don’t get me wrong, these kids certainly aren’t the usual lot.
Yet, I really wonder if they know the fundamentals. Do they even understand derivatives or just memorized the rule for polynomials? Can they even explain what a transistor is?
Normal curriculum takes 5 years to go from Algebra I to Calculus. Real Analysis, Linear Systems, etc. are fundamentals taught only in college…
Feels like too many are trying sprint before even learning to walk.
That's like saying the only way to do real engineering is with Google-scale Borg deployments. You can do quite a lot on very little hardware, r/StableDiffusion is a prime example.
You can do plenty of "real engineering" under normal conditions. But specifically when it comes to LLM engineering, no there's really not much you can do, they are called "large" for a reason. You can play around at small scale, but those lessons you learn will not be very relevant to the real problems in the market.
Sure you can gradually climb the ladder by demonstrating your skills bit by bit and getting access to more resources. It has very good prospects if you do manage to push through. But it's a hard and risky path, and you will not be able to get any interesting results for the longest time.
For a young middle-class student, it just doesn't make much sense. You can do much more impressive and impactful things with your time without getting into that black hole.
I know how to build an LLM, I know plenty of fellow young engineers that do too. It's really not that complex. But they can't do much with it without capital or access.
Good engineering has never been a bottleneck in this field, it's been all about having access to capital and taking smart but dangerous risks burning it on compute, without much idea of how long you need to keep burning for. There's still no end in sight, some are still managing to convince investors and keep burning, and we are seeing progress, but the business case is still unclear. If you want to get in that game, go ahead, but it's not something I would advice the average young engineer.
> I know how to build an LLM, I know plenty of fellow young engineers that do too. It's really not that complex.
I’m 40, and I don’t.I took that abstraction for granted and “left it to the big labs”. However I want to build my own LLM for learning purposes.
On needing big expensive hardware.. necessity is the mother of great innovation. Perhaps 18year olds trying to build their own LLMs in constrained resources environments will result in ground breaking ideas of achieving better intelligence than the one we currently have….
The world needs pragmatic folks who work at a higher abstraction and make LLMs useful, AND also folks who think why not “this other way”? And build newer ways to do fundamental things.
Given the usefulness of current LLMs, I would certainly encourage anybody to try and build their own LLMs, and see what they come up with…
Heck if they build a rack full of old laptops and run something with it that could be done “better” with modern servers, I’d still appreciate the learning running things on those little machines bring.
Well, that's not how it works. You don't just put some old laptops into a rack.
Maybe with a decent consumer GPU like a 4090, you could do experiments like distilling and fine tuning a small image model for edge deployment for specific tasks.
Even there, many use cases might require renting compute for $10/hour and investing a few hundred.
A LLM from scratch? Forget it. You can do theoretical experiments, but not build anything remotely useful with that kind of budget.
If you're talented enough to come up with revolutionary methods, maybe an university or AI lab would be the place to be.
To me the bottleneck is not even the compute, which is an issue for sure, but the data. All these large companies got their hands into petabytes of data, a lot of which of illegally acquired, but now they are large enough to pay the fines.
> On needing big expensive hardware.. necessity is the mother of great innovation. Perhaps 18year olds trying to build their own LLMs in constrained resources environments will result in ground breaking ideas of achieving better intelligence than the one we currently have….
10000%.
One of the things to think about when it comes to many kinds of "expensive" technology, is that from so many well-funded ventures, from the capitalists on down almost every decision-maker involved has not spent the majority of their life making every dollar count in some way or another.
Even more so when things are not just expensive by nature, but truly overpriced beyond that point.
>10000%
Once in a while you do get somebody who only spends a dollar and gets more out of it than a seasoned high-roller spending $10000. Most of the time the waste is borne by those who can afford to throw away $10000 more easily than an economizer can afford to lose one dollar, so nobody is crying about it.
With how ridiculously large the language models have gotten though, a 10000x improvement in actual intelligence does seem like it could be lurking unrecognized at a different point on the compass.
Agreed. It's hard to learn unless you have access to quite high end hardware, and even paying by the hour is expensive. There's a low ceiling on what you can learn without doing training runs.
You can however learn everything you need to know to get on the career ladder as a software engineer on a regular home PC.
While the topic here is narrow, the concept is broader.
Do you take the first step or rule it out because you don’t yet see the complete picture.
As a teenager I never hesitated to try things out. As a young adult I wanted the whole picture. Now I’m back to playing / trying things out. I kinda wish I’d not given it up. PG being a bit older and reminiscing - I bet he’s in that bucket too, whereas someone trying to establish themselves professionally probably (aka me early 20s) wants to see the path.
> . But specifically when it comes to LLM engineering, no there's really not much you can do, they are called "large"
The “large” qualifier dates back to pre-transformer language models, where even training a multi-million model was hard due to how poorly it scaled. GPT-2 was a large language model, despite being only 124 millions parameters.
Due to how much high quality data is readily available, anyone can now train a sub-billion (L?)LM on commodity hardware.
And I'm personally convinced that pretty much any enterprise use-case of an LLM (except coding) is better served by a fine-tuned small (<2B) model that is trained specifically on the task, rather than a generalist frontier model, so learning the engineering around fine-tuning is a key skill that companies will realize they need sooner than later.
Why? Oersted is correct, for any size class you can find an LLM that is free and well trained at this point. They are highly adaptable even without fine tuning, in-context learning is still superior to fine tuning in most cases also. And real world fine tuning is mostly about data gathering and cleaning. The actual adapter training is automated and put behind simple APIs.
I saw my first language model in action in 2014, I was writing blog posts about them back in 2016. In recent years, like many of us, I spent some time learning ML frameworks to see if it'd be a fun career pivot.
But:
1. It doesn't seem especially creative. All the frontier labs have nearly identical model personalities, capabilities and even app designs. We're not seeing them differentiate from each other, implying that the design space might not be that large. In which case the opinions and unique approaches of specific engineers aren't that important, they are interchangeable at the right level of skill, and what to do next is usually obvious to everyone.
2. It doesn't seem like a big job market. A lot of ML jobs were wiped out in recent years by the rise of LLMs. Lots of NLP specialists etc were suddenly replaceable with a cheap API. The jobs that remain have compacted into a small number of companies. It's a small community which greatly increases career risk, especially as so many are unprofitable and/or have strong ideological requirements.
3. It's unclear how much demand for better models there actually is. Do we actually need smarter models? In robotics clearly yes and robotics is interesting and high potential, but for pure LLMs/image models, most users are already incapable of setting tasks that stress the best models and are happy with the cheaper smaller ones.
Using the models on the other hand is a very large design space, and has a lot of scope for creativity. I see use cases for AI everywhere, but most companies seem to stop at putting a chatbot on their website or asking Copilot to rewrite an email before they send it. A lot of companies have hollowed out their IT departments over the past twenty years. It feels like a new golden age of consulting work could be upon us.
I really don't think I'd tell a 17 year old to learn how to train LLMs. Learn how they work and how to use them, sure, absolutely.
> We're not seeing them differentiate from each other, implying that the design space might not be that large.
There is the more likely reason they are not differentiating. They use almost exactly the same class of model. Everything is linear, parallelizable. It's incredible path dependence that's now invisible enough we think it's a natural law. Nature is not linear.
Exactly. They had the capital to gather massive amounts of data and compute and took a path that went well and pushed it to it's max. Are still pushing it. Are there better ways that don't need as much data and compute? Well, that's for the 17-year olds who don't have trillions to spend to figure out. I'm hoping they do so in a way that brings back affordable gaming gpus and memory sticks as a thing again.
> They are highly adaptable even without fine tuning, in-context learning is still superior to fine tuning in most cases also.
Good luck relying on in-context learning for a 600M LLM.
> The actual adapter training is automated and put behind simple APIs.
That's like saying it's worthless to learn infra because you can use serverless instead…
> All the frontier labs have nearly identical model personalities, capabilities and even app designs. We're not seeing them differentiate from each other, implying that the design space might not be that large
The design space for a generalist model isn't large, by definition. But the design space for specialized smaller models is much larger. If you can train a 200M model that, for your use-case, is competitive with a frontier one, then you'll make your company save a lot of money in tokens.
> 2. It doesn't seem like a big job market. A lot of ML jobs were wiped out in recent years by the rise of LLMs. Lots of NLP specialists etc were suddenly replaceable with a cheap API. The jobs that remain have compacted into a small number of companies.
We are in a strange place where a few companies are collectively burning a hundreds of billions a year to sell things a few pennies for the dollar. Of course it's going to be cheap and concentrated. How is it supposed to end though?
> 3. It's unclear how much demand for better models there actually is. Do we actually need smarter models?
That's the thing actually: I don't think we need better models this much, and if we don't need better models we need the cheapest possible model for a given use-case.
There is no reason to believe that generalist models need to be transformer (or transformer hybrids) it's just that people are risk averse and not spending their resources on the many scaled risky bets needed to explore the possibilities. There is more there.
True. I'm a big fan of Cactus's work on the needle family of Simple Attention Network: https://github.com/cactus-compute/needle
That looks like a really nice and well put together library.
But it feels like it sort of backs up my point about there being good models at every size class. Fine tuning Needle looks automated. Yes, you need to know basics like what validation loss means and how to use Python, but otherwise it's all about creating the dataset.
Someone had to come up with needle in the first place though. And it's the kind of thing that's going to come from a big lab with an AGI monopoly ambition.
But can you _sell_ that? If you can't you can't get a job doing it.
I suspect most who worked at Google did not work on Google infrastructure before getting there.
No, but the skills Google needed (back then) were just normal programming and sysadmin skills scaled up. I had eight interviews that covered Linux sysadmin, programming, debugging, networking, maths and more. If Google hadn't wanted me there'd have been plenty of other companies who needed those skills.
If you look at model training jobs a lot of the work at this point is creating RL gyms (normal programming work), but most people still think the work is all neural architecture research. Doing the former is fine but won't teach you much about how to build LLMs, whatever that means now. Doing the latter is a very hard market to get into: not many jobs and requirements are often like, "you must have published at one of the following conferences". Prior experience is assumed. Most of them seem to treat Google as ML university and source of new recruits. It's understandable given the cost of training runs.
> requirements are often like, "you must have published at one of the following conferences".
I'm not sure why it's like this. If you look at the real world, you have stuff like ggml, which is about as hardcore as it gets in the LLM space, and it was made buy just a guy. Same for this like ComfyUI
If you get enough academics in a place, they tend to close rank, and not let anyone in without the same credentials. Data science used to be like this, they were constantly on about how you need a Math Phd to even apply, yet when I met these guys IRL, most of them were just running Python math libraries.
These previous examples show that if you understand at least a part of the problem space, you can 100% contribute without academic credentials.
It does sound quite hard if that’s the mentality you’re approaching it with.
You can do it at home, for sure. I've done some NanoGPT training runs and modified the architecture, it wasn't that hard. Came up with some potential research ideas too. It does take money for GPU rental so for a 17 year old, it's not so easy unless their parents give them a budget. For an adult with income you can do it.
The question is more one of opportunity cost. At 17 you need to start finding your way in the world. It's best to learn skills lots of people need.
Interesting/capable diffusion models are much smaller than similarly interesting language models. But yes you could always scale things down to learn the fundamentals.
There are plenty of similarly tiny language models in the realm of tts too. Gatekeeping what’s interesting misses the forest for the trees.
I disagree with the premise.
Learning should not be done only as a direct path to getting paid.
Learn to create pattern matching and intuition to solve future problems.
When you are 17 it is a good time to understand how the world works so you can build on top of it in the future. If we assume most tech is going to have an LLM as part the stack, a solid basis in how LLMs work is likely to help you in future endeavors the same way a solid basis in how the web works helps you today.
Maybe a 17 year old should learn both. As a small anecdote when I was 17 I learned a lot about load balancers, failover, and building self-healing systems running small hosting company that had to be fault tolerant when I was attending high school. This wasn't at state of the art levels (e.g. I wasn't configuring gigabit routers or global CDNs -- but it was useful pattern matching for future problems)
I currently don't touch any of that tech, but I have working knowledge that still serves me today.
Think long term.
You haven't actually refuted their premise.
I am not the parent but I took it as them saying the premise was wrong to begin with, which I very much agree with. Learning should not be primarily directed by job availability.
A lot of startup companies are not training frontier models but help solve and optimize pain points of LLMs: cyber security, token usage, harnesses etc. These jobs don't require a PHD in machine learning but it does help if you understand LLMs at a deeper level.
A lot of startups succeed by lying to themselves about the quality of their solutions and focusing on selling a marketing story.
True, I'm tempted to put Antrhopic under the same category of lying startups so it enforces my point that understanding LLMs deeply could be useful
But you can train a small LLM with a gaming graphics card -- I managed one on a GTX 1660. I don't think pg is suggesting that you try to chase the frontier. It's more like building your own OS in the 80s, or web server in the 90s -- sure, you'll never match the commercial offerings or the big OS projects, but building something from scratch within the limits of the hardware you can afford is amazing educationally.
"I'd build the foundation of knowledge to base a startup on later"
what kinds of startups ?
In my experience, having a solid understanding of the next level down in the stack -- the foundation you're building your startup on -- is really helpful. We built a PaaS, and knowing enough about Linux internals to be able to work out what would be easy and what would be hard meant that we could focus our efforts on high bang-for-buck features.
So I'd say that understanding LLMs to the level that you get to by training your own baby one would be a solid foundation for pretty much anything built on top of the "real" ones.
Y'all are missing the point: It's probably less than 100 hours to learn the foundations of one of humanity's most-current breakthroughs. It's a disservice to any young hacker to not learn it. Here's your curriculum. Watch these:
-- 3Blue 1Brown's Neural Network Series: https://www.youtube.com/playlist?list=PLZHQObOWTQDNU6R1_6700...
-- Karpathy on LLMs: https://www.youtube.com/watch?v=7xTGNNLPyMI
-- Stanford CS336: https://www.youtube.com/watch?v=JuoVZkPBiKk
Then do this hands-on:
-- Karpathy's zero-to-hero: https://karpathy.ai/zero-to-hero.html
when you think about all of the advancements since Attention / GPT a lot of it has been somewhat more obvious than in other fields, as is typical with the massive flood of innovation that follows a big breakthrough.
Paul likely assumes there will be a sequence of additional papers with the same impact as Attention is all you need, which will spawn a lot of opportunity for a larger group of experts who are conversant enough to advance the field even if they do not themselves create such a major innovation. Not only is this deeply exciting, it is also highly meritocratic as there is still scarcity of the kind of intellect and creativity necessary to swim there.
Machine intelligence might soon surpass it, though, and Deepseek is 100% Chinese mainland educated. Paul's description of building an LLM from scratch is meant as a vague starting point for being an innovator of the highest value aspect of modern AI innovation, not as a specific prescription.
AI is the subtrate the future runs on.
And so I think the idea is more to understand tomorrow ... from first principles.
In the late 80's, as a teenager, I learned x86 assembly and C because that was the only way to squeeze out enough juice from my shitty CGA (and later VGA) card to programm the games/graphics that interested me.
I haven't written assembly in years.
But whatever I did in my career: it helped me and gave me an edge over my peers to have a foundation that is very close to the metal.
> AI is the subtrate the future runs on.
We don't know this. So many people are simply claiming this confidently, and a lot of them are betting their careers on it, but nobody has a crystal ball. Whenever someone tells you confidently, and without any doubt or qualifications, that something "is the future," be skeptical.
I remember when the Segway was definitely going to change urban planning worldwide.
Segways failed because they were a solution looking for a problem. You can't say that about current-gen LLMs, much less AI in general.
> AI is the subtrate the future runs on.
Current AI can automate significant amounts of grunt work in programming and math. It's good at running web searches and writing summaries. There are a few other niches where it is currently successful. But other than that, many corporate AI projects are spectacular failures.
So just given what we have in hand, assuming no further breakthroughs, then we're maybe looking at AI being somewhat bigger than the Internet. Which would make it a revolutionary technology, sure.
But to get from "a revolutionary technology" to "the substrate the future runs on", then you need to assume more breakthroughs: long-context operation over weeks or months, displacing human workers 100% instead of 75%, and the ability to directly economically compete with actual humans. And people are investing literal trillions of dollars to make that future come true, without really thinking through what truly competitive-with-human AI would actually mean. We might be looking at massive job loss, centralization of power, fully automated "companies" with no humans dominating markets, and other dystopian scenarios.
And in those worlds, it's unclear that being good at CUDA and matrix math will be all that helpful, careerwise. The AIs are already pretty good at that stuff. Data scientists get paid OK when they actually get hired, but it's not everything college students were promised in the 2010s, either.
We can't yet build a fully-general competitor for the human mind. But we're getting closer. And if we ever do build one, the consequences will be really weird in any number of ways. So I worry about visions of the future that assume AI keeps improving significantly, but that also assume it still somehow remains a "normal" technology that doesn't, for example, render most humans fundamentally uncompetitive.
> But to get from "a revolutionary technology" to "the substrate the future runs on", then you need to assume more breakthroughs: long-context operation over weeks or months, displacing human workers 100% instead of 75%, and the ability to directly economically compete with actual humans ... without really thinking through what truly competitive-with-human AI would actually mean. We might be looking at massive job loss, centralization of power, fully automated "companies" with no humans dominating markets, and other dystopian scenarios.
At 75% replacement of a worker we would already have huge job losses as each individual would be doing what several before did.
The only alleviation would be the creation of new equivalently paid jobs, which is no better than a hypothesis right now.
I keep saying AI is going to prove more impactful than cloud computing but less impactful than the sewing machine and a bunch of people get mad at me.
>> many corporate AI projects are spectacular failures.
Citation needed
I don't really agree, I think the future should run on humans.
AI is a great solution in niche areas but generally doesn't make much money. All the large companies are in the negative.
The steam engine was less of a bubble and was much more revolutionary and had a greater impact.
How did that edge manifest?
A single 3090 will train qwen 0.8B just fine. While it’s not a very capable model any training technique you would want to master can be used to make real progress. And all the skills you need to learn how to do this can be learned watching Andrej Karpathy’s zero to hero series (shame he quit educational content and went to anthropic)
This is a wildly incorrect and myopic view on the world.
Finetuning model is cheap and incredibly useful for deployment. You don't need to pre-train a frontier llm from scratch to make useful models.
There is tons of domains where you and fine-tune llms and deploy them for value in companies and for your own entrepreneurship ambitions. I have made this a big part of my career for the last few years and now I'm working on finetuning models for starting my own companies.
I find the fine tune approach more interesting than straight to RAG and MCP.
End of the day they're all customized data stores and protocols to interact with them. May as well stick to a uniform toolkit with fine-tunes.
Not that other tools aren't useful. But reaching straight for a bunch of infrastructure reliant services is like jumping in with k8s when you're still at a stage where basic mocks in code are sufficient.
I won't roll my own encryption or UI lib but want to stay focused on the incompleteness of the project I have to ship not all the buttons and knobs of some dependency or framework. Same old manage context switch problem.
Both of you are right. There is demand for tailored (fine-tuned) models; almost every enterprise would theoretically benefit from them.
But there are also a lot of prerequisites, namely does the enterprise have its sh*t together on a technical level. Does it have the processes and data pipelines available to train and benefit from these models? Probably not!
Applied ML is at the crown of a tech pyramid whereas most enterprises are still struggling at ground level. Being able to build from be ground is likely a safer skillset than only knowing how to work at the (non-existent) apex.
We finetune LLMs. Small ones like Gemma 4 for semantic tasks.
There are plenty of areas were we need people to do this for insurances, banks etc.
AI/ML exists on many levels.
Yes and no. I believe the point he is making is simply that there is no substitute for fundamentals and first-principles thinking.
We had scores of students study how microprocessors work and compilers work over decades, yet we have 3 or 4 major processor companies and a handful of programming languages. Yet, what they learned was probably crucial in their development as engineers.
We are also so early right now that even 2-3 years from now who knows how many LLMs and model firms survive (esp. given the "snake eating its tail" venture/investor funding situation)
This is kind of different though isn't it? Doing an assembly or compiler class has pretty clear benefits in this regard.
But LLMs are tools. Does a great engineer need to know how vscode works? Might be helpful to understand how extensions work, LSPs, and project configurations.
Usually when working with any tools, you need to understand how to get the most out of your tool for your needs and that's about it. Core fundamentals about how software and hardware works in general seems like it would be MUCH more useful than LLM core knowledge.
Aren’t llms tools in the same way compilers are tools?
"Necessity is the mother of invention" - limited hardware has always forced people to find cleverer ways of doing more with less. Current models are clearly nowhere near the efficiency limit (the brain does vastly more with far less power).
This is roughly how GPUs for neural networks got started: after Andrew Ng left Google Brain, he no longer had access to a 10,000-CPU cluster used to train the original DistBelief system. But his Stanford students could buy a GPU...
> Current models are clearly nowhere near the efficiency limit (the brain does vastly more with far less power).
I think this is disingenuous. One could say that drones are nowhere the efficiency limit either: a bee can fly for hours on the energy contained in just a few milligrams of honey, while our best battery-powered drones can't stay airborne for more than 30 minutes. But comparing energy efficiency of electric/mechanical devices to their biological counterparts is not an apples-to-apples comparison. There's a world of difference between the energy storage and delivery mechanisms.
And as many have pointed out already in the siblings, it's not just about the compute but the access to petabytes of training data.
I think his point is to do this to understand deeply what they can do, what they can’t do, and what they can almost do. And then find the highest value ‘almost’ use case and push there. Which doesn’t necessarily mean improve the llm, could be applying it in just the right way for the use case. Of course, the bitter lesson makes this hard and risky. But no more risky than investing your time in learning anything else these days.
I bet this will get less true over time though as the rate of change slows down, allowing specialized models/training for specific use cases that aren't TAM heavy enough for the big labs to go after them. It's just now any general model is the best thing to use for everything and you're wasting money to build something on what will certainly be obsolete by the time you can get it to market
I don't think pg is giving advice on what will lead most directly to a job, but rather what is the best learning for a 17yo.
A 17yo who trains their own LLM will have a much richer understanding of what AI is, how it works, what its potential capabilities and pitfalls are, versus someone who spends the same time doing something else.
You can train and run small models on an old gpu. That’s what I’m doing now at, well, much older than 17. Does it produce a useful model? No. Not even remotely.
However, I do learn stuff about models that takes it from “magic” to “useful tool I understand the limitations of.”
Do I do it for that reason? No not really, I’ve never had luck learning something because it would be good for my career. I do it because at my core I’m a bored teenager who wants to make the computer do cool shit.
Yes. There's a lot of demand for elite talent, and no demand for slightly sub-elite talent.
It is on my list to build a toy LLM from scratch.
Not that expect to make it big as a LLM researcher but building something from scratch gives a much deeper understanding than what you can get from simply using something.
Much in the same way as implementing and designing your own programming language makes you a much better programmer.
It is not a skill that you will use in your day to day life, but I think it is part of the fundamentals now. Sure, LLMs are in a bubble, just like the web during the dotcom bubble, but web didn't disappear, and I don't expect LLMs to, even after the bubble bursts.
I didn't write a LLM from scratch but it is on my "wishlist" so to speak. From what it seems, a GPT-1 class LLM can be done from scratch in a few days and tens of dollars of cloud compute or a high-end gaming GPU.
It is an exercise not unlike building a compiler, a school classic. You are unlikely to ever work on a compiler, but at least, now, you know your tools a little better. It is not about becoming an expert, that takes years, it is about knowing what you are doing.
If you intend to make software engineering your career, you will want more than surface knowledge. And that part is entirely on you, or on your school if you are a student. Companies will not pay for you to learn the fundamentals, they want short term returns, because you may leave at any time. But you as a software engineer may have 40+ years left, so it is worth thinking long term. Claude code may become obsolete a few years, but linear algebra is not going anywhere.
What about other machine learning related skills? Does this wave of LLM mean less need for that kind of work?
I would think not, but when I started to look into OCR options recently - assuming that obviously a dedicated tool would do a better job than an LLM - I was wrong (apparently).
Of course, because it's not the LLM that's special but the training data. Nowadays, your favourite AI service to generate code for an LLM whenever you ask for it.
I don’t think that’s correct, the data is not that special either, and getting a similar dataset is significantly easier than getting the compute capacity to use it, even if they are both relatively hard.
Probably this also is too cynical and simplistic, but: really what’s special is the ability to get this kind of capital, with the freedom to burn it on mad moonshots, with long enough leeway to actually get to see a few of the moonshots come true.
No wonder that the head of YC made this happen, this is exactly what they are world-leading at.
+1, I have a friend, math PhD that's been working on ML research 5+ years in London yet he has not been able to find any position.
The only jobs that he found he was highly over qualified or paid very little.
In any case, it doesn't look like there's this crazy rush to hire all ML talent, even the one that understand the math and technology deeply.
> +1, I have a friend, math PhD that's been working on ML research 5+ years in London yet he has not been able to find any position.
Maybe people simply don't want math PhDs but something else? Since 1-2 years ago I started doing consulting/freelancing in the ML space, but more on the infrastructure, deployments and similar stuff, as a general purpose developer, and I have a waiting list of clients interested in more work, some of them even trying to recruit me to work for them full-time as well. I'm based in continental Europe, fwiw.
How've you gone about getting into this btw? I have extensive experience in infra and pipeline rollout but have struggled to find freelance clients for this kind of thing. Would be great to tie it into ML as a learning opportunity there
Spent a year of freetime catching up on everything and learning as much as possible, started sharing what I've found works or not, write a bunch of comments on HN and elsewhere, and have a email in your profile, eventually people will find you if you put out good stuff :)
Also bunch of past workplaces who've adopted AI in various ways who reach out once they find out what my current focus lies, but that's harder for others to replicate unless you've already had a career as a developer.
whats ur contact, would love to chat
This only proves the original point which is that there is not much demand for actual machine learning expertise because that is only carried out in a small number of places and what demands there is is for the more basic software carpentry like infrastructure and operations rather than the actual technology and Engineering side of things
What parent says about "there are very few available positions" for "engineers that can do real LLM machine-learning" is fair, yeah, I'd agree with this.
I don't think the "incredibly small minority of companies in the world do any real training or optimisation" part is necessarily as true, as some parts of the work I do get is about helping them optimize training and infrastructure around training. Mind you, none of this is for building LLMs from scratch, it's 99% fine-tuning existing checkpoints.
I'd also agree with "paulg is in somewhat of a bubble" regardless of this, which is worth remembering whenever you read his content. Same goes for any person living in SF, and dare I say the US. But also, YMMV, I live and work in Europe, probably why I have this perspective.
I think Paul lives in Britain these days.
I think he does, but it seems he still considers himself a remote SV native rather than someone working in the UK's economy.
Worst of both worlds - no casual gossip feed in the Bay Area, no big dog meetings in the UK. (Which mostly has no idea he exists.)
As for the question - what are the odds LLMs will be anywhere near the top of the tech tree five years from now?
The trend seems pretty clear to me - local/offshore models are snapping at the heels of the big names in the US, and the current investment arc is insane.
I wouldn't bet on Anthropic or OpenAI being leaders five years from now. Longer term, I especially wouldn't bet on the US build-yourself-a-monopoly corporate model surviving AI at all.
Congrats!
Is it possible to see some of your old works? Personal research?
As always and everywhere, it's who you know (and who knows you) that matters.
I mean it makes sense right? Anthropic for instance has like, a couple hundred staff in London with plans to expand to somewhere just shy of a thousand. There are far far more ML/Maths/CS/Stats PhD's than there are openings. Especially in London there is no shortage of suitable candidates given Cambridge/Oxford/Imperial/UCL are surrounding it. 2% of the UK population has a PhD alone...
> 2% of the UK population has a PhD
Citation needed. I'm guessing you're not counting children.
I found this.
https://www.hesa.ac.uk/news/29-08-2024/higher-education-stud...
I think a lot of this is based on preconceptions. A lot of apps were made with Electron, because it was common wisdom that native is 'too hard'.
Now with LLMs, people write native apps in Rust, and I'd like to think some of them found that there isn't such a huge jump in difficulty they assumed there would be.
It never was native "too hard" it was always "too expensive".
That's the same case finding companies that will actually pay for hand made LLM instead of using something from big providers will be hard because most companies won't be able to afford it.
Yeah if you find a company that will do that stuff directly, good for you, but you will have to be very lucky and you will have to compete with other people who followed PG advice.
So I would rather learn all there is about properly using LLMs and integrating them with existing systems, that will most likely by useful for 90% of companies out there.
Building business niche harnesses is in my opinion much better direction. Knowing what will work best in specific cases is it FTS or vector search, optimisation of usage, getting best results while using cheaper models, knowing how to use tools to run models on the servers, and all the tooling around that like various MCP or just tooling that will be provided to models.
That is what I am currently busy with and I already have customers for that knowledge.
Oh yes I agree, LLMs are not that complex in principle, most engineers could build a toy version completely from scratch without too much difficulty.
But that’s the tip of the iceberg. If you have any ambitions of doing this professionally, it quickly becomes clear that all it’s all about knowing how to deal with problems that are only present at massive scale, when an LLM is actually L and becomes AI.
The mundane details about how to build a tiny autocomplete model and the maths behind it you can learn in a couple weeks easily. It’s not black magic, there are much harder areas of computers science.
This is a silly take. You can learn to build an LLM, there are great resources to do so (there are books about building them from scratch), you can use older model GPUs or rent them by the hour. The value of understanding them is really high for anyone building any application that uses an LLM at any point.
It similar to understanding how a very basic CPU works. Just because I'm not going to work at intel or nvidia or whatever optimizing the hell out of a chip, it doesn't mean I just throw my hands up and think "magic" - the basic architecture isn't that difficult, and the value of knowing it is astronomical for anyone writing software.
FWIW, at least 20 Y Combinator startups have published ML research recently at ICLR, NeurIPS, ICML, and so on.
I think a lot of people assume that only the big AI labs can do cutting edge research, but there's a strong argument you can do it as part of little tech as well.
> With all due respect, paulg is in somewhat of a bubble
I feel like the "ALWAYS HAS BEEN" meme is apropos here.
lol unnecessary and inefficient…
I just finished fine tuning Gemma e2b for local code completion on my local machine.
This comment just reinforces what the post actually means. We need people that are LLM natives, computing solves itself with time and with scale adjustments
That’s great but the industry has moved on to code generation and editing, not code completion.
1. I very much still write code myself without an LLM when I need top quality
2. That's why I have an agentic agent as well installed, Qwen 27B, outrageously good, better than sonnet 2 years ago. And it's mine, I can give it confidential info to work with since I own the whole chain. See where I'm going with this?
Can you tell me what specs your machine has? There is a difference between a few hours and a few days
A MBP m5 in this case, but the fine tune would have cost me like 10 bucks on RunPod, that's what I did with my previous setup.
The other thing to add as well is that the research teams who do the actual research work are relatively small and very specifically qualified which naturally keeps the barrier to entry high.
It is written in the first person, I suppose.
I think companies of all sizes will want their own models, or at least customised ones, for their own specific use cases or competition and security issues.
1. Both training and optimisation will get significantly cheaper and easier quickly.
2. Politics will probably get even more insane before a potential reprieve on the 20th of Jan 2029.
3. The big AI firms will become part of the surveillance capitalism network, if they're not already.
So I think for self-protection a lot of companies will be looking near to medium term AI independence.
The argument is sound, but the maths don't math for now, and it's unclear when/if they will.
For the time being, unless you truly have millions, the outcome from training will be very net negative, while focusing on building on top of existing AI will yield amazing things if you apply the same talent and effort.
When it does get cheaper, then it will be easier to acquire the skills and experience too, and the struggle you went through by trying to do it now will be somewhat wasted.
Besides, I am well versed in this field, and it is not rocket science. There are plenty of software engineering domains that are a lot more challenging, like high-end graphics, large-scale data engineering or kernel programming. People will learn to train LLMs when people want them to.
Right, just like companies don't use SAAS.
In reality, enterprises are happy to offload even risky tasks to others as long as they get some contractual guarantees about their data. Would they like more choice in who to buy from? Yes, but not enough to in-house such a specific discipline.
The cost of training a model from scratch is going to be cost prohibitive for the vast majority of companies (even if renting the hardware needed for the 1-2 month training time). It's an interesting learning exercise, and some of the things learned can be applied to other parts of the process. There's also the issue of needing a huge amount of data needed to get decent weights.
Fine-tuning a model or LoRA based on the companies data set is more feasible but you're likely going to need several runs as you test/try out different base models, parameters, etc. This is why there are a lot of fine-tuned models on huggingface based on base or instruction-trained models from the larger AI companies that have released open weight models (Microsoft, Google, IBM, Mistral, DeepSeek, Qwen, etc.).
Training is limited on memory first (storing training data and weights) and computation second. Realistically you need to own or rent 2-8 H100/B100 devices or Google's TPUs.
The majority of workflows for a company providing AI capabilities are likely best solved by tailoring a system prompt for the chosen model, evaluating the prompt and model with tools like promptfoo, and then running it on a compute cloud provider (including AWS Bedrock). If the company is big/financially well off enough they could look at buying the hardware needed to run it on their own servers.
For other uses like agentic software development you'd need to spin up a suitable model on a compute cloud provider (or local hardware if the model is small enough) and then tell your IDE/editor to use that model. You would need some way of benchmarking and evaluating the models to see if they are capable of doing the tasks you need. -- There have been some tests done by people on YouTube that suggests that Qwen 3.8 27B is a decent model, but your needs may vary.
Even for most organizations, testing AI systems is too cost prohibitive, so they YOLO in production, including public facing systems.
Most companies that build physical goods don't care for one second about their IT department other than how much money they can save per month, starting by outsourcing whole of it, thus they have little use for internal LLMs.
And it's across the industry, thinking banks, private banks, insurance, pharamcy etc don't outsource their IT, including development... I believe US outsource even more than Europe on this matter.
> With all due respect, paulg is in somewhat of a bubble, this is not congruent with the global situation.
Paul, I think, is talking about achieving outsized outcomes in relatively shorter timeframes (as the timing is just right to be investing in learning this tech) for high agency folks who can also afford the ordeal in wanting to maximize for impact & ambition. Of course, there's real risk one may get no where, but even in failure, given you were building the LLM yourself, you might end up with other adjacent, high reward opportunities.
> The reality is that an incredibly small minority of companies in the world do any real training or optimisation.
At the scale you are probably imagining, this is true - but take the hype out of the OP and what you have is just someone saying the field of data science exists and is growing.
Did you read his comments on this? It's not to actually do LLM research stuff, it's to trigger and unlock ideas.
I think his point is, if LLMs are the future (like computing is the future in the 90s), you should be an LLM-expert (equivalent of becoming a software developer).
I can see the point. It's unlikely that a 2.4T LLM will be integrated into, say, a pesticide drone. You'll still need some kind of LLMs to achieve maneuvers that "normal" programmings can't achieve.
But what if everything basically turn into that? Essentially, instead of build me a web app to solve X and do Y, build me an LLM to serve X and do Y. (unless the current LLMs are able to do it end-to-end but then they can hardly write coherent software/personal opinion).
This is like saying, “teens shouldn’t learn how to make their own game engine because no one is hiring for that.”
You’re missing the point. Understanding how Unity works fundamentally makes you a better Unity dev.
> You’re missing the point. Understanding how Unity works fundamentally makes you a better Unity dev.
Writing your own game engine makes you realize that the Unity engine is not really that well written....
Writing your own game engine makes you realise that no game engine is written well (when it is written to ship a game).
Except knowing how LLMs work don’t actually provide much understanding for using them. People don’t use LLMs the way we’ve built on most other tons or platforms. It’s more learning Unity hoping to be a better gamer.
this is just wrong idk why u came to that like obviously it does make u better at using them…
How so? I’ve been working on DNNs for over a decade. Not sure it helps me in any almost non-trivial way when it comes to using them.
1000%
That's not true, because everyone, everyone, everyone seems to want to do training. Which results in a 50 person company training, say, a voice model that then fails, because it's just not good enough.
In reality the problem is that it gets blasted out of the water by a much worse architecture trained on 10000x the infrastructure. And while I'm sure the freshly brought in ML student came up with a 10%, even 30% better architecture, it just doesn't matter. (and never mind that even OpenAI hasn't really solved a voice model yet. Try it. It can probably match 2026-quality call centers, but it's no substitute for an actually empowered human)
... and yet, if you look at what hyperscalers are getting paid for ... comfortably more than half the income is training. Which makes no sense on so many levels.
e.g. https://valueaddvc.com/blog/inference-chips-vs-training-chip... (I get it, not great first source, but st
The big question is whether companies hold enough proprietary data to do useful things that for e.g. Anthropic, etc. can't easily replicate.
For some very niche cases I think this is probably the case but for the vast majority, the company's data isn't as useful as they think it is or anywhere near the size needed.
Everyone says they want to do training, because it's sexy and an easy way to justify raising mad funding rounds. Some manage, most don't.
I don't know where you are located, but in EU, in China, and yes even in Silicon Valley, the vast majority of companies do not do any real AI engineering. There's nothing wrong with it, it's just not a smart path for most purposes. You can do amazing things without training, and if you try to train, you cannot get anything amazing unless you burn millions.
Very few people can afford to play the long game and cross that dessert. And, sure, you will not get far without good engineering, but good engineering is definitely not sufficient and is not the primary bottleneck.
It took a long time to cross that desert, and no sane company would want to get stuck in a desert, unless it's specifically an R+D "desert crossing" company.
I'm (more than) twice that age, but I've spent time learning this exactly this from videos by Andrej Karparthy and from books by Sebastian Raschka.
I didn't do it because it was useful to me in a practical sense. It's because LLMs are fascinating and I want to know how they work. From that perspective it's been a great experience. I have afirm grasp of the basics. This makes it much easier to understand frontier concepts like compressed latent attention. I can follow the field and understand it.
Not sure I would have got as much out of it at seventeen. I have a lot of background and experience which made it much easier to learn. I wasn't struggling with the linear algebra or with python. I already knew pytorch and neural networks. That helped a lot and I covered these tutorials fast and could skip over large sections. A few evenings and the odd weekend day over a couple of months was enough for me.
For seventeen year olds the tutorials are good enough to make it possible to learn this but it would have taken a lot longer to understand. On the other hand I would have learned a lot more. I think I would have learned a lot of valuable stuff.
However I also think 17 year old me was studying for his A levels and probably this was right choice in terms of maximising future opportunities. I'm not sure I think learning about LLMs instead is sensible. Indeed it might be bad advice. But I can absolutely agree with the sentiment.I think 17 year old me would have wanted to do this too.
Which resources from these two would you recommend? Or just blanket-recommend all their videos/books?
I kind of want to blanket recommend but that's not very helpful
I would suggest starting with Andrej Karpathy's YouTube video: https://youtu.be/kCc8FmEb1nY?is=oiDsrBYJg_MUUmoD
This video is excellent. I'm a huge fan. Also the video is zero commitment and instantly available which makes it a good way to check you are interested.
The book by Sebastian Raschka is slightly less accessible but very reasonably priced and the experience of working through a book is a lot nicer than skipping back and forth in a video (for me). Sebastian's blog posts on recent architectures are absolutely great too.
I've read Raschka's "Build Your Own LLM from Scratch" book and really enjoyed it. I haven't tried the code yet, but the code from his previous Python ML book worked great.
actually just use chatgpt. There is a new mode of learning thats now avaiable that doesnt require you to read about things that are already discovered leaving you with a shallow knowledge.
you can now play the inventor and start with question "i want to build next token prediction software" and go as far as you can with your current knowledge while brainstroming with chatgpt as a rubber duck.
dont not start with a course on probabality , linear algebra or calculus . do not watch 3 hr videos or 3blue animations .
there is agood video on this way to learn.
https://www.youtube.com/watch?v=cbiyPOn-__M&t=380s
I think ChatGPT is a great way to get depth on a topic. You can Q&A for a while until you get it, and it won’t get impatient with you.
That said, I think traditional resources are a better way to get breadth and to frame the topic. Just chatting about something can be a little disorienting imo.
so can u explain to me how they are works?
How much time are willing to spend and money are you willing to pay?
While knowledge is always great, I would encourage people not to seek advice from successful people like this (survivorship bias).
Moreover I am not sure it is even good advice? Would you advise a 17 y.o. to learn how transistors work or how to code (i.e. is LLM training the right level in the stack)? LLM training, a discipline where relevant work is already out of reach for 99.999% of budgets really as essential as this post implies?
> I would encourage people not to seek advice from successful people like this (survivorship bias).
Personally I don't see the problem, as long as you're aware there is survivorship bias involved here.
What's the alternative really, seek advice from unsuccessful people? That seems worse :)
Personally I do both, read about what worked for people, also read about what didn't work for people, then ignore both and do whatever the fuck I want.
It's best not to take advice on direction of careers from anyone. It's better to find and work on things that interest you, and then take advice from people that are amazing in that specific field. In mid 2000s in Australia all the "top people" were telling me not to get into a software engineering career because it was dead. It's certainly challenged right now, but it took off during those 15+ years.
“It's best not to take advice on direction of careers from anyone. It's better to find and work on things that interest you, and then take advice from people that are amazing in that specific field.”
What bucket should I put this advice in?
Understandable irony. The way I see it, there are no buckets, only context surrounding the advice and your awareness thereof. If the context is unknown, best to take it with a big chunk of salt... Or spend time familiarizing yourself with the context if you think it's worth the time/effort; working on things that interest you is a nice way of doing that.
But some advice is less specific than other advice though. Some things are always stupid, and some things are always smart, if you look at the context of our world and society. I find myself pursuing these "fundamental truths" with great interest lately, especially now that the world is changing so quickly.
> It's best not to take advice on direction of careers from anyone. It's better to find and work on things that interest you,
Agreed, my previous stated "ignore both and do whatever the fuck I want" approach has worked out very well for me in life, people should probably focus on identifying better what their gut tells them, rather than what randoms on the internet thinks and writes.
A lot of the advice that is relevant for top performers in a field has no relevance to an average person interested in that field.
>What's the alternative really, seek advice from unsuccessful people?
Seek advice from the averagely successful people, since that is statistically what you're most likely to be.
> seek advice from unsuccessful people
Intuitively, I would guess that they have a better grasp of what made them fail than successful people have of what made them succeed.
I don't know; I think people in general are just not great at this. Successful people tend to underrate luck and overrate the brilliance of their own decisions, but the rest of us are prone to either reversing that and blaming everyone but ourselves, or being so determined to take accountability (or just depressed) that we become overly self-critical, or simply not understanding why things happened the way they did and reaching for any explanation that resolves the chaos into something narratively satisfying.
Intuitively, that'd make sense if that unsuccessful person eventually found success, otherwise who knows if they actually picked up what made them unsuccessful in the first place?
I think it's reasonably common for people to understand what makes them unsuccessful but be unable or unwilling to resolve the problem.
For me, I know that one major reason for putting an upper limit on my success is my inability to form effective professional relationships. I know I should go to events and talk to people and use these relationships to my advantage, but that's just not something I've ever been good at, and I find it so incredibly unpleasant that I also just don't want to do it.
How do you know that's limiting your success?
Do you think the most successful people got that way by attending events? I thought they were hacking in their garages, building great products.
> How do you know that's limiting your success?
I know there were opportunities I could have benefited from if I'd had better relationships with specific people.
> Do you think the most successful people got that way by attending events?
That's not what I said.
Yep, many successful people greatly underappreciate the effect of simple dumb luck in their lives. And often they just make up complex reasoning chains, even wholly believing them, that more of it was in their control/talent, etc.
Nonetheless, there are many successful people I would gladly listen to for advice, though they are often successful in a different meaning than what venture capitalists would use (e.g. parents with great kids, managing to keep a healthy work-life balance, happiness, and maybe even having time to spend on some cool hobby project -- you are heros!)
same do unsuccesful ones. They usually overappreciate luck of succesful people from what I see, as a way of coping, many say "that guy was just lucky, i was unlucky". If you dig deeper, that person wasn't just "unlucky", they lacked the persistence, work ethics and other qualities compared to other succesful ones.
Of course, there are degrees and exceptions on every side, but the coping mechanism is very strong among people.
If there's a single unifying fault to the unsuccessful people I know (for any definition of unsuccesful, including my own failures) is that they're precisely bad at working out why they failed - if they even think to ask the question. The successful people are generally much clearer that it was studying hard or networking or natural talent that contributed to their success. The latter group may have people who don't realise luck played a part, but they massively increased their chances of good luck by the aforementioned behaviours.
Luck is a huge factor in success, so that logic leads nowhere. If one of the quacks selling how-to-succeed recipes had got it right, everyone would do it by now.
Just play the lottery once, pick the correct numbers and win $100mil.
If you can't do that - not sure anyone can help you.
Kids don’t even know what it is, even less actually feel what it is. At 17 you think that it won’t hit you, that you will be the one to survive until you don’t.
> What's the alternative really, seek advice from unsuccessful people?
Do both. Get advice from successful and unsuccessful people and take the diff
> What's the alternative really, seek advice from unsuccessful people? That seems worse :)
Learning from their mistakes (where it did go wrong) can be as valuable as.
I would seek advice from people who have a theory of why or why not they were successful. A lot of those results were happening in very specific contexts and usually should not be regarded as a blueprint, but as inspiration to whatever I do.
The unsuccessful person is much more likely to be actually connected to the daily needs and struggles of a normal person. Paul Graham hasn't seen the inside of a grocery store in the past twenty years.
I know who the 17 years old is closest to.
The better alternative is to always ignore unsolicited advice.
> Personally I do both
That’s what you get from listening to “successful people”. You get to learn about all the things they tried that failed, then the things that did work on that 24th try, which was successful.
The “survivorship bias” people always seem to assume that the “survivor” lucked into his fortune on his first try ever, so he can’t have learned anything, so we don’t have to listen to him. But that’s seldom the case.
I’ve written about this before:
https://expatsoftware.com/Articles/survivorship-bias.html
very good read. I get into the same discussion sometimes with people. If you're successful you're automatically labelled as "lucky".
There are successful people that failed many times before being successful. Somehow these days, previous failures and persistence seems to be ignored and they focus just on the luck you got on the 20th try.
> seek advice from unsuccessful people? That seems worse :)
Not sure why would you think so.
Inverse reasoning is very powerful, and unsuccessful people can give you plenty of "don't do this mistake", which the survivors would not even think about.
> unsuccessful people can tell you teach you plenty of "don't do this mistake",
But how can I know for sure that that particular mistake is actually why they were unsuccessful? Has exactly the same issue as listening only to successful people as they hardly know what actually made them successful most of the time, but they still compose large blog posts with their reasoning for why.
Again, I still think my approach of reading both but then regardless go my own way is the preferable approach, at least for me, ymmv.
>Personally I don't see the problem, as long as you're aware there is survivorship bias involved here.
Pretty tall ask, especially if this is targeted at 17 year olds...
Seek advice from people who have had a normal level of success. Not a one-in-a-million level.
I would say you should seek advice from someone has lucky and successful as Paul Graham but I wouldn’t take his public Twitter as advice it’s entertainment period.
Veritasium has a good piece for this kind of bias:
https://www.youtube.com/watch?v=3LopI4YeC4I
An advantage that is not "advisable", like being born in january, in a rich country, in an above average family, or just having luck, might have more influence on the outcome than any conscious action. It is almost sure that one-in-a-million level people only edge over the other 999,999 they competed with is just "have more luck".
It's not the only edge. Wildly successful people are usually lucky and talented/hard working.
Nobody said otherwise, even the video linked talks about talent (which might be also seen as luck!) and hard working. Say you have 100 people, and you pre-select the 10 hardworkers, that "hardwork" between 98 and 100% capacity: even if luck only accounts for 5% of the success, it doubles the 2% you can still extract from hardwork. Of course, someone that only works at 30% capacity has to be immensenly lucky to overcome the 70% they have until the 100%.
Of the 10 most talented and hardworking people above, I bet they have wildy different advise other than "do hardwork": use more LLM, use less LLM, keep away from computers, etc. And we are talking 1 in 100, not 1 in 1,000,000!
It comes to mind someone like Kary Mullis. He was no dumb, but I wont put him either at the peak of talented or even hard working people in the field. Yet, he was "lucky" enough to invent the PCR, which earned him deservedly the Nobel Price. Now that he was made to believe that he is some kind of illuminated and well above the top human, he started giving (at the bare minimum wrong, probably even dangerous) "advise" left, right and center: take LSD, astrology works, extraterrestial glowing racoons exist, etc. Downtune the guy a notch or two, and you can find thousands of Kary Mullis around giving useless advise as they were messiahs.
As someone who teaches AI at a university, understanding the basics of how the LLM works is really invaluable to understanding where and how they'll be applied.
You're right that it's probably a little too in the weeds, but it's also a nice clear and fun objective that teaches you the basics. Like building a TODO list in JavaScript to learn webdev or a Gameboy emulator in C++ to learn how a CPU works.
I would encourage young people to be born rich. It's the best time in 100 years to be advantaged. Why waste your potential by having your labor stolen?
> Moreover I am not sure it is even good advice?
I think it is. He isn't saying to learn how to train a LLM so that you can go on to train LLMs. He's saying to learn it so that you gain a deep understanding of how LLMs work. Ordinary startups can still benefit from things like training or fine tuning highly specialised smaller models, knowing how to select and configure an appropriate model for the task at hand, knowing what software to use and why, understanding what's going on behind the scenes instead of treating everything like a black box, having a higher level of intuition about LLMs generally, etc.
Most computer science courses do in fact teach things which are lower level than coding, such as how transistors work.
IMO these kind of advices never matters. Any individual still needs to make tens or hundreds of little decisions (every day) themselves, and that's what really makes all the difference.
> Would you advise a 17 y.o. to learn how transistors work or how to code
how many of us out here are doing work directly in what we got a degree in? I majored in economics and now I'm a CTO.
I would absolutely advise a 17 yo to learn how to code, understand how transitors work and how to code an llm. even if he never works on llms, you basically end up with a kid with applied knowlege of statistics, math, physics hardware, logic and a whole lot of practice in critical thinking.
It cannot be bad advice because learning something even slightly valuable is always good in a vacuum (as you mentioned)
Since he has not given any reference point it is equally as good advice as „just learn everything slightly valuable“.
So the real question is: „What should I not learn in favor of learning this.“
Or in other words: His comment is not usable because it can mean anything or nothing.
As a 17 y.o (way back in the last century) I didn’t need to be advised to learn about transistors. I just had a thirst for the knowledge. I would encourage everyone to learn something about transistors. They are one of mankind’s most useful discoveries.
Hey, I'm 19 CS undergrad but i don't really know what to do but I really wanna build something that can shape the world. Can you tell me more about why you would advice someone to learn about transistors? what are the possible career paths?
Not the original commenter, but in my view as a crusty old software engineer, the reason to learn how transistors is because they're interesting and the key to how all computation is implemented. Once you learn how transistors work, you can learn how logic gates work. And then how CPUs and memory work. Bit by bit, computers stop being magic boxes and start being something built from technologies you understand.
Now, for most software careers (including my own), that knowledge isn't directly practical. BUT, the most important things for a successful, fulfilling career are curiosity and a willingness to dive into subjects _without_ necessarily knowing how (or even if) that knowledge will yield practical applications. That drive is what's going to lead to insights and breakthroughs over your career that wouldn't happen if you just let abstractions be abstractions.
Thank you
Right - like advising people to learn nuclear power back in 1992. Perhaps a good idea, but super specific already. My gut is ML/AI is even more complex in 2026… I can’t even remember all the abbreviations and the new ones emerging. And every sub component, such as attention or embeddings, are actually a discipline of its own already.
I just don't think he realizes how saturated it got over the years. Or maybe he knows at a conscious level, but not subconsciously.
In 2000 (his era), it would have been really smart to study the source of Linux or Apache. Would have paid dividends over decades. Cuz that knowledge was so rare. The number of people hacking on LLMs now dwarfs the number of people hacking on web servers 30 years ago, by several orders of magnitude.
And if you turn back the clock even more, I mean just even having access to a computer, let alone owning one, would have put you at a massive advantage.
I don't know what to call it. The pioneers should be respected obviously, but at the same time you need to understand that for them, the game wasn't nearly as played out as it is now.
I just don't think you can afford to be dicking around with LLMs like you could afford to dick around with random Linux distros 20 years ago. Too many people willing to do it for free these days.
You don't wanna end up being the 2030 equivalent of a certain SNES emulator developer, or maintainer of a package manager for jailbroken iPhones, I mean the list goes on and on. Being a hacker doesn't automatically give you a path to being rich, or even making a decent living. It hasn't been that way for a while.
>Would you advise a 17 y.o. to learn how transistors work..
Hell yeah. Transistors are pretty awesome.
I agree more with Yann LeCunn's salty reply. Over long run, knowing how autoregressive language models work from scratch will be just one step in having foundational understanding, and they might become dated... like knowing how a CRT monitor work. Something of historical interest and good for learning, but not crucial to being well-rounded.
There are other types of models like diffusion models right now that are showing more efficiency and have a higher ceiling for improvement. Understanding math and fundamentals are more important.
Yann has been consistently wrong about the limits of LLMs.
I'll use this post as a shameless opportunity to tell more people about a little side project, I made:
http://languagemodelbuilder.com teaches you (in a few hours to days) how to build an LLM from scratch. It's entirely free, without accounts, and without data collection.
Beautiful work! What prompted you to build this? Pun not intended.
Nice! Please post screenshots and videos for people not ready or able to install yet.
This is the coolest link I saw on HN the last 2 months. You rock man !
Thanks a ton for building this.
I wish this was available for young folk in Romania: As it happens: macOS: 2.70% of desktop operating-system usage in Romania; OS X: 2.55%; Combined Apple desktop share: approximately 5.25%; Windows: 90.65%; Linux: 4.02% or so AI tells me.
This is great, thank you!
I am kind of amazed how negative the comments are here, especially on HN.
Learning to hack something together in high school using the latest technology (vacuum tubes, radios, microprocessors, web/javascript) has been a common theme in the tech world for generations. With LLMs and online tutorials, this isn't even a difficult suggestion. Do people think learning new tech is somehow wasted effort?
If he has said "to learn the math and programming skills needed to understand how to build LLMs" it'd have been much more positively received.
> I am kind of amazed how negative the comments are here, especially on HN.
I can't recall or point out exactly when, but there is a stark before/after moment where the opinions of anything pg went from "Interesting and maybe true in some ways" to what we see today, lots of knee-jerk reactions and hardly any comments about the actual content.
Hazarding a guess, I think the moment Altman became the CEO and later during COVID, the sentiment seemed to have been shifting towards what we see today. But this is all based on hazy memory, rather than looking at the data. I'm sure there is a blog post waiting to be written about analyzing the sentiment of comments to PGs articles on HN, and you'll see a shift somewhere.
Imo it's a breakdown of trust of the startup ecosystem as a whole. Repeatedly startups have enshittified and it's become undeniable that the investment apparatus around startups is partly responsible. We have seen a great driver of uncreative destruction, industries undermined, small businesses undermined just to drive masses of money into few pockets - less fairness for the people working in what is now the gig economy and ultimately prices and other costs that end up as high or higher than they were before for consumers. Not to mention the whole AI/OpenAI situation which many perceive as threatening their skillset per se, essentially tearing up the social contract that existed on this site.
The sycophancy on HN is starting to break down because there is a higher proportion of users sceptical towards the outputs of the VC and wider investment world than ones who believe they're potential beneficiaries of it.
Tech industry people are becoming less interested in HN as a warm handshake into the startup world because, frequently, they're disgusted by it. And this reflects on the sentiments people post on PG's articles.
Increasingly if those at YC want the same kind of low-bar praise they got before, they will need to get it from machines.
Because at some point in life everyone gets tired of fairytales. He started mending the anecdotes to his content which always rubs people the wrong way.
So, this comment of yours obviously isn't in the "knee-jerk reaction" category of comments, I suppose? What exactly from the linked tweet(s) are fairytales here? There is hardly any text at all, so strikes me as a comment about previous pg content, but then this would be one of those comments I talk about? Very confusing.
Your hand waving doesn't make it knee-jerk. It's just what happened to his writing since COVID. He goes for more of a shock and awe style and not everybody likes it. He's been writing for over 20 years now, hasn't he? His style has clearly changed, and an changing style attracts a different audience so it's no surprise his original readers might not connect with his newer work...
If you did a data analysis, I expect you would see an uptick with the 2023 Reddit API controversy. HN got a big influx of redditors with different sociopolitical perspectives and discussion norms.
This might be hard to disentangle from the tech correction layoffs of 2023 and 2024, but my perception is that most of the spite comes from Tech Outsiders opposed to jaded Developers.
> I can't recall or point out exactly when, but there is a stark before/after moment where the opinions of anything pg went from "Interesting and maybe true in some ways" to what we see today, lots of knee-jerk reactions and hardly any comments about the actual content.
Hard disagree. This submission is still being highly upvoted, while another recent post[1] on the harms caused by Graham’s fellows[2], with a fairly tame comment section, has been flagged. That is a constant on HN. It’s not a fluke, it’s as predictable as the sunrise and getting more pronounced.
I’m sure we’re both biased in our perceptions. Mine is that HN in general (certainly more than any other website) used to worship[3] everything he wrote, together with others like Musk, until things started to really go to shit and many eyes have been opened to the effects of the unfettered greed of rich tech guys out of touch with reality.[4]
[1]: https://news.ycombinator.com/item?id=49411762
[2]: A better English word is escaping me.
[3]: That word I choose hyperbolically but deliberately. It definitely was not “interesting and maybe true in some ways”, it was much more hardcore than that.
[4]: That is not “knee-jerk” but a slow realisation still ongoing.
> Mine is that HN in general (certainly more than any other website) used to worship[3] everything he wrote [...] It definitely was not “interesting and maybe true in some ways”, it was much more hardcore than that.
I guess it depends on what submission you look at, previous comment of mine solely based on memory. Now I went to https://news.ycombinator.com/from?site=twitter.com/paulg, clicked "More" a bunch of times, and seems my memory was more or less correct, none of the submissions I clicked on are "pg worship" (hyperbolic or not). Just one example: https://news.ycombinator.com/item?id=19418701
Maybe you need to enable "Show Dead" or something? Pgs articles on HN definitely never was free of any critique in the HN comments, just like any article. Although I do agree with you that it used to be different than it is today, and same with Musk too, and Altman, and probably more individuals, where they were lauded before but now pretty much just mentioning them poisons the conversation.
> Just one example
That post has barely any points and comments. It’s not a good indicator of general sentiment, it’s just an indicator of people who were on HN at that time.
> Maybe you need to enable "Show Dead" or something?
I have it enabled.
> Pgs articles on HN definitely never was free of any critique in the HN comments, just like any article.
Of course. I very explicitly wrote “in general (certainly more than any other website)”. That does not mean “always”, or “never”. HN is not a hive mind, there’s never going to be 100% agreement. The general trend is what’s being discussed, and we both agree that in general the sentiment on Graham used to be higher. We’re just disagreeing (we may be able to find ourselves agreeing through tough thorough thought, though[1]) on where exactly it is now and how to interpret it.
[1]: Sorry, can’t believe there was a real organic opportunity to use that sentence, had to take it.
Indeed, no hard disagree, merely details :) Overall you're right though, general/overall tone definitely shifted hard for a bunch of individuals over the years.
> Indeed, no hard disagree, merely details :)
Hard agree! Though the discussion was short, I thank you for it. Good start of the week, I wish you a good one.
I think it is more about people are a bit sick of filthy rich people giving this kind of advice. I would also not read anything he preaches.
Completely agreed. The point is the knowledge, the learning and the journey. If a kid has a passion for building or toying with LLMs, then of course, by all means, please start tearing them apart or even build and train your own model. You'll learn a ton, even if you won't necessarily end up using it here and now. The learning experience will compound and of course that will be useful.
The above is, after all, the whole genesis of the word 'hacker'. We should celebrate that.
How exactly does one go about "tinkering" with an LLM? Any architectural change you introduce needs fine tuning. That needs data and compute
I tried to modify the embedding output of bert to make it generate box embeddings instead of point ones. At the time I had access to university provided A100 gpus but even with all that a training run took half a day. Models these days I don't think I can train it in any reasonable time with that much compute.
https://thinkingmachines.ai/tinker/ https://github.com/thinking-machines-lab/tinker-cookbook
(As a TML person, I'm obviously biased, but I couldn't resist because of "tinkering").
TBF it's hard to imagine a real architecture change that wouldn't require a ton of compute, but you could certainly fine tune and play with different recipes, loss functions, etc. And Claude can carry you a lot of the way through doing this.
One fun task is to invent a tool and then train a small model to use it. You could export that small model and run it locally for free forever to do your thing. I think this is what a lot of Software Engineering will look like later.
There are a lot of other high level abstractions here to look at. Prime Intellect has one.
The other thing to play with is self-hosting small models, but IMO most of the interesting stuff is actually related to multi-gpu or multi-node inference so there's not necessarily a ton to learn here.
His writing was overrated: It's that simple. People now see his blog posts for what they are: decent blog posts.
Now I'm curious, do people actually tried to hack vacuum tubes or other big servers that's barely 1MB RAM? It seems like another thing that needs big investment to work properly, unlike those other techs where results can be shown even with little materials.
Yeah, there are retrocomputing hobbyists who mess around with sometimes-physically-large computers that were important many decades ago. I don't know if anyone is hacking on vacuum tubes of the kind that you could in principle build a computer with - there's a reason they became obsolete for digital computation almost as soon as the transistor was invented. On the other hand, I personally think it would be neat to try to build a CRT in a garage, which is of course a type of vacuum tube. I don't think this would be an easy garage project, but it does seem like might be tractable for someone who understand physical manufacturing and electronics well, has access to glassblowing equipment, etc.
There are indeed!
Vacuum tube computer: https://www.youtube.com/watch?v=KAlnJnYt5do
Making vacuum tubes in a garage: https://www.youtube.com/watch?v=-UEfqAWb3fE
> Now I'm curious, do people actually tried to hack vacuum tubes or other big servers that's barely 1MB RAM?
"Barely?"
It's insane to lump vacuum tubes together with servers with 1 MB RAM. My first PC, which I used for a decade, had 640KB RAM. And that was an upgrade from 512 KB RAM. My other PC had only 128KB RAM. None of these were considered the equivalent of (by then long dead) vacuum tubes in their day.
You can get a lot done in 1 MB.
> With LLMs and online tutorials, this isn't even a difficult suggestion.
Don't many of the commercial ones prevent you from using them to build LLMs?
I would say the reason for the negativity is not because it's a bad idea for a project, or that doing projects in general is a bad idea (it's not!), it's because it's a very specific thing that is not for everyone. The best thing about computing is the low barriers to entry. You can basically work on anything that takes your fancy. So those who are interested in ML will be drawn to learn about LLMs. They don't need anyone to tell them to do it. Telling everyone to do it reminds me of the "just learn to code" stuff of a decade ago. No, please don't, please find something you enjoy.
> Do people think learning new tech is somehow wasted effort?
No. But funnily enough that is a promise by some of the AI cretins and their boosters. Oh yeah best case scenario you learn how to build LLMs for us. We’ll employ you. And then ultimately that just becomes training data for the LLMs to do it themselves.
But why are people cynical? they ask.
I'd say, if you were 17, you'd probably be better off learning how to socialize or at least gracefully approach and relate to people your own age. That's probably the greatest challenge of the near future, sadly.
I appreciate the sentiment and I'm pretty curious how I could train an LLM, even are really basic one from 5yrs ago without nuts hardware.
That said, I a good starting point for a 17yo is reading about perceptrons[0], then the basics of neural networks[1] (ex. 3-layer perceptron) then writing a program to train a 3-layer perceptron and classifying the MNIST dataset[2] - a dataset of characters.
This can anywhere between a day and a week and you will demystify the basics of neural networks and work your way forward with more advanced contemporary concepts.
Fun fact: any multi-layer perceptron neural net can be reduced to a 3-layer perceptron network.
[0] https://en.wikipedia.org/wiki/Perceptron [1] http://geeksforgeeks.org/deep-learning/neural-networks-a-beg... [2] https://www.kaggle.com/datasets/hojjatk/mnist-dataset/data
Horrible advice. This may have been good advice 10 years ago, but not today. There are no positions for people who "kind of understand how toy LLMs work" because so many engineers do these days. Most of the real LLM optimization work is at the edge of research and highly proprietary and not something you could ever do without infra that costs millions.
But of course, 10 years ago this wasn't obvious.
you don't reach cutting edge immediately. you start with the basics
I think the answer then is to get a Phd? I mean, one should learn it to satisfy their curiosity and to build things but I don't think it's going to help you career in any significant way.
What would be better advice for a 17 year old?
Throw the computer and the smartphone out of the window.
Or more reasonably, the same old thing : use Linux, hack a little, why not learn programming basics. But learn to own your technology, fight against centralization of technology. The same old RMS story.
IDK where the tech industry is going, if there will be jobs anymore or not, but what I'm sure (and what have been the case for the last 10-15 years anyway) is that for most tech jobs, having good technical knowledge beyond the basics is pretty useless and will probably not be recognized.
If you can, stay a computer geek if that's your thing, but don't make it your career choice, the Eldorado is behind us.
Stay in school, get good grades.
Stay away from the internet and learn how to build a local business instead.
"it doesn't matter what you choose to study because no job is safe from being outsourced overseas, handed over to an indentured servant, or made redundant by a machine. you will spend your whole life surviving while the cannibalistic pedophiles who own everything invent new ways to make you own nothing. be frugal, don't get married, don't have children, do everything you can to stay healthy and independent, and you just might live a reasonably comfortable life."
> don't get married, don't have children
Bad advice. You’re going to need family connections and loyal people you can bet your life on to survive if the rest of what you’re saying is remotely true.
the likelihood of finding a partner you can bet your life on is nonexistent. the likelihood of the opposite outcome is well-known at this point.
Most reasonable advice I've seen under this post. Having graduated in 2025 I've experienced the horror of competing with infinitely many third worlders in my small country, who will gladly take 1/3 my pay and be serfs.
I really hope we get another hiring boom like in 2020 when I decided to study CS. Otherwise my career will be very rough. I love it and can't imagine doing anything else.
awwww…… so cute……
who would have guessed when first world were enjoying their heyday as they were colonizing the world, and teaching everyone and their mother to learn English.
Kinda sad now you have to compete with serfs from thirls world. chu chu chu. so cute.
shouldn’t have robbed others then. a richer world for you comes at poorer world for someone. we are playing infinite game in finite world.
> shouldn’t have robbed others then. a richer world for you comes at poorer world for someone. we are playing infinite game in finite world.
Young people are not responsible for the crimes of their ancestors centuries ago.
Horrible. Which country is this? Canada maybe?
Switzerland. I graduated from ETH Zürich.
Learn math
Yeah he's a moron with a lot of money, that's about it. I'm sure he's said the same thing about various other bags he had bets on throughout the years.
As someone who's at a similar age and was interested in learning how to do this, there just aren't enough resources to do so. Most LLM research is in the form of academic papers, and there isn't any 'popular' way to learn these things, and besides that all said research assumes you have a B200 cluster ready to go. If you have weaker hardware (say an 8GB nVidia GPU, which is what I have) you're going to be limited to fine tuning small models or torturing yourself working the GPU for days per iteration trying to run things like https://github.com/karpathy/nanochat, which is hardly an educational experience. Renting cloud GPUs is expensive, and at this age the most I could muster up for experimentation is probably $100 or so, which only gets me 25 or so hours on a B200 which just isn't enough. So why would I bother myself with this if I'm already at a disadvantage because of not having access to the right hardware and when surely there are better ways to spend my time? I concluded the only way to learn and be competitive is by finding work at an AI lab somehow (not happening at 17), or studying ML at the right university.
Putting this in contrast with programming, I learned coding when I was 8, and it was incredibly stimulating to learn because you can quickly iterate and there were thousands of books and YouTube tutorials that dumb everything down and teach you fundamentals. All you needed was a $300 computer, and you can learn nearly anything you want, without being gatekept from this or that because you don't have enough vRAM / an sm_100 GPU.
I think a problem a lot of people are grappling with here is that due to LLMs and AI generally, it’s basically impossible to predict what the future will look like or what jobs will still be around.
I’d probably say something like: do something you enjoy and seems like it might be useful, but accept that the pace of change may mean that whatever you study ends up being irrelevant.
Whatever solution there ends up being to this, it’s not going to be one that an individual 17 year old can implement. We’re past the point where individual good and bad choices matter that much to economic outcomes.
I would advise any somewhat ambitious 17 year old to avoid tech and get into healthcare if they can stomach human interactions and bodily fluids. Sure, it is not all sunshine and rainbows, but there will still be plenty of work helping people who are ill or elderly. Even in the worst-case economic scenario, medicine will be a more socially rewarding and stable life path.
This seems like a much more interesting question to me.
Telling other people's children what to do is easy and basically doesn't have any downside to being wrong. With your own children things are a bit different.
So: what are people here with school age children telling their own kids about the future? If their kids ask, what kind of careers would they encourage them to pursue, assuming they have the skills and interest?
When I was last in the Bay Area, maybe about a decade ago the bookshops were full of titles like "Python for Preschoolers" (I exaggerate, but only slightly). Clearly at the time a lot of people working in tech thought that cultivating an interest in programming was going to be the path to being a successful (by some metric) adult. Is that still the case?
We've wronged an entire generation by pushing "learn to code".
The real reason to recommend this is that they have an excellent chance of avoiding the "nerd-to-incel" pipeline that tech all-but-guarantees for its best nerds. Post GenAI boom, the social cost of working in tech combined with the coming collapse in high paying jobs, means that unironically coders should be learning a bit about coal mines.
In this regard, Healthcare is a polar opposite. It's pretty hard as a male nurse to not "accidentally" become a home wrecker.
Don't really understand why people are saying this is terrible advice. If you're 17, you should be learning everything. At that age, your brain is a sponge and your energy levels are the highest they will ever be.
None of us know what the future of work, education, or AI is going to look like. But your best bet is to become a life-long learner. Be it LLMs, musical instruments, physics, or business.
I get this is basically advice for young founders and entrepreneurs, but i would ignore that request and encourage 17 year olds to spend time trying to find a happy medium between work and life.
Being a super rich and an unhappy workaholic, or a super-impressive engineer who wakes up one day at 45 and realizes they regret wasting half their life (I ran into way too many of these) is a much worse fate than "not being rich from your startup" and working a relatively regular job while feeling fulfilled and happy by more than just work.
Especially in the US, which is uniquely bad at this and encourages people to work themselves to death, mental health and work life balance are much more valuable things for 17 year olds to focus on than finding good startup ideas.
In case you think i'm being a bit dramatic, let's look at the state of 17 year old mental health in the heart of Silicon Valley:
"The City of Palo Alto and the Palo Alto Unified School District approved a funded contract to place 24/7 human security guards and monitors at all four local Caltrain grade crossings, including the Churchill Avenue crossing directly adjacent to Palo Alto High School."
(in case it's not obvious, it's because of suicides by high school students)
The 17 year olds do not need advice on better startups, and this situation will never get better if we focus our advice on how to be better at work instead of how to be better at life. This will require redirecting the conversations.
Thank you. They need human contact, not more "sit in a room alone and get stressed as fuck for little ROI" tech bullshit. Unless the kid has a genuine, self-motivated interest in learning these things (a great, positive thing that should be nurtured), they should file pg's advice under "ok boomer."
Yeah, no way.
I'd move to the middle of nowhere and work multiple jobs on a farm and in construction. Learn how to grow food, and build things. Meet the farmer's daughter, and marry her. Then, buy my own land, grow my own food, and build my own things.
Slight problem with step “buy my own land”. Your working 2 blue collar jobs and paying rent are incompatible with this plan.
> Meet the farmer's daughter, and marry her.
I already do this. I live in a small village where there isn't even a wired Internet connection (wireless only). I work full-time with LLMs, on own product ideas and client projects (all LLM led).
I started investing in farms, have 50 pigs and 100+ chickens now. We are planning to grow to 100 pigs and 2000 chickens in a year. We will start growing Shiitake mushrooms in a few months too.
Most 17 year olds I know don’t want anything to do with AI and see the entire industry as an existential threat.
Nothing wrong with learning the theory and understanding the papers. Getting to that point you’ll have to get your fundamentals down. Might be an interesting exercise.
But as a future? I guess we’ll see. I suspect the next financial apocalypse will determine if there is one. Another AI Winter that may outlast all others so far.
> Most 17 year olds I know don’t want anything to do with AI
The few exceptional individuals will innovate what the millions will benefit from.
At 17, the mother of Isaac Newton removed Isaac from school and tried to make him a farmer. We all know that wasn't his destiny.
There’s no such thing as destiny.
You could also walk out your front door and get hit be a meteor tomorrow.
"...and then train..." and those dummies in openai and google wasting billions, while all they need is some 17 year old and few pizzas
If I were 17 again I'd prepare to go for volunteering overseas after high school for 1-2 years (plenty of free options in the EU where you might only need to cover the plane ticket). See the world, you learn a new language, help others and then think about what you want to do.
I built from scratch a sparse text embedding model trained on a 13T token corpus. Not the same as an LLM because there’s no transformer in the mix, but still I learned a shit load of things in order to solve all sorts of problems that emerge when you try to access big datasets and daily update tables with billions of rows. But if it wasn’t for a specific use case that I tried to solve I don’t think that whatever knowledge I gained could be utilized in the market. Sparse models are a very small niche and most people I’ve come across with similar knowledge are in academic circles, not business related ones. So even if LLMs are all the rage these days I doubt the demand for people who know how to build them is that high. Someone who knows how to setup an open weight model and expose an API might be more valuable to a company these days.
And what resources would you use for that?
(Besides the obvious nano gpt)
I'm not sure what 17 year old me would have done with YouTube tutorials for everything under the sun available.
I'm much older and less wise now, but I still afforded myself the opportunity to follow karpathy's tutorials to build a LLM from scratch. Got to play with a few ideas. Seen similar ideas turn up in frontier model work, which is quite gratifying.
There are so many ideas to try.
Currently playing with autoencoders that takes A and B and produce latents A', B', and C'. Reconstruction of A is from A' and C', B is from B' and C'
The idea is if C' can be made to improve both outputs, it must store as much information as it can about what is common to both inputs.
Mr. McGuire: I just want to say one word to you. Just one word.
Benjamin: Yes, sir.
Mr. McGuire: Are you listening?
Benjamin: Yes, I am.
Mr. McGuire: Plastics.
Benjamin: Exactly how do you mean?
Mr. McGuire: There's a great future in plastics. Think about it. Will you think about it?
---
I love this scene because it so perfectly captures what it's like to be young and given advice, however well-meaning, by an older generation living in a world that no longer exists for the young. And it's ambiguous and trite enough to be essentially useless even if the underlying idea isn't terrible.
I had the same inspiration!
I spent a days reviewing the lecture notes for CS336: Language Modeling from Scratch - and then trained a nanoGPT-esque model in PyTorch.
I'd recommend trying it for those who are curious. Computational bottlenecks become much more intuitive when you've looked at the overall process.
The only realistic approach is to train on a limited data set which is probably less usable than the comibnation of a custom RAG + one of the many available LLMs.
Also many people/kids don't have access to proper "productive" systems anymore, since the whole computing and electronics industry shifted to make "consumer"-devices like smartphones or laptops made for netflix, gaming and spotify.
Breaking the barrier to build a custom system, install linux (or developer tools for Windows, MacOS) is already a complex AND costly task. It was just way simpler in the late 90s and 00s to get something working.
If I were 61, I'd learn to not be so out of touch with the younger generation
Oddly, I just remembered I did the nearest possible thing to this when I was 17... back in 1991.
On an Amiga, I took various public domain text documents from cover disks and counted the probability of the next word given the previous word. Then spat out random sequences of words from it and printed them out. It was called "Splurge". Basically a very very simple single layer statistical language model.
Some of the sentences were randomly not bad sentences, which seemed amazing at the time!
That kind of thing (and Core Wars and Tierra etc) did lead me to getting a job at an artificial life startup at the end of the decade. But that was in turn about 10/15 years too early (no GPUs).
There's some lesson from this about timing, but honestly I've gained the most as a person when I did something that was fun, ethical and gained an audience. A tricky combination.
> Notice that what I would not do is try to start a startup. Instead I'd build the foundation of knowledge to base a startup on later.
There's also this little-known concept called learning things for learning's sake and not always trying to capitalize on it.
What about doing abliteration, weight pruning, representation engineering, etc, directly to open LLMs instead?
Building an LLM from scratch has a hard split between a tutorial project you can complete in a weekend (that's useless for actual usage) and then a solid 1km high brick wall if you want to create anything actually useful from scratch.
Modified open models have a very active community around them, without the need to look much further than Hugging Face.
I thought pg was trying to live forever. Has he learned LLMs from scratch?
I really don't understand Paul's reasoning here. Does he predict more scarcity on the model-level? That layer seems to be almost a commodity now + training is damn expensive.
If you are really 17, my advice is to identify use-case for AI (ideally relevant for businesses) that work most of the time and find ways to make them work pretty much every time. AI reliability is the scarcity right now.
I get it the sentiment behind the post…but it has some “let them eat cake” vibes though.
This might be of use
https://github.com/raiyanyahya/how-to-train-your-gpt
I'd learn a trade in all seriousness.
(Edit: And learn how honest business works)
With the hindsight of experience, the remnants of my 18-year old energy go “woah, that’s cool!” at plenty of engineering feats… and my decades-older second brain goes “well d’oh, I could’ve just learned a trade to work on that!”
I think the last one was seeing a skilled electronics repairman do surgery on a CT machine controller.
Depends if you’re 17 with rich parents or not.
This only changes whether you are naive enough to believe “honest” business means anything in today’s age. If anything, I worry being honest is holding back smart people who try to compete in a rigged game.
There's two ways to read my comment. I believe you read it along the lines of "you need to be rich to start a business".
What I meant was that if you're rich you don't need any trade school or to run a business, you can just sit on your ass living off interest from capital. Only "poor" people need to work for a living.
Honesty is a big problem today. I'd rather live with myself, knowing I didn't screw anyone...
> you are naive enough to believe “honest” business means anything in today’s age
Might be that these people are from outside the US as well, where things like "honest business" is very much possible today, probably most businesses I interact with AFK on a daily business are "honest businesses".
> Someone asked what I'd do if I were 17. I'd learn how to build LLMs from scratch, and then train ones as powerful as I could with whatever hardware I could get access to.
He's seeing a future for models running on everyday hardware just capable enough to do what the use case requires.
That is the idea of ai Not agi A specific model for each problem.
Agi is the academia solution Software is the practical solution
When I was not 17 at the times of GPT2, I decided to not bother with learning how to build LLMs because it’s too expensive for an individual. This escalated quickly.
telling a 17 year old to get into tech right now is horrible advice, literally telling them to get at the back of a line with a better part of a million more experienced people in it.
Telling them not to get into tech is also terribly reactionary advice. The truth of the matter is that we don’t yet know whether tech roles will be eliminated or if they’re just going to follow previous innovation breakthroughs where “one person producing way more work” makes software even more of a desirable industry to be involved in.
There really isn’t a very strong correlation between tech industry hiring strength and AI as of yet. Various studies that are out there haven’t even witnessed AI workflows contributing more than modest gains in software engineering efficiency. I.e., being able to write code 20-40% faster isn’t a seismic shift in the industry where everyone is getting laid off tomorrow and we’re all replaced by software.
Even with the questions surrounding the current job market, it’s still an incredibly good ROI career compared to so many other jobs out there.
For example, in my local area you can get a job as a registered nurse working nights in the emergency room and only make ~$115k.
I make almost double that telling an LLM what to do from my house in my pajamas during the day with less time spent in university.
Even if tech roles lose half their salary to automation pressure it’s still a really good gig.
“only make $115k”
This right here is why nobody is shedding tears for the massive employment crisis in tech.
You make double that shilling ai slopware while they work nights saving lives.
I would tell a 17 year old that the world will always need nurses, same can’t be said for guys sitting in their pajamas burning tokens.
What is happening now in tech has been a long time coming, and it can’t happen fast enough.
I just want to make it clear that I’m not assigning some kind of moral superiority to my financial situation and the amount that the economy values my labor per hour.
I don’t make the rules for how much each profession is able to make in compensation.
The world will always need nurses, but that doesn’t mean that it’s a fantastic career to get into if you have a neutral career preference and your primary consideration is university tuition ROI, expected compensation, work schedule, and day-to-day physical exertion.
My point isn’t to debate the virtues of each career, I am intending to stick to objective aspects of them.
In that sense, telling today’s kids that there’s no future in tech careers just because there’s a short term hiring slump is extremely premature. I certainly wouldn’t tell a kid who is passionate about tech to avoid the field just because the unemployment rate is currently 7%.
https://www.investopedia.com/bachelor-s-degrees-with-the-bes...
Spare me your attempt at turning this into a debate after sounding like every pompous dev Ive ever spoken to.
“I make almost double that telling an LLM what to do from my house in my pajamas during the day with less time spent in university.”
If thats not gloating I don’t know what is.
Perhaps I made a mistake in bringing my personal situation into this discussion. Delete that one sentence about my situation and you'll see my underlying argument is still there.
If you have anything to add to the topic at hand rather than critiquing my personality, I would love to hear it.
Is it really wise to tell kids to avoid tech when the #1 ROI bachelors degree is Computer Science and the #1 salary after 10 years degree is Computer Engineering, while the top 9 salaried bachelors degrees after 10 years are engineering degrees? Are we just telling kids to avoid tech/engineering on vibes or real labor force data?
What else can he say? His business depends on that line continuing to exist.
Shaka, when the walls fell...
I learned HTML when I was 17 in about 1995 and it's certainly taken me on a pretty fun career path. Less technical than LLMs for sure, but 'figure out where the industry is going and move what you're learning to there' is solid advice.
A teenager in China actually did that and got a paper accepted at ICML. The interview podcast is in Chinese, but you can ask AI to summarize it https://www.xiaoyuzhoufm.com/episode/6a8472b95aeb2a5712e8de7...
I think learning how to build your own agent harness framework from scratch and really understand each part of it, what are the modern components that a good agent harness are using these days, is more valuable than learn how to build LLMs, but that depends on what you want to do with your career.
Yeah sure, learning the internals of a technology that’s being actively developed will probably teach you things that remain useful for awhile even if your learning outcomes could end up being different than what he’s implying.
Whether that makes a good long-term investment is quite debatable. IMO, LeCun’s take (at the end) is far more forward-looking since it aims to gain more insight into what could come next based on what we know about the current state of the art.
All that being said, the extent to which these people capitalize on our tendency to be blinded by the halo effect is incredible. A constant stream of bite-sized aphorisms…
This is especially at a different level for early startup figures who happened to be in the right place at the right time and usually did little more than digitizing mundane, traditional day-to-day processes. Yet they’re treated as geniuses and prophets, with people hanging on their every word as though everything they say contains some deeper wisdom. PG and the like often strike me as broken clocks and they’re still profiting from having been very early players in the game, who had good instincts for commercialization and capitalism.
————
LeCun’s reply:
> I would try to figure out why LLMs can write my essays but not clean my bedroom. Then I would study topics in college and grad school that could help solve that problem. I'll figure out a set of methods and architectures beyond LLMs that can quickly learn to perform physical tasks as efficiently as humans and animals. That last item is also what I would if I were 30, 40, 50, or 66 years old
If i were 17, I'd try to distinguish who to take advice from, and would definitely learn that VCs have interest to spread a specific agenda in their message. Also, I would get drunk and have as much fun as could, as the misery of working under the treat of being replaced by AI, would simply kill any desire to live past 25.
Writing, supervising and training LLMs are now the purview of... even larger LLMs. Optimising CUDA kernels; hand-writing SIMD assembly to speed up data loading; tinkering with your particular brand of DRAM to see if there's anything to gain from optimising for its memory topology and NUMA --- these are now the job of AI.
There is very little reason for humans to get all too engrossed in this type of work now, today, with the hope of being good enough at it to command a high salary in 3-5 years. AI can already do it incredibly well, and they can do it persistently and doggedly 24 hours a day.
If this is the job of AI then why does AMD struggle to get the best performance on their hardware? Not enough LLMs?
I'm making an assumption here, but I think paulg is implying that "learning LLMs" today is like the equivalent of "learning computers" in the earlier days. We could even divide civilization into two eras: Before Transformers (BT) and After Transformers (AT).
You can build the program, but to train it is another beast, billions of docs, images, videos, which a mere mortal doesn't have access to
Second in line, build your own agent, that's more in our ballpark, then customize it to your needs, both virtual and physical
https://deeplearningwithpython.io/ is a decent (free) read for anyone looking to follow this advice.
This essentially comes down to choosing the search for substance (here in the form of technical depth) over short term gratification and quick wins in life.
I think this kind of mindset should be taught way more in school so that people really appreciate learning a subject deeply.
When I was 17, I made bad cartoons and was in a band. If I were 17, I'd spend more time learning music and design theory. I also learned PHP at this time, but that was low on the list, friendships came first.
I think it's more about the scar tissue (a.k.a. intuition) when building LLMs from scratch than whether it's transferable to job search. Maybe the person will decide they don't like LLMs and not develop that into a career, or maybe they become a researcher in another field because that's a better way to solve problems inherent in LLMs.
What corpus would one be advised to use for training? Should one limit oneself to legally downloadable documents or should one follow the examples of Altman, Amodei, and Zuckerberg and use LibGen and Anna's Archive?
The problem is tech moves so fast that by time kid is adult llm are outdated
My kids CS teacher asked me what they should do after AP CS.
Previously they had a class where they'd build apps for other teachers. Like tracking when clubs are, etc. But now that's become easy for teachers to vibe code themselves.
I suggested they shouldn't prereq this class on CS. Heck invite anyone in interested in "building things" and they can get practice at building apps for other people / themselves. Maybe that becomes a gateway TO CS - people who want to learn how things work under the hood.
I suggested post CS class for the CS people should probably be building an LLM or something :)
i was trying to build llms from scratch at 17. failed miserably because i did not know linear algebra. ended up in a different but adjacent field. when chatgtp got big suddenly there were so many people doing llms and i didnt want to compete like that. so much of what was happening was hype, and that really turned me off. am back to building llms from scratch, but like, its a journey teaching myself all the theory on top of my job. i have decent fundamentals, but i need a better grasp of all the advancements in the field in the past 5 years before i would feel comfortable designing anything. baby steps, essentially. am working on better understanding all the layers of an ai while implementing a rag on my local model as an experiment. 17 year olds should learn whatever theyre interested in but need fundamentals in order to do anything advanced.
What would you do if you’re 30 years old now?
As a platform engineer being based mainly out of Australia/Hong Kong, opportunities seem to be getting less unless targeting high frequency trading or banking.
It seems like building a startup with the help of some AI tools might be the best bet.
> It seems like building a startup with the help of some AI tools might be the best bet.
I'd only recommend taking that path if you already have your first paying customers standing by or are extraordinarily good at marketing.
As someone who doesn't work in this field (I'm in boring old Accounting/Finance), but would like to learn this field (both to future-proof my skillset, but also because I have an interest in it), would it be wise for me to learn math?
Is it something that's going to be fundamental for future technologies? I always plan to learn, but end up in a death spiral feeling like I'll invest so much time and energy only for the puck to have moved somewhere completely different.
Would gladly accept any advice :)
A 17yo can train a small GPT this weekend. nanoGPT is a few hundred lines. Understanding why it works is the part that takes a decade.
I'm usually a fan of pg, but this post is ignorant of modern AI technologies. Building an LLM from scratch is both a trivial and a useless exercise. There's probably in the range of 5000 github repos doing exactly that. What makes LLMs work is scale, and what makes engineering and training LLMs hard is also scale. And scale is not something you can achieve in your garage.
If the goal is to understand LLMs deeply, one would be better served by either joining one of the big AI companies or doing a PhD. And to be honest, I think this journey should have been started 5 years ago, because right now there's too much competition.
I wonder at the “worlds” Mr Graham envisions, and what is their cardinality. Is this the only 17 yro reimagining, or is there an army of 17yro, of which this LLM curious persona is but one?
Why is it important to train LLMs or even fine-tune them? LLMs have proven their point, costs are crashing, and there are more of them than most companies need.
The real value is to unlock meaningful insights and directions from existing data that is there inside companies.
I live far outside any tech city, so maybe I do not understand. But working with LLMs full-time, building for clients and tons of own experiments, I see no value in building on LLMs.
If I were 77 I'd do the same
I would (and am) going into MLOps. Not just the general infrastructure/systems administration but how to do inference optimization, caching, quantization, memory pinning, vfio passthrough of gpus etc.
Easier said than done - where do you get the B300s from?
Better to start working with harnesses, evals, statistical analysis, etc. - where you don't need the huge hardware for pre-training etc.
if i were 17, i'd learn a trade.
This might be a helpful resource: https://laurentiugabriel.github.io/token-town/
Ah nostalgia. At 17, I learned how to write programs in BASIC on a mainframe.
If I was 17 again I would sack off work and study and focus on chasing the opposite sex, without the angst I had at the time. I don't know a single person who regrets having had too much sex when they were young. I would not build an llm, too hard, too expensive to run. Learn how easy life is if your morals allow you to grift money of vcs into your own funds and retire.
The core point here is that AI is a massive thing (at the moment) so it's probably a good idea to understand it deeply. Not sure why people are so worked up about it.
> (at the moment)
That's why. That future is uncertain. So why gamble your future on something that's popular at the moment for something that could change completely a year later?
If you're 17, and seriously curious about how modern day AI works, you might as well just sit down and look at a couple of courses on linear algebra + calculus, machine learning, deep learning, and more LLM specific deep learning. Those courses will teach you how to go from writing your first perceptron to a MVP language model. But also so much more.
When I was 17, I wrote poetry and learned how to play guitar. I’m really happy that I did. That’s what I would do now, were I 17. I feel slightly bad for people who didn’t.
Sure, but isn't this a bit of a sneer? “I feel slightly bad for people who didn’t” has an air of superiority about it, and seems like a veiled swipe at those who lacked whatever qualities your 17-year-old self had. The guidelines have several different ways of asking us to avoid commenting like this.
As to the substance of your comment, there's nothing stopping a 17-year-old from writing poetry, learning guitar, and also learning about how LLMs work, if they're so inspired. Indeed I'm sure pg would encourage it, and that he did the equivalent of all those things himself when he was young. There's plenty of evidence of that in his essays: he short stories, published a scandalous school newspaper, played soccer, and studied fine art and philosophy. See:
https://www.paulgraham.com/worked.html
https://www.paulgraham.com/nerds.html
Same. But if I could go back, I would add skating as well. I slept on that one and spent too much time trying to be a hacker.
As an aside, for someone interested and who's an absolute beginner, can someone please recommend good resources on how to build LLMs from scratch? Thank you in advance.
1. Build an LLM from Scratch by Sebastian Raschka (https://sebastianraschka.com/llms-from-scratch/)
2. LLM from 0 to Hero, and nanoGPT by Andrej Karpathy
I second 1. I'm a newbie in neural networks and I think it's an excellent book! One of my barriers in ML is the resources, I find them overcomplicated or too simplistic without a mid term. It's not the case of this book, everything is well-explained. Neural Networks aren't fun for me, but this book makes it very interesting.
If you are serious about ML, you can email me to get recommendations on resources.
(Fully free, of course.)
Stanford CS336 is up on youtube from Spring 2026.
I think this is the best structured class out there that teaches how to scale LLMs . Hope the 17 year old knows linear algebra. Building an intuition for the shape of the matrices is important. A lot of understanding the 'building from scratch' means understanding choices like why RoPE instead of the original frequency based positional encoding. Start with Karpathy and then go to CS 336
Check the front page of this god forsaken website a few times a day and you'll get about 10 different posts a day about it.
... and it would be totally pointless.
I mean first that is already what plenty of 17yo are actually doing, because that is what they do at school or in parascholar activities. There are already countless of such tutorials where you can do that in an afternoon.
The pointless part though is precisely why Amazon and others are hunting for rare books, all the low hanging fruits have been picked already so just training a bigger model will simply mean burning more energy and money. Sure training a small one for the basic principle is a great pedagogical thing, training another one, medium, then maybe a large one, is also good in term of learning the process and architecture, but one should not expect it to be useful out of that context.
Pure players are precisely doing everything they can to corner the market by making their own scale unreachable by others. Smaller players with access to lesser infrastructure are thus betting on different market, e.g. embedded systems.
17yos should definitely build their (L)LMs from scratch and whatever bigger model they can train for free, or for cheap, but they should not expect that to bring them any riches.
Why would 17 year old do something that only brings them money? I do not think Mr Graham here is advocating for the path that makes most money as a result of learning how to train a model. I assume that tinkering and learning about LLMs is what enterprising 17 year olds will do to discover ways they can get a competitive edge or further the SotA with their insights further down the line.
No wonder the state of everything when 17 year olds are getting pressured to be “enterprising” and “get a competitive edge”. How about learning to be empathetic, respecting your fellow humans, caring for the place you live in, enhancing the lives of others? We shouldn’t be teaching 17 year olds to be greedy, selfish, self-aggrandising blowhards like Zuckerberg, Musk, and Graham. They are not good role models for the future of humanity.
he does mention that it would later on be about building a startup, which is about making money
It seems obvious to me that no matter what your age right now, you should want to be as broadly educated and intellectually curious as possible.
You should want to train a LLM from scratch as an intellectual curiosity itch that needs to be scratched.
The idea of learning one hot skill that has a pot of gold waiting at the end of it was a brief moment in time that came and went.
When I was 17, we would have said obviously support vector machines are the future. Neural networks overfit and don't work.
Seek advice from nice people that you actually know instead of rich people on the internet.
LLMs are the new compilers.
I don't think you can really call yourself a developer unless you at least have an idea how to build a more complex software project like a compiler, and maybe have built a toy one either at uni or for fun.
It's not clear how long this LLM age of AI will last (to be replaced by something better), but nowadays any developer should at least understand the basics of ANNs, and more than just the "hello world" of a cat vs dog CNN. An LLM/Transformer is maybe the equivalent of a compiler in that regard - something that we all use and is complex enough to present a bit of a challenge. You should at least understand the basics of how an LLM is built, and maybe building a toy LLM will/should become the new Comp. Sci. degree toy compiler replacement.
If you’re 17, you might learn hands on knowledge, like tacit knowledge in areas such as lathes, precision engineering, metrology, and other very niche fields. You can also try climbing and explore arts like music, painting, and drawing. And, of course, spend some time in nature.
LLMs are incredibly boring to me as a technology..Not in terms of what it can do, but how it works.
Same for me. Neural networks in general.
I first studied them in 2011, and I was like what? Just a bunch of partial derivatives?
I keep looking at AI to check if now it's something else but it keeps being gradient descent.
Okay, it's great that you can perform miracles using gradient descent but that doesn't make it captivating in any way.
It’s probably wise to learn how to build one to understand what you’re dealing with. However, if I were 17 I would lean how to apply an LLM to a problem instead of strictly building one.
Why not both? The best thing about LLMs is they make virtually any domain approachable.
An LLM isn’t hard to make - the training data is hard to get and prepare.
The big companies stole the data. The average person can’t do that
Not arguing how big companies got data, but there is plenty of public domain knowledge and content available to someone who wants to get it.
certainly better than wasting time with harness and agent workflows that will become irrelevant at the next evolution, same thing happened with 'prompt engineering'
Can any1 share any resource to learn that skill. I want something that has been tried by you. I can too search on the internet...
i am a small fan of pg, nevertheless i find this to be an exceptionally good take and it is strange to me to see so much piling on to this one in particular here.
learning about llms is not useful so that you can make llms later, you want to learn about it so that you can work on next generation architectures. llms before long i imagine will be left in the dust by ebm / physics oriented models especially that can have an embodied understanding of the world. but a lot of things you learn about them are transferable by doing something like this
Does anyone have a roadmap ?
Paul G is not writing this for a general audience of your run of the mill “engineer” hoping to be employed by someone. He is writing it for future founders. What knowledge / skills you need to develop today to be well positioned to have a startup worthy insight when you are 24.
Why are people so negative about this? It feels like a fun project and at 17 the stakes are not really high. Something one could easily do on summer break in a couple of weeks.
what hardware can a broke teenager get access to in a couple weeks? is this really better than learning how to program?
As if you couldn't learn what LLMs are at any Age.
The core technoology is pretty basic, developing a rudimentary understanding for why the individual parts work as well as they do is tricky.
Didn't his swiss watch essay say he'd essentially leave the industry because there will only be bloat from now on?
I read a bit about how LLM works, but as a hobbyist it is pretty frustrating that I won’t be building anything useful without throwing a lot of money at it
I assume in this hypothetical Cornell just gives away degrees to passersby.
I think pg answered the question as “what I’d do as a project” and not “what I’d do as a career.” So the critical comments are kind of missing the point, IMO.
I don’t see why learning how LLMs work is a bad project for a 17 year old.
Optimizing your entire career and the next decade+ of your life on LLMs? Yeah, probably not ideal. It’s almost always a bad idea to make long term decisions based on current trendy things.
And since everyone is using this topic to give their ideal advice to 17 year olds, my advice as a mid-30s guy: seriously consider becoming highly skilled at a specific thing, and don’t be scared off by the idea that it’ll take 5-10-15 years to get there.
When you’re 17-25, the timescale of a decade seems infinite. But it’s really not, and a decade spent “exploring and keeping your options open” sometimes just ends up with you being pretty decent but not amazing at a lot of random things.
Sometimes I wish I had just become a carpenter, chef, electrician, etc. – a specific skill set that leads to mastery over time, rather than the endless exciting-new-thing hamster wheel of working in tech.
> And since everyone is using this topic to give their ideal advice to 17 year olds, my advice as a mid-30s guy: seriously consider becoming highly skilled at a specific thing, and don’t be scared off by the idea that it’ll take 5-10-15 years to get there.
And this is a problem with modern society today: expecting 17 year-olds to know what they want to do professionally for the rest of their lives, and to focus heavily on professional development aligned to that.
By that age, I think it's not uncommon for individuals to have interests and perhaps even dreams, but a well-defined career focus that serves as the foundation of an actionable skills development plan? Nah. That just isn't common and I'd argue not desirable. 17 year-olds should be exploring their interests, enjoying early adulthood, learning valuable lessons in the social realm, etc. Not training themselves to become compliant little worker bees.
I'd just build an agent, it's easier than you think and you'd learn a lot about the "magic" of LLMs.
It’s really disheartening to see how many people don’t know shit about LLMs, by reading the comments… ironic given what OP is trying to say
Why is it disheartening? LLMs are a dead-end technology with respect to AI.
Have you been living under a rock? We might not get "super-intelligences" (if such a thing even exists) from LLMs but they've proven incredibly useful in basically anything relating to text, including code.
The point is that they’re going to be surpassed by a much better technology that doesn’t have the inherent and unavoidable downsides of LLMs.
Like, right now especially in the US you’d probably sound crazy saying that one day nobody’s going to buy a gasoline car, and that EVs are going to completely replace them. But that’s basically an inevitability based on the direction we know technology is going to go, it’s only a matter of when.
World models will definitely incorporate LLMs or at least text decoders in their final architecture, LLMs are here to stay.
Maybe this is a dumb analogy but is that kind of like saying “well EVs will still use lubricant! Cars that use oil are here to stay!”?
The point being that the way we heavily rely on LLMs specifically right now is likely to cease to be the case, and that in the future they’ll be almost like an irrelevant component.
There's far more shared concepts between them than you think.
You should check the code of both world models (JEPA class for example) and compare to GPT. Many of the tricks stay the same, representation is still embeddings, there is a loss function, etc.
The exact architecture will change, but unless there's a new discovery in that area, we've cracked the text component already. We're hitting the limits of LLMs because of the intrinsic limits of text as a medium. But the way we work with text is pretty much settled, fundamentally.
This is very interesting to me, and I have new things to read into, thank you.
2 years before everyone was doing custom training. What happened to all those today when frontier models itself become more powerful than custom trained ones?
If only we could train LLMs on commodity hardware, right?
LLMs will take away all jobs except yours, LLM maker. Keep at it, you're safe!
What are the best resources to learn how to build LLMs from scratch for 17 year olds?
I have my opinion on this but I'd like to hear the HN opinion, I will just say one thing:
If you are starting with little knowledge, like a 17 year old would, letting an LLM explain it to you is a terrible idea.
Andrej Karpathy has a great Youtube series on how to build LLMs from scratch. Perfect for somebody who just learned a lot of high school math. Would start there and get busy with some handson python coding.
Would building an LLM from scratch imply writing code by hand?
Why do intelligent people still use X? Thanks for the xcancel.com link!
If I were 17, I’d learn how to invest and build financial literacy, and plot potential growth of my networth throughout my life, before even thinking about a career. Then smoke a bowl.
I would not waste my time with yesterday's fad. The next unicorn generator will be something else.
Paul Graham:
>"Someone asked what I'd do if I were 17. I'd learn how to build LLMs from scratch..."
That's funny, Paul Graham, because if I were 17 again,
I'd learn how to program in LISP.
https://www.paulgraham.com/rootsoflisp.html
https://www.paulgraham.com/iflisp.html
https://www.paulgraham.com/hundred.html
(And/or other LISP derived languages... Clojure, Scheme, Racket, TinyScheme, etc.)
I guess "the grass is always greener..." as that old expression, that old "chestnut", goes... :-)
17 is such a fantastic age to be free and experience the world, you won't get that much of an advantage as these FOMO groomers are selling you into if you start now versus later.
If you are 17, go be yourself, whatever that is, in whatever way you want that to be, but do it so authentically and fully. Be unapologetic about what you love and what motivates you, and pursue that with passion and commitment.
I've come to accept that some people, when faced with impressive technology, simply want to use it. They genuinely have no interest in understanding how it works. Lately, its even become fashionable to shame people for trying to understand ("you still read code? gross, you know AI can do that for you"..)
I will never understand this mentality, to let yourself be so dependent on something you don't understand at all is to live like a child. But it is very common. I doubt too many 17 year olds will bother even trying to understand what an LLM is, let alone build one from scratch.
When I was 17 I was building Windows Phone apps, bad decision on my part.
I also did, and had written code for/on other mobile devices before, and did write a large number of even very ambitious software for other mobile devices later.
I do not see any past constructive experience as a waste of time.
Did you write anything for Symbian or PalmOS before WP?
Yes, absolutely. (Actually, "this friend of mine" goes back to programmable calculators ;) - my very first versatile palm computing devices.)
Incredible counterexample, but oddly relatable. I'd probably have achieved techbro 'post-economic' status earlier if I focused on Android dev instead of the shiny (and new at that time) Xamarin for Windows phones.
God I almost invested in Xamarin after Windows Phone got aborted, I did spend a lil time on UWP, but thank god Flutter came out not so long after that. After all these years I learned to stay away from Microsoft tech stack.
Paul has the same problem just about every tech obsessed engineer has (including myself), he thinks everyone else loves computers too. They don't. At all. I''m a self-taught ex-bartender and I can't tell you how many grown adults in the service industry I've tried to get into computer stuff and they had 0 interest.
And younger people I meet don't even own laptops. I had a genz/millennial cusp friend who wrote all her college papers on her iphone.
I think this is weird advice.
Learn how to make language models from scratch, yes. But learn how to use them, in the context of other machine learning tools, on very small hardware.
When the bubble bursts (and I still tend towards thinking it could burst rather than be deflated in a manageable way), the focus will be on uses of AI that are not like the hyperscalars' products.
People will still be interested in useful AI being added to small things — assistive technologies, home security, garden monitoring, their phones and smartwatches, robotics.
Instead of reductive, reactive make-an-anthropic-competitor advice like this, what about advising 17 year olds to focus on broad, integrated, helpful AI — or on going back through eighty years of history to look at AI projects that failed and reassess them?
Curious, what would you do if you are a 40 year old?
This is about as intelligent as say "If I were 17, I'd learn digital electroinics". You would waste your time. Sure, in theory it's useful, in reality it's not that useful.
yeah i loved tech so started learning circuity and soldering. but it was a waste of time i made my living learning how to program web applications.
All the negative comments here are really missing the point. At 17, you should be building your foundation. Kids that can make a custom CPU, or retrofit an old car with an electric motor, or screw around with nuclear energy... these are kids that are doing it just to see if they can. It's not about jobs, it's about curiosity and stretching limits and seeing what you can do, who you can be.
I remember when I caught PG on reddit arguing with some guy who'd said something mean about him. He didn't reveal who he was. But you could tell from his history - his first post was from before reddit opened to the public.
Good times.
I wonder if you can dig that out of the historical reddit database. I'd like to see that again. I love how everything is recorded now.
> I love how everything is recorded now.
I mean, except when it's censored. That part is a shame. HEY! Is there a tool that tracks the censored comments? I bet there is. And if there's not... that will be a delicious new project for my agents.
When you see anthropic job postings that say the job may not be available in a year... it really doesn't inspire much confidence. I say if you're 17, learn to weld, solder, and fit pipe...
That's applicable only if you live in the US.
if i were 17, i'd do these things:
1) LC until mediums
2) Calculus & linear algebra even if i understood nothing i'd just stare at vectors and derivatives.
Really? because I feel like LLMs are already pretty much a commodity, not to say their won't be advances in LLMs but I don't see the models themselves being all that ripe for disruption the way that the web was and such. I'm guessing chip design and manufacturing processes will be more important than models in the future.
It seems this is poor advice in that it’s suggesting young people should focus on the current problem as opposed to future problems. Focus on the current problem can result in making some money but it will result in making the incumbents more money, which is not disruptive. Isn’t the goal of radical software startups to maximize disruption?
If the two current bottlenecks, for this LLM madness that could very well be a bubble, are processing capacity and accuracy (a second processing problem) then what comes next? Isn’t that where young people should be looking or are we just giving up on innovation?
It’s crazy how much survivorship bias gets repackaged as generic advice.
Wait no it’s not, that was always happening.
What’s crazy is that people still believe in it.
I get the sense things have changed a bit since I graduated and there are lot more jobs in AI outside of academia these days, but it's still a very different field from other SWE pursuits, and it's not really accessible to hacker-minded people.
Learning AI isn't like learning HTML in the 90s then expecting to get a job at a tech company building websites. You can't just "learn how to build LLMs" and expect a frontier lab to hire you so I'd argue this is rather bad advise.
Additionally, unlike web development in the 90s you cant really do anything interesting yourself... All of the interesting/useful stuff will require huge amounts of compute and data so there isn't even much point in learning to start your own thing either.
As someone whose built many of NNs from scratch (hand written code, long before the days of LLMs), it's more or less useless knowledge if I wanted to work in a frontier lab or do anything interesting in the field.
I also think anyone thinking about going into a field which is basically a crossover of CompSci and Maths is absolutely insane right now. Even if you think there is a place for CompSci and Maths post LLMs, there's almost no chance anything you learn today will be relevant to the skills required in say 5-10 years.
YC Combinator guy says that with a time machine he would learn to build the currently trillions-valued or whatever technology. Okay.
No, that isn't what he said.
He said, if he is 17 _now_, with no family, no commitments, no pressure to start a career, he'd invest his time to learn to build LLMs from scratch instead of trying to start a company.
Okay sure it’s not a “time machine” in that sense. He wouldn’t go back forty years or whatever. And he’s still saying the same thing that I was alluding to.
Paul Graham wants you to work for him, not be him.
I don't think individuals have the resources to build an interesting llm. The l stands for large. You need a dataset too. Llms are only interesting because theyre large
And it's basically a weekend project to put transformers together in a ML library and train it.
The follow up comment,train it to play a game also doesn't make sense? Llms Sony really play games and there are better ml approaches to do that?
Terrible advice. If I were 17, genetics and bio tech at the next frontier, with opportunities to be more than another corporate drone. AI is a lot of bureaucracy and nepotistic who ya know already.
Let’s make the xcancel link the actual link, please!
On first glance, this is good advice and in general I'd give the same for this age group. With age, you'll see more of these scenarios come up and if you have experience and foresight, you can provide direction that may prove fruitful to young people. My son and his friend asked "what should we look into and learn?", about 15 years ago, I told them Python and Java; Python because of versatility and cryptocoins; Java for the long term stability in the job market. I despise Python personally, but I could see its potential and still do; especially for AI. At the end of the day, kids have more time than money and its great experience for them to get their hands dirty and find out what they might be interested in; its a long life.
Neural net maths are hardly above scientific high-school level.
Somewhere in rural America is a 17 year old that doesn't even have working plumbing in their house still.
I'm sure they'll get right on powering up their computer from the hamster wheel, Paul.
My heart goes to all the kids out there that didn't get the fair shake let alone fair access to tech that gets these condescending "learn to code/learn to LLM" bootstrappy talks from rich pricks that don't know what life really can be like for a lot of American kids out there.
If I were a billionaire, I'd learn how to give away a lot more of my wealth.
[flagged]
You can learn how to build an LLM from scratch while doing all of those things...
Is going to university really that good of advice nowadays?
Everybody goes to college nowadays and the average white collar has lots of debt and relatively minor financial benefits over a skilled trade worker.
edit: woah, so many people insulted by that. In my bubble and friends, me and another friend are the only people that make very good money compared to non-graduates. Plenty of others opened their shops, went into trades, one learned to tattoo fake eyelashes, one became a (successful) farmer and most make significantly more than the average law/chemist/mathematician/physics/architecture/languages graduates. Sure, the lowest salaries are to be found among the non-graduates too, but I don't see any evidence that graduates make that much more, and that graduating is worth it.
Some answers talking about how "formative college is", but my 25 years old friend with her own shop knows more about real life, business and economy than ivy league MBAs.
Depends on where you live (lots of countries have no tuition fees or far lower than the US), what funding you have, how good a university you go it, what you want to do (some careers require a degree), whether you will enjoy it, and whether you are there just for financial benefits or more than that.
Its not good advice for everybody, but it is good advice for a lot of people. What if you want to be a doctor? What if you want to work in R & D? Not everyone enjoys working in a shop or a farm. Also, how old is your friend group? If they are mid twenties you are ignoring the greater scope for advancement in a lot of white collar careers.
> my 25 years old friend with her own shop knows more about real life, business and economy than ivy league MBAs.
Within the narrow limits relevant to her business. How much does she know about macro-economics or financial economics, or scaling up a business? I also suspect you are comparing her to people who went straight on from bachelors to MBA (which is a bad path - study business after having some experience IMO) and lack experience. How will she compare in 10 years time when those people also have real world experience?
If you’re talking purely financial benefit: yes, it is still good advice, as most white collar jobs still require a degree.
If you’re talking about a place to mature, around others who are at a similar phase of life, also yes.
It is where most people meet their cofounders, for example, even if they don’t found anything until much later.
But it is not true financially, it really depends.
Plenty of blue collar workers make more than white collar ones, and have a huge debt free head start in life.
A plumber or electrician will make significantly more than the average bank employee or translator or nurse or teacher.
And they will also have an easier time starting a business as many trade workers are self employed and make much more than hired ones.
Going to college is worth it for basically everyone other than those that are choosing between a mediocre college / mediocre degree and a blue collar route. AND if college is going to put you into debt.
Financially, intellectually, socially, it’s a good idea - college is a formative period of life in American culture. That is more and more true the better the college gets. At the upper tier (Ivy League, etc.) you don’t really have to pay anything if your family isn’t already wealthy, and the connections and degree you’ll make more than pay for themselves.
Geberal college degree is good for mediocre white collar jobs but higher education is required for anything more. The richest drop out of college win a business lottery.
It depends on the university and your goals, I suppose.
Many people are surprised to learn how affordable elite colleges are if you genuinely need financial aid. I had no idea -- was pleasantly surprised when my alma mater took over 80% off of my tuition.
Indeed, would the advice be good if university was free?
Where I live it's basically free and I still sometimes regret not going into the trades. But I suspect this feeling might mostly be a "grass is greener" thing.
> Is going to university really that good of advice nowadays?
Just some anecdata, but every single one of my university professors was quite bad, but they think that since they are the professor, that means they are smart and the expert.
So get into debt and spend money is the advice?
Then vote for someone who will make the debts go away?
As opposed to what, neglecting the human experience to grind yourself to the bone for those who own capital, and then voting to uphold that capital? Makes no sense.
This also somewhat accurately describes what a VC backed startup does.
Risk is not debt
> Then vote for someone who will make the debts go away?
As opposed to vote for someone who gives even more power and money to the oligarchs? You bet your last dollar that people will choose the former over the latter.
yes?
I mean, have you seen the options for people graduating right now? How people are behaving?
Or forget the data, look at how the story of the new future technology is being told. The people making it recognize that it has the potential to put swathes of white collar workers out of jobs, and they are openly talking/warning/PR-ing about it.
People in tech and SV, the places which have a underlying culture of near delusional optimism, are talking about trying to avoid being part of "the permanent underclass".
Gambling is up, and prediction markets are being treated as financial investments. Wall street bets is a thing, and outright speculative investments are the hope people have to get ahead.
This is happening in the USA, forget the weaker or smaller economies.
When people see the future as one massive zero sum game, with no way to win by building, then they are going to change how they plan their future.
This is always such a nonsense question-answer thing, asking a person who already succeeded what they would do if they were young.
Even worse when they ask themselves.
Support your idea with arguments.
Can't this guy enjoy being rich in silence? His takes get worse with every passing year.
He did get rich by being pretty much the opposite of silent, so I'm guessing you can't just turn off that part, kind of comes with the package ;)
Venture capitalist suggests everyone to become his future employee, just as he has been (successfully) doing for his whole life.
If I were 17, but have the money I have now he means.
The times are different. When I was 17, we had to buy records; a 17yo today can listen to the whole of the available produced music, plus interviews and all other uncommon and related material, for free from the comfort of "here and now".
Possibilities exist now that did not exist before. Those who do not exploit this are fools.
I genuinely don't think telling young people to do anything tech-related is good career advice. We don't even know if entry level roles will ever come back. The situation couldn't be worse for these roles. PG thinks that some random teenager will build a startup and get rich from it, or some shit. Like get real, man. Completely out of touch, tech bro who hasn't worked a real tech job for the last like 20 years... Remind me how many startups succeed again, Paul? What about the market dynamics for LLMs and how one might "secure compute"?
These guys (VCs) entire livelihoods rely on tens of thousands of young people throwing away their early years of lives trying to get super rich. Only .01% even see moderate success but PG and co don't care and continue to pump the dream and urge folks to waste their lives to try and make them rich.
What the fuck does this guy know about? I'm sure if we went back through similar statements he's said over the years he's said the same thing about various technologies that are no longer relevant. The guy is a talentless hack who larps as a blogger and his only "redeeming" quality is having lots of money.
Owner of Golf Club Company says I should dedicate my life to golf lmfao.
The amount of people who missed the point here is absurd. He's advocating for learning about how LLMs work. For the sake of learning. Because no one's going to invent the next thing without at least some understanding of the current thing.
I think collectively we should all stop listening to Mr Graham..
He capitalizes on greed and hype but with a soft, sober and thoughtful voice so as to lull you with rationalism and now 20 years of his “disruption” has mostly ruined modern society and a whole generation of techies have been led astray into trying to “change the world” is the world of today (minus the magic technology really any better than 20 years ago?)
- Good for him and his Tech Bros, bad for the rest of society
Twitter mirror: https://nitter.net/paulg/status/2091544343589060625
So I am 15. Is it worth trying to build my own archive (s-1.site) of strategy ideas? Do I have any real differentiation? Or am I just wasting time? I figure that I can use it as proof that I have some know-how?
hackers and painters and kids and ROI and startups and capitalism and the destruction of nature and old guys with money talking like they know better in fascist social networks
> fascist social networks
Sometimes if you want to be heard you must go to the public square, whatever the flags there.
The rest of the post is unintelligible: add some verbs at least.
I do not think it is a proper thing to do for 17 y.o., unless they are exceptionally mathematically gifted, as proper understanding of how LLMs are trained requires a good grasp of calculus, understanding modern OS and SDE tools for proper implementation of pipeline etc.
I'd rather simply write another mnist implementation and check if I really like all that AI stuff at first place. Even then, before going into mature-on-the-way-to-dying tech (LLMs) I'd rather focus on fundamentals - good ols linear models, regressions, stat etc.
> I do not think it is a proper thing to do for 17 y.o
If I'd get a buck every time someone said something like this to me when I was in the 13-18 range, I wouldn't have a ton of money, but it's so very annoying when people tell you this.
Regardless if they're "gifted" or not, regardless if you believe in myths like that or not, let children explore what they want to explore, even if you don't understand what it is or why they want to explore that, just let people explore, regardless of age.
It was such a terrible experience being a young kid growing up, with so many adults spending hours trying to convince me to stop sitting in front of the computer so much doing whatever; "why are you even trying to learn that stuff, you have to go to school to understand anything of this" and so much other similar trash.
Sorry, not your fault and I'm borderline trauma-dumping now, but really sad to see this sort of gatekeeping on HN of all places, age is irrelevant to learning ANYTHING, in my humble opinion at least.
Kids, find anything interesting? Jump into it, ignore what adults tell you, and do whatever you feel like, you'll find your place eventually.
I just voiced my opinion. I just think buiding an LLM from the scratch for 17 y.o. is pointless exercise, advising a teenager to do so is borderline irresponsible, and frankly PG is simply virtue signalling here, as LLMs are still trendy, esp. in his circles.
There still will be varyy small number of outliers among youngsters who'd be able to extract tremensous value from such an excercise, but for most that'd be _IMO_ waste of of time, with illusion of understanding w/o actually having any.
> I just voiced my opinion.
Same! I just happened to disagree with your opinion, and frankly, I'd say trying to gatekeep what people learn is closer to "borderline irresponsible" compared to asking people to build/learn/do X.
> youngsters who'd be able to extract tremensous value from such an excercise
But they're youngsters, who are about "extracting value"? Life is about fun, not extraction, not value, not avoiding waste of time but literally enjoy what you do, nothing is more important (IMO).
Then who knows, doing fun stuff sometimes lead to useful stuff, like in my life. But if you only think about "extracting most value for time spent" or similar "optimization strategies", then you'd never discover this part of life.
> Life is about fun, not extraction, not value, not avoiding waste of time but literally enjoy what you do, nothing is more important (IMO).
This is, pardon, demagoguery. There is always "future fun" and "present fun" which a normal person would assign different nonzero weights (https://en.wikipedia.org/wiki/Discounted_utility). Besides, building a LLM _truly_ from the scratch, just using the famous 2017 paper and numpy manuals is not fun at all, esp. for a high schooler.
> Besides, building a LLM _truly_ from the scratch, just using the famous 2017 paper and numpy manuals is not fun at all, esp. for a high schooler.
To you it isn't, is my entire point here. But why extrapolate what you think is fun, to others? Sure, I don't find that fun either (although useful), but who am I to say it isn't fun for others?
We can continue this pointless conversation, in the tone "who you are to tell what is fun to others and whst is not". You'd be impervious to any argument stating that dealing with far beyound someone understanding and requiring countless hours of digging into difficult math is not fun even for those who thinks it should be fun, as they presumably, loves everything STEM.
> We can continue this pointless conversation
Yeah, if you see it as pointless, then I prefer not to. Have a enjoyable day!
Agree with this - started programming through learning scripting in ROBLOX when I was like 12 (this was back like 17 or 18 years ago) and it developed into a life-long passion for software engineering. I am thankful I had people around me (my parents), who were aware enough to realize I wasn't just playing video games and gave me the time I needed on the computer to learn and experiment with programming...
This also meant that by the time I was actually offered to take a programming class in school (junior year of HS), I had already been able to self-teach myself well beyond what that class was covering, thanks to just working on random projects that scratched an itch I had at the time, looking up anything I didn't know or understand, and internalizing those concepts over time.
In short though, I definitely agree, young kids and teens (and also, frankly, adults too!) should be encouraged to explore things that they have a passion for, without being told 'you need to go to school for this' or 'you cant understand this at your age'
I think he is basically saying YC has enough startups that are just making API calls.
Why would you tell people that the correct order is to build foundational knowledge before exploring a subject? For some (many?) people, a 'proper' understanding develops _after_ the exploration.
> Why would you tell people that the correct order
Because I can?. JK. Because that was my experience, of someone who is 2.5 older than 17?
> For some (many?) people, a 'proper' understanding develops _after_ the exploration.
I am afraid you have a too confrontational attitude here, but I'll answer anyway: because I do not believe you can simply "explore" such complex topics like building an LLMs. You'd simply be unable to build LLM drom scratch, unless you'd call cargo-cult chaining magic numpy incantations you've taken from Karpathy's tutorials "exploring".
If I were in "exploratory" state of mins, I'd rather go from entirely different side - I'd try playing with LoRA-ing existing small LLMs, such as venerable 2 y.o. Mistral Nemo, to get "feeling" for what training is and how hyperameters influence the process.
> unless you'd call cargo-cult chaining magic numpy incantations you've taken from Karpathy's tutorials "exploring"
That's what I would call exploring.
I too started exploring programming as a teen by cargo culting. Fooling around and getting results is what made it fun. Understanding came later.
> That's what I would call exploring.
Then it is not "building llm from scratch" in my book. Just mindlees following instructions. Could be educational yes, but only trivially useful, if you have no bloody idea what you are doing.
> Fooling around and getting results is what made it fun. Understanding came later.
It is not how LLMs are "built from scratch".
No it's not useful, no you don't know what you're doing, and no it is not 'proper'.
But you're learning.
Maybe just accept that not everyone starts from fundamental theory, and there are lots of people who start learning by fooling around.
> No it's not useful, no you don't know what you're doing, and no it is not 'proper'. But you're learning.
It is sold by PG as something special though.
> Maybe just accept that not everyone starts from fundamental theory, and there are lots of people who start learning by fooling around.
Even then LLMs are strange thing to advice to play with, when there are so much more interesting and theoretically accesible for a 17 y.o. so I wonder why would you'd particularly single it out.
> I do not think it is a proper thing to do for 17 y.o., unless they are exceptionally mathematically gifted
I attempted many projects at a young age that I was absolutely not equipped for. The result of the attempts more often than not left me equipped, every time it left me better off. This is terrible advice.
That'd would be a terrible advice if there weren't a plenty of other things "you are not equipped for", but far less daunting both theoretically and practically. Such as, say, convolutional neural networks, or some older ML tech. Or even something totally unrelated to ML.
Transformers are difficult to understand even to people with strong ML background, let alone a teenager.
You are assuming the 17yo in question as an untrained underdeveloped savage. If I were 17 in 2026, I would certainly have exploited all the availabilities from 2010 on - including YouTube, OpenCourseware, the Web simply (Sebastian Raschka etc.) and LLMs.
That 17yo would have already built many uncommon bases, and would build further.
That is, a 17yo with proper mentality.
>understanding modern OS and SDE tools for proper implementation of pipeline etc.
Can you provide an example?
How would you filter out garbage from your training data, for example? If you are trying to use someone elses corpus, would it be "from the scratch" then?
I quoted the wrong thing. I meant to quote that Calculus is needed for building LLMs when all the equations have been figured out already of what is needed.
Another great quote by PG. I've been really enjoying his essays recently - truly a great and curious mind.
If I were 17, I wouldn't be using a social media plattform run by racist neo-fascists...
If I were 17, I'd be going to parties, music festivals, chasing girls, and enjoying my youth.
But sure, make the kids even more depressed by telling them they need to learn how to build an LLM so they can get a job working themselves to death to make Paul and friends rich.
This community is filled with people who were obsessed with microcomputers or (depending on which generation) websites during their teenage years. HN was created by and is managed by people of this type. No doubt we're a minority, and the type you describe is more common, but if you're implying there's something wrong with teenagers being intellectually curious about technology, I can't help but think of the way "nerds" used to get put down and shamed in the past.
He bases this decision on all of the experience he has amassed, as a 61 year old man in the tech industry. An actual 17 year old, with 17 years of experience, would not think like this, nor should they.
Naturally 17 year olds don't think long-term like this which is why PG's advice is so useful. It gives them a pathway to follow that they likely wouldn't have reasoned otherwise.
I told my much younger brother when he was 12 what programming was and it'd be a great career. He looked into it and within months was writing CLI games. Eventually releasing his own unity 3d game on steam as a teen.
Eventually he got into CS and did really well because none of it was scary and new. He parlayed that into role at Meta out of university.
My point being, 17 year olds have time to learn new skills and guidance can go a long way.
Yes. And they almost certainly have a better understanding of their own situation that him. This is not a dig at Paul Graham, the closer anyone is in age, the better they understand what they have to deal with. I'm roughly in the middle between Paul G and the 17 year old, and even though I'm really quite fascinated with zoomer culture and probably come more in touch with it than most (due to relatives in the age range etc.) I realize I have very little idea what it's like to grow up in the world they grow up in.
Most. But, that isn't the point.
Either way, this isn't really advice for 17 year olds. Pg is thinking out loud about the pathways for founders.
What about a 19 year old?
>Whoa. I’m 19 and I trained a 100M language model from scratch. Did a v2 now with a new SFT experiment to see if I can get better results on same size.
This is the ultimate builder’s mindset. Tinkering with the hardest technical problems—even for frivolous things like games—always yields the highest return on curiosity. Time to go back to the fundamentals.