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.

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.

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.

> 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.

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.

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.

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.

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

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.

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.

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.

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)

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.

"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).

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

Yes. There's a lot of demand for elite talent, and no demand for slightly sub-elite talent.

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.

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.

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.

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.

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).

+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.

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.

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.

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

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.

> 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.

1000%

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.

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

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