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I didn't mean it literally in the sense of "the machine can produce 1000 copies of the same program!" (which indeed would not need an LLM to accomplish...). I meant industrial-era automation in the wider sense of "a machine and a human operator can do in one week the work previously done by 10 people in two months".

To put it another way: a generalisation of OP's question is "is it worth spending a lot of effort learning to do X manually, when a machine can do a lower-quality version of X massively faster and cheaper than I can ever do?" In the article, X = coding. I wanted to check if the arguments given for "yes, and" hold for different values of X and the first that came to my mind was rugs; probably unfortunate that I know nothing of that topic...

I don't think that the answers for one value of X always apply completely to other values of X (i.e. whatever happened to hand-woven rugs might well be very different than what happens with coding) but I found it an interesting thought experiment, because I can fully see a master artisan of any type of X giving a very close version of the article's arguments. Maybe finding when they were eventually right and when they were not for past scenarios can teach me something now. Maybe...

I spent a long time honing my trade and I did learn a lot in the process, so I want to believe this argument, I really do. But then I imagine it applied to a lot of pre-industrial era jobs and I am not so sure.

"Master, should I still learn to weave our beautiful Persian rugs by hand?"

"Yes, and... Have you seen one of these mass-produced rugs? They all look the same and their quality is terrible! And how would you ever operate one of those new machines if you don't know a good rug from a bad one? By learning to weave manually, you are also learning about choosing the right yarn, negotiating the right prices with the merchant down at the market, selecting a good apprentice to pass down the trade. All these things will always be useful!"

Yes, there are still artisans making and selling beautiful rugs at premium prices. But most people now are content with resting their feet on a cheap Ikea thing that they can replace every few years, so that market has shrunk to almost nothing.

Even assuming that inference is really profitable today's models don't learn new things by themselves, except in the limited sense of temporarily storing everything they need for a conversation in their context (and maybe leaving themselves little notes in md files like the guy from "Memento").

The difference with tyres is that If today's LLMs had been invented and had become "good enough" 50 years ago, you would have a cutover in their knowledge that excludes 50 years of information. Every "write me a program in Rust" conversation would involve LLMs filling up their context trying to learn Rust programming from scratch every time and probably doing a very bad job.

An example of that was when Fable disproved that mathematical conjecture and HN was full of other people who fed that information to other models (or Fable itself) and received incredulous answers from their LLMs. If something is proven true or false in math, the world of science moves on and that new piece of information can be used to build, prove or disprove other things. But an LLM is excluded from learning even from the very thing it just helped demonstrate and needs to re-discover it over and over again. In order to have an LLM that "lives" in a world where the Jacobian conjecture is false, you need to train a new model and add that information.

If you are using AI like you use Netflix (something you like but can live without if it gets too expensive) then you have a point. But if you are relying on AI for your business, the cost of AI sooner or later is going to be passed on to you and it's going to impact your margins. And it's probably goong to be sooner, rather than later.

The amount of money these AI companies are burning is unprecedented and there is simply not enough money in the economy to keep it going at a loss for ten years. Google (Google!) went cash flow negative and is issuing bonds, basically asking for a loan. That's the reason markets are getting so nervous.

And yes, choice is good but how many of these new models are trained from scratch vs distilled from frontier models? If OpenAI and Anthropic go down in flames how much of the cheaper choices we see today are going to keep evolving? I am not trying to make a prediction either way, I am just pointing out the uncertainty. Which, again, is fine if AI is a commodity you can easily cut, but not great if your company is betting big on AI.

Yes, it's like saying "we took off a big chunk of his brain but look! He can still breathe autonomously, swallow food and walk almost straight, which is like 95% of what he did before!"

"For instance we are already running out of people to lay off!"

But I want there to be an incentive to develop smaller, private models that can run on local machines that I own. I want that business model to win.

If it costs me slightly more in the short term, but I don't depend on any providers' price gouging as soon as I fully depend on them, I will consider it a win.

From COVID-era discussions (when virologists were briefly the stars of every talk show) I remember one explaining that it was less about fatality rates per se and more about the length of time you could carry the virus around and be nearly asymptomatic while still able to infect others.

I understand the jury is still out on whether a virus can be considered "alive" but, like us, it is capable of replicating itself and mutating. In that sense, it benefits from the same evolution strategies as more complex beings: a strain that gets its host very sick very quickly gets a lower chance to spread to a new host and multiply.

This creates an evolutionary advantage for strains of that virus that are less aggressive or at least develop the worst symptoms more slowly and more covertly.

On the other hand, if LinkedIn was actually good at finding jobs, they might be able to do more outplacement and less layoffs?

Where are all the apps? It's mostly visible in AI tooling itself. Harnesses, vibe coding tools and stuff with "claw" in the name saw a cambrian explosion.

And maybe using AI to use AI better is just masturbatory. But coders want interesting problems to solve. Pros also need software ideas they can monetize. And what problem is attracting more investment in money, time and neurons than the problem of making AI productive? (I am referring only to problems that can be solved in software....)

So the thing with AI is that right now it is both a tool AND a potentially very valuable problem to solve, that's why most of the AI "productivity" gains go into AI itself. At one point this self-refetential phase will have to end and people are going to see if these new AI tools, harnesses.claw-things are actually applicable to things people are willing to pay the real prices for (not the subsidized ones).

I have a teenager at home. Between freaking electron-powered Discord and Chrome, she's basically at the limit of her 12GB RAM (on windows 11)

I'm not saying that CEOs (or devs, for that matter) lie. But on AI I don't think we can rely on any self-reported results, positive or negative, based on surveys.

There is just too much incentive to say... no, to BELIEVE... both that AI yields 10x productivity that AI is useless.

I am swinging wildly between the two too, personally. The more time I spend with AI, the more I am developing this split personality where one part of me says "I hope this thing blows up before I lose my job and my children never have the chance to have an office job again" and the other one says "AI is actually not easy! You have to know how to use it well, deveop tools, plan, curate your context... This means I am acquiring useful skills here, tring to port Flappy Bird to COBOL".

And obviously, depending which side controls my cortex in that moment, I may err on the "AI is useless crap" or the "AI all the things!" side

Besides the ability to deal with text, I think there are several reasons why coding is an exceptionally good fit for LLMs.

Once LLMs gained access to tools like compilers, they started being able to iterate on code based on fast, precise and repeatable feedback on what works and what doesn't, be it failed tests or compiler errors. Compare this with tasks like composing a powerpoint deck, where feedback to the LLM (when there is one) is slower and much less precise, and what's "good" is subjective at best.

Another example is how LLMs got very adept at reading and explaining existing code. That is an impressive and very useful ability, but code is one of the most precise ways we, as humans, can express our intent in instructions that can be followed millions of times in a nearly deterministic way (bugs aside). Our code is written in thoroughly documented languages with a very small vocabulary and much easier grammar than human languages. Compare this to taking notes in a zoom call in German and trying to make sense of inside jokes, interruptions and missing context.

But maybe most importantly, a developer must be the friendliest kind of human for an LLM. Breaking down tasks in smaller chunks, carefully managing and curating context to fit in "memory", orchestrating smaller agents with more specialized tasks, creating new protocols for them to talk to each others and to our tools.... if it sounds like programming, it's because it is.

I assume that was exactly the author's point?