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Why I'm still bearish on LLMs after Navier-Stokes

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Re: Why I'm still bearish on LLMs after Navier-Stokes

#181

Post by keeda »

iron_albatross wrote: post #104
keeda wrote: post #73

The premise in the very first point seems off: > the frontier labs are priced according to the narrative that they have produced or will in the very near future produce a fully automated drop-in replacement for most knowledge workers... Even assuming this is how the AI companies are being valued (they're not), the numbers are off. The "value" of most knowledge workers -- based on what enterprises currently pay for th…

When thinking about these valuations, shouldn’t we try to quantify how much knowledge work becomes obsolete if other knowledge workers are automated? I.e. there are a huge amount of knowledge workers employed in businesses that create tools for other knowledge workers. AI won’t automate their work, those businesses will just cease to exist. And then there’s the second order effect: if all the knowledge workers get au…

Oh for sure, this was a simplistic analysis assuming AI adoption caps out at some X% of job responsibilities where X Unfortunately, I fear that may not be the most likely outcome. I've posted some comments on this before, but when I start thinking about how deeply everything will change once people figure out how to properly leverage AI, I see no outcome other than significant, widespread job losses.

As you indicated, at that point we will have much a bigger problem than the valuation of the AI industry. I'm not sure how it will get solved, I just know it will HAVE to be, because it would be an existential problem for everybody: people, governments, even the billionaires! Because now consider the 3rd order effects: if nobody can buy the stuff that's produced, how can billionaires get even richer? ;-)

Re: Why I'm still bearish on LLMs after Navier-Stokes

#182

Post by keeda »

zug_zug wrote: post #141
keeda wrote: post #73

The premise in the very first point seems off: > the frontier labs are priced according to the narrative that they have produced or will in the very near future produce a fully automated drop-in replacement for most knowledge workers... Even assuming this is how the AI companies are being valued (they're not), the numbers are off. The "value" of most knowledge workers -- based on what enterprises currently pay for th…

> The "value" of most knowledge workers -- based on what enterprises currently pay for them -- is $50 - 70 trillion annually. What do you mean? The sum of ALL US salaries is $13.4 Trillion per year. According to google $65T is the sum of ALL salaries Globally (not just knowledge workers). It's not reasonable to assume AI is a drop-in-replacement for any job yet (perhaps bottom tier customer support from oversees?). >…

It does not have to be 16% of all jobs, but 16% of any given job, i.e. AI stays in an augmentative role rather than a complete job automation. The simplistic analysis is if a tool makes you X% faster, that can be worth X% of your salary to your employer.

Unfortunately, I do fear that AI adoption will go beyond augmentation to automation, and I do fear an economic shock. Just posted this down-thread: https://news.ycombinator.com/item?id=49722616

Re: Why I'm still bearish on LLMs after Navier-Stokes

#183

Post by nmehner »

BobbyJo wrote: post #118
paimapi wrote: post #110

Earlier quoted context omitted.

so prove it! get a public repo out there, have it play against some open source engines also I think the operative letter in AGI is the G - and if the G is short for 'variably competent savant-like hyperfocus on certain kinds of software coding and not any other general skill' then its not really G at all, is it?

I suck at chess. Are you saying I can't be intelligent?

If you read all chess tutorials, strategy documentation and game archives on the internet and then would still suck at chess: yes.

Re: Why I'm still bearish on LLMs after Navier-Stokes

#184

Post by sph »

hackinthebochs wrote: post #123
what wrote: post #96

Earlier quoted context omitted.

Humans don’t code a $game engine to play $game, they can just play it. It seems like you are the one that has gone insane.

And how many years of direct play and study does it take for a human to get good at chess or any other game? Absolutely no human ever could be good at chess just by reading a few books, or even every book on chess. That's just not how the brain works. If LLMs could do that they would truly be superintelligence.

No, learning is definitely not a sign of super intelligence. I know words don’t mean anything anymore, but that is simply general intelligence, despite the claims we have reached this milestone.

Re: Why I'm still bearish on LLMs after Navier-Stokes

#185

Post by lelanthran »

hackinthebochs wrote: post #120
diehunde wrote: post #103

Earlier quoted context omitted.

AI bros: the LLM beats humans at solving Navier-Stokes and some old cypher. We are close to AGI Also AI bros: LLM can’t beat an avg chess player. But that doesn’t mean anything. It doesn’t count

>LLM can’t beat an avg chess player. Why should that matter?

> Why should that matter?

Because we want to use this as a replacement for humans, and the average human can learn the rules of chess without needing to see the rules explained hundreds of thousands of times in millions of games.

So, yeah, it matters if a model has millions of examples of something in its training set and still cannot follow the rules.

Re: Why I'm still bearish on LLMs after Navier-Stokes

#186

Post by hackinthebochs »

sph wrote: post #184
hackinthebochs wrote: post #123

Earlier quoted context omitted.

And how many years of direct play and study does it take for a human to get good at chess or any other game? Absolutely no human ever could be good at chess just by reading a few books, or even every book on chess. That's just not how the brain works. If LLMs could do that they would truly be superintelligence.

No, learning is definitely not a sign of super intelligence. I know words don’t mean anything anymore, but that is simply general intelligence, despite the claims we have reached this milestone.

No, but superhuman capabilities derived from ordinary learning is, which is what the parent comment described. Why is that not obvious?

Re: Why I'm still bearish on LLMs after Navier-Stokes

#187

Post by hackinthebochs »

minraws wrote: post #169
hackinthebochs wrote: post #150

Earlier quoted context omitted.

I would bet a lot of money that Astra can follow the rules of chess (perhaps if repeated within the context window). Also, this is a different argument than what I responded to.

I can write you a benchmark to prove it even with a heavy handed system prompt Astra will make an illegal move during the course of the games first few moves are generally ok since it's just throwing out learned moves.

I'd genuinely like to see the results of that.

Re: Why I'm still bearish on LLMs after Navier-Stokes

#188

Post by boesboes »

sigmoid10 wrote: post #40
joefourier wrote: post #31

Earlier quoted context omitted.

> current frontier models > Gemini 2.5 Pro, O3, Claude Sonnet 3.7 and ChatGPT 4.1 The gap in capabilities between those models which they tested, and actual current frontier ones is enormous. I would not trust that any conclusions they made are applicable.

The actual current frontier plays somewhere around GM level. https://chessbench-ai.github.io/#leaderboard It's also worth noting that the very latest models (GPT-6 and Fable 5.1) actually play worse than their immediate predecessors, so it is likely that the labs are not benchmaxxing for this yet. If they did, I'm sure they could come up with something superior to humans. But there is probably very little demand for…

Dumbest thing I’ve seen today

Re: Why I'm still bearish on LLMs after Navier-Stokes

#189

Post by hackinthebochs »

lelanthran wrote: post #185
hackinthebochs wrote: post #120

Earlier quoted context omitted.

>LLM can’t beat an avg chess player. Why should that matter?

> Why should that matter? Because we want to use this as a replacement for humans, and the average human can learn the rules of chess without needing to see the rules explained hundreds of thousands of times in millions of games. So, yeah, it matters if a model has millions of examples of something in its training set and still cannot follow the rules.

We're not talking about learning the rules of chess here, but playing a competent game from just being shown the rules. Why is it so hard for people to keep track of the thread of discussion?

Re: Why I'm still bearish on LLMs after Navier-Stokes

#190

Post by aprilthird2021 »

joefourier wrote: post #31
carodgers wrote: post #17

This April 2026 paper is a fun and related read. https://arxiv.org/html/2509.24239v4 Researchers asked frontier models to play chess. Have a look at the MAR rates in Table 3. When not explicitly told which moves were legal, no model identified legal moves at a rate better than 80%. Many asked for more illegal moves than legal moves. And even when explicitly told which moves were legal, the models continued to ask for…

> current frontier models > Gemini 2.5 Pro, O3, Claude Sonnet 3.7 and ChatGPT 4.1 The gap in capabilities between those models which they tested, and actual current frontier ones is enormous. I would not trust that any conclusions they made are applicable.

They still need supervision though
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