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

dank.systems

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

#421

Post by cavemandaveman »

derpyzza wrote: post #394
yshklarov wrote: post #209

Great article, but the lack of sentence capitalization makes it unnecessarily difficult to read. Apologies if this comment is off-topic, but it really is quite egregious, and since the article was submitted by the author I presume they are open to the feedback.

huh i didn't even notice, that's how i write all my blog posts too. it looks nicer to me and i don't have to bother checking for "proper" capitalization if everything's just lowercase anyway. didn't realize people struggled to read text that way though, maybe i should change my writing style if this is a common pain point

To me, it reads like a lazy fifth grader wrote it that just couldn't be bothered to press the shift key.

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

#422

Post by lionkor »

dgb23 wrote: post #237
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 gap in capabilities is mostly quantitative and not qualitative.

My read is that the improvements in quality are due to excessive use of "thinking" tokens (so, higher quantity and brute force), so I agree with that.

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

#423

Post by StevePrefontain »

moomoo11 wrote: post #366
lolakutty wrote: post #232

Earlier quoted context omitted.

> when you put these models in a loop, you are able to do more and more insane and cool things... Please share some of these insane things that you speak of..

i mean have you used any coding agents? if you’re getting slop code in 2026, that’s a smell and skill issue. fwiw i was pretty bearish on AI until i spent a month a couple months ago going deep into agentic workflows. use your imagination to solve problems people face and pay $$$ for today that is error prone and hard. i’ve got agentic workflows for the particular industry im building for, one of which that replaces…

Very hard to tell if this is satire. It reads like one of those old r/programmerhumor posts where people competed to come up with the worst designs possible for a volume control.

I could not think of a worse technology to use for an ETL pipeline than throwing LLMs at it and asking it to vibe out the correctness of the data every time it runs.

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

#424

Post by lionkor »

MattCruikshank wrote: post #420
cbolton wrote: post #417

Earlier quoted context omitted.

It's a great test of cognitive abilities. There are many claims that current LLMs surpass humans in cognitive abilities so it's noteworthy that they underperform on that test. Letting the model execute a chess program (that it wrote) would make sense if you're measuring its economic potential, but for cognition that would be cheating just like if you let a human run a chess program. The fact that the human would have…

> The fact that the human would have a much harder time writing a useful program is irrelevant. Why? There's a box. You give it a problem, and it comes up with a solution. Why does it matter to you if the box is strictly a LLM, or if the LLM can write code that it executes? Even neater if the box is self-contained with a local model. You provide electricity, and it comes up with solutions. Why does it matter if it ca…

The question is to what end? This is a benchmark task, because playing chess, or solving other well-understood problems is more of a party trick than it is useful.

If you let the LLM write a chess program, which it can ONLY do because there are already so many chess programs out there, then the benchmark becomes about recall of popular program source code, not chess.

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

#425

Post by orangedog »

moron4hire wrote: post #415
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 gap in capabilities between those models which they tested, and actual current frontier ones is enormous. Same story every 4 months and yet still no breakout, winning products. I've been hearing "the AI is good now" and "it 10x's my productivity" for a over a year now. If it were true, why aren't the all-in-AI using companies 10-15 years ahead of their competition yet? Why is it still all buggy, poorly designed…

I don't get why it is hard to understand there is middle ground. People are 10x their productivity, it isn't all buggy junk, but it isn't all it is hyped up to be either. It isn't that complicated.

If you hold the extreme position that there isn't any value in this, that's fine, but we're only having this discussion because these models have done what humans previously failed to do.

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

#427

Post by lionkor »

glitchc wrote: post #363
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…

Why not ask it to implement a chess engine first, and then use that to play against you? Does the LLM need to learn to play chess if it can build a chess engine to play for it instead?

With that approach, the benchmark falls apart. Of course it can write a chess engine, because it learned on lots of stolen source code of chess engines. This has nothing to do with the LLM's ability to reason.

Writing a well understood engine for a super popular problem does not count as reasoning about the problem.

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

#428

Post by tzone »

It all depends on what prompt you use though. You can just tell all current frontier models to write a chess engine first, and then play a game of chess against you using that engine.

It will probably do a pretty good job if you ask it that way (it will also burn a shit ton of tokens, but hey, that is part of the fun).

On that note, I actually had an overall harness (for experimenting) that was essentially like this: "for any task, instead of answering question directly, write a program to answer the question instead. test and verify the program before giving the answer".

It actually worked incredibly well on all "gotcha" LLM questions like math or counting letters in words and all sorts of stuff.

Of course it was ridiculously slow and very expensive but it was a proof of concept that it can actually be much more accurate on every task if you are willing to spend infinite amount of money.

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

#429

Post by brokensegue »

lelanthran wrote: post #379
Madmallard wrote: post #261

AI is just good at what it's got the most elaborate training data on. And by "good" I mean, is statistically most likely to spit something out that makes some kind of sense. I don't know how well it is studied, but I suspect it is possible there is language-related complexity constraints to the effectiveness of the LLM algorithms. Like perhaps context-free grammar related problems with adequate training data can be m…

> I don't know how well it is studied, but I suspect it is possible there is language-related complexity constraints to the effectiveness of the LLM algorithms. I believe that's obvious - humans don't think in words. Neither do animals. A machine that only thinks in words is obviously going to be deficient in some things, no matter how proficient it is in everything else.

Aren't the new models thinking in neuralese?

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

#430

Post by iron_albatross »

credit_guy wrote: post #132
iron_albatross wrote: post #104

Earlier quoted context omitted.

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…

I think you are committing the lump of labor fallacy [1]. Lots of jobs will disappear, but others will appear. Lots of things (both intellectual and material) that are produced nowadays by humans will be produced in the near future by AI. But humans will be needed to do new things. Take the Hugging Face incident. Why did it happen? Because the people whose task was to set up a testing framework took shortcuts. Why di…

That’s fair. I guess I’m also thinking that whatever new kinds of intellectual work that arise from the introduction of AI could be also done by AI. But maybe this comes from a lack of imagination again.
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