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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

#81

Post by minraws »

echelon wrote: post #78
minraws wrote: post #64

Earlier quoted context omitted.

I know HN readers and posters just read numbers and can't be bothered to read, but please read the methodology before making any claims. > About their ELO ratings from their own website: > A field-relative rating calculated within ChessBench. It compares performance among the tested models and is not a direct equivalent of a human chess rating. I am around 1600 elo in over the board I can mop up Astra Fable etc even…

The AI can write a chess bot program that will beat you. You're thinking about this the wrong way. The system is built and delivered as it is because that's how the providers make the most money. If they cared to have it perform well in chess games, you'd see a different shape and behavior. We shouldn't ask the multibillion dollar automated software generation system to play games with us any more than we should ask…

So AGI needs to be trained on something to work well on it. Lovely reasoning we have right here.

Delusion runs deep in HN circles.

I say that as someone heavily invested in AI startups and projects and as someone working in the field.

I think most people on HN should touch grass and find real human contact. Lmao

Incredible reasoning all around here.

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

#82

Post by willmarch »

skydhash wrote: post #72
bigstrat2003 wrote: post #61

Earlier quoted context omitted.

> Frontier labs don't care about chess. If OpenAI cared, GPT-7 could be a grandmaster+ level chess player. If the models were actually intelligent, the way that the boosters claim, they wouldn't need to be tuned to play chess in order to be good at it. That's kind of the point of intelligence, that it is generically applicable to whichever task one wishes.

Pretty much this. Feed it a book or two on chess, and you should have a decent (or good) player. That's the generic intelligence people have. The aims is not to be supremely talented at something, but being able to read a manual and figure how to use/play something. Mastery can be gained overtime.

If you gave a human a book or two on chess they would not become a decent player (they would be closer to 500-600 than 1100 ELO) and they would only get better after playing hundreds or thousands of games (often making illegal moves and moves that violate the rules of chess as they learn).

Your assumptions/intuition about generic human intelligence feels quite incorrect, considering LLMs currently play better than a brand new human player would (presumably without any attempt to fine tune them specific on chess, such as playing thousands of games).

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

#83

Post by sobellian »

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.

I tested both myself and a weak bot against Astra xhigh, https://lichess.org/study/27lCQqDa. It's still pretty bad at chess, though it takes longer to devolve into illegal moves.

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

#84

Post by woeirua »

knuppar wrote: post #19

Short and to the point! Open and cheap models will undercut the big labs continuously. The blast radius won't be pretty once spending commitments knock the door.

Open models wont be open for long. No one is going to release an open model capable of chaining zero-days. Even the Chinese aren't that reckless because it will just be turned around and used against them.

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

#85

Post by flyinglizard »

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…

You’re right; given that most of the money in the AI market is injected through OpenAI and Anthropic (which collect it through both selling equity and through customer revenue), the 7-8T is just a derivative of that.

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

#86

Post by willmarch »

harimau777 wrote: post #39
bravoetch wrote: post #28

Earlier quoted context omitted.

I was a young child when I learned chess by reading a short book, then practicing with a friend. That is not how LLMs learn. I'm no expert on LLMs, but if you showed a human all chess games and books in all history and then said 'play chess' and they still kept making illegal moves, they would have to have a brain injury.

It seems like an LLM potentially could learn that way if each practice game it participated in was added to its training data.

Yes, this is essentially how AlphaGo and AlphaZero algorithms work to train superhuman Go/chess/shogi agents. It’s an elegant algorithm that is analogous to how humans learn games.

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

#87

Post by striking »

echelon wrote: post #78
minraws wrote: post #64

Earlier quoted context omitted.

I know HN readers and posters just read numbers and can't be bothered to read, but please read the methodology before making any claims. > About their ELO ratings from their own website: > A field-relative rating calculated within ChessBench. It compares performance among the tested models and is not a direct equivalent of a human chess rating. I am around 1600 elo in over the board I can mop up Astra Fable etc even…

The AI can write a chess bot program that will beat you. You're thinking about this the wrong way. The system is built and delivered as it is because that's how the providers make the most money. If they cared to have it perform well in chess games, you'd see a different shape and behavior. We shouldn't ask the multibillion dollar automated software generation system to play games with us any more than we should ask…

It's not quite the same, but the in-flight chess game provided by Delta was known to be absurdly hard: https://news.ycombinator.com/item?id=46593395

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

#88

Post by famouswaffles »

bigstrat2003 wrote: post #61
famouswaffles wrote: post #48

Earlier quoted context omitted.

Frontier labs don't care about chess. If OpenAI cared, GPT-7 could be a grandmaster+ level chess player. In fact there's a google paper on grandmaster level chess without search with a 270M transformer. Outside that, there was gpt-3.5-turbo instruct which was incidentally a 1800 lichess elo player that didn't make any illegal moves even after a few thousand moves. Frontier labs care deeply about automating knowledge…

> Frontier labs don't care about chess. If OpenAI cared, GPT-7 could be a grandmaster+ level chess player. If the models were actually intelligent, the way that the boosters claim, they wouldn't need to be tuned to play chess in order to be good at it. That's kind of the point of intelligence, that it is generically applicable to whichever task one wishes.

If humans were actually intelligent, they wouldn't need to train and practice to play good chess. I mean, what level do you think people without any practice or training are ?

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

#89

Post by matteoraso »

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…

I don't see why this is such a big deal. Nobody's using LLMs for chess, but even if they are, just give them Stockfish as part of their harness. They don't need to do everything themselves as long as they're intelligent enough to use tools.

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

#90

Post by sobellian »

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…

If it's a GM then I'm Magnus Carlsen, https://lichess.org/study/27lCQqDa.
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