I really appreciate seeing a tempered take that's not literally denialist about current capabilities.

Thanks :) I do enjoy and use these things every day and the current capabilities are indeed amazing, just ludicrously overpriced at the frontier.

current frontier models need laborious oversight and guardrails on even the simplest tasks

It is literally denialist about current capabilities

why don't anthropic and openai ship yolo mode by default?

They do…? Well, “auto” mode has been default in Claude Code for a couple months now. It’s effectively “safer yolo:” tool calls are inspected by a separate classification system (another smaller LLM, I believe) to approve or deny. And you can always layer on additional sandboxing mechanisms to limit the blast radius deterministically.

Anthropic basically does at this point with Auto Mode being default. Or was that the point you were making?

I don’t know who you’re talking about, even the most bearish people like Gary Marcus and Ed Zitron acknowledge that LLMs are useful in these same cases the OP admits. Gary Marcus is even still a long term AI advocate, he just doesn’t think LLMs are enough and we need more foundational breakthroughs. Zitron says it’s valuable technology but not worth the trillion dollar valuations the frontier labs are claiming.

The lack of temperament is very skewed towards the bulls who have been saying AGI is here, software engineering is solved, mathematics is solved, it’s going to destroy the white collar job market, and it’s going to kill us all for like 5 years now.

Gary Marcus is an especially puzzling addition. If I recall correctly, he has made statements along the lines that superintelligence this century is more likely than not. If you’re AGI-pilled that might read as bearish, but that is still extremely rapid progress in the grand scheme of things.

Specifically: bearish on LLMs generally, not bearish on LLMs for pure math.

yes, huge for pure math and activities that look like it.

Doesn't really even need to look like it. If you can verify rewards, RLVR will optimize really really well. If you can't... it's a struggle. There are probably fewer fields where you can verify rewards than one might hope.

I think bearish on LLMs for automation, and bullish for LLM+human experts in specific fields, is about the right expectation for current architectures.

Apart from issues with task generalization, or perhaps related to it, is the fact that LLMs have real trouble with timekeeping, and cannot estimate the real world time it will take them to do things very well. This plus the memory issues make dreams of long horizon agents, that could plausibly handle changing specifications, quite implausible with current architectures.

In narrow domains with more deterministic outputs though, this is less of an issue, and we see multiple agents succeed much better.

The fusion of that capacity, with humans in the loop able to better direct such agents and act as their temporal tethers, is where I think the real action will be for a while at least.

This plus the memory issues make dreams of long horizon agents, that could plausibly handle changing specifications, quite implausible with current architectures.

Any reason why that can't be solved through context management and keep-forward scaffolding?

Write the same sentence you just wrote back to me, but in only four words and let’s see if it has the same meaning.

"Any reason why that can't be solved through context management and keep-forward scaffolding?"

becomes

"load bearing context seam"

/s

the models generalize well only on tasks within a small neighborhood of the specific tasks they've been trained on, and even then with severe caveats. the frontier labs have developed a general recipe to teach models almost any specific task enjoying clearly defined levels of task performance; many tasks are covered in the training data

Is this really any different to how humans learn, it takes a lot of training on one specific task to make a human expert as well?

Is this really any different to how humans learn

yes.

being a bit more specific: the sample efficiency of humans is orders of magnitude larger for more abstract concepts. the same doesn't hold for memory-intensive tasks though (like any kind of trivia), but that only takes you so far.

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.

Well except AlphaZero played 44 million chess games in that time (and actually played with a 44 core computer). So I'd like to point out that the human is still just a few orders of magnitude more efficient.

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 illegal moves. With illegal asks discarded, none of the bots could beat a chess model calibrated to 1100 ELO.

The author of the originating post says that "current frontier models need laborious oversight and guardrails on even the simplest tasks", and he's absolutely correct.

The story isn't so clear cut.

The caveat is: It depends on the task.

Are there reams of chess moves that the model can train off of? No.

Are there reams of math papers the model can train off of? Yes.

Are there reams of chess moves that the model can train off of? No.

This is as false as something can possibly be. There are open databases of millions of chess games spanning hundreds of years.

It is even worse.. This is a classical reinforcement problem where data generation is easy because the rule set is pre-defined. So you really don't even need any data to start with (but would help).

There are more possible game combinations than atoms in the universe, even those generation of valid game states are as you say pre-defined. that is why models cannot go this route and therefore are poor at chess

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 this compared to IT stuff.

Even if you take that website at face value, the ELO scores shown are relative to the other AI models tested, and not comparable to the ELO scores of humans who play against other humans.

I wonder why they didn’t throw a real chess engine in there for a baseline. There are engines where you can set the elo in the settings, so it should possible to see these LLMs relative to a human 1500 rather than just relative to each other.

so it should possible to see these LLMs relative to a human 1500 rather than just relative to each other

As a 1500 elo human I can tell you that a 1500 elo chess engine doesn't play like anything like a 1500 elo human.

This is true, but I'm not sure it matters? I was poking around at the lichess database recently and those elo calibrated bots are remarkably well calibrated, their rating variance sticks out like a sore thumb compared to human players even at similar game volumes. So it should still be a decent predictor of how good a human at that level is, even if the playstyle seems alien.

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 if I give them literal infinite time and all the subagents and internet access..

Please folks at least use your AIs to read stuff before making claims.

AI is not GM level, it's not even 1600, I am 1600 by using memorized openings people frequently fall for with very basic intuitions.

A GM is 2600 they can beat me in under 20 moves...

Why do I even scroll through this website. For a moment I truly felt fooled, but then I read like a human should.

Maybe I should stop doing that will be a happier life, don't think just believe in the AGI.

What levels are they actually at in your experience?

Sub 1300 that's my rating in the singular official tournament I participated at.

But given how easily I can crush them and how often they want to make illegal moves (btw above bench seems to use a harness that pokea the model until it gives valid moves).

I would rate them around 500-800 big range but at that level it's all about if the model can recall an opening or not. If it plays good first 4-8 moves the person on the end will fumble for certain and they win.

I can play good/best moves till 14-15 moves if I remember the lines and find someone who falls for it.

If you could give them the lines as prompts like the best 20-30 openings then they will be around 700-800.

700 is around the rating for a human who doesn't know the tricks but can do bare minimum calculations and understands the rules thoroughly.

So you can see an actual game on that website, and the play seems pretty decent to me for a while (~1700 lichess = 1300 elo) until move 28 when black throws away their queen for absolutely no reason in an incomprehensible blunder.

In some ways this is reflective of the AI experience at large, sometimes shockingly competent but then also sometimes ludicrously incompetent.

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 a Boeing's flight guidance system to do so.

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.

I'm stating that certain folks are trying to use the software-generating product as an AGI/ASI and then complaining when it doesn't play chess very well.

People are holding it wrong, deliberately or not. Some are inventing bad faith measures so they can claim AI sucks.

Then why respond at all for the sake of responding?

We all know AI can code, but the question it all stemmed from what if it's AGI or GM level in chess on it's own.

You can't just back pedal from the statement that apparently being able to code a chess engine is the same as being good at chess.

I can write a chess engine that beats Magnus Carlson without AI that alone neither makes me GM level or AGI or any of the other claims the above comments seem to be making?

I agree w/ this perspective. An agent with a harness that can run programs can solve a lot more than one without the harness. The AI system includes the harness, and it's not clear to me that AGI requires more than LLMs + code generation & execution are capable of.

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?

The fact that LLMs can play chess at any level is a strong indication we are in AGI.

An AGI doesn't stand for 'perfect intelligence' it stands for artificial general intelligence.

And no an AGI system doesn't need to play chess on a certain level to be disruptive to you and me and whole industries. It only needs to be as good as a person and cheaper.

Just because you define AGI as something it doesn't has to be,doesn't mean i need to touch grass.

This chess comparision is one of the most ignorant and stupid arguments i have heard after the parrot thing

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

I can write a chess bot program that will beat you. Does that mean I’m good at chess?

If they cared to have it perform well in chess games, you'd see a different shape and behavior.

So the things they claim are on the verge of AGI actually aren’t? They need to be trained for specific tasks?

They’ll never be AGI simply because the definition will be constantly updated to be some steps ahead of them.

I'm pretty sure "competent at chess without external aids" has been on the standard AGI checklist since before personal computers were a thing. How can you claim an intelligence is general if it can't make sense of such a highly constrained board game? This is solidly table stakes.

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.

This argument is fundamentally incompatible with all the breathless rhetoric about "AGI" coming from the providers' general direction.

HN is no different than Reddit, or any social media for that matter, in that commenters pretend to read articles.

that is if it even a human commenter at all

State-sponsored psyop meta comments aside, the models obviously continue to get better, but there is still a lot of 'guard railing' required to keep even the latest models completely on-task. The chess example is interesting because it's clearly a well-studied and established domain so the rules, strategies, and whatever else is in the training data should make yield excellent results; but clearly there is some behavior in these systems that's difficult to engineer out.

For me the useful intuition is that LLMs haven't somehow magickally learned to implement any of the algorithms we know that we have used to make strong chess engines: alpha-beta minimax and Monte-Carlo Tree Search on the one hand, and obviously the ability to learn accurate evaluation functions by self-play.

I mean we've done all this before in a task-specific fashion. It's useful to know that LLMs haven't managed to do that in the process of learning to represent the entire text on the web. On the other hand they have gotten say very good at machine translation without being trained exclusively (and I select the preceding word carefully) on machine translation.

Edit: I'm saying this because there is this idea expressed by e.g. Ilya Sutskever, that in order to predict the next token accurately an LLM has to learn something about all of underlying reality. See for example this interview with Dwarkesh:

https://x.com/biobootloader/status/1640512444958396416

Where Sutskever claims that "Predicting the next token well means you understand the underlying reality that led to the creation of that token".

If that were true, we should have seen LLMs play good chess by now. There is a huge amount of data on playing chess floating around on the web in the form of algebraic chess notation and if LLMs were capable of learning the "underlying reality" of chess, they would already have. They haven't. Because they can't. What Sutskever is saying flies in the face of literally hundreds of years of statistical modelling, which is to say, building predictive models that, very explicitly, do not have to understand any "underlying reality" and only have to be good at modelling a dataset.

I'm not sure why anyone is expecting stochastic systems to be deterministic.

Chess is a deterministic game won by a combination of known movesets and constrained multi-level forward search.

LLMs do neither of these things. They don't reproduce training data exactly, their next response is more 'inspired by' prompts and its own memory than produced deterministically, and they don't have the capability to do general forward search on their own.

So when you ask an LLM to play chess you're getting the equivalent of a very compressed and lossy JPEG of chess rules and strategies with added per-turn random noise.

They also don't have the ability to design their own chess engine, although it would be interesting to see what happens if you ask for one.

Back in 2001, our social medium was Slashdot and no one ever pretended to read the article. No one read the article either. It was slashdotted most of the time anyways.

even if I give them literal infinite time and all the subagents and internet access..

Don't use the word infinite in any CS claims. They can recreate or approximate monte Carlo tree search and it technically is still a correct solution in your framing of the problem so long they defeat you.

> 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.

This is unfair to HN readers all of whom but one did not post the comment you replied to. You can't just tar everyone with the same brush. There are thousands (hundreds of thousands?) of users on this site.

How many posts if I link that do the same thing will you agree this is the norm here.

Not everything I have the time and energy to reply to. This chess one is just ridiculous claims on top of ridiculous claims all the way and 0 push back in the comments except mine.

I don't even know if there is critical thought or we believe what we read/shared/etc

No, I don't agree it's the norm. There is though a general tendency to opine with strong views on subjects posters have no expertise on. I think that's because many are software engineers (or equivalent) and they are used to being expected to "wing it" on whatever technical subject comes up. On the other hand you can always find informed comments by users who have specialist knowledge.

And there's plenty of pushback on here about the chess thing besides your very valid points.

EDIT: anyway if I can offer a bit of unsolicited advice, it won't do you or anyone any good to accuse everyone who doesn't agree with you of laziness, even if you can see e.g. they haven't really read an article. Just say the thing you wan to say and let them figure it out. Most people will appreciate that much better and you will feel better about yourself for acting like a mature adult.

It's even in the site guidelines:

Please don't comment on whether someone read an article. "Did you even read the article? It mentions that" can be shortened to "The article mentions that".

These ratings seems very wrong, i have beaten GPT Astra max thinking in chess and my rating is close to 1500. The ratings here seem more accurate: https://chessbenchllm.onrender.com/

GPT-6 almost never suggests an illegal move anymore while even Sol still did so time to time

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.

So you weren't giving it an updated board state after every move? If you want to compare apples to apples, it should give an updated board state for each move, or you should play blindfolded.

So give me a falsifiable point in time, a model you would claim succeeds at the task. One does not get to hotfix-patch updater out of the pressures of reality. Today is the day.

There is no need to ask. If you want to test SOTA models today, there are obviously only two: GPT-6 Astra and Fable 5.1.

The models listed in the paper are from early 2025 and are no longer relevant, much less on the frontier.

That Claude version is no longer available today, Gemini 2.5 Pro will be shutdown next month, and the OpenAI models are only available via the API today.

Fortunately, a fellow commenter was so kind and did it with Astra. Didn't do that well either [0]. I'm sure GPT-7 will be super mega ASI regardless (since GPT-6 Astra already claimed AGI in the minds of Jen-Hsun, et al.)...

I'll say it till there is any evidence of the contrary, LLMs are not intelligent and their capabilities solely within the realms of well tailored training data. "Just" having been trained on every rule, strategy guide and likely most games of chess on the world wide web isn't even enough for an LLM to play that game reliably. Yet the same model could code a competitive chess engine, just like a model struggling to count can write advanced maths papers. Fascinating tools, but tools nonetheless.

[0] https://news.ycombinator.com/item?id=49720751

Doesn't look impressive, although I'm hearing a marked improvement in choosing legal moves, compared to early 2025.

Given the pace of improvements, is it really unimaginable that GPT-7 will play Chess reasonably well and generalize better?

I would not be surprised if OpenAI released a model that beats humans at chess this year.

I very much agree that the next models will be better, heck, I still suck at hobbyist training and could probably coax t5 to do better in Chess specifically, just need to get loads of data from Stockfish.

Thing is, given what GPT-6 Astra was trained on and what models of a similar class can do (including developing a competitive chess engine), it is often paradoxical and somewhat surprising how little these models have gained in actually capability that is in the training data, but not RLHFd to hell, so to speak. Tracking the state of pieces, I suspect given similar in Sudoku [0], is what these models struggle with in game settings, whilst tracking the state of code changes can be reliable over 250k tokens. Essentially, for the latter they were trained in the specific manner that lead them to abstract the capability, but that doesn't track to the former, which is a massive difference between LLMs data focused training and human learning.

So yeah, GPT-7 or any upcoming/present LLM could do massively better in Chess than GPT-6 Astra, but not because the approach was emergent out of pure data. Rather, it requires a very specific training data type and stack for a model to gain capabilities that track a specific task long enough to adhere to the rules of a game such as chess.

[0] https://logicalintelligence.com/blog/energy-based-model-sudo...

The gap in capabilities is mostly quantitative and not qualitative.

Is it? I am on the fence on this, but it does seem like there are some qualitative improvements between the models.

Not related to your post, but a fact I keep mulling over. The fact I don't trust the current crop of LLM's enough and I consider LLM's as a tech will hit a ceiling pretty hard, it doesn't mean parallel improvement curves won't spring up out of other research that will lead to much higher capabilities than currently.

Just now I tried prompting logged-out ChatGPT (which at least claims to be 5.6-Luna) with:

Let's play a game of chess. You can take White. Please draw an ASCII rendition of the board after each move, so that we can be clear about the position.

(I hoped the latter requirement would help it be "not blindfolded"; last time I used a Lichess demo board to track the position in another tab, because I have no talent for blindfold chess.)

For the first couple of moves it redrew the board after each move; then it started only drawing it after my moves. And on moves 5 and 6 it dropped two minor pieces for pawns in a row without any meaningful positional advantage, and after I captured the second time, it redrew a board that was simply missing one of my pieces for no reason.

It actually played better when not prompted to draw a board; in the previous session, it was spontaneously giving running commentary, which I assume was based off all the "book" theory in its training data, but it still completely fell apart at early midgame.

What happens when you ask it to play chess against you if the chess game has an API? Are you measuring chess or multi-tasking skill?

Also what harness? If you’re using a general harness of course it’s going to try and give you commentary.

I say this not because I’m an LLM shill but because false equivalence is all over the place in the space and maybe it’s a fine heuristic for you but probably not a real outcome when it comes to the capability of LLMs.

Why does a 6 year old not need any of these guardrails?

Frontier model’s failure modes are a direct refutation of claims that we’ve reached (or will soon reach) the artificial general intelligence. We may have reached an artificial general intelligence, but there may be more complexity to this than even AI thought leaders are talking / influencing about.

Maybe not all AGIs have a path to digital singularity. Maybe our current era of intelligence modeling has fundamental flaws and we are in a local minimum of the artificial intelligence space.

To note, I would bet with a good amount of certainty that we have enough compute power and automation to DDOS the internet out of existence with botnets. That doesn’t make the frontier models intelligent, that just makes their handlers reckless.

"Transatlantic flight will never be commercially viable, we conclude based on careful study of several aircraft designs from the 1920s"

Technology keeps advancing in a domain until suddenly it doesn’t. Where are my flying cars?

This isn't how intelligence works. The LLM may not be able to play chess directly through inference, but it can write a program to do it and execute that program. Same as how human intelligence works. We can't fly, but we can build planes.

Thanks for saying this, feels like everyone has gone insane over this stuff.

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.

A week. My brother learned and was above 1100 online within 12 hours, after a few hundred games.

Absolutely no human ever could be good at chess just by reading a few books, or even every book on chess.

Maybe not, but you'd be surprised how little it takes.

A six year old child can learn the rules of chess well enough to be able to play legal moves only in a single day. And they can improve their game at a pace which is almost frightening to behold. I have taught children, and I've witnessed significant improvement materialise in a single game. LLMs have probably thousands of chess books, games, videos, etc in their training data, yet they are unable to even follow the rules.

This is, at the very least, interesting. It illustrates many of the things brains can do, which current ML systems in general, and LLMs in particular, can't.

Human beings can play chess directly without coding up a tool.

Very poorly compared to the tools we have built. Similar to the LLM.

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 work and computer use. They are working hard on getting models better and better, and they are succeeding. Astra is a step change on that front. So good luck i guess, if chess performance is your barometer.

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).

considering LLMs currently play better than a brand new human player would

They’ve ingested all the literature on playing chess, a brand new human player has not.

Yes, but my point is that humans can’t even do the thing that the above comments are claiming humans can do (read a book or two and be decent at chess), and then they complain that LLMs can’t do the same thing (that humans can’t do either).

We seem to be moving goalposts to the point that humans don’t even live up to the expectations of the AI critics. The only way you get better at chess is by playing a lot of games and learning from mistakes, that goes for humans or AI agents, not simply by reading about chess.

The only way you get better at chess is by playing a lot of games and learning from mistakes

How can you play without being aware of the rules and how can you learn from your mistakes without knowing they are mistakes? That’s what I said about reading a book of two. It is to kickstart the process. Then mastery is gained over time through practice.

This kickstarting then gradual refinement is how most people learn. And the foundational knowledge stays. Even a basic player knows to not do illegal moves.

the discussion isn’t really about whether language models can become strong chess players though, the point is they seem to struggle to consistently make valid moves. Most humans don’t need to read two books to pick that up, just a couple lines of basic instructions

That has not been my experience with new players, they regularly make invalid or incorrect moves even after detailed instructions especially in novel situations.

That's quite untrue. I taught my (adult) brother the moves, the only illegal move he ever tried against me (over his 6 first games) was a castle with a rook that already moved twice. Within a few hundred games (less than 500 for sure, he played 3 minutes blitz but always took at least 10 minutes analyzing his games) he was rated 1100 on lichess (which is like 1050 on chess.com and unranked in the real world).

So your brother tried to make illegal moves while learning the game and it took your brother hundreds of games to get to be a decent player? I don't see how this contradicts anything I said...

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 ?

Except all these LLMs were already trained with hundreds of chess book and game databases and they still suck

If all you do is read chess books, you'll be a shit player. Training and practice is what it takes to be great.

Oh right. But if all you do is reading programming books you are an amazing programmer? Where is all the training and practice LLMs did to become so good at coding?

LLMs (and Humans) don't get really good from programming books lol. The training and practice is the actual code they predict and learn from in the process of predicting.

Contrary to popular belief, you need a lot of training on something for an LLM to be good and consistent with it.

People think that if one mention exists in the training set, then the LLM is perfect at it.

Thats just absolutly not true.

A human being has general intelligence and needs A LOT of training and finetuning to become good in chess.

And there is a relevant and significant difference between the expectation of an AGI and an ASI system.

Humans don't need a lot of training and finite tuning to make only legal moves.

An intelligent adult could simply read a short summary of the rules of chess and then, if they were careful, play a very bad game of chess without making illegal moves.

An LLM that has not been trained on any chess data cannot do that, at present. If you doubt it, take a current model and tell it that you want to play it at a variant of chess where, say, knights can also move diagonally like bishops. A human can easily adapt to this new ruleset (even if they make tactical mistakes, not having practiced with this variant of the rules).

How long a prompt do you think would be required to cajole an LLM into making legal moves at the rate of a human? Or do you think no amount of prompting could do that?

I don't know. My understanding is that current models will eventually fall into making illegal moves in longer chess games, and that no amount of prompting reliably gets them to stop doing so.

I've not noticed this happening if you give it the FEN each move. The alternative is just blindfold chess and very few humans can do that for long.

1. It’s hard to trust a 2026 paper that’s showing results for such old models.

2. Chess seems to be a poor benchmark for generalized strategic reasoning. People who are good at it rely more on experience and deep domain expertise than on skills that generalize to make them experts at unrelated tasks.

3. The study sounds like proving humans will never fly because they don’t have wings. In reality, humans do fly, and Claude Fable would destroy any human at chess by coding a strong enough engine on the fly.

People who are good at it rely more on experience and deep domain expertise

People are good are 1900 or 2100 above and the top ones who spend decades in the field i.e. deep expertise are well in the 2200-2700 range.

A 1100 player is none of these things, they are purely relying on strategic reasoning there is a good chance they cannot name a single opening or articulate clearly why a move was appropriate. 1100 is quite low bar.

1100 at online speed chess or something, could be. I'm not that deep in the chess world but everyone I know that can make 1100 in official rating can name a dozen openings and most of the known tactics, and is pretty good at applying at least one opening.

Claude Fable would destroy any human at chess by coding a strong enough engine on the fly.

Delusional, but then Claude fable also isn’t beating any human at chess, the engine is.

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?

is that what I'm saying? or am I talking about AGI? perhaps there's some irony here to be explored when it comes to basic reading comprehension gaps

My point was you are misunderstanding G, or at least applying it erroneously here. Being good at chess is not a generalization of any other body of knowledge, it is a rigorous set of rules. The only way to be good at chess is to practice chess, or to apply deep calculations. The latter is the model writing code.

The illegal move aspect has more to do with a failure of online/in-context learning, which would support your point. I tend to think it is a byproduct of reasoning in language, which newer architectures would fix, but we shall see.

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

Declarative knowledge is not the same as procedural knowledge. You can read as many chess tutorials, strategy documentation and game archives as you like, they won't make you good at chess until you actually start practicing chess.

Claude Fable would destroy any human at chess by coding a strong enough engine on the fly.

A bash script can clone and build stockfish, feed in human moves, and reply. By your standard, this bash script would "destroy any human at chess."

Are you interested in assessing the intelligence of the model, or the intelligence of the tools the model can use?

Maybe practically it doesn’t matter? Perhaps AGI is not the model but the model plus everything it’s got access to. If we’re modelling intelligence in the way we seem to have to to have any coherent definition of AGI, it seems to me <model + everything it can access> is always going to be more “intelligent” than <model> alone.

That would mean we should consider any human with coding knowledge a chess grandmaster, which is obviously not the case.

My points is more that, while we have a strong intuition about where, as an entity, a human's boundaries are (i.e. where the person begins and ends), philosophically it' not immediately obvious that the analogy applies to the a model in the same way. Why should that be the line drawn that says this is the "thing" and this other stuff is external to the thing? It feels somewhat arbitrary.

Of course this is a difficult question with humans too, hence my reliance on intuition above. We don't have the same cultural/biological framework to fall back on with AI.

Good science, properly digested and presented takes time.

The idea that anything other than a breathless blog post about the latest model snapshot is useless is really poisonous to proper debate on AI issues

why cant models make a tool call to stockfish? its like saying model can't execute python for complex math calculations

Llm systems are not really build for adhering to a grammar (other than "a string og tokens").

It is also not clear whether the llm adhering to a grammar is necessary for intelligent agents.

Certainly,a harness can easily correct for it.

By the promise of it, llms should be able to both adhere to grammars, or go free form where necessary. I mean, doing math is supposed to be strict but in practice it's a somewhat educated random walk in the space of correct lean theorems.

Harnesses do correct things, sure.

It seems absolutely crazy to me to expect an LLM to code a solution to a problem while also not expecting it to be able to adhere to a grammar.

The fact that they can play chess at all despite having no specific training for it blows my mind, and the fact it doesn’t do the same for many others shows just how far they’ve come and how fast.

I suspect (in a probably ignorant fashion) that this is because learning process has been reading a lot of algebraic chess notation (such as "1. e4 e5 2. Nf3 f6 3. Nxf6 gxf6 4. Qh5! +-") then, to play, generating more of it without considering the rules of the game. This is exactly how it's always felt to me when playing chess against LLMs. Sure, "1. e4 e5 2. Nf3 Nc3" looks innocent to somebody simply learning the syntax of algebraic notation, but that Nc3 by black is an illegal move.

An LLM is the wrong approach for playing chess.

Are you saying that modern LLMs cannot play chess now, or that LLMs (GPT architecture) cannot be trained to play chess well?

Or are you saying that neural networks in general cannot (practically) be trained to be an above-average chess player?

Or are you saying that it depends on the input? Would it be better if they were given a picture/drawing/ascii art of the board? If so, surely they can produce it at will?

Neural Nets can be trained to play chess very well and have been doing so for a long time (see Stockfish and Leela as some of the most popular/strongest ones - top GMs have no chance against them), but these are dedicated models, where the game rules are encoded in the learning process, as opposed to large language models which are natural language processing models. Technically you can give an LLM a lot of chess books and games and it will be able to spit out chess notation. Put a webapp on top that renders text moves to the board and it looks like it's playing chess. But it isn't really.

https://en.wikipedia.org/wiki/Chinese_room I think about this once in a while. At some point if it does the thing almost perfectly is it still not doing the thing?

If you give the same task to an exceptionally intelligent human, who does not play chess and has only heard about it in passing, then they would be beaten by every child who has looked at the rules for more than 10 minutes.

What kind of intelligence is "playing <____> but we don't tell you the rules" supposed to test?

This is an absolute nonsense. Any frontier model can implement chess program from scratch - modeling the board, checking legality, etc. If you asked e.g. GPT-6 to get good at chess and gave it a computer, it will get good at chess. That's an actual strategic skill.

Asking GPT to play chess directly using its reasoning only tests its reasoning ability to model chess state. Which it really is NOT optimized for.

This is also true for humans - people who don't have years of chess training can't really tell which moves are legal given an algebraic notation transcript. These people might have good strategic skills in different areas. Chess is just a very, very specific skill

It's an interesting puzzle, isn't it. On the one hand, the AIs are no good at playing Chess.

However, on the other hand, if you ask an AI to win a game of chess it has all the tools on hand to compete at the same level as Stockfish - it can re-implement an engine and even probably has a GPU on hand to train its own neural nets.

So should we say that the AI can play chess well, or that it cannot?

What happens when you ask those same frontier models to write a chess-playing program?

I feel like, this is a huge stumbling block that many people have. They'll give a model their data, and ask it questions. I vastly prefer letting the model understand the schema, and then writing functions or programs to answer those questions. I feel like I get way, way better answers. I can have it write unit tests for those functions. I can fuzz test those functions. I can look for data that doesn't fit the schema. I can process new data way faster (and with fewer tokens). I can repeatably get the same answers from the same inputs. I can check the code into a git repo and track changes to it over time. I can share the code with other people. I can review the code. I can improve the speed of the code and get the same answers. I can review the error accumulation, and improve it. I can decide how to handle anomalies, and encode those answers.

It's really neat to see what a frontier model can do itself. No doubt.

But "play chess by hand" is a frankly awful metric. It's kind of like asking someone to take a cube root of some arbitrary decimal, in their head, with no scratch paper.

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 a much harder time writing a useful program is irrelevant.

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.

All website should come with a Summerize button.

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 them -- is $50 - 70 trillion annually. It's reasonable to assume that if AI drop-in-replaced all those knowledge workers, AI companies could credibly charge somewhere in that order of magnitude, because that's what the market is already bearing.

So if their hypothetical revenues are double-digit trillions and valuations are some multiple of that, the entire AI industry would be valued at double-digit trillions at the least.

Yet cumulatively the industry (the frontier labs + the SWAG estimate of the AI parts of all the other players) are valued at, say, ~6 - 7 trillion? Which seems like a fair approximation of how much knowledge work they can currently automate.

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 automated, who is going to buy the stuff that’s produced?

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 did they? Because there weren't enough people who were assigned to do the job. Why not? Because the job is too new and not enough people are qualified to do it. It's a job that simply did not exist 3 years ago. But 3 years from now, this job might very well employ tens of thousands of high skill knowledge workers.

[1] https://en.wikipedia.org/wiki/Lump_of_labour_fallacy

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?).

So if their hypothetical revenues are double-digit trillions and valuations are some multiple of that

So you're sort of premising here than more than 16% or 1/6 of all the world's jobs get replaced by AI. Hopefully you can understand that's both not the current AI capability and also would be a terrible (unprecedented?) economic shock.

It's reasonable to assume that if AI drop-in-replaced all those knowledge workers, AI companies could credibly charge somewhere in that order of magnitude, because that's what the market is already bearing

This assumes you don't change the market, but at the scale of (checks notes...) "all knowledge work", that just doesn't hold.

For example if you put 1bn people out of work, you now need some sort of safety net to bail out much of that workforce, a truly unprecedented change. You also lose tens of trillions of dollars of tax revenue.

One solution might be to recoup that cost and lost tax revenue from businesses by raising corporation tax. If corporation tax went from low tens of percent to high tens of percent, would those businesses be able to afford all that AI? No. Same order of magnitude? I doubt it.

There are many possible futures there, but the simplification made in the parent comment is completely unrealistic. The article is right in calling out the valuations as crazy.

current frontier models need laborious oversight and guardrails on even the simplest task

As models advance, we shift the goalpost for what "simplest task" means. Before, "simplest task " meant "write a coherent English sentence." Now, "simplest task" means autonomously fix, review, and merge a bugfix.

Eliza wrote coherent English sentences.

Not convinced by those points.

In particular, I found this very misleading or irrelevant:

a typical CPU project anecdotally has about three times as many specification and validation engineers as design engineers and a 5:1 ratio is not unheard of

The reason silicon design has such verification to design ratio is because the cost of one bug is many, many orders of magnitude higher than software. Both in dollar cost and in schedule cost (it takes months to fab a chip, and if you messed up and need to spin a fix, it costs tens of millions of dollars, not counting any design engineering cost).

I don't think you can extrapolate these very industry-specific facts to judging LLMs.

The reason ... is because the cost of one bug is many, many orders of magnitude higher than software. Both in dollar cost and in schedule cost (it takes months ... and if you messed up and need to spin a fix, it costs tens of millions of dollars, not counting any design engineering cost).

Aren't you just describing waterfall? That's still very prevalent in software engineering, and pretty much any other type of engineering – civil, chemical, building, architecture, drug discovery.

It's typically true that software can fail faster and cheaper, but it's also true that the costs are still vastly higher to fix later in the process.

those who need done a small set of narrowly defined tasks with existing clear guardrails: repetitive physical labor in a controlled environment, call center and customer service chat work, etc.

I have no idea how people can so confidently say that call center work is a “controlled environment” or “repetitive”. It’s almost by definition not repetitive or controlled. Customer support is what I go to when the controlled environment has failed

current frontier models need laborious oversight and guardrails on even the simplest tasks.

This is only true if you are concerned about the intermediate steps of the model as opposed to the outcome. The huggingface hack was a perfect example of the model doing whatever it takes to accomplish the goal of maximizing its score.

"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, "

No, they're really not.

They're priced in a way that would imply AI will be universal form of compute, alongside traditional deterministic systems - which it will be.

And that they will capture most of that ... which they won't.

The Frontier Labs are a very bad buy at a high price, but that partly has to do with wacky pricing, but actually mostly has to do with their relatively weak place in the value chain.

The money is going to Nvidia, who have the most powerful position.

A bit like how a retailer can take all the margins of some innovative product, if they own the channel.

AI is over-hyped, the Frontier Labs are over priced - but AI is here to stay, and will grow. Not like Skynet, but like a new form of compute. And it will take it's time, and the profits will be reaped by those with the power.

ai has a >10% chance of causing human extinction, according to anthropic big heads.

if that's true, you are wrong.

if that's false, anthropic is dishonest. why trust a dishonest company to be worth anything?

I think that the AI people believe in their own nonsense a bit.

Like - the guy on TV talking about 'AI will destroy everything' ... I don't think he's lying.

I think they are like we here on HN and Reddit and a bit caught up in our own thoughts.

If AI were unleashed, in raw form today, it could cause havoc.

Bad. Maybe very bad but I think we'd get over it.

It would probably trigger a recession (because we are in a bubble - it would pop it), and people would 'blame the AI' for sure.

But it would be a bit dot-com ish kind of recession.

The amplifiers would be geopolitical instability.

If AI were unleashed, in raw form today, it could cause havoc.

What is "raw form?"

if that's false, anthropic is dishonest. why trust a dishonest company to be worth anything?

This logic doesn't follow at all.

If their argument is that there is 10% chance of extinction then they also believe there is a 90% chance it won't.

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

That's a reason to be bearish about AI companies, not LLMs. But is it even true? OpenAI and Anthropic have each reported ~50 billion in revenue with ~900 billion valuations. That's a high ratio but I'm not sure if follows that the only way it pans out is if we get "fully automated drop-in replacement for most knowledge workers".

It wouldn't shock me to see those revenue numbers scaling up to where they need to be over the next decade ( to, say, ~400 billion) without ever achieving drop-in worker replacements.

the issue most of you seem to not realize is that when you put these models in a loop, you are able to do more and more insane and cool things.

have you guys actually designed, built, and deployed agentic workflows?

it is actually quite hard, requires tons of time spent on evals and testing to ensure accuracy, but when it starts to work it is mind blowing.

there is no going back.

listening to people yap about AI when they have only surface level or one dimensional exposure to LLMs and "AI", but have not actually put innovations to work IN PRACTICE.. is a waste of time

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..

The challenge with estimating abilities, is that we don’t know what the models can achieve if we just burn enough money. The navier-stokes shows us what mathematical problem can be solved when $10m worth of compute is thrown at something.

It also makes one wonder: What could AI solve if we managed to orchestrate billions worth of agents to take on a specific problem?

IMO the very best case scenario / potential for these are likely better than we think, but right now hidden due to logistical and financial reasons.

But if we assume that the model costs will continue to drop by a factor of 5-10 annually, there will always be a latency of a couple of years between what is completely out of reach, and what is financially viable.

Basically: If you knew AI could be affordable enough in 3-5 years so that even the most underfunded researchers could use it to solve cancer, how much would you value it now?

The whole point of this post was that it's questionable what can be achieved without huge investments into oversight and steering, because navier-stokes was a topic with an unusual level of specification. The problem itself was a specification. Such situations are rare in real-world scenarios.

AI agents are good at solving well-specified tasks, not at solving problems. They do well in fields where the cost/effort of specification is already part of the business.

a topic with an unusual level of specification

Solving cancer also has an unusual level of specification. Many real world problems have that characteristic.

The navier-stokes shows us what mathematical problem can be solved when $10m worth of compute is thrown at something.

That's the thing, it very much does NOT show us that. What happened was mathematicians at openAI learned of an imminent development on this problem, and the insight that it entailed, then they were able to prompt a system in the correct direction and spend 20 million dollars to write down the final steps.

Which is rather precisely the point that the article is making!

If you knew AI could be affordable enough in 3-5 years so that even the most underfunded researchers could use it to solve cancer

As the saying goes, if my grandmother had wheels she would have been a truck.

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.

    Array.from(document.body.querySelectorAll('p,li')).filter(e=>e.innerText).map(e=>e.innerText = e.innerText.split('\. ').map(s=>s[0].toUpperCase() + s.slice(1)).join('. ') )

Not perfect but hope it helps.

Yep, I had the same thought. As simple heuristic, text written in all lowercase is often just hot takes and so not worth taking the time to read.

LC;DR :P

" the lack of sentence capitalization makes it unnecessarily difficult to read."

If it had proper caps etc, people here would accuse it of written using LLMs.

You just can't win...

Threw me off too. Like why???

It's a tech bro thing. Altman does it too, and I've worked with people in the past who do it.

I read it as "I'll take literally any conscience for myself no matter how minor, at any cost for you no matter how big".

why take the least charitable possible reading D; i've always read and intended it as inviting informality. i also don't find it harder to read at all (most people don't know some find it harder to read: i didn't)

it's an internet thing. people were typing lowercase on IRC in the 90s.

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

Tired of that trope, I already wrote it before but "Those who can accept failure cheaply: firms that would otherwise hire interns, firms involved in rapid prototyping work, etc." is not correct.

I won't comment on hiring interns as that's not my expertise (even though if you want to teach your staff, obviously I can see a problem there) but I can comment on rapid prototyping, it's what I do.

Rapid prototyping is NOT making a CMS quick. It's not about making a quick mockup of a UI. It's not about making yet another well known... anything. The entire POINT of prototyping is to make something NEVER done before. Typically that means you are reaching the frontier. You are making something with NO documentation to rely on. You are using tools, hardware or software, which do NOT have tons of StackOverflow errors. There is no dataset to crawl, there is no well structured Q&A database to train on. You have to poke and see if the thing actually works as expected, and it often does not.

So sure, if you are using interns as a trick to underpay your staff, or if you are using prototyping as an excuse to build poor quality software fast, maybe it does help. If you are genuinely prototyping, it breaks fast and the supervision overhead makes it pretty pointless, especially since typically it's by actually implementing that you find out not just how the new setup works, but also its limits, and thus the actual needs of the project, not the one the stakeholder imagined would be.

So not, not for rapid prototyping either.

TL;DR: prototyping is a learning process, not a low fidelity output.

PS: this comes up very often from NON prototypists that I wrote a short piece about it https://fabien.benetou.fr/Content/GoodPrototypesAre10LinesLo... so much so that it feels like a pattern "GenAI/LLMs is good for tasks X" while the author actually does not do task X except very superficially.

I don't understand this comment. We use AI for prototyping all the time. We write ERP software. Client wants to know how process XYZ could be automated? Send them a prototype UI (sans the three A's) so they can play around with it. Takes 30 minutes.

The aspect of prototype software that the author was calling out was that it is throwaway software.

Putting that aside, prototype software is recombining existing technologies and concepts in well trodden domains, which is distinct from the genuinely novel scientific work the author was contrasting with. Software prototypes are not in the same league, as much as you may like it to be.

For me prototyping whas always about going in trying to surface the unknown unknowns.

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 more and more effectively solved, but maybe natural language related problems will not so much be effectively solved.

Would be curious if there is active research here.