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.

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.

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.

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.

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.

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?

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

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.

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

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.

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.

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?

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

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?

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.

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.

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

"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.

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.