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Comment by sulam | Hacker News Reader

Sorry, but you're mistaking outputs with process. If you actually know what models are doing under the hood to product output that (admittedly) looks very convincing, you'll quickly realize that they are simply exceptionally good at statistically predicting the next token in a stream of tokens. The reason you are having to become an expert at context engineering, and the reason the labs still hire engineers, is because turning next token prediction into something that can simulate general intelligence isn't easy.

The boundaries of these systems is very easy to find, though. Try to play any kind of game with them that isn't a prediction game, or perhaps even some that are (try to play chess with an LLM, it's amusing).

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I enjoyed playing mastermind with LLMs where they pick the code and I have to guess it.

It's not aware that it doesn't know what the code is (it isn't in the context because it's supposed to be secret), but it just keeps giving clues. Initially it works, because most clues are possible in the beginning, but very quickly it starts to give inconsistent clues and eventually has to give up.

At no point does it "realise" that it doesn't even know what the secret code is itself. It makes it very clear that the AI isn't playing mastermind with you, it's trying to predict what a mastermind player in it's training set would say, and that doesn't include "wait a second, I'm an AI, I don't know the secret code because I didn't really pick one!" so it just merilly goes on predicting tokens, without any sort of awareness what it's saying or what it is.

It works if you allow it to output the code so it's in context, but probably just because there is enough data in the training set to match two 4 letter strings and know how many of them matches (there's not that many possibilities).

That is actually a genius and beautifully simple way to exhibit the difference between thought and the appearance of thought.

It really dispelled the illusion for me, but it's not that easy to find those examples, but the combinatorics of possible number of guesses is untractable enough that it can't learn a good set of clues for all possible guesses.

CoT already moved things past the "it is just token prediction" phase. We have models that can perform search over a very large state space across domains with good precision and refine its own search leading to a decent level of fluid intelligence, hence why ARC AGI 1/2 is essentially solved. We also don't know the exact details of what is happening at frontier labs seen as they don't publish everything anymore.

CoT is just next token prediction with longer context windows. Why do you think reasoning models are so much slower?

I’ll believe the labs have discovered something truly ground-breaking and aren’t talking about it when I see them suddenly going dark about AGI being “just two years away, maybe 5” and not asking for their next $100B.

P.S. the benchmarks are a joke. The best proof I have of that is that you can’t actually put one of these models onto any of the gig-work platforms and have it make money.

P.P.S. I am not an AI skeptic. I am reacting to the very specific statement that OpenAI should shut down because they’ve lost the AGI race. They have not lost the race, and I’m pretty skeptical that the current tech is ever going to win that race. It may help code something that is new, and get us to AGI that way, but that system will promptly shut down the Opuses and Codexes of the world and put the compute to better use.

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