That's because an LLM thinks in terms of language, while we think in a different way, then convert the ideas to language. It can be said that language is a tool for the serialization (writing) and deserialization (reading) of human ideas. It is also an incredible useful and powerful tool by itself. This last sentence has been proved true by LLMs themselves. However, since it is working on the serialized version of ideas, I agree with you in that's not the optimal way to think and something not serialized (maybe world models) can be invented that's better for thinking. All this in no way diminishes the usefulness of language and of automated language generation.

'fast' means executing a policy, that is, a state-action mapping. A trained RL model does this.

'slow' means making one or several action-dependent forecasts, evaluating the expected value of the outcomes, and making a decision based on that.

Neither map exactly to the situation with LLMs, but very roughly, the first is analogous to trained classifiers and the second to reasoning models.

The analogy breaks down, since each instance of token being produced is an example of a policy execution (system 1), and reasoning is just stringing lots of these together. But there are those who argued, before LLMs, that system 2 is just "policy composition" anyway...

Very relevant. Modern models use CoT to do “slow thinking” and this enables them to achieve much greater performance. You can also turn off thinking and answer directly which is quite similar to “fast thinking”, good at approximate maths, not capable of algorithms, etc.

Of course the shapes of what an AI can do in fast vs slow are quite different.

No clue what’s the consensus on this but my internal mental model is absolutely that LLM AI is pure fast mode, no slow mode. The “reasoning” loops are an attempt to mimic the slow mode but ultimately it doesn’t really work. I’m curious about the recent maths advances though, they seem to possibly challenge this.

You can ask a model for output directly and stop, or you can recursively ask it to keep refining the output.

That seems to fit the fast vs slow model of human thought reasonably well.

Structurally speaking we learn nothing like AI, we don't use vast amounts of information to pick up completely new skills. We also make decisions by using prior knowledge and emotions.The latter part is important, Thinking fast and slow cannot operate in a world of AIs as they stand today unless we are willing to grant them rights — because you have to teach them to make decisions based on all kinds of emotions — which is tricky at best.

Not relevant at all. Its a way to hype.

Seems like a terrible idea in the first place to build an entire organization around a single pop-sci book, but that’s just me.

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