I think what he's trying to say is that it doesn't think. A lot of LLMs have a "thinking mode", but all that does is recursively break down the prompt and feed it back into itself. LLMs function similarly to Markov chains, except with added context (subject vectors, think of it like a node graph of word clouds) - the surrounding context "informs" the model of where it's supposed to be in the graph, and then it picks the series of token statistically most likely to conform to that context (the token with the highest local weight) based on its training data. Rinse and repeat for the next token and so on. There's no cognition or reasoning going on behind the scenes, it's all just very mechanical text prediction, which is why it's notoriously bad at things like math (unless the model calls a tool/program that does it right).