I model LLMs as searchers. Give the input search and they match an output. The massiveness of the parameters and training data let them map data in a way searching looks like human thinking. They can also permutate a little and still stay in a space that can overlap with reality.

The human brain may be doing a very similar thing though, search and permutation via searched rules. It may be doing it just in a functional way, with more ability to search on massive data that may be with holes but filled with synthetic data via mind subprocesses on learned rules.

I think machines can eventually get there, especially if we can figure out how to harness continuous models instead of discrete ones. And I have a feeling that functional analysis may be the key.