In my highly-summarized opinion? When you have a challenging problem with tight constraints, like flight, independent solutions tend to converge toward the same analogous structures that effectively solve that problem, like wings (insects, bats, birds). LLMs are getting so good at mimicing human behavior that it's hard to believe their mathematical structure isn't a close analogue to similar structures in our own brain.* That clearly isn't all you need to make an AGI, but we know little enough about the human brain that I, at least, cannot be sure that there isn't one clever trick that advances an LLM into a general-reasoning agent with its own goals and purpose.
I also wouldn't underestimate the power of token prediction. Predicting the future output of a black-box signal generator is a very general problem, whose most accurate solution is attained by running a copy of that black box internally. When that signal generator is human speech, there are some implications to that. (Although I certainly don't believe that LLMs emulate humans, it's now clear by experimental proof that our own thought process is much more compactly modellable than philosophers of previous decades believed).
* That's a guess, and unrelated to the deliberately-designed analogy between neural nets and neurons. In LLMs we have built an airplane with wings whose physics we understand in detail; we also ourselves can fly somehow, but we cannot yet see any angel-wings on our back. The more similarities we observe in our flight characteristics, the more this signals that we might be flying the same way ourselves.