You'd be surprised to know that most of the statements in your first paragraph are conjecture and some are in dispute.

This is because autocomplete has no ability to reflectively consider its own internal state. It can't know that it doesn't know something, because it doesn't even know it is there as a thing that can know or not know things.

This is a topic of open research for transformers. The theory goes that in order to best predict the next token, it's possible for the model to create higher order representations that do in fact model "a reality of some sort" in some way. Its own internal state may well be one of these higher order representations.

Secondly, it is known that NNs (and thus autoregressive models) are universal function approximators, so from a computability point of view, there is as yet nothing in principle that rules out even simple AR models from being able to "brute force" find the function that approximates "AGI". It will likely be computationally inefficient compared to more refined methods, but a degree of "AGI" would have been achieved all the same.

I do generally agree with you though. It's just that these remain to be open questions that the fields of cogsci, philosophy, and ML are grappling with.

That leaves the possibility that AGI might in fact be really fancy auto complete. We just didn't know yet.