My impression is that the fundamental issue is that LLMs attempt to extract reasoning (executive execution) from data (relationship between tokens).
There's an open question about whether this is theoretically possible, but it doesn't seem like it to me.
Human generated data is an effect of reasoning. Attempting to extract executive function from it is kind of like taking an anti-derivative of a function.
This has always seemed like the root of hallucinations to me. It sort of follows the parallels to lossy compression that a lot of people draw. You're extracting some characteristics by observing the relationship between tokens, and then trying to argue that those characteristics are equivalent to the thing that generated the original tokens.
Surely there's some sort of overlap there, but viewed that way, it seems obvious that more and more parameters and scaling won't solve the fundamental problem. There's only so much meaning you can extract from token relationships.
It's like trying to derive the shape of a flame from the smoke it produces.
The original intelligence that created those tokens was driven by a whole universe of inputs, from hormones to starlight to gravity, not to mention all of the strange things about consciousness and parapsychology that is so poorly understood.
The machines are definitely useful for a certain class of tasks - those that don't require much executive function, and the useful work mostly involves pattern matching.
The problem is, we seem to be mistaking effect for cause and imagining that these things have greater capabilities than they'll ever posess.
The investors that don't understand this are indeed going to learn a bitter lesson.