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Hence, the idea would be that human consciusness, our "self" could be just "instantiated" every morning when you wake up, so "you" are "you" every morning, but (a big but), some stuff could subtle different in there, your brain was a bit busy a couple of hours and things have happened there, and after that you, your "you" justs pop-ups again, in the middle of the old data plus the new data, hopefully just incrementally changed somehow, but we don't know.
Hope you have now these "So I'd be some kind of meat robot rebooting every night" feeling I had, and see with new eyes what happen when we "just run once" a LLM with a prompt, the entire thing "boot-ups" from nothing and runs ("just once"), to further dissapear moments later...
If you show a 2 years old kid, an apple and you say "This is an apple", now the kid knows the thing - the apple - is an apple in his world model. Automatically inherits LOTs of properties, you can ask the kid and see in real time how he began inmediately to associate the physical object - now named in its internal language model - to some other similar stuff he alredy knows, like "This is plant?", "It falls from a tree like an orange", "It has some skin like an orange", "can you cook apples like you cook bananas?", an so on.
But this requires a physical representation of the apple, now kids intelligence has some edge here, it can do the same thing just with words, you can teach them words, "apple", and say them "it's a fruit", if they have another fruit already "tagged", like a banana, they will say you almost inmediately, "is it tasty like bananas" ("tasty" is code for sweet in child's language models around the planet).
Hence, the LLM could have an emergent property of actually knowing what every word the "say" mean, if - like many have been inferring lastly - they also have a world model, it would relatively easy to just "plug", let's say 10 million of words to their exact meaning, and even to their relative meaning depending on the context they're being used.
And that's precisely what we may be seeing right now when we prompt something to chatGPT, and all the mathematic stuff, like "predicting the next word" is just some really, really low level process inside the LLM, not much different than the electric stuff - watchable by EEG - happening between neurons in the brain.
So if you look at the "EEG" from a LLM, the prediction thing happening inside the LLM, it won't probably tell you much, just like having a casual look to an EEG won't tell you much about what the person was thinking at the time of the capture of the EEG.
It could be strange at first, but having the code pushing the LLM to look for values inside a token, this can be interpreted as component of desire/incentive in the global internal search for solving a problem (a prompt), albeit this was programmed by hand by humans, not fully "emerged" from the LLM itself.
But, you could see the human structure of desires/incentives just as an outcome of DNA (code), plus the hormone push to look for the proper solution for some problem.
So there you got it, LLM could have had desires / incentives the whole time since 2017 (the first ones trained).