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The primary problem with LLMs as they stand today isn't a lack of time to train them. It's is that they have a really primitive structure. They're like only part of what's in your head.
What an LLM can do, what generative AIs are doing, is basically take a very large amount of data, where each instance of data is annotated with something, and find commonalities between between them, which lets them learn to associate the annotation with characteristics of that data. And they can synthesize data based on those annotations.
That's quite useful for a number of things. It lets us do things that we couldn't do before. We can do really potent voice and image synthesis, upscale images, stuff like that.
But it's really simple and mechanical.
But what's going to happen here isn't that we take that approach and then throw more data or more hardware at it and it suddenly becomes a human.
And there's a lot of stuff that that doesn't do.
It doesn't permit for learning to perform tasks, say. It can't develop higher-level concepts of things than associations with the annotations. You can give Stable Diffusion 1000 years of continuous running, and it will never start sending you messages about living inside your computer.
So the existing things that people are calling "generative AI" -- those aren't going to be a human because they just don't have the infrastructure to do it. Some of the things they do might be part of something human-like, but they're missing a lot of parts.