>LLMs won't get intelligent. That's a fact based on their MO. They are sequence completion engines. They can be fine tuned to specific tasks, but at their core, they remain stochastic parrots.

This is absolutely wrong. There is nothing about their MO that stops them from being intelligent. Suppose I build a human LLM as follows: A random human expert is picked and he is shown the current context window. He is given 1 week to deliberate and then may choose the next word/token/character. Then you hook this human LLM into an auto-GPT style loop. There is no reason it couldn't operate with high intelligence on text data.

Not also that LLMs are not really about language at all anymore, the architectures can be used on any sequence data.

Right now we are compute limited. If compute was 100x cheaper we could have GPT-6, bring 100x bigger, we could have really large and complex agents using GPT-4 power models, or we could train on tupled text-video data of subtitles videos. Given the world model LLMs manage to learn out of text data, I am 100% certain that a sufficiently large transformer can learn a decent world model from text-video data. Then our agents could also have a good physical understanding.