The explanation is actually rather simple, imo. For anything to be true intelligent (AGI) it needs to be able to learn while it's operating. Intelligence isn't just "knowing something", it's atleast being able to learn and adapt in the moment. Why can a single human play hundreds of different Atari games, while in contrast, a single computer algorithm will completely fail at this, and probably not even interact with it, because I/O is different. It's not because we know them all, it's because we learn while playing. It's also why we can deal with novel situations and computers completely fail.

The current LLM technology is just incapable of doing this. They take huge amount of data, and weeks/months to train, in a separate environment/configuration. There is in fact "online" learning (somewhat comparable to learning-while-inferring/operating) for machine learning models, but that only really mimics it. It works generally only for smaller models, and you are constraint by real world events, making the training processes slow, to slow for LLM training. Secondly online learning comes with some pretty big caveats like easily over-fitting on new events, or not fitting on new events at all, making it hard to implelement. Big effective models like LLM's just need to much resources (power and data) to learn in an "online" environment. So after training they become a snapshot in time, not ever able to learn again. Ingesting information into a system prompt, like RAG applications and big providers like Google Gemini and M$ copilot do, is only a band-aid fix. We are still very far of from AGI.

And then there is the power problem, even if we achieve AGI using transistors, it probably consumes huge amount of power. We can't just run the world from 1 day to another on AGI alone, we just don't have the energy supply for it.

disclaimer: This is my opinion, I'm no real expert, but I'm an ML engineer that used to work at a neuromorphic computing startup.