You cannot get real, actual AGI (the same ability to perform tasks as a human) without a continuous cycle of learning and deep memory, which LLMs cannot do. The best LLM "memory" is a search engine and document summarizer stuffed into a context window (which is like having someone take an entire physics course, writing down everything they learn on post-it notes, then you ask a different person a physics question, and that different person has to skim all the post-it notes, and then write a new post-it note to answer you). To learn it would need RL (which requires specific novel inputs) and retraining (so that it can retain and compute answers with the learned input). This would all take too much time and careful input/engineering along with novel techniques. So AGI is too expensive, time consuming, and difficult for us to achieve without radically different designs and a whole lot more effort.
Not only are LLMs not AGI, they're still not even that great at being LLMs. Sure, they can do a lot of cool things, like write working code and tests. But tell one "don't delete files in X/", and after a while, it will delete all the files in "X/", whereas a human would likely remember it's not supposed to delete some files, and go check first. It also does fun stuff like follow arbitrary instructions from an attacker found in random documents, which most humans also wouldn't do. If they had a real memory and RL in real-time, they wouldn't have these problems. But we're a long way away from that.
LLMs are fine. They aren't AGI.