Look at (the comments on) the Genie announcement on the front page today or yesterday, and earlier generative "world models". People are itching to use those kind of models for the internal world representation of autonomous robots. More generally the fact that a model is "generative" does not mean it can not become an effective component in or pathway to AGI.
“Trying” is an overly generous interpretation of what’s going on.
Training an LLM is not actually working on AGI just as people building skyscrapers aren’t getting to the moon. It’s an inherent limitation on the approach.
Training LLMs is not the only thing people are trying. They dominate the public attention right now but there are people everywhere trying all kinds of approaches. Here's one from IBM: https://research.ibm.com/topics/neuro-symbolic-ai
First sentence: "We see Neuro-symbolic AI as a pathway to achieve artificial general intelligence"
Everyone is trying to get to AGI, and yes mostly through LLMs for now.
You said you don't believe LLMs are capable of ever getting there, so I offered a link showing people are trying other things as well. My point was never "Everyone is doing novel, non-LLM work towards AGI".
Not to mention obvious suspects (OpenAI, Anthropic etc). Just because you think it won't work doesn't mean they're not trying. Everyone is trying to get to AGI.
AGI needs to be able to generalize to real world tasks like self driving without needing task specific help from its creators.
But the current LLM process separates learning from interacting and the learning process is based on huge volumes of text. It’s possible to bolt on specific capabilities like say a chess engine, but you’re now building something different not an LLM.