I'm surprised that any of these companies consider what they are working on to be Artificial General Intelligences. I'm probably wrong, but my impression was AGI meant the AI is self aware like a human. An LLM hardly seems like something that will lead to self-awareness.
"Artificial General Intelligence (AGI) refers to a theoretical form of artificial intelligence that possesses the ability to understand, learn, and apply knowledge across a wide range of tasks at a level comparable to that of a human being."
Altman says AGI could be here in 2025: https://youtu.be/xXCBz_8hM9w?si=F-vQXJgQvJKZH3fv
But he certainly means an LLM that can perform at/above human level in most tasks rather than a self aware entity.
"Most people" naturally associate AGI with the sci-tropes of self-aware human-like agents.
But industries want something more concrete and prospectively-acheivable in their jargon, and so that's where AGI gets redefined as wide task suitability.
And while that's not an unreasonable definition in the context of the industry, it's one that vanishingly few people are actually familiar with.
And the commercial AI vendors benefit greatly from allowing those two usages to conflate in the minds of as many people as possible, as it lets them suggest grand claims while keeping a rhetorical "we obviously never meant that!" in their back pocket
Because of that, the discussion of what AGI means in its broadest sense, will never end.
So in fact such AGI discussion will not make nobody wiser.
The term itself (AGI) in the industry has always been about wide task suitability. People may have added their ifs and buts over the years but that aspect of it never got 'redefined'. The earliest uses of the term all talk about how well a machine would be able to perform some set number of tasks at some threshold.
It's no wonder why. Terms like "consciousness" and "self-awareness" are completely useless. It's not about difficulty. It's that you can't do anything at all with those terms except argue around in circles.
I would argue that learning is The definition of AGI, since everything else comes naturally from that.
The current architectures can't learn without retraining, fine tuning is at the expense of general knowledge, and keeping things in context is detrimental to general performance. Once you have few shot learning, I think it's more of a "give it agency so it can explore" type problem.