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.
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.
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.
"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