What is AGI?

I am not quite sure I fully understand

This article is more of an internal monlogue than a definitive statement.

AGI stands for Artificial General Intelligence. On paper, it means a machine that can perform a wide range of cognitive tasks at roughly human level, rather than excelling at one narrow function. That definition lasts about five seconds before it starts to decay. General turns out to be elastic. Human turns out to be selective. Intelligence becomes benchmarks, productivity, or economic output, depending on who is talking. For Shane Legg, AGI is the ability to achieve goals across many environments. For Marcus Hutter, it is a mathematical ideal no real system ever reaches. For François Chollet, it is adaptation to genuine novelty beyond the training distribution. Demis Hassabis frames it as human level cognition across the full spectrum of tasks. Sam Altman treats it as economic usefulness, systems that can do most valuable work better than people. Dario Amodei removes the threshold entirely by treating AGI as a gradient. Yann LeCun says we are nowhere near it because the architectures are wrong. Ajeya Cotra reduces it to large scale labour automation. BlueDot Impact openly acknowledges that the term itself is contested and prefers to talk about impact and capability ranges instead. Dan Hendrycks frames AGI less as a definition and more as a risk vector, emerging through scaling, competition, and arms race dynamics. None of these views are obviously wrong. That is the problem. AGI does not decay when systems fail. It decays when the meaning becomes flexible enough to satisfy everyone and precise enough to hold no one accountable.