This paper presents a theoretical proof that AGI systems will structurally collapse under certain semantic conditions — not due to lack of compute, but because of how entropy behaves in heavy-tailed decision spaces. The idea is called IOpenER: Information Opens, Entropy Rises. It builds on Shannon’s information theory to show that in specific problem classes (those with α ≤ 1), adding information doesn’t reduce uncer…
> specific problem classes (those with α ≤ 1), For the layman, what does α mean here?
Most of these don't have finite moments and are hard to do inference on with standard statistical tools. Nassim Taleb's work (Black Swan, etc.) is around these distributions.
But I think the argument of OP in this section doesn't hold.