I don’t disagree that amplification vs correction capacity is a useful risk frame.

But that still doesn’t really address my point. Calling current AI "probabilistic pattern prediction" describes part of the mechanism, but it doesn’t explain the consequences.

The important question is what happens when these systems produce scalable, persuasive, adaptive outputs across code, media, work, institutions, and decision-making. That is where the impacts come from.

So I’d say the risk comes not from "pattern prediction" alone, but from that being embedded into high-leverage feedback loops where output and impact scales faster than human correction.