Why the words used for AI incidents can distort how people judge the risks

sebkrier · x · 2026-07-25

This post argues that how AI incidents are described matters because labels act like compressed causal models.

Main points:

Terms like “reward hacking” are concrete and historically grounded, while words like “takeover” or “escape” can smuggle in sci-fi assumptions without tracing the actual causal chain.
Saying a model is “lying” implies intent; “confabulating” better captures a generative system filling gaps without truth-tracking.
The writer warns that exaggerated metaphors can mislead non-technical audiences and shape policy discourse in unhelpful ways.
The broader concern is that AI commentary often uses rhetorical labels for strategic effect rather than precision.

It is less a safety proposal than a critique of the language used in safety debates.

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