A common assumption underlies much contemporary thinking about AI risk: that time is corrective.

Models improve. Guardrails tighten. Feedback loops reduce error. Early failures are expected to fade as systems mature.

In many technical domains, this assumption is reasonable. In governance-relevant decision contexts, it is not.

Here, time often functions not as a corrective force, but as a risk amplifier. Certain classes of AI-generated outputs become more persuasive, more stable, and more institutionally dangerous the longer they persist.

This article examines that failure mode and names the mechanism behind it.