Pinned snapshot. This article is rendered from the catalog state captured at (closest snapshot at-or-before your requested date of ). The live version may differ — drop the
?asOf= parameter to see the current catalog state.Governance posture toward releasing frontier model weights publicly (Meta Llama, Mistral, DeepSeek vs. closed-weight Anthropic / OpenAI / DeepMind). EU AIA Recital 102 + Art. 53(2) carve-outs; CA SB-1047's failed framework; Meta Frontier AI Framework's explicit defence; emerging US export-control overlay.
Definition & scope
The cross-jurisdiction picture below shows how each of 45 tracked instruments treats this topic. The patterns vary substantially — and 33 regimes are silent, leaving gaps that future policy work could address.
Coverage across jurisdictions
Historical primacy & cross-jurisdiction tension
First addressed by Interim Measures for Generative AI Service Management on (implicit). Subsequent regimes have either codified, diverged from, or remained silent on this baseline.
- Forum-shoppingEU AI Act↔Executive Order 14179 — Removing Barriers to American Leadership in AI
- Forum-shoppingCalifornia SB-1047: Safe and Secure Innovation for Frontier AI Models Act↔UK Pro-Innovation Approach to AI Regulation (White Paper)
- Forum-shoppingMeta Frontier AI Framework↔G7 Hiroshima AI Process Code of Conduct
Compare jurisdictions: EU vs US · EU vs UK · EU vs CN
Enforcement & impact
Silent regimes — gap signal
Instruments that do not address Open-Weight Frontier Release — candidates for future policy work.
- Executive Order 14179 — Removing Barriers to American Leadership in AIUS
- UK Pro-Innovation Approach to AI Regulation (White Paper)UK
- G7 Hiroshima AI Process Code of ConductG7
- OECD AI Principles (Recommendation)OECD
- Council of Europe Framework Convention on AIcouncil_of_europe
- UN GA Resolution on Safe, Secure, Trustworthy AIUN
- NIST AI Risk Management FrameworkUS
- Bletchley Declaration on AI Safetyglobal
- NIST AI RMF Generative AI ProfileUS
- India Digital Personal Data Protection Act + AI Advisory (MEITY)IN
- Brazil AI Bill (PL 2338/2023)BR
- ASEAN Guide on AI Governance and EthicsASEAN
- UK-US AI Safety Institute Memorandum of Understandingglobal
- White House Voluntary AI CommitmentsUS
- Singapore Model AI Governance Framework for Generative AISG
- Japan METI AI Guidelines for BusinessJP
- General Data Protection Regulation (GDPR)EU
- EU General-Purpose AI Code of PracticeEU
- OMB Memorandum M-24-10 (Advancing Governance, Innovation, and Risk Management for Agency Use of AI)US
- GSA Generative AI and Specialized Computing Infrastructure Acquisition Resource GuideUS
- DoD Responsible AI Strategy and Implementation PathwayUS
- FedRAMP AI Cloud Procurement GuidanceUS
- DFARS Subpart 252.204 (Safeguarding Covered Defense Information and Cyber Incident Reporting)US
- California SB-53: Transparency in Frontier Artificial Intelligence Act (TFAIA)US
- California SB 243: Companion ChatbotsUS
- Revised Product Liability Directive (Directive (EU) 2024/2853)EU
- UNESCO Recommendation on the Ethics of Artificial IntelligenceUNESCO
- Directive (EU) 2024/2831 on improving working conditions in platform workEU
- Provisions on the Administration of Deep Synthesis of Internet Information ServicesCN
- New York RAISE Act: Responsible AI Safety and Education ActUS
- TAKE IT DOWN Act (Tools to Address Known Exploitation by Immobilizing Technological Deepfakes on Websites and Networks Act)US
- Italy Law No. 132/2025 on Artificial Intelligence (Legge 23 settembre 2025, n. 132)IT
- Japan AI Promotion Act (Act on the Promotion of Research, Development and Utilization of AI-Related Technologies)JP
See also
Further reading
11 academic & grey-literature sources bearing on this topic — catalogued metadata with a primary link; one-line findings are ✦ AI-generated summaries, labeled as such (charter §7.9). Browse the full literature index.
- On the Societal Impact of Open Foundation Models Preprint✦ AIProposes a marginal-risk framework, finding current research "insufficient to effectively characterize the marginal risk of open foundation models relative to pre-existing technologies."
- Considerations for governing open foundation models Peer-reviewed✦ AI"Open foundation models can benefit society by promoting competition, accelerating innovation, and distributing power," but regulation risks an uneven impact on open vs. closed models.
- Dual-Use Foundation Models with Widely Available Model Weights (NTIA Report) Research institute✦ AIRecommends the US government monitor but not currently restrict open-weight models, assessing case-by-case whether 'marginal risks' over closed models or pre-existing technology warrant action.
- Rethinking open source generative AI: open-washing and the EU AI Act Peer-reviewed✦ AIA 14-dimension survey of 45+ systems finds many self-described 'open source' models are 'open weight at best' and providers seek to 'evade scientific, legal and regulatory scrutiny' under the EU AI Act's open-source exemption.
- On Evaluating the Durability of Safeguards for Open-Weight LLMs Preprint✦ AIShows tamper-resistance safeguards for open weights are fragile and hard to assess, cautioning that 'even evaluating these defenses is exceedingly difficult and can easily mislead audiences' — undercutting safeguard-conditioned…
- The Gradient of Generative AI Release: Methods and Considerations Peer-reviewed✦ AIMaps six access levels for generative AI where "each level, from fully closed to fully open, can be viewed as an option along a gradient," grounding release-policy tradeoffs.
- Open-Sourcing Highly Capable Foundation Models: An evaluation of risks, benefits, and alternative methods for pursuing open-source objectives Research institute✦ AIArgues that for some highly capable models "open-sourcing may pose sufficiently extreme risks to outweigh the benefits," and evaluates alternative routes to open-source objectives.
- Open (For Business): Big Tech, Concentrated Power, and the Political Economy of Open AI Preprint✦ AIArgues 'even the most open of open AI systems do not, on their own, ensure democratic access...nor does openness alone solve the problem of oversight,' and that openness rhetoric can entrench Big Tech power.
- Hazards from Increasingly Accessible Fine-Tuning of Downloadable Foundation Models Preprint✦ AIGrounds the open-weight marginal-risk debate technically: 'increasingly accessible fine-tuning methods may increase hazard through facilitating malicious use and making oversight...more difficult.'
- Structured Access: An Emerging Paradigm for Safe AI Deployment Peer-reviewed✦ AIProposes 'structured access' (controlled, arm's-length cloud interactions) as a middle path between open release and full closure, restricting dangerous capabilities while preserving beneficial use and scrutiny.
- Release Strategies and the Social Impacts of Language Models Preprint✦ AIDocuments OpenAI's GPT-2 staged-release experiment, arguing 'staged release allows time between model releases to conduct risk and benefit analyses' and proposing publication norms for powerful models.
References
The primary instrument sources behind the article's classifications.
- EU-AIA-2024: Art. 53(2) + Recital 102/104 — explicit open-source GPAI exemption (with caveats for systemic-risk models)
- US-EO-14110: §4.6 NTIA report on dual-use foundation models specifically addresses open-weight risk; not binding obligation
- CN-GENAI-2023: Art. 8 — registration / safety assessment applies regardless of weight release modality
- SEOUL-2024: Frontier AI Safety Commitments apply to all 16 signatories regardless of open/closed weight stance (Meta is signatory)
- CA-SB-1047: Vetoed bill — would have required covered models (incl. open-weight releases) to adopt a safety & security protocol + self-certified compliance, with independent third-party audits from 2026 (Anthropic + Meta objected on different grounds)
- AU-AI-STRATEGY-2024: Continental strategy frames AI capacity-building — open access to weights aligns with capacity goals
- ANTHROPIC-RSP-2024: RSP applies to Anthropic's models which are closed-weight; framework does not address third-party open release
- OPENAI-PREPAREDNESS-2023: Framework applies to OpenAI deployments (closed-weight); does not address third-party open release
- DEEPMIND-FSF-2024: Framework applies to Google DeepMind deployments (mostly closed); third-party open release not addressed
- META-FRONTIER-2024: Framework's distinctive feature — explicit defence of open-weight release as governance posture; halt-training commitment if 'critical risk' threshold reached without mitigations
- CA-SB-942: Cal. Bus. & Prof. Code § 22757.3(c) (added by SB 942, operative Aug. 2, 2026) — a covered provider that LICENSES its GenAI system to a third party must require by contract that the licensee preserve the § 22757.3(b) disclosure capability, and must revoke the license within 96 hours if the licensee disables it; reinforced by § 22757.3.2 (added by AB 853, operative Jan. 1, 2027), which bars a GenAI hosting platform distributing a system's source code or model weights from knowingly hosting a non-disclosing system
- UN-GDC-2024: GDC Objective 5 capacity-building partnerships (A/RES/79/1, Annex I)
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12 instruments tracked.