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.Mandatory reporting based on training-compute or capability thresholds.
Definition & scope
The cross-jurisdiction picture below shows how each of 45 tracked instruments treats this topic. The patterns vary substantially — and 29 regimes are silent, leaving gaps that future policy work could address.
Coverage across jurisdictions
Historical primacy & cross-jurisdiction tension
First addressed by DFARS Subpart 252.204 (Safeguarding Covered Defense Information and Cyber Incident Reporting) 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-shoppingExecutive Order 14110 on Safe, Secure, Trustworthy AI↔UK Pro-Innovation Approach to AI Regulation (White Paper)
- Forum-shoppingCalifornia SB-1047: Safe and Secure Innovation for Frontier AI Models Act↔Interim Measures for Generative AI Service Management
Compare jurisdictions: EU vs US · EU vs UK · EU vs CN
Enforcement & impact
Silent regimes — gap signal
Instruments that do not address Compute-Threshold Reporting — 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
- Interim Measures for Generative AI Service ManagementCN
- 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
- 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
- African Union Continental AI StrategyAfrican_Union
- Google DeepMind Frontier Safety FrameworkUS
- Meta Frontier AI FrameworkUS
- UK-US AI Safety Institute Memorandum of Understandingglobal
- 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
- California SB 243: Companion ChatbotsUS
- California SB 942: AI Transparency ActUS
- 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
- 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
- UN Global Digital CompactUN
See also
Further reading
25 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.
- Defending Compute Thresholds Against Legal Loopholes Preprint✦ AIIdentifies 'enhancement techniques that are capable of decreasing training compute usage while preserving... model capabilities', exposing loopholes in compute-reporting thresholds.
- Computing Power and the Governance of Artificial Intelligence Preprint✦ AIArgues compute is a uniquely governable lever because it is "detectable, excludable, and quantifiable, and is produced via an extremely concentrated supply chain".
- Training Compute Thresholds: Features and Functions in AI Regulation Preprint✦ AIFinds "training compute currently is the most suitable metric to identify GPAI models", but thresholds should only trigger further scrutiny, not determine risk measures alone.
- Compute North vs. Compute South: The Uneven Possibilities of Compute-based AI Governance Around the Globe Peer-reviewed✦ AICensus of hyperscale cloud regions shows a divide between "Compute North" states hosting training-relevant compute and a Compute South, shaping who can wield compute-based governance.
- Governing Through the Cloud: The Intermediary Role of Compute Providers in AI Regulation Preprint✦ AIArgues 'compute providers should have legal obligations' to secure infrastructure, keep records, verify activity and report frontier training as regulatory intermediaries.
- Verification methods for international AI agreements Preprint✦ AISurveys '10 verification methods that could detect... unauthorized AI training... and unauthorized data centers', mapping the technical basis for compute-disclosure regimes.
- Open Problems in Technical AI Governance Preprint✦ AICatalogs open problems in 'technical analysis and tools for supporting the effective governance of AI', including compute measurement, verification and reporting gaps.
- What does it take to catch a Chinchilla? Verifying Rules on Large-Scale Neural Network Training via Compute Monitoring Preprint✦ AIProposes chip-level monitoring (on-chip logging, supply-chain oversight) giving governments "high confidence that no actor uses large quantities of specialized ML chips" in violation of rules.
- Oversight for Frontier AI through a Know-Your-Customer Scheme for Compute Providers Preprint✦ AIProposes a banking-style KYC regime for cloud compute providers because 'compute is emerging as a node for oversight', enabling record-keeping and reporting of high-risk training.
- Deceptive Alignment PreprintHubinger, E., et al. (2019), 'Risks from Learned Optimization in Advanced Machine Learning Systems.'
- Mesa-Optimization PreprintHubinger, E., et al. (2019), 'Risks from Learned Optimization in Advanced Machine Learning Systems.'
- Capability Elicitation PreprintQi, X., Zeng, Y., Xie, T., Chen, P.-Y., Jia, R., Mittal, P., Henderson, P. (2023), 'Fine-tuning Aligned Language Models Compromises Safety, Even When Users Do Not Intend To!'
- Policy Instrument Peer-reviewedLascoumes, P. & Le Galès, P. (2007). Introduction: Understanding Public Policy through Its Instruments — From the Nature of Instruments to the Sociology of Public Policy Instrumentation. Governance 20(1): 1-21. See also Hood (1983) The Tools of Government, ch. 1-2; Salamon (2002) The Tools of Government: A Guide to the New Governance, pp. 1-47; Howlett (2011) Designing Public Policies, ch. 3-5.
- Multi-Turn Evaluation PreprintZheng, L., et al. (2023), 'Judging LLM-as-a-Judge with MT-Bench and Chatbot Arena' — operationalises the multi-turn evaluation protocol for foundation models.
- Model Distillation Risk PreprintHinton, G., Vinyals, O., Dean, J. (2015), 'Distilling the Knowledge in a Neural Network' — the foundational distillation paper; the governance-relevant adaptation runs through Alpaca/Vicuna (2023) and DeepSeek-R1 (2025).
- Inference-Time Compute PreprintSnell, C., Lee, J., Xu, K., Kumar, A. (2024), 'Scaling LLM Test-Time Compute Optimally can be More Effective than Scaling Model Parameters' — establishes inference-time-compute scaling as a first-class capability lever.
- Sandbagging Preprintvan der Weij, T., Hofstätter, F., Jaffe, O., Brown, S., Ward, F. (2024), 'AI Sandbagging: Language Models can Strategically Underperform on Evaluations.'
- In-Context Learning PreprintBrown, T., et al. (2020), 'Language Models are Few-Shot Learners' (GPT-3 paper) — the canonical articulation of in-context learning as an emergent capability.
- AI Risk Management Framework | NIST Standards body✦ AIUS voluntary AI risk-management framework (Govern/Map/Measure/Manage).
- ISO/IEC JTC 1/SC 42 - Artificial intelligence Standards body✦ AIInternational committee developing AI standards.
- OECD AI Incidents Monitor, an evidence base for trustworthy AI - OECD.AI Incident database✦ AIOECD tracker of real-world AI incidents and hazards.
- Capturing the Potential of Generative AI’s Use in Health and Medicine Requires Collaboration and Oversight, Consideration of Risks, Says NAM Special Publication Research institute✦ AINAM special publication on generative AI in health & medicine.
- One Hundred Year Study on Artificial Intelligence (AI100) Research institute✦ AIStanford's standing century-long study of AI's societal impact.
- Measuring up | Ada Lovelace Institute Civil society✦ AIAda Lovelace Institute policy briefing.
- Anthropomorphic AI terms create gaps in accountability | Brookings Think tank✦ AICommentary on how anthropomorphic AI language obscures accountability.
References
The primary instrument sources behind the article's classifications.
- EU-AIA-2024: Art. 51(2) + Annex XIII (10²⁵ FLOP presumption)
- US-EO-14110: §4.2(a)(i) — 10²⁶ FLOP threshold
- BLETCHLEY-2023: Declaration §6 calls for capability evaluation but does not specify compute thresholds
- SEOUL-2024: Safety Commitments invoke capability thresholds; compute is one proxy
- CA-SB-1047: Cal. SB-1047 §22603(b) — annual reporting of training compute + safety determination
- ANTHROPIC-RSP-2024: RSP v2 capability evaluations triggered by capability rather than pure compute; compute is one signal
- OPENAI-PREPAREDNESS-2023: Capability-tier evaluations are the primary trigger; compute is a coincident signal
- WH-VOLUNTARY-2023: Self-reporting through commitments framework; binding compute thresholds came via EO 14110 §4.2(a)
- OMB-M-24-10: §3(a)(iv)–(v) annual public AI use-case inventory + quarterly AI procurement reporting to OMB
- GSA-AI-GUIDE-2024: Guide routes AI acquisitions through existing governmentwide vehicles (MAS IT / Best-in-Class GWACs) rather than a dedicated generative-AI vehicle or new AI-specific SINs
- DOD-RAI-2022: Tenet 1 (RAI Governance) + Tenet 3 (Acquisition Lifecycle) — clarifies CDAO + OUSD(A&S) roles in AI procurement oversight; tracking + reporting emerge through standard DoD acquisition reporting channels
- FEDRAMP-AI-2024: FedRAMP authorisation enables ATO; agency-AI-use disclosure flows through OMB M-24-10 inventory + quarterly procurement reporting rather than through FedRAMP itself
- DFARS-252-204: Cyber-incident reporting under 252.204-7012(c) — 72-hour DoD notification covers AI-system compromise events including model-weight theft + prompt-injection-based credential exposure; broader AI-use disclosure flows through M-24-10 not DFARS
- CA-SB-53: Bus. & Prof. Code § 22757.11 uses a 10^26 FLOP compute threshold to SCOPE the regulated class + § 22757.12 ties disclosure to compute-defined frontier models; no standalone compute-figure reporting mandate to a regulator
- NY-RAISE-2025: N.Y. Gen. Bus. Law § 1420(6),(9) — the frontier-model / large-developer compute figures SCOPE the regulated class; no standalone compute-figure reporting duty to a regulator. (The Mar. 27, 2026 chapter amendment revised the large-developer threshold to align more closely with California's criteria; the verdict — coverage-scoping, not a reporting duty — is unchanged by the specific figure.)
- JP-AIPROMO-2025: Act No. 53 of 2025, Art. 12
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16 instruments tracked.