AI Governance & Policy (Overview)

Overview

AI governance encompasses the policies, regulations, standards, and coordination mechanisms aimed at managing risks from advanced AI systems. The governance landscape is rapidly evolving, with approaches ranging from national legislation to international treaties to voluntary industry commitments. As of early 2026, no single governance framework has achieved comprehensive coverage of frontier AI risks, but multiple overlapping efforts are creating an increasingly dense regulatory environment.

Legislation and Regulation

Major regulatory frameworks and legislation across jurisdictions:

International:

• EU AI Act: The world's first comprehensive AI regulation, adopting a risk-based approach to regulate foundation models and general-purpose AI
• Council of Europe Framework Convention on AI: First legally binding international AI treaty, establishing human rights standards

United States:

• California SB 1047PolicySafe and Secure Innovation for Frontier Artificial Intelligence Models ActCalifornia's SB 1047 required safety testing, shutdown capabilities, and third-party audits for AI models exceeding 10^26 FLOP or $100M training cost; it passed the legislature (Assembly 48-16, Sen...Quality: 66/100: Pioneering state-level frontier AI safety bill (vetoed but influential)
• California SB 53: First US state law regulating frontier AI models through transparency requirements
• US Executive Order on AIPolicyUS Executive Order on Safe, Secure, and Trustworthy AIExecutive Order 14110 (Oct 2023) established compute thresholds (10^26 FLOP general, 10^23 biological) and created AISI, but was revoked after 15 months with ~85% completion. The 10^26 threshold wa...Quality: 91/100: Federal executive action on AI safety and security
• NIST AI Risk Management FrameworkPolicyNIST AI Risk Management Framework (AI RMF)Comprehensive analysis of NIST AI RMF showing 40-60% Fortune 500 adoption with implementation costs of $50K-$1M+ annually, but lacking quantitative evidence of actual risk reduction and inadequate ...Quality: 60/100: Voluntary framework for managing AI risks
• US State AI LegislationAnalysisUS State AI Legislation LandscapeComprehensive tracking of US state AI legislation shows explosive growth from ~40 bills in 2019 to 1,080+ in 2025, with only 11% passage rate but real enforcement beginning (Texas AG settlement, Co...Quality: 70/100: Growing landscape of state-level AI regulation
• New York RAISE Act: State legislation requiring safety protocols for frontier AI
• Texas TRAIGA: Comprehensive AI governance act signed in 2025

Other jurisdictions:

• Canada AIDAPolicyArtificial Intelligence and Data Act (AIDA)Comprehensive analysis of Canada's failed Artificial Intelligence and Data Act (AIDA), which died in Parliament in January 2025 after three years of development. Documents specific failure modes in...Quality: 46/100: Canada's Artificial Intelligence and Data Act
• Colorado AI ActPolicyColorado Artificial Intelligence ActColorado's AI Act (SB 205) is the first comprehensive US state AI regulation targeting algorithmic discrimination in employment, housing, and other consequential decisions, with enforcement beginni...Quality: 53/100: State-level AI regulation focused on high-risk systems
• China AI RegulationsPolicyChina AI Regulatory FrameworkComprehensive analysis of China's AI regulatory framework covering 5+ major regulations affecting 50,000+ companies, with enforcement focusing on content control and social stability rather than ca...Quality: 57/100: China's evolving approach to AI governance including generative AI rules

Analysis:

• Failed and Stalled AI Policy ProposalsAnalysisFailed and Stalled AI ProposalsAnalysis of AI policy failures reveals systematic patterns: 150+ federal bills with 0% passage rate, industry lobbying up 141% YoY to 648 companies, and 97% public support for AI safety rules versu...Quality: 63/100: Tracking proposals that did not advance and why

Compute Governance

Technical governance approaches leveraging the physical infrastructure of AI:

• AI Chip Export ControlsPolicyUS AI Chip Export ControlsComprehensive empirical analysis finds US chip export controls provide 1-3 year delays on Chinese AI development but face severe enforcement gaps (140,000 GPUs smuggled in 2024, only 1 BIS officer ...Quality: 73/100: US policies restricting advanced AI chip exports, particularly to China
• Compute ThresholdsConceptCompute ThresholdsComprehensive analysis of compute thresholds (EU: 10^25 FLOP, US: 10^26 FLOP) as regulatory triggers for AI governance, documenting that algorithmic efficiency improvements of ~2x every 8-17 months...Quality: 91/100: Using training compute as a measurable threshold for regulatory triggers
• Compute MonitoringApproachCompute MonitoringAnalyzes two compute monitoring approaches: cloud KYC (implementable in 1-2 years, covers ~60% of frontier training via AWS/Azure/Google) and hardware governance (3-5 year timeline). Cloud KYC targ...Quality: 69/100: Approaches to tracking and verifying AI training runs
• Hardware-Enabled Governance: Technical mechanisms in AI hardware for monitoring and enforcement
• International Compute RegimesConceptInternational Compute RegimesComprehensive analysis of international AI compute governance finds 10-25% chance of meaningful regimes by 2035, but potential for 30-60% reduction in racing dynamics if achieved. First binding tre...Quality: 67/100: Proposals for international coordination on compute governance

International Coordination

Mechanisms for cross-border cooperation on AI safety:

• International AI Safety SummitsEventInternational AI Safety Summit SeriesThree international AI safety summits (2023-2025) achieved first formal recognition of catastrophic AI risks from 28+ countries, established 10+ AI Safety Institutes with $100-400M combined budgets...Quality: 63/100: Series of international summits on AI safety starting with Bletchley Park (2023)
• Bletchley Declaration: First international agreement on AI safety signed by 28 countries
• Seoul DeclarationPolicySeoul Declaration on AI SafetyThe May 2024 Seoul AI Safety Summit achieved voluntary commitments from 16 frontier AI companies (80% of development capacity) and established an 11-nation AI Safety Institute network, with 75% com...Quality: 60/100: Follow-up international commitment on frontier AI safety
• International Coordination Mechanisms: Bilateral dialogues, multilateral treaties, and institutional networks

Industry Self-Regulation

Voluntary commitments and industry-led safety frameworks:

• Responsible Scaling PoliciesApproachResponsible Scaling PoliciesComprehensive analysis of Responsible Scaling Policies showing 20 companies with published frameworks as of Dec 2025, with SaferAI grading major policies 1.9-2.2/5 for specificity. Evidence suggest...Quality: 62/100: Framework pioneered by Anthropic tying safety requirements to capability levels
• Voluntary Industry CommitmentsPolicyVoluntary AI Safety CommitmentsComprehensive empirical analysis of voluntary AI safety commitments showing 53% mean compliance rate across 30 indicators (ranging from 13% for Apple to 83% for OpenAI), with strongest adoption in ...Quality: 91/100: Commitments secured by the Biden administration from major AI labs
• Model Registries: Centralized databases for tracking frontier AI models

Governance Assessment

• AI Governance and PolicyCruxAI Governance and PolicyComprehensive analysis of AI governance mechanisms estimating 30-50% probability of meaningful regulation by 2027 and 5-25% x-risk reduction potential through coordinated international approaches. ...Quality: 66/100: Broader analysis of governance approaches and their effectiveness
• Policy Effectiveness AssessmentAnalysisAI Policy EffectivenessComprehensive analysis of AI governance policy effectiveness finds compute thresholds and export controls achieve 60-75% compliance while voluntary commitments show <30% behavioral change, but only...Quality: 64/100: Evaluating which governance interventions actually reduce risk

Key Tensions

Speed vs. thoroughness: The pace of AI capability development outstrips the pace of legislative and regulatory processes in most jurisdictions.

National vs. international: AI development is global but governance is primarily national, creating coordination challenges and regulatory arbitrage risks.

Voluntary vs. mandatory: Industry self-regulation (RSPs, voluntary commitments) is faster to implement but lacks enforcement mechanisms. Legislation provides enforcement but is slower and harder to update.

Compute governance as bottleneck: Compute is the most governable input to AI development (physical, concentrated, measurable), but effective compute governance requires international coordination that remains elusive.