Legislation

Model specifications are explicit documents defining AI behavior, now published by all major frontier labs (Anthropic, OpenAI, Google, Meta) as of 2025. While they improve transparency and enable external scrutiny, they face a fundamental spec-reality gap—specifications don't guarantee implementatio

Government-run institutions dedicated to evaluating frontier AI systems for dangerous capabilities and safety properties. Pioneered by the UK AISI in 2023, with analogues in the US (USAISI), EU, Japan, and others. Play a key role in pre-deployment evaluations and responsible scaling policy thresholds.

International and national organizations that develop technical standards for AI systems, including measurement methodologies, safety requirements, and evaluation protocols. Key bodies include ISO/IEC JTC 1/SC 42, NIST, and IEEE, whose standards increasingly inform government AI regulation.

Legal and institutional frameworks for protecting AI researchers and employees who report safety concerns. The bipartisan AI Whistleblower Protection Act (S.1792) introduced May 2025 addresses critical gaps in current law, while EU AI Act Article 87 provides protections from August 2026.

The Artificial Intelligence and Data Act (AIDA) was Canada's proposed federal AI legislation, introduced as Part 3 of Bill C-27 (the Digital Charter Implementation Act, 2022). Despite years of debate and amendment, the bill died on the order paper when Parliament was dissolved in January 2025.

World-first international agreement on AI safety signed by 28 countries at the November 2023 AI Safety Summit, committing to cooperation on frontier AI risks. Follow-up summits in Seoul (May 2024) and Paris (February 2025) expanded commitments.

California's Transparency in Frontier Artificial Intelligence Act, the first U.S. state law regulating frontier AI models through transparency requirements, safety reporting, and whistleblower protections. Signed September 29, 2025 after Governor Newsom vetoed the more ambitious SB 1047 a year earlier.

China has developed one of the world's most comprehensive AI regulatory frameworks through a series of targeted regulations addressing specific AI applications and risks. Unlike the EU's comprehensive AI Act, China's approach is iterative and sector-specific, with new rules issued as technologies emerge.

The Colorado AI Act (SB 24-205) is the first comprehensive AI regulation enacted by a US state. Signed into law on May 17, 2024, it takes effect June 30, 2026. Establishes a risk-based framework regulating AI systems making consequential decisions, with developer documentation requirements and deployer impact assessments.

Compute governance uses computational hardware as a lever to regulate AI development. Because advanced AI requires enormous amounts of computing power, and that compute comes from concentrated supply chains, controlling compute provides a tractable way to govern AI before models are built.

Framework analyzing compute monitoring approaches for AI governance, finding that cloud KYC targeting 10^26 FLOP threshold is implementable now via three major providers controlling 60%+ of cloud infrastructure, while hardware-level governance faces 3-5 year development timelines.

Analysis of compute thresholds as regulatory triggers, examining current implementations (EU AI Act at 10^25 FLOP, US EO at 10^26 FLOP), their effectiveness as capability proxies, and core challenges including algorithmic efficiency improvements that may render static thresholds obsolete within 3-5 years.

The Council of Europe's AI Framework Convention represents the first legally binding international AI treaty, establishing human rights-focused governance principles across 57+ countries, though it has significant enforcement gaps and excludes national security applications. While historically signi

The EU AI Act is the world's first comprehensive legal framework for artificial intelligence. Adopted in 2024, it establishes a risk-based approach to AI regulation, with stricter requirements for higher-risk AI systems. Fines up to 7% of global revenue for violations.

Evals-based deployment gates require AI models to pass safety evaluations before deployment or capability scaling. The EU AI Act mandates conformity assessments for high-risk systems with fines up to EUR 35M or 7% global turnover, while UK AISI has evaluated 30+ frontier models.

Understanding why AI governance proposals fail is as important as understanding successes. Failed efforts reveal political constraints, industry opposition patterns, and the challenges of regulating rapidly evolving technology.

Technical mechanisms built into AI chips enabling monitoring, access control, and enforcement of AI governance policies. RAND analysis identifies attestation-based licensing as most feasible with 5-10 year timeline, while an estimated 100,000+ export-controlled GPUs were smuggled to China in 2024.

The International AI Safety Summit series represents the first sustained effort at global coordination on AI safety, bringing together governments, AI companies, civil society, and researchers to address the risks from advanced AI.

Multilateral coordination mechanisms for AI compute governance, exploring pathways from non-binding declarations to comprehensive treaties. Assessment finds 10-25% chance of meaningful regimes by 2035, but potential for 30-60% reduction in racing dynamics if achieved.

International coordination on AI safety involves multilateral treaties, bilateral dialogues, and institutional networks to manage AI risks globally. Current efforts include the Council of Europe AI Treaty (17 signatories), the International Network of AI Safety Institutes (11+ members), and the Paris Summit 2025 with 61 signatories.

A deterrence framework proposed by Dan Hendrycks, Eric Schmidt, and Alexandr Wang in their 2025 paper 'Superintelligence Strategy'. MAIM posits that rival states will naturally deter each other from pursuing unilateral AI dominance because destabilizing AI projects can be sabotaged through an escalation ladder from espionage to kinetic strikes. Part of a three-pillar framework including nonproliferation and competitiveness.

Centralized databases of frontier AI models that enable governments to track development, enforce safety requirements, and coordinate international oversight, serving as foundational infrastructure for AI governance analogous to drug registries for the FDA.

State legislation requiring safety protocols, incident reporting, and transparency from developers of frontier AI models. Signed December 2025, effective January 2027, with civil penalties up to $3M enforced by the NY Attorney General.

The NIST AI Risk Management Framework (AI RMF) is a voluntary guidance document developed by the National Institute of Standards and Technology to help organizations manage risks associated with AI systems.

Proposals to pause or slow frontier AI development until safety is better understood, offering potentially high safety benefits if implemented but facing significant coordination challenges and currently lacking adoption by major AI laboratories.

Responsible Scaling Policies (RSPs) are voluntary commitments by AI labs to pause scaling when capability or safety thresholds are crossed. As of December 2025, 20 companies have published policies, though SaferAI grades the three major frameworks 1.9-2.2/5 for specificity.

SB 1047, the Safe and Secure Innovation for Frontier Artificial Intelligence Models Act, was California state legislation that would have required safety testing and liability measures for developers of the most powerful AI models. Passed the legislature with strong majorities (Senate 32-1, Assembly 45-11) but was vetoed by Governor Newsom, who cited concerns about regulating based on model size rather than risk.

The Seoul AI Safety Summit (May 21-22, 2024) was the second in a series of international AI safety summits, following the Bletchley Park Summit in November 2023.

Consensus document from the 2025 Singapore Conference on AI (SCAI), authored by 88 researchers from 11 countries, organizing AI safety research into a defence-in-depth framework across three areas: Assessment, Development, and Control.

Texas TRAIGA is a state-level AI regulation focused on intent-based liability for harmful AI practices rather than comprehensive safety requirements. Passed with near-unanimous support (House 146-3, Senate unanimous). Creates enforcement mechanisms and a regulatory sandbox but avoids prescriptive technical safety standards.

The United States has implemented unprecedented export controls on advanced semiconductors and semiconductor manufacturing equipment, primarily targeting China. These controls represent one of the most significant attempts to constrain AI development through hardware governance.

The Executive Order on Safe, Secure, and Trustworthy Artificial Intelligence, signed by President Biden on October 30, 2023, is the most comprehensive US government action on AI to date. It establishes safety requirements for frontier AI systems, mandates government agency actions, and creates oversight mechanisms.

The US government possesses extensive legal authority to direct, commandeer, or access the $700B+ in commercial AI infrastructure owned by US companies, through the Defense Production Act (priority contracts), CLOUD Act (global data access), FISA 702 (warrantless surveillance of non-US persons), IEEPA (emergency commerce regulation), and executive orders. Historical precedents include WWII industrial mobilization, post-9/11 PRISM surveillance, and COVID-era DPA invocations. Current military-commercial integration (Pentagon contracts with Anthropic, Google, OpenAI, xAI; GenAI.mil serving 3M personnel) creates pre-existing channels for rapid mobilization.

In the absence of comprehensive federal AI legislation, US states have become laboratories for AI governance. As of 2024, hundreds of AI-related bills have been introduced across all 50 states, with several significant laws enacted.

In July 2023, the White House secured voluntary commitments from leading AI companies on safety, security, and trust. These commitments represent the first coordinated industry-wide AI safety pledges, establishing baseline practices for frontier AI development.