The White House released legislative recommendations outlining a National Policy Framework for Artificial Intelligence, structured around seven pillars addressed to Congress. The framework covers child safety, community protection, intellectual property, anti-censorship, innovation, workforce, and federal preemption. It positions state regulation as the primary threat to U.S. competitiveness and frames preemption as the central legislative priority.

Primary frame: Innovation and competitiveness

A signed executive order (June 2, 2026) directing the federal government to deploy advanced AI for cyber defense while explicitly disclaiming any authority to regulate private AI development — framing deregulation itself as innovation policy. Operationally it is a cybersecurity-and-national-security instrument: it orders agencies (Defense, DHS/CISA, Treasury, NSA, OMB) to harden federal and critical-infrastructure systems within 30–60 days, creates an interagency AI Cybersecurity Clearinghouse for vulnerability scanning and patch coordination, and establishes classified NSA-led benchmarking to designate 'covered frontier models' by their cyber capabilities. It invites developers into a voluntary framework offering 30-day pre-release federal access to frontier models (with IP and confidentiality protections), but states emphatically that nothing in it authorizes any 'mandatory governmental licensing, preclearance, or permitting' for AI development or release. The Attorney General is directed to prioritize prosecution of AI-enabled computer crimes under existing statutes. Unlike the White House's legislative framework, it is operative now — but as an executive order it can be undone by a future president.

Primary frame: Deregulation + national-security cyber

The most comprehensive federal AI bill to date, spanning 17 titles and hundreds of pages. Despite being framed as implementing the White House's deregulatory vision, the bill contains significantly more regulatory density than the White House framework suggests. It creates multiple enforcement pathways, mandatory reporting obligations, and a risk-based evaluation program.

Primary frame: Comprehensive federal regulation

OpenAI advocates for a specific sequencing of governance: federal framework first, state alignment second, federal incentive third. The position endorses mandatory federal testing of frontier systems using classified government capabilities before deployment. CAISI would serve as the primary evaluative institution.

Primary frame: Prevention-first safety

OpenAI's most expansive policy document to date, moving well beyond its earlier safety-focused position to propose a comprehensive industrial policy agenda for the transition to superintelligence. The document is organized around two pillars: building an open economy with broad participation and shared prosperity, and building a resilient society through safety systems, alignment, and governance. It proposes a Public Wealth Fund giving every citizen a stake in AI-driven growth, portable benefits decoupled from employers, adaptive safety nets with automatic triggers, a 32-hour workweek pilot, modernized taxation of capital over labor, and a global network of AI Safety Institutes. The framing explicitly invokes the Progressive Era and the New Deal as precedents for the scale of institutional response required.

Primary frame: Industrial policy and shared prosperity

OpenAI's June 2026 blueprint for federal frontier-AI governance, framed around 'democratic governance' — the principle that democratic governments, not private companies, should set the rules. It advances a three-part strategy. First, 'reverse federalism': Congress should codify the emerging consensus from state frontier-safety laws (California's SB 53, New York's RAISE Act, Illinois's SB 315) into a national framework — severe-risk evaluations (cyber, CBRN, loss-of-control, misalignment, and recursive self-improvement), public safety frameworks and transparency reports, annual third-party audits, critical-incident reporting, model-weight security, and whistleblower protections — and then preempt state laws covering the same frontier-safety risks, while leaving states authority over youth protection, energy and environment, and AI literacy. Second, build CAISI into the premier federal institution for frontier evaluation, with statutory authority, CHIPS-style hiring, classified compute, and a mandatory pre-release evaluation of the most capable models — though CAISI would only evaluate and recommend, never approve or block deployment, and developers could ship if CAISI misses a statutory deadline. Third, a whole-of-government resilience strategy: international safety coordination, compute-advantage protection via export controls, a ban on government use of unevaluated frontier systems, and biodefense and cyber investment so defense outpaces offense. Recursive self-improvement (RSI) is treated as the defining governance challenge of the decade.

Primary frame: Reverse federalism + frontier safety

The Center for Humane Technology's most comprehensive AI policy document, structured around seven principles for how AI should be built, deployed, and governed. The Roadmap operates across three intervention domains -- norms, laws, and product design -- arguing that no single reform is sufficient and that change requires layered, simultaneous pressure on the AI development paradigm. CHT explicitly draws parallels to the Big Tobacco and nuclear weapons movements, framing its theory of change as identifying high-leverage intervention points and applying coordinated civil society pressure across them. Unlike industry or legislative proposals, this is a civil society document setting expectations for what governance should look like rather than introducing bills.

Primary frame: Humane technology and public interest

Rolled out July 21, 2026, Warner's agenda is the most legislatively dense package any single member of Congress has put behind AI — seven bills organized around four stated priorities: building AI infrastructure responsibly, promoting competition and safety, preparing workers for economic disruption, and strengthening America's national security advantage. The framing is explicitly anti-tradeoff: "Artificial intelligence will reshape nearly every aspect of our economy and society. The only question is whether Congress is going to help shape that future or spend the next decade scrambling to catch up." On infrastructure, the Data Center Tax Accountability and Disclosure Act forces large AI data centers to publish energy, water, emissions, and backup generation data, and conditions federal bonus depreciation on meeting efficiency standards — with the revenue from limiting that depreciation dedicated to a National Workforce Transition Fund. On competition and safety, the AI AGENT Act builds a rights-and-duties regime for consumer-facing AI agents accessing major platforms, and the SAFE AI Act bans federal procurement of models that generate CSAM or non-consensual intimate imagery while creating private remedies for survivors. On national security, the Secure AI Development Act mandates secure testing environments for advanced models before deployment and imports aviation-style voluntary incident reporting into AI. The agenda builds on Warner's earlier workforce measures — the AI-Related Job Impacts Clarity Act with Sen. Hawley, the Economy of the Future Commission with Sen. Rounds, and the Investing in American Workers Act — converting what had been a data-and-commission strategy into a full regulatory program.

Primary frame: Responsible innovation across four pillars

A federal policy proposal from the New York Assemblymember who authored the RAISE Act, now pitching a contingency-based direct payment program designed to activate automatically if AI meaningfully displaces American workers. The AI Dividend is explicitly framed as 'fire insurance' — not a prediction that mass unemployment will occur, but preparation in case it does. The proposal is notable for three novel funding mechanisms: a token tax on AI computation, federal equity warrants in frontier AI companies (out-of-the-money, exercisable only if companies multiply dramatically in value), and tax reform eliminating the accelerated depreciation subsidy for AI capital that currently makes automation cheaper than hiring. Revenue flows to three buckets: direct payments to Americans, workforce transition and education investment, and public AI safety/oversight infrastructure. Bores frames the timing urgency around a closing political window — demanding equity stakes in AI companies after they have already captured the value is far harder than structuring it now.

Primary frame: Contingency-based economic insurance

The most developed Democratic proposal for AI governance, focusing on worker protection and economic redistribution alongside safety and competitiveness. Kelly treats AI primarily as an economic disruption problem requiring institutional investment, proposing an industry-funded AI Horizon Fund for worker retraining and infrastructure.

Primary frame: Economic redistribution

The most interventionist and structurally critical position in the current debate, treating AI governance as inseparable from questions of corporate power and wealth inequality. Sanders' proposals include a national data center moratorium, a robot tax, and calls to break up major AI companies.

Primary frame: Democratic control

Khanna's April 2026 manifesto in The Nation, building on his earlier Seven Principles but substantially more developed and politically explicit. Self-identifying as an 'AI democratist' (neither accelerationist nor doomer), Khanna frames AI policy as inseparable from the broader fight against billionaire wealth concentration in a 'new Gilded Age.' Notably published as a Silicon Valley representative who has co-hosted town halls with Sen. Bernie Sanders on AI oligarchy, the piece invokes FDR's New Deal as the template for the scale of response required and proposes a Future Workforce Administration funded by a wealth tax. Khanna explicitly attacks Trump's December 2025 executive order authorizing the DOJ to sue states over AI safety regulations.

Primary frame: AI democratism and economic redistribution

Introduced August 6, 2026 and referred to Energy and Commerce and to the Judiciary, this is a sense-of-the-House resolution asserting that every American and community affected by an AI data center should have the right to transparency, local autonomy, and — stated plainly in the operative text — the right to oppose them. Its findings are the argument: more than 4,400 registered data centers now operate across the United States with at least one facility in every state; developers have used nondisclosure agreements and shell companies to conceal ownership, water use, electricity demand, and tax incentives; AI data centers could consume up to 32 billion gallons of water a year by 2028, with many sited in water-stressed regions; and diesel generators, gas turbines, and cooling systems create air pollution and persistent noise linked to asthma, heart disease, and sleep disruption. The finding that carries the political weight is the last one: construction may create hundreds of well-paying jobs, but even the largest completed facilities often employ fewer than 150 permanent workers. From there the resolution enumerates ten rights, running from a 2,500-foot residential siting ban through enforceable community benefit agreements to clawback provisions on subsidies. It is non-binding and creates nothing — its function is to give the local opposition movement a federal vocabulary.

Primary frame: Community consent and local control

Introduced August 6, 2026 and referred jointly to Education and Workforce and to Ways and Means, this is the first bill to put actual statutory text behind the idea of taxing AI computation and routing the proceeds into public jobs. Title I adds a new subchapter to the Internal Revenue Code imposing an excise tax on foundation model developers, calculated as the greater of a percentage of the fair market value of tokens processed or a percentage of revenue from AI services — with rates that automatically escalate as unemployment rises. Titles II through IV take 100 percent of that revenue into a trust fund and spend it through a Work Protection Administration inside the Department of Labor, whose acronym is a deliberate echo of the New Deal's Works Progress Administration. The WPA awards competitive grants to states, localities, tribes, school districts, universities, and nonprofits to hire people directly into 17 enumerated categories of public work — child care, education, health, elder and disability care, housing, homelessness services, violence prevention, scientific research, infrastructure, arts and libraries, conservation, wildfire and disaster preparedness, facility repair, parks, local journalism, and WIOA training. The bill is unusual in coupling the tax trigger to the labor market it is meant to protect: the levy is imposed only on models used commercially or used to shrink a workforce, and its rate is indexed to the U-4 unemployment measure.

Primary frame: Automation tax funding public employment

California's evolution from the vetoed SB 1047 to SB 53 illustrates the real-time negotiation between ambition and political feasibility in AI governance. SB 53 targets large frontier developers with over $500M annual revenue and requires transparency reports on safety testing, with critical safety incident reporting within 15 days standard or 24 hours if imminent harm.

Primary frame: Transparency

New York's Responsible AI Safety and Education Act establishes reporting and safety governance for frontier AI developers. It covers companies with over $500M in revenue developing frontier models and requires publicly disclosed safety and security protocols. The act includes civil penalties of $1M for initial violations, escalating to $3M for repeat offenses.

Primary frame: Compliance

A House discussion draft from Rep. Jay Obernolte organized into four titles: frontier AI governance, workforce, cybersecurity, and research and international cooperation. It federalizes the SB 53 / RAISE-style transparency model — large frontier developers (>$500M revenue) must write and publicly post a frontier AI framework, file pre-deployment reports, and report critical safety incidents to CAISI within 15 days (or 24 hours if there is an imminent risk of death or serious injury), backed by fines of up to $1M per day and federal and state AG injunctions. CAISI is established in statute within the Department of Commerce to set voluntary security standards and to license Independent Verification Organizations (IVOs) that audit developer frameworks. Its preemption clause is deliberately narrow: it bars only state laws specifically targeting AI model development, expressly preserves laws of general applicability, common law remedies, and regulation of AI use and deployment, and sunsets three years after enactment. Title II builds the most elaborate AI workforce-data apparatus of any proposal — WARN Act layoff disclosures, AI-sensitive occupation forecasts, an AI Workforce Research Hub, and a study of a Rapid AI Adjustment Assistance Program.

Primary frame: Federal transparency + workforce data