Mapping the AI Regulation Landscape

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

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

Senator Warner's AI agenda is not a single bill but a coordinated three-part strategy to build the institutional infrastructure for evidence-based AI workforce policy. First, a bipartisan letter (co-signed by Warner, Hawley, Banks, Hassan, Kelly, Kaine, Hickenlooper, Young, and Rounds) urging the Bureau of Labor Statistics and Census Bureau to rapidly expand AI labor market data collection across existing surveys — CPS, JOLTS, and the National Longitudinal Survey. Second, the AI-Related Job Impacts Clarity Act (with Sen. Hawley), requiring quarterly disclosures from publicly traded companies and federal agencies on AI-driven layoffs, hires, unfilled positions, and retraining — reported to DOL with NAICS codes and published on the BLS website. Third, the Economy of the Future Commission Act (S.4046, with Sen. Rounds), establishing a 10-member bipartisan legislative commission to develop consensus recommendations on workforce development, education, social safety nets, taxation, open-source AI, transportation safety, energy, and robotics. The commission must deliver employment projections by NAICS code at 5- and 10-year horizons within 7 months, and full legislative recommendations within 13 months. Together, the three measures form a pipeline: collect the data, mandate its disclosure, then channel it into bipartisan legislative recommendations.

Primary frame: Data-driven workforce governance

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

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

Governance Structure

Who regulates AI, through what institutions, and with what enforcement tools

Should the U.S. create a new federal AI regulatory body, or distribute authority across existing agencies?

Scope and Application

What gets regulated, how thresholds are defined, and when intervention occurs

Is a revenue-based threshold (like $500M) the right way to identify which AI systems require governance, or does it miss important risks?

Political Positioning

Partisan dynamics, coalition possibilities, and competing theories of the problem

Can a bipartisan coalition form around child safety and transparency while deferring harder questions about preemption and liability?

Enforcement Mechanism vs. Regulatory Scope

X-axis: Narrow Scope (AI-specific) → Broad Scope (Economy-wide)

Y-axis: Voluntary / Industry-led → Mandatory / Government-enforced

Prevention vs. Liability & Regulatory Authority

X-axis: State-led Authority → Federal-led Authority

Y-axis: Liability / Post-hoc → Prevention / Pre-deployment

Innovation Priority vs. Worker Protection

X-axis: Low Worker Priority → High Worker Priority

Y-axis: Low Innovation Priority → High Innovation Priority

Pre-deployment Obligations vs. Federal Preemption

X-axis: Weak Obligations → Strong Obligations

Y-axis: Weak Preemption → Strong Preemption