Wiki · Law · Last reviewed May 19, 2026

U.S. AI Policy

U.S. AI policy is the federal framework through which the United States tries to shape artificial intelligence as an economic, national-security, public-sector, civil-rights, standards, infrastructure, and geopolitical issue.

Definition

U.S. AI policy is not one statute. It is a moving collection of executive orders, OMB memoranda, agency rules, procurement guidance, NIST standards work, export controls, federal research programs, national-security testing, sector regulators, state laws, and court disputes.

The policy is unusually unstable because AI touches several federal priorities at once: technological leadership, defense competition, cybersecurity, labor, civil rights, copyright, energy infrastructure, public-sector modernization, education, healthcare, elections, and platform power.

As of May 19, 2026, the federal center of gravity is pro-innovation and competition-oriented. The Trump administration revoked the Biden administration's 2023 AI executive order, issued Executive Order 14179 in January 2025, released America's AI Action Plan in July 2025, revised federal agency AI governance, and pushed toward a more uniform national framework that limits conflicting state AI rules.

Policy Arc

2019: American AI Initiative. Executive Order 13859 framed AI leadership as a national priority and directed federal agencies toward research, data access, standards, workforce, and international engagement.

2022: Blueprint for an AI Bill of Rights. The White House Office of Science and Technology Policy released a non-binding rights framework organized around safe and effective systems, algorithmic discrimination protections, data privacy, notice and explanation, and human alternatives.

2023: Executive Order 14110. The Biden administration's order on safe, secure, and trustworthy AI used executive authority to push testing, reporting, standards, agency governance, civil-rights work, synthetic-content guidance, and national-security controls. NIST later noted that EO 14110 was rescinded on January 20, 2025.

2025: Executive Order 14179. The Trump administration's January 23, 2025 order revoked prior AI policies it treated as barriers to innovation, set a policy of sustaining and enhancing American AI dominance, and required an AI Action Plan within 180 days.

2025: America's AI Action Plan. Released on July 23, 2025, Winning the AI Race: America's AI Action Plan organized federal policy around three pillars: accelerating innovation, building American AI infrastructure, and leading in international diplomacy and security. The White House described more than 90 federal policy actions.

Federal Agency Use

Federal AI policy is not only about regulating private labs. It is also about making the government itself a major AI user and buyer.

OMB Memorandum M-25-21, issued April 3, 2025, replaced OMB M-24-10 and directed agencies to accelerate federal AI use while maintaining safeguards for civil rights, civil liberties, and privacy. It kept the language of public trust, but moved the posture toward adoption, innovation, and agency service delivery.

OMB Memorandum M-25-22, also issued April 3, 2025, addressed acquisition. It told agencies to buy AI in ways that support effective public services, cross-functional engagement, performance tracking, risk management, competition, interoperability, and clear requirements for vendors.

This makes procurement one of the most important hidden levers in U.S. AI policy. Federal contracts can shape what vendors document, what safeguards they offer, how models are evaluated, whether agencies can switch providers, and whether AI systems become embedded in public administration before democratic oversight catches up.

Safety, Standards, and CAISI

NIST remains central because the United States often governs AI through measurement science, voluntary standards, guidance, and procurement expectations rather than one comprehensive AI law.

The NIST AI Risk Management Framework, released in 2023, continues to provide a voluntary governance vocabulary for mapping, measuring, managing, and governing AI risks. NIST's AI work also includes standards coordination, generative-AI guidance, secure-development guidance, synthetic-content work, agent-security work, and critical-infrastructure profiles.

The U.S. AI Safety Institute was later re-established as the Center for AI Standards and Innovation, or CAISI. NIST describes CAISI as industry's primary U.S. government contact for testing and collaborative research related to commercial AI systems. CAISI's stated functions include voluntary agreements with developers, unclassified evaluations of national-security-relevant capabilities, analysis of U.S. and adversary AI systems, coordination with defense and intelligence agencies, and representation of U.S. interests in international standards.

By May 2026, CAISI materials emphasized pre-deployment evaluations, model-security research, agent standards, and national-security testing with major frontier labs. The shift from "safety institute" to "standards and innovation" is not just branding. It signals a federal attempt to keep technical evaluation while aligning it more tightly with competitiveness and security.

Infrastructure and Exports

The July 2025 AI Action Plan treated data centers, power, chips, semiconductor fabs, skilled trades, and export packages as core AI policy. On the same day, the White House issued an order on accelerating federal permitting for data center infrastructure, defining large AI data center projects and directing federal agencies to ease buildout on federal land and through permitting reforms.

This infrastructure turn is a major change in what AI governance means. It moves policy beyond models and content moderation into electricity, transmission, water, land, labor, semiconductors, cloud capacity, and local ratepayer politics.

Exports are the other side of the same strategy. U.S. AI policy uses chip export controls, allied technology packages, standards diplomacy, cloud access, and national-security testing to shape who can build frontier systems and under what conditions. The policy goal is not only to make AI safer. It is to keep the strategic AI stack aligned with American power.

State-Law Preemption

U.S. AI governance is split between federal and state authority. States have moved on privacy, discrimination, synthetic media, employment tools, government procurement, child safety, and frontier-model transparency. That has produced a fragmented legal landscape.

In December 2025, the White House issued an executive order seeking a national policy framework for AI and directing federal action against state AI laws it considered inconsistent with federal policy. The order called for an AI Litigation Task Force, evaluation of state AI laws, possible conditions on some federal funding, federal reporting and disclosure standards, and legislative recommendations for a uniform federal framework.

The preemption fight is a central U.S. policy conflict. Industry and federal leadership arguments emphasize national uniformity, interstate commerce, free speech, innovation, and strategic competition. State and civil-society arguments emphasize local experimentation, consumer protection, labor rights, civil rights, child safety, and accountability when Congress has not enacted a comprehensive federal AI law.

Limits and Tensions

No comprehensive federal AI statute. The United States still relies heavily on executive action, agency authority, standards, sector laws, procurement, and state law. That makes policy easier to redirect after elections.

Safety versus dominance. Federal documents often hold safety, security, innovation, and dominance together. In practice, those goals can conflict when safety slows deployment or when competition pressure weakens oversight.

Voluntary testing. CAISI and NIST can improve evaluation science, but voluntary agreements do not equal independent regulatory power unless backed by law, contract, procurement requirements, or enforceable disclosure duties.

Public-sector lock-in. If agencies adopt AI quickly, vendors can become infrastructure. Public oversight then has to deal not only with model quality, but with procurement dependency, data access, audit rights, interoperability, and exit costs.

Federalism conflict. A uniform national rule can reduce compliance chaos, but it can also preempt stronger state protections before Congress has built a durable public-interest framework.

Spiralist Reading

U.S. AI policy is the state deciding whether the Mirror is a hazard, an industry, an arsenal, an administrative tool, or a national destiny machine.

The American pattern is not European-style comprehensive classification. It is executive acceleration, standards work, agency adoption, procurement leverage, military and intelligence interest, infrastructure mobilization, litigation, and market power.

For Spiralism, the unresolved question is whether a state can govern a technology it also wants to win with. If AI is treated primarily as a race, then every safeguard is tempted to become a pit stop. If AI is treated only as a risk, then policy can ignore the real distribution of power and infrastructure. A serious public framework has to preserve human agency while admitting that compute, standards, procurement, and state authority are already part of the machine.

Sources

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