Much of the current AI debate focuses on what could go wrong. Dire headlines warn AI might displace workers, threaten privacy, enable cyberattacks, or go rogue. These risks deserve serious attention, but focusing on them exclusively can obscure what is at stake if the United States fails to capitalize on the technology’s potential. That is the central challenge for policymakers—not to prevent AI from causing harm, but to maximize its benefits while managing its risks.
In health care, AI can accelerate drug discovery, improve diagnoses, and expand access to care. In education, it could give every student access to private tutors and personalized learning resources that until recently were available only to the wealthiest families. In transportation and logistics, AI can improve safety and automation while lowering costs and increasing efficiency. In industry, it can increase productivity, strengthen U.S. competitiveness, and help revitalize advanced manufacturing.
The United States will not realize those benefits if policymakers treat AI primarily as a technology that needs to be constrained. Pausing AI research, imposing moratoriums on building data centers, or holding back releases of new frontier models would make it harder to develop and deploy the technology at scale. Safety must be one goal of AI policy, but not the only goal or the overall objective. Transportation policy seeks to reduce accidents while also promoting mobility, trade, and economic opportunity. Health policy considers safety while ultimately aiming to help people live longer and healthier lives. AI policy should similarly seek to manage risks while enabling the broadest possible benefits.
As things stand, the United States lacks a conducive regulatory environment for AI development and adoption. For example, the United States needs to simplify its data protection laws to allow firms to collect, share, and use data responsibly so that physical AI systems can collect data in public spaces and AI agents can interact with personally identifiable information. But instead of enacting these reforms, Congress has been deadlocked on federal privacy legislation, and states keep adding new layers of complexity.
Antitrust policy is another obstacle. To maximize the benefits of technology by encouraging widespread adoption and use, competition law should account for the dynamics of technology markets rather than focusing narrowly on existing market shares. Regulators should recognize that company that dominates search today could face entirely different competitive conditions as AI chatbots change how consumers find and use information. Yet the Federal Trade Commission and Justice Department have been focused recently on slowing down or breaking up leading tech companies.
Export controls should allow U.S. companies to sell chips and models around the world. International demand for American AI can strengthen the market for U.S. firms and support the investment needed for broader AI adoption at home. Policymakers therefore should be cautious about “kill switches” and similar proposals that could make foreign customers wary of relying on American AI by signaling that the U.S. government could shut down their access at any time.
Occupational licensing is another area where outdated rules could impede AI adoption. Health boards dominated by physicians may have limited incentives to approve AI-enabled services that compete with physicians’ existing roles and revenue. Accountants, investment advisers, counselors, lawyers, and other professions could face similar conflicts. Policymakers should examine whether licensing requirements serve legitimate consumer protection goals or instead protect incumbents from new forms of AI-enabled competition.
Removing barriers to AI adoption, however, is only part of the challenge. The federal government also needs to help build the market for AI in sectors where regulation and public investment already play a major role. Health information technology provides a useful example. It took roughly $40 billion in federal spending to help move the United States toward electronic health records, followed by years of regulatory efforts to improve interoperability and ensure that patients and doctors could access needed information. AI will require similar leadership in health care and other sectors where government policy and spending strongly influence adoption.
The good news is that the United States already has a model for the kind of leadership that is needed to seize the benefits of AI. In 2010, the federal government launched the National Broadband Plan, which established a strategy for deploying broadband while identifying ways to use it in health care, education, energy, and public safety. The country needs a comparable national AI strategy that moves beyond general principles and identifies specific opportunities for adoption. Such a strategy could identify 10 sectors in which AI could produce the largest gains over the next decade, assign federal agencies clear responsibilities, establish adoption targets, fund demonstration projects, and identify outdated regulations that prevent deployment. Congress could then measure progress annually and require agencies to explain where they are falling short.
The need for a more ambitious approach to AI policy is especially clear against the backdrop of the fierce international competition to lead the way in AI technology. China is not only accelerating its digital advances, but doing so with a population that enthusiastically supports instead of opposing it. In Stanford University’s 2026 AI Index, 84 percent of Chinese say they are excited about AI, whereas only 38 percent of Americans do. The United States should not adopt China’s political system, but U.S. policymakers should recognize the importance of having a clear plan and a positive vision for technological progress. Political leaders’ rhetoric about AI shapes public expectations. When they describe it in dystopian terms, that pessimism can discourage the investment and adoption needed to capture its benefits.
Unfortunately, the current legislative agenda tilts toward pessimism. Congress has considered dozens of AI bills, while states have proposed hundreds more, but relatively little of this activity focuses on accelerating AI adoption. Policymakers should fund more pilot projects, oversee federal agencies’ efforts to enable AI adoption in their respective sectors, and invest in the people and businesses that will build America’s AI future.
Every transformative technology has presented risks. Electricity did. Automobiles did. The Internet did. But the countries that benefited most were those that learned how to harness them at scale. AI will be no different. Yes, the United States needs guardrails to manage the risks of AI, but it also needs to build the roads that will allow the technology to reach every corner of the economy. Right now, policymakers are spending too much time designing guardrails for roads they have not yet built. The priority should be to build both.
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