As Americans waited to see what economic policies would be enacted in the new presidential term, the Hoover Prosperity Program hosted a timely conference on the pressing challenges facing the US economy. The panel discussion on “How Should the US Economy Adapt to the AI Boom?” was of particular import to the Silicon Valley audience, bringing together distinguished scholars from across Hoover and Stanford University: Jonathan Levin, president of Stanford University and professor of economics at the Stanford Graduate School of Business (GSB); Steven J. Davis, senior fellow at the Hoover Institution; and Justin Grimmer, senior fellow at Hoover and professor of political science at Stanford. The panel was moderated by Amit Seru, senior fellow at Hoover and professor of finance at Stanford GSB.
The AI Revolution Is Already Upon Us
Amit Seru opened the discussion with an acknowledgment that artificial intelligence (AI) is not a futuristic possibility—it is already reshaping daily life, the economy, and governance structures. AI has contributed over $400 billion to the US economy as of 2024, with projections suggesting a $4.4 trillion impact by 2030.
However, Seru noted, as productivity potential soars, so do concerns about job displacement, equity, and governance. The panel therefore focused on three central themes: labor and workforce adaptation, antitrust concerns and innovation, and AI regulation.
Labor and Workforce Adaptation
Steven Davis highlighted that AI’s impact on labor markets is multifaceted and consistent with historical technological shifts that have displaced some jobs while creating others. However, he stressed that AI might be less disruptive than past upheavals in sectors such as manufacturing: Job losses from AI are expected to be more dispersed across industries and geography, lessening concentrated economic hardship. Remote work further reduces locational constraints, making job transitions smoother for displaced workers.
Davis underscored that AI often complements rather than replaces workers. From generative AI tools aiding document preparation to diagnostic tools assisting healthcare professionals, these technologies enhance productivity without necessarily eliminating jobs. Still, the need for reskilling and labor market adaptation remains critical. He cautioned against overregulation and emphasized the principle of “do no harm” in policymaking—allowing innovation to continue without preemptively regulating AI based on theoretical harms that may or may not come to pass.
Davis also noted that AI diffusion will be gradual, tempered by organizational adaptation and complementary investments, such as skills development for workers. He expressed cautious optimism that the US economy could manage workforce transitions without severe disruptions, provided policy responses remain thoughtful and adaptive.
Antitrust Concerns and Innovation
Jonathan Levin focused on how AI is reshaping markets and raising complex questions about market design, competition, and innovation. He pointed to the emergence of AI-powered platforms in areas including digital advertising, ride-sharing, and logistics, noting that these markets often exhibit winner-takes-all dynamics, where a few firms earn significantly more than their competitors. Firms that produce data and hardware infrastructure are likely to have disproportionate shares of market power in the short run, though given the intense and growing competition, this dominance may diminish over the longer term.
Levin emphasized the importance of designing antitrust policies that recognize these unique characteristics of AI-driven markets. He stressed that traditional antitrust tools may fall short when faced with rapidly evolving technologies and network effects—i.e., when widespread adoption of a product or platform among consumers augments its dominance in relation to other firms. Policymakers will need to think creatively about how to maintain market fairness and competition while fostering innovation.
Levin also addressed the role of universities in this evolving landscape. Universities have a critical role in conducting foundational research, providing education, and serving as neutral forums for policy debates. However, they face challenges in retaining talent, given industry’s significant advantages in offering resources and compensation. Levin urged stronger collaborations between academia, industry, and government to ensure balanced and effective AI development.
Regulating AI
Justin Grimmer addressed the challenges associated with regulating AI firms. He noted that AI’s predictive, processing, and generative capabilities each bring distinct regulatory issues. Uses of predictive AI (such as for risk assessments in criminal justice) and generative AI (such as for content creation) raise critical questions about fairness, privacy, and potential for misuse.
Grimmer pointed out the talent asymmetry among labor forces in industry, government, and academia. Industry’s superior ability to attract top talent often leaves regulators and public institutions at a disadvantage in understanding and overseeing complex AI systems. This imbalance could result in weaker oversight or regulations that are driven by those less familiar with technological realities than those in the industries they regulate.
He also highlighted that fairness discussions must be grounded in comparisons to existing systems. For example, while algorithmic decision making may carry biases, it is essential to evaluate whether these systems perform better or worse than human alternatives. Grimmer advocated for a pragmatic approach in AI governance, one that acknowledges relative improvements rather than seeking unattainable perfection.
International coordination emerged as another key challenge. Grimmer suggested that just as treaties exist for controlling nuclear weapons and addressing climate change, the United States should actively participate in shaping international frameworks for AI governance. The stakes are global, and unilateral action may be insufficient.
Academia’s Role in Facilitating AI Adaption
Throughout the panel, there was a consensus that while AI presents enormous opportunities for productivity and innovation, it also poses serious challenges for labor markets, competition policy, and governance. The panelists emphasized that effective adaptation will require coordinated efforts across the private sector, government, and academia.
Watch the full panel conversation here.
Read more about the conference findings here.
About the Hoover Prosperity Program
The Hoover Prosperity Program conducts evidence-based research on the institutions and policies that foster economic prosperity amid today’s public policy challenges. The program is dedicated to producing research that empowers citizens and policymakers to make informed decisions about a core question: What combination of laws, institutions, policies, and regulations is most likely to foster long-term economic prosperity?
