AI, Internet Governance, and Emerging Technologies 

This brief analyzes US federal approaches to artificial intelligence governance under the previous Trump and Biden administrations, along with developments in the early months of Trump’s second term. Despite differing priorities, both administrations relied primarily on soft law—nonbinding executive orders, voluntary industry standards, and advisory frameworks—over formal regulation. The result has been a market-driven, decentralized governance model that emphasizes innovation and competitiveness but offers limited enforcement and oversight. In contrast to regulatory-heavy models in the European Union and China, the US approach remains rooted in public-private coordination, industry leadership, and flexible federal action

Limitations in the prevailing measures hamper the accurate evaluation of the domestic artificial intelligence (AI) workforce. Accelerating the timeline for revising the Standard Occupational Classification system to create a technical AI occupational category could increase the prospects that AI policies will improve AI competitiveness, especially through more granular measures of the way AI is changing the nature of jobs. While AI is unquestionably a future foundation for national security and economic competitiveness in the United States during the digital era, American interests will be best served by a deliberate, measured, and data-driven workforce policy that builds on national strengths.

This policy brief examines the evolving landscape of artificial intelligence governance in the United States and focuses on the tension between federal soft law approaches and emerging state-level regulations. Soft law refers to nonbinding guidelines, voluntary standards, and informal frameworks that aim to guide innovation without imposing rigid legal constraints. Whereas federal policy has favored voluntary standards and industry-led initiatives, several states have proposed or enacted binding regulations that mirror the more prescriptive models of the European Union and China. Through case studies of Virginia, Texas, and Colorado, the brief assesses the risks of regulatory fragmentation and argues that a more coordinated federal framework—grounded in soft law—can preserve innovation while guiding state-level governance. The brief concludes by offering recommendations for a model governance approach that balances flexibility, national coherence, and state autonomy.