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What Is the Opportunity Cost of State AI Policy? A New Cato Policy Analysis Discusses

by June 10, 2025
June 10, 2025 0 comment

Jennifer Huddleston

In the first half of 2025, over 1,000 bills related to artificial intelligence (AI) have been introduced at the state level. This follows Colorado becoming the first state to pass a comprehensive law significantly regulating AI in both its development and deployment last year. What might this mean for the future of AI, as well as for the overall policies that states and the federal government take on innovation?

SEE MORE: Is a State AI Patchwork Next? AI Legislation at a State Level in 2024

Cato just released the “Opportunity Costs of State and Local AI Regulation” policy analysis authored by FAI Research Fellow Joshua Levine. This paper examines the trade-offs involved in state and local-level AI regulation through the lens of opportunity cost, an economic concept of the cost of the opportunity forgone by a particular action.

Among this paper’s key points:

  • Beyond just AI policy, state approaches to tax, energy, and personal liability policy can also encourage and discourage investments in technology, including AI.
  • Current state-level AI policy discussions range significantly, including those seeking merely to study and understand the technology or its impact on a particular industry, as well as regulation on deployment or development, resulting in a wide range of opportunity costs for various regulations.
  • The costs of state and local AI regulation include hindering the diffusion of this technology and creating significant opportunities for regulatory capture and rent seeking. Regulation risks setting a predetermined outcome for a technology in its infancy.

SEE MORE: Words to Fear: I’m From the State Government, and I’m Here to Help with AI Risk

This discussion comes as debate over a potential moratorium on state-level AI policy continues. Considering the impact of state-level AI policy, however, the conversation extends not only to what the policy would do but also to what might not be accomplished in the meantime.

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