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Civitas Outlook
Topic
Politics
Published on
May 19, 2026
Contributors
Jonathan Hartley

AI Regulation is Coming to Your State

Contributors
Jonathan Hartley
Jonathan Hartley
Research Fellow
Jonathan Hartley
Summary
Regulation signals a seriousness about an important emerging policy issue but does not reliably generate better outcomes for workers.
Summary
Regulation signals a seriousness about an important emerging policy issue but does not reliably generate better outcomes for workers.
Listen to this article

Increasingly, state legislators are raising their ire toward Generative Artificial Intelligence (GenAI).  AI is not polling well amidst the general public, perhaps because of widespread confusion or misunderstanding about what the technology can do. GenAI has been adopted by a large fraction of workforces and consumers in the United States and around the world. Alongside co-authors, I estimate between 30 to 40 percent of the U.S. labor force is using GenAI tools at work in 2026 (See Hartley, Jolevski, Melo, and Moore (2025)). It’s quite possible that lower income individuals will be some of the greatest beneficiaries of GenAI in the years to come as lower skilled individuals can use such tools to complete tasks that were otherwise overly complicated. For instance, those who were unable to code before the advent of GenAI now have the capability of asking large language models (LLMs) like ChatGPT and Claude to complete tasks they were not able to complete previously. By lowering the barrier to entry for programming, LLMs allow individuals to leapfrog into the digital economy by building apps or performing freelance technical work. The scope with which anyone can now use AI agents to complete tasks is enormous. Meanwhile, high-skilled workers at many large technology firms including Amazon, Meta and Microsoft are feeling some negative local labor market consequences as these companies engage in AI-led restructuring to create efficiencies.

In other sectors, LLMs can help people, especially those below the median income, learn dominant trade languages—ike English or Mandarin—or translate complex technical manuals into local dialects, opening up global labor markets. Farmers in rural areas can also use LLMs to diagnose crop diseases from descriptions or images and get advice on soil management and climate-resilient planting.

Read the full essay on The Foundation for Research on Equal Opportunity.

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