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Lawmakers Push for Better Data as AI’s Workforce Impact Comes into Focus

 |  March 11, 2026

Concern about artificial intelligence’s potential impact on the U.S. workforce is rising among voters, economists and policymakers, prompting new efforts in Congress to improve how federal agencies measure the technology’s economic effects. Lawmakers from both parties are pressing labor and statistical agencies to update national surveys and data collection methods, arguing that better information is essential to crafting policy responses to what many see as a looming labor market disruption.

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    The push reflects a growing recognition on Capitol Hill that the federal government lacks reliable, detailed data about how AI is already reshaping employment patterns across industries,” according to Politico. While economists and technology companies broadly agree that AI could increase productivity and create new types of jobs over time, many also expect a period of near-term disruption as automation replaces some existing roles.

    A bipartisan group of nine senators recently urged the Department of Labor, the Bureau of Labor Statistics and the Census Bureau to update their core workforce surveys to better capture AI’s influence on employment and workplace practices, NextGov/FCW reports. The lawmakers asked the agencies to incorporate AI-specific questions into surveys such as the Current Population Survey, the Job Openings and Labor Turnover Survey, and the National Longitudinal Survey.

    In a letter to the agencies, the senators argued that current federal data systems were not designed to track the rapid adoption of generative AI across the economy. They noted that evidence from academia, the private sector and the media provides conflicting signals about whether AI will produce job losses or job growth, making it difficult for policymakers to assess the technology’s true economic impact.

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    The senators wrote that federal statistical agencies must become “adaptable and responsive” as the U.S. workforce approaches what they described as an inflection point driven by AI adoption. More detailed data, they said, would help researchers, policymakers and labor market participants better understand how the technology is affecting job creation, layoffs and workplace culture.

    Those calls for improved measurement come as public anxiety about AI’s labor impact intensifies, per Politico. A February survey conducted by The Economist and YouGov found that roughly 63% of Americans believe AI will lead to fewer jobs, including 62% of Republicans. At the same time, the labor market has shown signs of slowing, adding to concerns that automation could accelerate job displacement.

    WHAT’S NEXT IN TECHREG

    Research led by Stanford University economist Erik Brynjolfsson found that employment among workers aged 22 to 25 in occupations most exposed to AI has declined relative to peers since generative AI tools emerged in late 2022, even as more experienced workers in those same fields have remained relatively stable.

    These early indicators have prompted lawmakers to consider additional policy responses beyond improved data collection. Several bills introduced in Congress focus on expanding workforce training programs designed to help displaced workers transition to new roles. Other proposals call for employers or federal agencies to report data on AI-related layoffs or hiring slowdowns.

    Despite the growing attention, Congress has yet to enact significant legislation addressing AI’s labor market implications. Per Politico, lawmakers remain divided between those who want to minimize regulatory constraints to maintain U.S. leadership in AI development and those who argue that the government must prepare workers for potentially significant economic disruption.

    For now, improved data collection may represent the most immediate area of bipartisan agreement. By expanding federal surveys and statistical reporting, lawmakers hope to gain a clearer picture of how AI is changing employment patterns across sectors and demographics.