What has Congress said about artificial intelligence and jobs?

Reviewed by Jason Burns, Editorial Steward · Last updated

Members of Congress have said, in on-the-record hearings, that artificial intelligence will reshape the U.S. workforce — creating some jobs, displacing others, and requiring new investment in worker training, transition support, and labor protections. As Bernard Sanders, U.S. Senator (Vermont), put it on the record before the Senate HELP Committee hearing, 'AI and the Future of Work: Moving Forward Together' (May 16, 2024): "Artificial intelligence and robotics must benefit the working people of our country, not just corporate CEOs and wealthy stockholders."

Source: www.help.senate.gov/hearings/ai-and-the-future-of-work-moving-forward-together

Editor's note: Estimates of AI's effect on employment vary widely and are updated frequently. Numbers cited in any single hearing should be checked against current Bureau of Labor Statistics data before being treated as authoritative.

What this means in plain English

In Senate labor hearings, senators from both parties have said AI will change many jobs — some entire occupations will shrink, some will grow, and many will change shape. Chair Sanders and others have emphasized that whether the gains flow to workers or only to shareholders depends on policy choices such as training investment, portable benefits, and labor protections.

What this does not mean

A Senate hearing does not create labor rights. Statements from members of Congress about AI and jobs are not, on their own, mandates on private employers or on federal agencies.

How to use this information

Employers should track two policy streams that flow from these hearings: (1) worker-notice and consultation rules around AI-driven monitoring and decision-making, and (2) federal training and re-skilling investment that may become available through labor-department programs.

Risks and limitations

Predictions about total job gains or losses from AI are highly uncertain and depend on assumptions about deployment speed, complementarity, and regulation. Treat single-figure predictions with caution.

Sources

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