Sponsors (5)
Introduced December 3, 2025 by James E. Banks · Last progress December 3, 2025
The bill strengthens public data, forecasts, and research capacity to help workers and workforce systems respond to AI-driven job changes, but it introduces new reporting burdens, privacy risks, and modest federal spending with limited long-term guarantees.
Unemployed, displaced, and mid-career workers will get clearer, regular AI-related labor-market forecasts and retraining guidance to plan career transitions and upskilling.
State and local workforce agencies and policymakers will receive actionable, occupation-level forecasts and standardized data to better target training dollars and in-demand occupation lists.
Creation of an AI Workforce Research Hub plus recurring analyses and prize competitions will build public research capacity, scenario planning, and validated methods for understanding AI's labor impacts.
Collecting and sharing unit- or record-level workforce and employer data, even with safeguards, raises privacy and reidentification risks for individuals and small firms.
Enhanced WARN disclosures, new reporting obligations, and requirements to estimate AI-attributable layoffs will increase compliance costs and proprietary/competitive concerns, especially for small businesses.
The bill authorizes several million dollars (including a $24M account plus other multi-year authorizations) of federal spending over FY2026–2030, increasing taxpayer outlays without guaranteed long-term benefits.
Based on analysis of 7 sections of legislative text.
Requires federal data collection, prize competitions, and occupation-level forecasting to measure AI's labor impacts and studies to guide training grants and a Rapid AI Adjustment Assistance design.
Creates a federal program to measure and forecast how artificial intelligence (AI) will change jobs and occupations, by collecting new data, running prize competitions to develop benchmarks, improving layoff and employer reporting, and directing the Bureau of Labor Statistics and other agencies to publish regular prediction-interval employment forecasts for a prioritized set of occupations. It also authorizes modest funding for benchmark competitions and studies, requires public input and interagency consultation, authorizes temporary hiring of technical staff, and directs studies on how to use the new data to improve workforce training grants and a possible Rapid AI Adjustment Assistance program for displaced workers.