Sponsors (5)
The bill invests in data, forecasts, and temporary technical capacity to help workers and policymakers anticipate and respond to AI-driven labor changes, but does so at added taxpayer expense and with new reporting burdens, privacy risks, and limited long-term continuity that could blunt effectiveness if participation or safeguards are weak.
Workers (including unemployed people and those considering reskilling) and training providers get recurring, actionable data and forecasts (with prediction intervals) about AI-driven occupational and task changes, improving decisions about reskilling, career moves, and training program design.
Federal agencies gain enhanced technical capacity (temporary hiring authorities, expert recruitment, forecasting teams) and standardized tools/benchmarks to analyze AI labor impacts and implement workforce programs more effectively.
Workers affected by mass layoffs will receive clearer disclosures when AI substantially contributed (type of AI, how used, upskilling efforts) and employers/trainers get semiannual machine-readable statistics on AI adoption to target retraining.
The federal government will incur new spending and authorized outlays (multiple authorizations across agencies and prize pools), increasing taxpayer costs with benefits that are uncertain or may not persist after short funding windows.
Employers, educational institutions, and state/local workforce boards face new compliance and administrative burdens (reporting, standardized data elements, WARN disclosures, integrating forecasts into in‑demand lists), imposing costs especially on small businesses and local agencies.
Key data efforts rely on voluntary firm participation and limited data-sharing, risking unrepresentative or biased statistics that could mislead policymakers and workers or blunt the effectiveness of forecasting and training programs.
Based on analysis of 7 sections of legislative text.
Creates a federal program to measure, forecast, and report how artificial intelligence (AI) is changing jobs and workforce needs, and to give states, training providers, and employers better data and tools to prepare. It funds research, pilot statistics, prize competitions for benchmarking and forecasting, temporary technical hires, voluntary employer data-sharing, and new employer disclosures when AI contributes to mass layoffs. Requires the Labor Department and partner agencies to publish a list of at least 15 occupations likely affected by AI, produce range-based employment forecasts with evaluation and public archives, add AI questions to federal surveys, provide technical assistance to workforce programs, and run short-term projects with limited funding and phased sunsets to build federal capacity and inform training and policy responses.
Introduced December 3, 2025 by James E. Banks · Last progress December 3, 2025