Introduction

There is deep uncertainty about how significant AI’s workforce effects will be, how quickly they will emerge, and which groups they will affect most. Some economists expect only modest effects on employment in the near term, while several leading AI developers and other economists warn of the possibility of large-scale labor market impacts within one to five years. This uncertainty creates a dual risk: policymakers may be unprepared for labor‑market disruption, and talent shortages could slow AI adoption and sap U.S. growth.

America’s AI Action Plan recognized this and directed the Department of Labor to establish an AI Workforce Research Hub “to lead a sustained Federal effort to evaluate the impact of AI on the labor market and the experience of the American worker.”

The pending launch of the Hub offers an opportunity to strengthen how policymakers monitor and understand AI’s workforce impacts. This paper proposes several options for the AI Workforce Research Hub to collect data and enable frequent analyses so that policymakers can be agile in planning for, and responding to, the workforce impact of advanced AI. The Hub could consider the following four-part approach to enhance policymaker visibility into the labor market impacts of advanced AI.

1. Establish data-sharing partnerships with leading AI developers to gather data on AI usage. Work with leading AI developers such as OpenAI, Anthropic, Microsoft, xAI, Meta, and Google to establish agreements and standards for sharing anonymized AI usage statistics that can be linked to occupation-level employment, earnings, and hiring statistics.

2. Establish data-sharing partnerships with payroll and hiring platforms to gather data on employment, wages, and hiring. Work with firms such as ADP, LinkedIn, Revelio Labs, and Indeed to establish data-sharing agreements for data on employment, wages, hiring trends, and skill demands across the economy, so that these data can be linked to AI usage statistics and made available to researchers.

3. Enhance federal data collection to measure AI usage. Support the Bureau of Labor Statistics and U.S. Census Bureau to add an AI usage supplement to the Current Population Survey (CPS) and AI expenditure items to the Annual Integrated Economic Survey (AIES), while continuing the AI Supplement to the Business Trends and Outlook Survey (BTOS).

4. Build analysis and forecasting capacity through a voluntary expert committee. Engage leading economists to analyze combined datasets and produce regular forecasts on AI’s workforce impacts.

The first three components focus on assembling the underlying data by bringing together information on AI usage, employment, wages, and hiring into a secure and consistent home within the Hub. With improved data on both AI diffusion and key economic indicators, researchers could examine questions that are currently difficult to answer, such as how adoption in particular occupations relates to wages, hiring demand, or job turnover for different categories of workers in those occupations over time. The fourth component would then task a small group of independent experts to analyze these linked datasets and produce regular reports for the Department of Labor, other relevant agencies, congressional committees, and the public. This last component would ensure that emerging workforce effects are monitored and interpreted consistently over time.