Our Work

Measuring AI’s Impact on Workers Requires a Better-Funded BLS

• September 15, 2026
• Beck Holtzman

On March 5, a bipartisan coalition of senators published a statement urging the Census Bureau and the Bureau of Labor Statistics (BLS) to improve their “collection, analysis, and dissemination of high-quality, timely data regarding artificial intelligence.” The question of whether — and how severely — AI will displace workers has become one of the most consequential and least settled questions in contemporary labor economics.  

 Accurate data is the backbone of our national response to labor displacement. To inform public policy, business decisions, and individual career choices, the BLS needs greater funding to generate accurate, AI-specific data using modern statistical methodology.  

 Over the past year, apocalyptic statements made by tech leaders have fueled widespread concern around AI’s future role in the labor force. Microsoft AI CEO Mustafa Suleyman, for instance, predicted in early 2026 that within 18 months, nearly all white-collar work would be automated. Studies from Stanford and Brookings predict significant disruption to white-collar employment. Meanwhile, reports from the Yale Budget Lab (YBL), the European Investment Bank (EIB), and Goldman Sachs assessed more moderate improvements in productivity.  

 For example, the EIB report found that firms that adopted AI increased labor productivity by 4%, with no adverse effects on their employment. The YBL report concluded that in the first 33 months since ChatGPT’s release in November 2022, there has been no major economic disruption in the labor market. By contrast, the Stanford study measured a 16% relative decline in employment among early-career workers aged 22 to 25 between 2022 and 2025. Additionally, it found that employment growth for young workers has been stagnant, with substantial declines in occupations heavily exposed to AI. In 2024, Brookings advocated for a working paper by the University of Pennsylvania and OpenAI with similar results, which estimated that roughly 19% of U.S. workers are in occupations where at least half of their tasks could be disrupted by generative AI.  

 The EIB used data from 12,000 firms across the EU and 800 firms in the United States. But in general, most of these studies rely heavily on data from the BLS, including Occupational Employment and Wage Statistics (OEWS), the Current Population Survey (CPS), and the Department of Labor’s O*NET task database. Key surveys for labor force statistics, like the CPS, are jointly sponsored by the Census Bureau and the BLS. 

 The BLS publishes 10-year occupational projections annually, but the agency itself acknowledges its projection methods are “not designed to capture extremely rapid technological change.” To enable the best possible decision-making by national stakeholders, better labor projections and data are both indispensable. 

 In November 2025, the Census Bureau added AI questions to the Business Trends and Outlook Survey (BTOS), a necessary starting point for the collection of AI-specific data. The BLS states clearly that the CPS needs modernization, as a response to both declining response rates and lack of AI-specific data. In April 2026, the BLS outlined what this modernization should look like:  

 The BLS has already begun the implementation of the internet self-response (ISR) instrument, which would run in conjunction with the CPS and provide critical additional data. The adoption of the internet survey response instrument and parallel surveys, as recommended in their report, addresses lower response rates and insufficient AI data. These modernization efforts go hand in hand with existing economic literature on AI data best practices. Common practices include the use of task-based AI exposure scores and LLM-based task annotations, which better capture the changing scope of AI’s impacts. Reform proposals tend to call for expanded data collection and better frameworks to process it. This proposal would cost an additional $60 million a year in funding. Currently, however, the agencies are unable to conduct testing at the scale necessary to ensure the accuracy of the new surveys due to funding constraints. 

 The big picture: The real (inflation-adjusted) funding for the BLS has declined by 13.8% since 2016. In January 2025, the BLS almost resorted to cutting the sample size of the CPS until a last-minute revision was made to the continuing resolution that allowed the BLS to spend CPS funds at a faster rate. The gravity of these potential cuts prompted two former BLS commissioners to urgently call upon Congress for funding.  

 For FY2026, the Trump administration’s budget request would be a $56 million decrease from the FY2025 budget, citing proposed reorganization efforts that would save money and move the BLS under the Department of Commerce. This sharp drop in necessary funding would almost guarantee furloughs and a stark decrease in the quantity and quality of BLS data at the moment it is needed most. 

 The BLS funding decline makes it difficult to maintain current operations, let alone respond to potential AI-induced labor volatility. For the BLS to generate concrete employment data in the world of AI, it will take funding to hire data scientists with AI expertise, to train existing staff on new tools and methodologies, and to secure reliable data-sharing agreements with private sector partners.  

 These reforms are essential. Without them, the labor market will remain impossible to measure accurately for the foreseeable future. Timely labor statistics are an invaluable asset when fast-paced economic conditions arise, so now more than ever it is necessary for the federal government to invest in its statistical agencies.