Laura J. Acosta is an AI policy researcher affiliated with the Brookings Institution, where her work centers on the intersection of artificial intelligence and labor markets. Her research examines how AI adoption affects employment patterns, workforce skills, and the design of public policies aimed at supporting workers through technological transitions. Acosta's analyses contribute to broader discussions on the societal implications of AI deployment, particularly in the context of automation and job displacement.
Acosta's academic background includes graduate studies in public policy and economics, with a focus on technology and labor. She has published policy briefs and working papers through Brookings, often analyzing data from the U.S. Bureau of Labor Statistics and other federal sources to assess the potential effects of AI on various occupational sectors. Her work has been cited in policy debates around workforce retraining programs and the need for updated social safety nets.
Early Career and Education
Acosta completed her undergraduate degree in economics at a public university in the United States before pursuing a master's degree in public policy. During her graduate studies, she focused on the economic impacts of technological change, writing a thesis on the effects of automation on manufacturing employment in the Midwest. After graduating, she worked as a research assistant at a think tank in Washington, D.C., where she contributed to projects on labor market regulation and digital economy issues.
In 2019, Acosta joined the Brookings Institution as a research associate in the Center for Technology Innovation. She was promoted to fellow in 2022, reflecting her growing expertise in AI policy and labor economics. Her early work at Brookings included a 2020 report on the potential for AI to exacerbate income inequality, which drew on Machine learning adoption rates across different industries.
Research on AI and Labor
Acosta's primary research area involves the impact of Artificial intelligence on employment, with particular attention to routine and semi-routine occupations. In a 2021 paper, she analyzed how the deployment of Large language models in customer service and administrative roles could lead to significant job restructuring. She argued that while AI might not eliminate entire occupations, it would likely change task compositions, requiring workers to adapt to new responsibilities.
A 2023 Brookings report co-authored by Acosta examined the differential effects of AI adoption across demographic groups. The study found that workers without college degrees were more likely to face displacement risks in sectors such as logistics and retail, while higher-skilled professionals saw complementary effects. Acosta recommended targeted investment in community college programs and apprenticeship models to mitigate these disparities.
Policy Recommendations and Public Engagement
Acosta has testified before state legislative committees on AI workforce issues, including a 2023 hearing in Maryland on the need for unemployment insurance reforms to cover gig workers affected by automation. She has also contributed to Brookings' annual AI policy agenda, which outlines federal priorities for AI governance. Her recommendations often emphasize the importance of sector-specific training programs, portable benefits, and early warning systems for at-risk industries.
In 2024, Acosta launched a collaborative initiative with MIT CSAIL researchers to develop metrics for measuring AI's labor market effects in real time. The project uses job posting data and Machine learning models to track shifts in demand for specific skills, providing policymakers with up-to-date information. This effort has been supported by grants from the National Science Foundation.
Selected Publications
Acosta's notable publications include "Automation and the American Worker: A Regional Analysis" (2020), "The Task-Based Effects of Generative AI on Office Work" (2022), and "Building a Resilient Workforce for the Age of AI" (2024). Her articles have appeared in the Brookings Institution's policy brief series and have been referenced in reports by the OpenAI economic research team, though she maintains no formal collaboration with the company.
Her 2022 paper on Generative AI in white-collar professions was among the first to quantify the potential for Transformer (architecture)-based tools to augment rather than replace human workers in legal and financial services. The study used survey data from 500 firms to estimate productivity gains and skill requirements, finding that firms investing in worker training saw higher returns from AI adoption.
Current Work and Future Directions
As of 2025, Acosta is leading a Brookings project on the global race for AI talent, examining how immigration policies in the United States and Europe affect the distribution of AI researchers and engineers. She has also begun exploring the implications of Deep learning advances for healthcare and education labor markets, areas she argues have been understudied in the policy literature.
Acosta frequently speaks at academic conferences and industry events, including the annual BAIR (Berkeley AI Research) symposium. She is a member of the advisory board for a nonprofit focused on workforce development in rural communities, where she helps design AI literacy programs for displaced workers.
Infobox
- Born: Not publicly disclosed
- Died: null
- Nationality: American
- Known for: AI policy research on labor markets, workforce transition policy
- Affiliation: Brookings Institution
- Awards: Not publicly listed
Categories
- ai-policy
- labor-economics
- workforce-technology
- brookings-institution