The authors explore the relationship between occupational exposure and technologies, wages, and employment related to artificial intelligence (AI). Using natural language processing (NLP), the authors evaluate occupational exposure to all technology patents in the United States, as well as to specific AI technologies, including machine learning, NLP, speech recognition, planning control, AI hardware, computer vision, and evolutionary computation.

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Artificial Intelligence and the Labor Force

A Data-Driven Approach to Identifying Exposed Occupations

by Tobias Sytsma, Éder M. Sousa

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Research Questions

  1. How have general technology patents affected the U.S. workforce since 1980, and how are AI-specific patents shifting that impact?
  2. How high is the U.S. workforce's general level of occupational exposure to AI technologies, and what occupations are particularly vulnerable?
  3. How important are specific skills and tasks, as well as routineness, to occupational exposure to AI, and how might these findings inform policy discussions?

The rapid development of artificial intelligence (AI) has the potential to revolutionize the labor force with new generative AI tools that are projected to contribute trillions of dollars to the global economy by 2040. However, this opportunity comes with concerns about the impact of AI on workers and labor markets. As AI technology continues to evolve, there is a growing need for research to understand the technology's implications for workers, firms, and markets. This report addresses this pressing need by exploring the relationship between occupational exposure and AI-related technologies, wages, and employment.

Using natural language processing (NLP) to identify semantic similarities between job task descriptions and U.S. technology patents awarded between 1976 and 2020, the authors evaluate occupational exposure to all technology patents in the United States, as well as to specific AI technologies, including machine learning, NLP, speech recognition, planning control, AI hardware, computer vision, and evolutionary computation.

The authors' findings suggest that exposure to both general technology and AI technology patents is not uniform across occupational groups, over time, or across technology categories. They estimate that up to 15 percent of U.S. workers were highly exposed to AI technology patents by 2019 and find that the correlation between technology exposure and employment growth can depend on the routineness of the occupation. This report contributes to the growing literature on the labor market implications of AI and provides insights that can inform policy discussions around this emerging issue.

Key Findings

  • There are no occupations in the United States that are completely unexposed to general technology patents. By 1989, all occupations were exposed to technology patents to some extent.
  • Work tasks that are typically carried out less frequently have the highest technology patent exposure.
  • By 2020, nearly all occupations are exposed to AI technology patents, defined as the ability of computers and machines to simulate human intelligence, to some degree, but the level of exposure varies across occupation groups, time frame, and technology categories.
  • In 2019, up to 15 percent of workers were employed in occupations that were highly exposed to AI technologies.
  • The nature of occupational exposure to technology patents has changed over time. In contrast to earlier decades, occupations that require more education and pay higher wages have become more exposed to technology patents in general.
  • Greater exposure to NLP, speech recognition, and evolutionary computation technology patents is associated with employment growth declines for occupations that specialize in more-routine tasks.

Research conducted by

Funding for this research was provided by gifts from RAND supporters and income from operations. The research was conducted by RAND Education and Labor.

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