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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.

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Over the past decade, more than 10 billion dollars has been invested in Pittsburgh tech companies, with more than 3.5 billion invested in 2021 alone. With the context of such strong sectoral growth in mind, RAND Corporation researchers set out to characterize the science- and technology-focused (STF) workforce ecosystem in the Pittsburgh region and suggest policy changes and investment opportunities to future-proof the ecosystem. Researchers sought to define STF occupations in a regionally relevant way, characterize the current state of the STF ecosystem, identify barriers and facilitators to participation in the STF ecosystem, and develop strategies to facilitate the STF ecosystem's continued growth. To achieve these goals, the research team used qualitative and quantitative methods. The research team selected Boston and Nashville as peer regions to further contextualize quantitative findings. Researchers found that Pittsburgh has a sizable share of STF employment relative to the United States and to Nashville. However, additional investments and changes to policy can safeguard the region's strengths and support Pittsburgh as a flourishing science and technology hub. Recommendations include improving market conditions to support expansion of the STF workforce; supporting and engaging communities of color and other locally underrepresented groups; building out regionally relevant, data-backed career pathways; and crafting and implementing a regional STF strategy.