This dissertation examines impacts of Artificial Intelligence (AI) on labor market outcomes and educational choices. The first chapter focuses on labor supply by exploring the relationship between the growth of AI and college major choices. The second chapter turns to labor demand, studying the impacts of AI job postings on labor market outcomes of heterogeneous skill groups. The last chapter analyzes how AI adoption in firms affects gender wage gaps.The first chapter explores how the rise in AI shapes college major choice. I propose a new method to measure how well a major prepares students to work with AI by matching phrases for AI subfields with college major descriptions. I then define AI skill-related majors as those that provide AI-related skill training. Those majors that are most complementary to AI have systematically high growth rates of bachelor’s degree conferrals from 1990 to 2019. In contrast, I find evidence suggesting that majors that are most exposed to AI-driven substitution grow relatively slowly, especially at elite universities.In the second chapter, I study effects of AI on employment and wages for heterogeneous skill groups in the U.S. by introducing and analyzing a task-based framework. I first categorize labor into four skill groups based on skill specializations: (1) abstract and AI-intensive; (2) abstract-intensive but not yet AI-related; (3) routine-intensive; and (4) manual-intensive. The demand for AI skills is then measured by matching phrases for AI-developing skills to descriptions of online job postings. I document a consistent upward trend in the share of AI postings for the high-skilled AI-complement group during my sampling period, 2012-21. There is a strong growth in both employment and wages for abstract and AI-intensive occupations associated with an increasing demand for AI skills, while abstract but not-yet-AI occupations have much smaller growth. Middle-skilled occupations experience wage declines associated with an increase in the standard deviation of the intensity that AI-developing skills are required for job tasks. Employment and wage gaps between abstract and AI-intensive occupations and other skill groups widen as the labor market favors workers with AI skills, consistent with my theoretical model's implications. I also discuss whether AI is possibly a general-purpose technology.The last chapter analyzes the link between gender wage gaps and AI adoption. Using a real-time, high-frequency data on AI adoption in business, I construct measures for current, expected, and continuing AI adoption. AI adoption at the state-month level narrows within-occupation gender wage gaps in mean hourly wages, whereas AI adoption at the industry-month level exhibits a non-monotonic pattern in within-industry, between-occupation gender wage gaps across different percentiles of the wage distribution. The gap widens at the 10th percentile and the median, but shrinks at the 90th percentile. However, using data on online job postings that require AI skills, I find that a higher share of AI postings benefits women more than men across the wage distribution.
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