Across the University of California system, undergraduate enrollment in traditional computer science programs has declined for two consecutive years, while overall college attendance in the United States has slightly increased. The scale of the decline is limited but notable, resembling the post–dot-com crash downturn and indicating a change in student expectations about the future of technology-related work.
UC San Diego represents a significant outlier. After introducing a dedicated artificial intelligence major, the campus has seen strong demand for this program even as conventional computer science enrollment contracts. This pattern suggests that students are not disengaging from technology fields overall, but reallocating their interest toward AI-specific pathways that they perceive as more aligned with emerging labor market opportunities.
In China, this reallocation is further advanced. Universities there have integrated AI across multiple disciplines, treating it as foundational infrastructure rather than a specialized subfield. AI-related competencies are being embedded into core curricula, with some institutions creating entire colleges focused on AI and requiring students in varied disciplines to develop proficiency with AI tools. In this context, AI literacy is shifting from a differentiating skill to a standard requirement.
American universities are attempting to adapt to similar pressures. Both elite private institutions and large public universities are launching AI-centered majors, departments, and colleges. These programs frequently combine machine learning with areas such as decision science, cybersecurity, ethics, and public policy, based on the assumption that AI will influence sectors beyond software, including business, law, health care, and the arts. Initial enrollment figures indicate substantial student interest in these new offerings.
The transition is generating internal friction within academia. Some faculty members are rapidly incorporating AI into teaching and research, while others express concern about issues such as academic integrity, potential job displacement, and the possible weakening of foundational technical skills. Administrators advocating for accelerated AI integration report resistance from within their institutions, even as they argue that graduates without AI competencies may face reduced competitiveness in the job market.
Parents are also influencing student choices. After years of encouraging computer science as a relatively low-risk, high-return major, many now anticipate that routine programming tasks may be increasingly automated. As a result, they are directing students toward fields such as mechanical and electrical engineering, which they view as less vulnerable to AI-driven substitution.
Available enrollment data indicates that the primary trend is not a broad withdrawal from technology disciplines, but a redistribution within them. As AI-focused degrees expand across U.S. universities, students are shifting away from legacy computer science tracks toward programs that they believe will position them more directly within the core technologies of the next phase of economic development.
