In response, many firms are now focusing on building tangible products rather than merely shifting algorithms for commerce or generating more social media. In the aerospace and defence sectors, for example, AI is seen not as an end but a tool. It is a means to enhance human creativity and productivity rather than replace it. “Software is not in the greatest position with AI,” Delian Asparouhov, who runs a firm focused on in-space manufacturing, told me. “Now people are shifting to hard tech. Designing and building spaceships still needs people.”
This could spell trouble for elite universities, but represents a major advantage for schools that teach the practical skills companies actually need. So far, these opportunities are largely concentratedin red and purple states in the Midwest and South — the regions most focused on reshoring manufacturing and other industries from overseas.
Attitudes are shifting alongside these economic changes. One recent survey found that roughly 83% of Generation Z feel that learning a skilled trade can be a better pathway to economic security than college — including 90% of those already holding college degrees. Indeed, as college enrolment has dropped between 2020 and 2023, trade school enrolment grew by 10%. These changes suggest that as practical skills gain value, regions offering them are likely to attract both talent and jobs.
California is already losing hard tech jobs to emerging players in Texas and the South. Between 2022 and 2023, Texas led the country in new tech jobs, while California remained largely flat. Florida came in second, with Georgia, Tennessee, and North Carolina also posting significant gains. Looking ahead, CompTIA (the Computing Technology Association) projects that Texas, Mississippi, Tennessee, and South Carolina will see the fastest growth in tech over the next decade.
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Erik Brynjolfsson, Bharat Chandar and Ruyu Chen:
This paper examines changes in the labor market for occupations exposed to generative artificial intelligence using high-frequency administrative data from the largest payroll software provider in the United States. We present six facts that characterize these shifts. We find that since the widespread adoption of generative AI, early-career workers (ages 22-25) in the most AI-exposed occupations have experienced a 13 percent relative decline in employment even after controlling for firm-level shocks. In contrast, employment for workers in less exposed fields and more experienced workers in the same occupations has remained stable or continued to grow. We also find that adjustments occur primarily through employment rather than compensation. Furthermore, employment declines are concentrated in occupations where AI is more likely to automate, rather than augment, human labor. Our results are robust to alternative explanations, such as excluding technology-related firms and excluding occupations amenable to remote work. These six facts provide early, large-scale evidence consistent with the hypothesis that the AI revolution is beginning to have a significant and disproportionate impact on entry-level workers in the American labor market.