Skills and Future of Work

As employers increasingly seek workers who can apply AI alongside domain expertise, universities should integrate AI across disciplines—not just standalone AI or computer science programs—to prepare graduates for the modern workforce.

Labor unions have claimed that General Motors laid off more than 1,000 workers at its Factory Zero EV facility because it installed 50 AI-integrated manufacturing robots, but the layoffs were far more likely driven by weaker-than-expected EV demand and shifting production priorities. Policymakers should focus on helping workers adapt to automation through retraining rather than resisting productivity-enhancing technologies.

Too many academics now trade objectivity for narrative, ignoring inconvenient facts that get in the way of the story they want to tell.

U.S. visa policies are limiting the flow of foreign expertise that is critical to strengthening American manufacturing, innovation, and scientific leadership. Congress should create a dedicated visa category with expedited processing for technical experts and researchers who advance U.S. industrial competitiveness.

Fears that AI will trigger widespread job losses are increasingly contradicted by new evidence showing that firms adopting AI intensively hire more workers—including entry-level employees—and expand employment across a wide range of occupations. Rather than slowing AI adoption, policymakers should accelerate it through a national AI strategy while pushing back against misleading narratives that undermine productivity, competitiveness, and economic growth.

Cities that are successfully scaling AI are investing in workforce upskilling alongside governance and technology deployment. Case studies from Washington, DC, San Jose, Seattle, and Cleveland show that employee training and AI literacy are critical to turning pilot projects into lasting improvements in public service delivery.

Echoing social capitalism, the encyclical gets technology and employment wrong, succumbing to the lump-of-labor fallacy and short-term protection over long-term progress.

South Korea produces large numbers of STEM graduates, but too many are attracted to medicine, and too few go into engineering. Korea should rebalance its education financing and university incentives to ensure that enough engineers are ready to work in advanced industries.

Proposals to tax AI computing power are proliferating as concerns about AI grow. But an AI compute tax would slow productivity growth, drive investment abroad, and do little to protect workers or preserve the tax base.

As AI-powered cyber threats become more advanced, the federal government should modernize the CyberCorps SFS program by integrating AI-security training, reforming cyber hiring pipelines, and expanding training infrastructure to build a stronger cybersecurity workforce.