Competitiveness Through Immigration
Summary
Immigration reform is a national security imperative. A net inflow of science and technology talent is a defining source of strength and key competitive advantage for the United States. Highly skilled science and technology workers provide our nation with an economic edge and drive innovation. However, intensifying competition for skilled workers abroad and self-imposed barriers to immigration at home are deterring potential talent from coming to the United States, instead routing them to competitor countries.
The Biden-Harris Administration should act to attract and retain foreign science and technology talent through a focused overhaul of U.S. immigration laws and procedures. Specifically, the Administration should draw top talent to the United States by streamlining the visa process and providing greater flexibility for foreign scholars and workers. Steps should be taken to ground visa processes in evidence-based procedures, expand visa limits and classes, redesign security-screening procedures to ease bottlenecks, and reallocate resources to build analytic capabilities. Doing so will enhance our national competitiveness, a top government-wide priority. Imminent action is crucial: the suppressed demand for U.S. visa services due to the COVID-19 pandemic has opened a once-in-a-century window to implement reform.
AI’s high-stakes outputs directly shape American economic, industrial, and technological outcomes. They must not be accepted entirely on the basis of institutional faith rather than verifiable evidence.
The clearinghouse will also neutrally mediate the assignment of auditors to audits—strengthening independence, trust, and ethical governance across the AI ecosystem.
Soft law was never meant to be a permanent solution. Treating it as one, and letting the sandcastle stand in for the skyscraper indefinitely, is how we end up with a decade of voluntary commitments and no enforceable accountability to show for it.
Jobseekers, employees at smaller organizations, and local public workers need more than a weekend crash course in AI. They need practice choosing useful tasks, protecting data, testing outputs, and explaining where human judgment remains necessary.