THE LONG VIEW

Bottom Line Up Front: Frictions in the cross-border flow of scientific knowledge pose a significant threat to U.S. innovation, particularly in strategically vital fields like semiconductors, quantum science, and artificial intelligence, due to deep integration with global research networks. Threat Identification: The U.S. innovation ecosystem is dependent on a globally distributed supply chain of scientific knowledge, not just domestic research. Policies or geopolitical events that restrict international collaboration, data sharing, or researcher mobility can disrupt the flow of foundational knowledge that feeds into patented technologies. This dependency is especially acute in high-tech sectors with rapid advancement cycles. Probability Assessment: High likelihood within the next 3–5 years (2026–2031). Increasing geopolitical tensions, export controls on research, visa restrictions for international scholars, and data localization laws are already introducing frictions. The current date (2026-06-01) places the U.S. in a period of heightened technology competition, making such disruptions not only plausible but actively unfolding [Esposito, 2026]. Impact Analysis: Reduced connectivity in the knowledge supply chain leads to longer development cycles, duplicated efforts, and lower innovation productivity. Critical national technology priorities—explicitly including Semiconductors, Quantum Science, and AI—face delayed breakthroughs and weakened competitive positioning globally. The economic and security implications are substantial, affecting both private-sector leadership and national R&D objectives. Recommended Actions: (1) Strengthen bilateral and multilateral science cooperation agreements with trusted partners; (2) Exempt fundamental research from export controls while safeguarding applied technologies; (3) Invest in digital infrastructure to enhance open-access scientific knowledge sharing; (4) Monitor the health of the global knowledge supply chain using citation and collaboration metrics as early-warning indicators. Confidence Matrix: - Threat Identification: High confidence (supported by multi-generational citation tracing) - Probability Assessment: Moderate to High confidence (based on observed policy trends and simulation outcomes) - Impact Analysis: High confidence (quantified reductions in innovation productivity in simulation) - Recommended Actions: Moderate confidence (policy effectiveness context-dependent but aligned with historical evidence of open science benefits) [Esposito, 2026].