Abstract: Developed through a Policy Research Project at the LBJ School of Public Affairs under the guidance of Professor Dilawar Syed, this paper introduces the TRUST Framework, a five-stage roadmap to help Taiwanese AI infrastructure companies navigate U.S. market entry. It highlights how policy, permitting, and infrastructure constraints are now the primary drivers of execution.
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AI Infrastructure Is Becoming a Policy Problem: A Framework for Taiwanese Companies Entering the U.S. Market
As part of a Policy Research Project within the LBJ School of Public Affairs at the University of Texas at Austin, graduate students in Professor Dilawar Syed’s course on U.S. Business Competitiveness were tasked with examining a question that is quickly becoming central to the next phase of AI infrastructure development: How can existing U.S. federal and state economic, industrial, and public infrastructure policy support Taiwanese companies seeking to localize and expand in order to meet the demands of the U.S. AI infrastructure buildout?
Castle Peak Advisors was invited to support this effort by providing industry perspective across geopolitical dynamics, macroeconomic trends, and the practical realities of working with Taiwanese companies entering the U.S. market. Drawing on our experience at the intersection of U.S.–Taiwan industrial collaboration, particularly in semiconductors, data centers, and advanced manufacturing, we contributed insights to help inform the students’ analysis. The result is a structured framework designed to translate a complex and fragmented policy environment into something more actionable.
Translating Policy into Action: The TRUST Framework
The presentation introduces the Taiwan Roadmap for U.S. Stateside Technology (TRUST) Framework, a five-stage model intended to guide Taiwanese AI infrastructure companies through U.S. market entry.
The analysis begins with a clear observation. AI infrastructure demand in the United States is accelerating rapidly, with hyperscale capital expenditures expanding at a pace that is beginning to outstrip the capacity of existing systems to support it. At the same time, critical bottlenecks are emerging across power availability, advanced chip packaging, thermal management, and skilled labor.
Layered on top of these constraints is a policy environment that is not lacking in support, but in coordination. Federal initiatives aimed at strengthening domestic manufacturing and supply chain resilience operate alongside state and local regulatory frameworks governing land use, permitting, energy procurement, and environmental compliance. These processes often move sequentially rather than in parallel, introducing uncertainty and extending development timelines. The TRUST Framework is designed to navigate this reality.
It combines a three-lens analysis of demand, regulatory, and economic factors with a state-level assessment of infrastructure readiness and policy conditions. Through case-based analysis, including states such as Indiana and Arizona, the framework illustrates how Taiwanese companies can align their capabilities with specific U.S. capacity gaps while managing regulatory and operational risk. The core takeaway is straightforward. The limiting factor for AI is no longer just compute. It is the ability to build and operate infrastructure at scale within real-world policy, resource, and regulatory constraints.
For Taiwanese companies, localization in the United States is becoming both an opportunity and a requirement. The ability to navigate federal, state, and local policy environments will increasingly determine speed to market and long-term competitiveness.
View the final presentation here: U.S. AI Infrastructure Policy Roadmap