Building National Compute Capacity for High-Throughput AI Services: Economic and Policy Implications

The increasing deployment of artificial intelligence has shifted the economic and infrastructural focus from model training to high-throughput, low-latency inference services for widespread consumer and business applications. This shift necessitates the development of robust national compute capacity to secure economic competitiveness, enhance national security, and maintain technological sovereignty. This paper examines three primary models for building such capacity—direct public investment, incentives for private sector development, and Public-Private Partnerships (PPPs)—and presents a comparative evaluation across strategic, fiscal, and governance dimensions. Building on case studies of large-scale AI infrastructure projects, the analysis incorporates total cost of ownership (TCO) considerations, inference pricing dynamics, and supply chain security risks. The paper introduces two practical policy tools: a structured decision-making framework and a phased roadmap for implementation, each visualized through accompanying diagrams to support adoption by policymakers. Findings emphasize that while PPPs offer the most pragmatic balance between control and agility, their success depends on rigorous fiscal governance and international collaboration to mitigate supply chain vulnerabilities. The results provide both analytical and actionable guidance for nations seeking to develop sovereign AI capacity in a rapidly evolving technological and geopolitical environment.