Toward Measuring AI Infrastructure Investment and Economic Resilience Across Ten Economies: Financing Architectures, Capital Formation, and Deployment Timing

<jats:p>Countries are expanding AI infrastructure through markedly different combinations of public and private capital, yet cross-country assessment is complicated by inconsistent investment accounting, incomplete national coverage, and the lag between expenditure and usable capacity. This study compares financing architectures across ten economies: the United States, United Kingdom, Canada, Japan, South Korea, Germany, France, Singapore, China, and India. Public AI Infrastructure Investment Share (PAIIS), Private AI Infrastructure Investment Share (PrAIIS), and AI infrastructure investment intensity (AI-IIS) are treated as bounded measurement concepts rather than presently comparable national statistics. The analysis distinguishes source quality, realization status, AI specificity, accounting basis, capital-formation eligibility, and deployment timing. Qualifying AI-specific realized monetary records are available from five economies, but national coverage remains incomplete and several records combine capital and operating expenditure. France provides completed-project funding with unresolved payment timing; Germany and Singapore provide procurement evidence; China provides realized broader-compute CAPEX that is too broad for an AI-only numerator; and India reports mission-wide expenditure without an infrastructure-specific split. The study therefore compares financing architecture, monetary evidence, and deployment timing rather than constructing national investment rankings. Macroeconomic and labor-market indicators are reported only as descriptive context. The findings show that scope, accounting treatment, realization status, deployment timing, and coverage must be aligned before meaningful cross-country investment comparisons or subsequent resilience analysis can be undertaken.</jats:p>