Counting AI: A blueprint to integrate AI investment and use data into US national statistics | 국외연구자료

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Counting AI: A blueprint to integrate AI investment and use data into US national statistics

Brookings

2026.01.16

In this article, we first describe three reasons why AI’s impact on the economy may be under-estimated, focusing on the treatment of AI as expensed intangible capital; the mismeasurement of quality change and service flows; the scalability and spillovers of AI; the J-curve created by complementary organizational investments; and the missing value of free or bundled AI services. Then, we discuss the parallel problem of undercounted costs, including cyber risk, privacy loss, and intellectual property erosion, and argue that AI creates both intangible assets and “intangible liabilities” that fall outside current national accounts. We also lay out a two-horizon measurement agenda: a near-term Generative AI Intensity Index that combines statistical surveys with provider telemetry to track AI adoption by sector and region, and a medium-term reform of national and satellite accounts that separately identifies AI-related capital, services, labor reallocation, and household time use. We conclude with several policy recommendations, including the creation of an interagency AI measurement task force, the development of NIST-led standards for AI usage metrics, and the systematic integration of AI measures into Bureau of Economic Analysis (BEA) and Bureau of Labor Statistics (BLS) products so that macroeconomic, labor market, and infrastructure policy rests on statistics that reflect the actual scale and distribution of AI adoption.

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