Measuring the AI economy

Anton Korinek and Patrick McKelvey
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Patrick McKelvey: Bank of Canada

No WP26-9, Working Paper Series from Peterson Institute for International Economics

Abstract: The authors construct a macroeconomic estimate of total AI production for the United States, combining inference and research and development/training activities and applying quality adjustments based on the evolution of API prices at fixed performance levels and the pace of algorithmic progress. They estimate that nominal AI compute spending grew over 140 percent per year each in 2024 and 2025, raw compute capacity grew over 200 percent per year, and quality-adjusted AI output grew over 2,000 percent per year. These growth rates reflect three compounding forces: expanding data-center capacity, continued improvements in chip efficiency, and rapid algorithmic progress. The authors then employ their estimates to develop a nascent framework for "AI GDP" that tracks the AI economy as a coherent whole rather than dispersed across standard industry classifications. Quality-adjusted AI GDP grew by more than 2,500 percent each in 2024 and 2025. The measures in the paper complement traditional national accounts by providing visibility into a fast-moving sector whose activity is difficult to isolate in existing statistics. The measures may serve as building blocks for satellite accounts that track AI's growing role in the economy. The conceptual framework and methodology used in this paper are described in detail in the technical appendix.

Keywords: artificial intelligence; national accounts; GDP mismeasurement; AI satellite accounts; quality-adjusted prices; algorithmic progress; AI GDP (search for similar items in EconPapers)
JEL-codes: E01 E22 O33 O47 (search for similar items in EconPapers)
Date: 2026-05
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