Measuring the AI Economy

Anton Korinek and Patrick McKelvey

Staff Working Papers from Bank of Canada

Abstract: We construct a macroeconomic estimate of total AI production for the United States, combining inference and R&D/training activities and applying quality adjustments based on the evolution of API prices at fixed performance levels and the pace of algorithmic progress. We estimate that nominal AI compute spending grew over 140% per year each in 2024 and 2025, raw compute capacity grew over 200% per year, and quality-adjusted AI output grew over 2000% per year. These growth rates reflect three compounding forces: expanding data-center capacity, continued improvements in chip efficiency, and rapid algorithmic progress. We then employ our 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 2500% each in 2024 and 2025. Our measures complement traditional national accounts by providing visibility into a fast-moving sector whose activity is difficult to isolate in existing statistics, and they may serve as building blocks for satellite accounts that track AI’s growing role in the economy.

Keywords: Models and tools; Econometric; statistical and computational methods; Monetary policy; Real economy and forecasting; Structural challenges; Digitalization and productivity (search for similar items in EconPapers)
JEL-codes: C43 E01 E22 E31 O33 O47 (search for similar items in EconPapers)
Pages: 22 pages
Date: 2026-06
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Persistent link: https://EconPapers.repec.org/RePEc:bca:bocawp:26-20

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