Beyond GDP: Economic Measurement in the AI Era

Dr. Jiemin Guo is an internationally recognized authority on economic measurement, with more than 30 years of professional experience across government, international organizations, and academia, specializing in national accounts, input-output analysis, global value chains, and methodologies and applications in national accounts and input-output supply-use compilation and modeling. He served as a senior economist at the U.S. Bureau of Economic Analysis (2001–2025), where he led research on input-output accounts and supply-use tables, the measurement of global value chains, and the integration of U.S. industry accounts with the System of National Accounts, receiving the U.S. Department of Commerce Gold Medal Award; he represented the United States in the APEC Technical Group on Trade in Value Added and in ministerial-level trade-statistics negotiations under the U.S.–China Joint Commission on Commerce and Trade. From 2007 to 2012 he headed quarterly national accounts at the OECD Statistics Directorate in Paris and led the OECD's technical cooperation program with China's National Bureau of Statistics. He served as Vice President of the International Input-Output Association and as senior adviser to the International Civil Aviation Organization (ICAO). He was a visiting scholar at MIT and serves on the editorial board of Economic Systems Research. He has made profound contributions to national accounts, input-output analysis, global value chains, and to methodologies and applications in national accounts and input-output supply-use compilation and modeling, publishing extensively, delivering numerous academic presentations, and participating in guiding the compilation of national accounts in multiple countries. He received his Ph.D. from the University of Illinois at Urbana-Champaign.

Since ChatGPT's launch in November 2022, artificial intelligence has been transforming every aspect of human life at unprecedented speed, leading a new industrial revolution. Yet GDP barely registers this revolution — the moment is ripe for the "Beyond GDP" agenda. This lecture surveys the emerging field of AI and economic measurement from the perspective of national accounts: why AI investment initially depresses measured productivity (the J-curve); the welfare GDP cannot see — how free AI services challenge the production boundary (GDP-B); how to measure AI — from task exposure and actual usage data to AI thematic accounts at national statistical offices; SNA 2025's new rules treating data as a produced asset and their valuation puzzles; and China's practice and gaps: digital-economy accounting, the world's first data-asset accounting rule, and the missing national-accounts methodology for generative AI. The lecture offers economics researchers a beyond-GDP measurement framework and points to future research frontiers.