Power Hungry: How AI Will Drive Energy Demand

Christian Bogmans, Patricia Gomez-Gonzalez, Ganchimeg Ganpurev, Giovanni Melina, Andrea Pescatori and Sneha Thube

No 2025/081, IMF Working Papers from International Monetary Fund

Abstract: The development and deployment of large language models like ChatGPT across the world requires expanding data centers that consume vast amounts of electricity. Using descriptive statistics and a multi-country computable general equilibrium model (IMF-ENV), we examine how AI-driven data center growth affects electricity consumption, electricity prices, and carbon emissions. Our analysis of national accounts reveals AI-producing sectors in the U.S. have grown nearly triple the rate of the private non-farm business sector, with firm-level evidence showing electricity costs for vertically integrated AI companies nearly doubled between 2019-2023. Simulating AI scenarios in the IMF-ENV model based on projected data center power consumption up to 2030, we find the AI boom will cause manageable but varying increases in energy prices and emissions depending on policies and infrastructure constraints. Under scenarios with constrained growth in renewable energy capacity and limited expansion of transmission infrastructure, U.S. electricity prices could increase by 8.6%, while U.S. and global carbon emissions would rise by 5.5% and 1.2% respectively under current policies. Our findings highlight the importance of aligning energy policies with AI development to support this technological revolution, while mitigating environmental impacts.

Keywords: generative AI; data centers; energy and the macroeconomy; climate change and growth; CGE models (search for similar items in EconPapers)
Pages: 32
Date: 2025-04-22
New Economics Papers: this item is included in nep-ain, nep-ene, nep-env and nep-reg
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