## Power Hungry: How AI Will Drive Energy Demand

_IMF Working Papers, April 22, 2025_

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**Canonical URL:** [Power Hungry: How AI Will Drive Energy Demand](https://www.imf.org/en/publications/wp/issues/2025/04/21/power-hungry-how-ai-will-drive-energy-demand-566304)

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## Bibliographic details
- Authors: Christian Bogmans, Patricia Gomez-Gonzalez, Ganchimeg Ganpurev, Giovanni Melina, Andrea Pescatori, Sneha D Thube
- Published: April 22, 2025
- Series: IMF Working Papers
- DOI: https://doi.org/10.5089/9798229007207.001

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### Summary
- The development and deployment of large language models like ChatGPT require expanding data centers that consume vast amounts of electricity.
- The paper uses descriptive statistics and a multi-country computable general equilibrium model (IMF-ENV) to examine how AI-driven data center growth affects electricity consumption, electricity prices, and carbon emissions.
- Analysis of national accounts finds AI-producing sectors in the U.S. have grown nearly triple the rate of the private non-farm business sector.
- Firm-level evidence shows electricity costs for vertically integrated AI companies nearly doubled between 2019-2023.
- Simulations of AI scenarios in the IMF-ENV model are based on projected data center power consumption up to 2030.
- The AI boom will cause manageable but varying increases in energy prices and emissions depending on policies and infrastructure constraints.

### Key findings and statistics
- AI-producing sectors in the U.S. have grown nearly triple the rate of the private non-farm business sector.
- Electricity costs for vertically integrated AI companies nearly doubled between 2019-2023.
- Under scenarios with constrained growth in renewable energy capacity and limited expansion of transmission infrastructure:
  - U.S. electricity prices could increase by 8.6%.
  - U.S. carbon emissions would rise by 5.5% under current policies.
  - Global carbon emissions would rise by 1.2% under current policies.
- The analysis covers projected data center power consumption up to 2030.

### Model, scenarios, and scope
- Model used: IMF-ENV, a multi-country computable general equilibrium (CGE) model.
- Empirical evidence: national accounts analysis and firm-level electricity cost data.
- Time horizon for projections: up to 2030.
- Geographic focus: U.S. and global outcomes are examined; subject tags include Europe, North America, and Global.

### Policy implications and recommendations
- Align energy policies with AI development to support AI expansion while mitigating environmental impacts.
- Address infrastructure constraints by expanding renewable energy capacity and transmission infrastructure to limit increases in electricity prices and emissions.
- Consider policy measures that can moderate the price and emissions impact from rapid data center and AI-related power demand growth.

_Italic: Source: Power Hungry: How AI Will Drive Energy Demand, IMF Working Paper No. 2025/081 (April 22, 2025)._

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_Source: https://www.imf.org/en/publications/wp/issues/2025/04/21/power-hungry-how-ai-will-drive-energy-demand-566304_