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Implications of Artificial Intelligence-Related Data Center Electricity Use and Emissions: A Workshop

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Topics

Global adoption of artificial intelligence (AI) has been highlighted as a main source of uptick in data center construction and investment in recent years, alongside investments in cloud computing. Since data center loads are large and require continuous power, they need the buildout of energy sources that can provide continuous electricity, such as nuclear or fossil, or grid-scale storage to complement renewables. This poses challenges for both local electric grids and broader climate goals. This workshop aims to understand how to map, measure, and mitigate the impacts of AI as they relate to data center electricity usage.

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Description

The National Academies of Sciences, Engineering, and Medicine will organize a public workshop to explore trends, drivers, and implications of data center electricity use and greenhouse gas emissions related to artificial intelligence. The workshop will bring together a diverse group of expert stakeholders from the electricity industry, the computing industry including producers and users of AI, and the policy making and regulatory community to discuss areas such as:

  • Trends, drivers, and tradeoffs related to AI that have the potential to impact energy demands;
  • Application of AI tools to facilitate energy and water efficiency improvements in data centers;
  • Standardizing benchmarks and accounting methods for energy efficiency and carbon emissions within the Information Technology sector;
  • Trends in electricity markets, electrification, and options for all sectors to meet net-zero goals under increased demand for electricity;
  • Regional considerations related to data center siting and clean generation resource availability.

The aim of the workshop is to understand how to map, measure, and mitigate the impacts of AI as they relate to data center electricity usage. Possible outcomes include describing the proportion of current data center electricity use and emissions related to AI; identifying a range of scenarios for potential increased electricity use and emissions; identifying options to mitigate potential increased electricity use and emissions; and catalyzing conversations among stakeholders on new approaches to understanding and meeting these challenges.

Collaborators

Committee

Chair

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Staff

Kasia Kornecki

Lead

Jasmine Victoria Bryant

Nneka Udeagbala

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