AI is revolutionizing industries from healthcare to manufacturing, driving unprecedented levels of efficiency and innovation. However, this technological advancement comes at a significant cost: substantial electricity consumption. The computational power required to sustain AI’s rise is doubling roughly every 100 days. On average, a ChatGPT query requires nearly 10 times as much electricity to process as a Google search. According to the International Energy Agency (IEA), AI could increase global electricity demand by up to 5% by 2025, adding over 80 terawatt-hours (TWh) of annual energy consumption. Data centers worldwide currently consume 1-2% of overall power, but this percentage is expected to rise to 3-4% by the end of the decade. Goldman Sachs analysts predict AI will represent about 19% of data center power demand by 2028.
This surge in energy demand is primarily due to the energy-intensive processes of training and inference in AI models, which rely on high-performance computing (HPC) systems to manage vast amounts of data and complex calculations.
There are over 8,000 data centers in operation globally, with approximately one-third located in the U.S. In 2019, the global energy demand for AI training and inference was around 40 TWh, comparable to Switzerland’s annual consumption. As AI utilization accelerates and models become more data-intensive, the IEA projects that this energy demand for AI will triple by 2025, driven by regions like China, India, and Southeast Asia. In the U.S., overall electricity demand is poised to grow 2.4% annually, compared to essentially zero in prior years. Data centers could account for 7-10% of total U.S. electricity demand in the next few years, compared to 2-3% at the end of 2023, necessitating around $50 billion in new generation capacity investments.
Conversely, AI offers opportunities to enhance energy efficiency by optimizing data center operations, reducing AI models’ energy intensity, and supporting smart grid management and renewable energy integration. For example, AI could shift the most taxing tasks to off-peak hours, thus balancing the load on power grids. Such advancements can help mitigate some inflationary pressures by improving overall energy efficiency. Moreover, AI-driven productivity gains across various sectors could offset inflationary impacts by boosting economic output and efficiency.
In conclusion, while AI is set to drive significant increases in global electricity demand due to its energy-intensive computational needs, it also holds the potential to improve energy efficiency. The dual impact of AI on energy consumption and efficiency highlights the importance of strategic investments in power infrastructure and the development of AI technologies that are more energy efficient. From an investment point of view, investors should be on the lookout for any cyclical dips in the global utilities sector to build long-term portfolio allocations that will offer exposure to this secular demand for electricity.
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