A skeptic’s take on AI electricity load growth

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Mar 6, 2025

Jonathan Koomey, founder of Koomey Analytics and expert on data center energy demand, questions the hype surrounding AI-driven electricity load growth. He recalls past overestimations in energy predictions and emphasizes the importance of energy efficiency in AI. The discussion navigates concerns about the sustainability of massive investments in AI infrastructure, the unpredictability of energy demand, and the historical patterns of computing efficiency improvements. Koomey's insights provide a balanced perspective on the future of data centers and electricity consumption.

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ANECDOTE

Debunking Internet Electricity Use Predictions

Around 2000, some predicted the internet would use half of all US electricity in 10 years.
Research by Jonathan Koomey and others at Lawrence Berkeley National Lab debunked this, finding it was only 3%.

INSIGHT

Efficiency Mitigates Growth

Data center electricity use doubled between 2000 and 2005.
The industry responded with efficiency technologies, limiting growth from 2010 to 2018 despite increased compute demand.

INSIGHT

Hyperscale Data Centers

Growth in computing continued, but the industry prioritized efficiency.
This involved hardware and software redesign and a shift to more efficient hyperscale data centers.

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The predictions are coming in hot. Data centers could grow to consume more than 9% of U.S. electricity generation by 2030, according to EPRI. That’s more than double its current estimated data center load. AI will increase global data center power demand 165% by 2030, says Goldman Sachs. And billions of dollars are at stake. Utilities, megasite developers, and data center operators are all basing major decisions on predictions like these.

But they’re also the kinds of predictions we’ve seen before. In 1999, when the internet was growing fast, a couple researchers claimed it would grow to consume half of all U.S. power generation within a decade — until a team at Lawrence Berkeley National Laboratory debunked it.

Jonathan Koomey was one of those researchers. Although today’s predictions about energy usage are tamer than those in 1999, Jonathan still has questions about the current hype around AI power demand. He's is now the founder and president of Koomey Analytics, which has published multiple papers on the topic, including a recent report for the Bipartisan Policy Center: Electricity Demand Growth and Data Centers: A Guide for the Perplexed.

So what are the assumptions that go into these new predictions? And how do they hold up to scrutiny?

In this episode, Shayle talks to Jonathan about why he questions the hype around AI load growth predictions and why he believes energy constraints will incentivize the AI industry to focus on efficiency. Shayle and Jonathan cover topics like:

The time lags and proprietary data that hinders precise data center load estimates, both in historical analyses and future predictions
The difficulty of reproducing the predictions of even prominent institutions like the IEA
The two basic assumptions that go into predictions: AI demand and AI power requirements
Why Jonathan believes conventional wisdom relies on questionable sources, like Nvidia’s business plan
The unexplored areas of AI energy efficiency, like computer architecture, software improvements, algorithms, and special purpose computers

Recommended resources

Lawrence Berkeley National Laboratory: 2024 United States Data Center Energy Usage Report

Nature: Will AI accelerate or delay the race to net-zero emissions?

Joule: To better understand AI’s growing energy use, analysts need a data revolution

WSJ: Internet Hype in the ’90s Stoked a Power-Generation Bubble. Could It Happen Again With AI?

Open Circuit: The data center boom: ‘All the cheap power is gone’

Credits: Hosted by Shayle Kann. Produced and edited by Daniel Woldorff. Original music and engineering by Sean Marquand. Stephen Lacey is executive editor.

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