A skeptic’s take on AI electricity load growth

Catalyst with Shayle Kann

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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