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Tech companies keep finding new ways to bring AI into every facet of our lives. AI has taken over my search engine results, and new virtual assistants from Google and OpenAI announced last week are bringing the world eerily close to the 2013 film Her (in more ways than one).

As AI has become more integrated into our world, I’ve gotten a lot of questions about the technology’s rising electricity demand. You may have seen the headlines proclaiming that AI uses as much electricity as small countries, that it’ll usher in a fossil-fuel resurgence, and that it’s already challenging the grid.

So how worried should we be about AI’s electricity demands? Well, it’s complicated.

Using AI for certain tasks can come with a significant energy price tag. With some powerful AI models, generating an image can require as much energy as charging up your phone, as my colleague Melissa Heikkilä explained in a story from December. Create 1,000 images with a model like Stable Diffusion XL, and you’ve produced as much carbon dioxide as driving just over four miles in a gas-powered car, according to the researchers Melissa spoke to.

But while generated images are splashy, there are plenty of AI tasks that don’t use as much energy. For example, creating images is thousands of times more energy-intensive than generating text. And using a smaller model that’s tailored to a specific task, rather than a massive, all-purpose generative model, can be dozens of times more efficient. In any case, generative AI models require energy, and we’re using them a lot.

Electricity consumption from data centers, AI, and cryptocurrency could reach double 2022 levels by 2026, according to projections from the International Energy Agency. Those technologies together made up roughly 2% of global electricity demand in 2022. Note that these numbers aren’t just for AI—it’s tricky to nail down AI’s specific contribution, so keep that in mind when you see predictions about electricity demand from data centers.

There’s a wide range of uncertainty in the IEA’s projections, depending on factors like how quickly deployment increases and how efficient computing processes get. On the low end, the sector could require about 160 terawatt-hours of additional electricity by 2026. On the higher end, that number might be 590 TWh. As the report puts it, AI, data centers, and cryptocurrency together are likely adding “at least one Sweden or at most one Germany” to global electricity demand.

In total, the IEA projects, the world will add about 3,500 TWh of electricity demand over that same period—so while computing is certainly part of the demand crunch, it’s far from the whole story. Electric vehicles and the industrial sector will both be bigger sources of growth in electricity demand than data centers in the European Union, for example.

Still, some big tech companies are suggesting that AI could get in the way of their climate goals. Microsoft pledged four years ago to bring its greenhouse-gas emissions to zero (or even lower) by the end of the decade. But the company’s recent sustainability report shows that instead, emissions are still ticking up, and some executives point to AI as a reason. “In 2020, we unveiled what we called our carbon moonshot. That was before the explosion in artificial intelligence,” Brad Smith, Microsoft’s president, told Bloomberg Green.

What I found interesting, though, is that it’s not AI’s electricity demand that’s contributing to Microsoft’s rising emissions, at least on paper. The company has agreements in place and buys renewable-energy credits so that electricity needs for all its functions (including AI) are met with renewables. (How much these credits actually help is questionable, but that’s a story for another day.)

Instead, infrastructure growth could be adding to the uptick in emissions. Microsoft plans to spend $50 billion between July 2023 and June 2024 on expanding data centers to meet demand for AI products, according to the Bloomberg story. Building those data centers requires materials that can be carbon intensive, like steel, cement, and of course chips.

Some important context to consider in the panic over AI’s energy demand is that while the technology is new, this sort of concern isn’t, as Robinson Meyer laid out in an April story in Heatmap.

Meyer points to estimates from 1999 that information technologies were already accounting for up to 13% of US power demand, and that personal computers and the internet could eat up half the grid’s capacity within the decade. That didn’t end up happening, and even at the time, computing was actually accounting for something like 3% of electricity demand.

We’ll have to wait and see if doomsday predictions about AI’s energy demand play out. The way I see it, though, AI is probably going to be a small piece of a much bigger story. Ultimately, rising electricity demand from AI is in some ways no different from rising demand from EVs, heat pumps, or factory growth. It’s really how we meet that demand that matters.

If we build more fossil-fuel plants to meet our growing electricity demand, it’ll come with negative consequences for the climate. But if we use rising electricity demand as a catalyst to lean harder into renewable energy and other low-carbon power sources, and push AI to get more efficient, doing more with less energy, then we can continue to slowly clean up the grid, even as AI continues to expand its reach in our lives.

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