Environment & Energy

What you need to know about AI and climate change
We take a hard look at the good, the bad, and the whoa of AI.

by DAISY SIMMONS
SEPTEMBER 4, 2025

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But here’s the thing about any AI tool: Despite their differences, they all share an insatiable appetite for energy – lots of it. And as they scale up, their hunger only grows. Early machine learning systems ran comfortably on desktop computers with minimal power consumption. Some of today’s most prominent AI systems use 100,000 GPUs (the specialized chips that crunch AI calculations), drawing as much electricity as a small city and filling server farms that span several football fields. For perspective, Meta’s flagship AI system relied on about 16,000 of these chips, a setup that would fit in a single, much smaller facility. As we speak, clusters of more than 300,000 GPUs are entering the drawing board, too.

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The climate reality: What we know and what we don’t

Every step of AI computing comes with a carbon cost. According to new analysis from MIT Technology Review, AI data centers now consume 4.4% of all U.S. energy, with projections showing AI alone could use as much electricity as 22% of U.S. households by 2028. These centers typically use electricity that’s 48% more carbon-intensive than the U.S. average.

The training process – where AI systems learn by digesting huge datasets – requires astronomical amounts of energy. Training GPT-4, for its part, gobbled through enough energy to power San Francisco for three days, at a cost of over $100 million.

And training accounts for just 10-20% of AI’s energy use. The real energy hog is inference – what happens every time someone asks a question, generates an image, or gets an AI recommendation. The MIT Technology Review study found that a simple text query uses about as much energy as riding six feet on an e-bike, while generating a five-second video burns the equivalent of a 38-mile ride.

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