We did the Math on AI’s Energy Footprint. Here’s the Story you Haven’t Heard.
coolgopher writes:
https://www.technologyreview.com/2025/05/20/1116327/ai-energy-usage-climate-footprint-big-tech/
AI's integration into our lives is the most significant shift in online life in more than a decade. Hundreds of millions of people now regularly turn to chatbots for help with homework, research, coding, or to create images and videos. But what's powering all of that?
[...] Given the direction AI is headed-more personalized, able to reason and solve complex problems on our behalf, and everywhere we look-it's likely that our AI footprint today is the smallest it will ever be. According to new projections published by Lawrence Berkeley National Laboratory in December, by 2028 more than half of the electricity going to data centers will be used for AI. At that point, AI alone could consume as much electricity annually as 22% of all US households.
[...] Racks of servers hum along for months, ingesting training data, crunching numbers, and performing computations. This is a time-consuming and expensive process-it's estimated that training OpenAI's GPT-4 took over $100 million and consumed 50 gigawatt-hours of energy, enough to power San Francisco for three days. It's only after this training, when consumers or customers "inference" the AI models to get answers or generate outputs, that model makers hope to recoup their massive costs and eventually turn a profit.
"For any company to make money out of a model-that only happens on inference," says Esha Choukse, a researcher at Microsoft Azure who has studied how to make AI inference more efficient.
As conversations with experts and AI companies made clear, inference, not training, represents an increasing majority of AI's energy demands and will continue to do so in the near future. It's now estimated that 80-90% of computing power for AI is used for inference.
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