The shift to digital promised a cleaner, greener economy, but today the embodiment of that transformation is straining energy grids and crashing data centers. Without an intervention, the artificial intelligence (AI) and renewable revolutions seem headed for a clash. Generative AI (GenAI) caught the world off guard with its incredible power—and with it, incredible power consumption. A World Economic Forum study says the energy required to run AI tasks is already rising at an annual growth rate above 26%. By 2028, GenAI could be consuming more electricity than Iceland.
Can wind, solar, and hydro really provide enough power to keep up? And if they can’t, will utilities be forced to keep fossil fuels on the front burner? Experts say an EcoAI is possible, but the way machine-learning models are trained and AI applications are coded will need to change. The energy industry also needs big investments in infrastructure to deliver clean power where and when it’s needed.
Why does GenAI use so much energy?
Large language models (LLMs) are the engine under GenAI’s figurative hood, and they have an unquenchable thirst for fuel. A peer-reviewed study from late 2023 forecasts that apps like ChatGPT will consume between 85 and 134 terawatt hours (TWh) by 2027. That’s roughly equivalent to what the Netherlands and Sweden use in a year.
Shahab Mousavi, a Stanford University AI researcher who specializes in sustainability, says three recent shifts have made cloud computing more energy intensive. “What happened in a nutshell is that Nvidia and its competitors steadily improved their graphics processing units [GPUs] that are customized for high-performance computing and parallel processing, laying the groundwork for the data centers with incredibly high processing power that enable today’s AI systems,” he says.\
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