The demand for generative AI is set to drive a tenfold increase in data centre electricity consumption by 2030, according to figures from Bloomberg Intelligence (BI).
BI’s research indicates that the surge in energy requirements to support AI workloads will also fuel a growing need for solar power and battery storage solutions.
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Enterprises worldwide are racing to integrate AI into their existing operations, with companies like Nvidia positioning themselves to meet the growing demand through innovative hardware solutions.
Nvidia's upcoming Blackwell GPU is a prime example, with the company noting that demand is “well above supply.”
According to BI, the launch of Blackwell GPUs in 2025 could result in a 1% increase in total U.S. power consumption — or a 50% surge in data centre energy usage — within its first year.
BI further predicts that data centres could see a significant rise in demand, potentially consuming up to 17% of the US's total electricity by 2030.
With already strained energy infrastructure, BI research suggests data centre operators could turn to renewables to shore up a more cost-effective supply.
The research firm estimates that solar, wind and battery storage solutions will play a key role in meeting increased demand from US data centres, while also offsetting declines in coal and other fossil fuels.
BI estimates that data centre demands for renewables could provide alternative energy companies like First Solar, Enphase, and Sunnova with a lucrative opportunity, with analysis suggesting that combined 2026 revenue could approach $40 billion for US-listed solar firms.
However, BI also suggests the rise in demand for data centre power could prove profitable for the natural gas industry.
BI estimates that data centres could increase natural gas demand by at least three billion cubic feet (bcf) a day — potentially rising to 10-plus bcf a day by 2030.
Meanwhile nuclear energy “may miss [the] AI revolution,” as BI estimates that next-generation nuclear reactors likely won’t come online in time to support generative AI workloads, with build outs expected to take at least 10 years.
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