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AI models consume tons of energy and increase greenhouse gas emissions.Tech firms and governments say an energy revolution must happen to match the pace of AI development.Many AI leaders are rallying around nuclear energy as a potential solution.
Advances in AI technology are sending shockwaves through the power grid.
The latest generation of large language models requires significantly more computing power and energy than previous AI models. As a result, tech leaders are rallying to accelerate the energy transition, including investing in alternatives like nuclear energy.
Big Tech companies have committed to advancing net zero goals in recent years.
Meta and Google aim to achieve net-zero emissions across all its operations by 2030. Likewise, Microsoft aims to be “carbon negative, water positive, and zero waste” by 2030. Amazon aims to achieve net‑zero carbon across its operations by 2040.
Major tech companies, including Amazon, Google, and Microsoft, have also struck deals with nuclear energy suppliers recently as they advance AI technology.
“Energy, not compute, will be the No. 1 bottleneck to AI progress,” Meta CEO Mark Zuckerberg said on a podcast in April. Meta, which built the open-source large language model Llama, consumes plenty of energy and water to power its AI models.
Chip designer Nvidia, which skyrocketed into one of the most valuable companies in the world this year, has also ramped up efforts to become more energy efficient. Its next-generation AI chip, Blackwell, unveiled in March, has been marketed as being twice as fast as its predecessor, Hopper, and significantly more energy efficient.
Despite these advancements, Nvidia CEO Jensen Huang has said allocating substantial energy to AI development is a long-term game that will pay dividends as AI becomes more intelligent.
“The goal of AI is not for training. The goal of AI is inference,” Huang said at a talk at the Hong Kong University of Science and Technology last week, referring to how an AI model applies its knowledge to draw conclusions from new data.
“Inference is incredibly efficient, and it can discover new ways to store carbon dioxide in reservoirs. Maybe it could discover new wind turbine designs, maybe it could discover new materials for storing electricity, maybe more effective materials for solar panels. We should use AI in so many different areas to save energy,” he said.
Moving to nuclear energy
Many tech leaders argue the need for energy solutions is urgent and investing in nuclear energy.
“There’s no way to get there without a breakthrough,” OpenAI CEO Sam Altman said at the World Economic Forum in Davos in January.
Altman has been particularly keen on nuclear energy. He invested $375 million in nuclear fusion company Helion Energy and has a 2.6% stake in Oklo, which is developing modular nuclear fission reactors.
The momentum behind nuclear energy also depends on government support. President Joe Biden has been a proponent of nuclear energy, and his administration announced in October it would invest $900 million in funding next-generation nuclear technologies.
Clean energy investors say government support is key to advancing a national nuclear agenda.
“The growing demand for AI, especially at the inference layer, will dramatically reshape how power is consumed in the US,” Cameron Porter, general partner at venture capital firm Steel Atlas and investor in nuclear energy company Transmutex, told Business Insider by email. “However, it will only further net-zero goals if we can solve two key regulatory bottlenecks—faster nuclear licensing and access to grid connections—and address the two key challenges for nuclear power: high-level radioactive waste and fuel sourcing.”
Porter is betting the incoming Trump administration will take steps to move the needle forward.
“Despite these challenges, we expect the regulatory issues to be resolved because, ultimately, AI is a matter of national security,” he wrote.
AI’s energy use is growing
Tech companies seek new energy solutions because their AI models consume much energy. ChatGPT, powered by OpenAI’s GPT-4, uses more than 17,000 times the electricity of an average US household to answer hundreds of millions of queries per day.
By 2030, data centers—which support the training and deployment of these AI models—will constitute 11-12% of US power demand, up from a current rate of 3-4%, a McKinsey report said.
Tech companies have turned to fossil fuels to satisfy short-term demands, which has increased greenhouse gas emissions. For example, Google’s greenhouse gas emissions jumped by 48% between 2019 and 2023 “primarily due to increases in data center energy consumption and supply chain emissions,” the company said in its 2024 sustainability report.
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