AI’s power density requirements similarly necessitate a new set of electricity infrastructure enhancements—like advanced conductors for transmission lines that can move up to 10 times as much power through much smaller areas, cooling infrastructure that can address the heat of vast quantities of energy-hungry chips humming alongside one another, and next-generation transformers that enable the efficient use of higher-voltage power. These technologies offer significant economic benefits to AI data centers in the form of increased access to power and reduced latency, and they will enable the rapid expansion of our 20th-century electricity grid to serve 21st-century needs.
Moreover, the convergence of AI and energy technologies will allow for faster development and scaling of both sectors. Across the clean-energy sector, AI serves as a method of invention, accelerating the pace of research and development for next-generation materials design. It is also a tool for manufacturing, reducing capital intensity and increasing the pace of scaling. Already, AI is helping us overcome barriers in next-generation power technologies. For instance, Princeton researchers are using it to predict and avoid plasma instabilities that have long been obstacles to sustained fusion reactions. In the geothermal and mining context, AI is accelerating the pace and driving down the cost of commercial-grade resource discovery and development. Other firms use AI to predict and optimize performance of power plants in the field, greatly reducing the capital intensity of projects.
Historically, deployment of novel clean energy technologies has had to rely on utilities, which are notoriously slow to adopt innovations and invest in first-of-a-kind commercial projects. Now, however, AI has brought in a new source of capital for power-generation technologies: large tech companies that are willing to pay a premium for 24-7 clean power and are eager to move quickly.
These “new buyers” can build additional clean capacity in their own backyards. Or they can deploy innovative market structures to encourage utilities to work in new ways to scale novel technologies. Already, we are seeing examples, such as the agreement between Google, the geothermal developer Fervo, and the Nevada utility NV Energy to secure clean, reliable power at a premium for use by data centers. The emergence of these price-insensitive but time-sensitive buyers can accelerate the deployment of clean energy technologies.
The geopolitical implications of this nexus between AI and climate are clear: The socioeconomic fruits of innovation will flow to the countries that win both the AI and the climate race.
The country that is able to scale up access to reliable baseload power will attract AI infrastructure in the long-run—and will benefit from access to the markets that AI will generate. And the country that makes these investments first will be ahead, and that lead will compound over time as technical progress and economic productivity reinforce each other.
Today, the clean-energy scoreboard tilts toward China. The country has commissioned 37 nuclear power plants over the last decade, while the United States has added two. It is outspending the US two to one on nuclear fusion, with crews working essentially around the clock on commercializing the technology. Given that the competition for AI supremacy boils down to scaling power density, building a new fleet of natural-gas plants while our primary competitor builds an arsenal of the most power-dense energy resources available is like bringing a knife to a gunfight.
The United States and the US-based technology companies at the forefront of the AI economy have the responsibility and opportunity to change this by leveraging AI’s power demand to scale the next generation of clean energy technologies. The question is, will they?
Michael Kearney is a general partner at Engine Ventures, a firm that invests in startups commercializing breakthrough science and engineering. Lisa Hansmann is a principal at Engine Ventures and previously served as special assistant to the president in the Biden administration, working on economic policy and implementation.
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