Integration of artificial intelligence (AI) is poised to significantly accelerate the development of commercial-scale geothermal energy across the United States. While geothermal resources lie abundantly beneath the surface, identifying optimal drilling locations has historically posed considerable challenges. Recent advancements in AI are transforming exploration and discovery, making geothermal development faster, more cost-effective, and efficient.
According to a report from the U.S. Department of Energy (DOE), substantial technical and non-technical barriers must be overcome to expand geothermal energy as a national solution. The report notes that subsurface exploration is the foremost challenge due to the high costs and complexities involved.
Zanskar Energy, a startup based in Utah, is now piloting an innovative AI-integrated model designed to address these obstacles. The company has demonstrated repeated success in identifying commercially viable geothermal locations. Their first project, the Lightning Dock facility in New Mexico, has been recognized as “the most productive pumped geothermal well in the U.S.”, as estimated by Zanskar. Recently, the company announced the discovery of a second deep geothermal site at the Pumpernickel site in northern Nevada.
“Our vertically-integrated, AI-native approach to geothermal development is delivering the speed to discovery and speed to development necessary to meet the new paradigm of rapid energy demand growth,” said Carl Hoiland, co-founder and CEO of Zanskar, in a statement reported by Clean Technica. He emphasized that their latest drilling results reinforce the belief that conventional geothermal resources are more abundant and larger than previously thought, providing a low-cost pathway to deliver gigawatts of reliable, carbon-free baseload power.
Geothermal energy stands out as a promising clean technology capable of providing continuous power production, unlike variable renewable sources such as wind and solar. This reliability positions geothermal as a crucial player in enhancing energy security, especially as the demand for clean energy continues to rise.
Despite geothermal energy currently accounting for just 0.4% of the U.S. energy mix, rapid technological advancements could change this dynamic. Enhanced drilling technologies and AI integration are expanding the potential for geothermal energy access globally. Theoretically, heat from the Earth’s core is accessible from nearly anywhere, contingent on sufficient drilling depth. Innovations in geothermal systems are leveraging technologies from the oil, gas, and even nuclear fusion sectors to enable deeper drilling.
As AI continues to revolutionize geothermal development, it also drives increased interest in the sector. With global energy demand projections escalating, both private and public investors are adopting a comprehensive approach to energy production. In the U.S., geothermal energy enjoys broad bipartisan support, positioning it to benefit from the “artificial intelligence-driven power boom,” as noted by Bloomberg.
A report published by the Rhodium Group earlier this year estimates that geothermal energy could economically meet up to 64% of the anticipated demand growth by the early 2030s. This projection highlights a remarkable potential for growth in a sector that has historically contributed a limited share of the clean energy landscape.
Nonetheless, the rapid advancements in geothermal technology and policy are outpacing the development of skilled labor. The industry is confronting a significant workforce shortage that could hinder its projected growth. As the sector gains visibility and becomes a more viable employment option, it is hoped that the next generation of engineers and geologists will engage with the opportunities available.
The intersection of AI technology and geothermal energy development represents a pivotal moment in the quest for sustainable energy solutions. Should geothermal energy continue to break through technological barriers and benefit from a supportive policy framework, its potential to contribute to the energy landscape is substantial.
