NextFin News - On January 21, 2026, geothermal exploration startup Zanskar announced a significant breakthrough in its mission to redefine the global energy landscape, claiming that conventional hydrothermal resources could deliver a staggering 1 terawatt (TW) of power—an order of magnitude higher than current industry projections. To accelerate this vision, the company closed a $115 million Series C funding round led by Spring Lane Capital, with participation from major climate and industrial investors including Lowercarbon Capital and Munich Re Ventures. Based in the United States, Zanskar is utilizing advanced artificial intelligence and data science to locate "blind" geothermal systems that lack traditional surface indicators like hot springs, which the company believes represent 95% of all viable geothermal resources.
The current U.S. Department of Energy (DOE) GeoVision analysis projects approximately 60 gigawatts (GW) of geothermal capacity by 2050, representing roughly 10% of the nation's electricity supply. However, Holland, the CEO of Zanskar, argues that these figures are overly conservative because they rely heavily on next-generation Enhanced Geothermal Systems (EGS) while overlooking the massive potential of conventional hydrothermal systems. While EGS involves hydraulic fracturing to create reservoirs in hot dry rock, conventional geothermal taps into naturally occurring permeable reservoirs. According to Holland, the stagnation of conventional geothermal—which currently generates only 4 GW in the U.S.—is a result of a "search problem" rather than a resource limit.
Zanskar’s technical approach involves a sophisticated two-stage AI workflow. First, supervised machine learning models are trained on decades of public and proprietary data, including gravity surveys, magnetotelluric data, and even accidental discoveries from oil and gas drilling. Once a potential site is identified, the company employs Bayesian Evidential Learning (BEL) to quantify uncertainty and falsify competing geological hypotheses. This methodology has already yielded tangible results: Zanskar successfully restored output at an underperforming plant in New Mexico and identified two new prospects with a combined potential exceeding 100 megawatts (MW). Edwards, the company’s CTO, noted that the success rate of "three for three" in early explorations validates the scalability of their discovery engine.
From a financial perspective, the move toward a 1 TW potential represents a fundamental shift in the risk profile of geothermal energy. Historically, the industry has been plagued by high upfront drilling risks and "dry holes," which often lead to the "valley of death" for startups. By reducing exploration risk through predictive modeling, Zanskar aims to lower the cost of capital and unlock non-recourse project finance. Lazard’s levelized cost of energy (LCOE) estimates for geothermal currently range between $60 and $110 per megawatt-hour (MWh). If Zanskar can successfully reduce drilling outlays by 10-20% through better targeting, geothermal could become highly competitive with other firm power sources like nuclear or natural gas with carbon capture.
The broader implications for the U.S. energy grid under the administration of U.S. President Trump are significant. As the administration emphasizes energy independence and grid reliability, geothermal offers a unique advantage: a capacity factor of 70-90%, providing the "firm" baseload power that intermittent renewables like wind and solar cannot. Unlike EGS, which has faced scrutiny over induced seismicity and high water usage, conventional hydrothermal systems are generally seen as more stable and easier to permit if the discovery process is streamlined. However, bottlenecks remain, particularly in the permitting of transmission lines and the availability of specialized drilling rigs.
Looking forward, the success of Zanskar’s thesis will depend on its ability to move from pilot successes to a pipeline of at least 10 confirmed sites. If the company can demonstrate a consistent "hit rate" in the U.S. West, where high heat flow and existing transmission infrastructure overlap, it could trigger a massive reallocation of capital toward geothermal. The integration of AI into subsurface exploration not only promises to uncover 1 TW of power but also sets a precedent for how data-driven approaches can revitalize stagnant industrial sectors. As the global demand for carbon-free, 24/7 power intensifies, the "overlooked" heat beneath the surface may finally become a primary pillar of the modern energy economy.
Explore more exclusive insights at nextfin.ai.
