Geothermal Energy’s Hidden Giant: Zanskar’s AI Uncovers 1 Terawatt of Overlooked Power Potential

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Geothermal Energy’s Hidden Giant: Zanskar’s AI Uncovers 1 Terawatt of Overlooked Power Potential

In the race to decarbonize America’s power grid, a massive energy source lies directly beneath our feet, largely untapped and underestimated. While the U.S. Department of Energy projects geothermal power could generate 60 gigawatts by 2050, startup Zanskar’s leadership believes the true potential approaches a staggering 1 terawatt—a figure that could fundamentally reshape the nation’s energy landscape. This discrepancy highlights a critical oversight in conventional geothermal assessment, one that emerging artificial intelligence technologies are now poised to correct.

Geothermal Energy’s Conventional Stagnation and Enhanced Promise

The United States currently generates approximately 4 gigawatts from conventional geothermal sources, representing only a modest increase over the past decade. This stagnation stems from traditional exploration methods that rely on surface indicators like hot springs or volcanic activity. Consequently, experts estimate that about 95% of viable geothermal systems remain undetected because they lack these visible surface expressions.

Meanwhile, enhanced geothermal systems (EGS) have captured significant attention and investment. These systems utilize hydraulic fracturing techniques—similar to those used in oil and gas extraction—to create permeability in deep, hot rock formations. Companies including Fervo Energy and Sage Geosystems have pioneered this approach, which the Department of Energy identifies as crucial for achieving its 60-gigawatt projection by mid-century.

Zanskar’s AI-Powered Discovery Revolution

Zanskar, founded by CEO Carl Holland and CTO Joel Edwards, employs a sophisticated two-stage artificial intelligence methodology to identify overlooked geothermal resources. First, supervised machine learning models analyze diverse datasets, including geological surveys, seismic information, and historical accidental discoveries. These models identify patterns invisible to human analysts, pinpointing promising subsurface locations.

Following computational identification, field teams conduct ground validation at selected sites. For development planning, Zanskar utilizes Bayesian evidential learning (BEL), a specialized AI approach that builds probabilistic models based on existing data. BEL generates and tests multiple hypotheses about subsurface conditions, quantifying uncertainty and identifying optimal drilling locations. The company has developed proprietary geothermal simulation software to fill data gaps, creating comprehensive resource assessments.

The Quantifiable Success of Modern Exploration

Zanskar’s approach has demonstrated remarkable effectiveness in practical applications. The startup successfully resuscitated a declining power plant in New Mexico and discovered two new sites with combined potential exceeding 100 megawatts. These achievements contributed to Zanskar securing a $115 million Series C funding round led by Spring Lane Capital, with participation from numerous prominent climate technology investors.

“We’ve achieved success with three of three exploration projects,” stated CTO Joel Edwards. “This raises an exciting question: What becomes possible when we scale to ten sites, or one hundred?” The company currently maintains a pipeline of sites capable of supporting at least one gigawatt of generating capacity, primarily concentrated in the geothermally rich western United States.

Financial Pathways and Industry Transformation

Geothermal development faces significant financial hurdles, particularly the “valley of death” between pilot projects and commercial-scale deployment. Zanskar aims to bridge this gap by identifying ten confirmed sites to attract project finance investors, who typically offer lower-cost capital than venture capitalists. This transition from venture funding to infrastructure financing represents a critical maturation step for climate technology startups.

The broader implications extend beyond single companies. Modern drilling technologies, combined with AI-driven discovery, could increase output from individual geothermal systems by an order of magnitude. When multiplied across potentially hundreds of overlooked sites, this creates what Holland describes as “a terawatt-scale opportunity”—dwarfing current governmental projections and positioning geothermal as a cornerstone of reliable, renewable baseload power.

Conclusion

Geothermal energy stands at an inflection point, transitioning from a niche renewable resource to a potentially dominant clean power source. Zanskar’s AI-driven methodology challenges long-held assumptions about conventional geothermal limitations, revealing orders of magnitude more potential than previously recognized. As artificial intelligence transforms resource discovery and characterization, geothermal power may finally achieve the scalability necessary to contribute significantly to global decarbonization efforts. The realization of even a fraction of this terawatt-scale potential would fundamentally alter energy transition timelines and strategies.

FAQs

Q1: What is the main difference between conventional and enhanced geothermal systems?
Conventional geothermal taps naturally occurring fractures and fluid reservoirs near the Earth’s surface, typically indicated by surface features like hot springs. Enhanced geothermal systems (EGS) create artificial reservoirs by injecting water into deep, hot rock formations, making geothermal possible in locations without natural permeability.

Q2: Why has conventional geothermal development been stagnant?
Development has been limited by traditional exploration methods that rely on visible surface indicators, which only exist for about 5% of viable geothermal resources. High exploration costs, drilling risks, and lengthy development timelines have further constrained growth in this sector.

Q3: How does Zanskar’s AI technology improve geothermal discovery?
Zanskar uses machine learning to analyze multiple data types—including geological, seismic, and historical data—to identify subsurface patterns indicating geothermal potential. Their Bayesian evidential learning approach then models probabilities for resource characteristics, reducing uncertainty and improving drilling success rates.

Q4: What is a “terawatt-scale opportunity” in geothermal energy?
A terawatt equals 1,000 gigawatts. The Department of Energy projects 60 gigawatts of U.S. geothermal capacity by 2050. Zanskar suggests that with modern discovery and extraction technologies, the actual potential could be 15-20 times greater, fundamentally changing geothermal’s role in the energy mix.

Q5: What regions have the greatest geothermal potential in the United States?
The western United States, particularly along the tectonic plate boundaries of the Pacific Ring of Fire, contains the highest temperature gradients and most accessible resources. States including California, Nevada, Oregon, and Utah host significant identified and potential geothermal resources.

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