Zanskar uses a proprietary AI‑powered exploration platform to analyse vast geological, thermal, satellite and geophysical datasets across regions — aiming to discover geothermal resources that traditional methods have missed. By combining machine‑learning models with modern data‑collection and drilling methods, Zanskar claims it can predict promising underground heat sources (hot water or steam reservoirs) with greater accuracy, effectively turning what was once a gamble into a data‑driven prospect hunt.
In 2025, Zanskar’s approach delivered concrete results: the company announced discovery of a new geothermal site called Big Blind in western Nevada — described as the first “blind” geothermal system confirmed as commercially viable by industry in over 30 years. Prior to drilling, the site had no surface indicators or prior exploration history; AI‑driven predictions helped target promising locations, and exploratory wells intersected a sufficiently hot permeable reservoir at relatively shallow depth — conditions suitable for power generation.
This breakthrough is not isolated. Earlier in 2025, Zanskar had also confirmed a geothermal well at another site — Pumpernickel in northern Nevada — where temperatures of about 137 °C were found, validating their AI‑based discovery methodology. The company says its pipeline of “greenfield” (previously unexplored) geothermal prospects is now the largest in the industry, suggesting a major scalability potential for geothermal energy if their AI‑driven process is repeated across many sites.
The significance is broader than just one company’s wins. By dramatically improving the success rate and lowering the cost and risk of geothermal exploration, AI can make geothermal — which offers stable, carbon‑free, baseload power — a more viable and scalable alternative in the global energy mix. This represents a promising model of how advanced AI and climate‑tech goals can align: using intelligent data analysis to unlock sustainable energy resources that were previously considered too uncertain or expensive to exploit.
