Zanskar
Zanskar is applying vertical data moats to industrial, representing a series c vertical AI play with core generative AI integration.
As agentic architectures emerge as the dominant build pattern, Zanskar is positioned to benefit from enterprise demand for autonomous workflow solutions. The timing aligns with broader market readiness for AI systems that can execute multi-step tasks without human intervention.
Zanskar develops data-driven technology to identify and develop geothermal energy resources.
Zanskar’s core advantage is its proprietary AI-driven platform for geothermal resource discovery, mapping, and development, which enables them to identify and de-risk thousands of new geothermal sites rapidly and accurately.
Vertical Data Moats
Zanskar is leveraging proprietary, domain-specific geoscience and geothermal data to train AI models, creating a competitive advantage in geothermal energy exploration and development.
Unlocks AI applications in regulated industries where generic models fail. Creates acquisition targets for incumbents.
Agentic Architectures
There are indications of autonomous or semi-autonomous AI systems providing real-time operational insights and optimization, suggesting agent-like behavior in managing geothermal assets.
Full workflow automation across legal, finance, and operations. Creates new category of "AI employees" that handle complex multi-step tasks.
Continuous-learning Flywheels
The mention of real-time insights and repeatable, data-driven processes hints at feedback loops where operational data could be used to improve models, though explicit continuous learning is not confirmed.
Winner-take-most dynamics in categories where well-executed. Defensibility against well-funded competitors.
Zanskar operates in a competitive landscape that includes Fervo Energy, Eavor Technologies, Sage Geosystems.
Differentiation: Zanskar emphasizes an AI-native, data-driven approach with a focus on building the first AI foundation model for geothermal, whereas Fervo is known for using horizontal drilling and fiber-optic sensing but less focus on AI-driven resource discovery.
Differentiation: Eavor’s differentiation is its closed-loop geothermal system (Eavor-Loop), while Zanskar’s is its AI-driven, data-centric approach to mapping and developing geothermal resources.
Differentiation: Sage Geosystems focuses on energy storage and hybrid geothermal solutions, while Zanskar’s core is AI-powered geothermal resource discovery and development.
Zanskar claims to be building the first AI foundation model for geothermal exploration and development, applying advanced machine learning to subsurface mapping—a domain traditionally dominated by manual geoscience and physics-based modeling. This is an unusual technical choice, as foundation models are typically associated with language, vision, or general-purpose domains, not geoscience.
Their workflow integrates AI-driven discovery, development, and real-time operations optimization. The explicit mention of real-time insights for maximizing energy output suggests a closed-loop system that continuously ingests field data, updates models, and tunes operations—a level of automation and feedback not commonly seen in legacy energy companies.
The company positions itself as 'AI-native,' indicating that their core business processes (from site selection to drilling and operations) are fundamentally built around data and AI, rather than retrofitting AI onto existing workflows. This could enable repeatable, scalable geothermal site development, which is a major technical challenge due to the heterogeneity and uncertainty of subsurface resources.
They emphasize shareholder ownership for all employees and a culture of field experience, which may help align technical and operational teams—potentially reducing the friction between data scientists and field engineers, a common bottleneck in industrial AI deployments.
The company repeatedly claims to be 'AI-driven', 'AI-native', and building the 'first AI foundation model for geothermal', but provides no technical specifics, examples, or evidence of proprietary AI models or unique data assets. The language is buzzword-heavy and lacks substance.
There is no clear evidence of a defensible data moat, proprietary technology, or technical differentiation. The approach appears replicable by competitors, especially incumbents with access to similar data and AI capabilities.
The offering as described could be interpreted as a feature (AI-assisted geothermal site identification and optimization) that could be absorbed by larger energy or geoscience incumbents, rather than a standalone, defensible product.
Zanskar's execution will test whether vertical data moats can deliver sustainable competitive advantage in industrial. A successful outcome would validate the vertical AI thesis and likely trigger increased investment in similar plays. Incumbents in industrial should monitor closely for early signs of customer adoption.
Source Evidence(5 quotes)
"Zanskar uses data and an AI-driven playbook to unlock this limitless energy source at scale."
"Building the first AI foundation model for geothermal"
"Zanskar is the first AI-native geothermal energy company."
"We use modern geoscience, artificial intelligence, and advanced field and drilling methods to accurately map the Earth’s subsurface"
"Application of AI foundation models specifically to geothermal exploration and resource mapping, an uncommon vertical for foundation model deployment."