The optimal location of the AI data center is identified using a rigorous, multicriteria, systems-engineering methodology. The primary selection criterion is the delivered cost of electricity, evaluated on a lifecycle basis and incorporating the cost and availability of wind and solar generation, battery energy storage systems (BESS), transmission access, and carbon credits required to offset residual grid-supplied power.
Beyond power economics, the methodology evaluates a comprehensive set of technical, operational, and risk parameters. These include digital connectivity (proximity to high-capacity fiber routes, latency, distance to cable landing stations, and network redundancy); cooling feasibility, including water availability and suitability for advanced, water-efficient cooling technologies; and land availability and constraints, such as slope, geotechnical conditions, flood risk, and seismic exposure
Additional criteria include logistics and supporting infrastructure, covering access to roads, ports, and construction supply chains, as well as associated cost implications. Environmental and social considerations are integrated throughout the analysis to avoid ecologically sensitive areas, minimize social impacts, and ensure regulatory and permitting readiness.
This structured, data-driven approach ensures that site selection decisions are defensible, bankable, and optimized for long-term reliability, sustainability, and gigawatt-scale AI operations, rather than driven by single-factor or short-term considerations.