Our multi-modal AI infrastructure supports hyperscale training centers, distributed compute meshes, and latency-sensitive edge sites under a single, coherent architecture. It gives technical and executive leaders a clear path to scale AI workloads without being constrained by legacy data center footprints or fragile, capacity-constrained grids.
Together, these elements create a long-horizon AI infrastructure stack that can be replicated, relocated, and expanded as demand evolves.
Unify land, power, data center design, GPU systems, networking, and managed operations into a single, repeatable AI factory model that can be drop-shipped and commissioned in weeks instead of years.
Deliver AI infrastructure that operates autonomously from traditional grids using MMR-based energy, while meeting stringent national security, regulatory, and data sovereignty requirements.
Large-scale, high-density facilities optimized for sustained AI training and mission-critical enterprise workloads that integrate with existing Tier III and Tier IV campuses.
A distributed AI compute layer integrating space-based and terrestrial communication networks.
Low-latency inference infrastructure deployed close to data generation and end users.
Containerised, rapidly deployable AI factories designed for scalable training and massive energy output.
As AI workloads accelerate, Project Genesis assumes power demand, density, and energy mix will shift dramatically through 2035.
| Metrics | Current (2026) | 5-Year Forecast (2031) | 10-Year Forecast (2036) |
|---|---|---|---|
| Global AI Power Load | ~25,000 MW | ~75,000 MW | ~150,000+ MW |
| Standard Cluster Size | 10–50 MW | 100–500 MW | 1,000 MW+ (1 GW scale) |
| Rack Density (Average) | 30–50 kW | 100–500 MW | 200 kW+ (immersion-ready infrastructure) Primary Power Source |
| Primary Power Source | Grid (Mixed) | Grid + Renewables | Nuclear (SMR/MMR-driven AI campuses) |