AI infrastructure
AI Data Center Development
Purpose-built development strategies for high-density compute, cloud and artificial intelligence infrastructure.
Demand drivers
- Growth of generative AI
- GPU-intensive workloads
- Cloud expansion
- Enterprise AI adoption
- Data sovereignty requirements
- Low-latency infrastructure needs
- Energy availability constraints
- Expansion of high-performance computing
What makes a site suitable
- Large-scale power availability
- Grid proximity
- Fiber connectivity
- Land size and zoning
- Cooling potential
- Water or alternative cooling options
- Environmental constraints
- Permitting environment
- Access roads and logistics
- Expansion capacity
- Political and regulatory stability
- Utility cooperation
- Construction feasibility
From raw land to data center campus potential
A structured path from initial screening to development readiness.
- 01
Location screening
- 02
Power screening
- 03
Fiber and connectivity review
- 04
Land control strategy
- 05
Environmental and permitting assessment
- 06
Campus planning
- 07
Utility engagement
- 08
Technical feasibility
- 09
Strategic partner alignment
- 10
Development-readiness planning
Technical considerations
Practical design topics addressed at concept and feasibility stages.
Power density
From conventional racks to high-density AI training environments.
Redundancy
N+1, 2N and concurrent maintainability concepts at concept stage.
Cooling strategy
Air, liquid and hybrid concepts aligned with workload and climate.
Utility-scale requirements
Substation capacity, interconnection studies and delivery.
Phased development
Modular campus phasing aligned with power delivery.
Anchor tenant readiness
Site characteristics suitable for hyperscale or enterprise demand.
Scalability
Land footprint and grid headroom for long-horizon expansion.
Grid resilience
Multiple feeds, backup strategy and resilience planning.
Sustainability considerations
Renewable adjacency and efficiency planning where feasible.