AI Factory Data Centers: What the New Development Format Actually Requires

AI factories are a distinct class of data center infrastructure with power densities, cooling requirements, and construction constraints that standard hyperscale development assumptions do not cover.

The term "AI factory" has migrated from Nvidia marketing language into the working vocabulary of institutional data center developers. In June 2026, Nvidia announced a partnership with IREN to deploy up to 5 GW of AI infrastructure globally, with Texas's Sweetwater positioned as the flagship site for the company's DSX AI factory architecture. The designation is not cosmetic. AI factories have structural requirements that differ from conventional hyperscale and colocation development in ways that matter for site selection, design, construction, and underwriting.

Understanding those differences is now a developer competency, not a technical nicety.

What Makes an AI Factory Different

A conventional hyperscale data center is designed around general compute: mixed CPU and GPU workloads, variable rack densities, and load profiles that fluctuate across the facility's operational life. An AI factory is designed around one workload: training and inference at scale for large language models and other AI systems. The implications cascade through every design assumption.

Power density. Standard hyperscale development targets average rack power densities of 10 to 20 kW per rack. AI factory architecture operates at 50 to 100 kW per rack, with some configurations pushing higher as GPU generations advance. The electrical infrastructure design -- busways, PDUs, switchgear, UPS topology -- is fundamentally different at that density. Per-megawatt construction costs for AI-ready infrastructure run up to $25 million per megawatt, compared to $11.3 million for standard data center construction. The gap is real and it does not close as the build scales.

Cooling architecture. Air cooling is not viable at 50 kW per rack. AI factories require liquid cooling: direct-to-chip, rear-door heat exchangers, or immersion cooling depending on the density target and the tenant's hardware configuration. Liquid cooling infrastructure changes the structural requirements for the facility. Water loop design, heat rejection capacity, water rights, makeup water availability, and cooling tower permitting all become site selection variables that are secondary in air-cooled facilities.

Networking infrastructure. AI training workloads require ultra-low latency communication between GPUs within and across racks. This drives specific requirements for intra-facility networking: high-density fiber, precise cable management, and physical layout that minimizes latency. These requirements constrain floor layout in ways that standard data hall design does not.

Power continuity requirements. AI training runs cannot tolerate interruption. A hyperscale facility can tolerate brief maintenance windows for certain workloads. A training cluster in the middle of a 30-day run has a fundamentally different continuity requirement. This affects UPS topology, generator sizing, fuel logistics, and the redundancy architecture the facility must deliver.

Site Criteria for AI Factory Development

The site selection criteria for an AI factory diverge from hyperscale criteria at several points.

Power must be deliverable, not just available. The distinction between power availability and power deliverability has become standard in data center diligence, but it is especially acute for AI factories. A 500 MW AI factory is not a future expansion option -- it is the base case. The site must support that load from the utility, from behind-the-meter generation, or from a combination. Sites that can deliver 50 MW but "could support" 500 MW with future grid upgrades are not AI factory sites. They are early-stage development options.

Land configuration matters more. High-density cooling infrastructure and the networking layout requirements of AI training clusters produce facility footprints that differ from standard data halls. AI factories tend to require more cooling infrastructure per square foot of white space, more mechanical yard area for heat rejection equipment, and precise building orientation relative to cooling towers and electrical switchgear. Sites with irregular geometry or constrained utility corridors can limit the buildable configuration in ways that are not obvious from aerial review.

Fiber route resilience. AI training clusters communicate at sustained high bandwidth internally, and they connect externally to data and model repositories. Fiber route resilience -- multiple diverse routes, low-latency backbone connectivity -- is a site evaluation criterion that carries more weight for AI factory siting than for general-purpose colocation.

Water. An AI factory running direct-to-chip liquid cooling with closed-loop heat rejection requires substantially less water than an evaporative-cooled facility of the same power. But the cooling system must still reject heat, and in water-stressed markets, the permitting pathway for even a small makeup water requirement can create entitlement risk. Water strategy belongs in the pre-LOI evaluation.

Underwriting an AI Factory

The financial model for an AI factory differs from a standard hyperscale facility in three areas.

Construction cost. The $25 million per megawatt figure for AI-ready infrastructure is a ceiling, not an average, but it sets a realistic upper bound for dense GPU-class facilities. A 100 MW AI factory represents $2.5 billion in construction cost at full density. The electrical infrastructure and cooling system account for a disproportionate share of that budget relative to the building shell, which means changes to design assumptions late in the development process are more expensive.

Lease structure. AI factories are typically leased to a hyperscaler or large enterprise AI operator on long-term contracts with power and cooling specifications built into the lease. The tenant's hardware evolution over a 10-15 year lease term creates a design risk: the facility must either be built with flexibility to accommodate higher densities as GPU generations advance, or the lease must address upgrade obligations explicitly. This is a negotiating point with meaningful capital implications.

Power cost pass-through. At 50-100 kW per rack, power is the dominant operating cost. Most AI factory leases are structured with power cost passed through to the tenant. The developer's underwriting is therefore primarily focused on construction cost, financing, and the credit quality of the lease counterparty rather than energy cost forecasting.

Where AI Fits in the Development Workflow

AI tools are most useful in AI factory development at three stages. Pre-LOI site screening can layer power deliverability data, cooling water availability, fiber route mapping, and land configuration analysis to identify which sites are structurally compatible with AI factory requirements before a team spends time on confirmatory diligence. Design-stage modeling can compare cooling topology options -- rear-door versus direct-to-chip versus immersion -- against site water constraints and construction cost targets. And procurement tracking is essential given the extended lead times for high-voltage electrical equipment and liquid cooling infrastructure at the scale AI factories require.

The development format is new enough that there are few institutional comparables to benchmark against. Teams building their first AI factory are effectively developing the playbook. The developers who do it twice will have a structural advantage over those doing it for the first time.