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World desk6 min

How to Choose a Location for an AI Data Center

The best AI data center location is the one that can prove it will deliver the project’s power, cooling, connectivity and approvals on schedule—not simply the one that looks promising on a map.
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Choose a site by proving that it can deliver the required power on the required schedule, then checking whether cooling, water, connectivity, land, permits and resilience make the project workable. A nearby power line, a cool climate or an attractive incentive is not enough on its own. The right location depends on the AI workload and on evidence specific to the parcel and its utility, network operators and authorities.

Define the facility before comparing places

Start with the job the data center must do. A facility built for large training runs may have different density, growth and network needs from one serving inference close to users. Those requirements determine what counts as a viable site; without them, headline figures such as acreage or megawatts are difficult to interpret.

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Write down the project assumptions

  • Workload: training, inference or a mix, including any latency-sensitive services.
  • Expected IT load, the schedule for bringing it online and the planned expansion horizon.
  • Rack and power density, availability target and intended cooling approach.
  • Latency tolerance and locations of users, other facilities and network hubs.
  • Required resilience, including backup and disaster-recovery arrangements.

ASHRAE’s AI Data Center Energy Performance Framework recommends matching site planning to workload requirements and anticipated future density. Treat the assumptions as a versioned design brief: if they change, recheck the sites against the new requirements.

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Make deliverable power the first hard gate

Power is not a site attribute you can infer from a map. A nearby substation or transmission line does not establish that the utility can provide the required capacity, or when it can do so. ASHRAE identifies grid capacity, interconnection, utility expansion plans, transformers and switchgear as early feasibility and schedule considerations.

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Ask for evidence from the utility or system operator

For each candidate, seek written, site-specific answers on:

  • Current available capacity and how much is committed to other customers.
  • The status and expected date of the interconnection, including any study or approval still outstanding.
  • Required network upgrades, their delivery schedule and responsibility for their cost.
  • Utility plans or constraints that could change the proposed capacity or date.
  • Expected availability of essential equipment, particularly transformers and switchgear.

Compare the dates and conditions against the project’s load ramp, not just its eventual target. A site that might eventually support the full load may still fail if it cannot serve the first phase on time. Separate confirmed capacity from estimates, conditional allocations and aspirational utility plans.

Treat on-site generation as a separate project dependency

Generation or storage may be part of a power strategy, but it does not make a site independent of permitting, fuel or other resource needs, emissions constraints, operating arrangements and grid connection requirements. Record those dependencies and their schedules explicitly rather than counting proposed equipment as firm power.

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Test cooling against both climate and water

A cool climate can help, but it is not a complete cooling assessment. Temperature and humidity interact with workload density and the proposed cooling system. Water-based cooling can reduce electricity use while increasing water consumption; hotter conditions can also raise cooling demand. The U.S. Geological Survey’s 2026 science synthesis discusses these linked energy and water effects, so compare both at each candidate site.

Model the intended design at the actual site

Have the design team assess expected ambient temperature and humidity against the planned IT load and cooling approach. Ask the water supplier to confirm available volumes, delivery timing, infrastructure work and any allocation constraints. Establish the wastewater discharge route, applicable requirements and permits as well. A nominal water source is not proof that the required volume can be delivered or discharged when needed.

Compare cooling alternatives on their electricity and water consequences together. Document the assumptions behind each estimate, including the facility’s expected load and operating conditions, so a low-water option is not judged without its energy implications or vice versa.

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Verify connectivity for the workload

How far a facility can be from its users depends on the service it must deliver. Determine latency tolerance for the intended workload, then ask network operators for evidence of available fiber and mobile coverage, route diversity and committed upgrades. Do not assume that a regional connectivity claim proves that the parcel has diverse routes or a deliverable connection.

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The UK AI Growth Zone application criteria, for example, call for operator letters on current coverage and planned extensions. They do not establish a universal latency threshold. For any location, the project team must set a workload-appropriate target and verify that the proposed network design can meet it.

Establish that the parcel can be built and expanded

Technical feasibility depends on land and delivery as well as utilities. Confirm control of the site, contiguous developable area, room for expansion, construction access and logistics, and compatibility with zoning. Ask the relevant authorities to identify the planning route, environmental reviews and likely approval schedule. Include water abstraction or supply and wastewater discharge permissions where relevant.

Map the agencies and delivery partners responsible for each approval and infrastructure dependency. Early engagement with local authorities and residents can surface concerns about power demand, water use, noise, visual effects and land use while there is still time to respond in the design. Record likely local benefits and concerns rather than treating community acceptance as a late-stage communications task.

Screen parcel-level hazards and operational resilience

Regional hazard maps are useful for initial screening, not a substitute for parcel-level environmental, engineering or regulatory assessment. Check relevant flood, wildfire, seismic and other local hazards. Their consequences can affect facility design, insurance, approvals, continuity and schedule.

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Assess resilience as a system: power, cooling and network paths, backup arrangements, disaster recovery and access during emergencies. A site with a favorable hazard profile can still be operationally fragile if critical services share a single path or if emergency access is vulnerable. Have specialists assess the actual parcel and proposed design before treating a regional screen as a final risk judgment.

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Compare candidates only after checking hard constraints

Use the same project assumptions for every candidate. First eliminate sites without credible power delivery and schedule, or without a feasible cooling, water and permitting path. Then compare the remaining sites on factors where trade-offs are meaningful. A weighted score can help organize those trade-offs, but it should not allow a low land price or incentive to compensate for an unbuildable connection.

Comparison area Evidence to compare What it helps answer
Power Confirmed capacity, allocation firmness, interconnection date, upgrades, equipment lead times, energy cost and emissions profile. Can the required load arrive on the project schedule, and on what conditions?
Cooling and water Design fit to temperature and humidity, sustainable water volume, water stress, discharge capacity and approvals, and expected energy-water trade-off. Can the site cool the planned workload within its resource and permit constraints?
Connectivity Providers, route diversity, committed upgrades, and latency to users and network hubs. Can the network support the workload and its resilience needs?
Land and delivery Developable area, ownership or control, zoning, construction logistics, expansion room and schedule confidence. Can the facility and planned phases be built on the parcel?
Risk and resilience Local hazards, independent power, cooling and network paths, backup plans and emergency access. How robust is operation against disruption?
Regulatory and community Approval route, environmental review, local concerns and benefits, and effects on noise, water and the grid. Is there a credible route to approval and durable local acceptance?
Economics and flexibility Land and infrastructure costs, incentive conditions, workforce, phased growth and adaptability to changing hardware density. Which feasible site best fits the project’s cost, delivery and expansion priorities?

For every score, state the evidence, assumptions, owner and confidence level. Mark unknowns as unknowns rather than silently treating them as favorable. Revisit the comparison if utility dates, water availability, equipment schedules, policy or project requirements change.

Keep jurisdiction-specific thresholds in context

Published thresholds can illustrate what a particular program or law covers, but they are not general design standards. The UK Department for Science, Innovation and Technology’s AI Growth Zone application criteria, accessed October 4, 2026, require evidence of at least 500 MW of power access by 2030, sufficient water for 500 MW of AI infrastructure, at least 100 acres available by 2028, and planning consent or a deliverable route by 2028. These are requirements for that program, not universal minimums for choosing an AI data-center site.

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Separately, the White House’s Executive Order 14318, dated July 23, 2025, defines a “Data Center Project” for the order’s stated purposes as a facility requiring more than 100 MW of new load for specified AI activities. That definition is not an engineering threshold for site selection. Check the relevant program criteria and current legal status for the project’s jurisdiction and date before relying on either example.

The USGS 2026 synthesis is useful background on energy infrastructure and cooling considerations, but it does not rank optimal locations. Its spatial analysis covers Western U.S. states and Alaska and omits several land-suitability and permitting issues essential to a parcel decision. Use it as context, not as a shortlist of winning sites.

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