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Data Center World 2026 framed AI infrastructure as a power, cooling, grid and community challenge—not merely a race to add server space. The Washington, D.C., conference’s theme, “Innovation at Scale,” underscored that projects built for short-term capacity can become liabilities if they cannot secure electricity, adapt to changing hardware, manage water use or earn local support.
What Data Center World 2026 says has changed
The event report describes an industry moving from conventional enterprise facilities toward infrastructure designed around AI workloads. Bill Kleyman, executive chair of data-center programs at AFCOM, said, “The data center industry is scaling at a previously unimaginable pace.”
Joe Kava, formerly Google’s vice president of data centers, contrasted today’s visibility with the cloud industry’s earlier role: “Twenty years ago, this conversation was very different. We were known as ‘the cloud’ back then. We were just the folks trying to make sure the servers didn’t melt and the lights stayed on. We were the back office. And now it’s a very different conversation.”
Scott Armul of Vertiv described an inflection point in which “the old ways of doing things, the old ways of thinking about design, the old ways of thinking about developing product and building data centers are becoming stale.” In practical terms, AI is forcing operators to coordinate compute, electrical infrastructure, thermal management, construction schedules and public impact from the beginning of a project.
How large could the electricity requirement become?
The numbers vary by geography and by what gets built. An EPRI 2026 scenario analysis places U.S. data centers at 9% to 17% of electricity generation in 2030, compared with roughly 4% to 5% currently. The range reflects different assumptions about projects under construction or in planning that ultimately become operational.
The International Energy Agency estimates global data-center electricity demand at 485 TWh in 2025 and about 950 TWh in 2030—around 3% of global electricity demand. The IEA also reports that global data-center electricity demand grew 17% during 2025. These are worldwide electricity estimates, not measures of U.S. consumption.
The conference report separately cited an estimate of up to 17% of U.S. electricity consumption by 2030. Because that passage does not identify the forecast’s underlying publisher or methodology, the EPRI range is the more clearly qualified U.S. reference.
Omdia analyst Maxine Holt, as reported at the conference, forecast global IT spending of $6.07 trillion in 2026, up 10% year over year. Omdia analyst Vlad Galabov predicted the data-center market would exceed $1.9 trillion by 2030. Those are analyst estimates reported by the event coverage, not independently verified totals.
AI changes the physical design of a facility
Rack density and power delivery
Related conference coverage described traditional racks in the 30–40 kW range, current systems reaching hundreds of kilowatts and some designs approaching a megawatt. These figures describe the direction of industry designs, not a universal load for every AI rack.
At those densities, power distribution becomes a design constraint. Operators must plan electrical paths, protection, redundancy and maintenance access around the actual accelerator configuration. A rack-mount power distribution unit (PDU) is one component in that chain, but its rating and topology must match the facility’s voltage, phase, monitoring and resilience requirements; the conference coverage does not endorse a particular product.
Cooling and water
Higher rack loads produce more heat, making cooling inseparable from power planning. Speakers discussed liquid cooling for high-density systems and hybrid facilities in which air- and liquid-cooled environments coexist. Liquid systems can support concentrated heat loads, while water availability, treatment, leak management and operating procedures become part of the sustainability and reliability discussion.
Cooling choices are therefore not a simple replacement decision. They depend on workload density, climate, building design, available water, maintenance capability and the need to accommodate older air-cooled equipment alongside newer liquid-cooled systems.
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Training versus inference
AI training is a tightly coupled workload: large groups of accelerators exchange data continuously, so proximity and network latency influence layout and interconnect design. Inference must deliver responses broadly and reliably, which can favor distributed capacity and different availability patterns. A campus optimized for one workload may not be optimal for the other.
| Design question | Why AI makes it more important |
|---|---|
| Workload | Training emphasizes tightly coupled compute; inference emphasizes broad, responsive availability. |
| Rack and distribution architecture | Accelerator loads can reach hundreds of kilowatts and, in some designs, approach megawatt scale. |
| Cooling | Liquid, air and hybrid approaches may need to coexist as hardware changes. |
| Grid relationship | Large, variable loads require coordinated utility connections, on-site generation or storage strategies. |
| Deployment method | Prefabrication, factory integration and modular construction are being used to compress schedules. |
| Public impact | Electricity demand, water use, construction and land-use effects can determine whether a project proceeds. |
Why grid connections are becoming the schedule risk
The IEA identifies constrained energy-equipment and chip supply chains, along with delays in grid connections and approvals, as physical bottlenecks. A site can have land and financing yet remain unable to operate at scale until transformers, switchgear, generation equipment and a utility connection are available.
Conference speakers discussed on-site generation as a near-term bridge while operators pursue grid-connected capacity. Storage can help manage load variation and power quality, but it does not remove the need for a durable electricity supply. The appropriate mix depends on local grid conditions, emissions objectives, permitting and the facility’s resilience requirements.
Front-loaded engineering, factory-integrated modules and campus-scale coordination were presented as ways to respond to compressed timelines. They can reduce work performed at the site, but they do not guarantee faster approval, equipment delivery or interconnection.
Community acceptance is part of project delivery
Electricity and water demand make data centers visible to people who may not use the facilities directly. Amber Caramella of Netrality said, “We know that demand is outweighing the supply, and so we need to do all the things that we’re doing to accommodate that, but the one thing that we’ve known has been a negating factor is community pushback.”
The event coverage links early engagement with smoother development. Lawson-Shanks said Aligned engages schools, church groups and local leaders before zoning meetings. That example does not establish one best engagement model, and the coverage does not quantify public sentiment. It does show why a proposal’s local explanation matters before formal hearings: residents may have questions about power, water, noise, traffic, land use, jobs and long-term obligations.
What “build for legacy” means in this context
Kava’s closing instruction was: “Don’t build for capacity, build for legacy. Build systems that are as sustainable as they are powerful.” In this conference context, legacy means making infrastructure decisions that remain defensible after the first AI deployment changes.
- Power: Plan for a credible long-term grid relationship rather than treating temporary generation as the whole solution.
- Adaptability: Leave room for changing accelerator generations, rack layouts and the mix of training and inference.
- Thermal stewardship: Match air, liquid or hybrid cooling to density, reliability and local water conditions.
- Resilience: Coordinate redundancy, storage, maintenance access and power-quality controls with the actual workload.
- Public legitimacy: Engage local institutions early and explain infrastructure impacts before zoning decisions.
The central lesson from Data Center World 2026 is that AI scale turns a data-center project into a regional infrastructure decision. Capacity still matters, but electricity, equipment supply, cooling, approvals and community trust determine whether that capacity can be delivered and sustained.
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