Industry analysis details critical power and cooling challenges in building AI-ready infrastructure, highlighting undersupply of supporting systems.

Big GPU investments, cloud capacity, and cutting-edge chips have been driving the AI race, but a new challenge is emerging in corporate data centers. IT leaders deploying AI at scale must ensure their infrastructure can support it.

Gartner predicts that global data center electricity demand will increase by 26% in 2026, driven primarily by AI infrastructure. This year, AI-optimized servers alone will consume nearly one-third of total data center electricity. Deloitte estimates that U.S. AI data center power demand could surge from 4 GW in 2024 to 123 GW in 2035.

As compute demand outpaces supporting infrastructure, the strain on enterprise data centers is acute. Racks are denser, heat loads are rising, and electrical systems are approaching their design limits. In many facilities, adding another AI cluster has become an engineering challenge.

Solving these problems requires more careful infrastructure planning. Key questions now center on power availability, cooling capacity for additional AI clusters, and how much headroom to reserve for future hardware.

The answers will determine how quickly AI moves into production and whether existing data centers can scale to meet demand.

Data center infrastructure evolves slowly compared to AI models. Upgrading transformers, switchgear, and utility connections takes months or years, forcing IT leaders to rethink AI expansion strategies. Rather than building new facilities, many organizations are modernizing existing data centers in phases—creating dedicated AI zones, strengthening power distribution, and deploying high-density racks. This approach accelerates deployment while protecting existing infrastructure investments.

Even hyperscalers follow this path. Amazon's Titus initiative adapts existing data center designs for the next generation of AI systems through liquid cooling, flexible power architectures, modular server arrangements, and faster construction methods—enabling future AI systems without relying solely on new facilities.

Modernizing also means improving visibility. DCIM platforms and intelligent power distribution units provide granular data on power consumption, thermal capacity, and rack utilization. Digital twins allow companies to simulate how new AI deployments will perform before hardware arrives. This insight directs investments where they yield the highest return.

Robert Danforth, Director of Engineering, Simulation, and Advanced Development at Rehlko, emphasizes viewing AI infrastructure as a complete system rather than isolated components. "We're seeing a move away from individual pieces of infrastructure toward thinking about the entire operating ecosystem. The question isn't whether a generator, battery system, cooling solution, or control platform performs well on its own. It's whether all of those systems can work together effectively under highly dynamic conditions. That is why simulation and digital twin technologies are getting so much attention. They allow operators to understand how these interactions play out before infrastructure is deployed and help identify potential challenges early in the design process," he said.

GPUs are just the beginning. The bigger challenge is preparing data centers for the next generation with modular UPS systems, overhead busways, and scalable power distribution.

Modern GPU clusters generate heat that conventional air cooling cannot manage. The focus is not simply keeping facilities cool, but removing heat directly from chips to maintain AI performance. Organizations need hybrid cooling systems—liquid cooling for high-density AI racks combined with conventional air cooling for traditional workloads. This phased approach aligns with brownfield modernization trends, enabling AI expansion without complete rebuilding.

Cooling technologies are advancing rapidly. Direct-to-chip liquid cooling is becoming standard for new AI deployments, capturing heat at the source before it spreads throughout the server. NVIDIA is advancing this with its 45-degree liquid cooling system, using warm liquid in a closed loop to reduce reliance on energy-intensive chillers and lower water usage. Microsoft is testing microfluidic cooling with tiny channels etched into chip silicon, allowing coolant to flow through and remove heat more effectively than traditional methods.

Many enterprise AI setups leave valuable GPU resources underutilized. Training tasks compete for the same accelerators, inference tasks run on hardware over-provisioned for much larger models, and organizations often reserve capacity as precaution—resulting in expensive infrastructure sitting idle.

Improving utilization starts with better workload placement. Large-scale model training can leverage cloud resources during demand spikes, while routine inference runs on smaller, task-specific models closer to users or on existing enterprise infrastructure. AI orchestration platforms effectively schedule workloads across available GPUs. Techniques like model quantization, batching, and inference optimization reduce compute needs while maintaining business outcomes, enabling organizations to perform more AI work without adding servers or increasing power consumption.

Capacity planning is no longer an annual exercise but a continuous process. Infrastructure, AI, and operations teams must share performance data to determine where workloads should run and when new capacity is genuinely needed.

Building AI-ready infrastructure extends beyond adding more GPUs. Lasting success depends on effectively managing model performance, power supply, and cooling systems. Steve Altizer, CEO of Compu Dynamics, notes that AI has made infrastructure integration essential. "In previous generations of data center development, mechanical, electrical, IT, and operations teams could often work in parallel and piece everything together later. AI removes that tolerance. A change in rack density affects electrical distribution, structural needs, thermal strategy, commissioning, service access, and site operations."

As AI workloads evolve, infrastructure must adapt alongside them. Rather than rebuilding facilities for each new hardware generation, organizations should standardize core infrastructure—power, cooling, and network backbones—while allowing IT systems to scale as workload needs change. "The goal is to support the next generation of AI deployments without making every hardware change a major redesign," Altizer says.

Each new AI deployment influences energy demand, water use, and long-term resilience. Regulatory pressure is intensifying. Chile blocked a major Google data center project over water consumption concerns and aquifer impact. Europe is tightening restrictions, with the Netherlands imposing strict limits on hyperscale facilities and Amsterdam extending its municipal ban on new hyperscale data centers through 2030.

For CTOs and infrastructure teams, these pressures are reshaping AI planning. Scaling AI must balance growth against sustainable power grids, water sources, and operating budgets.