Artificial Intelligence and Data Center Correlation

Artificial intelligence has quickly become part of everyday work. We all know that. From chip designers to AI researchers, the focus is on turning AI into a practical assistant that helps people move faster and do more. This growing convenience, however, often hides what is happening behind the scenes at the infrastructure level.

A study by the Environmental and Energy Study Institute (EESI) reports that a medium-sized data center can consume up to 110 million gallons of water per year, primarily for cooling. Today, we have the privilege of assigning an AI assistant to our daily work for around 20 bucks month, gaining a noticeable boost in productivity. That privilege is supported by large physical systems that consume significant amounts of water and energy. To maintain availability close to 99.99999%, cloud providers like AWS or Azure operate hundreds of data centers worldwide, increasing the overall physical resource footprint behind what appears to be a simple digital service.

What Happened Before the AI Boom

These water-usage numbers for a mid-sized data center are recent (June 25, 2025), which raises a natural question: what was different before the AI era? Power and cooling have always been part of data center infrastructure, so that alone is not New. The difference lies in how these systems are used today.

The underlying physics has not changed. GPUs, servers, racks, and network switches still obey the same physical laws. What has changed is usage intensity. The AI boom has pushed infrastructure utilization much higher and for much longer duration. Every deep learning or generative AI workload ultimately runs on physical bare metal, regardless of how much virtualization or abstraction is applied on top.

This shift—from mixed, bursty workloads to sustained, compute-dense workloads—means systems now operate closer to their thermal and power limits for extended periods. As a result, physical constraints that were once manageable have become dominant factors in system performance.

Short answer: thermodynamic complexity, details discussion here.

Why Simulation Became Necessary

As computing intensity increases, heat generation becomes unavoidable. When heat is produced faster than it can be removed, temperatures rise beyond safe limits. This leads to performance degradation and potential IT hardware damage. In computer systems, this problem is well known and often appears as localized high-temperature regions, commonly called hot zones.

Because of this, reducing hot zones has long been a concern in data center design. Research work by Long Phan and collaborators, published as early as 2017—well before the current AI-driven scale-up—anticipated challenges that now shape modern data center and colocation operations. Their studies examined airflow and cooling behavior using hot- and cold-aisle layouts, which are common in data centers.

To study these effects, the researchers used physics-based simulation tools such as EnergyPlus, developed by the National Renewable Energy Laboratory (NREL). EnergyPlus was used to evaluate temperature variation and cooling performance under different operating conditions.

This work matters because there are infinitely many possible ways to design, locate, and operate a data center. Small differences in layout, airflow strategy, materials, or operating settings can lead to very different long-term outcomes. Today, these simulation-based methods directly support planning and operational decisions, as AI workloads push physical infrastructure closer to its limits.

Why Simulation Matters for Return on Investment

In data centers, return on investment (ROI) is mostly decided before the first RACK is installed. Simulation helps at this early stage, when design changes are still cheap and flexible. Instead of guessing, teams can test how layout, Heating, Ventilation, and Air Conditioning (HVAC) strategy, and operating conditions affect energy use and long-term cost.

Simulation also makes future costs visible. By modeling energy consumption, HVAC behavior, cooling demand, and utility expenses over time, planners can see how today’s decisions shape tomorrow’s operating cost. This reduces trial-and-error, avoids over-design, supports better capital planning and maximize ROI.

Digital Twins build on this idea. They are a more integrated form of simulation—a virtual representation of a physical system. Using a digital twin, planners can evaluate designs from GPU power management to RACK layout, network switches, and power infrastructure before capital is committed. Explore NVIDIA AIR—you might find it interesting. This supports investment planning by reducing uncertainty around cost and performance.

Physics-based tools such as EnergyPlus support this process by modeling heat transfer, airflow, and system behavior using physical principles. Used this way, simulation becomes a practical planning tool rather than an academic exercise—helping teams make informed technical and financial decisions early.

Physics Still Sets the Limits

Much of the work on hot zones, peak load, data center thermal behavior is grounded in physics-based simulation, not abstract models. Tools such as EnergyPlus matter because they explicitly model heat transfer, airflow, material properties, and boundary conditions using physical principles. This makes them suitable for studying how hot zones form, persist, and shift across different parts of a data center as workloads change.

As AI workloads run hotter and longer, the usefulness of physics-based models becomes easier to see. Work that once focused on isolated thermal cases now helps answer real, day-to-day questions about layout choices, cooling strategies, and long-term efficiency. In this way, physical simulation tools link earlier research to today’s planning and operational needs—not by changing the physics, but by applying it at the right scale.