AI Data Centers Need More Than More Cooling: They Need Faster Engineering

Artificial intelligence is transforming digital work, but its impact is increasingly physical. AI at a massive scale requires far more power than ever before. And the data centers that house those AI servers are struggling to keep up with demand. In fact, a report from Deloitte estimates that by 2035, power demand from AI data centers in the United States alone could grow by more than thirty times.

However, the issue isn’t just a matter of power usage. The data centers that house these AI servers need to account for the sheer level of heat that the technology creates. Modern GPU clusters can reach 50 kW per rack and beyond. That’s a staggering tenfold increase from the standard compute servers of just the last decade.

The cooling systems that have long been in place for data centers to sufficiently manage IT workloads are being outpaced by these new heat outputs. The infrastructure simply cannot keep up, leaving those tasked with designing data center cooling systems a new challenge. The engineers in charge of designing AI-ready infrastructure are increasingly finding that traditional engineering workflows can’t keep up with the scale and speed of AI deployment.

Perhaps paradoxically, AI is both increasing demand for data center capacity and transforming the engineering process used to build that capacity. The same advances in AI that are driving unprecedented infrastructure requirements are also beginning to accelerate how engineers model, validate, and optimize the physical systems that support those workloads.

In effect, AI is becoming part of the process used to design the infrastructure that runs AI.

In light of this, many engineering teams are adopting cloud-native, AI-accelerated simulation workflows that allow them to evaluate thermal performance, cooling strategies, and infrastructure tradeoffs before construction begins.

When it comes to today’s data center infrastructure, the stakes mean any misstep can be devastating. Proving performance before construction has become the imperative for long term success, not reliance on assumptions, rules of thumb, or late-stage validation.

Traditional Cooling Infrastructure Under Strain

Unfortunately for engineering teams, AI workloads are fundamentally different from traditional cloud-based compute. There is no ebb and flow with AI, the network demands, heat output, and power requirements are constant.

That shift is, in part, exposing a key failing of many data centers. Many were built under the assumption that such a sustained demand wouldn’t be needed. And because these cooling systems are often highly energy-intensive themselves, it’s quickly becoming untenable to ‘overcool’ and assume that will cover a data center’s needs. Taking this approach and prioritizing uptime will cause costs and power usage to rapidly soar out of control.

At the end of the day, the crossroads many data centers find themselves at is not a matter of ‘more heat’. The defining risk that AI growth brings is a much tighter margin for error.

For data center developers, every delay in the validation process can impact customer commitments, capacity planning, or energy costs.

Historically, engineering teams could compensate for uncertainty through overprovisioning, conservative design assumptions, and late-stage validation. AI infrastructure changes that equation. The pace of deployment, capital investment required, and increasing rack densities leave far less room for trial-and-error engineering. Decisions that could be validated later now need to be proven much earlier in the design process.

The New Reality: Proving Thermal Performance Before Breaking Ground

With the margin for error shrinking, engineering teams are moving thermal analysis earlier in the design process, while changes are still inexpensive and the design is still flexible. Rather than waiting until commissioning to discover whether airflow patterns, rack layouts, containment strategies, or cooling equipment placement are sufficient, they can model airflow and heat transfer before construction begins.

That lets engineers identify hot spots, test cooling strategies, and compare design options under realistic operating conditions. A team can evaluate whether cold air is reaching high-density racks, whether hot exhaust is recirculating into equipment inlets, and whether cooling capacity is being used efficiently.

This is where platform architecture matters. For teams working under intense pressure, simulation cannot remain confined to a small group of specialists with access to dedicated HPC resources. Utilizing a cloud-native simulation platform makes high-fidelity analysis accessible to entire engineering teams, not just a small cadre of individuals. This allows those teams to run studies, compare design options, and collaborate without building or maintaining their own compute infrastructure.

With Engineering AI layered into that workflow, the role of simulation itself begins to change. Historically, simulation has been constrained by expertise, time, and computational resources. Running high-fidelity studies often required specialist knowledge, dedicated hardware, and lengthy iteration cycles.

Engineering AI, utilizing near autonomous agents that automate and accelerate engineering design, simulation, and analysis workflows, helps lower those barriers by accelerating model setup, surfacing relevant insights, and enabling teams to evaluate more design options in less time. Rather than reserving simulation for a final validation step, engineering teams can use AI-accelerated workflows to explore alternatives continuously throughout the design process.

The result is not simply faster simulation. It is faster innovation.

So, what does this all look like in reality for engineering teams? Consider a company that makes cooling and ventilation systems for large facilities needed a faster way to test new equipment designs. Usually, these companies have to build physical prototypes, bring in outside specialists, and spend several weeks checking whether air moved and mixed properly inside the system.

But when this company decides to use simulation software to create a virtual version of the test setup, the equation changes. Taking this approach lets engineers test airflow and temperature performance on a computer before building the real product.

And the results often deliver real impact. The pre-testing phase can be cut down to as short as to 2–3 and engineering time can drop down to 40 hours rather 85 in more traditional workflows.

But the value here is also much bigger than just saving time. The real value lies in the engineering team’s ability to ask more questions and explore possibilities earlier. What happens if rack density increases? What if airflow paths change? What if redundancy assumptions fail?

Enabling a heightened level of exploration like this is what elevates simulation from an analytical tool to a crucial component of infrastructure design strategy.

AI Infrastructure Requires AI-Accelerated Engineering

The next phase of AI infrastructure will not be defined by facility size, rack density, or cooling capacity alone. It will also be defined by how quickly engineering teams can prove that those systems will work before they are built.

That is where the industry’s next competitive advantage will emerge. Data center developers that bring simulation earlier into the design process, make it accessible across engineering teams, and pair it with AI-accelerated workflows will be better positioned to make confident decisions before capital is committed and construction begins.

As AI continues to reshape the physical demands placed on data centers, it will also reshape the way those facilities are engineered. The organizations that lead this next era will not simply react to higher heat loads or tighter energy constraints. They will build design processes capable of anticipating them.