Current headlines focus on how AI is driving demand for new data centers. While that is true, another shift is happening on the ground. AI is accelerating how quickly data center requirements evolve during design and construction.
On active projects, owners are now weighing the cost and logistics of adjusting core systems, including power, cooling, layout, to accommodate AI demands. Changes are coming faster than a typical design and construction cycle can absorb, which means teams are building with the expectation that requirements will evolve and planning accordingly.
Power Density Is Moving Faster Than the Build Cycle
One of the clearest examples of this shift is power demand at the rack level.
Even within the same generation of GPUs, the difference is significant. A lower-tier configuration might require around 550 watts, while a higher-end version in that same line can push closer to 1000 watts. That’s nearly double the power demand without a fundamental change in the platform.
That increase doesn’t stay isolated to the rack. It drives changes across the entire system:
- Cooling requirements increase
- Electrical distribution needs to be reevaluated
- Supporting infrastructure has to scale
From a construction standpoint, those changes translate into real cost. It’s not uncommon for adjustments tied to these types of shifts to land in the six- to seven-figure range.
Cooling Is a Good Example of How These Changes Play Out
Cooling is one of the most significant areas of change.
Traditional data center loads are relatively steady. AI workloads have more spikes, generate heat much more quickly, and require liquid cooling solutions.
While future data center projects will account for liquid cooling in the design phase, projects already under construction are making the transition to liquid cooling in a targeted manner. In some facilities, a small block of four or five racks may be designated for liquid cooling, while the rest remains air-cooled.
Even with this targeted approach, the impact is significant. Changes require:
- Piping systems to support coolant distribution
- Additional mechanical equipment
- Sensors for temperature, humidity, and leak detection
- Integration with building management and fire alarm systems
The shift carries through multiple trades and building systems.
Small Adjustments Scale Quickly
In the field, it’s immediately apparent how quickly small changes can scale.
Take something like a busway system, which is essentially a powered distribution system running across the data hall that allows racks to be plugged in where needed. If you shift that system slightly, it may require adjusting cable trays across every row, moving structural support and coordinating with other overhead systems.
When you multiply these changes across dozens of rows, a minor layout change can turn into a six-figure cost impact.
This situation is not new to data center construction, but the frequency of those adjustments is increasing as AI requirements continue to evolve.
The Industry Is Already Adapting
One of the more important points is that this isn’t something teams are just reacting to. It’s something experienced teams have learned to plan for.
On earlier projects, liquid cooling was not fully defined until late in the process. That led to larger change orders and more rework. In one case, integrating that scope late resulted in a seven-digit cost increase.
On projects moving into construction now, that same scope is anticipated earlier. It’s built into the budget as an allowance, coordinated ahead of time, and priced more accurately. The cost doesn’t go away, but the impact is more controlled, with less rework, fewer schedule disruptions, and less need to expedite materials or redesign on the fly.
For projects now in design, it has become part of the standard approach. The scope is defined, pricing is understood, and execution is more predictable.
That cycle—learn, adjust, standardize—is happening across multiple aspects of data center construction right now.
There Are Still Real Constraints
At the same time, not every change can be absorbed mid-project.
Power infrastructure is a good example. If a tenant decides they want to increase power demand beyond what was originally planned, that can trigger much larger conversations. Upgrading feeder cables, transformers, or distribution systems can mean millions of dollars and significant rework.
At that point, owners must decide if they want to absorb the cost and delay to upgrade now or move forward with the current design and adjust on the next project.
Those decisions are happening more frequently as technology continues to evolve during the construction timeline.
Building for Flexibility Is Becoming the Standard
The reality is that the fit-out of data centers takes anywhere from six to twelve months. In that time, hardware specifications change.
That means when you deliver a new facility, it’s based on a spec that’s already several months old by the time it’s fully operational.
That’s not a failure in the process. It’s just the pace of the industry right now.
What’s changing is how teams respond to that reality. Instead of trying to predict the exact end state, the focus is shifting toward:
- Designing systems that can accommodate upgrades
- Building in allowances for evolving scope
- Coordinating earlier across trades to reduce downstream impacts
The goal isn’t to get everything perfect on day one. It’s to make sure the facility can adapt.
Experience Matters More Than Ever
AI is accelerating change in data center construction, but it’s not rewriting the fundamentals.
What’s different is the pace and the need to understand how one change ripples through the data hall.
Teams that have been through that learning curve are better positioned to manage it. They know where to expect adjustments, how to plan for them, and how to keep projects moving without unnecessary disruption.
Experience is becoming the real differentiator as the next wave of data centers takes shape.
Key Takeaways
- Having people involved who understand how to balance speed with budget control is critical to delivering as close to “on time within budget” as possible
- AI is pushing project teams to the limits as it causes us to consistently re-adjust to new industry tech & construction systems to support that technology
- Having budgets and contracts set up for the realities on the ground are more important than ever
