The following viewpoint is written by Mark Knipfer, who leads data center services at Integrated environmental solutions
The rapid growth of artificial intelligence is causing some of the fastest infrastructure build-out the data center industry has ever seen. Hyperscalers, cloud providers and colocation operators are racing to add capacity to support increasingly compute-intensive AI workloads.
For the construction and engineering community, this wave of development brings new challenges. Data centers have always been technically complex facilities, but the scale and speed of AI infrastructure deployment is forcing project teams to rethink some long-held assumptions about how these buildings are designed and entered. This applies not only to new builds, but increasingly to existing facilities that must be adapted to support higher-density, AI-driven workloads.
Energy availability is the new constraint
Perhaps the most significant change is that energy availability is increasingly becoming the main constraint on development.
In many markets, utilities are struggling to keep up with the demand created by large-scale data center projects. These facilities can require hundreds of megawatts of electrical capacity, and interconnection timelines are stretching as grid operators work to expand generation and transmission infrastructure. For developers, this means that securing power, and getting that power online quickly, has become one of the defining challenges of new projects. For operators of existing facilities, it also raises a parallel question: how to increase usable capacity within current power constraints.
This pressure is changing the economics of data center design. Decisions affecting power demand, cooling performance and infrastructure efficiency now have direct implications for how quickly facilities can be delivered and how much capacity can be deployed in a given location. In practice, these decisions can also determine whether projects move smoothly from design to construction, or encounter costly redesigns once infrastructure limitations become clear.
Cooling strategies are evolving rapidly
At the same time, AI workloads are driving significant changes in data center cooling.
High-density computing environments built around specialized GPUs and AI accelerators generate far more heat than traditional enterprise workloads. Rack power densities that were once considered exceptional are becoming more common in AI clusters.
As a result, developers and engineering teams must evaluate a wider range of cooling approaches. Conventional air-based systems are still common, but many new installations are exploring direct-on-chip liquid cooling or hybrid approaches that combine air and liquid infrastructures. For retrofit projects, these options are often limited by existing plant, space constraints, and business continuity requirements, making the assessment even more critical.
Each strategy introduces different design considerations. Liquid cooling can improve heat removal for high-density racks, but it also introduces additional infrastructure requirements, such as coolant distribution networks, heat exchangers, and modified heat rejection systems. Hybrid approaches can offer flexibility, but require careful integration with existing facility infrastructure.
These decisions are not purely mechanical design issues, but influence a facility’s energy consumption, water use, operational resilience and long-term scalability.
Engineering decisions are increasingly interconnected
What makes these choices particularly difficult is that they are deeply interconnected.
A change in cooling strategy may affect electrical infrastructure requirements. Power limitations may influence equipment selection. Heat rejection strategies may depend on regional climatic conditions or water availability. In adaptation scenarios, these interdependencies are amplified, as new systems must be integrated with existing infrastructure that was not designed for today’s operating conditions.
Historically, engineers have often evaluated these systems using peak load calculations, designing installations to operate under the most extreme operating conditions likely to be encountered. And while these calculations are still essential to ensure reliability and redundancy, peak scenarios represent only a small part of how data centers actually operate.
Most of the time, facilities operate under part-load conditions as IT workloads vary and environmental conditions change throughout the year. Infrastructure systems that appear efficient at peak load may behave very differently during day-to-day operation.
For engineering teams responsible for providing reliable and efficient facilities, understanding these dynamics is increasingly important.
Infrastructure assessment before the design process
Another defining characteristic of today’s data center expansion is the pace of development.
Demand for AI computing capacity is compressing project timelines. Developers are under pressure to deliver new facilities as quickly as possible, often while navigating uncertain energy availability and evolving technology options. At the same time, operators are looking to modernize existing facilities to extend their useful lives and support new workloads without waiting for new capacity to come online.
This makes the initial assessment of infrastructure strategies particularly valuable.
By examining how different cooling architectures, equipment configurations, and infrastructure systems interact, engineering teams can identify potential performance issues before construction begins. Early analysis can also help developers understand the tradeoffs between competing design options and make more informed decisions about which strategies best align with project goals.
For complex facilities such as AI data centers, this type of initial assessment can reduce risk during the later stages of design, construction and commissioning.
A new phase for data center development
Data center construction has always required close collaboration between developers, engineers and contractors. As the AI infrastructure expands, this collaboration becomes even more important.
Power availability, cooling performance and facility scalability are no longer isolated design considerations, but interconnected challenges that shape the way projects are planned and delivered, whether for new developments or the transformation of existing assets.
For the engineering and construction community, the rapid rise of AI infrastructure represents a new phase in data center development. Meeting the demand for next-generation computing capability requires project teams to evaluate infrastructure decisions more holistically and earlier in the design process.
As these facilities continue to grow in scale and complexity, this more integrated approach to engineering will become essential. Successful teams will be those able to evaluate infrastructure decisions earlier, understand how systems interact under real operating conditions, and translate those insights into designs that can confidently move from concept to construction.
Mark Knipfer leads data center services at Integrated Environmental Solutions, where he works with engineering and developer teams to evaluate performance and infrastructure strategies for high-performance facilities.
