Abhilash Kunnatoor Margabandu is vice president of infrastructure engineering at EdgeCore Digital Infrastructure.
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For the past two decades, data center development strategy followed a relatively predictable curve. Workloads expanded steadily, rack densities rose incrementally and infrastructure design evolved in measured cycles. AI broke that rhythm.
In the span of just a few years, we’ve seen U.S. data center demand surge to record levels, with annual leasing activity reaching historic highs and forecasts pointing even higher. Gigascale-level campuses, or environments capable of delivering gigawatt-level IT capacity, that once seemed extraordinary are now becoming the norm.
It takes great skill and planning to bring these modern facilities to life. In today’s landscape, the defining engineering challenge isn’t about how to build bigger, but how to create infrastructure that can continuously evolve alongside AI’s advancements. That shift is forcing leaders to rethink how these facilities are designed and built. These are the areas leaders need to focus on today to remain successful tomorrow:
Legacy facilities were optimized for stability. Designs assumed a relatively fixed power density and baseline cooling techniques. Once in operation, the goal was to run as consistently as possible for years. However, that mindset is no longer the case with today’s demands.
Rack densities have skyrocketed, while cooling methods that were once considered specialized just a few years ago are now becoming mainstream. Additionally, power requirements are shifting faster than utility timelines can accommodate. In this gigascale environment, a data center cannot be a static asset. It must function as a dynamic platform.
Agility starts with a fungible, baseline MEP architecture. Electrical systems must allow capacity to scale in blocks without disrupting operations. Distribution paths must also accommodate higher amperage and evolving models. As density requirements continue evolving, data center developers must absorb that change without a complete structural overhaul.
Mechanical design faces a similar inflection point. Air cooling is being replaced by either liquid-based solutions for higher-performance AI hardware or a mix of air- and liquid-based solutions for a diverse use case, but even liquid cooling isn’t a single endpoint. Direct-to-chip and hybrid systems are advancing in parallel.
The "winning" strategy isn’t betting on one method. It’s about developing facilities that can integrate multiple approaches over time. But designing for that adaptability is fundamentally different from sticking with one blueprint for years to come.
While cooling is the most visible transformation, power is the underlying constraint. The next wave of disruption is more about delivering significantly higher power densities. As AI clusters consolidate, electrical infrastructure must move more energy through less space while doing so efficiently, reliably and safely.
As part of their energy strategy, developers must explore other supply avenues, like on-site generation and advanced storage capabilities, that can manage volatility and capacity constraints. While commercial deployment at scale may take years, the direction is clear: Infrastructure must be prepared for new forms of baseload power.
Once again, agility is the connector. Facilities built with rigid assumptions about power sources may struggle to adapt as the energy mix evolves.
AI-driven demand has compressed project timelines across the board. Hyperscalers are seeking rapid delivery of large capacity blocks, often with evolving technical specifications. That pressure has reshaped construction and project management practices.
Prefabrication, standardized design elements and parallel workstreams are now central to reducing build times. Supply chain strategy has also become a critical concern of engineering teams, particularly for long-lead electrical components.
However, accelerating delivery introduces its own risks. Speed that undermines resiliency is counterproductive. The challenge is to create repeatable frameworks that enable faster deployment while preserving operational integrity.
This is where agility extends beyond physical infrastructure into strategy. Decision-making must be streamlined, while engineering teams need authority to adjust specifications as customer workloads shift.
The conversation around gigascale campuses often lands on the availability of land, power and fiber routes. But equally as important is human capital.
High-density AI data centers need engineers who understand the connection between all systems—mechanical, electrical and software. For instance, a modification in cooling strategy affects electrical load distribution, while a shift in design influences planning. These concentrations require multidisciplinary fluency.
The industry also faces a critical shortage of skilled trade workers and experienced professionals. Competition for talent is intense. The long-term solution isn’t hiring more aggressively but investing in continuous upskilling opportunities. It’s also important to foster adaptability as a core professional trait.
Engineers entering the industry must expect that the various tools, standards and best practices they will learn will evolve rapidly. Developers who prioritize learning opportunities will outpace those who restrict themselves to static experience.
In the next five to 10 years, densification will continue as AI hardware becomes more powerful, energy-intensive and complex. At the same time, energy markets will be scarce, and new cooling and power technologies will emerge to meet the evolving chip design topography.
The most resilient gigascale campus developers won’t be those who perfectly predicted future scenarios. They’ll be the ones who engineered for optionality and stayed agile over the years. In previous eras, resilience meant resisting disruption. But in the AI acceleration era, resilience means absorbing it.
Gigascale infrastructure is becoming the physical foundation of the digital era, but its success will hinge less on megawatts and more on engineering architectural foresight. As industry leaders evaluate their next wave of investment, the question is not simply whether a campus can support today’s workload. It’s about whether or not it’s engineered to handle tomorrow’s uncertainty.
Agility isn’t an added feature of the data center. It’s the organizing principle that will determine which developers endure future shifts and which are left redesigning in response to endless change.
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