Planning for plug-and-play systems

What does it take to design a data center for a world where the technology inside it may change several times before the building even opens?

In this episode of Tech Talks Daily, I sit down with Jackson Metcalf, Principal at Gensler, to talk about how AI is forcing a complete rethink of data center design. Jackson has spent nearly two decades working on critical facilities, and in our conversation he explains how the shift from traditional cloud workloads to dense AI environments is changing everything from building form and cooling strategy to long-term infrastructure planning.

What struck me most in this conversation is the sheer mismatch in timescales. Data centers can take two and a half to three years to design and build, while chip and GPU roadmaps are evolving in cycles of months. Jackson explains why that means designing for a fixed end state no longer makes sense. Instead, the future may belong to facilities built with flexibility at their core, spaces that can be reconfigured, upgraded, and even conceptually rebuilt over time rather than treated as static assets.

We also talk about what hyper-flexibility actually means in practice. This is not just a buzzword. It is about designing buildings with enough structural and engineering headroom to support very different cooling and power models over their lifespan. As AI workloads push cabinet densities to levels that would have sounded impossible only a few years ago, the need for plug-and-play mechanical and electrical infrastructure becomes far more than a design preference. It becomes essential.

Another fascinating part of the conversation centers on sustainability. Jackson shares why durable, well-built structures can create long-term environmental value, even in an industry often criticized for its energy demands. We discuss embodied carbon, adaptive reuse, and why a high-quality building may have a much better second life than something built purely for short-term speed. That leads into a wider conversation about repositioning underused real estate, from former industrial facilities to vacant office buildings, as potential digital infrastructure.

We also get into the growing energy challenge behind AI. With demand for power rising fast, and the US grid under increasing pressure, many operators are now weighing options such as on-site natural gas generation while waiting for cleaner long-term alternatives to mature. Jackson offers a thoughtful perspective on the tension between urgent infrastructure needs and environmental responsibility, as well as the uncertainty surrounding future energy roadmaps.

Looking further ahead, I ask Jackson what will define a successful data center campus in the years to come. Will it be raw megawatts, adaptability, carbon intensity, location strategy, or something else entirely? His answer opens up a much bigger conversation about whether these buildings can become more connected to the communities around them, and what role they may play in a future where digital infrastructure is no longer hidden in the background, but central to how society functions.

So if AI is pushing data center design to extremes, how do we build facilities that are ready for what comes next without becoming obsolete almost as soon as they open? And what does sustainable, adaptable digital infrastructure really look like in practice?