Building Flexible AI Data Centers With EdgeCore

How can data center developers meet soaring demand for AI capacity without locking billions of dollars into buildings that may no longer fit tomorrow’s workloads?

In this episode of Tech Talks Daily, I speak with Steve Conner, president of EdgeCore Digital Infrastructure, about the decisions sitting beneath the rapid expansion of AI infrastructure. Steve has worked in and around the data center sector since 1998, including the dot-com era and the later growth of cloud computing. That history gives him a measured view of the current rush to build large facilities quickly.

Steve argues that AI has intensified what he calls shiny object syndrome. The opportunity is large, but training, inference, and cloud workloads do not all ask the same things of a building. Rack density, floor loading, cooling, electrical design, available space, network distance, and proximity to cloud regions can all affect whether a campus can adapt when customer requirements change.

We discuss why EdgeCore has chosen to preserve flexibility in its facilities. A training-focused building might be made smaller because dense racks require less floor space, but future inference workloads may need a wider footprint. EdgeCore therefore accepts additional space in some designs, reinforces floors for heavier equipment, and enables liquid cooling even when a lower-density workload may not need it immediately. Steve presents those decisions as insurance against expensive retrofits or stranded capacity.

The conversation also examines EdgeCore’s recently secured $1.5 billion in financing. Steve says the covered buildings were fully leased and designed to support mixed cloud and AI workloads. For him, the financing reflects continuing demand both inside established cloud regions and in surrounding markets, while the mixed-use design gives the customer options as requirements develop. These figures and interpretations remain EdgeCore’s account of the investment.

Site selection is another major part of the equation. Power availability receives much of the attention, but Steve adds network distance, workforce availability, long-term political support, and relationships with utilities and local authorities. He describes looking beyond crowded locations such as Ashburn while remaining close enough to established cloud regions to support different use cases.

For me, the most valuable part of the discussion concerns communities. Steve says developers should begin meeting local leaders and understanding local needs before purchasing land. EdgeCore’s examples include support for chambers of commerce, first responders, hospitals, fire services, and workforce development. His argument is that a company cannot simply purchase goodwill after construction begins. It has to be present early and demonstrate that the relationship runs both ways.

We also address public concerns about water, emissions, energy demand, and jobs. Steve argues that many modern data centers use cooling systems that do not consume water for routine cooling, though his comments apply to the facilities and designs he knows and should not be generalized to every data center. He also notes that AI facilities consume substantial power while arguing that developer-funded transmission upgrades can benefit other users of the grid.

The episode closes with a wonderfully plain analogy. Steve describes the data center as the plate rather than the meal. The infrastructure serves whatever workload the customer needs, which is precisely why the plate must be designed for a menu that keeps changing.

Are developers doing enough to prepare AI data centers for changing workloads while earning the confidence of the communities around them? Listen to the episode and share your thoughts with me.