Steve Altizer, Compu Dynamics: Integration has to be foundational. It has to start at the first planning conversation, not after the equipment is selected or once the building is already designed. In previous generations of data center development, mechanical, electrical, IT, and operations teams could often work in parallel and bring the pieces together later. That worked when the load profile was more predictable and the facility had more room to absorb change. Before the introduction of ChatGPT, there was very little change to absorb. AI removes that tolerance. A change in rack density can affect electrical distribution, structural requirements, thermal strategy, commissioning, service access, and the way the site is operated. These are no longer independent decisions. They are all part of one performance system. As AI systems move toward POD-scale platforms, the boundary between IT and facility infrastructure becomes much harder to separate. The challenge is that AI workloads are too varied for a one-size-fits-all approach. Training clusters, inference nodes, enterprise AI environments, and edge sites can all have different requirements for density, cooling architecture, network connectivity, security, site conditions, and serviceability. That is why many companies are adopting a modular approach, while others are embracing hybrid models where turnkey modular AI capacity is integrated into larger campus environments.  At the campus level, that means standardizing the backbone infrastructure that serves the site (utility power feeds, central cooling capacity, and network pathways), while allowing the IT environment and the integrated critical infrastructure components to evolve as workload requirements change. The goal is not modularity for its own sake. The goal is to support the next generation of AI deployments without forcing every hardware change to become a major redesign. AI infrastructure cannot be planned as a collection of disparate systems. It has to be designed as one coordinated

The data center industry has never been more visible, more vital, or more challenged. Support for AI and its overall industry impact has pushed digital infrastructure into the public conversation. It has become clear that the sector is confronting unprecedented demands for everything from power to basic infrastructure. That convergence is the focus of Data Center Insights 2026, a two-day virtual event taking place July 15–16, 2026, produced by Endeavor B2B’s Data Center Frontier, Cabling Installation & Maintenance, ISE, Lightwave, and SecurityInfoWatch. Designed for data center owners, operators, engineers, IT leaders, and the people supporting the next generation of data center development, the event offers a concentrated look at the technologies and strategies shaping the future of digital infrastructure. The program arrives at a crucial moment. AI workloads are changing almost every assumption behind data center design. Rack densities are rising, liquid cooling is becoming mainstream, and fiber networks are being rethought for 400G and beyond. Power constraints are now central to site selection. Security is becoming highlighted and operators are being asked to build faster, scale larger, be more resource efficient and maintain resilience in an environment where downtime carries higher consequences than ever. Data Center Insights 2026 is structured to help attendees make sense of this moment. Rather than treating data center infrastructure as a set of separate disciplines, the event brings together experts across cooling, cabling, fiber, power distribution, modular design, AI infrastructure, and operational strategy. The result is a practical, cross-functional program built around the real-world questions now facing the industry. What will I learn at this event? The event opens with “Expert Roundup: The State of the Data Center Industry,” featuring perspectives from Steven Carlini of Schneider Electric.This session sets the stage by examining the forces driving change across the data center landscape in 2026.

Steve Altizer, Compu Dynamics: The defining challenge is keeping pace with the rate of change in the IT environment. It takes time to design, permit, build, and commission a data center. AI hardware operates on a completely different timeline. New GPU families are being introduced every 12 to 18 months, and from one generation to the next, rack power densities can double or even triple. At prototype scale, you can design around a single cluster or a specific density profile. At production scale, that approach becomes a real liability. The facility has to support today’s deployment while remaining adaptable for the next compute profile. We are not just talking about adding more power. We are preparing for major architectural shifts, including the move toward DC power delivery or cooling systems that may rely on two-phase liquid to remove heat at scale. That is what becomes materially harder. You are no longer solving for a single, static deployment. You are solving for a moving target inside a live operating environment. This is where strategic modularity proves its value. It helps decouple the lifecycle of the building from the lifecycle of the IT hardware. Instead of treating the data center as one monolithic design, modularity creates a more agile framework that can absorb new power and cooling architectures without requiring a full facility retrofit every time the IT roadmap shifts. At Compu Dynamics Modular, we are seeing this play out in real time. The value of a turnkey modular approach is not simply speed. It is the agility owners need to keep pace with ever-evolving rack densities, power delivery requirements, and cooling architectures.

Matt Vincent is Editor in Chief of Data Center Frontier, where he leads editorial strategy and coverage focused on the infrastructure powering cloud computing, artificial intelligence, and the digital economy. A veteran B2B technology journalist with more than two decades of experience, Vincent specializes in the intersection of data centers, power, cooling, and emerging AI-era infrastructure. Since assuming the EIC role in 2023, he has helped guide Data Center Frontier’s coverage of the industry’s transition into the gigawatt-scale AI era, with a focus on hyperscale development, behind-the-meter power strategies, liquid cooling architectures, and the evolving energy demands of high-density compute, while working closely with the Digital Infrastructure Group at Endeavor Business Media to expand the brand’s analytical and multimedia footprint. Vincent also hosts The Data Center Frontier Show podcast, where he interviews industry leaders across hyperscale, colocation, utilities, and the data center supply chain to examine the technologies and business models reshaping digital infrastructure. Since its inception he serves as Head of Content for the Data Center Frontier Trends Summit. Before becoming Editor in Chief, he served in multiple senior editorial roles across Endeavor Business Media’s digital infrastructure portfolio, with coverage spanning data centers and hyperscale infrastructure, structured cabling and networking, telecom and datacom, IP physical security, and wireless and Pro AV markets. He began his career in 2005 within PennWell’s Advanced Technology Division and later held senior editorial positions supporting brands such as Cabling Installation & Maintenance, Lightwave Online, Broadband Technology Report, and Smart Buildings Technology. Vincent is a frequent moderator, interviewer, and keynote speaker at industry events including the HPC Forum, where he delivers forward-looking analysis on how AI and high-performance computing are reshaping digital infrastructure. He graduated with honors from Indiana University Bloomington with a B.A. in English Literature and Creative Writing and lives in southern New Hampshire with

For much of the past decade, the conversation surrounding AI infrastructure has been dominated by one resource above all others: power. Utilities have become strategic partners. Natural gas generation, small modular reactors, microgrids and behind-the-meter power have become central themes across virtually every major data center conference. Developers increasingly speak about securing megawatts years before they discuss servers. But another infrastructure constraint is quietly following the same trajectory: Water. According to executives from Emergence Water and Nimbus Advanced Process Cooling Systems, water is rapidly evolving beyond its traditional role as a sustainability metric and becoming one of the primary determinants of where AI campuses can be built, how they are cooled, and how efficiently they will operate over the coming decade. Speaking with Data Center Frontier Editor in Chief Matt Vincent on the latest DCF Show podcast, Emergence Water Chief Product Officer Leif Percifield and Nimbus Technical Director Vamsi Mokkapati described an industry where water has effectively joined power and fiber as foundational infrastructure for AI development. “From a community perspective, water is absolutely the number one priority about where and why a data center gets built,” Percifield said. “From the developer, it’s pretty binary. They either have water available to them—or they don’t.” Water Is Becoming a Site Selection Constraint The shift reflects the changing realities of AI infrastructure. Traditional enterprise data centers often viewed water primarily through sustainability reporting or Power Usage Effectiveness (PUE) discussions. AI facilities operating at unprecedented rack densities have fundamentally altered that equation. Liquid cooling, hybrid cooling architectures and increasingly sophisticated thermal management strategies all place new emphasis on reliable long-term water availability. Equally important, communities are beginning to scrutinize water usage with the same intensity previously reserved for electrical demand. Percifield says those conversations are increasingly determining whether projects move forward at all.

Environmental conditions add another layer of complexity. Anthony Santora, managing director of IT for the USGA, describes the championship network as a data center without the usual comforts. There’s dust, rain, wind, and wide temperature swings instead of clean, controlled air. Hardware resides in trailers and weatherproof enclosures, not in racks behind raised floor tiles. For network engineers who spend most of their time on office campuses and in colos, that’s an important reminder: Critical infrastructure increasingly sits in places that look nothing like a traditional wiring closet. User behavior is just as hostile. The U.S. Open has its own term — the “Tiger effect” (though one could argue it’s now the Scottie effect) — for what happens when tens of thousands of fans follow a single golfer. The hot spot moves with the group, and the RF design must cope with a dense, moving cluster of devices. That pattern should sound familiar to anyone who supports large conferences or festivals; it’s the same phenomenon, just under a different name. Building an AI‑ready, fault‑tolerant course network Cisco’s answer to this environment is a fully redundant, mobile core design. Instead of a single large core in a building, the network collapses into dual trailers that serve as cores on the go, typically anchored at the NBC broadcast compound and another central location. Each core hosts Cisco Secure Firewall appliances, FMCs, core Catalyst switches, DHCP, UPS, and generators, all in pairs. Rodriguez was matter-of-fact about the philosophy: “We do everything in pairs as much as we can.” If one fails, its twin picks up the load.