The Data Center Frontier Show

As AI data centers follow available power into new markets, network infrastructure is moving much earlier into the site selection process.

In this episode of the Data Center Frontier Show, DCF Editor-in-Chief Matt Vincent speaks with Scott Bergs, CEO of Kirkwood Infrastructure Group, about how hyperscale and high-density compute are changing the physical network requirements behind data center development.

For years, fiber could often be addressed after land and power were secured. Bergs says that model is breaking down. Hyperscale and neo-cloud campuses increasingly require multiple physically diverse, high-capacity, low-latency network paths — infrastructure that may be difficult or impossible to add late in the development cycle.

That means connectivity planning now has to begin alongside power, roads, permitting and other site infrastructure, sometimes before the ultimate tenant is even known.

Bergs also explains the evolution of Kirkwood Infrastructure Group from the team behind DF&I, which built dark fiber infrastructure across Northern Virginia and Maryland. Kirkwood is now expanding across the Southeast and into the Midwest as large data center campuses move into markets where power may be available but communications infrastructure remains relatively thin.

The conversation gets into the physical implications of that expansion. Bergs describes customer demand progressing from lit services to dark fiber, then dedicated cables and increasingly dedicated conduit capacity. High-density applications are driving fiber counts from 864 fibers to as many as 6,912 per cable, while conduit itself is becoming a strategic asset for security, routing control and future network expansion.

Vincent and Bergs also discuss hollow-core fiber, including its potential latency advantages and the engineering tradeoffs posed by its larger form factor and different implementation requirements. Bergs’ larger point: network developers need to build enough physical pathway capacity today to accommodate whatever fiber technology becomes dominant tomorrow.

The episode also examines several emerging infrastructure bottlenecks, including rights-of-way, permitting pressure on agencies such as the U.S. Army Corps of Engineers and transportation departments, and growing strain on the fiber manufacturing supply chain.

Perhaps the clearest indication of how much the network map is changing comes from inter-campus connectivity. Bergs says links that once might have stretched two to 30 miles can now extend 250 miles or more.

“What might have traditionally been thought of as a long-haul segment or path for us is just another inter-campus connectivity corridor,” he says.

The conversation closes with another increasingly important part of infrastructure development: community acceptance. Bergs argues that early, factual engagement is becoming as important as early engineering.

“It’s very difficult to fight emotion with facts,” he says. “But you can sometimes prevent negative emotion with positive facts if they’re presented first.”

Listen to the full conversation for a detailed look at how power, fiber, conduit, permitting and community engagement are converging in the next generation of AI data center development.

AI Infrastructure’s New Site Selection Equation: Power, Fiber and Permitting
AI data center development may begin with power, but it increasingly depends on whether connectivity can be built at the same scale and on the same schedule.

In this episode of the Data Center Frontier Show, Matt Vincent, Editor-in-Chief of Data Center Frontier, speaks with Jeff Wabik, CTO of DC Blox, about how AI infrastructure is changing the relationship between data center development and fiber networks.

Wabik explains how DC Blox’s site-selection strategy has evolved over the past decade—from finding “dirt close to eyeballs,” to securing more land, to chasing available power, and now to evaluating whether massive, diverse fiber routes can be constructed alongside new campuses.

The conversation explores:

Why 864-count fiber is now among the smallest deployments DC Blox commonly sees

How hyperscalers are becoming major builders of terrestrial and subsea network infrastructure

Why new conduit systems may include 10 to 14 ducts from the outset

How fiber permitting can take 12 to 18 months and influence routing decisions

Why DC Blox may route around jurisdictions with histories of permitting delays

Fiber lead times stretching to 70–80 weeks for large-count cable

Skilled-trades and operations workforce shortages

Why community engagement around water, noise, environmental impacts and tax benefits is moving much earlier in the development process

Wabik describes the current AI infrastructure buildout as “beautiful insanity”—an era in which power, connectivity, permits, equipment, labor and community acceptance increasingly have to come together at the same time.

“A data center without connectivity is an expensive warehouse.”

Listen to the full conversation for a ground-level look at how fiber is becoming part of the critical path for AI data center development.

The AI infrastructure race is largely about getting more computing into data centers faster. But increasingly, operators also need a plan for getting yesterday’s hardware back out while it still has value.

In this episode of the Data Center Frontier Show, recorded live at the third annual DCF Trends Summit in Reston, Virginia, DCF Contributing Editor Doug Black speaks with Josh Humm, Data Center Solutions Manager at Dynamic Lifecycle Innovations, about how accelerated AI hardware cycles are changing IT asset disposition, or ITAD.

Humm says traditional enterprise infrastructure might remain in service for three to five years. Newer GPU systems, by comparison, can face refresh cycles of just 18 to 24 months. That compressed timeline is colliding with another AI-era reality: the equipment itself is getting heavier, more specialized and more difficult to remove.

AI systems can include liquid-cooling manifolds, proprietary configurations and units weighing 5,000 to 6,000 pounds, requiring specialized rigging and decommissioning procedures. At the same time, valuable processors, memory, storage and networking components can depreciate quickly once equipment is taken offline.

“The faster we can get the materials out of your building, the more it’s worth, the more we can return to your program,” Humm says.

The result, he argues, is that ITAD should become part of lifecycle planning rather than something operators begin thinking about only when hardware reaches end of life.

The conversation examines how operators can design decommissioning workflows into facility operations, maintain defensible chains of custody, securely destroy data and determine whether retired equipment should be resold whole, harvested for components or recycled.

Humm also discusses the risks created by vendor handoffs across onsite decommissioning, transportation, processing, remarketing and recycling. He recommends scrutinizing providers for data-security and environmental certifications, downstream transparency and the ability to scale as AI refresh projects grow larger.

The economics can be substantial.

Humm describes a recent project involving an approximately 8- to 10-MW enterprise data center in Colorado whose owner was migrating from on-premises infrastructure to the cloud. Dynamic removed racks and equipment, wiped hard drives onsite and shredded drives that could not be successfully sanitized.

After roughly three months, Humm says the project returned more than $17 million net to the customer — about $15 million more than expected.

That outcome highlights a larger issue emerging around AI infrastructure: decommissioning is not necessarily just a disposal cost. In a strong secondary market for memory, processors and other components, disciplined asset disposition can return capital to the next hardware cycle.

Black and Humm close with two questions operators should ask prospective ITAD partners before a major refresh begins: Can I trust you? And can you scale with me?

As GPU infrastructure turns over faster, those questions are likely to become a much larger part of data center operations.

As AI workloads continue to reshape the data center landscape, operators are looking for practical ways to evolve existing infrastructure without overbuilding for tomorrow. In this conversation, we explore how intelligent rack power infrastructure can help data centers support both traditional and AI workloads while improving visibility, efficiency and scalability.

The discussion covers the role of intelligent monitoring, open integration and rack-level solutions in creating power infrastructure that can adapt as data center requirements continue to change.

AI is changing more than compute—it is changing how the entire physical infrastructure of the data center must be designed. In this episode, we'll explore why organizations should stop thinking about cabinets, power and cooling as separate decisions and instead view them as an integrated system. We'll discuss how this approach improves efficiency, simplifies deployment and creates a more flexible foundation for high-density AI environments. 

Along the way, we'll examine the evolving role of the IT cabinet, the industry's transition from air cooling to hybrid and liquid cooling, and the practical questions organizations should be asking as rack densities continue to climb. From rear door heat exchangers to long-term thermal management strategies, listeners will gain practical insights into building infrastructure that's ready for the next generation of AI workloads.