Uptime and cooling performance both depend on something that is often overlooked during the data system design process: power quality. In this podcast, "Power Quality: The Conversation Data Centers Can't Afford to Ignore," we talk with a Power Quality expert about harmonic distortion in data centers — what it is, how it is created, and how poor power quality impacts data center cooling, uptime and performance. We will also dig into solutions engineers can employ to mitigate poor power quality in their data center.

Power quality deserves a seat at the table alongside the metrics data center operators already obsess over — availability, PUE, and thermal performance. Total Harmonic Distortion, or THD, gives engineers a real, measurable way to gauge the health of their electrical system, turning power quality from an abstract concern into something concrete that can be monitored, benchmarked, and acted on.

This discussion will focus on where harmonics actually come from — and it's something of a paradox. Many of the same components installed specifically to make data centers more efficient, such as variable frequency drives and EC fans, are also responsible for generating the harmonic distortion that undermines power quality. Understanding which specific equipment, in which specific areas of a facility, pose the greatest risk to power quality, gives engineers a practical way to identify where problems are most likely to originate.

Once data center engineers understand the source of harmonics, we’ll dig into the interdependency between power quality and thermal cooling in data centers. Clean, stable power is required to cool a facility properly, and proper cooling is required to run a data center efficiently — the two don't operate independently, one relies on the other. When harmonics go unmanaged excess heat accelerates equipment wear, protective devices trip unexpectedly, cooling systems degrade, and thermal throttling sets in. Left unaddressed, the result is unplanned downtime — the very outcome every data center is designed to avoid.

The physical layout of a facility matters too. "Grey space" and "white space" — the mechanical/support areas and the core IT data hall, respectively — each present different harmonic challenges and require different mitigation approaches. There isn't one single location in a data center where power quality can be fixed once and for all; effective mitigation means evaluating the facility as a whole, rather than searching for a single point of resolution.

Finally we’ll explore solutions available for engineers to help mitigate harmonics in data centers. One of the primary solutions we will focus on will be active harmonic filters. Active harmonic filters continuously monitor power quality of the system and make real-time corrections to mitigate harmonics. Where these filters are installed matters as much as the technology itself, and getting placement right has a direct impact on effectiveness. We’ll also discuss how engineers can take a hands-on approach to maintaining power quality by calculating their own harmonic distortion levels to determine how much filtering capacity they actually need — turning an otherwise theoretical challenge into something they can size, plan for, and solve.

Ultimately, this podcast makes the case that power quality deserves the same level of design attention as uptime and cooling — because in reality, all three are connected. Whether you're a data center engineer, facility owner, or simply responsible for keeping systems running reliably, understanding harmonic distortion isn't a peripheral concern. It's foundational to how well a data center performs.

As data centers race to support AI workloads, liquid cooling, and accelerated construction timelines, operators are facing new challenges around reliability, efficiency, and infrastructure visibility. Many facilities still rely on commercial-grade instrumentation across critical cooling and utility systems, creating blind spots that can increase operational risk and reduce confidence in performance.

In this episode, we explore why leading operators are adopting industrial-grade instrumentation to gain more accurate insight into flow, temperature, pressure, and fluid quality across their facilities. The conversation covers the critical measurement points that impact uptime, how liquid cooling is changing infrastructure requirements, and the role measurement technology plays in helping operators commission faster, reduce risk, and confidently scale for future growth.

Listeners will walk away with practical guidance for evaluating instrumentation strategies, selecting technology partners, and preparing their facilities for the next generation of data center demands.

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 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.