Advertise on podcast: The Data Center Frontier Show

The data center construction industry continues to evolve at an unprecedented pace. As artificial intelligence accelerates demand for computing capacity, data center developers, owners, general contractors, and equipment suppliers face growing pressure to deliver projects faster, manage increasingly complex supply chains, and navigate a rapidly changing market. Understanding these challenges—and how industry professionals are responding to them—is essential to building the infrastructure that powers our digital world. In this episode, BluePrint Supply Chain takes a closer look at its third annual State of the Industry Report on the Data Center Construction Supply Chain, examining what industry professionals are saying about the challenges, priorities, and opportunities shaping mission-critical construction. Joining the conversation is Bill Tierney, Chief Sales Officer for BluePrint Supply Chain, who will discuss the report’s findings and explore what they reveal about the current state and future direction of the data center construction supply chain. The third year of the report marks an important milestone. While individual annual surveys provide a valuable snapshot of industry sentiment, three years of responses begin to reveal something more meaningful: emerging patterns, shifting priorities, and longer-term trends. By comparing responses across reporting periods and examining them against broader market forces, BluePrint can begin to distinguish temporary challenges from persistent industry issues—and identify where expectations, behaviors, and supply chain strategies are changing. Whether you are a data center owner, developer, general contractor, procurement leader, equipment supplier, or logistics professional, this episode offers an opportunity to gain perspective on the forces shaping mission-critical construction—and what the industry's collective experience can teach us about building smarter, more resilient supply chains.

Alberta's data center industry has moved from ambition to execution. But what does it take to turn a relatively small regional market into a gigawatt-scale digital infrastructure hub? In this episode of the Data Center Frontier Show, Editor in Chief Matt Vincent welcomes Vlad Oujegov, president and founder of the Western Canada Data Centre Alliance, founder of Data Centre West, and National Data Center Power Systems Lead at Schneider Electric. Fresh from Data Centre West 2026 in Calgary, the conversation examines Alberta's remarkable transformation, including Meta's CAD $13 billion, 1-GW data center development in Sturgeon County, and the industrial, workforce and community challenges accompanying the province's emerging AI infrastructure economy. Oujegov offers an especially revealing perspective on the industry's growing self-awareness. Across discussions of power, cooling, technology and policy, operators and suppliers are increasingly emphasizing voluntary commitments to environmental performance, efficiency and community benefits. Yet that progress is occurring alongside intensified public scrutiny and opposition to new data center development. The discussion also explores the overlooked challenges of expanding a regional industry tenfold, from skilled labor and commissioning capacity to the local warehouses, service networks and supply chains required to support hyperscale infrastructure. Vincent and Oujegov examine why credible megawatts increasingly matter more than ambitious announcements, how unrealistic proposals can undermine legitimate developments, and whether data centers should face regulatory requirements beyond those applied to other large industrial facilities. Drawing on lessons from Northern Virginia's Data Center Alley and Alberta's own community engagement efforts, the episode considers a larger question: Can the data center industry build the physical infrastructure of AI while earning the public trust necessary to sustain its expansion? As Alberta begins turning gigawatt-scale plans into construction activity, this conversation offers a timely examination of what it really means to build not just data centers, but an entire data center industry. Read more: DCF's in-depth coverage of Data Centre West 2026.

The data center industry's race for power is accelerating, but what if some of the capacity needed to support AI growth is already available inside existing facilities? In this episode of the Data Center Frontier Show, DCF Editor-in-Chief Matt Vincent speaks with Rahul Kar, founder and CEO of Hammerhead AI, about the company's approach to unlocking underutilized electrical capacity through AI-driven orchestration. Kar explains how Hammerhead's ORCA platform uses reinforcement learning to coordinate power, cooling, and compute workloads, allowing operators to accommodate additional GPU capacity within existing electrical limits. The conversation explores the distinction between unused power and safely recoverable capacity, including the operational safeguards necessary to protect existing customer service-level agreements. The discussion also examines why latency-tolerant AI inference workloads may be particularly suited to flexible power management, and how greater infrastructure utilization could reshape the economics of colocation, GPU deployment, and AI cloud services. Key topics include: Why the AI power crisis is partly an infrastructure utilization problem. How ORCA integrates facility power systems, cooling infrastructure, and GPU-level controls. The performance trade-offs involved in accommodating additional inference workloads. Hammerhead's reported 30% increase in GPU capacity within a fixed power envelope. The commercial value of recovering usable megawatts without waiting for new utility interconnections. Hammerhead's production deployments, industry collaborations, and expansion plans for 2027. Kar also considers how more intelligent infrastructure management could complement new power generation and data center construction as AI demand continues to accelerate. Listen to the full conversation for a closer look at how software-defined orchestration could help the industry produce more AI compute from the power it already has.

As AI workloads continue to drive growing demand for data center power, operators are considering how new intelligence can help operators make better use of complex infrastructure. In this conversation, Matt Vincent, Editor in Chief of Data Center Frontier, sits down with Sadiq Syed, SVP Digital Energy Software at Schneider Electric to explore how EcoStruxure Foresight, Schneider Electric’s AI-powered operations platform for the built environment, is helping bring energy, power and building management together, and why the future of AI will depend on unlocking both energy and intelligence.  The discussion explores how AI-powered insights and intelligent infrastructure management can help operators improve visibility, efficiency and resilience, while breaking down the separation between building, facility and energy systems. Sadiq will also discuss why connecting these traditionally separate domains will be increasingly important as data centers scale for AI, enabling a more unified and intelligent approach to managing critical infrastructure.

In general pros and bullet points, how would you summarize what’s important in this podcast. This podcast will explore why air cooling continues to matter as liquid cooling becomes more common in AI and high-density data center environments. The goal is not to position air cooling and liquid cooling as competing technologies, but to explain how they increasingly work together as part of a broader thermal management strategy. Liquid cooling is receiving significant attention because of the growth of AI workloads and higher rack densities. It can be highly effective at removing heat directly from high-performance processors, especially in applications where traditional air cooling may no longer be sufficient on its own. However, liquid cooling does not necessarily eliminate the need for airflow elsewhere in the facility. Important points for the conversation include: Air and liquid cooling will increasingly coexist. Many data centers will operate in hybrid environments where high-density AI racks use liquid cooling while lower-density servers, storage, networking equipment, power electronics, and other infrastructure continue to rely on air. Liquid cooling does not remove every thermal load. Even when heat is captured directly from processors, other components may still generate heat that needs to be managed through air movement. Residual heat within and around racks can also remain part of the thermal equation. The transition to liquid cooling will often be gradual. Most operators are not replacing an entire facility at once. Existing air-cooled equipment may continue operating for years while new liquid-cooled systems are added. This creates mixed environments that require careful coordination between air and liquid cooling systems. Heat still has to go somewhere. Liquid cooling captures and transports heat, but that heat still has to be rejected from the facility. Fans and air-moving equipment can continue to play an important role in heat rejection and auxiliary cooling systems. System-level efficiency matters more than focusing on one technology. Cooling performance depends on how air movement, liquid loops, controls, heat rejection, and other components work together. A highly efficient component does not automatically create an efficient overall cooling system. Airflow should not become an afterthought. If liquid cooling is added without properly considering airflow, operators can still experience unnecessary energy use, poor thermal distribution, or reduced overall system performance. Modern air movement is becoming more intelligent. Advances in fan efficiency, variable-speed operation, controls, sensors, and digital connectivity allow airflow to respond more closely to actual cooling demand. This can help reduce energy use while maintaining reliable operating conditions. Environmental impact should be evaluated at the full-system level. The conversation should avoid broad claims that one cooling method is automatically more sustainable than another. Operators should consider water use, energy consumption, climate, system efficiency, heat rejection, and operating conditions when evaluating cooling strategies. Air-cooled data centers can still be viable. Not every facility or workload requires liquid cooling. Depending on rack density, equipment, climate, and system design, air cooling may still be capable of meeting performance and efficiency requirements. Flexibility will be increasingly important. AI workloads, rack densities, and equipment designs are changing quickly. Data centers that can adapt their cooling architecture over time will be better positioned to support future requirements without unnecessary redesign. The podcast should help listeners understand that the future of data center cooling is not simply a move from air to liquid. In many cases, the more realistic future is a combination of both. The key takeaway should be that cooling strategy needs to be based on the actual thermal requirements of the facility. Liquid cooling will play an increasingly important role in high-density applications, but efficient and intelligent airflow will continue to support hybrid environments, lower-density equipment, residual heat management, heat rejection, and overall facility performance. The discussion should remain educational and practical rather than promotional. It should help listeners think about cooling as a complete system and understand where air and liquid technologies each provide value as data center requirements continue to evolve.

Data Center Frontier Show Podcast | DCF Trends Summit 2026 As AI infrastructure expands at unprecedented speed, data center operators face a growing financial and operational challenge: What happens to GPU systems when their first production assignment ends? In this episode of the Data Center Frontier Show, recorded onsite at DCF Trends Summit 2026 in Northern Virginia, DCF Contributing Editor Doug Black speaks with Linda Li, Chief Strategy Officer of Re-Teck / Li Tong Group, about why GPU lifecycle management must begin at the design and procurement stage—not when equipment is ready for retirement. Drawing on decades of reverse supply chain experience in telecommunications and electronics, Li explains how rapidly evolving accelerators, increasing power densities, and shifting cooling requirements are changing the economics of AI infrastructure. The conversation examines four considerations for GPU disposition: remaining operational capability, component-level economics, data security and regulatory compliance, and critical-material recovery. GPUs no longer suited to frontier AI training may retain substantial value for inference, enterprise applications, academic research, or component harvesting. Li also discusses the importance of continuous asset evaluation, secondary-market timing, secure chains of custody, and designing hardware for second and third operational lives. The central warning: Companies that fail to establish lifecycle strategies at the executive level risk creating significant amounts of stranded capital as valuable equipment sits idle while market conditions change. For AI infrastructure operators investing billions in successive generations of compute, the next life of the hardware is becoming as important to financial planning as its first deployment. Listen to the conversation for a practical examination of how GPU lifecycle planning, circular infrastructure design, and reverse supply chain management are becoming essential disciplines in the AI data center economy.

The conversation around data center power has focused on replacing diesel. But for many hyperscalers and developers, modern standby diesel remains a practical solution amid grid constraints, permitting challenges, and growing reliability demands. In this discussion, Matt Vincent, Editor in Chief of Data Center Frontier, sits down with Brad Meissner, Director of Product Management at Generac, to explore why diesel continues to play a critical role in data center power strategies—and how low-NOx engines, HVO compatibility, and next-generation emissions controls are redefining diesel technology in 2026.

AI data centers are creating a new kind of power problem. As thousands of GPUs ramp together, fast and synchronized changes in electrical demand can place new pressure on UPS systems, generators and the broader power infrastructure supporting the compute load. In this episode of the Data Center Frontier Show, DCF Editor in Chief Matt Vincent speaks with Frank DeLattre, President Americas at Musashi Energy Solutions, about how hybrid supercapacitors could help data centers manage those rapid power transients — and why AI may be forcing the industry to rethink the role of energy storage altogether. DeLattre explains the difference between storing energy and managing power, and why high-power, short-duration storage may become increasingly important as AI infrastructure scales. The discussion explores placing energy storage at multiple points in the power chain, from the UPS to systems closer to the server rack, where transients can potentially be addressed before they propagate upstream. The conversation also examines whether short-duration storage can help operators get more compute from existing electrical capacity by buffering momentary peaks, as well as the tradeoffs between energy buffering and throttling GPU performance. DeLattre also raises a provocative question around traditional UPS design: If backup generators can accept load within seconds, how much stored energy does a modern AI data center actually need for ride-through? The discussion looks ahead to a future in which operators may think less in terms of a single “UPS battery” and more in terms of an energy storage architecture — combining different technologies, durations and locations to solve different power problems across the AI data center.

AI and high-performance computing are changing how data centers are designed and powered. Rack densities have more than doubled in recent years, and power demands continue to climb. For data center operators and engineers, the pressure to scale faster, reconfigure more often, and support higher loads has made the power distribution decisions more important than ever. This QuickChat video will focus on overhead busway as the optimal power distribution system for modern data center environments. Chris Osian, Product Manager at Starline, will discuss why busway outperforms traditional RPP-based approaches as power densities increase, and what the right power distribution infrastructure needs to deliver: the capacity to support increasing power densities, the flexibility to change without downtime, and the ability to scale with the facility over time. With over 30 years of proven performance in mission-critical environments, Starline is the name data centers have trusted to deliver on all three.

Using AI UPS Systems to Tame AI Load Swings AI data centers are creating a power challenge that goes beyond simply securing enough megawatts. Large AI computing clusters can introduce rapid swings in electricity demand, creating new power-quality and grid-stability concerns for utilities and data center operators. In this interview recorded live at the 2026 Data Center Frontier Trends Summit, DCF Contributing Editor Doug Black speaks with Asser Elsamahy, P.E., vice president of engineering at ON.energy, about how battery-based power systems are emerging as one response. Elsamahy says battery energy storage itself is not new. Systems have been deployed globally at gigawatt scale for roughly two decades. What is changing is their role inside the data center. As AI workloads introduce larger and faster fluctuations in power demand, batteries and power electronics can increasingly act as a buffer between the compute environment and the electric grid. ON.energy’s approach is what the company calls an AI UPS, or medium-voltage UPS, built around a double-conversion architecture. Instead of relying on a single set of inverters, the system uses two. One side faces the grid and can respond to utility requirements such as voltage ride-through, frequency ride-through and ramp-rate limits. The other faces the data center load and supplies what the computing equipment requires, including rapid surges and changes in demand. The result is an architecture designed to separate the behavior of the AI load from the behavior presented to the grid. Elsamahy contrasts that approach with a conventional parallel battery energy storage system using a single inverter set. “It kind of is in a constant struggle of, do I support the grid or do I support the load?” he said. “I have to pick one or the other. It’s very difficult to do both.” The batteries can also be recharged in multiple ways. A facility can draw modestly more power than its compute load requires during off-peak periods, or it can use downward portions of the AI load profile to replenish stored energy. Elsamahy describes the battery as a “reservoir of energy” that gives operators greater flexibility over when they draw power and how they respond to changing conditions. That flexibility can also support peak shaving. Using a hypothetical 1-GW data center, Elsamahy explains that stored energy could allow the facility to reduce its instantaneous grid draw during a period of system stress — potentially lowering a 1-GW demand to 800 MW while continuing to support the internal load. The goal is not necessarily to disconnect from the grid, he notes. At these scales, the more practical value is having a lever to reduce demand when grid conditions require it. That capability is becoming increasingly relevant as utilities and grid operators scrutinize large-load interconnections more closely. Elsamahy says ON.energy is seeing strong demand from hyperscalers and data center developers looking several years ahead and designing around requirements such as voltage and frequency ride-through and limits on how quickly loads can ramp. His expectation is straightforward: those requirements will become stricter as AI data centers account for a larger share of electricity demand. For an industry focused heavily on securing power, the conversation points to another emerging requirement for AI infrastructure: controlling not only how much power a data center consumes, but how that demand appears to the grid. In that environment, battery storage may increasingly become part of the core power-quality architecture of the AI data center rather than simply another backup resource.

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.

Communities across the U.S. are being asked to make increasingly consequential decisions about data centers at the same time that AI infrastructure development is moving faster than traditional municipal planning processes. On this episode of the Data Center Frontier Show, DCF Editor-in-Chief Matt Vincent speaks with Ilissa Miller, founder and CEO of iMiller Public Relations and a board member of the Open Infrastructure Exchange (OIX), about a new effort to help communities prepare before specific data center projects arrive. At the center of the conversation is the OIX Digital Infrastructure Framework, designed to give municipal planners and elected officials a structured set of questions for evaluating data centers and other digital infrastructure. Rather than prescribing what communities should approve, Miller says the framework is intended to help them determine where digital infrastructure fits within their own long-term land-use strategies. Many municipalities, she notes, still do not explicitly recognize data centers as an acceptable land use — even on parcels already designated for industrial activity. That can send developers into conditional-use or zoning processes while residents and public officials are still trying to understand what exactly is being proposed. Miller argues that comprehensive planning offers a better starting point. Communities should be deciding in advance where different types of digital infrastructure might be appropriate, what information developers will need to provide, and how data centers fit alongside other economic-development priorities. The need has become particularly acute as opposition to data centers increasingly overlaps with broader public anxiety about artificial intelligence. “Most opposition is about more than the data center itself,” Miller says. Concerns over AI governance, automation and societal impacts are legitimate topics for debate, she argues, but they are not necessarily the same questions local officials must answer when evaluating land use, power, water, noise, construction and community impacts. For developers, Miller says earlier community engagement is equally important. Engagement should begin before a conditional-use application and before a project design has effectively been finalized. Developers should hold listening sessions, understand local business and political dynamics and give communities a meaningful opportunity to influence project plans. “The key to those engagements and those listening events is actually listening,” Miller says. That means showing communities what was heard, explaining what changed as a result and tailoring community benefits to actual local priorities rather than arriving with a standardized package. But Miller also emphasizes that communications cannot compensate for a poorly conceived project. “Not all data center developments should be built,” she says. Her firm will decline engagements involving certain attempts to rezone agricultural or otherwise inappropriate land for data center development. Miller argues that developers should look first toward properly zoned industrial locations, redevelopment opportunities and sites where intensive land use already exists. That principle gets to the larger question of social license: community trust begins with project execution and site selection, not simply better messaging. The conversation also explores perhaps the most difficult challenge facing both the industry and municipalities — speed. AI chip development is occurring on cycles measured in months or a few years, while data center development, municipal planning and infrastructure investment historically operate over much longer timelines. Miller says the industry itself is struggling to absorb that pace. Looking five years ahead, she envisions communities incorporating digital infrastructure directly into comprehensive master plans, distinguishing between hyperscale AI campuses, colocation, carrier hotels, edge facilities and other forms of digital infrastructure. Not every community needs — or can support — a massive AI data center. But Miller argues that communities still need to understand the infrastructure requirements of their hospitals, manufacturers, businesses and public services. The objective is not to ensure that every data center gets built. It is to ensure that when a proposal does arrive, the community is no longer beginning the conversation from zero.

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 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.