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.