The Data Center Frontier Show

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.