The Data Center Frontier Show

Powering AI When the Grid Can’t: The New Behind-the-Meter Playbook

The Data Center Frontier Show

Show Notes

The AI infrastructure boom is rapidly reshaping how the data center industry thinks about power. What was once a relatively straightforward utility procurement exercise is evolving into a complex strategy spanning onsite generation, fuel logistics, financing, and system architecture. That reality framed a recent special edition of The Data Center Frontier Show Podcast, which recast and updated a pivotal DCF Trends Summit 2025 session: From Grid to Onsite Powering: Optimizing Energy Behind the Meter for Data Centers. Moderated by Fengrong Li, Senior Managing Director at FTI Consulting, the panel explored how operators are responding as interconnection timelines stretch and AI workloads surge. Li’s framing emphasized a core shift: onsite power is moving from contingency planning to critical-path infrastructure. From the OEM perspective, David Blank of Siemens Energy noted that behind-the-meter deployments have accelerated sharply over the past year as developers confront multi-year waits for firm utility capacity.

“Everyone would prefer grid power,” Blank said. “But in many cases, reliable access isn’t available for five, ten, even ten-plus years.”

Panelists agreed that AI’s scale and speed are driving a structural rethink. Brian Gitt of Oklo described the moment as a return to industrial roots, with large loads once again building dedicated generation to meet growth timelines. At the same time, new technical pressures are emerging. AI clusters can produce sharp load swings, forcing developers to deploy fast-response buffering technologies such as batteries, flywheels, and supercapacitors to maintain stability. Despite differing technology paths—including gas turbines, hydrogen fuel cells, and advanced nuclear—the panel aligned on one common theme: modularity. Phased power blocks increasingly mirror how AI campuses are actually built and financed. The discussion also highlighted the growing importance of contract structures. Long-term offtake commitments, capacity reservations, and credit support are increasingly required to unlock equipment queues and fuel supply. Other panelists included Marty Trivette of AlphaStruxure and Yuval Bachar of ECL. The event was hosted by Data Center Frontier’s Matt Vincent. The takeaway was clear: in the AI era, energy strategy has moved to the critical path—and for many operators, that path now runs behind the meter.

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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.

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