NEW YORK, June 18, 2026 (GLOBE NEWSWIRE) — As artificial intelligence drives unprecedented growth in data center power demand, existing energy storage technologies may no longer be sufficient to meet the needs of next-generation computing infrastructure, according to a new white paper released today by Qnetic.
The report, “AI-Grade Energy Storage: Why AI Data Centers Need a New Category of Energy Storage,” argues that the energy storage industry has historically been organized around two primary categories – battery energy storage systems (BESS) and long-duration energy storage (LDES) – but neither was designed for the unique operating requirements of AI workloads.
The timing is significant. Global data center electricity demand is projected to more than double by 2030, while U.S. data centers could account for nearly half of all electricity demand growth this decade. As AI infrastructure scales, utilities, grid operators, and developers are increasingly seeking new ways to manage volatile power demand while maintaining reliability, resilience, and cost efficiency.
AI Is Creating a New Energy Challenge
Unlike traditional industrial loads, AI training and inference clusters create highly dynamic demand profiles that can fluctuate by hundreds of megawatts within seconds. At gigawatt-scale facilities, power ramp rates can exceed 1,000 MW per second, creating new challenges for utilities, grid operators, and data center developers seeking to maintain reliable operations.
According to Qnetic, these requirements are creating the need for a new category of infrastructure: AI-grade energy storage.
“Over the last decade, energy storage has been largely optimized around renewable integration and peak shifting,” said Michael Pratt, CEO of Qnetic. “AI changes the problem. Data centers now require storage resources that can respond in milliseconds, cycle continuously for decades, operate safely alongside critical digital infrastructure, and support increasingly dynamic power loads. We believe AI is creating an entirely new category of energy storage requirements.”
Defining the Requirements for AI-Grade Energy Storage
The new white paper identifies six characteristics that define AI-grade energy storage:
• Millisecond response times to rapidly changing loads
• Unlimited daily cycling without performance degradation
• High-power output to support volatile computing demand
• Multi-hour energy storage capacity
• Operational lifetimes measured in decades
• Intrinsic safety without thermal runaway risk
The report argues that while lithium-ion batteries have become the dominant storage technology for many grid applications, they were not designed for the intensive cycling, long operating life, and safety requirements associated with AI infrastructure. Likewise, many long-duration storage technologies prioritize energy duration but lack the response speed and operational flexibility required by modern AI workloads.
The impact of AI infrastructure on power systems is already reshaping energy planning across the United States. States including Texas, Virginia, and California are evaluating new approaches to grid reliability, dispatchable generation, and energy storage as they prepare for rapidly growing data center demand.
At the same time, developers are increasingly pairing data centers with on-site generation, renewable energy resources, and microgrids. In these environments, energy storage plays a critical role in balancing fluctuating loads, improving generation efficiency, and maintaining power quality for mission-critical computing operations.
Why Flywheels Are Built for AI Infrastructure
The white paper concludes that flywheel energy storage systems are particularly well suited to these emerging requirements because they store energy mechanically rather than chemically, enabling unlimited cycling without degradation, rapid response times, long operating life and enhanced safety characteristics.
Unlike conventional batteries, flywheels are not subject to capacity fade, augmentation requirements, or thermal runaway risks. These characteristics become increasingly important in AI data center environments, where storage systems may be called upon to cycle continuously, respond instantly to changing loads, and remain in service for decades.
Qnetic’s flywheel systems use high-speed composite rotors suspended on magnetic bearings inside vacuum enclosures to store and dispatch electricity. The technology can provide multi-hour energy storage while maintaining full performance throughout a 30-year operating life.
The report also notes that flywheels can improve the performance of both grid-connected and behind-the-meter generation systems by absorbing rapid load fluctuations and reducing operational stress on generation assets.
“AI infrastructure will require a different class of energy storage than the grid has relied on in the past,” Pratt said. “The question is no longer how much energy we can store. It’s how quickly, safely and reliably we can deliver that energy in an environment where demand changes constantly. That’s the challenge AI-grade energy storage is designed to solve.”
The full white paper, “AI-Grade Energy Storage: Why AI Data Centers Need a New Category of Energy Storage,” is available for download at www.qnetic.energy.
About Qnetic
Qnetic develops long-duration flywheel energy storage systems for utility, industrial, and data center applications. The company’s solid-state mechanical batteries provide multi-hour energy storage, rapid response, unlimited cycling, and a 30-year operating life without degradation. Qnetic is headquartered in New York, with manufacturing operations in Sacramento, California, and a technology center in Shanghai. For more information, visit www.qnetic.energy.
Investor note
Qnetic’s current crowdfunding round in partnership with DealMaker offers retail investors an opportunity to participate in the company’s next growth phase. To review offering documents, risk disclosures, and investor FAQs, visit https://invest.qnetic.energy.
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