According to the National Electrical Manufacturers Association (NEMA), U.S. energy demand is projected to grow 50% by 2050. Electrical manufacturers have invested more than $10 billion since 2021 in new technologies to expand grid and manufacturing capacity, also reducing reliance on materials from China by 32% since 2018. Power access, sustainable infrastructure, and land acquisition have become critical factors shaping where and how data center facilities are built. As we previously reported in Data Center Frontier, investors realized this years ago, viewing these facilities both as technology assets and a unique convergence of real estate, utility infrastructure, and mission-critical systems that can also generate revenue. One of those investors is global asset manager Blackstone, which through its Energy Transition Partners private equity arm, recently acquired Shermco Industries for $1.6 billion. Announced August 21, the deal is part of Blackstone’s strategy to invest in companies that support the growing demand for electrification and a more reliable power grid. The goal is to strengthen data center infrastructure reliability and expand critical electrical services. Founded in 1974, Texas-based Shermco is one of the largest electrical testing organizations accredited by the InterNational Electrical Testing Association (NETA). The company operates in a niche yet important space: providing lifecycle electrical services, including maintenance, testing, commissioning, repair, and design, in support of data centers, utilities, and industrial clients. It has more than 40 service centers in the U.S. and Canada. In addition to helping Blackstone support its electrification and power grid reliability goals, the Shermco purchase is also part of Blackstone’s strategy to increase scale and resources—revenue increases without a substantial increase in resources—thus expanding its footprint and capabilities within the essential energy services sector.  As data centers expand globally, become more energy intensive, and are pressured to incorporate renewables and modernize grids, Blackstone’s leaders plan to leverage Shermco’s

Schneider Electric and Compass Datacenters: Prefabrication Meets the AI Frontier “We’re removing bottlenecks and setting a new benchmark for AI-ready data centers.” — Aamir Paul, Schneider Electric In another sign of how collaboration is accelerating the next wave of AI infrastructure, Schneider Electric and Compass Datacenters have joined forces to redefine the data center “white space” build-out: the heart of where power, cooling, and compute converge. On September 9, the two companies unveiled the Prefabricated Modular EcoStruxure™ Pod, a factory-built, fully integrated white space module designed to compress construction timelines, reduce CapEx, and simplify installation while meeting the specific demands of AI-ready infrastructure. The traditional fit-out process for the IT floor (i.e. integrating power distribution, cooling systems, busways, cabling, and network components) has long been one of the slowest and most error-prone stages of data center construction. Schneider and Compass’ new approach tackles that head-on, shifting the entire workflow from fragmented on-site assembly to standardized off-site manufacturing. “The traditional design and approach to building out power, cooling, and IT networking equipment has relied on multiple parties installing varied pieces of equipment,” the companies noted. “That process has been slow, inefficient, and prone to errors. Today’s growing demand for AI-ready infrastructure makes traditional build-outs ripe for improvement.” Inside the EcoStruxure Pod: White Space as a Product The EcoStruxure Pod packages every core element of a high-performance white space environment (power, cooling, and IT integration) into a single prefabricated, factory-tested unit. Built for flexibility, it supports hot aisle containment, InRow cooling, and Rear Door Heat Exchanger (RDHx) configurations, alongside high-power busways, complex network cabling, and a technical water loop for hybrid or full liquid-cooled deployments. By manufacturing these pods off-site, Schneider Electric can deliver a complete, ready-to-install white space module that arrives move-in ready. Once delivered to a Compass Datacenters campus, the

Microsoft’s newest AI data center in Wisconsin, known as “Fairwater,” is being framed as far more than a massive, energy-intensive compute hub. The company describes it as a community-scale investment — one that pairs frontier-model training capacity with regional development. Microsoft has prepaid local grid upgrades, partnered with the Root-Pike Watershed Initiative Network to restore nearby wetlands and prairie sites, and launched Wisconsin’s first Datacenter Academy in collaboration with Gateway Technical College, aiming to train more than 1,000 students over the next five years. The company is also highlighting its broader statewide impact: 114,000 residents trained in AI-related skills through Microsoft partners, alongside the opening of a new AI Co-Innovation Lab at the University of Wisconsin–Milwaukee, focused on applying AI in advanced manufacturing. It’s Just One Big, Happy AI Supercomputer… The Fairwater facility is not a conventional, multi-tenant cloud region. It’s engineered to operate as a single, unified AI supercomputer, built around a flat networking fabric that interconnects hundreds of thousands of accelerators. Microsoft says the campus will deliver up to 10× the performance of today’s fastest supercomputers, purpose-built for frontier-model training. Physically, the site encompasses three buildings across 315 acres, totaling 1.2 million square feet of floor area, all supported by 120 miles of medium-voltage underground cable, 72.6 miles of mechanical piping, and 46.6 miles of deep foundation piles. At the rack level, each NVL72 system integrates 72 NVIDIA Blackwell GPUs (GB200), fused together via NVLink/NVSwitch into a single high-bandwidth memory domain capable of 1.8 TB/s GPU-to-GPU throughput and 14 TB of pooled memory per rack. This creates a topology that may appear as independent servers but can be orchestrated as a single, giant accelerator. Microsoft reports that one NVL72 can process up to 865,000 tokens per second. Future Fairwater-class deployments (including those under construction in the UK and Norway)

Recently, Data Center Frontier sister publication Electronic Design (ED) released an eBook curated by ED Senior Editor James Morra titled In the Age of AI, A New Playbook for Power Supply Design, with a collection of detailed technology articles focused on understanding the nuts and bolts of delivering power to AI-centric data centers. This compendium explores how the surge in artificial intelligence (AI) workloads is transforming data center power architectures and includes suggestions for addressing the issues. Breaking the Power Barrier As GPUs like NVIDIA’s Blackwell B100 and B200 cross the 1,000-watt threshold per chip, rack power densities are soaring beyond 100 kW, and in some projections, approaching 1 MW per rack. This unprecedented demand is exposing the limits of legacy 12-volt and 48-volt architectures, where inefficient conversion stages and high I²R losses drive up both energy waste and cooling load. Powering the Next Era of AI Infrastructure As AI data centers scale toward multi-megawatt clusters and rack densities approach one megawatt, traditional power architectures are straining under the load. The next frontier of efficiency lies in rethinking how electricity is distributed, converted, and protected inside the rack. From high-voltage DC distribution to wide-bandgap semiconductors and intelligent eFuses, a new generation of technologies is reshaping power delivery for AI. The articles in this report drill down into five core themes driving that transformation: Electronic Fuses (eFuses) for Power Protection Texas Instruments and others are introducing 48-volt-rated eFuses that integrate current sensing, control, and switching into a single device. These allow hot-swapping of AI servers without dangerous inrush currents, enable intelligent fault detection, and can be paralleled to support rack loads exceeding 100 kW. The result: simplified PCB design, improved reliability, and robust support for AI’s steep and dynamic current requirements. The Shift from 48 V to 400–800 V High-Voltage DC (HVDC)

Global Fusion Momentum: France, Europe, and a New Competitive Context As CFS, Google, Eni, and Helion press ahead, other fusion efforts worldwide are also making waves, reminding us this is a global race, not a U.S.-exclusive pursuit. In France, the CEA’s WEST tokamak recently achieved a new benchmark by sustaining plasma for more than 22 minutes (1,337 seconds) at ~50 million °C, breaking previous records and demonstrating improved plasma control and stability. That milestone underscores the incremental but essential progress in continuous operation, one of the key prerequisites for any commercially viable fusion system. Meanwhile, ITER, the international flagship built in southern France, continues its slow-but-steady assembly. Despite years of delays and cost overruns, ITER remains central to global fusion ambitions. It’s not expected to produce significant fusion output until the 2030s, but its role in validating large-scale superconducting magnet systems, remote maintenance, tritium breeding, plasma control, and heat management is essential to de-risking downstream commercial fusion designs. Elsewhere in Europe, Proxima Fusion (Germany) is gaining attention. The company is developing a quasi-isodynamic stellarator design and has recently raised €130 million in its Series A, showing that alternative confinement geometries are earning investor support. While that path is more speculative, it adds needed diversity to the fusion technology portfolio. Germany’s Wendelstein 7-X Raises the Bar Germany added another major milestone to the fusion timeline this fall. At the Max Planck Institute for Plasma Physics, researchers operating the Wendelstein 7-X stellarator sustained a high-performance plasma for 43 seconds, setting a new world record for continuous fusion confinement. The run demonstrated stability and control at temperatures exceeding 30 million °C, proving that stellarators, once viewed mainly as scientific curiosities, can now compete head-to-head with tokamaks in performance. Unlike tokamaks, which rely on strong external currents to confine plasma, stellarators use a twisted

The decision also reflects a future of AI workloads running on heterogeneous computing and networking infrastructure, said Lian Jye Su, chief analyst at Omdia. “While it makes sense for enterprises to first rely on Nvidia’s full stack solution to roll out AI, they will generally integrate alternative solutions such as AMD and self-developed chips for cost efficiency, supply chain diversity, and chip availability,” Su said. “This means data center networking vendors will need to consider interoperability and open standards as ways to address the diversification of AI chip architecture.” Hyperscalers and enterprise CIOs are increasingly focused on how to efficiently scale up or scale out AI servers as workloads expand. Nvidia’s GPUs still underpin most large-scale AI training, but companies are looking for ways to integrate them with other accelerators. Neil Shah, VP for research at Counterpoint Research, said that Nvidia’s recent decision to open its NVLink interconnect to ecosystem players earlier this year gives hyperscalers more flexibility to pair Nvidia GPUs with custom accelerators from vendors such as Broadcom or Marvell. “While this reduces the dependence on Nvidia for a complete solution, it actually increases the total addressable market for Nvidia to be the most preferred solution to be tightly paired with the hyperscaler’s custom compute,” Shah said. Most hyperscalers have moved toward custom compute architectures to diversify beyond x86-based Intel or AMD processors, Shah added. Many are exploring Arm or RISC-V designs that can be tailored to specific workloads for greater power efficiency and lower infrastructure costs. Shifting AI infrastructure strategies The collaboration also highlights how networking choices are becoming as strategic as chip design itself, suggesting a change in how AI workloads are powered and connected.