Omniverse DSX Blueprint Unveiled Also at the conference, NVIDIA released a blueprint for how other firms should build massive, gigascale AI data centers, or AI factories, in which Oracle, Microsoft, Google, and other leading tech firms are investing billions. The most powerful and efficient of those, company representatives said, will include NVIDIA chips and software. A new NVIDIA AI Factory Research Center in Virginia will use that technology. This new “mega” Omniverse DSX Blueprint is a comprehensive, open blueprint for designing and operating gigawatt-scale AI factories. It combines design, simulation, and operations across factory facilities, hardware, and software. • The blueprint expands to include libraries for building factory-scale digital twins, with Siemens’ Digital Twin software first to support the blueprint and FANUC and Foxconn Fii first to connect their robot models. • Belden, Caterpillar, Foxconn, Lucid Motors, Toyota, Taiwan Semiconductor Manufacturing Co. (TSMC), and Wistron build Omniverse factory digital twins to accelerate AI-driven manufacturing. • Agility Robotics, Amazon Robotics, Figure, and Skild AI build a collaborative robot workforce using NVIDIA’s three-computer architecture. NVIDIA Quantum Gains  And then there’s quantum computing. It can help data centers become more energy-efficient and faster with specific tasks such as optimization and AI model training. Conversely, the unique infrastructure needs of quantum computers, such as power, cooling, and error correction, are driving the development of specialized quantum data centers. Huang said it’s now possible to make one logical qubit, or quantum bit, that’s coherent, stable, and error corrected.  However, these qubits—the units of information enabling quantum computers to process information in ways ordinary computers can’t—are “incredibly fragile,” creating a need for powerful technology to do quantum error correction and infer the qubit’s state. To connect quantum and GPU computing, Huang announced the release of NVIDIA NVQLink — a quantum‑GPU interconnect that enables real‑time CUDA‑Q calls from quantum

Infrastructure and Supply Chain Race Cloud competition is increasingly defined by the ability to secure power, land, and chips— three resources that dictate project timelines and customer onboarding. Neoclouds and hyperscalers face a common set of constraints: local utility availability, substation interconnection bottlenecks, and fierce competition for high-density GPU inventory. Power stands as the gating factor for expansion, often outpacing even chip shortages in severity. Facilities are increasingly being sited based on access to dedicated, reliable megawatt-scale electricity, rather than traditional latency zones or network proximity. AI growth forecasts point to four key ceilings: electrical capacity, chip procurement cycles, latency wall between computation and data, and scalable data throughput for model training. With hyperscaler and neocloud deployments now competing for every available GPU from manufacturers, deployment agility has become a prime differentiator. Neoclouds distinguish themselves by orchestrating microgrid agreements, securing direct-source utility contracts, and compressing build-to-operational timelines. Converting a bare site to a functional data hall with operators that can viably offer a shortened deployment timeline gives neoclouds a material edge over traditional hyperscale deployments that require broader campus and network-level integration cycles. The aftereffects of the COVID era supply chain disruptions linger, with legacy operators struggling to source critical electrical components, switchgear, and transformers, sometimes waiting more than a year for equipment. As a result, neocloud providers have moved aggressively into site selection strategies, regional partnerships, and infrastructure stack integration to hedge risk and shorten delivery cycles. Microgrid solutions and island modes for power supply are increasingly utilized to ensure uninterrupted access to electricity during ramp-up periods and supply chain outages, fundamentally rebalancing the competitive dynamics of AI infrastructure deployment. Creditworthiness, Capital, and Risk Management Securing capital remains a decisive factor for the growth and sustainability of neoclouds. Project finance for campus-scale deployments hinges on demonstrable creditworthiness; lenders demand

At this year’s Data Center Frontier Trends Summit, Honghai Song, founder of Canyon Magnet Energy, presented his company’s breakthrough superconducting magnet technology during the “6 Moonshot Trends for the 2026 Data Center Frontier” panel—showcasing how high-temperature superconductors (HTS) could reshape both fusion energy and AI data-center power systems. In this episode of the Data Center Frontier Show, Editor in Chief Matt Vincent speaks with Song about how Canyon Magnet Energy—founded in 2023 and based in New Jersey with research roots at Stony Brook University—is bridging fusion research and AI infrastructure through next-generation magnet and energy-storage technology. From Fusion Research to Data Center Reality Founded in 2023, Canyon Magnet Energy emerged from the advanced-magnet research ecosystem around Stony Brook and now operates a manufacturing line in Newark, New Jersey. Its team draws on decades of experience designing the ultra-strong magnetic fields that enable the confinement and stability of fusion plasma—but their ambitions go far beyond the laboratory. “Super magnets are the foundation of fusion,” Song explains in the interview. “But the same high-temperature superconductors that can make fusion practical can also dramatically improve how we move and store electricity in data centers.” The company’s magnets are built using REBCO (Rare Earth Barium Copper Oxide) tape, which operates at around 77 Kelvin—cold, but far warmer and more manageable than traditional low-temperature superconductors. The result is a zero-resistance pathway for electricity, unlocking new possibilities in power transmission, energy storage, and grid integration. Why High-Temperature Superconductors Matter Since their discovery in 1986, high-temperature superconductors have progressed from exotic physics experiments to industrial-scale wire and magnet manufacturing. Canyon Magnet Energy is among a new generation of companies moving this technology into the AI data-center context—where efficiency and instantaneous power responsiveness are increasingly critical. With AI training clusters consuming power at hundreds of megawatts per campus,

The aim, said Gogia, “is continuity, not cost efficiency. These deals are forward leaning, relying on revenue forecasts that remain speculative. In that context, OpenAI must continue to draw heavily on outside capital, whether through venture rounds, debt, or a future public offering.” He pointed out, “the company’s recent legal and corporate restructuring was designed to open the doors to that capital. Removing Microsoft’s exclusivity makes room for more vendors but also signals that no one provider can meet OpenAI’s demands. In several cases, suppliers are stepping in with financing arrangements that link product sales to future performance. While these strategies help close funding gaps, they introduce fragility. What looks like revenue is often pre-paid consumption, not realized margin.” Execution risks, he said, add to the concern. “Building and energizing enough data centers to meet OpenAI’s projected needs is not a function of ambition alone. It requires grid access, cooling capacity, and regional stability. Microsoft has acknowledged that it lacks the power infrastructure to fully deploy the GPUs it owns. Without physical readiness, all of these agreements sit on shaky ground.” Lots of equity swapping going on Scott Bickley, advisory fellow at Info-Tech Research Group, said he has not only been astounded by the funding announcements over the last few months, but is also appalled, primarily, he said, “because of the disconnect to what this does to the underlying technology stocks and their market prices versus where the technology is at from a development and ROI perspective … and from a boots on the ground perspective.” He added that while the financial pledges involve “huge, staggering numbers, most of them are tied up in ways that are not necessarily going to require all the cash to come from OpenAI. In a lot of cases, there is equity swapping. You have

“AI will be essential to the future of business and society, driving innovation that demands a network to match,” Scott Lawrence, senior vice president and chief product officer at Verizon Business said in a statement. “This deal with Amazon demonstrates our continued commitment to meet the growing demands of AI workloads for the businesses and developers building our future.” This is not the first time that two companies have partnered. Verizon has previously adopted AWS as a preferred public cloud provider for its digital transformation efforts. The collaboration also extends to joint development of private mobile edge computing solutions, delivering secure, dedicated connectivity for enterprise customers. These efforts have been targeted at industries such as manufacturing, healthcare, retail, and entertainment.

Supermicro has introduced a new business line, Data Center Building Block Solutions (DCBBS), expanding its modular approach to data center development. The offering packages servers, storage, liquid-cooling infrastructure, networking, power shelves and battery backup units (BBUs), DCIM and automation software, and on-site services into pre-validated, factory-tested bundles designed to accelerate time-to-online (TTO) and improve long-term serviceability. This move represents a significant step beyond traditional rack integration; a shift toward a one-stop, data-center-scale platform aimed squarely at the hyperscale and AI factory market. By providing a single point of accountability across IT, power, and thermal domains, Supermicro’s model enables faster deployments and reduces integration risk—the modern equivalent of a “single throat to choke” for data center operators racing to bring GB200/NVL72-class racks online. What’s New in DCBBS DCBBS extends Supermicro’s modular design philosophy to an integrated catalog of facility-adjacent building blocks, not just IT nodes. By including critical supporting infrastructure—cooling, power, networking, and lifecycle software—the platform helps operators bring new capacity online more quickly and predictably. According to Supermicro, DCBBS encompasses: Multi-vendor AI system support: Compatibility with NVIDIA, AMD, and Intel architectures, featuring Supermicro-designed cold plates that dissipate up to 98% of component-level heat. In-rack liquid-cooling designs: Coolant distribution manifolds (CDMs) and CDUs rated up to 250 kW, supporting 45 °C liquids, alongside rear-door heat exchangers, 800 GbE switches (51.2 Tb/s), 33 kW power shelves, and 48 V battery backup units. Liquid-to-Air (L2A) sidecars: Each row can reject up to 200 kW of heat without modifying existing building hydronics—an especially practical design for air-to-liquid retrofits. Automation and management software: SuperCloud Composer for rack-scale and liquid-cooling lifecycle management SuperCloud Automation Center for firmware, OS, Kubernetes, and AI pipeline enablement Developer Experience Console for self-service workflows and orchestration End-to-end services: Design, validation, and on-site deployment options—including four-hour response service levels—for both greenfield builds