AI infrastructure: MediaTek will work with Nvidia’s NVLink Fusion ecosystem to enable customers to develop custom AI infrastructure designed to integrate with Nvidia rack-scale systems and AI factories. Local AI computing: The companies will continue to collaborate on multiple generations of Nvidia RTX Spark and DGX Spark PC chips, powering consumer PCs, AI developer supercomputers, and enterprise-class workstations, that integrate Nvidia GPUs with MediaTek SoCs. Automotive: MediaTek and Nvidia will continue developing platforms for AI-powered, software-defined vehicles in the era of physical AI.  “MediaTek is one of the world’s great semiconductor companies, with exceptional expertise in system-on-chip design, connectivity, leading performance and power efficiency,” said Jensen Huang, founder and CEO of Nvidia, in a statement. “Together, we’re building platforms that bring Nvidia accelerated computing to new markets and give customers the freedom to create differentiated AI systems at enormous scale.” NVLink is a high-performance interface, but this move also helps lock in customers to the Nvidia platform, since NVLink Fusion is not about to hook up to AMD processors. For MediaTek, the Nvidia investment provides a big pile of cash and access to Nvidia’s infrastructure as it attempts to establish itself as a major supplier of custom data center silicon.

Fiber Becomes Part of the Build A decade ago, DC Blox might have prioritized land near existing fiber from AT&T, Verizon, Zayo or another established network provider. That calculation is different today. As data center campuses have grown and hyperscalers have become a larger share of the customer base, Wabik said connectivity has increasingly become another construction package associated with the project itself. “If Zayo or Verizon or AT&T just happens to be there close by, that’s a good thing,” he said. “But fiber construction is inherently anymore just part of the construction component.” That changes the site-selection question from Is there fiber nearby? to Can fiber be built here at the scale and diversity the customer requires? For DC Blox, Wabik said that can mean assessing whether sufficient public right-of-way exists to establish three or sometimes four diverse fiber paths into a data center. That distinction is important, as AI workloads push infrastructure into markets where power, land and energy options may be more abundant than established carrier density. The hyperscalers themselves have also become major network builders. Wabik characterized them provocatively as today’s telecom providers, pointing to the scale of terrestrial fiber they commission as well as the growing role of companies such as Amazon, Google and Meta in subsea cable development. The point is less that traditional carriers have disappeared than that hyperscalers increasingly design, commission and control enormous portions of the connectivity required to support their own infrastructure. DC Blox now sees requests for 864-count fiber as routine and, in some cases, 1,728-count cable. That would have been difficult to imagine during an earlier era when a handful of fibers from an established carrier could satisfy a data center’s connectivity requirements. AI-Scale Fiber Gets Physical The scale becomes clearer when the discussion moves from abstract network

For decades, data centers consumed electricity much like other large commercial customers: power arrived from the utility, while batteries and diesel generators stood behind it to protect the load. AI is starting to break that model. As data center campuses grow toward hundreds of megawatts and, in some cases, gigawatt scale, developers are increasingly taking responsibility for an energy system that once sat largely outside the data center boundary. Natural gas supply, onsite generation, fuel cells, batteries, controls and the behavior of the compute load itself are increasingly becoming parts of the same infrastructure system. That was the central thread running through “Power First: The New Playbook for Delivering AI Data Centers,” an Aug. 4 session at the Data Center Frontier Trends Summit 2026 in Reston, Virginia. Moderated by Fengrong Li, Senior Managing Director at FTI Consulting, the panel brought together Jim Summers, CEO of GPC Infrastructure; Shankar Achanta, EVP and Chief Product and Technology Officer at FuelCell Energy; Judith Judson, Executive Vice President at Calibrant Energy; and Yuval Bachar, Founder and CEO of EdgeCloudLink. The discussion began with the immediate constraint — the grid cannot deliver capacity on the timetable AI developers increasingly require — but quickly moved beyond the familiar concept of “bridge power.” The larger question was what happens when the data center itself becomes an energy system. From Backup Power to Prime Power Behind-the-meter generation is not new. What has changed is its role and scale. “Traditionally, behind-the-meter generation has been for backup and the sizes were smaller,” Achanta said. “But what they’re seeing is the demand for the power is growing rapidly due to the data center load.” Interconnection queues, transmission limitations and equipment supply constraints are pushing onsite generation into what Achanta called the “front seat,” supplying primary power rather than waiting behind the

The joint reference design appears in Trane’s Continuum Rubin DSX and Eaton’s Beam Rubin DSX platforms. The goal is a pre-coordinated architecture stretching from grid power to the chip rather than requiring developers and engineering teams to independently assemble electrical and mechanical systems for each project. The systems are also intended to exchange operating data. Rather than cooling and electrical systems responding independently, the systems can instead exchange leading indicators and respond more dynamically to changing operating requirements. This is an approach that closely mirrors NVIDIA’s larger DSX philosophy. Trane and Eaton are also designing the architecture to accommodate future liquid-cooling and direct-current power-distribution technologies. That future-proofing matters as rack power densities continue to rise. An electrical and cooling plant optimized for one GPU generation may otherwise become a constraint several hardware generations later. The Broader DSX Buildout The Lancium, Cloverleaf and Trane/Eaton agreements are part of a considerably wider expansion of the DSX ecosystem. Earlier deals show NVIDIA moving into many of the same infrastructure layers through partnerships spanning powered land, electrical design, digital twins and even project financing. In May, NVIDIA and IREN announced plans to support as much as 5 GW of DSX-aligned AI infrastructure across IREN’s global development pipeline, with the companies identifying IREN’s 2 GW Sweetwater campus in Texas as an expected flagship DSX deployment. NVIDIA also received a five-year right to purchase up to 30 million IREN shares at $70 each, representing a potential investment of as much as $2.1 billion. The infrastructure ecosystem has widened as well. Siemens, NVIDIA and Fluence, incorporating nVent design considerations, have developed a DSX Vera Rubin-aligned electrical, power and controls architecture extending from the utility connection to the rack. ABB is integrating digital models of medium-voltage switchgear, power-distribution equipment and UPS systems into the Omniverse DSX Blueprint, while Vertiv

Texas has no shortage of proposed data center megawatts. The harder question is how many of them are real. That distinction is becoming central to the Electric Reliability Council of Texas (ERCOT) as the state works through an unprecedented wave of AI, hyperscale and other large-load requests. In June, ERCOT said it was tracking more than 438 GW of proposed large loads, nearly 89% associated with data centers. By Aug. 3, Gov. Greg Abbott said ERCOT was considering approximately 474 GW of connection requests, roughly 90% from data centers and more than five times the system’s record peak demand. Neither figure represents a forecast of what will actually get built. And that is increasingly the point. ERCOT’s new Batch Zero process is beginning to put harder boundaries around Texas’ enormous development pipeline, asking which projects have enough maturity, technical information and commitment to warrant space in the transmission plan. At the same time, new requirements surrounding voltage ride-through and dynamic modeling are forcing another realization on the AI infrastructure industry: at hundreds of megawatts, a data center is no longer simply a customer at the edge of the grid. Its behavior can affect the grid itself. For developers, utilities and investors, Texas is becoming a large-scale test of what separates an announced AI campus from executable infrastructure. The Queue Is Not the Grid The sheer scale of ERCOT’s large-load queue can obscure how early many projects remain. ERCOT’s April 2026 monthly report offered a revealing snapshot. Large-load applications totaled 445.8 GW through 2033, but 321 GW had no studies submitted to ERCOT. Another 93.7 GW was under ERCOT review, while 22 GW had met the applicable Section 9.5 requirements. Against that enormous development funnel, ERCOT reported just 5.9 GW of observed energized large loads, with another 3.2 GW approved to

The AI infrastructure race is largely a story about getting more computing into data centers faster. But the accelerated hardware cycle is creating an equally consequential problem at the other end of the rack: getting yesterday’s equipment back out while it is still valuable. GPU systems built around increasingly dense and specialized AI architectures are beginning to challenge traditional assumptions about IT asset disposition, or ITAD. Where conventional enterprise infrastructure might remain in service for three to five years, newer GPU platforms can face refresh cycles of 18 to 24 months, according to Josh Humm, Data Center Solutions Manager at Dynamic Lifecycle Innovations. That compression changes the economics as well as the mechanics of decommissioning. “The faster we can get the materials out of your building, the more it’s worth, the more we can return to your program,” Humm said. Humm joined DCF Contributing Editor Doug Black for a DCF Show podcast recorded at the third annual Data Center Frontier Trends Summit, held Aug. 4-6 in Reston, Virginia. Their conversation focused on a less visible part of the AI infrastructure buildout: what happens to servers, accelerators, memory, storage and networking gear when the next generation arrives. The answer increasingly touches facility operations, data security, logistics, sustainability and potentially millions of dollars in recoverable hardware value. AI Hardware Changes the Exit Path AI systems create some obvious physical challenges for decommissioning. Traditional ITAD teams accustomed to pulling 1U and 2U servers out of air-cooled racks may instead encounter liquid-cooling manifolds, substantially heavier systems and equipment requiring specialized rigging and handling procedures. Humm said some systems can weigh between 5,000 and 6,000 pounds. “We’re not pulling out just 1U, 2U servers out of racks anymore,” he said. Liquid cooling adds another layer. Removing infrastructure designed around direct-to-chip or other liquid-cooling architectures can