The biggest driver in today’s global economy is the data center, and in the process, data centers are generating and taking more heat than ever before. Heat — the generation of it and public concerns about it — is the most pressing issue facing the data center industry. In response, data center managers and engineers are viewing the problem in new ways, devising innovative, next-generation thermal control strategies that manage the problem more effectively. In the minds of IT managers and the public, data centers and AI are joined at the hip, along with increasing power demands and associated issues of environmental impact, water usage, and higher utility costs. As community activists, political leaders, and now even some prominent AI industry CEOs call for limits on AI development and data center construction, it’s imperative that the data center industry better manage the heat they’re producing — and taking. Sponsored Resources: Texas Instruments’ portfolio of data center thermal management systems spans the full coolant path. It’s been under active development at TI for decades and has evolved in response to the rising power, computing demands, and complexity of advanced data centers. Traditional thermal management techniques can’t keep up with AI’s intense computing requirements. For decades, air cooling was sufficient. Fans blew cool air across hot components and carried heat away, keeping data centers and AI servers operating reliably. But with AI training and inference pushing rack power beyond 20kW to 40kW, air alone can no longer remove heat quickly enough. In addition, air cooling is noisy and consumes too much energy. AI server racks are coming online now that draw 100kW of power, and they’re on their way to more than a megawatt in a few years. Each generation of servers grows denser, more powerful, and hotter as they move and compute massive volumes

“Operators need cooling infrastructure that can adapt as GPU platforms and rack densities evolve,” said Scott Smith, general manager of LiquidStack, in a statement. “CDU 2.X combines the performance and flexibility customers need today with the headroom to prepare for what comes next, allowing them to configure cooling around their facility and deployment strategy rather than designing the facility around the CDU.” A key feature is the platform’s support for different deployment configurations, including end-of-row and rack-adjacent installations. The idea is to give data center operators more flexibility in how they place their equipment with increasingly high thermal loads. The system offers configurable control-valve, power-feed and redundancy options, including dual-feed A/B configurations and automatic transfer switch support. These features allow operators to tailor the CDU to different facility architectures and resiliency requirements.

Nvidia introduced NVLink Fusion as part of a broader effort to make its interconnect technology available to companies developing custom AI processors. The approach allows third-party silicon to be incorporated into Nvidia-oriented data center architectures, potentially extending Nvidia’s influence beyond its own GPUs. A similar strategy is already being pursued with inference-chip developer d-Matrix. Apple also already operates specialized servers for its Private Cloud Compute system, which handles Apple Intelligence workloads that require processing beyond the user’s device. Scaling that infrastructure, however, reportedly creates bandwidth, cost, and performance challenges. A commercial AI server would allow Apple to extend its silicon strategy into the data center while giving customers direct access to Apple processors rather than Apple’s own cloud services.

Western Pennsylvania is becoming a test bed for one of the most consequential changes underway in data center development: the migration from simply finding grid capacity to building the power supply along with the data center. Two projects illustrate that transition particularly well. Aligned Data Centers is advancing the roughly $10 billion, 2-GW Project Phoenix campus at the former Bruce Mansfield coal-fired power station site in Shippingport, Beaver County. About 60 miles to the east, the former Homer City Generating Station is being transformed into the Homer City Energy Campus, centered on as much as 4.4 GW of new natural-gas generation and a proposed Amazon Web Services campus that could ultimately include 39 data center buildings. The projects share a number of similarities. Both reuse former coal-generation properties. Both already possess much of the infrastructure that greenfield data center developers spend years trying to obtain: high-voltage transmission, industrial zoning, water infrastructure, pipeline access, large parcels and proximity to the Marcellus and Utica natural-gas fields.But technically and commercially, they are also very different. Project Phoenix is essentially a data-center-led development using behind-the-meter generation, supplemented by a separate plan to repower the former coal station. While Homer City is the reverse: a power-generation project being constructed first, with the hyperscale data center development forming around the available power supply. This isn’t a competition, but it is a comparison on speed of delivery and the issues both development models face. Project Phoenix: Aligned Moves Into Pennsylvania Aligned calls Shippingport its first Pennsylvania campus and a regional flagship. The company formally broke ground on Project Phoenix on September 10, 2026, describing it as a 2-GW campus spanning three data center facilities and representing roughly $10 billion in regional investment. Aligned estimates the development will support approximately 3,000 construction jobs and 640 full-time jobs in

Type-5 support is aimed at data center integration. “It lets the tenant network prefixes be advertised directly into the physical fabric, so that really that’s basically how modern data center networks are built,” Carrez explained. “So really helps OpenStack fit into those environments without extra gateway layers that we’ve seen in use before.” Routable tenant addresses: The OVN BGP integration gains a route leaking option. An operator turns it on with the leak_routes attribute of a subnet. The extended OVN features follow the same approach. “OVN BGP features that let the tenant addresses be routable directly from the underlay, and that again is exposing how modern data centers are built directly into OpenStack,” Carrez said. Lower memory use: In deployments that use Open vSwitch, a monitoring daemon tracked keepalived state changes for HA routers. Hibiscus replaces the daemon with a shell script. The change applies to every HA router, so the savings add up across a deployment. “The 15 times reduction in memory footprint for the high availability router monitoring is, I think, really interesting,” Carrez said.

The power challenge surrounding artificial intelligence is increasingly about more than finding enough megawatts. AI data centers can also introduce rapid changes in electricity demand as large clusters of accelerators ramp workloads up and down. Those swings create a different kind of infrastructure problem: how to serve highly dynamic compute loads without passing that volatility directly onto the electric grid. That challenge is helping move battery energy storage deeper into data center power architecture. In an onsite podcast interview recorded live at the Data Center Frontier Trends Summit 2026, DCF Contributing Editor Doug Black spoke with Asser Elsamahy, P.E., vice president of engineering at ON.energy, about the emerging role of battery-based power quality infrastructure for AI data centers. Elsamahy said battery power systems themselves are hardly new. Energy storage has been deployed at gigawatt scale around the world for roughly two decades. What is new is the way the technology is being adapted to the operating characteristics of large AI facilities. “They’re new to the data center industry, but they’re not necessarily new in the market,” Elsamahy said. “They’ve been deployed at gigawatt scale already, multiple gigawatts all over the world.” The difference now is the load. Major swings in AI computing demand can create additional stress for grid operators already confronting rapid growth in large-load interconnection requests. Elsamahy said that dynamic is accelerating interest in energy storage as a way to manage the interface between AI infrastructure and the grid. From Battery Storage to an “AI UPS” ON.energy’s approach is built around what the company calls an AI UPS, or medium-voltage uninterruptible power supply. The architecture differs from the parallel battery energy storage system, or BESS, configuration commonly used for standalone grid storage. ON instead uses a double-conversion design with two sets of inverters. One inverter set faces the