At Data Center Frontier, we rely on industry leaders to help us understand the most urgent challenges facing digital infrastructure. And in the fourth quarter of 2025, the data center industry is adjusting to a new kind of complexity.  AI-scale infrastructure is redefining what “mission critical” means, from megawatt density and modular delivery to the chemistry of cooling fluids and the automation of energy systems. Every project has arguably in effect now become an ecosystem challenge, demanding that electrical, mechanical, construction, and environmental disciplines act as one.  For this quarter’s Executive Roundtable, DCF convened subject matter experts from Ecolab, EdgeConneX, Rehlko and Schneider Electric – leaders spanning the full chain of facilities design, deployment, and operation. Their insights illuminate how liquid cooling, energy management, and sustainable process design in data centers are now converging to set the pace for the AI era. Our distinguished executive panelists for this quarter include: Rob Lowe, Director RD&E – Global High Tech, Ecolab Phillip Marangella, Chief Marketing and Product Officer, EdgeConneX Ben Rapp, Manager, Strategic Project Development, Rehlko Joe Reele, Vice President, Datacenter Solution Architects, Schneider Electric Today: Engineering the New Normal – Liquid Cooling at Scale  Today’s kickoff article grapples with how, as liquid cooling technology transitions to default hyperscale design, the challenge is no longer if, but how to scale builds safely, repeatably, and globally.  Cold plates, immersion, dielectric fluids, and liquid-to-chip loops are converging into factory-integrated building blocks, yet variability in chemistry, serviceability, materials, commissioning practices, and long-term maintenance threatens to fragment adoption just as demand accelerates.  Success now hinges on shared standards and tighter collaboration across OEMs, builders, and process specialists worldwide. So how do developers coordinate across the ecosystem to make liquid cooling a safe, maintainable global default? What’s Ahead in the Roundtable Over the coming days, our panel

At the 2025 Data Center Frontier Trends Summit (Aug. 26-28) in Reston, Va., the conversation around AI and infrastructure moved well past the hype. In a panel sponsored by Schneider Electric—“AI for Good: Building for AI Workloads and Using AI for Smarter Data Centers”—three industry leaders explored what it really means to design, cool and operate the new class of AI “factories,” while also turning AI inward to run those facilities more intelligently. Moderated by Data Center Frontier Editor in Chief Matt Vincent, the session brought together: Steve Carlini, VP, Innovation and Data Center Energy Management Business, Schneider Electric Sudhir Kalra, Chief Data Center Operations Officer, Compass Datacenters Andrew Whitmore, VP of Sales, Motivair Together, they traced both sides of the “AI for Good” equation: building for AI workloads at densities that would have sounded impossible just a few years ago, and using AI itself to reduce risk, improve efficiency and minimize environmental impact. From Bubble Talk to “AI Factories” Carlini opened by acknowledging the volatility surrounding AI investments, citing recent headlines and even Sam Altman’s public use of the word “bubble” to describe the current phase of exuberance. “It’s moving at an incredible pace,” Carlini noted, pointing out that roughly half of all VC money this year has flowed into AI, with more already spent than in all of the previous year. Not every investor will win, he said, and some companies pouring in hundreds of billions may not recoup their capital. But for infrastructure, the signal is clear: the trajectory is up and to the right. GPU generations are cycling faster than ever. Densities are climbing from high double-digits per rack toward hundreds of kilowatts. The hyperscale “AI factories,” as NVIDIA calls them, are scaling to campus capacities measured in gigawatts. Carlini reminded the audience that in 2024,

“The big change that’s really started to happen in late 2024 early 2025 is that the FinOps practice started to expand past the cloud,” Storment said. “A lot of organizations got really good at using FinOps to manage the value of cloud, and then their organizations went, ‘oh, hey, we’re living in this happily hybrid state now where we’ve got cloud, SaaS, data center. Can you also apply the FinOps practice to our SaaS? Or can you apply it to our Snowflake? Can you apply it to our data center?’” The FinOps Foundation’s community has grown to approximately 100,000 practitioners. The organization now includes major cloud vendors, hardware providers like Nvidia and AMD, data center operators and data cloud platforms like Snowflake and Databricks. Some 96 of the Fortune 100 now participate in FinOps Foundation programs. The practice itself has shifted in two directions. It has moved left into earlier architectural and design processes, becoming more proactive rather than reactive. It has also moved up organizationally, from director-level cloud management roles to SVP and COO positions managing converged technology portfolios spanning multiple infrastructure types. This expansion has driven the evolution of FOCUS beyond its original cloud billing focus. Enterprises are implementing FOCUS as an internal standard for chargeback reporting even when their providers don’t generate native FOCUS data. Some newer cloud providers, particularly those focused on AI infrastructure, are using the FOCUS specification to define their billing data structures from the ground up rather than retrofitting existing systems. The FOCUS 1.3 release reflects this maturation, addressing technical gaps that have emerged as organizations apply cost management practices across increasingly complex hybrid environments. FOCUS 1.3 exposes cost allocation logic for shared infrastructure The most significant technical enhancement in FOCUS 1.3 addresses a gap in how shared infrastructure costs are allocated and

Enterprises will connect to and manage orbital workloads “the same way they manage cloud workloads today,” using optical links, the spokesperson added. The company’s approach is to “continuously launch new hardware and quickly integrate the latest architectures,” with older systems running lower-priority tasks to serve out the full useful lifetime of their high-end GPUs. The company declined to disclose pricing. Aetherflux plans to launch about 30 satellites at a time on SpaceX Falcon 9 rockets. Before the data center launch, the company will launch a power-beaming demonstration satellite in 2026 to test transmission of one kilowatt of energy from orbit to ground stations, using infrared lasers. Competition in the sector has intensified in recent months. In November, Starcloud launched its Starcloud-1 satellite carrying an Nvidia H100 GPU, which is 100 times more powerful than any previous GPU flown in space, according to the company, and demonstrated running Google’s Gemma AI model in orbit. In the same month, Google announced Project Suncatcher, with a 2027 demonstration mission planned. Analysts see limited near-term applications Despite the competitive activity, orbital data centers won’t replace terrestrial cloud regions for general hosting through 2030, said Ashish Banerjee, senior principal analyst at Gartner. Instead, they suit specific workloads, including meeting data sovereignty requirements for jurisdictionally complex scenarios, offering disaster recovery immune to terrestrial risks, and providing asynchronous high-performance computing, he said. “Orbital centers are ideal for high-compute, low-I/O batch jobs,” Banerjee said. “Think molecular folding simulations for pharma, massive Monte Carlo financial simulations, or training specific AI model weights. If the job takes 48 hours, the 500ms latency penalty of LEO is irrelevant.” One immediate application involves processing satellite-generated data in orbit, he said. Earth observation satellites using synthetic aperture radar generate roughly 10 gigabytes per second, but limited downlink bandwidth creates bottlenecks. Processing data in

He said he had earlier told analysts in a separate call that margins for AI workloads in these data centers would be in the 30% to 40% range over the life of a customer contract. Kehring reassured that there would be demand for the data centers when they were completed, pointing to Oracle’s increasing remaining performance obligations, or services contracted but not yet delivered, up $68 billion on the previous quarter, saying that Oracle has been seeing unprecedented demand for AI workloads driven by the likes of Meta and Nvidia. Rising debt and margin risks raise flags for CIOs For analysts, though, the swelling debt load is hard to dismiss, even with Oracle’s attempts to de-risk its spend and squeeze more efficiency out of its buildouts. Gogia sees Oracle already under pressure, with the financial ecosystem around the company pricing the risk — one of the largest debts in corporate history, crossing $100 billion even before the capex spend this quarter — evident in the rising cost of insuring the debt and the shift in credit outlook. “The combination of heavy capex, negative free cash flow, increasing financing cost and long-dated revenue commitments forms a structural pressure that will invariably finds its way into the commercial posture of the vendor,” Gogia said, hinting at an “eventual” increase in pricing of the company’s offerings. He was equally unconvinced by Magouyrk’s assurances about the margin profile of AI workloads as he believes that AI infrastructure, particularly GPU-heavy clusters, delivers significantly lower margins in the early years because utilisation takes time to ramp.

Addressing the challenge Modern AI accelerators now draw more than 700W per GPU, and multi-GPU nodes can reach 6kW, creating concentrated heat zones, rapid power swings, and a higher risk of interconnect degradation in dense racks, according to Manish Rawat, semiconductor analyst at TechInsights. Traditional cooling methods and static power planning increasingly struggle to keep pace with these loads. “Rich vendor telemetry covering real-time power draw, bandwidth behavior, interconnect health, and airflow patterns shifts operators from reactive monitoring to proactive design,” Rawat said. “It enables thermally aware workload placement, faster adoption of liquid or hybrid cooling, and smarter network layouts that reduce heat-dense traffic clusters.” Rawat added that the software’s fleet-level configuration insights can also help operators catch silent errors caused by mismatched firmware or driver versions. This can improve training reproducibility and strengthen overall fleet stability. “Real-time error and interconnect health data also significantly accelerates root-cause analysis, reducing MTTR and minimizing cluster fragmentation,” Rawat said. These operational pressures can shape budget decisions and infrastructure strategy at the enterprise level.