@import url(‘https://fonts.googleapis.com/css2?family=Inter:[email protected]&display=swap’); a { color: #1796c1; } .ebm-page__main h1, .ebm-page__main h2, .ebm-page__main h3, .ebm-page__main h4, .ebm-page__main h5, .ebm-page__main h6 { font-family: Inter; } body { line-height: 150%; letter-spacing: 0.025em; font-family: Inter; } button, .ebm-button-wrapper { font-family: Inter; } .label-style { text-transform: uppercase; color: var(–color-grey); font-weight: 600; font-size: 0.75rem; } .caption-style { font-size: 0.75rem; opacity: .6; } #onetrust-pc-sdk [id*=btn-handler], #onetrust-pc-sdk [class*=btn-handler] { background-color: #1796c1 !important; border-color: #1796c1 !important; } #onetrust-policy a, #onetrust-pc-sdk a, #ot-pc-content a { color: #1796c1 !important; } #onetrust-consent-sdk #onetrust-pc-sdk .ot-active-menu { border-color: #1796c1 !important; } #onetrust-consent-sdk #onetrust-accept-btn-handler, #onetrust-banner-sdk #onetrust-reject-all-handler, #onetrust-consent-sdk #onetrust-pc-btn-handler.cookie-setting-link { background-color: #1796c1 !important; border-color: #1796c1 !important; } #onetrust-consent-sdk .onetrust-pc-btn-handler { color: #1796c1 !important; border-color: #1796c1 !important; background-color: undefined !important; } Welcome to Nomads at the Summit, a new podcast series from Data Center Frontier in partnership with the Nomad Futurist Foundation. Recorded live at the 2025 Data Center Frontier Trends Summit (Aug. 26-28), here we sit down with industry leaders, innovators, and change-makers shaping the future of digital infrastructure. Join hosts Nabeel Mahmood and Phillip Koblence of Nomad Futurist, alongside DCF editorial leadership including Editor at Large Melissa Farney and Senior Editor David Chernicoff, for these candid conversations that highlight the ideas, talent, and technologies driving the next chapter of the data center industry. Whether you attended the DCF Trends Summit in person or are just now tuning in from afar, Nomads at the Summit gives you a behind-the-scenes look at the people and innovations defining what’s next in digital infrastructure. <!–> EPISODE LIST ]–> Waste Heat to Water – The Path Towards Water Positive Data Centers In this DCF Trends-Nomads at the Summit Podcast episode, Matt Grandbois, Vice President at AirJoule, introduces a game-changing approach to one of the data center industry’s most pressing challenges: water sustainability. As power-hungry, high-density environments collide with growing water scarcity

Data center provider Equinix has launched its Distributed AI infrastructure, which includes a new AI-ready backbone to support high performance distributed AI deployments spanning multiple data center facilities, a global AI Proving Ground to test new solutions, and Fabric Intelligence to better support next generation enterprise workloads. Equinix designed Distributed AI from the ground up to support the scale, speed, and complexity of modern intelligent systems, such as autonomous, agentic AI capable of reasoning, acting, and learning independently. AI is inherently distributed, drawing on multiple data sources in different locations. To effectively train a model, data must be drawn from multiple locations and processed where it lies, not moved around. This requires a new kind of infrastructure that is globally distributed, deeply interconnected, and fully programmable. Distributed AI links more than 270 data centers in over 60 markets, effectively including almost all Equinix’s facilities, according to the vendor.

The software stack supports three other prototype applications to help enable quantum networking and the data center. The first is what Pandey describes as a network-aware distributed quantum compiler that lets quantum algorithms run across multiple networked processors. “The compiler is the piece of technology you need to enable practical, pragmatic, distributed quantum computing. It takes a quantum workload, a quantum circuit, and it partitions it so that it runs in a distributed environment, in a connected set of qubits or quantum compute nodes,” Pandey said. Significantly, it’s multivendor; the quantum compute nodes can be from the same vendor or from other vendors, such as IBM: “It could be as messy a brownfield, heterogeneous environment as you want. It doesn’t matter to the compiler, which will take an algorithm, partition it across any heterogeneous, brownfield environment,” Pandey said.  “What makes it unique, and an industry-first, is that it accounts for quantum interconnect requirements between processors and supports distributed quantum error correction. Existing compilers target circuits for only single computers,” Pandey stated. “Ours compiles circuits for network-connected computers potentially made of heterogeneous quantum compute technologies and can distribute that partitioned circuit across an entire data center of processors, all connected through a quantum network.” The distributed quantum error correction is a key feature of the software. Error correction ensures the accuracy and reliability of quantum computations and is a challenge for any distributed or standalone network.  The Cisco software in this case understands the error correction intricacies of each of the quantum computing modalities in the network, and “we can ensure that those are carried over from node to node, giving us a distributed or a holistic view of the entire distributed environment and result,” Pandey said.  In addition, “we are developing our own algorithms [to determine] the best way, using our network, to do a

The new strategic partnership between OpenAI and NVIDIA, formalized via a letter of intent in September 2025, is designed to both power and finance the next generation of OpenAI’s compute infrastructure, with initial deployments expected in the second half of 2026. According to the joint press release, both parties position this as “the biggest AI infrastructure deployment in history,” explicitly aimed at training and running OpenAI’s next-generation models.  At a high level: The target scale is 10 gigawatts (GW) or more of deployed compute capacity, realized via NVIDIA systems (comprising millions of GPUs).  The first phase (1 GW) is slated for the second half of 2026, built on the forthcoming Vera Rubin platform.  NVIDIA will progressively invest up to $100 billion into OpenAI, contingent on deployment of capacity in stages.  An initial $10 billion investment from NVIDIA is tied to the execution of a definitive purchase agreement for the first gigawatt of systems.  The equity stake NVIDIA will acquire is described as non-voting / non-controlling, meaning it gives financial skin in the game without governance control.  From a strategic standpoint, tying investment to capacity deployment helps OpenAI lock in capital and hardware over a long horizon, mitigating supply-chain and financing risk. With compute frequently cited as a binding constraint on advancing models, this kind of staged, anchored commitment gives OpenAI a more predictable growth path (at least in theory; that said, the precise economic terms and risk-sharing remain to be fully disclosed.) Press statements emphasize that millions of GPUs will ultimately be involved, and that co-optimization of NVIDIA’s hardware with OpenAI’s software/stack will be a key feature of the collaboration.  Importantly, this deal also fits into OpenAI’s broader strategy of diversifying infrastructure partnerships beyond any single cloud provider. Microsoft remains a central backer and collaborator, but this NVIDIA tie-up further

AI has taken the technology industry by storm, with enterprises deploying emerging applications to create business value. Amid this shift, operators are leveraging network automation, optical innovation and more to support enterprise AI use cases. Still, the technology ecosystem must balance AI’s opportunities with its challenges. While AI can improve operations, it can also leave companies more vulnerable to cyberattacks. As organizations deploy more AI tools and employees increasingly use them, the overall attack surface expands and opens more security gaps. This article explores how internet carriers are building their networks to support enterprises, while also discussing how operators are establishing trust with customers. Table stakes: reliability, diversity and reach AI’s requirements are similar to content distribution, cloud networking and previous industry shifts, but place even greater pressure on carrier-delivered enterprise network services.  In these services, network diversity is integral, allowing carriers to eliminate single points of failure in the event of an outage, then quickly reroute traffic through the next best available path. This improved reliability is vital for enabling real-time enterprise AI operations amid increased instances of network disruption due to geopolitical sabotage or accidental damage. As more hyperscalers build sprawling AI data center campuses, network reach will also prove even more crucial. By continuously expanding their network footprints, carriers can help enterprises access these sites no matter where they’re located, with operators’ high-capacity connectivity infrastructure facilitating the transfer of massive data volumes between these campuses. Similar to how content distribution networks rely on a robust network underlay, backbone connectivity provides the high-capacity, long-haul transport underpinning the delivery of AI inferencing responses. While the backbone itself does not cache or deliver these responses, its densely interconnected networks ensure that this AI traffic reaches regional and access networks, which then distribute responses to end users. Lightspeed: optical innovation With

Rising thermal pressure on AI hardware AI workloads and high-performance computing have placed unprecedented strain on data center infrastructure. Thermal dissipation has emerged as one of the toughest bottlenecks, with traditional methods such as airflow and cold plates increasingly unable to keep pace with new generations of silicon. “Modern accelerators are throwing out thermal loads that air systems simply cannot contain, and even advanced water loops are straining. The immediate issues are not only the soaring TDP of GPUs, but also grid delays, water scarcity, and the inability of legacy air-cooled halls to absorb racks running at 80 or 100 kilowatts,” said Sanchit Vir Gogia, CEO and chief analyst at Greyhound Research. “Cold plates and immersion tanks have extended the runway, but only marginally. They still suffer from the resistance of thermal interfaces that smother heat at the die. The friction lies in the last metre of the thermal path, between junction and package, and that is where performance is being squandered.” Cooling costs: the next data center budget crisis Cooling isn’t just a technical challenge but also an economic one. Data centers spend heavily to manage the immense heat generated by servers, networking gear, and GPUs. Hence, the cost of cooling a data center is also a significant expense. “As per 2025 AI infra buildouts TCO analysis, over 45%-47% of data center power budget typically goes into cooling, which could further expand to 65%-70% without advancement in cooling method efficiency,” said Danish Faruqui, CEO at Fab Economics. “In 2024, Nvidia Hopper H100 had 700 watts of power requirements per GPU, which scaled in 2025 to double with Blackwell B200 and Blackwell Ultra B300 to 1000 W and 1400 watts per GPU. Going forward in 2026, it will again more than double by Rubin and Rubin Ultra GPU to 1800W