“There are very beefy workloads that you must have that performance for to run the enterprise,” he said. “The Fortune 500 mainstream enterprise customers are now … adopting Epyc faster than anyone. We’ve seen a 3x adoption this year. And what that does is drives back to the on-prem enterprise adoption, so that the hybrid multi-cloud is end-to-end on Epyc.” One of the key focus areas for AMD’s Epyc strategy has been our ecosystem build out. It has almost 180 platforms, from racks to blades to towers to edge devices, and 3,000 solutions in the market on top of those platforms. One of the areas where AMD pushes into the enterprise is what it calls industry or vertical workloads. “These are the workloads that drive the end business. So in semiconductors, that’s telco, it’s the network, and the goal there is to accelerate those workloads and either driving more throughput or drive faster time to market or faster time to results. And we almost double our competition in terms of faster time to results,” said McNamara. And it’s paying off. McNamara noted that over 60% of the Fortune 100 are using AMD, and that’s growing quarterly. “We track that very, very closely,” he said. The other question is are they getting new customer acquisitions, customers with Epyc for the first time? “We’ve doubled that year on year.” AMD didn’t just brag, it laid out a road map for the next two years, and 2026 is going to be a very busy year. That will be the year that new CPUs, both client and server, built on the Zen 6 architecture begin to appear. On the server side, that means the Venice generation of Epyc server processors. Zen 6 processors will be built on 2 nanometer design generated by (you guessed

When DartPoints CEO Scott Willis took the stage on “the Distributed Edge” panel at the 2025 Data Center Frontier Trends Summit, his message resonated across a room full of developers, operators, and hyperscale strategists: the future of AI infrastructure will be built far beyond the nation’s tier-one metros. On the latest episode of the Data Center Frontier Show, Willis expands on that thesis, mapping out how DartPoints has positioned itself for a moment when digital infrastructure inevitably becomes more distributed, and why that moment has now arrived. DartPoints’ strategy centers on what Willis calls the “regional edge”—markets in the Midwest, Southeast, and South Central regions that sit outside traditional cloud hubs but are increasingly essential to the evolving AI economy. These are not tower-edge micro-nodes, nor hyperscale mega-campuses. Instead, they are regional data centers designed to serve enterprises with colocation, cloud, hybrid cloud, multi-tenant cloud, DRaaS, and backup workloads, while increasingly accommodating the AI-driven use cases shaping the next phase of digital infrastructure. As inference expands and latency-sensitive applications proliferate, Willis sees the industry’s momentum bending toward the very markets DartPoints has spent years cultivating. Interconnection as Foundation for Regional AI Growth A key part of the company’s differentiation is its interconnection strategy. Every DartPoints facility is built to operate as a deeply interconnected environment, drawing in all available carriers within a market and stitching sites together through a regional fiber fabric. Willis describes fiber as the “nervous system” of the modern data center, and for DartPoints that means creating an interconnection model robust enough to support a mix of enterprise cloud, multi-site disaster recovery, and emerging AI inference workloads. The company is already hosting latency-sensitive deployments in select facilities—particularly inference AI and specialized healthcare applications—and Willis expects such deployments to expand significantly as regional AI architectures become more widely

Brian Ortbals, senior vice president from World Wide Technology, which is one of Cisco’s biggest and most important partners stated: “Cisco engaged partners early in the process and took our feedback along the way. We believe now is the right time for these changes as it will enable us to capitalize on the changes in the market.” The reality is, the more successful its more-than-half-a-million partners are, the more successful Cisco will be. Platform approach is coming together When Jeetu Patel took the reigns as chief product officer, one of his goals was to make the Cisco portfolio a “force multiple.” Patel has stated repeatedly that, historically, Cisco acted more as a technology holding company with good products in networking, security, collaboration, data center and other areas. In this case, product breadth was not an advantage, as everything must be sold as “best of breed,” which is a tough ask of the salesforce and partner community. Since then, there have been many examples of the coming together of the portfolio to create products that leverage the breadth of the platform. The latest is the Unified Edge appliance, an all-in-one solution that brings together compute, networking, storage and security. Cisco has been aggressive with AI products in the data center, and Cisco Unified Edge compliments that work with a device designed to bring AI to edge locations. This is ideally suited for retail, manufacturing, healthcare, factories and other industries where it’s more cost effecting and performative to run AI where the data lives.

Customers are very focused on modernizing their network infrastructure in the enterprise in preparation for inferencing and AI workloads, Robbins said. “These things are always multi-year efforts,” and this is only the beginning, Robbins said. The AI opportunity “As we look at the AI opportunity, we see customer use cases growing across training, inferencing, and connectivity, with secure networking increasingly critical as workloads move from the data center to end users, devices, and agents at the edge,” Robbins said. “Agents are transforming network traffic from predictable bursts to persistent high-intensity loads, with agentic AI queries generating up to 25 times more network traffic than chatbots.” “Instead of pulling data to and from the data center, AI workloads require models and infrastructure to be closer to where data is created and decisions are made, particularly in industries such as retail, healthcare, and manufacturing.” Robbins pointed to last week’s introduction of Cisco Unified Edge, a converged platform that integrates networking, compute and storage to help enterprise customers more efficiently handle data from AI and other workloads at the edge. “Unified Edge enables real-time inferencing for agentic and physical AI workloads, so enterprises can confidently deploy and manage AI at scale,” Robbins said. On the hyperscaler front, “we see a lot of solid pipeline throughout the rest of the year. The use cases, we see it expanding,” Robbins said. “Obviously, we’ve been selling networking infrastructure under the training models. We’ve been selling scale-out. We launched the P200-based router that will begin to address some of the scale-across opportunities.” Cisco has also seen great success with its pluggable optics, Robbins said. “All of the hyperscalers now are officially customers of our pluggable optics, so we feel like that’s a great opportunity. They not only plug into our products, but they can be used with other companies’

On November 4, 2025, Google unveiled Project Suncatcher, a moonshot research initiative exploring the feasibility of AI data centers in space. The concept envisions constellations of solar-powered satellites in Low Earth Orbit (LEO), each equipped with Tensor Processing Units (TPUs) and interconnected via free-space optical laser links. Google’s stated objective is to launch prototype satellites by early 2027 to test the idea and evaluate scaling paths if the technology proves viable. Rather than a commitment to move production AI workloads off-planet, Suncatcher represents a time-bound research program designed to validate whether solar-powered, laser-linked LEO constellations can augment terrestrial AI factories, particularly for power-intensive, latency-tolerant tasks. The 2025–2027 window effectively serves as a go/no-go phase to assess key technical hurdles including thermal management, radiation resilience, launch economics, and optical-link reliability. If these milestones are met, Suncatcher could signal the emergence of a new cloud tier: one that scales AI with solar energy rather than substations. Inside Google’s Suncatcher Vision Google has released a detailed technical paper titled “Towards a Future Space-Based, Highly Scalable AI Infrastructure Design.” The accompanying Google Research blog describes Project Suncatcher as “a moonshot exploring a new frontier” – an early-stage effort to test whether AI compute clusters in orbit can become a viable complement to terrestrial data centers. The paper outlines several foundational design concepts: Orbit and Power Project Suncatcher targets Low Earth Orbit (LEO), where solar irradiance is significantly higher and can remain continuous in specific orbital paths. Google emphasizes that space-based solar generation will serve as the primary power source for the TPU-equipped satellites. Compute and Interconnect Each satellite would host Tensor Processing Unit (TPU) accelerators, forming a constellation connected through free-space optical inter-satellite links (ISLs). Together, these would function as a disaggregated orbital AI cluster, capable of executing large-scale batch and training workloads. Downlink

The pattern points to an evolving GPU ecosystem: while top-tier chips like Nvidia’s new GB200 Blackwell processors remain in extremely short supply, older models such as the A100 and H100 are becoming cheaper and more available. Yet, customer behavior may not match practical needs. “Many are buying the newest GPUs because of FOMO—the fear of missing out,” he added. “ChatGPT itself was built on older architecture, and no one complained about its performance.” Gil emphasized that managing cloud GPU resources now requires agility, both operationally and geographically. Spot capacity fluctuates hourly or even by the minute, and availability varies across data center regions. Enterprises willing to move workloads dynamically between regions—often with the help of AI-driven automation—can achieve cost reductions of up to 80%. “If you can move your workloads where the GPUs are cheap and available, you pay five times less than a company that can’t move,” he said. “Human operators can’t respond that fast automation is essential.” Conveniently, Cast sells an AI automation solution. But it is not the only one and the argument is valid. If spot pricing can be found cheaper at another location, you want to take it to keep the cloud bill down/ Gil concluded by urging engineers and CTOs to embrace flexibility and automation rather than lock themselves into fixed regions or infrastructure providers. “If you want to win this game, you have to let your systems self-adjust and find capacity where it exists. That’s how you make AI infrastructure sustainable.”