“The AWS AI Factory seeks to resolve the tension between cloud-native innovation velocity and sovereign control. Historically, these objectives lived in opposition. CIOs faced an unsustainable dilemma: choose between on-premises security or public cloud cost and speed benefits,” he said. “This is arguably AWS’s most significant move in the sovereign AI landscape.” On premises GPUs are already a thing AI Factories isn’t the first attempt to put cloud-managed AI accelerators in customers’ data centers. Oracle introduced Nvidia processors to its Cloud@Customer managed on-premises offering in March, while Microsoft announced last month that it will add Nvidia processors to its Azure Local service. Google Distributed Cloud also includes a GPU offering, and even AWS offers lower-powered Nvidia processors in its AWS Outposts. AWS’ AI Factories is also likely to square off against from a range of similar products, such as Nvidia’s AI Factory, Dell’s AI Factory stack, and HPE’s Private Cloud for AI — each tightly coupled with Nvidia GPUs, networking, or software, and all vying to become the default on-premises AI platform. But, said Sopko, AWS will have an advantage over rivals due to its hardware-software integration and operational maturity: “The secret sauce is the software, not the infrastructure,” he said. Omdia principal analyst Alexander Harrowell expects AWS’s AI Factories to combine the on-premises control of Outposts with the flexibility and ability to run a wider variety of services offered by AWS Local Zones, which puts small data centers close to large population centers to reduce service latency. Sopko cautioned that enterprises are likely to face high commitment costs, drawing a parallel with Oracle’s OCI Dedicated Region, one of its Cloud@Customer offerings.

On the hardware front, HPE is targeting the AI data center edge with a new MX router and the scale-out networking delivery with a new QFX switch. Juniper’s MX series is its flagship routing family aimed at carriers, large-scale enterprise data center and WAN customers, while the QFX line services data center customers anchoring spine/leaf networks as well as top-of-rack systems. The new 1U, 1.6Tbps MX301 multiservice edge router, available now, is aimed at bringing AI inferencing closer to the source of data generation and can be positioned in metro, mobile backhaul, and enterprise routing applications, Rahim said. It includes high-density support for 16 x 1/1025/50GbE, 10 x 100Gb and 4 x 400Gb interfaces. “The MX301 is essentially the on-ramp to provide high speed, secure connections from distributed inference cluster users, devices and agents from the edge all the way to the AI data center,” Rami said. “The requirements here are typically around high performance, but also very high logical skills and integrated security.” In the QFX arena, the new QFX5250 switch, available in 1Q 2026, is a fully liquid-cooled box aimed at tying together Nvidia Rubin and/or AMD MI400 GPUs for AI consumption across the data center. It is built on Broadcom Tomahawk 6 silicon and supports up to 102.4Tbps Ethernet bandwidth, Rahim said.  “The QFX5250 combines HPE liquid cooling technology with Juniper networking software (Junos) and integrated AIops intelligence to deliver a high-performance, power-efficient and simplified operations for next-generation AI inference,” Rami said. Partnership expansions Also key to HPE/Juniper’s AI networking plans are its partnerships with Nvidia and AMD. The company announced its relationship with Nvidia now includes HPE Juniper edge onramp and long-haul data center interconnect (DCI) support in its Nvidia AI Computing by HPE portfolio. This extension uses the MX and Junipers PTX hyperscaler routers to support high-scale, secure

When it announced its CPO-capable switches, Nvidia said they would improve resiliency by 10 times at scale compared to previous switch generations. Several factors contribute to this claim, including the fact that the optical switches require four times fewer lasers, Shainer says. Whereas the laser source was previously part of the transceiver, the optical engine is now incorporated onto the ASIC, allowing multiple optical channels to share a single laser. Additionally, in Nvidia’s implementation, the laser source is located outside of the switch. “We want to keep the ability to replace a laser source in case it has failed and needs to be replaced,” he says. “They are completely hot-swappable, so you don’t need to shut down the switch.” Nonetheless, you may often hear that when something fails in a CPO box, you need to replace the entire box. That may be true if it’s the photonics engine embedded in silicon inside the box. “But they shouldn’t fail that often. There are not a lot of moving parts in there,” Wilkinson says. While he understands the argument around failures, he doesn’t expect it to pan out as CPO gets deployed. “It’s a fallacy,” he says. There’s also a simple workaround to the resiliency issue, which hyperscalers are already talking about, Karavalas says: overbuild. “Have 10% more ports than you need or 5%,” he says. “If you lose a port because the optic goes bad, you just move it and plug it in somewhere else.” Which vendors are backing co-packaged optics? In terms of vendors that have or plan to have CPO offerings, the list is not long, unless you include various component players like TSMC. But in terms of major switch vendors, here’s a rundown: Broadcom has been making steady progress on CPO since 2021. It is now shipping “to

But the concern for enterprise IT leaders is whether Nvidia’s financial stakes in UALink consortium members could influence the development of an open standard specifically designed to compete with Nvidia’s proprietary technology and to give enterprises more choices in the datacenter. Organizations planning major AI infrastructure investments view such open standards as critical to avoiding vendor lock-in and maintaining competitive pricing. “This does put more pressure on UALink since Intel is also a member and also took investment from Nvidia,” Sag said. UALink and Synopsys’s critical role UALink represents the industry’s most significant effort to prevent vendor lock-in for AI infrastructure. The consortium ratified its UALink 200G 1.0 Specification in April, defining an open standard for connecting up to 1,024 AI accelerators within computing pods at 200 Gbps per lane — directly competing with Nvidia’s NVLink for scale-up applications. Synopsys plays a critical role. The company joined UALink’s board in January and in December announced the industry’s first UALink design components, enabling chip designers to build UALink-compatible accelerators. Analysts flag governance concerns Gaurav Gupta, VP analyst at Gartner, acknowledged the tension. “The Nvidia-Synopsys deal does raise questions around the future of UALink as Synopsys is a key partner of the consortium and holds critical IP for UALink, which competes with Nvidia’s proprietary NVLink,” he said. Sanchit Vir Gogia, chief analyst at Greyhound Research, sees deeper structural concerns. “Synopsys is not a peripheral player in this standard; it is the primary supplier of UALink IP and a board member within the UALink Consortium,” he said. “Nvidia’s entry into Synopsys’ shareholder structure risks contaminating that neutrality.”

Organizations should reassess redundancy However, he pointed out, “the deeper concern is that CME had a secondary data center ready to take the load, yet the failover threshold was set too high, and the activation sequence remained manually gated. The decision to wait for the cooling issue to self-correct rather than trigger the backup site immediately revealed a governance model that had not evolved to keep pace with the operational tempo of modern markets.” Thermal failures, he said, “do not unfold on the timelines assumed in traditional disaster recovery playbooks. They escalate within minutes and demand automated responses that do not depend on human certainty about whether a facility will recover in time.” Matt Kimball, VP and principal analyst at Moor Insights & Strategy, said that to some degree what happened in Aurora highlights an issue that may arise on occasion: “the communications gap that can exist between IT executives and data center operators. Think of ‘rack in versus rack out’ mindsets.” Often, he said, the operational elements of that data center environment, such as cooling, power, fire hazards, physical security, and so forth, fall outside the realm of an IT executive focused on delivering IT services to the business. “And even if they don’t fall outside the realm, these elements are certainly not a primary focus,” he noted. “This was certainly true when I was living in the IT world.” Additionally, said Kimball, “this highlights the need for organizations to reassess redundancy and resilience in a new light. Again, in IT, we tend to focus on resilience and redundancy at the app, server, and workload layers. Maybe even cluster level. But as we continue to place more and more of a premium on data, and the terms ‘business critical’ or ‘mission critical’ have real relevance, we have to zoom out

Microsoft did not immediately respond to a request for comment. Microsoft’s constraints Analysts say the twin departures mark a significant setback for Microsoft at a critical moment in the AI data center race, with pressure mounting from both OpenAI’s model demands and Google’s infrastructure scale. “Losing some of the best professionals working on this challenge could set Microsoft back,” said Neil Shah, partner and co-founder at Counterpoint Research. “Solving the energy wall is not trivial, and there may have been friction or strategic differences that contributed to their decision to move on, especially if they saw an opportunity to make a broader impact and do so more lucratively at a company like Nvidia.” Even so, Microsoft has the depth and ecosystem strength to continue doubling down on AI data centers, said Prabhu Ram, VP for industry research at Cybermedia Research. According to Sanchit Gogia, chief analyst at Greyhound Research, the departures come at a sensitive moment because Microsoft is trying to expand its AI infrastructure faster than physical constraints allow. “The executives who have left were central to GPU cluster design, data center engineering, energy procurement, and the experimental power and cooling approaches Microsoft has been pursuing to support dense AI workloads,” Gogia said. “Their exit coincides with pressures the company has already acknowledged publicly. GPUs are arriving faster than the company can energize the facilities that will house them, and power availability has overtaken chip availability as the real bottleneck.”