The acquisition of VMware by Broadcom has caused many enterprise IT leaders to reexamine their infrastructure strategies. For organizations running vSphere 8, the October 2027 end-of-support deadline is rapidly becoming a planning priority. What may appear to be a routine upgrade is driving bigger discussions about cost, flexibility, cloud strategy, and long-term infrastructure direction.  Many organizations have not only begun evaluating alternatives but also are leaving VMware.  “VMware has been a great, innovative company,” says Harsha Kotikela, senior director of product and solutions marketing at Nutanix. “But since the acquisition, their business model has fundamentally changed, and that is what is forcing IT leaders to adapt.” Sticker shock, vendor lock-in, and the need for flexibility One of the biggest catalysts has been licensing costs. Organizations that had grown accustomed to predictable contracts have encountered significant pricing increases, creating what Kotikela describes as “sticker shock.” At the same time, some enterprises are reevaluating their vendor relationships due to concerns about support availability and changes in partner engagement models. Beyond immediate operational concerns, IT leaders are also focused on future requirements. Hybrid cloud environments have become the norm, with applications and data distributed across data centers, public clouds, and edge locations. AI initiatives are adding another layer of complexity, requiring infrastructure that can support workloads wherever they need to run. “The future is about flexibility,” Kotikela says. “If enterprises want to implement AI at the edge, in the data center, or in the cloud, they need the capability to manage that environment without creating silos.” That flexibility is becoming a critical factor in infrastructure decisions. Organizations increasingly want platforms that support multiple deployment models, open APIs, and cloud-native technologies to minimize the risk of vendor lock-in. How a future-ready platform addresses IT and business requirements Nutanix positions its architecture around openness and choice, according to

“Nvidia has something like 85% of the AI accelerator chip market,” he pointed out. “Sure, they have nowhere to go but down, but that’s still going to take them a while. More importantly, they have literally spent decades working with practitioners in AI and ML and compute-intensive fields, indoctrinating them into their CUDA software ecosystem. Rewriting that tool chain will take institutional change at most organizations, which means years, if not decades, to uncouple.” “Organizations that think they’ve achieved agnosticism because they’re using high-level abstractions like PyTorch, well,  they have come closest,” he observed. “But just cutting and pasting the same code into AMD Instinct can lead to memory and dependency errors. It’s like VM lift and shifts to the public cloud 10 years ago. Easier, but still possible to screw up.” Nonetheless, Annand said that the deal, if it goes through, is still good news for enterprises. 

To close industry watchers, it’s really no secret that the AI infrastructure race has entered another phase; one where capital formation itself may become as strategically important as GPUs, power procurement, or liquid cooling. And in launching Helix Digital Infrastructure, investment giant KKR is making a calculated wager that hyperscalers no longer simply need developers or financiers. They need a partner capable of orchestrating capital, energy, connectivity, and data center execution as a unified platform. The significance of that strategy is underscored by the executive chosen to lead it. Adam Selipsky, the former CEO of Amazon Web Services and one of the industry’s most experienced cloud operators, will serve as Co-Founder and CEO of Helix, bringing firsthand experience from the very class of customers the new venture intends to serve. A New Model for AI Infrastructure Helix launches with more than $10 billion in long-duration committed capital from founding investors including KKR, the Kuwait Investment Authority (KIA), NVIDIA, and Vistra. But the headline number tells only part of the story. The company has been structured around an increasingly important thesis: that AI infrastructure can no longer be assembled piecemeal. Rather than treating data centers, electrical supply, transmission capacity, and fiber connectivity as separate procurement exercises, Helix proposes a vertically coordinated approach in which a single organization manages and finances the entire infrastructure stack. According to KKR, the objective is to reduce execution risk and accelerate deployment for hyperscale customers facing unprecedented AI demand. As AI factories grow from hundreds of megawatts toward gigawatt-scale campuses, synchronization among land acquisition, utility planning, financing, construction, and technology deployment has emerged as one of the industry’s defining challenges. Helix is effectively positioning itself as an operating platform designed to simplify that complexity. Why Selipsky Matters The appointment of Adam Selipsky may be the announcement’s

“The enterprise data centers, even the new ones, tend to be far, far smaller than new hyperscale deployments,” Killian said. “Not uncommon to see enterprises deploy a quarter meg or one meg or two, maybe up to 10 megs. Whereas the hyperscale guys are deploying 40 up to 300 meg facilities.” But scale alone does not tell the story. For every one of the roughly 20 hyperscale users that dominate headlines, Killian noted, there may be 50 to 100 times as many large and mid-sized enterprise users. Those companies run critical business systems, purchase hardware, software, telecom and services, employ large data center teams, and often operate multiple facilities across domestic, edge, EMEA and Asia-Pacific footprints. In other words, enterprise demand may be smaller in unit size, but it remains massive in aggregate. And as AI shifts from training to inference, the enterprise data center could become newly strategic. Enterprise AI Is Not Hyperscale AI Killian’s central point is that enterprise infrastructure requirements differ materially from hyperscale requirements. Hyperscalers are primarily optimizing for massive scale and speed to market. Enterprises, by contrast, tend to prioritize reliability, flexibility, integration into broader IT systems, and audit and compliance. That difference has major implications for developers and colocation providers. “The real industry opportunity is to take some of the innovation and the economies of scale that we’re seeing from the hyperscale builds to deliver smaller chunks of data center capacity,” Killian said. That might mean adapting lessons from 40 MW or 100 MW campuses into enterprise-ready deployments of 2 MW, 4 MW or 8 MW. Killian pointed to providers such as DataBank and Flexential as examples of companies working to deliver hyperscale-derived efficiencies in smaller enterprise increments. He also noted that QTS and other large campus developers may reserve portions of multi-building campuses

This is a crucial point for AI infrastructure. In some markets, water can be as politically and operationally difficult as power. Evaporative cooling and cooling towers can consume large volumes of water, while discharge permits can slow projects or limit operations. Gradiant claims HyperSolved can expand access to alternative sources such as municipal reuse and impaired supplies, reduce reliance on freshwater, protect cooling performance through integrated treatment and AI-enabled operations, and minimize discharge through high-recovery concentration and reuse. The platform uses containerized systems for immediate or temporary capacity while also supporting permanent infrastructure and lifecycle operations from commissioning onward. That fits the AI data center buildout, where developers may need bridge capacity during construction, phased water infrastructure, or interim systems while permanent treatment plants are completed. This can address the speed of deployment issue that plagues many data center solutions. Water is becoming a siting and scaling variable that has to be addressed. A site may have land and power prospects, but if water sourcing, reuse, or discharge cannot be solved, the project will face higher costs, delays, and local opposition. Gradiant is positioning itself as the managed water layer for hyperscale AI, similar to how power providers, cooling vendors, and network suppliers each own critical infrastructure domains. The Pattern: Hybridization, Standardization, and Industrial Scale The announcements included here make it clear that cooling is seeing significant attention from technology vendors, and not just state-of-the-art new technologies such as direct-to-chip, but also traditional data center air cooling. T-Global and SiPearl are working on high-conductivity materials and two-phase modules for HPC chips. Castrol is providing fluids for direct-to-chip and immersion environments. These are technologies aimed at the heat source itself, where higher chip power and rack density are overwhelming conventional approaches. The reference design offerings from Johnson Controls acknowledges the importance

At Data Center Frontier, we rely on industry leaders not only to help us understand the most urgent challenges reshaping digital infrastructure, but also to illuminate the broader technological, operational, and market forces driving the industry’s evolution. And in the Second Quarter of 2026, those challenges increasingly revolve around a fundamental shift in emphasis: the industry is moving beyond discussing AI infrastructure in theory and into the far more demanding work of deploying, operating, and scaling it in production.  The era when hyperscale announcements and GPU roadmaps dominated the conversation is giving way to one defined by execution; where power availability, thermal management, construction schedules, supply chains, and operational discipline determine whether ambitious plans become functioning AI factories. That transition is exposing new realities. Rack densities continue to climb, liquid cooling is becoming mainstream, electrical architectures are evolving, and project timelines are compressing even as capital commitments reach unprecedented levels.  Success increasingly depends not on optimizing individual systems in isolation but on orchestrating tightly integrated environments where compute, power, cooling, networking, and facility operations function as a unified whole. At the same time, moving from pilot deployments to industrial-scale AI infrastructure introduces an entirely different class of challenges around reliability, maintainability, commissioning, and repeatable execution. For our Q2 Executive Roundtable, we brought together senior leaders whose expertise spans AI infrastructure design, mission-critical deployment, advanced thermal management, and engineering innovation to examine where the industry stands today, and what it will take to bridge the gap between AI ambition and AI deployment at scale. Drawing on perspectives from hyperscale execution, liquid cooling, and next-generation power and facility engineering, their insights explore the practical realities of building the AI factory at industrial scale.