By David Gordon
Publication Date: 2026-03-25 09:00:00
Sponsored Post For most of the past decade, enterprise virtualization was the kind of infrastructure that nobody argued about. It worked, it scaled, and the economics, while never exactly cheap, were at least predictable.
Then AI arrived in earnest, and the assumptions baked into those stacks started showing their age. The licensing disruption from Broadcom’s VMware acquisition made the headlines, but underneath this lay a deeper architectural problem. That was already building before any vendor changed a price list.
What does AI demand that legacy virtualization can’t deliver?
AI workloads such as inference engines, training pipelines, and the data movement between them need bare-metal-like performance, high-density compute, and low-latency interconnects. Traditional hypervisor architectures weren’t designed around those requirements. They were built for conventional enterprise workloads that were predictable, relatively modest, and tolerant of the overhead that virtualization introduces. At AI scale, that overhead stops being a rounding error and starts being a genuine constraint on what the system can do.
Management is another issue. Most enterprise VM estates have accumulated tools and processes over years, each one solving a specific problem in a specific environment. Trying to run AI workloads through that kind of fragmented stack means inconsistent provisioning and unpredictable performance. There’s no clean way to move workloads between on-premises clusters…
