Is AI Infrastructure Broken? A Candid Conversation with Volumez

AI adoption is accelerating, but most enterprises are still stuck in the pilot phase. Cloud costs keep climbing, GPUs go underutilized, and data pipelines struggle to keep pace. If AI is the future, why is the infrastructure built to support it so often stuck in the past?

In this episode, recorded live in Silicon Valley during the IT Press Tour, I sit down with John Blumenthal, Chief Product Officer at Volumez, and Diane Gonzalez, Senior Director of Business Development and Product. Together, we unpack what is really holding AI back and explore how Data Infrastructure as a Service (DIaaS) could change the equation.

We explore:

• Why traditional AI infrastructure models are inefficient and unsustainable
• How DIaaS enables just-in-time, automated infrastructure tuned to each workload
• The role of GPU and data scientist efficiency in determining AI ROI
• How Volumez achieved industry-leading results in the MLCommons benchmark
• Why hybrid and multicloud strategies demand a fundamentally different infrastructure approach

John and Diane share firsthand insights from working at the intersection of data, cloud, and AI infrastructure. They argue that achieving meaningful return on AI investment requires more than hardware upgrades or clever provisioning. It means embracing automation, profiling cloud capabilities in real time, and architecting pipelines that adapt to the specific demands of each phase in AI and ML workflows.

Whether you're building AI platforms, running data science teams, or managing cloud infrastructure, this conversation offers a grounded look at how to make AI actually scalable.

Are you wasting your most valuable resources or are you ready to run AI workloads at full speed with none of the bloat?