AI is everywhere, yet scaling it remains a serious challenge for many enterprises. Organizations are eager to move beyond pilot projects, but they quickly run into obstacles—cloud costs spiral out of control, GPUs sit idle due to inefficiencies, and infrastructure bottlenecks slow down innovation. If AI is supposed to be the future, why is the underlying infrastructure still holding it back?

In this episode, I'm joined by John Blumenthal, Chief Product Officer at Volumez, and Diane Gonzalez, Senior Director of Business Development and Product, to unpack the hidden inefficiencies of AI infrastructure and explore whether Data Infrastructure as a Service (DIaaS) is the answer. They explain why traditional AI infrastructure models are failing and why manual optimization simply can't keep up with modern AI workloads.

John argues that the problem is beyond human management, stating, "No human being is going to be able to answer that question—to actually arrive at that optimization. So you have to turn it over to a machine." We explore why cloud spending is so difficult to control, how businesses are struggling to achieve a return on their AI investments, and what it will take to make AI infrastructure more cost-effective and scalable.

Diane shares real-world examples of how AI teams are losing valuable time and resources trying to work around infrastructure constraints. She explains how DIaaS dynamically composes infrastructure on demand, eliminating bottlenecks and ensuring businesses can run AI workloads at full speed without overspending.

With AI adoption accelerating, organizations are rethinking their approach to cloud infrastructure. But is DIaaS the missing piece that will finally make AI truly scalable? And are businesses ready to let automation take the lead in optimizing their cloud environments? Join the conversation and share your thoughts.