Moving Compute at the Speed of Demand with Mike Maniscalco of QumulusAI

In this episode, Mike Maniscalco, CEO of QumulusAI, shares how his background as a computer scientist and software entrepreneur led him into AI infrastructure and ultimately to QumulusAI. Mike explains how the rapid advancement of AI, particularly reasoning models and agentic tools, made one thing increasingly clear: more capable AI requires more tokens, and more tokens require more compute infrastructure. That realization brought him back to the infrastructure layer powering the AI revolution.

Mike discusses QumulusAI’s differentiated approach to delivering GPU compute through a distributed, inference-first model built around “hyperspeed.” Rather than focusing exclusively on massive, multi-year hyperscale campuses, the company identifies smaller pockets of existing powered data center capacity that can be upgraded and brought online faster. This allows QumulusAI to respond to customers who need compute in months rather than waiting years for new infrastructure.

The conversation explores why inference represents such a significant opportunity as AI moves from training models to putting them into production. Mike also breaks down QumulusAI’s FACTS framework (flexibility, access, cost, trust, and speed) and explains how flexibility across locations, vendors, architectures, and supply chains can help accelerate deployment.

Looking ahead, Mike believes AI infrastructure demand is still in its early innings. As multimodal AI, agentic applications, robotics, autonomous vehicles, and other emerging use cases consume more compute, he sees distributed infrastructure becoming increasingly important, especially as latency and location matter more. QumulusAI’s ambition is to evolve into a true AI cloud capable of moving compute at the speed of demand.

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