2026 Event Schedule

AI facilities are no longer scaling like conventional data centers. As rack densities move from tens of kilowatts toward hundreds, operators are managing a new class of infrastructure challenge: power, thermal, controls, white space, and services now behave as one interdependent system. In this session, Scott Armul will present a practical operator framework for planning and scaling AI infrastructure as a physical AI engine, where the facility itself becomes part of the compute equation.  

The session will address four realities shaping AI deployments today: extreme densification, compressed time-to-capacity (time to token), rapid campus-scale expansion, and the operational risk created by disparate systems. Scott will outline how operators can reduce these risks by shifting from component-by-component decisions to a converged infrastructure approach that integrates the power train, thermal chain, and controls/services layer as a coordinated system.  

Attendees will also see how repeatable AI building blocks—such as 12.5 MW units scaled into larger campus architectures—can improve deployment predictability, reduce onsite complexity, and preserve flexibility across future compute generations. The emphasis is on operational outcomes: faster deployment, better utilization, and lower integration friction at scale.  

Attendees will leave with three takeaways:     

1. A clear framework for treating AI facilities as integrated physical systems, not isolated infrastructure domains.           

2. A practical scaling model for using repeatable building blocks to accelerate deployment while maintaining flexibility.

3. An operator-focused approach to converged power, thermal, and controls integration that improves reliability, efficiency, and time-to-capacity.