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Power, Pressure, and Precision: What It Takes to Keep AI Online 24/7

The rise of generative AI and large-scale model training has transformed data centers into high-intensity “AI factories,” where workloads are no longer predictable or gradual. Unlike traditional cloud environments, AI data centers generate highly volatile and fast-changing power loads. Research shows that large-scale AI workloads can trigger rapid, synchronized spikes and drops in electricity…

By Applied Digital · April 8, 2026, 3:34 PM UTCAi InfrastructureAi WorkloadsApplied DigitalArchitects of Acceleration Volume Ii
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Key takeaways

01

The rise of generative AI and large-scale model training has transformed data centers into high-intensity “AI factories,” where workloads are no longer predictable or gradual.

02

Unlike traditional cloud environments, AI data centers generate highly volatile and fast-changing power loads.

03

Research shows that large-scale AI workloads can trigger rapid, synchronized spikes and drops in electricity…

The rise of generative AI and large-scale model training has transformed data centers into high-intensity “AI factories,” where workloads are no longer predictable or gradual. Unlike traditional cloud environments, AI data centers generate highly volatile and fast-changing power loads. Research shows that large-scale AI workloads can trigger rapid, synchronized spikes and drops in electricity demand—sometimes shifting tens of megawatts in seconds—placing significant stress on electrical and cooling infrastructure. As enterprises race to deploy AI at scale, the stakes are clear: even seconds of downtime can disrupt massive computational processes and delay high-value outputs.

So how do operators keep these volatile, always-on AI environments stable, resilient, and online?

Welcome to Architects of Acceleration Volume II. In the latest episode, host Philbert Shih, Founder and Managing Director of Structure Research, is joined by Arron Combs and Wayne Watson of Applied Digital. Together, they explore the operational realities of running a 100-megawatt AI data center—unpacking how teams manage fluctuating GPU loads, maintain uptime, and respond to risks in real time.

What you’ll learn…

  • Why AI workloads are harder to manage than traditional data center demand: Understand how rapid, unpredictable GPU power swings force operators to rethink cooling, capacity, and response strategies.
  • How maintenance and redundancy evolve in always-on environments: Learn why zero-downtime expectations are raising the bar for preventive maintenance, backup systems, and operational discipline.
  • How operators stop failures before they happen: Explore how massive volumes of telemetry are transformed into clear, prioritized alerts—helping teams catch small issues before they become critical incidents.

Arron Combs is Director of Site Operations at Applied Digital, bringing over 20 years of experience managing critical infrastructure across data centers, energy, and industrial operations. He has led large-scale facility operations and construction programs, including maintaining 100% uptime in high-density data centers and overseeing major commissioning and capacity expansion projects. His expertise spans operations management, electrical and mechanical systems, emergency response, and team leadership in high-pressure, mission-critical environments.

Wayne Watson is VP of Data Center Operations at Applied Digital, with over 20 years of experience leading operations in nuclear and hyperscale data center environments. He specializes in commissioning, operational strategy, and scaling large-scale facilities, with a strong track record in maintaining uptime, managing multimillion-dollar budgets, and leading high-performing technical teams. Previously, he held senior leadership roles at Compass Datacenters and Flexential, where he drove process improvements, supported major data center builds, and oversaw operations across large multi-site portfolios.

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