What 3 Data Centre Leaders Are Saying About Designing for AI at Scale

As AI workloads accelerate, data centre design is being pushed into uncharted territory.
To understand where the real pressure points are emerging, we asked three leaders working across design, engineering, and delivery the same five questions.
Here’s where they agree, where they don’t, and what it means for the future of data centre infrastructure.

Where Is Data Centre Design Under the Most Pressure from AI?

Byron Taylor
Schedule is always the biggest hurdle to overcome. Long lead equipment decisions require a sufficiently detailed basis of design early on, while power generation, permitting, and network security all introduce additional complexity.

John Sasser
The biggest challenge is getting power and cooling to high-density cabinets, potentially up to 600kW, in time to support rollout.

Scott Muller
The evolution in technology processing AI compute demands necessitates a flexible and scalable infrastructure design to meet changing technology requirements.

Key Takeaway

AI is not creating a single pressure point, it is exposing multiple simultaneous constraints across schedule, power, cooling, and infrastructure flexibility.

Where Does Design Fail to Match Real-World Operation?

Byron Taylor
Computational Fluid Dynamics modelling and required equipment spacing often push the limits of what is actually achievable in real-world environments.

John Sasser
Designs often assume steady-state operation and ideal load diversity, but real-world deployments are far more dynamic and uneven.

Scott Muller
The creation or integration of cross-discipline monitoring systems is critical to efficient operation. Too often, systems operate in silos, forcing operations teams to work across multiple platforms to monitor different systems.

Key Takeaway

There is a disconnect between modelling assumptions and operational reality, particularly around load variability, system integration, and environmental constraints.

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What Late-Stage Design Changes Cause the Most Disruption?

Byron Taylor
Changes to security boundaries are often one of the most disruptive late-stage design issues.

John Sasser
Increasing rack density or changing cooling strategies late in design creates ripple effects across power, space, and schedule.

Scott Muller
Increased power and cooling requirements can alter the internal footprint and prevent full utilisation of the allocated square footage.

Key Takeaway

Late-stage changes impact multiple systems at once, making early alignment on density, cooling, and security critical.

Which Early Design Decision Has the Biggest Downstream Impact?

Byron Taylor
Decisions around behind-the-meter power generation and procurement have the greatest long-term impact.

John Sasser
Early decisions on power architecture and cooling approach define cost, scalability, and speed of deployment.

Scott Muller
System selection that does not support or allow capacity upgrades or scalability to meet evolving requirements.

Key Takeaway

Early-stage decisions are increasingly irreversible in practice, locking in cost structure, flexibility, and delivery speed.

What Conversations Does the Industry Need to Be Having More Openly?

Byron Taylor
Power availability and water consumption need to be discussed more openly.

John Sasser
The industry needs better alignment on real AI workloads and the trade-offs between speed, efficiency, and flexibility.

Scott Muller
Long lead items and the need to design systems that can be modified or upgraded in the future to keep data centres aligned with current demands.

Key Takeaway

The industry is still not aligned on what “right” looks like—especially around power, sustainability, and AI-driven demand profiles.

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