Two Workloads, Two Methods: Why the Site Selection Framework Built for AI Training Will Fail AI Inference

Power First vs. Fiber First: How the Training-Inference Split Requires Two Parallel Site Evaluation Frameworks

The site selection methodology built for AI training is producing the wrong answer for AI inference.

And inference is about to become the dominant workload. Training is latency-insensitive. It tolerates 100ms between regions. It goes where the power is. Geography is optional.

Inference is the opposite.

Sub-50ms round-trip for consumer applications. Sub-10ms for real-time. It must be where the users are. Geography is mandatory.

Two workloads. Two completely different site selection criteria:

→ Training: power quantum first, land size, water, cost — fiber secondary, metro irrelevant

→ Inference: fiber diversity and IXP proximity first, latency radius to population centers, metro required — power secondary

McKinsey projects inference surpasses training as the dominant AI workload by 2030. Industry data puts the crossover at late 2026 to early 2027.

Operators still running one evaluation framework for both are building the wrong facilities in the wrong markets.

Today's issue of The PillarPoint Brief: how to run two site selection methodologies in parallel — and the workload classification question that must be answered before the first screen.

Is your current site evaluation process differentiated by workload type — or are you still running one methodology for all AI opportunities?

Review the full analysis: Two Workloads, Two Methods: Why the Site Selection Framework Built for AI Training Will Fail AI Inference