For years, efficiency was measured one way. It worked until AI changed the equation. Traditional efficiency metrics don’t tell the full story. Liquid cooling is becoming the default for AI infrastructure, but the way we measure efficiency requires tracking data center efficiency metrics beyond Power Usage Effectiveness (PUE), which only captures energy overhead. AI demands a different question: How much compute does that energy actually produce?
Goldman Sachs estimates that 76 percent AI servers deployed by the end of 2026 will be liquid-cooled. As this shift from air cooling occurs, operators will turn to power compute effectiveness (PCE) and water usage effectiveness (WUE) as important AI data center KPIs in addition to PUE. This is necessary because PUE only compares total facility power to IT power, without accounting for compute outcomes.
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What is replacing PUE in data centers?
Even when data centers operate at PUE levels of 1.4 to 1.8, systems can be inefficient if they limit rack density, increase airflow, and constrain compute performance. PUE tracks energy overhead, but that isn’t enough for power-intensive AI workloads, which require metrics like PCE to determine data center energy efficiency in converting power to AI tokens.
