The AI heat trap: why data centers must rethink thermodynamics
The adoption of generative AI and large language models has introduced a new thermal reality
by https://www.techradar.com/uk/author/michael-poto · TechRadarOpinion By Michael Poto published 9 April 2026
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For decades, the data center industry has operated on a relatively predictable model of thermodynamics. Operators built a hall, filled it with servers, and circulated cold air through the floor or aisles.
The heat load remained predominantly stable, electrical loads increased gradually, and cooling systems could be sized with conservative, static margins.
Artificial intelligence (AI) has fundamentally changed that stability.
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Michael Poto
Product Manager, Global Chilled Water Systems, Vertiv.
The rapid adoption of generative AI and large language models (LLMs) has introduced a new thermal reality. Unlike some tasks, training an AI model experiences large fluctuations in computational workload rather than steady output.
Power draw can increase dynamically in seconds, often across mixed hardware estates with very different thermal characteristics.
This creates localized hot spots that traditional air cooling struggles to mitigate fast enough.
Without a coordinated cooling response, this thermal variability may affect the performance and lifespan of the very hardware it is meant to protect.
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