Artificial intelligence is driving a fundamental shift in global data center infrastructure. AI workloads require significantly higher compute density, specialized hardware, and advanced cooling systems compared to traditional enterprise applications.
McKinsey estimates that global data center capacity demand could grow between 19% and 22% annually through 2030, largely driven by AI adoption [4].
Understanding AI Workloads
AI workloads typically fall into two categories: training and inference. Training large AI models involves continuous processing across clusters of high‑performance processors, while inference workloads support real‑time application usage. Both place sustained demands on power availability, cooling capacity, and infrastructure resilience.
The Evolution of Cooling Systems
The widespread use of GPU‑accelerated servers has increased per‑rack power density, challenging conventional air‑cooled data center designs. According to the Uptime Institute, many operators are now adopting liquid‑cooling or hybrid cooling systems to support higher thermal loads and improve energy efficiency [5].
A New Category of Data Centers
Goldman Sachs notes that a new category of AI‑optimized data centers is emerging globally, purpose‑built to support high‑performance computing workloads. These facilities represent a structural evolution in how digital infrastructure is designed and deployed [6].
References
- 4 McKinsey & Company – The Future of Data Centers in the AI Era, 2024
- 5 Uptime Institute – Data Center Cooling and Power Trends, 2024
- 6 Goldman Sachs – Data Center Investment Outlook, 2025