U.S. Data Center Construction Market Outlook Report 2026-2031 Featuring Key Investors - Apple, Applied Digital, AWS, CyrusOne, DataBank, Digital Realty, Equinix, Google, Meta, Microsoft, NTT, Vantage
View original at finance.yahoo.comU.S. Data Center Construction Market Outlook Report 2026-2031 Featuring Key Investors - Apple, Applied Digital, AWS, CyrusOne, DataBank, Digital Realty, Equinix, Google, Meta, Microsoft, NTT, Vantage Company Logo The U.S…
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The AI industry is set to increase the size of hyperscale data centers significantly, with capacity for AI workloads expected to almost triple over the next five to six years
60% confidenceNew data centers are being designed to support high-density AI-based workloads with the rising adoption of AI across several industries
60% confidencePartnerships among data center operators, cooling technology vendors, chip manufacturers, and energy innovators are becoming essential for developing sustainable AI infrastructure
60% confidenceLiquid cooling is becoming a significant trend that major colocation operators are implementing and is emerging as a key differentiator for colocation providers as AI and compute-intensive workloads continue to grow
60% confidenceThe U.S. Data Center Construction Market was valued at USD 83.97 Billion in 2025, and is projected to reach USD 154.49 Billion by 2031, rising at a CAGR of 10.70%
60% confidenceDemand for data centers in the New York-New Jersey market remains high, driving vacancies down to less than 6%
60% confidenceThe Southeastern U.S. is expected to be the largest market in 2031 in terms of power capacity, with a share of around 32% in 2031, followed by the Midwestern U.S.
60% confidenceTexas and Arizona contribute to over 90% of the data center investments in the Southwestern U.S. region
60% confidenceAs computing power demands and performance expectations rise, driven by AI, IoT, and ML advancements, the need for power usage will increase, resulting in higher temperatures within IT infrastructure
60% confidenceAI-based data centers utilize GPUs as they can perform massively parallel computations, train and run AI models dramatically faster, use power more efficiently for each AI task, support real-time AI inference, and enable high-density, scalable facilities
60% confidence