The bottom line: Data centers cannot match exponential AI demand at the same pace due to constraints in physical buildout, power supply, and regulatory approvals.
Global demand for compute capacity for AI applications is growing rapidly, yet data centers are increasingly hitting physical and regulatory bottlenecks in expansion. A study by NTT DATA reveals the infrastructure gap between aspiration and reality.
Data centers worldwide are planning massive capacity expansions to meet demand from language models, training processes, and AI inference. The required hardware and energy resources are growing faster than new facilities can actually be built.
For CTOs and IT infrastructure leaders, this creates a fundamental dilemma: planned data center investments must be synchronized with both hardware supply chains (GPUs, processors, memory) and available power infrastructure. Delays in any of these areas jeopardize entire cloud and AI deployments.
Regulatory hurdles compound the problem. Approval processes for major data center projects stretch over years, while the AI market sets new standards in months. In many regions across Europe and North America, authorities are still working toward standards for energy efficiency, data protection, and network prioritization.
The study “Can Data Centers Keep Pace with AI? A Global Data Center Outlook” from NTT DATA systematically examines this conflict and shows that expansion alone without strategic planning can lead to operational costs, availability gaps, and regulatory penalties.
Source: itwelt.at · Published 2 July 2026
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