
AI infrastructure spending is forecast to push global data center capex beyond $3 trillion by 2030, driven by hyperscalers and AI-specialized clouds.
Global data center capital spending is projected to exceed $3 trillion by 2030, with AI infrastructure driving the majority of this investment through hyperscalers, sovereign AI programs, and specialized cloud providers. AI accelerators are expected to account for about one-third of this spending, but the full cost of AI infrastructure also includes servers, specialized networking, and storage systems. Power availability is becoming the biggest constraint on expansion, with modern data centers requiring hundreds of megawatts and some campuses being designed at gigawatt scale, making grid connection timelines and electricity sourcing critical factors in project planning. The four largest US cloud providers are expected to account for about half of global data center capex, and their purchasing power and use of custom chips could advantage large cloud providers over enterprises considering their own infrastructure.

OpenAI is adding an energy-technology role as it expands flexible-load, generation and storage strategies across its growing AI data center portfolio.

The 760 MW Eddystone coal plant will remain online longer as PJM faces rising data center load, generator retirements, and delayed power resources.

AI workloads push data center rack densities beyond the limits of air cooling. Three liquid cooling approaches offer different trade-offs and benefits.
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