The cost of compute: A $7 trillion race to scale data centers - McKinsey

McKinsey Quarterly The cost of compute: A $7 trillion race to scale data centers 

 April 28, 2025 | Article AI is fueling high demand for compute power, spurring companies to invest billions of dollars in infrastructure. But with future demand uncertain, investors will need to make calculated decisions. 
 

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 Amid the AI boom, compute power is emerging as one of this decade’s most critical resources. In data centers across the globe, millions of servers run 24/7 to process the foundation models and machine learning applications that underpin AI. The hardware, processors, memory, storage, and energy needed to operate these data centers are collectively known as compute power—and there is an unquenchable need for more.

 About the authors 

 This article is a collaborative effort by Jesse Noffsinger , Mark Patel , and Pankaj Sachdeva , with Arjita Bhan, Haley Chang, and Maria Goodpaster , representing views from McKinsey’s Technology, Media & Telecommunications Practice.

 
 Our research shows that by 2030, data centers are projected to require $6.7 trillion worldwide to keep pace with the demand for compute power. Data centers equipped to handle AI processing loads are projected to require $5.2 trillion in capital expenditures, while those powering traditional IT applications are projected to require $1.5 trillion in capital expenditures (see sidebar “What about non-AI workloads?”). Overall, that’s nearly $7 trillion in capital outlays needed by 2030—a staggering number by any measure. 1 McKinsey Data Center Demand Model, McKinsey Data Center Capex TAM Model, and expert interviews. 

 What about non-AI workloads? 

 While AI workloads dominate the conversation, non-AI processing loads remain a significant portion of data center activity. These include traditional enterprise IT tasks such as web hosting, enterprise resource planning systems, email, and file storage. Non-AI loads are less compute-intensive and can operate efficiently on central processing units rather than the specialized graphics processing units or AI accelerators that AI workloads require. They also tend to have more predictable usage patterns and lower power densities, which allow for less demanding cooling and energy requirements. As a result, data centers focused on non-AI processing typically have different infrastructure needs, capital intensity, and operational considerations compared with those optimized for AI.

 
 To meet this demand, companies across the compute power value chain must strike a balance between deploying capital quickly and doing so prudently. To improve the odds that their data center investments will provide strong returns, companies can tackle projects in stages, assessing ROI at each step. Still, a lack of clarity about future demand makes precise investment calculations difficult.

 
 The compute power value chain is complex—from the real estate developers that build data centers to the utilities that power them, to the semiconductor 

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