1、Technology,Media&Telecommunications PracticeThe cost of compute:A$7 trillion race to scale data centersAI 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.This

2、 article is a collaborative effort by Jesse Noffsinger,Mark Patel,and Pankaj Sachdeva,with Arjita Bhan,Haley Chang,and Maria Goodpaster,representing views from McKinseys Technology,Media&Telecommunications Practice.April 2025Amid the AI boom,compute power is emerging as one of this decades most crit

3、ical 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 powerand there is

4、an unquenchable need for more.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 powe

5、ring traditional IT applications are projected to require$1.5 trillion in capital expenditures(see sidebar“What about non-AI workloads?”).Overall,thats nearly$7 trillion in capital outlays needed by 2030a staggering number by any measure.1What about non-AI workloads?While AI workloads dominate the c

6、onversation,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 proces