A data center is a physical facility that organizations use to house their critical computer applications and data. Several types of data centers are in operation including enterprise data centers that a single company houses on corporate campus; managed services data centers run by third party providers; colocation data centers where a company rents space within another’s data center; and cloud data centers where off-premises data and applications are hosted by a cloud services provider such as Amazon Web Services (AWS), Microsoft (Azure), or IBM Cloud or others.
The growth of AI is driving the rapid demand for data centers. According to IBM, AI is technology that enables computers and machines to simulate human learning, comprehension, problem solving, decision making, creativity and autonomy, and applications and devices equipped with AI can see and identify objects. Computers are being programmed to understand and respond to human language, learn from new information and experiences, make detailed recommendations to users and experts, and act independently, replacing the need for human intelligence or intervention. Generative AI is a type of artificial intelligence designed to generate content without human intervention, including text, images, and even music. This technology uses complex algorithms and machine learning models to memorize patterns and rules from existing data that generates new content similar in style and structure.
Predictions of the macroeconomic impact of AI are all over the place but the technology is likely to lead to greater productivity. For example, Goldman Sachs estimates that the use of Gen AI in finance is expected to increase global gross domestic product (GDP) by 7% or nearly $7 trillion, and it should boost productivity growth by 1.5%, and AI will start having a measurable impact on US GDP in 2027 and begin affecting growth in other economies around the world in the years that follow. The foundation of the Goldman Sachs forecast is the finding that AI could automate around 25% of labor tasks in advanced economies and 10-20% of work in emerging economies. PwC predicts, in the near-term, the biggest potential economic uplift from AI is likely to come from improved productivity which includes automation of routine tasks, augmenting employees’ capabilities and freeing them up to focus on more stimulating and higher value adding work. PwC further predicts capital-intensive sectors such as manufacturing and transport are likely to see the largest productivity gains from AI, given that many of their operational processes are highly susceptible to automation, and the GDP uplift from product enhancements and subsequent shifts in consumer demand, behavior and consumption emanating from AI will overtake the productivity gains, potentially delivering more than $9 trillion of additional GDP in 2030.
According to JLL, the growing demand of AI is driving up demand for data center storage capacity which is expected to grow from 10.1 zettabytes (ZB) in 2023 to 21.0 ZB in 2027, for a five-year compound annual growth rate of 18.5%. Not only will this increased storage generate a need for more data centers, but generative AI’s greater energy requirements – ranging from 300 to 500+ megawatts – will also require more energy efficient designs and locations. The need for more power will require data center operators to increase efficiency and work with local governments to find sustainable energy sources to support data center needs.
The reality is that AI is coming but its growth and expansion and economic productivity benefits are dependent on the development of more data center projects across the United States.
