AI is fueling high demand for compute power, spurring companies to invest billions of dollars in infrastructure.
A research by McKinsey 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, bringing the total investment requirement to a staggering $7 trillion by 2030.
These massive investment numbers are fueled by several factors including mass adoption of gen AI, the integration of AI applications across industries, the competitive infrastructure race between hyperscalers and enterprises to build proprietary AI capacity, and Geopolitical priorities with governments investing heavily in AI infrastructure.
McKinsey’s analysis identifies five types of data center investors: builders (real estate developers, design firms, and construction companies) , energizers (energy providers, cooling/electrical equipment manufacturers, and telecom operators), Technology developers and designers (semiconductor firms and IT suppliers producing chips and computing hardware for data centers), Operators (hyperscalers and colocation providers) and AI architects (AI model developers). Out of the $5.2 trillion investment forecast for AI infrastructure, 15 percent ($0.8 trillion) will flow to builders for land, materials, and site development. Another 25 percent ($1.3 trillion) will be allocated to energizers for power generation and transmission, cooling, and electrical equipment. The largest share of investment, 60 percent ($3.1 trillion), will go to technology developers and designers, which produce chips and computing hardware for data centers.
Despite the enormous growth potential of AI, CEOs are hesitant to invest in compute power capacity at maximum levels because they have limited visibility into future demand.
Uncertainty about whether AI adoption will continue its rapid ascent and the fact that infrastructure projects have long lead times make it difficult for companies to make informed investment decisions.
To succeed in this rapidly evolving landscape, companies must strike a balance between rapid capital deployment and prudent financial management. To improve the odds that their data center investments will provide strong returns, companies can tackle projects in stages, assessing ROI at each step.
Ultimately, the ability to predict and anticipate compute power demand will determine who emerges as the winners in the AI-driven computing era. The winners of the AI-driven computing era will be the companies that anticipate compute power demand and invest accordingly.
