The Future of Compute Power Investment in AI
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.
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. Overall, that’s nearly $7 trillion in capital outlays needed by 2030—a staggering number by any measure.
Where is the investment going?
To qualify our $5.2 trillion investment forecast for AI infrastructure, it’s important to note that our analysis likely undercounts the total capital investment needed, as our estimate quantifies capital investment for only three out of five compute power investor archetypes—builders, energizers, and technology developers and designers—that directly finance the infrastructure and foundational technologies necessary for AI growth.
Critical considerations for AI infrastructure growth
As companies plan their AI infrastructure investments, they will have to navigate a wide range of potential outcomes. In a constrained-demand scenario, AI-related data center capacity could require $3.7 trillion in capital expenditures—limited by supply chain constraints, technological disruptions, and geopolitical uncertainty. These barriers are mitigated, however, in an accelerated-demand scenario, leading to investments as high as $7.9 trillion.
The race for competitive advantage
The winners of the AI-driven computing era will be the companies that anticipate compute power demand and invest accordingly. Companies across the compute power value chain that proactively secure critical resources—land, materials, energy capacity, and computing power—could gain a significant competitive edge.
FAQ
Q: What are the key challenges in predicting compute power demand for AI?
A: The key challenges include uncertainties in AI use cases and rapid innovations in AI technologies that could impact efficiency gains.
Q: How can companies in the compute power value chain invest prudently?
A: Companies can assess ROI at each investment step, tackle projects in stages, and stay informed about market trends.
Conclusion
Investing in compute power for AI is a critical aspect of driving innovation and staying competitive in the rapidly evolving technological landscape. Companies must carefully consider their investment strategies, assess future demand projections, and prioritize efficiency in order to navigate the complexities of the compute power value chain successfully.
