Key Takeaways:
- Investment in AI infrastructure presents a compelling opportunity for private equity (“PE”) funds and is increasingly becoming a target of investment.
- Brookfield estimates that $7T will be spent on AI-related infrastructure over the next 10 years, extending beyond just data centers.
- Firms building out portfolios of interconnected companies powering AI must consider structural, regulatory, and financial issues to ensure success.
The infrastructure powering artificial intelligence (AI) is equally as important as the technology itself. What began as a rush to invest in the latest AI startups has now evolved into something more significant: investing in the development of the fully integrated infrastructure systems that enable AI to operate at scale. Leading PE firms have already been pouring billions into data centers alone, transforming them from a niche investment to a staple of their portfolios.
To put it in perspective, a report from Brookfield estimates that total spending on AI-related infrastructure could exceed $7 trillion within the next 10 years. This investment is broken down into the following categories:
- $2 trillion for developing new data center capacity
- $0.5 trillion for baseload power and electricity transmission infrastructure
- $4 trillion for GPU partnerships and chip design/manufacturing
- $0.5 trillion for dedicated fiber connectivity, cooling solutions, and semiconductor and robotics manufacturing
AI does not rely solely on data centers, creating significant opportunities for investment beyond just the land acquisition and facility construction. AI requires massive reliable power inputs, high-speed data transmission, and localized processing capabilities. This is prompting PE firms to expand into adjacent sectors that were previously outside their typical investment scope.
For example, investing in energy solutions is a critical component of AI infrastructure strategies. Similarly, there is a renewed interest in fiber networks and connectivity assets, as efficient data movement is essential for AI applications, especially those requiring real-time processing. Edge computing has also increased the need for processing power closer to the point of use, driving investment into smaller, distributed facilities and edge nodes that complement centralized data centers.
Because there are so many components making up the infrastructure behind AI, it opens the opportunity for a new platform strategy for PE firms. It allows firms to build a vertically integrated portfolio of companies that span power, connectivity, and compute, with multiple acquisitions across sectors and long-term development pipelines, all with the goal of delivering a unified service offering.
This integrated platform approach raises governance and structural considerations for sponsors as they consider how to allocate capital across various strategies, manage conflicts between funds, and structure investments. Co-investment vehicles, joint ventures, and continuation funds are viable options, but each presents its own challenges. An integrated platform also comes with its own level of risk as one issue in one layer of the platform can have a ripple effect throughout the platform. As a result, more sophisticated structuring of intercompany agreements, supply chain arrangements, and contingency planning is required.
There is also a great deal of regulatory consideration that come with these industries. Energy, telecommunications, and data-related assets are all subject to a complex web of regulations and compliance requirements and combining them in a single platform multiplies the regulatory burden. The capital intensity of integrated AI infrastructure is also significant, and the cash flow profiles of these different asset classes can vary widely. To blend all of these into one cohesive financing structure requires innovative thinking.
Demand for AI infrastructure is not only growing; it is becoming increasingly interconnected. To capture the immense value, sponsors must be able to assemble and operate integrated platforms that offer end-to-end solutions. This approach supports premium valuations and creates multiple exit pathways. It’s about moving beyond the data center to build comprehensive systems that will power the future.
