Key Points

• The rapid growth of AI and electrification has accelerated the demand for reliable power and grid modernization. At the same time, recent geopolitical tensions in the Middle East have highlighted the importance of supply chain resilience and energy security.
• These dynamics underscore the growing need for critical energy infrastructure, as well as for the discipline and experience required to navigate constrained markets.
• Regulatory and technological constraints, not just demand, are increasingly determining where infrastructure assets can be built and scaled.

A Shifting Landscape Fueled by Strong Demand

The infrastructure investing landscape is shifting, shaped by a combination of tailwinds and headwinds. One tailwind is strong demand for power from data infrastructure—including data centers, telecom towers and fiber networks. As a result, data-related assets have become a core focus of infrastructure investing.

AI has been a significant catalyst for the growth of data infrastructure, alongside other technological developments such as cloud computing and increased mobile usage. While AI is increasing the scale and intensity of activity across data infrastructure, it is also intensifying existing power and location limitations.

By 2030, global data center electricity demand is expected to reach nearly 1,000 terawatt-hours (TWh) under base-case scenarios, reflecting the rapid expansion of AI-driven workloads (see below). This also highlights the need to debottleneck the grid through continued investment in power generation, grid capacity and interconnection.

This electricity demand is driven primarily by AI workloads that fall into two broad categories (see below):

1. Model training: Model training is the process of teaching a machine learning model to recognize patterns and identify relationships by exposing it to large datasets.¹ This requires significant computing power and must be located in areas with sufficient energy availability.
2. Real-time inference: Inference is the process in which a trained AI model generates new outputs by reasoning and making predictions based on new data.² It requires low latency, meaning infrastructure must be located close to end users.

These workload types help to highlight a broader dynamic: while strong demand is driving the need for new capacity, it does not necessarily determine where or how quickly that capacity can be delivered. Outcomes may increasingly depend on distinct power, location and connectivity requirements, underscoring the idea that constraints—not just demand—are becoming a defining factor in the development of data infrastructure.

Constraints and Obstacles

Data infrastructure operates as an integrated system, with performance primarily depending on access to power, location and network connectivity. Constraints in any one of those areas can limit the ability to deliver capacity, even when demand is strong.

AI workloads help illustrate this dynamic. Some of the power needed to drive AI can be located far from end users in large-scale campuses with abundant power. However, much of the demand remains tied to smaller facilities near end users. This creates a potential constraint: the ability to deliver power in the right place, at the right time. The workload requirements also serve as examples of headwinds for the growth and development of data infrastructure.

Potential constraints are not limited to AI. The ongoing expansion of mobile broadband coverage requires infrastructure to be able to handle increased data usage. Even with this demand, land permitting and interconnection constraints can lengthen infrastructure development timelines, while regulatory processes and labor shortages can delay projects and disrupt schedules.

Interestingly, obstacles vary across regions, reflecting their different stages of digital maturity (see below). In some markets, the priority is to continue to expand basic coverage. In others, rapid adoption is driving network densification and increasing demand for fiber and interconnection. More developed and highly penetrated markets are increasingly constrained by limits on power, land and network capacity.

Still, across all environments, we see a consistent underlying pattern: The ability to deliver infrastructure is shaped less by demand and more by the ability to overcome or remove structural constraints that can limit where infrastructure can be delivered, developed and expanded over time.

The Impact of Constraints

With the speed of AI advancement, infrastructure constraints have had several impacts across the value chain.³ For example, hyperscalers are prioritizing industrial equipment suppliers that can offer reliability, products that can meet specific technical and operational requirements, and punctual delivery. For their part, suppliers are shifting their operating models to focus on execution readiness and the ability to co-engineer products. Data center customers are favoring more modular, integrated equipment for power, cooling and control systems.

Key stakeholders across the AI value chain are becoming more selective about their inputs and long-term partners. In this environment, we believe that the investors that have the necessary capital, sourcing advantages and operating expertise may be better positioned to navigate power, connectivity and location constraints.

Bottom Line

In our view, data infrastructure offers a significant opportunity for investors, but it will take skill and expertise to access these opportunities. The next phase of data infrastructure investing will be defined not by who builds the most, but by who can consistently identify, secure and execute on the most constrained opportunities.

For investors, that will require access to scarce resources, such as power, land and interconnection, combined with business models that may offer revenue visibility and the ability to expand over time. And investors should seek partners who have global sourcing and origination channels across the infrastructure value chain, access to capital and the expertise to execute across a range of market environments.

Read More in our Alts Quarterly Q3 2026.

END NOTES

1. Kyle Aubrey, “What’s the Difference Between Deep Learning Training and Inference?” NVIDIA blog, July 29, 2016. Updated October 2025.

2. Aubrey, “What’s the Difference Between Deep Learning Training and Inference?”

3. Maria Goodpaster et al., “The $7 Trillion Data Center Buildout: How Industrials Can Capture Their Share,” McKinsey & Company, March 27, 2026.

A WORD ABOUT RISK

As an asset class, private credit comprises a large variety of different debt instruments. While each has its own risk and return profile, private credit assets generally have increased risk of default, due to their typical opportunistic focus on companies with limited funding options, in comparison with their public equivalents. Because private credit usually involves lending to below-investment-grade or non-rated issuers, yield on private credit assets is increased in return for taking on increased risk.

Investments in real estate-related instruments may be affected by economic, legal or environmental factors that affect property values, rents or occupancies of real estate.

Infrastructure companies may be subject to a variety of factors that may adversely affect their business, including high interest costs, high leverage, regulation costs, economic slowdowns, surplus capacity, increased competition, lack of fuel availability and energy conservation policies.

Alternative investments often are speculative and include a high degree of risk. Investors could lose all or a substantial amount of their investment. High-yield bonds are subject to interest-rate risk. When interest rates rise, bond prices fall; generally, the longer a bond’s maturity, the more sensitive it is to this risk. Yields are subject to change with economic conditions. Yield is only one factor that should be considered when making an investment decision.

Investment opportunities related to artificial intelligence and emerging technologies involve significant risks including rapid technological change, regulatory uncertainty, market speculation, and the possibility that anticipated technological advances may not materialize as expected. AI-focused investments may be highly volatile and speculative in nature. Forecasts regarding AI adoption, data center demand, electricity consumption, and related infrastructure development are inherently uncertain and may not develop as anticipated.

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Annualized Net Return is the geometric mean of the returns with respect to one year. It represents periodic returns rescaled to a period of one year.

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