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Real assets, AI and the case for a total portfolio approach

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The evolution of real assets investment is often expressed through the lens of megatrends. Societal needs are evolving, reconfiguring the physical and operational systems required to support future growth. In the real assets arena, AI, digitization and the energy transition are reshaping demand for power, connectivity, logistics capacity and digital infrastructure, while reinforcing the need for long-term capital to finance, develop and operate underlying assets.

Rapid growth in data usage and artificial intelligence has increased demand for data centers, fiber networks and connectivity assets, while the global energy transition is arguably creating new investment opportunities across renewable generation and grid infrastructure. Increasingly, capital-constrained governments are relying on private capital to help finance these developments, expanding the role that institutional investors can play in delivering essential infrastructure.

In the AI boom, the role of real estate includes securing and productizing scarce data center sites – land, entitlements, and buildings in locations with power and fiber optic access – to enable computing capacity to be delivered at scale. Structural AI demand is converted into durable cashflows through leasing structures including co-location and hyperscale built-to-suit properties.

Through real assets, access to the future economy becomes broader and more immediate. Allocations to these areas are attracting investors not merely as portfolio ballast, but as a means of accessing structural growth through assets with tangible utility and potentially durable cashflows. In this respect, real assets are not simply “housing” the AI boom; they are also helping make it investable, particularly for investors seeking resilience alongside thematic exposure.

There is a “picks and shovels” logic to this approach, but these allocations are more sophisticated than the phrase implies. Accessing AI through real assets is not simply about avoiding technology risk, but about gaining exposure to the enabling assets and systems on which AI deployment depends. Opportunities tend to be underpinned by business models that are more asset-backed, more cashflow-oriented and, critically for investors, less reliant on a narrow set of corporate winners in public equities.



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