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Wall Street's AI bet: Own the infrastructure, collect the rent

Wall Street's AI bet: Own the infrastructure, collect the rent

Wall Street's leading alternative asset managers are placing their bets on the AI boom by investing in the infrastructure that drives it, securing long-term earnings through leases. Concerns over potential defaults from debt-ridden neocloud companies and the risk of AI investments not meeting expectations have spurred these managers to focus on high-quality, long-term tenants.

Blue Owl Co-CEO, Marc Lipschultz, uses his company's physical infrastructure, leasing it out for 15-20 years with annual rent escalators, providing investors with a predictable and growing income stream. The company emphasizes limiting risks, including those associated with short-term leases and token pricing.

Data centers are a crucial component of the AI boom, housing the servers and computing equipment required to train and run AI models. As major technology firms strive to expand computing capacity, demand for such facilities has skyrocketed, presenting alternative asset managers with an unprecedented opportunity. Blue Owl currently operates or is constructing over 130 data centers, with a combined capacity of nearly 14 gigawatts, and has pipeline capacity of another 21 gigawatts.

The company does not consider AI investment a separate bet, instead taking on the credit risk of major players like Microsoft, Amazon, and Meta.

Ares Management's data center development strategy targets metropolitan areas such as Tokyo, Osaka, London, and Sao Paulo, pre-letting projects to investment-grade hyperscalers under 12- to 15-year leases with no termination rights or escalators. Blackstone has also emphasized its focus on physical infrastructure and long-term contracted assets, with the majority of its data center portfolio having initial lease terms of 15 to 20 years.

The company's counterparts are rated investment grade, and the average market capitalization stands at $2.5 trillion.

Written by urgent.news from CNA - Business's reporting — not their text. Machine-written — may contain errors; check the original before relying on it.

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