Data Centers: Financing the AI Buildout

Stijn Van Nieuwerburgh | Episode 161

For the latest episode of Markus’ Academy Stijn Van Nieuwerburgh presented his recent paper: Financing the AI Buildout. Van Nieuwerburgh is the Earle W. Kazis and Benjamin Schore Professor of Real Estate and Professor of Finance at Columbia University's Graduate School of Business.

Watch the full talk below. A summary in three bullets:

• The new wave of AI development is driving a new wave of physical capital formation that is both unusual in scale and in composition

• The buildout is changing who owns and finances AI infrastructure. Hyperscalers are moving away from fully self-funding data centers and are increasingly relying on landlords, debt, SPVs, private credit, and securitized structures

• This new architecture differs from the old model: leverage is rising, obligations are moving off balance sheet, and risk may be ending up with investors who are harder to identify and monitor

Highlights

[04:32] Why AI changed the Economics of Data Centers

• Modern data centers are much more than a warehouse with servers in it. They require an order of magnitude more power, backup generators, bespoke liquid cooling, fiber-optic cables…

• A frontier data center with 200 MW power capacity costs $8.2bn. The planned U.S. data center capacity of 200 GW implies $8.2tn in CAPEX over roughly a decade

• AI-related investment today essentially accounts for all U.S. GDP growth. The planned investment exceeds historical infrastructure booms in railroads, electrification, highways, and telecom fiber

• Half of the investment will be for computing equipment, another third for data facility infrastructure, and the rest for new power capacity. In data centers the tenant owns the IT equipment, while the landlord provides the building and energy infrastructure

• In the past data centers were owned by publicly listed real estate investment trusts, and each data center would house many tenants. Hyperscalers (e.g. Meta, Google), with their complex hardware needs, have transformed the market into a single-tenant market with large bespoke AI campuses

[22:25] The changing ownership and financing of the AI buildout

• Hyperscalers historically self-financed and owned data centers, but escalating AI CAPEX and limited free cash flow increasingly push them toward leasing real estate and, increasingly, IT hardware

• Data center leases are like corporate bonds. Landlords prioritize having creditworthy tenants, so hyperscalers are a natural fit. AI model firms (e.g. OpenAI) are startups, and so they rent hyperscalers’ capacity via compute contracts

• 60% of the financing will be equity and 40% debt. Most of the hyperscaler equity is for IT needs. Most of the debt is for data centers, and is highly leveraged at ~70%

• In the next 4 years Morgan Stanley expects 1.15tn in private debt, bonds and securitizations. For a sense of scale, in 2007 there were 2tn in subprime mortgage securitizations

• Corporate bond issuance by hyperscalers has surged, but the dominant incremental funding channel for new data centers is long-maturity, highly levered structured finance rather than traditional on-balance-sheet debt

• Securitizations come in two forms: (1) Commercial mortgage-backed securities borrow by collateralizing the buildings (which are in turn backed by the lease payments); they have very little diversification, often with a single tenant and a single borrower. (2) Asset-backed securities borrow against collateral like GPUs

[38:20] The New Finance Architecture of the AI Buildout Transfers Risk and Increases Opacity

• A new structure is emerging, illustrated by the Meta–Blue Owl Louisiana campus. Originally owned entirely by Meta, it is the largest facility to date (2 GW)

• Meta then sold an 80% equity stake to Blue Owl for $2.5bn, and as a joint venture SPV they issued $27.3bn in debt. With ~90% leverage, it is the largest investment grade bond issuance in U.S. history.

• This long-maturity amortizing bond is bankruptcy remote from Meta’s balance sheet. Back of the envelope: Meta would have paid 120bps less if it had financed it with unsecured corporate debt on its own balance sheet. The bonds are trading above par and for technical reasons it has much lighter reporting requirements to the SEC

• Instead of a 20-year lease, Meta signed five 4-year leases. Meta has the right not to renew them, but if this happens they have to pay the landlord a minimum residual value. Even if one of the two outcomes will happen with certainty, accounting rules allow Meta to record neither the future leases nor the residual guarantee as a liability

• With the lower reported liabilities, Hyperscalers preserve software-like equity multiples by shifting physical capital and leverage onto SPVs. Hyperscalers do not want to trade at the multiples of infrastructure companies

[50:26] What could go wrong?

• The bullish case is that vacancy rates are at all-time-lows, rent growth is at-all-time highs, capital is being deployed by the companies with the strongest balance sheets on earth, while there is no speculative development in the sense that nothing is being built without knowing who will occupy it

• However hyperscalers face rising costs of capital and increasing leverage, while revenue growth is uncertain and there is a lot of concentration risk. Credit default swaps on hyperscalers have climbed in recent months

• The second risk is technological disruption: quantum computing, much more efficient chips, or inference shifting to phones could rapidly depreciate data center collateral

• We know little about who is bearing risks. If they shift to pension funds and insurers, households will be on the line, while wealthier households will bear the costs if the risks shift to private credit

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* Hosted by Markus Brunnermeier. Summary produced by Pablo Balsinde (PhD Student, Stockholm School of Economics).