AI Infrastructure Is Becoming a Credit Story

For the last several years, most of the AI conversation has centered on models, chips, software, and capability. Increasingly, the more consequential question is becoming: who is financing the physical infrastructure underneath AI, and who ultimately carries the risk?

A recent Brookings Papers on Economic Activity analysis by Columbia University professor Stijn Van Nieuwerburgh projects that U.S. AI investment in data center buildings, power systems, networking infrastructure, specialized chips, and related equipment could total $10.3 trillion from 2025 through 2032, averaging 3.63% of U.S. GDP per year. The paper argues that the buildout would be larger relative to the economy than historic U.S. canal, railroad, electrification, highway, and telecommunications investment booms.

That is an extraordinary amount of capital. But the more interesting development may be how the financing is changing.

From balance-sheet spending to a broader capital stack

Early AI infrastructure spending could largely be absorbed by the enormous operating cash flows and balance sheets of the hyperscalers. Morgan Stanley estimates roughly $2.9 trillion of global data-center-related capex through 2028. Its broader framework assumes about $1.4 trillion can be covered by hyperscaler cash flows, leaving an estimated $1.5 trillion to come from outside capital.

That means the structure is moving beyond:

Big Tech spends its own cash.

And toward a more familiar infrastructure-finance chain: banks lend, private-credit funds lend, special-purpose entities borrow, assets are securitized, developers assume completion risk, tenants sign long-term commitments, and investors buy the paper.

The technology may be revolutionary. The financing is still finance.

The underlying technology can be unprecedented while the financial mechanics remain familiar. Leverage still matters. Counterparty risk still matters. Completion guarantees still matter. Tenant concentration still matters. Power availability still matters. Construction overruns still matter. And someone always has to absorb the loss when assumptions fail.

Morgan Stanley’s estimate of the $1.5 trillion external financing gap illustrates how quickly the capital stack is broadening. Its framework assigns the largest share to private credit and asset-based finance, with additional funding from corporate debt, securitized credit, and a mix of sovereign, private-equity, venture, and other capital.

Risk is migrating, not disappearing

That migration is already visible. SoftBank recently launched more than $11 billion of high-yield bonds, with expected yields around 9% to 10%, in part to fund its OpenAI investment. The financing is notable not because one bond sale proves a systemic problem, but because it shows how AI investment is reaching farther into leveraged capital markets.

At the same time, Oracle-linked infrastructure financing has become a useful case study in how execution risk and credit risk can meet. Reuters reported that roughly $18 billion of loans tied to Project Jupiter, a New Mexico data-center development, were trading below par amid concerns about regulatory delays and Oracle’s growing debt exposure. Bloomberg separately reported that Oracle sent the project developer a force-majeure notice designed to protect Oracle against payment obligations if delays prevent the project from coming online as planned.

None of these developments individually proves that AI infrastructure financing is unstable. Together, however, they demonstrate something important:

The AI boom is no longer confined to technology-company balance sheets.

It is spreading into credit markets, project finance, private capital, structured finance, real estate, utilities, and construction. That changes the risk map.

For builders, lenders, and investors, the logo is not the credit analysis

A hyperscaler or major AI company may sit somewhere in the economic chain. That does not necessarily mean it is your direct obligor.

The underwriting questions become much more traditional:

• Who owns the project?
• Who borrowed the money?
• Who signed the lease?
• Who guarantees completion?
• Who absorbs cost overruns?
• What happens if the tenant delays occupancy?
• What happens if power delivery slips?
• What happens if compute economics change before the debt matures?
• What collateral actually supports the financing?

Those questions can get obscured when an enormous technology brand sits somewhere near the project. But a famous tenant is not the same thing as payment security.

AI data centers should increasingly be analyzed for what they are: large, capital-intensive infrastructure projects with technology-demand risk layered on top.

The opportunity is enormous

None of this means the buildout is irrational. Quite the opposite. AI may become one of the most important infrastructure investment cycles in modern history.

The opportunity spans power generation, transmission, substations, fiber, cooling, construction, semiconductors, networking, real estate, equipment finance, private credit, and project finance.

The mistake would be assuming that enormous opportunity eliminates financial risk. Usually it does the opposite. The larger the capital requirement becomes, the more important underwriting becomes.

AI may be new. Capital cycles are not.

We have seen versions of this story before. Transformational infrastructure attracts capital. Capital attracts leverage. Leverage enables faster construction. Faster construction creates excess capacity somewhere. Then markets discover which projects were economically durable and which were built on assumptions that did not survive contact with reality.

AI may ultimately justify every dollar invested and more. Or some parts of the capital stack may be significantly overbuilt before demand catches up.

Both can be true:

AI can transform the economy, and some AI infrastructure investments can still lose money.

That is the distinction I think executives, CFOs, lenders, builders, and investors should be watching.

The AI conversation is rapidly evolving from What can the technology do? to Who is financing what it takes to do it, and who owns the downside?

That may become one of the most important finance questions of the AI era.

Sources

• Brookings Papers on Economic Activity — Financing the AI buildout
• Morgan Stanley Research — Bridging the Data Center Financing Gap
• Morgan Stanley — Credit Markets’ Role in the AI Financing Gap
• Financial Times — SoftBank high-yield bond financing for OpenAI investment
• Reuters — Oracle-linked Project Jupiter debt under pressure
• Bloomberg — Oracle force-majeure notice on Project Jupiter