How AI’s Borrowing Boom Is Reshaping Credit Markets

These company-level figures depend on the cutoff date and on what is counted as AI-related financing. Bond issuance, leases, private-credit arrangements, joint ventures, and project-finance vehicles do not always appear in comparable datasets, so the figures should be treated as snapshots rather than a definitive ranking.

Why cash-rich technology companies are borrowing

The shift is notable because these companies historically relied heavily on internal cash flow. AI infrastructure requires several expensive investments at once: advanced processors, networking equipment, data-center buildings, land, cooling systems, and electricity supply. The spending arrives before the full revenue from AI services is certain.

Reuters reported that combined technology-company spending was expected to exceed $730 billion in 2026, up from an earlier estimate of about $700 billion. Other estimates in the supplied reporting put hyperscaler capital expenditure even higher, illustrating the uncertainty around the final scale of the buildout.

Debt allows companies to start construction and secure capacity before future AI revenue is fully realized. Leases, equity, private credit, infrastructure funds, and joint ventures broaden that financing pool. The trade-off is that more of the execution risk moves from corporate cash balances to bondholders, lenders, and project investors.

What has changed in credit markets

The immediate effect is a large increase in long-duration investment-grade supply. As more technology companies issue bonds, investors become more selective about how much paper they will absorb and at what price. Reuters reported that major issuers were borrowing at steadily higher yields as demand cooled.

That matters beyond the hyperscalers. Corporate borrowers compete for many of the same duration-sensitive buyers, so a flood of new technology debt can lead to:

• higher required yields;
• wider credit spreads;
• larger new-issue concessions;
• greater refinancing costs; and
• more use of non-dollar, private, or off-balance-sheet financing.

The Treasury-market effect is more indirect. Heavy corporate issuance can increase competition for long-duration capital and contribute to pressure on long-term yields, but Treasury yields remain primarily determined by macroeconomic conditions, inflation expectations, monetary policy, and fiscal supply. AI borrowing is therefore an additional source of duration demand—not a standalone explanation for Treasury-market moves.

The Bank for International Settlements has also noted that hyperscaler bond issuance rose sharply, that much of the borrowing was long term, and that credit-default-swap spreads increased particularly for lower-rated issuers as investors questioned the eventual returns from the projects.

NVIDIA is trying to create a second financing channel

NVIDIA’s August partnerships with Apollo, BlackRock, Blackstone, Brookfield, Goldman Sachs, and KKR are designed to establish independent compute-financing platforms capable of mobilizing more than $500 billion of third-party capital over time.

The proposed structure is different from simply having NVIDIA or a hyperscaler issue another corporate bond. It is intended to let outside investors finance data centers, power infrastructure, AI factories, and NVIDIA hardware through dedicated pools of capital. The company describes the approach as making compute and full-stack AI infrastructure an investable asset class.

If it works, the model could reduce the amount of infrastructure that must sit directly on hyperscaler balance sheets. It could also connect AI projects with private equity, private credit, infrastructure funds, and project-finance investors. But moving assets away from a parent company does not eliminate risk; it changes who owns the assets, who lends against them, and who absorbs losses if utilization or revenue falls short.

The risks investors are watching

1. Long-term rates can make the buildout more expensive

Higher long-term Treasury yields raise the all-in cost of corporate bonds, leases, and project debt. They also reduce the present value of distant AI cash flows. A business model that looks attractive when financing is cheap can become less compelling when projects must support higher interest costs.

2. AI revenue may arrive after the debt is committed

Infrastructure financing is often arranged years before a facility reaches full utilization. That creates a timing risk: debt service begins on a fixed schedule, while demand for computing capacity and revenue from AI products may develop more slowly.

The central question is not whether hyperscalers can still borrow. Many have substantial financial capacity. The question is whether aggregate borrowing grows faster than durable AI demand and cash generation.

3. Oracle shows how credit concerns can become company-specific

Oracle has become a closely watched case because it is financing an aggressive AI expansion while carrying more sensitivity to leverage and refinancing conditions than the largest cash-rich peers. Reports have described a significant widening in Oracle’s credit-default-swap spreads, although the exact levels vary by source and date.

Wider CDS spreads do not mean that default is imminent. They indicate that the cost of insuring the company’s debt has risen and that investors are demanding more compensation for perceived credit risk.

4. GPUs create an asset-liability mismatch

AI hardware can lose economic value faster than the bonds or infrastructure loans used to finance it mature. If newer processors make existing GPUs less competitive, or if utilization drops, collateral values could decline while the debt remains outstanding.

This is a different risk from ordinary real-estate financing. A data-center building may remain useful for years, but the computing equipment inside it can face rapid technological obsolescence.

5. Projects may depend on a small number of tenants

Data-center and project-finance structures can be exposed to a limited group of hyperscale tenants. A spending slowdown, contract renegotiation, or weaker cloud demand from one major customer could affect several projects at once, especially where facilities were built for specialized AI workloads.

6. Credit-market capacity may become the constraint

The largest technology companies may have room to add debt without immediately losing investment-grade status. But the bond market’s ability to absorb that debt at favorable prices is not unlimited. Goldman Sachs research cited in the supplied reporting has pointed to a potential gap between what hyperscalers could borrow and what the dollar investment-grade market can comfortably absorb.

That helps explain the expansion of alternative channels: euro and other foreign-currency bonds, private investment-grade credit, infrastructure funds, leases, and joint-venture project bonds. These channels diversify funding, but they can also make the true level of leverage harder to assess across the entire AI ecosystem.

The bottom line

AI borrowing has changed the financing model of the buildout. What began as a technology investment funded largely from corporate cash is becoming a broad capital-markets cycle involving public bonds, private credit, infrastructure funds, leases, and project finance.

The strongest near-term market effect is heavier investment-grade supply and greater selectivity from bond investors. The longer-term risk is an asset-liability mismatch: infrastructure and hardware may be financed today against AI demand and revenue that arrive later—or fail to arrive at the expected scale.

The dividing line is whether the buildout becomes a productive infrastructure cycle or a leverage cycle. Borrowing is sustainable if utilization, pricing, and AI-service revenue rise before hardware depreciation and debt-service costs become binding. It becomes fragile if capacity expands faster than durable demand.