October 29, 2026
Thursday’s market drop brought back a question investors would rather leave in the server room: who is paying for the AI buildout, and what happens if the money runs short?
On October 22, the Nasdaq fell 2.6% and the S&P 500 lost 1.21%. That was not proof that artificial intelligence is a fad. It was a reminder that investors are weighing enormous spending against the cost of financing it, and asking whether the returns will arrive in time.
The investment is large enough to matter beyond Wall Street. Depending on what gets counted, recent estimates put broad AI- and technology-related investment at roughly a third to nearly half of U.S. GDP growth. That is not a single official statistic about AI: estimates differ based on whether they include wider technology spending and subtract imported equipment. The Federal Reserve makes the measurement problem plain: national accounts don’t have an “AI investment” line, and expensive imported chips can make gross spending look bigger than its domestic contribution to GDP.
So, yes, the buildout is real, and borrowed money is playing a growing role in financing it.
Three ways borrowed money gets into the AI buildout
Corporate bonds are the familiar route. A company sells bonds to investors and promises to pay interest and repay the principal later. The company gets money now to build data centers, buy equipment and expand power capacity. The Bank for International Settlements says bond issuance by hyperscalers topped $100 billion in 2025. Bonds are not inherently a problem; the test is whether future cash flows can comfortably cover the bills.
SPVs move some of the borrowing to another legal entity. Picture a purpose-built company that owns a data center, borrows from private-credit funds, and collects rent or capacity payments from a large technology firm. The tech firm may hold a minority stake, sign a long-term lease, or guarantee parts of the deal. That can shift the upfront construction cost away from its balance sheet, but it doesn’t make the economics vanish. The BIS calls these arrangements “shadow borrowing”: obligations that can resemble debt while sitting mostly outside the tenant company’s reported debt totals. If rent stops or the project needs refinancing, the lenders and guarantors still have a problem.
Vendor financing links the seller to the customer’s ability to buy. Sometimes a chipmaker invests in a customer, lends to it, guarantees financing, or agrees to buy capacity if another buyer doesn’t show up. The customer uses the money or support to build infrastructure and purchase equipment from that same supplier. It can help get racks installed and online this year, but it also means the supplier may be helping to fund the demand for its own product.
That distinction matters when people discuss the $20 billion Nvidia-Groq headline. It should not be treated as proof of a $20 billion loan to a chip buyer: coverage described the transaction as a deal involving Groq assets and technology, not a simple customer loan. The broader vendor-financing concern is visible elsewhere in disclosed investment, lease and credit-backstop arrangements. CNBC reported that Nvidia had $36 billion in off-balance-sheet AI cloud agreements or backstops, as well as other large guarantees and commitments. These are not all funded loans, and they do not mean the company must pay the full amounts. They are, however, promises with potential costs if a project or customer fails.
Leverage makes the ride steeper in both directions
Borrowing can make a successful investment more profitable. If a data center earns enough to pay operating costs and interest, the owners can earn a return on their own smaller slice of the project while using borrowed capital to build something much larger.
But the loan still has a payment schedule if customers arrive late, power connections stall, or demand falls short. Debt doesn’t wait patiently for the business plan to work.
That’s the recurring pattern in boom-and-bust stories. Railroads in the 1800s were a transformative technology. Telecom networks in the late 1990s were real infrastructure. Housing was real shelter in the 2000s. The danger wasn’t that the underlying thing was imaginary. It was that capacity was built ahead of reliable revenue, with debt that had to be serviced on time whatever happened to demand.
What makes this cycle worth watching
First, money can run in a circle. If Nvidia invests in an AI company that then buys Nvidia chips, or a cloud provider finances a customer that rents back its computing capacity, the money may be circulating among companies that depend on each other. That doesn’t automatically make the sales false or the technology useless. It does make an important question harder to answer: how much demand comes from independent customers paying from their own operating revenues?
Second, depreciation assumptions affect reported profit. Data centers and chips wear out, lose value, or become less useful as technology changes. Companies estimate how long equipment will remain productive and spread its cost over that time. A longer estimated life means less expense is recorded each year, which can make current earnings look stronger, even though it doesn’t put cash in the bank. As one concrete example, Meta said extending the estimated life of most servers and network assets to 5.5 years reduced 2025 depreciation expense by $2.92 billion. That is a disclosed accounting estimate, not evidence by itself that anything improper happened. It is a reason to ask whether the assumed useful life matches the speed of the business.
Third, the market is concentrated. A small group of giant companies accounts for a large share of major U.S. indexes, and AI spending and expectations are concentrated among them too. When one company’s earnings call or spending forecast moves sharply, the ripple can reach an ordinary 401(k). That can happen even if a saver never bought that company directly: a plain S&P 500 index fund owns companies in proportion to their market value. As Fidelity explains, the biggest companies make up a historically large share of the index.
Should regular folks care?
Yes, but not because every household needs to predict whether AI spending will pay off. Most people’s exposure is indirect and may be larger than they realize. A target-date fund or plain S&P 500 fund can carry substantial exposure to the same megacap companies driving the AI boom. The regular saver did not necessarily pick that trade; the index may have picked it for them.
The practical response is not to guess the top or bottom of the market. Nobody does that reliably. It is to understand what you own, avoid having money needed in the next few years concentrated in volatile investments, and consider whether your overall portfolio is diversified in a way that fits your goals and comfort with risk.
Watch the bills, not the buzz
The most useful warning signs are about the financing, not the headlines. Are AI-linked borrowers making interest and principal payments? Are data-center SPVs being downgraded or struggling to refinance? Are hyperscalers cutting capital-spending guidance? Are customers using the capacity and paying for it, or are projects leaning on new guarantees and fresh borrowing to keep the cycle moving?
A falling share price can reflect changing sentiment. That matters, but sentiment can shift from one week to the next. Debt service is more concrete: it comes due on a schedule. Watch whether the cash arrives when the payments are due.
And keep the counterargument in view. AI may deliver real productivity gains and revenue that justify much of today’s investment. In that case, some of the current alarm will look overdone. The Federal Reserve’s review notes that large investments often come before broad productivity gains become visible, while also finding that aggregate productivity effects have yet to clearly emerge. The waiting period is part of the uncertainty.
The market is trying to price both possibilities at once: a technology that could transform business, and a financing boom that could get ahead of its cash flows. That tension is why Thursday happened, and why a rough market day is not, by itself, a verdict on AI.
Be mindful, be watchful and good luck.
This article is for educational purposes only and is not investment advice. Financial decisions depend on your circumstances; consult a qualified financial professional before acting.