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Growing financing obligations could compound the pain of a downturn
By
[Asa Fitch](https://archive.is/o/6rUhG/https://www.wsj.com/news/author/asa-fitch)
Aug. 25, 2026 5:30 am ET
Nvidia CEO Jensen Huang is increasingly tapping the company’s financial strength to keep customers buying. Manami Yamada/Reuters
[Nvidia’s](https://archive.is/o/6rUhG/https://www.wsj.com/market-data/quotes/NVDA) [NVDA -2.91%decrease; down pointing triangle](https://archive.is/o/6rUhG/https://www.wsj.com/market-data/quotes/NVDA) current financial position looks rock solid to say the least. But the financial engineering it is using to keep revenue growing introduces risks that could eventually cause real pain.
Nearly four years into an artificial intelligence boom that has brought Nvidia hundreds of billions of dollars in profit, the company is increasingly tapping its financial strength to keep customers buying its chips. Those chips have become the ubiquitous computational workhorses of the AI boom, a status quo Nvidia has an interest in maintaining.
Nvidia recently provided backstops that could put it on the hook for some $230 billion in lease obligations and residual-value deals, including a [$105 billion backstop](https://archive.is/o/6rUhG/https://www.wsj.com/articles/openai-locks-in-lease-for-huge-data-center-in-ohio-with-backing-from-nvidia-7474bb9c) for an [OpenAI](https://archive.is/o/6rUhG/https://www.wsj.com/topics/subject/openai) lease in Ohio and [potentially up to $125 billion](https://archive.is/o/6rUhG/https://www.wsj.com/tech/ai/why-wall-street-and-nvidia-are-building-an-exotic-money-pipeline-for-the-ai-boom-346ba482) in “residual-value support” for financing deals with Wall Street heavyweights. That support would likely entail Nvidia’s assuring that the value of assets backing loans won’t fall below a predetermined level.
Nvidia also [recently signed deals](https://archive.is/o/6rUhG/https://blogs.nvidia.com/blog/nvidia-unlocks-ai-compute-at-scale-capital-partners-to-power-ai-infrastructure-buildout/) with two young cloud-computing companies in Australia where it acts as a customer of last resort if they can’t find others to rent their computing power to. In exchange, Nvidia gets a share of the companies’ revenue from those assets above an agreed-upon level. The backstops give lenders confidence to extend financing with which the companies buy Nvidia’s chips.
Those promises don’t include Nvidia’s [agreement last year](https://archive.is/o/6rUhG/https://www.sec.gov/Archives/edgar/data/1769628/000176962825000047/crwv-20250909.htm) to buy unsold cloud-computing capacity from [CoreWeave](https://archive.is/o/6rUhG/https://www.wsj.com/market-data/quotes/CRWV) as part of a $6.3 billion deal. Nor do they include the equity stakes Nvidia has taken in many of the cloud companies and AI labs that it is providing backstops for, including CoreWeave. The company had $72.5 billion of public and private-company stakes at the end of its last fiscal quarter.
Nvidia can easily absorb some big impacts, with more than $80 billion of cash and marketable securities on its balance sheet as of its last fiscal quarter. It also isn’t likely that Nvidia would be liable for its entire lease backstops, which reduce over time.
But Nvidia’s venture into finance appears poised to become much deeper. The company [described its deals](https://archive.is/o/6rUhG/https://blogs.nvidia.com/blog/nvidia-unlocks-ai-compute-at-scale-capital-partners-to-power-ai-infrastructure-buildout/) with the two cloud-computing players in Australia as a “new business model,” suggesting it could act as a financial guarantor many more times over. And its financial backstops for OpenAI and the Wall Street firms create an expectation that Nvidia will lend more support to other large data center projects.
Part of what Nvidia is doing is spreading its bets so it is less reliant on a handful of tech giants like [Amazon.com](https://archive.is/o/6rUhG/amazon.com/) and [Microsoft](https://archive.is/o/6rUhG/https://www.wsj.com/market-data/quotes/MSFT). Those companies are running out of financial steam.
Yields on AI-linked Big Tech bonds [are surging](https://archive.is/o/6rUhG/https://www.wsj.com/finance/investing/how-to-play-the-flood-of-ai-bonds-279f6ba5) because there is such a large supply of them. With no end in sight for debt issuances, borrowing costs for AI projects will likely stay high. And the tech companies are already [heavily committed to data center projects](https://archive.is/o/6rUhG/https://www.wsj.com/tech/ai/why-big-techs-ai-spending-is-3-trillion-higher-than-it-seems-e1067bb2), with $904 billion of leases not yet started and $1.52 trillion of purchase commitments largely related to AI, per their most recent financial statements.
There is thus some logic in Nvidia trying to generate sales from smaller and less-creditworthy companies. But history also provides examples of companies for whom courting less-creditworthy customers backfired spectacularly.
In the dot-com bubble, telecom-equipment leaders like Lucent Technologies, which was spun out of [AT&T](https://archive.is/o/6rUhG/https://www.wsj.com/market-data/quotes/T) in the mid-1990s, sought to court a new breed of small-time operators created following industry deregulation in the U.S.
To boost sales in a fast-growing market, Lucent extended financing to these less-creditworthy customers for purchases of its products. It guaranteed third-party loans to customers. And it made off-the-books guarantees to distributors of its products, according to an [SEC complaint](https://archive.is/o/6rUhG/https://www.sec.gov/files/litigation/complaints/comp18715.pdf%23:~:text=During%20Lucent's%20fiscal%20year%201999%20until%20the,resulted%20in%20significant%20uncertainties%20surrounding%20those%20transactions.) in 2004 that resulted in a settlement. It agreed to buy back any unsold equipment from those distributors, but improperly recognized the sales to distributors as revenue, the SEC alleged.
Lucent’s financial engineering was a major factor in its downfall. It ended up downsizing to about a third of its original workforce and merged with France’s Alcatel in 2006.
Vendor financing “helped make possible growth rates that did not seem possible,” Lisa Heffernan wrote in a 2004 book about the company’s demise. “Vendor financing,” she wrote, “opened doors that were shut for a reason.”
Nvidia, of course, is no Lucent. It is far stronger financially. All indications are that supplies of AI chips aren’t likely to catch up with demand for at least another year or two. But the scale of the financing deals it is entering into looks increasingly risky in the longer term.
“The question is, is this a trend?” said Wolfe Research analyst Chris Caso. “Does this become the principal way of financing these data centers, and the chip company provides backstops? That would be more concerning, in my view.”
The overarching problem for Nvidia is that its deals concentrate the risk it is taking on AI.
Participating in a larger slice of the industry downstream of those chips, including lease backstops, residual-value guarantees and even equity stakes in customers, compounds the risks of an eventual weakening in AI chip demand. Instead of a simple reduction in revenue and profit, a downturn scenario could stress Nvidia’s balance sheet. Reductions in the value of its investments could hit profit further.
It isn’t clear what kinds of internal controls Nvidia has to guard against getting overextended financially. Nor has the company said whether it is setting aside cash reserves as a buffer for specific financing or lease backstops. Analysts will likely ask questions about those measures when the company reports earnings on Wednesday. Investors have a lot of confidence in Nvidia’s technological prowess but might have less in its financial engineering.
Nvidia’s venture into financing could help it fulfill shifting demand. But it leaves the company with vanishingly little room for error as it tries to stay at the forefront of the AI boom.
**My take away:** AI demand is increasingly supported by financing relationships among chipmakers, hyperscalers, AI laboratories, and financial institutions. This financing allows customers to keep purchasing infrastructure even when their independent revenue may not justify the spending. The system can continue while capital remains available, but a reduction in hyperscaler spending, weak AI monetization, or rapid hardware depreciation could break the cycle and set off a tsunami across the financial system.
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