Just a few years ago, Nvidia was primarily celebrated as the champion of GPUs and accelerated computing platforms. Today, Jensen Huang’s company structures, guarantees, and sometimes directly funds a substantial portion of the world’s AI data-center builds.
This pivot, hailed by markets as a growth accelerator, now draws the gaze of analysts and rating agencies. Central banks are watching more broadly for the financial risks tied to the AI investment boom, notably rising debt and the interdependencies between technology players and financiers.
In August last year, Nvidia announced agreements with several major asset-managers (Apollo, BlackRock, Blackstone, Brookfield, Goldman Sachs and KKR) to create platforms aimed at financing the construction of computing infrastructures. The stated objective: mobilize more than $500 billion of external capital outside the company.
The principle is to bring more private capital into funding the infrastructures required for AI development. Depending on the deals, these financings can take various forms, while Nvidia can provide financial backing to reduce the risk borne by investors.
In particular, it can guarantee a portion of the residual value of certain equipment or infrastructures. In the OpenAI data-center project developed in Ohio, Nvidia has committed to guarantee up to $105 billion in payment obligations. This type of guarantee helps financiers reduce their exposure to certain project risks, but it also creates a conditional exposure for Nvidia.
Financing AI Infrastructure to Accelerate Adoption
Nvidia has also extended payment terms for some of its largest clients under multi-year contracts. The average payment period has risen from 45 to 60 days, taking the amount of accounts receivable to $63.1 billion—the sums still owed to Nvidia.
To that, a third lever is added: Nvidia’s direct equity stakes in its clients. The company has stepped up investments in key ecosystem players, including $30 billion committed to OpenAI and $10 billion to Anthropic.
Moreover, Nvidia confirmed several long-term datacenter leases, some of which have yet to come into effect. In Texas, the Financial Times identified Nvidia as the tenant for a Hut 8 campus. The contract starts at 15 years and $19.6 billion in value, but could reach about $50.2 billion with renewal options.
By helping turn computing power into a long-term asset that can be financed like commercial real estate or certain infrastructures, Nvidia broadens the circle of investors who can finance AI-related projects. Insurers, pension funds, or private-debt funds can thus access an asset class they might have previously found too risky or too hard to finance directly.
The upside is double. On one hand, every data center built around its processors helps secure future demand for its chips, software, and CUDA ecosystem. On the other hand, Nvidia’s chief financial officer, Colette Kress, estimates that clients benefiting from the group’s financial backing could account for roughly a quarter of its activity next year.
The risks identified by analysts
One can situate this arrangement within a longer narrative: supplier credit. A practice that has accompanied the rise and fall of other tech sectors. The comparison has its limits: the pace of AI development is such that firms like OpenAI would struggle to raise on traditional financial markets the sums needed to build out compute capacity at the scale they deem indispensable.
Several red flags recur in analyses of Nvidia’s all-out strategy.
According to its own financial documents, four direct customers each accounting for more than 10% of revenue represented about 61% of sales in the third quarter of fiscal 2026. Nvidia does not name them in its reports, referring to them only as “Customer A,” “Customer B,” etc.
Analysts therefore speculate, frequently naming Microsoft, Amazon, Google, OpenAI, or xAI as likely candidates, though no official confirmation is given. On top of this concentration are the financing commitments tied to IA infrastructure that could total up to $250 billion if Nvidia were to back them in full.
Furthermore, by helping finance the infrastructure for purchasing its own chips, Nvidia ties a portion of its growth to capital it itself mobilizes, rather than to an entirely independent end demand.
If the revenues generated by actual AI use—subscriptions, enterprise contracts, online services—prove insufficient, some data centers may not produce enough cash flow to honor their financial commitments. Difficulties would ripple to the financiers, but could also affect Nvidia if its guarantees are called or if demand for its chips slows.
A Strategy Under Scrutiny
Finally, even with a cap, the support Nvidia provides for the residual value of certain equipment exposes it to two risks: whether the involved clients will meet their financial obligations, and the actual value of used GPUs if a new generation renders them obsolete more quickly than expected.
The extension of payment terms adds another risk factor. The larger Nvidia’s receivables grow, the more exposed the company is to the consequences of a potential default by one of its major clients.
Current financing structures value GPU clusters as assets whose operation can extend over several years, akin to certain infrastructures or industrial equipment. In company accounts, these assets can be amortized over five to six years. Yet some investors believe their real economic lifespan could be closer to two to three years, before a new generation of chips makes them less competitive. If this hypothesis holds, profits could be overstated and mid-term equipment renewal needs underestimated.
Part of these financings can, through structured deals, end up in insurers’ or pension funds’ portfolios. The question then becomes their exposure to credit risk concentrated in a still-young sector, particularly if several projects encounter difficulties at once.
What Limits Are There to Financing?
The Bank of England and the Bank for International Settlements (BIS) both highlight the rise of external financing for AI infrastructures and the risks associated with the growing interconnection between tech companies and the financial system. A sharp correction in AI investments could transmit to the data-center financiers and, beyond, to the major technology groups.
For the moment, none of these signals has slowed Nvidia’s stock trajectory. With about $80 billion in cash on hand and cash flow generation running into the hundreds of billions annually, Nvidia possesses a financial capacity unmatched by most players it helps finance.
Yet that is precisely what makes the situation engaging. The question is no longer whether Nvidia can absorb the failure of a few clients, but how far its commitments could extend if it keeps backing the growth of its ecosystem.
The more Nvidia helps convert future compute demand into assets financed today, the more a portion of its own financial risk becomes tied to the ability of this ecosystem to generate the expected revenues.
