Nvidia is using its balance sheet to fuel the AI boom. Is it a double-edged sword?

From a strategy being dubbed “balance sheet as a service” to Nvidia itself being described as the central bank of artificial intelligence, the chipmaker’s growing role in financing the AI ecosystem is attracting more attention from investors.

Nvidia has transformed itself from a semiconductor company into one of the most important financial backers of the AI infrastructure boom.

Its public and private equity holdings reached $95.6 billion at the end of July, up from less than $100 million in early 2020, while the company has said its broader equity investments now total roughly $99 billion.

Its portfolio includes stakes in Intel, CoreWeave, Coherent, Nokia, Synopsys and Nebius, among others.

At the same time, Nvidia is increasingly providing guarantees, backstops and other forms of financial support to companies building the data centers required to run AI models.

That strategy could help accelerate infrastructure spending and secure future demand for Nvidia’s chips.

But it also raises a more uncomfortable question for investors: what happens if AI infrastructure supply grows faster than demand?

Nvidia is becoming more than a chipmaker

Nvidia’s latest financing initiatives demonstrate how dramatically its role in the AI industry has changed.

The company agreed to buy up to an estimated 500 megawatts of unused data-center capacity from CoreWeave for $6.3 billion.

If CoreWeave cannot rent out the capacity to other customers, Nvidia effectively becomes the customer of last resort.

Nvidia has also provided significant financial support for data-center developments, including a planned compute campus in Ohio where OpenAI is expected to be a tenant.

The company ultimately agreed to provide a guarantee of up to $105 billion to help OpenAI lease the sprawling facility, according to reports.

That figure was substantially lower than the $250 billion commitment that had previously been discussed following investor concerns over Nvidia’s exposure.

Nvidia has separately contributed $30 billion to the record-breaking funding round OpenAI completed in March.

The scale of those transactions has led some investors to question whether Nvidia is simply financing the expansion of an industry that then uses the resulting capital to purchase Nvidia products.

That is the essence of the circular-financing concern.

In a typical circular arrangement, a company provides financial support to customers that subsequently use some of that support to buy its products.

Critics argue that such structures can make demand appear stronger than it would be without the financing.

The comparison has prompted memories of financing practices during the dot-com bubble, although the underlying economics of today’s AI infrastructure market are very different.

Nvidia CEO Jensen Huang has rejected the idea that the company is using investments to artificially inflate its growth.

“I think the only regret that I have is that I didn't invest more and sooner,” Huang said of Nvidia’s investments in AI laboratories.

Morgan Stanley sees a $200 billion exposure

Morgan Stanley has taken a closer look at the potential risks.

The bank recently initiated credit coverage of Nvidia with a neutral view, arguing that the company’s exceptional growth is turning balance-sheet strength into a strategic AI financing tool while introducing new risks.

“We initiate credit coverage of Nvidia with a neutral view as exceptional growth turns balance sheet strength into a strategic AI financing tool - and introduces new risks,” Morgan Stanley analyst Lindsay Tyler wrote.

The analysts estimate that Nvidia could have total credit exposure of roughly $200 billion by the end of 2028.

Importantly, most of that figure would not represent conventional borrowing.

Morgan Stanley estimates that around $170 billion could come from guarantees and backstops that currently sit off Nvidia’s balance sheet but could become financial obligations if conditions deteriorate.

The bank has dubbed the strategy “balance-sheet-as-a-service.”

The central question is how investors should treat Nvidia’s support packages, lease commitments and guarantees.

While they are not necessarily debt in the traditional sense, they can create financial obligations that become significant if the projects or customers Nvidia supports run into trouble.

That distinction matters because Nvidia’s enormous profitability and cash generation give it considerable financial flexibility today.

The concern is less about whether Nvidia can afford these commitments under normal circumstances and more about whether multiple guarantees could be triggered simultaneously during an AI downturn.

Nvidia wants to unlock $500 billion of AI investment

That question has gained urgency following Nvidia’s announcement of “repeatable financing platform” agreements with Apollo, BlackRock, Blackstone, Brookfield, Goldman Sachs and KKR, aimed at mobilizing more than $500 billion in third-party capital for AI infrastructure projects.

The goal is to bring institutional capital into an industry where enormous upfront investments are required to construct data centers, purchase GPUs and develop the supporting infrastructure.

Nvidia has emphasized that it is not putting up the entire $500 billion.

Instead, the company may provide residual-value support for up to 25% of an individual opportunity, depending on the project.

In practical terms, that means Nvidia could absorb a defined portion of the loss if the GPUs or other assets backing a project ultimately prove to be worth less than expected.

At 25% of $500 billion, the theoretical maximum would amount to $125 billion.

That does not mean Nvidia has committed to $125 billion in cash payments.

The actual exposure would depend on the projects financed, the amount drawn, asset values, and the circumstances under which guarantees are triggered.

Still, the size of the potential obligation explains why investors are paying closer attention.

Nvidia CEO Jensen Huang has argued that bringing major financial institutions into the process represents “the beginning of an open capital market for AI infrastructure.”

The approach could also help address concerns about circular financing by shifting more of the funding burden to outside investors rather than Nvidia itself.

The real risk comes if AI demand weakens

The more difficult issue is what happens if the assumptions underpinning the AI buildout prove too optimistic.

Morgan Stanley has used a $35 billion chip-lease agreement involving Broadcom and Google-designed tensor processing units for Anthropic as one template for understanding how these structures could work.

Under similar arrangements, chips can be sold to private credit vehicles funded by institutional investors, with the manufacturer providing some degree of support.

Morgan Stanley estimates that if Nvidia signed 15 comparable financing arrangements by the end of 2028, its tail-risk exposure could peak at nearly $90 billion shortly afterward, taking into account drawdowns and amortization.

The bank also noted that rating agencies have shown a preference for treating roughly $125 billion of Nvidia’s financing structures as debt-like.

The more opaque area is Nvidia’s individual arrangements with customers and neocloud companies.

Nvidia recently described a “revenue-sharing and credit-support model” designed to expand access to its chips beyond the largest hyperscalers.

One possible structure would involve Nvidia guaranteeing a minimum price for GPU capacity rented by neocloud providers, while taking a share of revenue above that threshold in addition to its traditional chip sales.

The arrangement could help smaller AI infrastructure providers secure financing and give Nvidia another route to expand its addressable market.

But it also creates a common point of vulnerability.

If AI demand weakens, GPU rental prices could fall, data-center capacity could remain unused, and the value of older GPUs could decline.

Those are precisely the circumstances in which Nvidia’s guarantees could become more expensive.

Nvidia is effectively insuring its own market

Vested Finance has argued that the headline numbers need to be put into perspective.

Nvidia’s enormous revenue and cash-generation capacity mean that $200 billion of potential exposure does not automatically translate into a balance-sheet crisis.

Morgan Stanley’s calculations put gross leverage at around 0.4 times currently, rising to roughly 0.7 times if growth flattens in 2028.

The company could therefore absorb a considerable amount of additional financial obligations before approaching the type of leverage levels that would normally trigger serious credit concerns.

But the timing of potential losses matters.

“The real issue is when the guarantees get called,” Vested Finance said.

Residual-value support would become relevant if used GPUs were worth less than expected.

Revenue guarantees could become costly if neoclouds failed to rent their capacity at profitable rates.

CoreWeave-style backstops could be triggered if data-center capacity went unused.

All three scenarios ultimately point to the same underlying problem: AI compute demand falling short of supply.

That creates an unusual relationship between Nvidia’s financial commitments and its operating business.

If AI demand falls sharply enough to trigger guarantees, Nvidia could simultaneously face weaker orders for its own chips.

In other words, the company could be required to provide financial support to an ecosystem at precisely the time when its own revenue and cash flow are coming under pressure.

Vested Finance described Nvidia as effectively selling put options on its own end market, comparing it to an insurer writing earthquake policies on buildings located in its own city.

The analogy captures the central concern.

Nvidia benefits enormously when AI infrastructure demand remains strong, but its financial exposure could rise when that same demand begins to weaken.

GPU depreciation adds another layer of uncertainty

The financing strategy is also closely connected to a long-running debate over the useful life of AI accelerators.

Compute operators have argued that GPUs can remain economically useful well beyond the six-year depreciation schedules commonly used for accounting purposes.

Skeptics believe the rapid pace of technological advancement means some hardware could become economically obsolete within three to five years.

The difference matters enormously for Nvidia’s financing arrangements.

If GPUs retain their value for a long time, residual-value guarantees may never be called. Nvidia could then help facilitate hundreds of billions of dollars of infrastructure investment while taking relatively limited losses.

If GPU values fall rapidly, however, the company could be forced to absorb the difference between expected and actual asset values.

And those losses could emerge during an industry downturn, when Nvidia’s own sales are under pressure.

Nvidia’s commitments are rising rapidly

The company's latest disclosures illustrate the scale of the expansion.

Nvidia reported supply and capacity commitments of $279 billion, up sharply from $119 billion in the previous quarter, primarily because of commitments to secure memory.

Total future commitments stood at approximately $366 billion.

The company also reported another $56 billion of AI-cloud and third-party lease commitments, while maximum gross guarantees reached $108.5 billion.

Up to $105 billion of that amount is connected to the OpenAI and SB Energy data-center development in Ohio.

Separately, Nvidia’s partnerships with major financial institutions are intended to mobilize more than $500 billion of third-party capital for AI infrastructure.

The commitments are not necessarily evidence of financial weakness.

In fact, they could strengthen Nvidia’s competitive position by helping customers obtain scarce infrastructure and ensuring that its chips are deployed at scale.

But the complexity of Nvidia’s financial relationships with its customers is increasing.

“This does not automatically make the revenue circular. It does mean Nvidia is increasingly helping to create and finance the ecosystem into which it sells,” said Charu Chanana of Saxo.

Investors now have more than GPU sales to watch

For years, the primary question for Nvidia investors was straightforward: how many GPUs can the company sell, and at what margins?

That equation is becoming more complicated.

Nvidia’s equity investments, guarantees, leases, revenue-sharing agreements and infrastructure commitments are increasingly intertwined with the growth of the AI market.

“These arrangements can help Nvidia secure scarce supply, accelerate customer deployments and expand its addressable market,” Chanana said.

“But they also make the company’s risk profile more complex. Investors increasingly need to consider customer credit quality, leases, guarantees, revenue-sharing agreements and Nvidia’s equity investments—not just GPU shipments," said Chanana.

The company's financial position remains strong, and its financing strategy could ultimately prove highly profitable if AI demand continues to expand at the pace Nvidia expects.

The strategy could also help solve one of the industry's biggest challenges: finding enough capital to build the data centers needed to support increasingly powerful AI models.

But the very success of the model could make the risks harder to see during the boom.

As long as AI demand continues to exceed supply, Nvidia’s guarantees are unlikely to look particularly threatening. GPU values remain high, data centers stay occupied, and customers continue ordering chips.

The test will come if that equation changes.

Nvidia has spent years building the infrastructure, software and semiconductor ecosystem around the AI boom. It is now increasingly helping finance it as well.

That could make the company an even more powerful beneficiary of continued AI expansion. But it also means that, should the AI cycle eventually turn, Nvidia may have more than its chip business at stake.