Nvidia allegedly turned to financing to maintain demand for its GPUs: according to Wccftech, citing the Wall Street Journal, Jensen Huang pushed for agreements with AI startups and cloud providers to expand their data centers and keep buying the company's chips. The backdrop was reportedly an excess of silicon and memory that Nvidia had committed to TSMC, not simply a lack of manufacturing capacity.
The distinction matters: the account does not prove that there was no shortage of components, but describes another pressure on the market. Nvidia allegedly had to place supply commitments it had already secured while some of its biggest customers reduced their dependence on commercial GPUs.
Financing as a way to address excess chips
Wccftech places the episode at an August 2026 meeting between Huang and Nvidia CFO Colette Kress, who became interim CEO. According to that publication and its attribution to the Wall Street Journal, Kress wanted to contain circular financing commitments involving smaller cloud providers and large AI companies, including OpenAI and Anthropic.
The most specific figure concerns OpenAI. Discussions allegedly considered guaranteeing computing capacity of around 8 gigawatts and up to $250 billion; Kress reportedly lowered the signed limit to around 4.25 gigawatts and up to $125 billion. The difference does not prove that OpenAI received or used that capacity: it describes the size of the guarantees reportedly under consideration, and the cap that was allegedly ultimately agreed upon.
The mechanism combines two roles: Nvidia would sell GPUs and, at the same time, help finance companies that need to build the infrastructure to buy and put them into service. If the published account is accurate, financial backing could turn some of the supply that might otherwise have gone without an immediate buyer into actual orders. It also shifted some of the risk: in addition to selling hardware, Nvidia was exposed to its customers' ability to finance and deploy those data centers.
“coming at him like an avalanche”
— Jensen Huang
Wccftech attributes that description to Huang, referring to the TSMC wafer and memory commitments Nvidia had ahead of it. The image provides context for the decision: a buildup of committed components can become a commercial problem if end demand or the infrastructure needed to use them does not advance at the same pace.
Major customers move away from standard GPUs
The explanation Huang allegedly gave privately, according to Wccftech, is that major cloud providers were shifting toward chips designed in-house. If those customers reduce purchases of commercial components, Nvidia loses some of the demand that had helped justify its manufacturing orders. Financing smaller operators and AI companies would reportedly have helped make up for that retreat.
That shift has a direct consequence for negotiations between manufacturers and customers: a GPU buyer is no longer necessarily a customer who only compares performance and price. In this account, it also matters who can finance an expansion, assume guarantees, and turn a capacity reservation into operational infrastructure. For companies that depend on these agreements, access to computing capacity may be tied to broader commercial and financial commitments.
Wccftech also cites an estimate from Ed Zitron: at least $200 billion worth of Nvidia AI GPUs were reportedly sitting idle in warehouses after being purchased by cloud providers without enough electrical capacity or immediate user demand. This is an estimate attributed to Zitron, not a figure confirmed by Nvidia in the cited material. The publication also mentions that Amazon faced a similar problem, without providing further details.
The cost of sustaining demand
For Nvidia, financing buyers could protect sales and prevent an excess of chips from putting pressure on prices. But this strategy also concentrates risks: if customers cannot secure power, infrastructure, or demand for their services, financing does not make the GPUs get used. It only allows purchases and deployment to continue for longer, with guarantees that could increase Nvidia's exposure.
The other alternative mentioned in the report was reducing future orders to TSMC. Wccftech says Nvidia contacted the foundry to scale back upcoming orders. Adjusting purchases can limit future excess, though it does not eliminate commitments already made or, by itself, resolve what to do with existing inventory. In the account, financing and order cuts were responses to different stages of the same problem.
For developers and companies renting AI capacity, the practical takeaway is not that GPUs are immediately available or that prices must fall. Rather, some demand could depend on financing agreements and data center construction, not just stated computing needs. If the backed projects are never deployed, financed purchases can coexist with unused GPUs; if they are, they sustain orders and infrastructure activity.
What the report supports
The published account connects an internal meeting, guarantee limits, and supply commitments, but does not establish that Nvidia deliberately created a general GPU shortage or that financing alone determined prices. The central claim is narrower: Huang allegedly advocated using Nvidia's balance sheet to support buyers while the company faced an excess of chips and memory.
The contrast between the initial limit attributed to OpenAI and the cap Kress reduced it to shows the potential scale of the exposure, but does not clarify how much money was actually disbursed, what terms the guarantees carried, or how many GPUs ended up operating in data centers. The open question is whether purchase commitments could turn into real usage demand at the pace Nvidia needed.
Sources: TechPowerUp, Wccftech
