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Nvidia's AI moat is shifting from chips to capital

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Why This Matters

Nvidia is shifting its competitive strategy from solely relying on its technological edge in chips to leveraging its substantial capital reserves to dominate AI infrastructure development. This approach allows Nvidia to secure key partnerships and maintain its leadership in the rapidly expanding AI market, even as competitors close the technology gap. The company's financial strength enables it to influence the AI ecosystem significantly, ensuring sustained growth and industry relevance.

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NVIDIA CEO Jensen Huang delivers a speech during a keynote event at COMPUTEX on June 02, 2026 in Taipei, Taiwan. Cheng Chia Huang | Getty Images

Nvidia's massive head start in artificial intelligence turned the chipmaker into the world's most valuable company. Now, almost four years into the generative AI boom, competitors like Advanced Micro Devices and Google have chipped away at Nvidia's technology lead, pushing the company to take advantage of its other great asset: capital. Following last week's pact with Wall Street firms to pursue $500 billion worth of financing for Nvidia's graphics processing units, Nvidia said on Monday that it's providing up to $105 billion for a giant OpenAI data center in Ohio, offering a backstop of sorts should the ChatGPT creator see its fortunes turn. For Nvidia, the strategy involves fueling the AI boom by whatever means necessary, recognizing that demand for critical infrastructure is seemingly insatiable but that a handful of companies — the hyperscalers — account for an outsized amount of purchases. With its quarterly free cash flow up 18-fold over the past three years to $48.5 billion in the latest period, Nvidia is using the strength of its balance sheet and credit rating to ensure there's no dramatic slowdown following 12 straight quarters of revenue growth above 55%.

"They remain dominant, but they're very paranoid about making sure they don't lose ground," said Ram Bala, associate professor of AI and analytics at Santa Clara University's Leavey School of Business. Nvidia declined to comment. In a note to clients on Monday, analysts at Cantor brushed off concerns that Nvidia is effectively buying revenue through its financial maneuvering. They reiterated their buy rating and said the latest agreement is a "clear signal that the current AI investment cycle will be elongated and durable." "We view this less as circular and more facilitating the coming AI buildout while at the same time creating additional competitive moats that will continue to enable NVDA to remain THE AI leader," the analysts wrote. Nvidia is swimming in money. Its cash generation is so great that the company said in May that it was increasing its quarterly dividend to 25 cents a share from a penny, and announced a new $80 billion stock buyback plan. The company pledged "to return roughly 50% of free cash flow to shareholders this year."

One way the company has been putting its cash pile to work is through equity investments in companies across the AI ecosystem, including some businesses — like model developers and neoclouds — that spend heavily on Nvidia's chips and systems. Nvidia held $30.2 billion in marketable equity securities as of the most recent quarter, up from $12.9 billion a year earlier. In February, Nvidia invested $30 billion in OpenAI, which relies on training capacity from Vera Rubin, the chip giant's most advanced system. Monday's agreement included a $1.5 billion investment in SB Energy, a SoftBank affiliate that's building and managing the data center at the PORTS-Pike Technology Campus in Pike County, Ohio, through a 20-year lease to OpenAI. In addition to the SB Energy investment, Nvidia said it's putting its financial support behind about 4 gigawatts of development at the Ohio site for portions of lease and power and "a specified residual-value commitment," as data centers open between 2028 and 2030.

Expanding access

Nvidia CEO Jensen Huang acknowledged the significance of the company's financial prowess in a post on X about the agreement. "Frontier AI labs have extraordinary demand for training and inference compute, but many are growing faster than their balance sheets and long-term credit profiles can support," Huang wrote. "They may have strong customer demand and rapidly growing revenue yet still lack the decades-long infrastructure contracts and investment-grade financing capacity needed to secure the AI factory infrastructure independently." A week prior, Huang was on set at CNBC surrounded by six of Wall Street's leading financiers to announce the arrival of Nvidia graphics processing units as a new asset class. In signing a memorandum of understanding with firms including Goldman Sachs , Apollo Global Management, Blackstone and BlackRock, Huang indicated that the next phase of the AI buildout will be funded in part by third-party backers, who can start investing in GPUs the way they do real estate. "These are revenue-generating assets now," Huang told CNBC. "They're productive, they're long-lived, they're fungible, they're flexible."

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