Nvidia projects $673 billion in sales as AI demand widens

Nvidia is projecting 70% revenue growth in fiscal 2028, which would put annual sales at roughly $673 billion if the current Wall Street consensus for fiscal 2027 holds. That would move the chipmaker ahead of Apple and Alphabet by revenue, leaving only Amazon among US tech companies with higher projected sales.

CFO Colette Kress delivered the forecast on August 26, 2026. It is far above the 44% average analyst estimate tracked by LSEG and marks a change in Nvidia’s disclosure: the company has not previously provided a forecast this far into the future, although CEO Jensen Huang has offered shorter-range indications of expected AI chip demand.

Nvidia’s fiscal 2027 second-quarter results provided the immediate evidence for the outlook. Quarterly revenue reached $96.2 billion, more than double the year-earlier figure, while data-center revenue rose 117% to $89 billion. Nvidia’s shares climbed about 4% in extended trading in one account, while another market report put the session’s high at 5.6%. The difference reflects the trading range reported after the earnings release, not a change to the company’s forecast.

Supply, rather than demand, is now setting the near-term ceiling. Huang said shortages in components including memory prevented Nvidia from making a still higher projection as AI infrastructure consumes a growing share of global chip and memory capacity.

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“Our demand is much greater than 70%. Our supply allows us to confidently deliver 70%, and we’re going to continue to work with our supply chain to increase on on that.”

Nvidia is broadening its customer base

Investors have worried that Nvidia’s growth depends too heavily on a small group of hyperscalers building data centers for a handful of frontier AI labs. Huang said the next phase will involve a wider set of buyers, including regional AI companies, neocloud providers, startups and conventional enterprises.

Nvidia groups those customers under the label ACIE. The company is selling them more than GPUs. Huang said its technology can supply much of the surrounding data-center stack, making Nvidia a broader infrastructure partner for organizations that cannot assemble the system themselves. The customer category was previously “largely invisible,” he said, but it now includes a growing number of companies deploying AI for useful production work rather than merely funding large-scale model training.

“This time last year, one lab alone was driving the buildout; today, we have a golden age of new AI labs and startups, multiple frontier labs scaling in parallel, a thriving open-model ecosystem and physical AI coming online — with strong momentum across the U.S. and around the world.”

A customer base spread across regional providers, startups and enterprises could reduce dependence on any one hyperscaler’s capital budget. It also gives Nvidia more opportunities to sell networking, systems and other data-center components alongside its processors, although the supplied results do not break out how much revenue comes from those products.

The demand is already affecting companies outside the US. European semiconductor stocks rose after Nvidia’s results: ASML gained about 2.5%, while STMicroelectronics, Infineon Technologies and BE Semiconductor each rose between 2% and 4%. The moves suggest investors expect continued spending on the equipment and components needed to build AI infrastructure, even as broader European indexes declined or held flat.

Nvidia is financing the infrastructure that buys its chips

Nvidia’s role now extends beyond selling hardware. The company is investing in model developers including OpenAI and Anthropic, backing neocloud providers that rent Nvidia-powered compute, and helping arrange financing for data-center construction.

One example is $105 billion in financial support for a large compute campus under construction in Ohio, where OpenAI is expected to be the tenant. Nvidia has also announced a partnership with major Wall Street firms to arrange up to $500 billion in data-center financing. Those arrangements have raised concerns about circular financing: Nvidia helps fund customers or infrastructure, and that money can then flow back to Nvidia through purchases of its products.

Huang defended the strategy by arguing that frontier AI companies need unusually large amounts of capital before they have the balance sheets required to borrow cheaply.

“This is the first generation of startups that needed tens of billions of dollars to get funded. When was the last time anybody heard of a startup that needed billions of dollars to get off the ground and needed tens of billions of dollars to become profitable? That just never happened. But that’s really the nature of AI. The cost of building AI, the cost of deploying AI, it’s very capital intensive.”

He said many of those companies are not investment grade and lack the operating and financial history needed to secure low-cost capital. Nvidia wants to invest in them, support them and encourage them to build on Nvidia’s technology, tying the company’s financial exposure to future demand for its computing platform.

“They’re not investment grade. They don’t have the track record, the capital track record, the financial track record, to be able to capture or secure capital at a low cost. And this is where Nvidia could be helpful.”

Huang also said Nvidia’s infrastructure can be redeployed across customers and workloads if an individual AI company falters, limiting the risk of being tied to one borrower or tenant.

“The money we’ve invested is going to generate tremendous returns. I think the risk is low.”

That is Nvidia’s defense, not an independent assessment of the financing risk. The company’s earnings show that demand is real, but the financing structure makes Nvidia both a supplier and an increasingly direct participant in the capital required to buy its systems. The unresolved question is whether the broader ACIE customer base can sustain the projected growth without Nvidia’s balance sheet continuing to subsidize the expansion.