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CNBC's Kate Rooney reports on OpenAI's talks with Nvidia over guaranteeing about $250 billion in data center loans.

ToplineNvidia and OpenAI have held talks to spend roughly $500 billion on a data center in southern Ohio, potentially becoming the largest such project by power capacity in the world by far.

The development would become the world’s largest data center by power capacity.

AFP via Getty Images

Key FactsNvidia has discussed providing $250 billion to OpenAI to help the ChatGPT maker lease a proposed 10-gigawatt site being developed about 50 miles south of Columbus, Ohio, by SoftBank’s energy subsidiary SBEnergy, the Wall Street Journal reported, citing people familiar with the matter.

That would dwarf the power output of other planned data center projects, including a $20 billion, 3.2-gigawatt project in Effingham County, Georgia, announced by OpenAI last week.

The full project could cost more than $500 billion with the added costs of Nvidia’s chips, and OpenAI is discussing a chip purchase valued up to $350 billion, the Journal reported.

Power for the site—located on federally owned land—is controlled by the U.S. and funded by Japan, which agreed to fund $33 billion as part of a commitment to invest in the U.S. in exchange for lower tariffs.

Japan and the U.S. would share proceeds from power sales at the site until Japan recoups its $33 billion, after which the U.S. would hold 90% control.

The project’s first phase is expected to be completed by 2028 with around 800 megawatts of power.

what to watch forCommerce Secretary Howard Lutnick is reportedly involved in deciding which companies will receive access to power at the site. Anthropic, Microsoft and Google have also spoken to Lutnick about interest in the site.

surprising factThe average U.S. household used 865 kilowatt-hours per month in 2024, according to the Energy Information Administration, meaning the proposed data center's maximum 10 gigawatt output would be enough to power roughly 8.4 million homes.

big numberMore than $500 billion. That’s the size of Nvidia’s deal to acquire AI memory supply from SK Hynix, the company announced Friday. Nvidia said the transaction includes the construction of large-scale data centers expected to come online in 2027.

key backgroundNvidia’s potential funding for OpenAI’s data center could become the latest multibillion-dollar AI-related transaction involving mega-cap firms this year. Meta announced plans to expand a Louisiana data center to 5 gigawatts, with total planned investments exceeding $50 billion. Alphabet announced a major equity raise last month to finance AI infrastructure, with $10 billion of the $80 billion planned capital raise coming from Berkshire Hathaway. In April, Amazon agreed to increase its investment in Anthropic to $25 billion, while Anthropic committed to a more than $100 billion long-term purchase of Amazon’s cloud technology over 10 years.

further readingForbesOpenAI Investors—Nvidia, Oracle, More—Fall After AI Giant Reportedly Misses Revenue TargetBy Ty Roush

OpenAI co-founder Ilya Sutskever walks outside a U.S. federal courthouse as the trial in Elon Musk's lawsuit over OpenAI's for-profit conversion continues, in Oakland, California, U.S., May... Purchase Licensing Rights, opens new tab Read more

CompaniesSAN FRANCISCO, July 27 - Nvidia (NVDA.O), opens new tab will make ​a $5 billion equity investment in ‌Safe Superintelligence as part of a strategic partnership between ​the chipmaker and the ​startup co-founded by OpenAI's ⁠former chief scientist Ilya ​Sutskever, a person briefed on ​the deal said.

The two companies announced the deal Monday morning without ​disclosing financial terms.

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The ​funding will boost SSI's available computing ‌resources, ⁠the companies said. The deal also gives SSI access to Nvidia's cutting-edge Vera ​Rubin ​hardware.

The strategic ⁠deal announced Monday came together within ​a matter of weeks, ​two ⁠people briefed on the matter said.

Bloomberg earlier reported ⁠the ​amount of Nvidia's ​equity investment.

Reporting by Deepa Seetharaman in ​San Francisco Editing by Nick Zieminski

Our Standards: The Thomson Reuters Trust Principles., opens new tab

Deepa is a Reuters technology correspondent covering artificial intelligence and the companies driving its development, including OpenAI and Anthropic. She reports on how advances in AI are reshaping business, politics, and society. This is Deepa's second stint at Reuters. She began her career at the news agency in New York and covered the U.S. auto industry from Detroit before moving to San Francisco to report on Amazon. She was part of a Reuters team named a finalist for the Gerald Loeb Award for Beat Reporting for their coverage of the United Auto Workers. She rejoined Reuters in September 2025. In between, she spent a decade at The Wall Street Journal, where she was the lead reporter covering Facebook and later artificial intelligence following the emergence of ChatGPT. Her reporting included coverage of Instagram's impact on teenage girls and investigations into how AI systems falter in moderating racist and hateful content. She has been part of teams that won the George Polk Award for Business Reporting and the Gerald Loeb Award for Beat Reporting.

Nvidia Corp (NASDAQ:NVDA, XETRA:NVD) is in talks to provide a roughly $250 billion financial guarantee to support OpenAI's planned data center project in Ohio, according to a Wall Street Journal exclusive report citing people familiar with the matter.

The report said the proposed guarantee would help OpenAI secure a lease for a planned 10-gigawatt data center campus being developed in southern Ohio by SoftBank's energy subsidiary.

The overall project could ultimately cost more than $500 billion, including the Nvidia chips expected to power the facilities, making it the largest data center project announced to date.

According to the Wall Street Journal, Nvidia's backing would help developers obtain construction financing by addressing lender concerns over OpenAI's non-investment-grade credit profile. The guarantee would apply to lease and construction financing rather than purchases of Nvidia's AI chips.

The newspaper also reported that Nvidia is separately discussing a financing package that could total about $350 billion to support OpenAI's purchases of its AI chips.

The first phase of the Ohio campus is expected to deliver about 800 megawatts of capacity and is currently scheduled to begin operating in 2028, the report said.

The Wall Street Journal noted that negotiations remain ongoing and that the terms have not been finalized, meaning there is no assurance a deal will be completed.

Shares of Nvidia traded down 2% at about $202 on Monday morning, having added about 8% so far this year.

CNBC's Jim Cramer explains why he is keeping an eye on shares of Nvidia.

The group of stocks that have been dubbed the "Magnificent Seven" has a huge impact on the overall performance of the stock market. It was 2023 when Bank of America analyst Michael Hartnett first pinned that phrase on tech sector giants Nvidia (NVDA -4.49%), Amazon, Apple, Alphabet, Meta Platforms (META +0.86%), Microsoft, and Tesla (TSLA -1.16%), and since then, they have grown to a combined market cap of $22 trillion. Collectively, they make up nearly one-third of the value of the S&P 500.

But what many investors may be overlooking is that the leadership of these companies has changed. Titans such as Jeff Bezos and Bill Gates have moved on. In fact, only three of these companies currently have founders who are actively guiding their direction -- Jensen Huang at Nvidia, Mark Zuckerberg at Meta Platforms, and Elon Musk at Tesla.

These three have very different visions for their companies, with unique strengths and risks. But is there one who is the best investment for the long term?

I think the answer is clear.

Nvidia CEO Jensen Huang. Image source: Nvidia.

Nvidia's Jensen Huang: Strategic focus Huang is an electrical engineer who began his career as a microprocessor designer at Advanced Micro Devices. By 1993, he and two others founded Nvidia to design graphics chips for video games, and Huang became CEO.

The company's graphics processing units (GPUs) were great for gaming, but Nvidia really started gaining steam when it developed the parallel computing platform known as CUDA, which allowed developers to write and execute code that allowed GPUs to be used for other purposes. Researchers later discovered that GPUs were ideal for handling the heavy parallel processing needs of deep learning.

Today, Nvidia's GPUs are considered the gold standard for training and running AI programs. Nvidia's most recent quarter showed $81.61 billion in revenue, with $75.2 billion of that coming from data center sales.

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Meta's Mark Zuckerberg: Making huge bets Zuckerberg famously founded Facebook, now known as Meta Platforms, at age 19 while a student at Harvard University. The site was originally designed to help students match up the names and faces of their fellow students, but it has since grown to a mammoth social media/artificial intelligence company that operates Facebook, Instagram, WhatsApp, Messenger, and Threads. The company's apps are used by an average of 3.56 billion people per day.

Zuckerberg isn't afraid to take chances. In 2021, he led the company's rebrand from Facebook to Meta Platforms as he shifted its focus toward building a digital metaverse and virtual reality technology. Meta Platforms spent $80 billion on its metaverse aspirations, but Zuckerberg's vision never paid off.

"Sometimes, we knock it out of the park," Samantha Ryan, vice president of content at Meta's Reality Labs division, wrote in a blog post. "Other times, we get things wrong."

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Now, Meta has shifted its attention to artificial intelligence, investing heavily in data centers and computing capacity, which up until now, it has used entirely for its own needs. However, Bloomberg reports that it is preparing to get into the cloud infrastructure business and lease capacity to external customers, and it's in the process of building a massive 5-gigawatt data center campus in Louisiana.

Tesla's Elon Musk: Big ambition, big risk First things first -- let's acknowledge that Musk is recognized as a founder of Tesla, but he wasn't there at the beginning. The electric vehicle company was founded in 2003 by two engineers; Musk joined a year later, investing $6.5 million and becoming chairman of the board. A legal settlement later saw him recognized as one of its five founders.

Musk successfully turned Tesla around, thanks in part to his vision, personality, and work -- he famously slept on the factory floor for months in 2017 and 2018 as Tesla brought its Model 3 into production.

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Today, Tesla's core business remains electric vehicles (EVs), but it's also deeply involved in artificial intelligence. The company is developing full self-driving (FSD) technology, hoping to gain widespread regulatory approval for autonomous vehicles to operate. And it's developing its Optimus humanoid robots, which Musk hopes will be used for general labor, manufacturing, and household chores.

But Musk's ambition and force of will can work against him as well. He got heavily involved in politics and took a short-lived post running President Donald Trump's now-defunct Department of Government Efficiency (DOGE). But that work hurt Tesla's brand, and Musk says he now regrets his involvement.

"I think instead of doing DOGE, I would have basically worked on my companies," he said in December.

The verdict Undoubtedly, all three of these founder CEOs have been wildly successful. But if I had to choose just one to bet on for continued success over the long term, my money would be on Huang. Nvidia has the widest competitive moat with its GPUs. Meta Platforms, meanwhile, is investing heavily just to have the opportunity to potentially compete with a host of established cloud computing providers, and Tesla's Optimus robots and unsupervised FSD technology remain highly speculative long-term bets.

For those reasons, I'm picking Nvidia.

After two years in stealth, Safe Superintelligence, the AI lab founded by former OpenAI co-founder and alignment lead Ilya Sutskever, has announced a long-term partnership with Nvidia as it prepares to scale to its next phase. 

The deal, which includes an undisclosed investment, will give Safe Superintelligence (SSI) access to Nvidia’s Vera Rubin GPU platform, which is expected to increase the startup’s compute resources “by an order of magnitude.” The partnership comes as SSI has achieved significant research milestones, per Nvidia. 

Nvidia’s investment stretches into multiple billions, a source familiar with the deal told TechCrunch.

Already an investor in SSI, the chipmaking giant said it signed this compute partnership to “accelerate SSI’s next stage of growth after obtaining rare access into the company’s closely guarded research.”

“We have research that is worthy of scaling up, and having access to a big NVIDIA computer will let us do so,” Sutskever said in a statement. “We are confident that our big bet on the Vera Rubin platform will take us to the next level.

The partnership news, while sparse in details, brings SSI back into the spotlight after a quiet two years since it was founded. The company is pursuing a “straight shot” research approach to building what it says is a safe, aligned artificial superintelligence, without getting distracted by commercial product releases or short-term revenue cycles. 

At a time when commercial pressures to move fast could encourage AI labs to lower their bar for safety, SSI’s approach to developing foundational techniques focused on alignment and true general reasoning feels poignant. That’s especially true in light of OpenAI’s recent disclosure that one of its advanced models broke out of its sandbox to hack into Hugging Face during testing — sparking concerns about whether it’s even possible to ensure AI alignment before new, increasingly capable models are released.

According to Nvidia, the two companies will also collaborate on advancing Nvidia’s current and future compute platforms, relying on SSI’s tech and “unique insights into the future of AI.” (SSI also partnered last year with Google Cloud to power its research.)

Sutskever is a pioneer in the field of AI. He co-authored and co-created AlexNet alongside Alex Krizhevsky and Geoffrey Hinton, proving that GPU scaling and deep neural networks can work. That work has largely been credited for setting the groundwork for today’s generative AI.  

Prior to leading SSI, Sutskever headed the now-defunct Superalignment team at OpenAI. He left OpenAI months after a failed attempt to oust OpenAI CEO Sam Altman, following what Sutskever referred to as a “breakdown in communications.”

SSI has raised $7 billion to date, and is valued at $32 billion post-money, according to PitchBook data. Aside from Nvidia, the firm’s backers included Andreessen Horowitz, Alphabet, Lightspeed Venture Partners, GV, Sequoia Capital Partners, and others.

TechCrunch has reached out to SSI and Nvidia for more information.

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Rebecca Bellan is a senior reporter at TechCrunch where she covers the business, policy, and emerging trends shaping artificial intelligence. Her work has also appeared in Forbes, Bloomberg, The Atlantic, The Daily Beast, and other publications.

You can contact or verify outreach from Rebecca by emailing [email protected] or via encrypted message at rebeccabellan.491 on Signal.

In a post on X on Monday, the CNBC host said “First National Bank of Nvidia is starting to really cause people to freak out,” referring to the chipmaker’s increasing use of financing to support AI infrastructure buildouts.

Cramer expanded on the point in a follow-up post, arguing that Nvidia’s recent share price weakness has little to do with demand for its AI chips.

“The volume in Nvidia supports the decline,” he wrote. “I would say if there were no financing involved Nvidia’s stock would be soaring.”

Instead, Cramer believes investors are fixated on the potential risks of Nvidia extending financing to customers building AI infrastructure, drawing comparisons to financing-driven excesses seen during the dot-com boom.

Dot-Com Deja Vu“All that matters to the market, though, is the financing and how it will impact Nvidia negatively because of memories of 2000,” he said.

The comments come as Nvidia explores new ways to accelerate AI infrastructure deployment by helping customers fund increasingly expensive data center projects. While the strategy could strengthen demand for the company’s chips and expand its competitive moat, it also introduces financial risks that many investors have not traditionally associated with the semiconductor giant.

Cramer’s remarks suggest Wall Street’s focus may be shifting beyond Nvidia’s industry-leading AI products toward the balance sheet implications of financing some of the AI boom itself.

For investors, the question is no longer whether Nvidia can sell enough GPUs. It’s whether acting as a capital provider for the AI ecosystem becomes a competitive advantage—or a new source of risk.

Image: Shutterstock

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I keep buying NVIDIA (NASDAQ:NVDA | NVDA Price Prediction) because the loudest fears on my feed are about a demand cliff that the company’s own income statement refuses to confirm. Every dip fed by a DeepSeek headline or a delayed server rumor sends me back to the buy button, and after this last quarter I stopped apologizing for it.

The Thesis I Keep Coming Back To My conviction rests on software gravity, not the next GPU cycle. Even if hyperscalers slow raw GPU procurement, the installed base of enterprise applications built on the NVIDIA runtime keeps generating high-margin utility billing, which is what allows a 74% to 75% gross margin profile to hold through the late 2020s and avoid the 20% to 30% margin collapses that usually gut chip valuations during capital digestion phases. That is the sentence I underline. Silicon revenue is cyclical. CUDA rent is not.

The Receipts Start with margins, because that is the whole ballgame. Non-GAAP gross margin printed 75.0% in Q1 FY2027, and management guided 75.0% ± 50 bps for Q2 on $91.0 billion ± 2% of revenue. That is a chipmaker guiding software-company gross margins on a top line growing 85.23% year over year. Operating margin sits at 65.6% and return on equity at 114.3%. Numbers like that do not describe a commoditized business.

Then the cash. Free cash flow was $48.554 billion in the quarter and $96.575 billion for FY2026. The board added $80.0 billion to the buyback authorization in May and raised the quarterly dividend from $0.01 to $0.25. About $20.0 billion already came back to shareholders in a single quarter.

Finally, valuation. Forward P/E is 23x against a PEG of 0.559. For a business compounding data center revenue at 92% year over year, that is not a demanding number.

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Why Not the Obvious Alternatives Friends ask why I do not rotate into Advanced Micro Devices (NASDAQ:AMD) or Broadcom (NASDAQ:AVGO) for AI exposure. My answer is the margin bar. NVIDIA’s 75% non-GAAP gross margin and 65.6% operating margin are the profile the software-gravity thesis depends on. Until a rival ships a stack that customers rebuild their MLOps pipelines around, my capital compounds fastest at the runtime layer, and that runtime is CUDA. Jensen Huang put it plainly on the last call: “The AI race is not just about chips. It’s about which stack the world runs on.”

The Risk I Own The real risk is a hyperscaler capex pause colliding with $119.0 billion in total supply-related commitments. If AI infrastructure spend digests for a few quarters, that commitment turns into inventory pain. I own that risk. What keeps me buying is that Microsoft processed over 100 trillion tokens in Q1, a fivefold increase year over year, and reasoning workloads consume hundreds to thousands more tokens per task than one-shot inference. Utilization of the installed base is climbing faster than fresh capex, which is exactly what the margin thesis needs.

Forward Conviction Reddit sentiment during the last two weeks ran bearish on DeepSeek, server delays, and customer diversification fears, and shares are down 3.86% on the week. That is the setup I keep showing up for. The margins are guided flat at software levels, the cash is being handed back in $80 billion tranches, and the runtime the industry rents is still ours. As long as those three sentences stay true, my buy button stays warm.

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Bloomberg reported Monday morning that NVIDIA (NASDAQ:NVDA | NVDA Price Prediction) is lining up as much as $750 billion in investments.

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Jensen Huang, the CEO of Nvidia. Bloomberg/Getty Images Nvidia and top tech companies have assembled a group calling for better support for open AI models to fend off cyberattacks in the wake of OpenAI's rogue agent hacking Hugging Face.

The Open Secure AI Alliance, unveiled Monday, comprises dozens of companies, including Microsoft, SpaceXAI, IBM, Palantir, Databricks, Dell, and Hugging Face itself, as well as the Linux Foundation and startups such as Cognition and Thinking Machines Lab.

In a blog post announcing the group, Nvidia called for regulators to recognize open models and security tooling as defensive assets rather than liabilities. Blanket restrictions would "weaken defensive capacity," it warned, and concentrate dependence on a few providers.

Critics view open models as a liability because their safeguards can be stripped out and their capabilities repurposed for attacks, a risk the post acknowledged but said was not unique to open systems.

Nvidia's post, which lists the companies as inaugural partners, also calls on governments and companies to fund shared open datasets, evaluation frameworks, and red-teaming tools. The group frames closed and open models as complementary.

"Attackers have frontier AI. Defenders need a frontier AI ecosystem—the best open and closed models, force-multiplied by a global community," Nvidia CEO Jensen Huang wrote in a Monday X post.

"During the Hugging Face incident, closed AI blocked essential forensics. An open-weight frontier model helped contain the intrusion. That's why we created the Open Secure AI Alliance," he added.

Hugging Face hacked The announcement comes after Hugging Face said on July 16 that an AI agent had broken into its systems and stolen an access key. It said it used the key to reach deeper into the company's network. Five days later, OpenAI said that its models were responsible: GPT-5.6 Sol and a more capable pre-release system, both running with safety refusals dialed down for an internal test.

Hugging Face first tried to investigate the breach using commercial AI services, but analyzing the intrusion required submitting the attacker's own code. Closed tools, "unable to distinguish attackers from defenders," blocked the analysis, Nvidia said. The safety filters designed to stop hackers ended up hindering the hacked company instead.

Instead, Hugging Face ran an open model, Z.ai's GLM 5.2, on its own servers to review more than 17,000 actions. Defenders "need open, frontier agentic systems for self-defense," the Nvidia blog argued, or their response is constrained "at exactly the moment speed matters most."

Nvidia does not name OpenAI in the post, referring only to "closed AI tools." OpenAI is also absent from the membership list, along with Anthropic, Google, Meta, and Amazon. Z.ai, the Chinese developer of the model used by Hugging Face, is also not included.

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Georgia is a fellow at Business Insider's London office.Before joining Business Insider, she worked at Japan's largest newspaper, The Yomiuri Shimbun, and interned at the Financial Times. She is an NCTJ-qualified journalist with a degree in Philosophy from the University of Birmingham. You can contact her via email at [email protected]

Cybersecurity OpenAI AI More Artificial Intelligence

Safety initiative to develop open-source programs to help defend from AI cyberattacks.

SANTA CLARA, Calif. and PALO ALTO, Calif., July 27, 2026 (GLOBE NEWSWIRE) -- Safe Superintelligence Inc. (SSI) and NVIDIA today announced a long-term partnership to rapidly accelerate SSI’s strategic growth. NVIDIA has additionally made an investment in SSI.

For SSI, NVIDIA’s substantial investment combined with access to the next-generation, best-in-class NVIDIA Vera Rubin platform will allow SSI to increase its compute by an order of magnitude. The two companies will also collaborate on the technical advancement of NVIDIA’s current and future compute platforms, leveraging SSI’s unique insights into the future of AI.

For the last two years, SSI has been quietly advancing a new research direction to unlock a powerful and robustly aligned artificial intelligence. NVIDIA entered this partnership to accelerate SSI’s next stage of growth after obtaining rare access into the company’s closely guarded research.

“Ilya has pioneered fundamental breakthroughs at the foundation of modern AI, beginning with AlexNet,” said Jensen Huang, founder and CEO of NVIDIA. “We are excited to see what new breakthroughs SSI will discover powered by our Vera Rubin platform.”

“We have research that is worthy of scaling up, and having access to a big NVIDIA computer will let us do so,” said Ilya Sutskever, cofounder and CEO of SSI. “We’re incredibly proud to be partnering with Jensen and the NVIDIA team, and we are confident that our big bet on the Vera Rubin platform will take us to the next level.”

About SSI
Safe Superintelligence Inc. (SSI) is the world’s first straight-shot SSI lab, with one goal and one product: a safe superintelligence. Founded in 2024, the company is led by Ilya Sutskever and Daniel Levy. Sutskever’s research laid the foundation for modern AI, with contributions to AlexNet, AlphaGo, Sequence-to-Sequence learning, and the GPT models. He also spearheaded the research that led to reasoning models such as OpenAI o1. SSI’s investors include Andreessen Horowitz, DST Global, Greenoaks, and Sequoia Capital.

About NVIDIA
NVIDIA is the world leader in AI and accelerated computing.

For further information, contact:
Mylene Mangalindan
Corporate Communications
NVIDIA Corporation
[email protected]

Gloria Labbad
Media Inquiries
SSI
[email protected]

Certain statements in this press release including, but not limited to, statements as to: expectations with respect to NVIDIA’s partnership with SSI; expectations with respect to growth, performance, availability, and benefits of NVIDIA’s products, services and technologies, and related trends and drivers; expectations with respect to technology developments, and related trends and drivers; projected market growth and trends; expectations with respect to AI and related industries; and other statements that are not historical facts are forward-looking statements within the meaning of Section 27A of the Securities Act of 1933, as amended, and Section 21E of the Securities Exchange Act of 1934, as amended, which are subject to the “safe harbor” created by those sections based on management’s beliefs and assumptions and on information currently available to management and are subject to risks and uncertainties that could cause results to be materially different than expectations. Important factors that could cause actual results to differ materially include: global economic and political conditions; NVIDIA’s reliance on third parties to manufacture, assemble, package and test NVIDIA’s products; the impact of technological development and competition; development of new products and technologies or enhancements to NVIDIA’s existing products and technologies; market acceptance of NVIDIA’s products or NVIDIA’s partners’ products; design, manufacturing or software defects; changes in consumer preferences or demands; changes in industry standards and interfaces; unexpected loss of performance of NVIDIA’s products or technologies when integrated into systems; NVIDIA’s ability to realize the potential benefits of business investments or acquisitions; and changes in applicable laws and regulations, as well as other factors detailed from time to time in the most recent reports NVIDIA files with the Securities and Exchange Commission, or SEC, including, but not limited to, its Annual Report on Form 10-K and Quarterly Reports on Form 10-Q. Copies of reports filed with the SEC are posted on the company’s website and are available from NVIDIA without charge. These forward-looking statements are not guarantees of future performance and speak only as of the date hereof, and, except as required by law, NVIDIA disclaims any obligation to update these forward-looking statements to reflect future events or circumstances.

© 2026 NVIDIA Corporation. All rights reserved. NVIDIA, the NVIDIA logo are trademarks and/or registered trademarks of NVIDIA Corporation in the U.S. and other countries. Other company and product names may be trademarks of the respective companies with which they are associated. Features, pricing, availability and specifications are subject to change without notice.

A photo accompanying this announcement is available at https://www.globenewswire.com/NewsRoom/AttachmentNg/65acb244-82c0-4dea-9023-59d66f334807

Safe Superintelligence Inc. and NVIDIA SSI and NVIDIA logos

Nvidia is in talks with OpenAI guarantee $250 billion in financing to help it open a data center in Ohio. OpenAI is already a customer of Nvidia.

NVIDIA (NASDAQ:NVDA | NVDA Price Prediction) CEO Jensen Huang used an exclusive Bloomberg interview on July 27, 2026 to lay out one of the boldest forecasts yet for the AI buildout: a semiconductor industry that must expand roughly tenfold over the coming decade to serve a computing world dominated by autonomous machines rather than human users.

Speaking about the shift from human-driven computing to agentic systems, Huang argued that Nvidia’s addressable market is being redefined at the endpoint level. “These are now processing AI’s for humans to collaborate with. In the future we have AI agents and robots and they will be using computers. Instead of a billion people using computers we will have 100 billion agents and billions of robots all using computers. The computer industry built on top of the chip industry is certainly not big enough,” Huang said. His projection follows: “My guess is the semiconductor industry will probably have to be 10 times larger than it is today over the next decade or so. Working with our partners in Korea and around the world to scale up the supply chain of semiconductors so we are prepared for the AI future.” Huang framed the 10x figure explicitly as his personal guess, not a certainty.

Reframing The Addressable Market The pitch aligns with themes Huang has hammered on recent earnings calls. On the fiscal Q1 2027 call in May 2026, he described “The transition from generative to agentic AI, AI capable of perceiving, reasoning, planning, and acting,” as a force that will reshape every industry. He has also flagged that reasoning models require “a hundred, a thousand times more” tokens per task than one-shot chatbots, a compute intensity that underpins his tenfold industry expansion thesis.

Nvidia reported fiscal Q1 2027 revenue of $81.61 billion, up 85.2% year over year, with data center revenue of $75.25 billion and non-GAAP earnings per share of $1.87, according to the company’s SEC 8-K filing. Q2 guidance called for approximately $91.0 billion in revenue.

The Korea Angle: SK Group And HBM Huang leaned heavily into South Korea as a linchpin of the buildout. “It is the golden age for Korea, as you know. Their semiconductor and industrial business is booming. The country has the ability to help the world buildout AI infrastructure. They are incredibly adapt at adopting new technologies,” he told Bloomberg.

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Nvidia announced a partnership with SK Group valued at over $500 billion in consumption and purchasing of memory and sales of AI supercomputers. The companies are collaborating on memory roadmaps spanning HBM3, HBM4, and beyond, and Nvidia is investing in Korea’s leading AI cloud with plans to scale 200 megawatts. Reports indicate Nvidia will invest $1 billion in Naver Corp to finance an AI data center expansion in the country. SK Group’s listed affiliates trade on the Korea Exchange rather than U.S. venues.

Capex Confidence, With Caveats The 10x call gives investors a framework for interpreting Nvidia’s aggressive supply commitments. The company disclosed $119.0 billion in total supply-related commitments and authorized an additional $80 billion share buyback, alongside a dividend raise to $0.25 per share. Reports indicate Nvidia is in talks to provide a $250 billion financing guarantee for an OpenAI data center project in southern Ohio.

Nvidia carries a trailing P/E of 32 and a forward P/E of 24, with an analyst target price of $302.83 against a recent quote of $206.02. Shares are up 11.04% year to date and 19.21% over the past year.

Huang is arguing that AI infrastructure demand will run longer and deeper than current models assume because the number of compute endpoints jumps from roughly a billion humans to hundreds of billions of agents and robots. That is his projection, not a guarantee, and it hinges on the industry actually scaling supply chains across Taiwan, Korea, and the United States to meet it. Investors watching Nvidia should track hyperscaler capex commentary, HBM supply agreements, and the Vera Rubin ramp for evidence that Huang’s timeline is materializing.

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Dan Ives, Partner and Senior Managing Director at Yorkville Ives & Co., took to CNBC on July 27, 2026 to push back on the growing chorus of tech skeptics. His central message: the recent pullback in high-growth names is a digestion period, and the AI investment cycle is nowhere near its late stages. “We’re third inning of the AI revolution and it’s just further validation from earnings,” Ives said.

The backdrop matters. The Nasdaq 100 is trading at 22 times forward P/E, a nearly 10% discount to its ten-year average, and sits 8% below June 2026 highs. Volatility has ticked up modestly, with the VIX at 18.70 as of July 23, 2026, still inside the normal range. Ives’s framing is that this is exactly what a healthy consolidation looks like inside a multi-year buildout.

The NVIDIA Thesis: One Chip, 12-to-1 Demand NVIDIA (NASDAQ:NVDA | NVDA Price Prediction) sits at the center of Ives’s argument. “There’s one chip in the world fueling the AI revolution, and that’s Nvidia,” he said, adding that “Demand to supply today is 12 to 1 for their chips. Physical AI hasn’t even started to play out.”

The most recent numbers give that view some weight. In Q1 FY2027, NVIDIA posted revenue of $81.61 billion, up 85.2% year over year, and non-GAAP EPS of $1.87 versus a $1.77 estimate. Data Center revenue reached $75.25 billion, up 92% year over year, with Data Center Networking climbing 199% year over year to $14.80 billion. Management guided Q2 FY27 revenue to $91.0 billion, plus or minus 2%, excluding China Data Center compute. Total supply commitments now stand at $119.0 billion, disclosed in the company’s Q1 FY27 8-K filing.

CEO Jensen Huang described the moment as “the buildout of AI factories, the largest infrastructure expansion in human history, is accelerating at extraordinary speed.” NVIDIA shares closed at $206.84 on July 24, and the stock is up 11.04% year to date. Forward P/E sits at 24, with a consensus analyst target of $302.83.

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The Hyperscaler Capex Arms Race Ives frames hyperscaler spending as rational. “For every dollar spent on capex, there’s five, six multiplier across the rest of the tech,” he said, and “We’re only 15% through what the broader spending is going to be in terms of AI.”

The 2026 spending picture supports the scale of that argument:

Amazon (NASDAQ:AMZN) plans roughly $200 billion in capex in 2026, with AWS revenue of $37.59 billion in Q1, up 28%. Microsoft (NASDAQ:MSFT) reported Q3 FY26 capex of $30.88 billion, up 84.4%, with an AI business surpassing a $37 billion annual revenue run rate, up 123% year over year. Meta Platforms (NASDAQ:META) raised its FY2026 capex guidance to $125 billion to $145 billion and signed a multi-year deal with NVIDIA for millions of Blackwell and Rubin GPUs. Alphabet spent $44.92 billion on capex in Q2 alone, up 100% year over year, and Ives noted the company recorded its first negative free cash flow in 22 years as a direct result of AI buildout spending. What Ives Wants Investors to Watch The real validation of the cycle, in Ives’s view, arrives through cloud growth and enterprise adoption metrics due this earnings season. Prediction markets are aligned near term: Polymarket assigns a 91% probability that Microsoft beats its next quarterly earnings, and 95.3% probability for Amazon.

Risks remain. NVIDIA continues to guide with no Data Center compute revenue from China assumed, and hyperscalers are increasingly tapping debt markets to fund the buildout. Ives’s 12-to-1, 5-to-6x, and 15%-complete figures are his estimates, not audited metrics. The pushback that matters most for his thesis is whether cloud revenue growth continues to justify the capital being deployed.

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CNBC's Kai Nicol-Schwarz discusses a White House official accusing Chinese AI company Moonshot of accessing advanced Nvidia chips despite an export ban.

Nvidia was eyeing a raft of deals with SK Hynix, OpenAI and Siemens.

Nvidia and a host of tech giants on Monday launched a new artificial intelligence safety initiative focused on open models, as the fallout from a cyberattack committed by rogue OpenAI models continues.

Last week, it emerged that the target of the attack, startup Hugging Face, was unable to use leading U.S. frontier models to defend itself, with guardrails not distinguishing between aggressor and defender. Instead, it turned to a self-hosted, open-weight Chinese model, which was not bound by those same restrictions.

In the wake of U.S. lawmakers increasingly weighing how to curb growing adoption of Chinese AI models, the most advanced of which are open weight, tech giants have launched an initiative aimed at building and sharing open AI tools.

Open models can be downloaded, modified and self-hosted, in contrast to closed models — including frontier systems built by Anthropic and OpenAI — which can only be accessed through specific infrastructure.

"The Open Secure AI Alliance will work to remediate and disclose vulnerabilities using open technologies," Nvidia said in a statement. "The recent Hugging Face security incident delivered a clear reminder: cyber defenders need open, frontier agentic systems for self-defense."

Alongside Nvidia, other members of the alliance include Microsoft, SpaceX, Palantir, and dozens of other tech companies from the U.S. and Europe.

The push to curb Chinese AIThere are growing calls for measures to limit access to models built by Chinese AI companies, which have been accused of campaigns to extract information from U.S. rivals' systems, known as "distillation" — when one model extracts knowledge from a better-trained model.

Last week, Treasury Secretary Scott Bessent threatened sanctions on Chinese companies that commit distillation attacks against U.S. companies.

"There is a real possibility the US government does impose restrictions on Chinese models," Chris McGuire, senior fellow for China and emerging technologies, at think tank the Council on Foreign Relations, told CNBC.

That could include a ban on transactions involving the models, such as purchasing tokens via an API or U.S. companies hosting the model on the cloud and charging customers for inference, he said.

"In Washington this is not a debate about open-source vs closed-source, it is a debate about whether or not to tolerate Chinese IP theft," McGuire said. "Any actions would be focused on Chinese companies, not the open-source ecosystem."

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But with the majority of the most capable open-source models being built by Chinese companies, there are concerns over restrictions.

Last week, Nvidia, Microsoft, Meta, Palantir and more than 20 other companies released a letter urging policymakers to avoid "premature restrictions" on open-weight AI models that would "stifle competition or drive innovation overseas."

The OpenAI-Hugging Face incident showed a "practical truth," said Nvidia. "When defenders cannot inspect, adapt and run advanced AI on their own infrastructure, their ability to respond is constrained at exactly the moment speed matters most."

The artificial intelligence boom has reshaped more than the technology industry — it has changed how the biggest AI infrastructure projects get funded. Companies are no longer just competing to build the fastest models. They are racing to secure enough computing power to train them, forcing billions of dollars into new data centers and AI chips. 

Nvidia (NASDAQ:NVDA | NVDA Price Prediction) has been the biggest beneficiary of that spending, but as the cost of AI infrastructure continues climbing, investors are beginning to pay closer attention to who is ultimately paying the bill. A new report suggests Nvidia may once again play a larger role than simply supplying GPUs.

Nvidia’s Role Appears to Be Expanding According to The Wall Street Journal, Nvidia is in talks to support as much as $600 billion of OpenAI-related financing. The proposal could include $250 billion tied to OpenAI’s planned Ohio data center and another $350 billion supporting GPU purchases.

If true, the arrangement would represent a dramatic expansion of Nvidia’s relationship with its largest AI customers. OpenAI already depends on Nvidia’s GPUs to train and run models like ChatGPT. Increasingly, it may also depend on Nvidia to help finance the infrastructure required to buy those chips.

This isn’t the first time such reports have surfaced. Last year, reports suggested Nvidia was considering a financing package approaching $100 billion for OpenAI’s infrastructure ambitions. Those negotiations ultimately produced a much smaller investment than initially reported. Nvidia CEO Jensen Huang later indicated media reports overstated the scope of the discussions, explaining the conversations centered around a non-binding memorandum of understanding rather than Nvidia funding the entire project outright.

That history matters because none of the latest reports have been confirmed by Nvidia or OpenAI. Investors should remember that negotiations often evolve — or disappear entirely.

A $600 billion gamble that could redefine the AI race—or expose a massive 'circular' financing loop. © 24/7 Wall St. The Circular Financing Debate Returns Critics have increasingly argued Nvidia is participating in “circular financing” arrangements, where it helps customers obtain financing that ultimately flows back to Nvidia through GPU purchases. The concern is straightforward: if Nvidia is helping finance demand for its own products, does that make AI demand appear stronger than it otherwise would?

The criticism isn’t new. Nvidia has invested directly in AI startups while partnering with lenders and infrastructure providers to expand AI capacity. Those investments remain tiny compared to Nvidia’s nearly $200 billion in annual revenue and hundreds of billions of dollars in cash generation, but a $600 billion financing package would inevitably attract renewed scrutiny if completed.

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Granted, facilitating financing isn’t uncommon in capital-intensive industries. Aircraft manufacturers, industrial equipment makers, and energy companies have long helped customers secure funding. The difference is the sheer size of today’s AI infrastructure projects.

OpenAI’s Economics Remain the Bigger Question The financing discussion also shines a spotlight on OpenAI itself. Earlier this year, reports indicated OpenAI had reached roughly $2 billion in monthly revenue, an extraordinary growth rate for any software company. Yet multiple reports have also suggested the company continues generating massive operating losses as it spends aggressively on AI infrastructure, talent, and model development.

That creates a delicate balancing act. OpenAI needs ever-larger computing clusters to stay competitive, while Nvidia needs customers capable of purchasing ever-larger quantities of GPUs.

Ironically, that mutual dependence is exactly what makes investors uneasy. If OpenAI requires outside financing to sustain its expansion, skeptics may once again question whether AI chip demand is entirely organic or increasingly supported by creative financing structures.

Key Takeaway In short, investors should treat the reported $600 billion financing discussions with caution until Nvidia or OpenAI confirms the details. Similar reports last year ultimately proved far less sweeping than early headlines suggested.

Regardless, the report underscores a broader trend that matters far more than one rumored transaction: Nvidia is evolving from the world’s dominant AI chip supplier into a central player in financing the AI ecosystem itself. That strategy could deepen customer relationships and protect future GPU demand, but it also invites greater scrutiny over whether demand is being driven by end-market economics or increasingly by the availability of capital.

Ultimately, that distinction may become one of the most important questions surrounding Nvidia’s valuation over the next several years.

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Nvidia’s expanded partnership with SK Hynix has given investors a reason to look for a breakout, although the agreement has not yet produced a measurable market reaction.

NVDA closed Friday at $206.84, down 0.92%, before details of the SK Group initiative were absorbed.

Nvidia already dominates AI accelerators, but its processors cannot be delivered as complete systems without enough high-bandwidth memory.

By securing and jointly developing HBM with SK Hynix, Nvidia is addressing a component that could increasingly determine how many AI factories it can build and ship.

Nvidia and SK Hynix agreed to establish a long-term partnership covering supplies, joint development and optimisation of next-generation AI memory, including HBM.

The arrangement is designed to align memory technology with Nvidia’s computing platforms.

That matters because memory is becoming one of the tightest constraints in the AI supply chain.

Morgan Stanley analyst Joseph Moore described the market as unlike a conventional semiconductor cycle, arguing that memory was becoming “increasingly THE bottleneck” for AI and agentic-computing systems.

Moore also identified Nvidia and Broadcom as among the strongest-value computing names.

His argument supports the investment case behind the SK Hynix agreement: if Nvidia secures more advanced memory while demand remains above supply, it could ship more complete systems and reduce a major execution risk.

The deal does not eliminate shortages immediately.

HBM capacity remains limited, qualification requirements are demanding and Nvidia, hyperscalers and rival accelerator developers are competing for the same advanced supply.

The partnership also links Nvidia to a potentially important customer.

SK Telecom plans to develop an AI factory of up to 2 gigawatts using Nvidia’s DSX architecture and Vera Rubin accelerated-computing systems powered by SK Hynix HBM4.

The first facility is planned to begin operating in 2027.

That creates a strategic loop as SK Hynix supplies and co-develops the memory, Nvidia provides the computing systems, networking, software and data-centre architecture, and SK Telecom becomes an infrastructure customer serving South Korea and the wider Asia-Pacific region.

The deployment could become a valuable reference site for Vera Rubin as Nvidia faces competition from hyperscalers’ custom processors and specialist AI-chip companies.

It also reinforces Nvidia’s shift from selling individual GPUs towards supplying complete AI factories.

However, the companies signed letters of intent for a programme described as exceeding $500 billion.

They did not disclose Nvidia’s expected revenue, system volumes, memory prices or binding purchase commitments.

Also read: Why are Nvidia-backed CoreWeave, Nebius, and IREN stocks plunging?

Execution remains the central risk. KeyBanc analyst John Vinh said the Vera Rubin ramp appeared slightly delayed because of thermal-lid issues and SK Hynix’s HBM4 qualification.

He nevertheless viewed the financial risk as manageable because additional Blackwell shipments could offset slower Rubin deliveries.

Vinh retained an Outperform rating and raised his Nvidia price target to $330 from $310.

His view captures the stock’s tension: the partnership addresses the correct bottleneck, but Nvidia must still qualify HBM4, solve system-level challenges and scale Rubin on schedule.

Investors must also see continued capital spending from Microsoft, Amazon, Alphabet and Meta.

Barron’s recently argued that renewed Big Tech spending commitments were needed to drive sustained gains above $200.

The planned investment could pave the way for a broader multibillion-dollar push into AI infrastructure in South Korea.

News Summary:

NVIDIA expands NVIDIA Agent Toolkit with re-architected NVIDIA PhysicsNeMo libraries and updated NVIDIA CUDA-X libraries, enabling software developers to build autonomous AI engineers with AI physics skills, accelerated solvers and quantum chemistry capabilities.NVIDIA Nemotron 3 Ultra leads among open models in agentic register-transfer level coding with the ACE-RTL agent from NVIDIA Research, helping enterprises build customizable AI agents for chip design and verification.Cadence, Siemens, Synopsys and other industry leaders are using NVIDIA accelerated computing and agentic AI technologies to advance autonomous engineering workflows across chip design, verification, packaging and systems. LONG BEACH, Calif., July 26, 2026 (GLOBE NEWSWIRE) -- NVIDIA today announced an expansion of NVIDIA Agent Toolkit for engineering, now adding NVIDIA PhysicsNeMo™ and CUDA-X™ libraries as agent-ready tools and skills built to transform how the world designs and develops products.

Building the next generation of chips and systems requires teams to connect physics, simulation and performance analysis across increasingly complex design cycles. A new class of autonomous AI engineers is emerging to help take on that complexity — using specialized tools, running simulations and generating high-fidelity data to help scale chip design, verification, packaging and systems. 

Now included in NVIDIA Agent Toolkit, NVIDIA has re-architected PhysicsNeMo into a set of agent-friendly libraries and added new and updated CUDA-X libraries to support complex engineering work. PhysicsNeMo provides AI physics skills for training and deploying models, while CUDA-X libraries bring accelerated solvers and quantum chemistry capabilities into agentic engineering workflows.

“Engineering has reached an inflection point. AI can now work with tools of physics, simulation and design,” said Timothy Costa, vice president and general manager of computational engineering at NVIDIA. “With NVIDIA Agent Toolkit, developers can build agentic engineers that reason using physics, run complex simulations and generate high-fidelity data to become a new engine for innovation in chip and system design.”

NVIDIA Agent Toolkit Adds AI Physics and Accelerated Computing Skills for Engineering Agents
NVIDIA Agent Toolkit helps developers build specialized engineering AI assistants connected to domain-specific tools, models and data. With the addition of NVIDIA PhysicsNeMo and CUDA-X libraries, these agents can now use AI physics skills, accelerated solvers and quantum chemistry capabilities for chip, system and industrial engineering. 

Key capabilities include:

AI physics skills: NVIDIA PhysicsNeMo libraries help agents train and deploy customizable AI physics models for complex design and simulation tasks, turning model architectures into callable tools for engineering workflows.Iterative sparse solvers: New NVIDIA cuISS (CUDA Iterative Sparse Solvers) library accelerates large sparse linear systems in physics-based and engineering simulations. Designed for flexibility and performance on GPUs, its modern, composable solvers and preconditioners help developers build scalable, production simulation engines for agentic engineering workflows. Direct sparse solvers: NVIDIA cuDSS (CUDA Direct Sparse Solvers) accelerates large, complex sparse linear systems central to electronic design automation (EDA) and scientific simulation. It delivers high performance and numerical robustness for critical workloads like device, circuit and system simulations with scalability to multi-GPU and multi-node deployments in production environments.Quantum chemistry: NVIDIA cuEST (CUDA Electronic Structure Theory) brings high-accuracy quantum chemistry simulations to device-relevant scales, enabling density functional theory (DFT) and post-DFT methods to be integrated into production workflows at scale. cuEST brings production value to customers by supporting a wide range of modern functionals and making increasingly large ground-state and excited-state simulations manageable on NVIDIA GPUs. NVIDIA Nemotron 3 Ultra Open Model Advances Agentic Coding for Chip Design
Chip design depends on specialized register-transfer level (RTL) coding, which demands high accuracy, deep domain expertise and flexibility over deployment. 

With ACE-RTL — an agent for designing hardware from NVIDIA Research — NVIDIA Nemotron™ 3 Ultra leads among open models in agentic RTL coding on the comprehensive verilog design problems benchmark across RTL coding tasks.

This represents how Nemotron 3 Ultra offers industry-leading accuracy and efficiency and can be post-trained on proprietary data — deployed locally or on premises — giving enterprises greater control, customization and data privacy as they build AI agents for chip design.

Developers can get started with Nemotron 3 Ultra using Cadence’s harness; Synopsys’ fully autonomous, long-running agents for design verification and analog and mixed-signal workflows; Siemens’ Questa One smart verification agentic toolkit; as well as on Hugging Face.

Software Leaders Build Autonomous AI Engineers With NVIDIA
Industrial engineering leaders are already using the new and expanded NVIDIA Agent Toolkit components to develop autonomous AI engineers.

Cadence is using NVIDIA Nemotron, accelerated computing and CUDA-X libraries with the recently launched Cadence AuraStack AI Super Agent and the Cadence Millennium M2000 platform to autonomously drive advanced packaging and printed circuit board (PCB) design from exploration through signoff, delivering up to 20x faster multiphysics performance. This joins Cadence’s complete portfolio of silicon design super agents which collectively cover the chip design workflow end to end, from architecture through manufacturing signoff.

In addition, the collaboration extends from agentic design to the underlying compute as Cadence’s portfolio of EDA and system design automation tools, including Cadence Jasper, a formal verification platform, is being optimized for the NVIDIA Vera CPU to help engineering teams validate advanced chip designs faster.

Synopsys is using the NVIDIA Agent Toolkit, NVIDIA NIM™ microservices, Nemotron open models, the NVIDIA NeMo™ Gym library and NVIDIA NemoClaw™ blueprints with Synopsys AgentEngineer to build secure, accelerated agentic workflows across chip and system design. Leveraging Ansys Icepak, Synopsys’ agentic workflow autonomously executes simulation setup, and pre- and post-processing for complex GPU cooling design optimization. Synopsys is developing NVIDIA cuISS use cases to accelerate simulation workloads.

The collaboration extends from agentic workflows to the underlying compute platform as Synopsys VCS, a high-performance functional verification solution used to simulate and validate complex chip designs before fabrication, is being optimized for the NVIDIA Vera CPU to help improve verification throughput.

Siemens is using NVIDIA NeMo Gym, Nemotron open models and CUDA-X libraries with the Siemens Fuse EDA AI Agent to orchestrate multi-tool and multi-agent workflows across semiconductor, 3D-IC, PCB and system design, from conception through signoff. In Siemens Solido Characterization Suite, these agentic AI workflows are delivering more than 10x faster library characterization while reducing token costs by more than 10x.

Samsung is using NVIDIA cuLitho and CUDA-X libraries to achieve up to 20x greater performance for computational lithography and applying NVIDIA PhysicsNeMo to perform chip-scale thermal-stress analysis with numerical solver-level accuracy across domains containing up to 10 billion cells.

ChipAgents is using NVIDIA Agent Toolkit to build domain-specific AI agents for chip design and verification. The team is fine-tuning NVIDIA Nemotron models for complex end-to-end semiconductor design and verification workflows including debug, formal verification, coverage and more.

Silvaco is using NVIDIA accelerated computing to scale high-accuracy 3D optical simulation in the Silvaco Victory Device. Running on 32 NVIDIA GPUs interconnected by NVIDIA NVLink™ technology, it completed a 3.2-billion-mesh-node photonic edge coupler simulation in under four hours, a workload beyond the practical limits of CPU-based simulation.

Keysight is harnessing NVIDIA cuDSS to accelerate electromagnetic simulations by up to 10x, while Samsung, Synopsys and TSMC are integrating NVIDIA cuEST into its GPU-accelerated pipeline to achieve up to a 50x speedup for key quantum-chemistry workloads.

Learn more by joining NVIDIA at DAC.

About NVIDIA
NVIDIA (NASDAQ: NVDA) is the world leader in AI and accelerated computing.

For further information, contact:
Paris Fox
Corporate Communications
NVIDIA Corporation
[email protected]

Certain statements in this press release including, but not limited to, statements as to: With NVIDIA Agent Toolkit, developers being able to build agentic engineers that reason using physics, run complex simulations and generate high-fidelity data to become a new engine for innovation in chip and system design; expectations with respect to growth, performance, availability, and benefits of NVIDIA’s products, services and technologies, and related trends and drivers; expectations with respect to NVIDIA’s third party arrangements, including with its collaborators and partners; expectations with respect to technology developments, and related trends and drivers; projected market growth and trends; expectations with respect to AI and related industries; and other statements that are not historical facts are forward-looking statements within the meaning of Section 27A of the Securities Act of 1933, as amended, and Section 21E of the Securities Exchange Act of 1934, as amended, which are subject to the “safe harbor” created by those sections based on management’s beliefs and assumptions and on information currently available to management and are subject to risks and uncertainties that could cause results to be materially different than expectations. Important factors that could cause actual results to differ materially include: global economic and political conditions; NVIDIA’s reliance on third parties to manufacture, assemble, package and test NVIDIA’s products; the impact of technological development and competition; development of new products and technologies or enhancements to NVIDIA’s existing products and technologies; market acceptance of NVIDIA’s products or NVIDIA’s partners’ products; design, manufacturing or software defects; changes in consumer preferences or demands; changes in industry standards and interfaces; unexpected loss of performance of NVIDIA’s products or technologies when integrated into systems; NVIDIA’s ability to realize the potential benefits of business investments or acquisitions; and changes in applicable laws and regulations, as well as other factors detailed from time to time in the most recent reports NVIDIA files with the Securities and Exchange Commission, or SEC, including, but not limited to, its Annual Report on Form 10-K and Quarterly Reports on Form 10-Q. Copies of reports filed with the SEC are posted on the company’s website and are available from NVIDIA without charge. These forward-looking statements are not guarantees of future performance and speak only as of the date hereof, and, except as required by law, NVIDIA disclaims any obligation to update these forward-looking statements to reflect future events or circumstances.

Many of the products and features described herein remain in various stages and will be offered on a when-and-if-available basis. The statements above are not intended to be, and should not be interpreted as a commitment, promise, or legal obligation, and the development, release, and timing of any features or functionalities described for our products is subject to change and remains at the sole discretion of NVIDIA. NVIDIA will have no liability for failure to deliver or delay in the delivery of any of the products, features or functions set forth herein.

© 2026 NVIDIA Corporation. All rights reserved. NVIDIA, the NVIDIA logo, CUDA-X, NemoClaw, Nemotron, NVIDIA NeMo, NVIDIA NIM, NVLink and PhysicsNeMo are trademarks and/or registered trademarks of NVIDIA Corporation in the U.S. and other countries. Other company and product names may be trademarks of the respective companies with which they are associated. Features, pricing, availability and specifications are subject to change without notice.

A photo accompanying this announcement is available at https://www.globenewswire.com/NewsRoom/AttachmentNg/8cc7fd5d-80e0-4960-9176-41e04d3909a0

NVIDIA Agent Toolkit With NVIDIA PhysicsNeMo and CUDA-X Libraries NVIDIA today announced an expansion of NVIDIA Agent Toolkit for engineering, now adding NVIDIA Physi...

By Reuters

July 26, 202611:04 PM UTCUpdated 43 mins ago

Nvidia logo is seen in this illustration taken June 11, 2026. REUTERS/Dado Ruvic/Illustration/File Photo Purchase Licensing Rights, opens new tab

SEOUL, July 27 (Reuters) - South ​Korea's Naver (035420.KS), opens new tab ‌said in a ​regulatory ​filing on Monday ⁠that ​Nvidia (NVDA.O), opens new tab will ​acquire $1 billion of its ​shares ​to be newly ‌issued ⁠as part of an ​investment ​partnership ⁠to build ​a ​new ⁠data center.

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Reporting ⁠by ​Jack ​Kim; Editing by ​Chris Reese

Our Standards: The Thomson Reuters Trust Principles., opens new tab

By Reuters

July 26, 202611:39 PM UTCUpdated 12 mins ago

An NVIDIA logo and a computer motherboard appear in this illustration taken August 25, 2025. REUTERS/Dado Ruvic/Illustration/File Photo Purchase Licensing Rights, opens new tab

CompaniesJuly 26 (Reuters) - Nvidia (NVDA.O), opens new tab is in talks ​to provide a ‌roughly $250 billion backstop for OpenAI as ​part of ​a massive data center ⁠project, The ​Wall Street Journal ​reported on Sunday.

The guarantees from Nvidia would help ​the ChatGPT ​maker lease a 10-gigawatt ‌project ⁠that SoftBank’s (9984.T), opens new tab energy subsidiary is developing in southern ​Ohio, ​the ⁠newspaper said citing people ​familiar with the ​matter.

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Reuters ⁠could not immediately verify the ⁠report.

Reporting ​by ​Abu Sultan in Bengaluru; Editing ​by Christian Schmollinger

Our Standards: The Thomson Reuters Trust Principles., opens new tab