The robot accomplishes all this in one place: the simulator. Nvidia's simulation world, Omniverse (the company's virtual environment platform), serves as the environment where robots are "born." It functions as a cognitive kindergarten where humanoid robots learn to walk, operate, understand, react, fall, and rise. Just as an infant develops cumulative motor and cognitive abilities, the robot learns within an artificial world governed by real-world physical laws.

The simulator generates thousands of situational variations: a glass falling at a different angle, a slightly higher step, weak lighting, a person crossing too quickly in the movement path – to teach the robot to react to as many scenarios as possible.

"If we want to build intelligence that understands the physical world and operates within it, we need to teach it in a world similar enough to reality so it can function within it safely, efficiently, and controllably," Rev Lebaredian, Nvidia's vice president of simulation technologies and Omniverse, said in an exclusive conversation with Israel Hayom.

A defining moment in the journey

Lebaredian joined Nvidia in 2002, after working in the film industry. Early in his career, he worked at production houses like Disney and Warner Bros., and later founded a startup developing advanced rendering technologies. In cinema, the rendering process transforms raw graphics into realistic images that appear as if filmed by a camera – a process that was particularly slow and demanding in the early 2000s, sometimes requiring hours of computation for each frame.

As part of his work, he contributed to creating effects in films like "Armageddon," "X-Men," "The Sum of All Fears," and Disney's "Mighty Joe Young," a film nominated for an Oscar for effects thanks to the digital gorilla character at the story's center.

In the early 2000s, Nvidia was primarily a gaming chip manufacturer, far from the AI giant it is today, valued at approximately $5 trillion. Lebaredian joined exactly when Nvidia's flagship product, the graphics processing unit (GPU), began transforming, and he accompanied the company from the crude computer games era of the early 2000s to today's AI revolution, changing the world at rapid speed.

"I joined Nvidia at a defining moment in its journey, precisely when we launched the ability to program shaders (programmable graphics functions) directly on the GPU. This significantly accelerated rendering capabilities, but more importantly, this was the moment the GPU opened for the first time to free programming. I worked then on the first programming language for graphics processors, CG, which became the first brick on the path to CUDA (Nvidia's parallel computing platform), the language dominating parallel computing today," he recounted.

Today, as head of the company's simulation division – Omniverse – Lebaredian is among the handful of senior executives leading simulation and physical intelligence at the company. Nvidia believes this field will drive the next major technological revolution, bringing artificial intelligence into the physical space of daily life. In this revolution, the division Lebaredian heads will have one of the most significant roles.

"Nvidia CEO and founder Jensen Huang said years ago that the most important algorithms will be those understanding the physical world and capable of influencing it," Lebaredian stated.

From language understanding to world understanding

Those algorithms Huang discussed years ago are materializing today in a new field of artificial intelligence: the world model. Just as a language model learns from billions of sentences to predict which word will come next with the highest probability, and thus essentially understand language, meaning, and context – a world model learns to predict what will happen next in the physical world. Namely, how an object will move, how force will affect, what will happen if a door opens too quickly, or where an object placed at this or that angle will roll.

"A world model is the central foundation of the next revolution: physical intelligence, meaning AI that understands not just words, but the universe," Lebaredian explained. According to him, this is a statistical model developing a probabilistic understanding of dynamic reality, not of text. This model will essentially be the robot's "brain," decoding the environment's visual information and knowing how to operate, where to turn to avoid an obstacle, and what force to apply to crack an egg while making an omelet, for example.

But to do this, it needs data of a type that doesn't exist on the internet. Not words, but material, movement, acceleration, friction, light, temperature, interactions, human environments, and physical infrastructures. The training process is fundamentally similar to that of language models – learning from countless examples and situations – except that here the examples must come from the physical world itself.

"The major problem with physical intelligence," Lebaredian explained, "is that we don't have a digital archive of physics. We need to capture it from reality – and that's expensive, dangerous, and limited. The solution is to recreate reality in simulation, and then produce synthetic data from it."

According to Lebaredian, Nvidia's simulation world is not merely a three-dimensional model. It is an engine of natural laws. A city where every lamppost, sidewalk, car, and tree branch is coded to behave as in reality. In this environment, a robot can walk thousands of simulated years in a short time, accumulating experience impossible in the real world.

Releasing the "genie" from the GPU

To understand Nvidia's role in the AI revolution and the magnitude of the mission the company placed on Lebaredian's shoulders, one must return to the story's beginning – and trace the development of one of recent decades' most influential components: the graphics processing unit.

This development did not amount to gradual increases in performance. This is deep evolution, where each new GPU generation changed the computer's very nature. To such an extent that some believe that without Nvidia, not only would a large language model not function at the required speed, but we might not have imagined the very possibility.

Language models, world models, and advanced robotics all feed on enormous parallel computing power, the kind that needed to be born before theoretical thinking about them became possible. Twenty years ago, the GPU was a dedicated graphics unit designed to accelerate computer games. It was designed as a "drawing machine," receiving a series of fixed commands defining how a three-dimensional object should appear on screen. All stages were rigid: how light falls, how reflection forms, whether the material is shiny or matte. The processor could execute these tasks quickly, but nothing existed beyond this.

"In the early 2000s, everything was very simple and limited," Lebaredian recalled. "You couldn't write your own code. Performance was high, but flexibility didn't exist." According to him, the field's first significant revolution occurred when Nvidia opened the shading stage to programming. Instead of built-in models, developers could write their own functions, recreate light and material laws, and build graphic worlds as they imagined them. The change then appeared as a breakthrough for the gaming world alone, but in practice, it freed the GPU from its initial engineering constraints.

The drawing machine became a machine that understood somewhat more about how the world behaves. The hardware ceased being a black box and became an open platform. This was the moment the seed was planted that later became a computing superplatform.

"I've been at Nvidia for 23 years," Lebaredian said, "and almost throughout this entire period, the company has dealt with the question of what else the GPU can be beyond what it was designed for."

"Far beyond what we imagined"

Lebaredian recounted that as shader programs became more flexible, more and more developers identified potential within the GPU far exceeding graphics. Thus, for example, academic researchers began using the graphics processor for physics calculations – they took the same shading function that calculates light and adapted it to compute airflow, water movement, or particle dynamics. The graphics processor's essence as a computer with powerful parallel computing capabilities gradually became clear.

"We saw researchers using it for things completely unrelated to graphics – physical simulations, fluid dynamics, molecules. This was the moment we understood our processors could serve far beyond what we imagined," he stated.

At this stage, Nvidia understood it must change direction and give this computing body a new form. In 2006, CUDA (Nvidia's parallel computing platform) launched, a software environment allowing regular code to run on the GPU. No more disguising scientific problems as graphics, no more manipulating textures or pixels – but a complete computer capable of processing large arrays, running loops, and executing complex algorithms quickly. Historically, this was the turning point at which the GPU ceased to be a graphics accelerator and became a general-purpose computing engine.

The network that learned to "see"

Here arrived another defining moment in the development of artificial intelligence, made possible by Nvidia's programming language. AlexNet – that groundbreaking 2012 neural network learning to identify objects in images with high accuracy like cats, dogs, cars – ran on CUDA. AlexNet marked the beginning of the past decade's computer vision era, with countless applications from smart security cameras to facial recognition systems in smartphones. That same processor, previously drawing shadows, became a machine learning model to identify complex patterns – learning to "see."

Here, it became clear how critical this link was. Those telling AI's history usually emphasize algorithmics but almost always ignore the fact that behind all this stood infrastructure that realized the vision: parallel computation of enormous data quantities at speeds and prices that enabled the very idea of large models.

In a sense, had the GPU not first freed itself from its graphic constraints, we might not have been able to think about a language model as a feasible project. In retrospect, the GPU appears to have undergone the most dramatic transformation chain in computing history: from drawing machine to scientific computer, from graphics accelerator to global AI engine, and from imaging system to virtual reality source, raising the next generation's robots.

Nvidia did not merely improve the GPU. It reinvented it repeatedly until it became the foundation supporting today's entire artificial intelligence revolution – and likely will be tomorrow's as well. "We are only at the beginning of the process of creating foundational world models. No one will 'own' them or be their exclusive owner – this is a project all humanity will need to contribute to," Lebaredian concluded.

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The meeting was organized by Amit Krig, senior vice president and head of Nvidia's development center in Israel. The emotional gathering at the company's US headquarters included the Israeli management team, who were at headquarters for professional meetings.

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Alongside Nvidia CEO Jensen Huang, the "Architects of AI" recognized by TIME include a cadre of rival and allied tech titans who are collectively reshaping the global landscape. Key figures featured in the report include Sam Altman of OpenAI, whose ChatGPT ignited the current boom; Mark Zuckerberg of Meta, who is integrating AI into social platforms; and Elon Musk, who is rapidly building massive data center infrastructure for xAI. The group also encompasses hardware and model leaders such as AMD CEO Lisa Su, Anthropic CEO Dario Amodei, and Google DeepMind CEO Demis Hassabis, as well as global investors and innovators like SoftBank's Masayoshi Son and Baidu's Robin Li, all of whom are described as "grabbing the wheel of history" to drive the technological revolution.

2025 was the year when artificial intelligence's full potential roared into view, and when it became clear that there will be no turning back.

For delivering the age of thinking machines, for wowing and worrying humanity, for transforming the present and transcending the… pic.twitter.com/mEIKRiZfLo

— TIME (@TIME) December 11, 2025

In the days leading up to the unveiling, reports proliferating on social media suggested the 2025 accolade belongs not to one person, but to a collective dubbed "The Architects of AI."

The a circulated cover art depicted a striking digital reimagining of the legendary "Lunch atop a Skyscraper" from 1932. Rather than steelworkers dining on a beam, the graphic showcases the tycoons and intellects engineering our digital destiny, according to Polymarket.

BREAKING: TIME Person of the Year reportedly leaked. pic.twitter.com/oe3okxsXoZ

— Polymarket (@Polymarket) December 10, 2025

The cover was confirmed to be authentic. Sitting center stage is Jensen Huang, the CEO of Nvidia, whose chips are powering this entire revolution, in no small part thanks to the Israeli company Mellanox he bought for a meager 6.9 billion dollars, which made the AI speed and bandwidth on his revolutionary chips possible to begin with. He's joined by household names like Elon Musk (Tesla, xAI), who made autonomous driving part of every day life, and Mark Zuckerberg (Meta), who transformed social media with AI. But the bench goes deeper, including OpenAI's Sam Altman, who made Chat-GPT a household name and revolutionized the way people retrieve data and interact with the world, and AMD's Lisa Su, whose chips have declared war on NVIDIA. Google DeepMind's Demis Hassabis is also on the leaked cover, a nod to the California giant that made AI latest breakthrough with Gemini 3.0, and made everyone question whether it is the next big thing – potentially outranking Chat-GPT because of its more sophisticated inner workings and easier supply chain. Also on the cover were another Chat-GPT rival, Anthropic's Dario Amodei, and the "Godmother of AI" herself, Fei-Fei Li, a Chinese-American computer scientist whose work on AI long before the revolution should be perhaps noted as the big bang of this new era.

It's a powerful visual: the people literally building the infrastructure of the 21st century, just like the steelworkers built the 20th.

The leak triggered chaos for gamblers. Viral posts on X noted that markets had "Artificial Intelligence" as the heavy favorite. However, when the cover showed people, the "AI" bet crashed. Since the winners are "The Architects of AI" (humans) and not "Artificial Intelligence" (the tech), the wager lost its value immediately.

When the cover leaked showing actual people instead of a generic robot, the betting rules kicked in. Since the title is reportedly "The Architects of AI" (humans) and not "Artificial Intelligence" (the tech), the value of the "AI" bet crashed from a sure thing to nearly zero in seconds.

Based on the betting odds chart from the various betting agencies, these were the following predictions:

Top political & world figures

• Pope Leo XIV (7–34%)

• Donald Trump (3–25%)

• Volodymyr Zelenskyy (11–12%)

• Jerome Powell (~9%)

• Benjamin Netanyahu (3–9%)

• Xi Jinping (~6%)

Cultural & media stars

• MrBeast (11–14%)

• Joe Rogan (8–12%)

The "tech" candidates (now likely part of the "architects")

• Jensen Huang (17–33%)

• Sam Altman (6–14%)

• Elon Musk (2–14%)

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Like so many stories worth telling these days, Rosenberg's also begins in the early morning hours of that cursed Saturday. "Right after I understood the dimensions of the event, I realized it would also affect our employees," he said. "Nvidia has more than 5,000 employees in Israel, many of whom live and work in the south, and we started trying to find out what was happening with them."

"We were always there with them in the evening, from seven to nine, in sirens and missiles, and all this time we also had to take care of other employees who were affected, evacuated from home, or who lost family members and friends," he said. "It was a difficult and somewhat crazy routine. Nvidia had five employees whose family members were kidnapped to Gaza, and some were murdered there, and we even recruited new employees over time who also have relatives who are hostages. In addition, employees lost brothers and friends in the war, and company employee Amit Chayut fell in battle. We understood that we had to do everything we could to help them. It was clear to me that this was my most important mission and that of the company."

These years have been dramatic not only for Nvidia itself, but for the entire tech world. "I remember the pivotal day when they announced OpenAI's ChatGPT, exactly three years ago," Rosenberg said. "We all at the company understood that something had fallen, that the AI field was really breaking through, as we hoped would happen. This, of course, didn't happen in one day. The company built itself toward this moment. OpenAI worked with Mellanox on the technology back then, and we knew how important our products were for the AI era. It was a gradual rise until explosion – and when it happened, it happened fast and in big leaps, and it was important to preserve the organization's identity."

"Satisfaction is not just good conditions," Rosenberg explained. "Money is, of course, important and critical – salary, stocks, financial horizon – but a leading company needs to create satisfaction and interest and security for employees. From the CEO to the most junior manager, it's clear to everyone that we need to ensure our employees have it good and that they will want to continue working with us – and we invest a lot in this."

"On October 7, I didn't have a 'playbook' that says what to do if a company employee is kidnapped. We didn't know how to deal with something like this, but I hope I did the right things along the way. I also have no idea if anything I did somehow helped bring Avinatan home, but I tried to give his family as much strength as possible, because they were in a very lonely and difficult place."

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A CNBC report that key customer Meta could partner with Google to use its Tensor Processing Units (TPUs) caused a 3% dip in Nvidia's stock. Nvidia asserted its chips are more flexible and powerful than the custom ASIC chips like Google's TPUs, claiming its product "offers greater performance, versatility, and fungibility than ASICs," CNBC reported.

We're delighted by Google's success — they've made great advances in AI and we continue to supply to Google.

NVIDIA is a generation ahead of the industry — it's the only platform that runs every AI model and does it everywhere computing is done.

NVIDIA offers greater…

— NVIDIA Newsroom (@nvidianewsroom) November 25, 2025

Analysts report Nvidia holds more than 90% of the AI chip market, but Google's proprietary chips are gaining attention as a potent, less costly substitute. Unlike Nvidia, Google does not sell its TPUs directly but offers them for lease through Google Cloud and uses them internally.

Earlier this month, Google launched its highly-rated Gemini 3 AI model, which was trained on its custom TPUs, not Nvidia GPUs. A Google spokesperson commented that demand for both custom TPUs and Nvidia GPUs is accelerating, and Google "We are committed to supporting both, as we have for years."

Nvidia CEO Jensen Huang addressed the TPU competition in his recent earnings call with investors, noting Google buys his GPUs and that Gemini can run on Nvidia technology. Huang confirmed contact with Demis Hassabis, Google DeepMind CEO, who texted that the theory on creating stronger AI by using more data and chips – known as "scaling laws" – is "intact." Nvidia believes this will drive even more demand for its systems, CNBC reported.

Hassabis texted that the theory on creating stronger AI by using more data and chips – known as "scaling laws" – is "intact." Nvidia believes this will drive even more demand for its systems, CNBC reported.

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NVIDIA just shattered records again. $NVDA reports Q3 Revenue of $57.0 Billion, up 62% year-over-year. • GAAP EPS: $1.30 (+67% YoY) • Net Income: $31.9B The AI boom isn't slowing down—it's accelerating. https://t.co/tskrzW9jNg#NVIDIA $nvda

— Israel Hayom English (@IsraelHayomEng) November 19, 2025

NVIDIA (NASDAQ: NVDA) continued its dominance of the artificial intelligence sector, reporting record-breaking revenue for the third quarter that significantly outperformed Wall Street projections.

The chip giant posted revenue of $57.0 billion, marking a staggering 62% increase compared to the same period last year and comfortably beating the analyst consensus of $55.09 billion.

NVIDIA achieved a gross margin of 73.4% and an operating income of $36.0 billion, translating to an exceptional operating margin of 63.2%. The company's profitability was equally striking, with operating income reaching $36.0 billion, demonstrating exceptional operational efficiency even as it scales. Net income followed suit, jumping 65% year-over-year to nearly $31.9 billion (or $1.30 per share). Looking forward, the company signaled that the AI boom is far from over, issuing guidance for fourth-quarter revenue of approximately $65 billion and noting that demand for its next-generation Blackwell chips is already "off the charts."

"During the first nine months of fiscal 2026, NVIDIA returned $37.0 billion to shareholders in the form of shares repurchased and cash dividends. As of the end of the third quarter, the company had $62.2 billion remaining under its share repurchase authorization," the company said in a statement.

Smashing the projections

NVIDIA didn't just meet expectations; it surpassed them by nearly $2 billion on the top line.

• Revenue Beat: Actual revenue of $57.0 billion vs. Consensus of $55.09 billion (a beat of $1.91 billion).

• EPS Beat: Non-GAAP earnings per diluted share came in at $1.30, beating the consensus estimate of $1.26.

The primary engine for this outperformance was the data center segment, which generated a record $51.2 billion in revenue alone: up 66% from a year ago. This single segment now accounts for the vast majority of the company's total intake.

Guidance blows past estimates

Perhaps more impressive than the current results is NVIDIA's outlook for the future. The company provided fourth-quarter revenue guidance of $65.0 billion (plus or minus 2%).

This forecast stands well above the consensus estimate of $61.84 billion, suggesting a difference of over $3 billion between NVIDIA's internal visibility and Wall Street's models. The company also forecasted strong non-GAAP gross margins of roughly 75.0% for the upcoming quarter.

NVIDIA Q3 Fiscal 2026: Actual vs. Consensus

Note: Q4 Revenue Outlook represents the midpoint of the provided guidance range (+/- 2%).

"Insatiable" Demand for AI

The driving force behind the surge remains the company's next-generation hardware. In the press release, NVIDIA founder and CEO Jensen Huang emphasized that demand is outstripping supply.

"Blackwell sales are off the charts, and cloud GPUs are sold out," Huang stated. "Compute demand keeps accelerating and compounding across training and inference, each growing exponentially."

Huang described the current market environment as a "virtuous cycle of AI," noting that the ecosystem is scaling rapidly across various industries and countries. "AI is going everywhere, doing everything, all at once," he added.

BREAKING: Nvidia $NVDA earnings

EPS $1.3 vs $1.26 expected
Revenue $57 Billion vs $55 billion expected

We are breaking it down LIVE at https://t.co/b1CM1ZxP19 pic.twitter.com/qTZEcDXqSy

— unusual_whales (@unusual_whales) November 19, 2025

The corporation published its third-quarter numbers and future outlook on the investor relations segment of its web portal at 4:20 p.m. ET, about half an hour after Wall Street trading concluded. A subsequent discussion with management began at around 5 p.m. ET via the same platform, arriving as anxiety regarding the immense capital expenditures on artificial intelligence by Silicon Valley giants increases. The stunning beat appeared to soothe the concerns of circular funding between the major AI players, with after hours trading showing green candles for the 5-trillion company. 

Before the smashing earnings report, observers questioned if CEO Jensen Huang would validate the AI revolution's ongoing surge or perhaps warn us that demand for the GPUs (its processing units that allowed fast computations with AI capacities) had plateaued, less than a decade after his company made perhaps the most important decision in its history: buying a small Israeli company named Mellanox, from Yoqne'am.

Gaining essential Ethernet and InfiniBand tools for high-performance AI centers made the 2019 Mellanox deal a pivotal moment. That acquisition rendered the Jewish state and the tech giant "inextricably linked", establishing Israel as the company's second-biggest R&D hub outside America.

Nvidia controls roughly 80% of global AI GPU spending in 2025. Its data center unit generated $41.1 billion in Q2. Reports provide insights into AI adoption and cloud spending. CEO Jensen Huang projects $3-4 trillion in infrastructure spending by 2030. Financial Times noted that the company's performance is a barometer for the sector.

The report was a a beat across the board, and a record, just weeks after the company has recently reached a record market cap of 5 trillion dollars, the first company ever to do so.

Transitioning from a graphics processor manufacturer to an AI infrastructure titan marks the firm's successful metamorphosis, driven by the stagnation of traditional silicon progress known as Moore's Law.

NVIDIA just shattered records again. $NVDA reports Q3 Revenue of $57.0 Billion, up 62% year-over-year. • GAAP EPS: $1.30 (+67% YoY) • Net Income: $31.9B The AI boom isn't slowing down—it's accelerating. https://t.co/tskrzW9jNg#NVIDIA $nvda

— Israel Hayom English (@IsraelHayomEng) November 19, 2025

Recent performance history shows the equity has consistently surpassed Wall Street predictions in its previous three financial summaries, bolstering shareholder belief in operational fulfillment amidst unprecedented requests for AI frameworks. Yet, the last profit victory, the August earnings report for Q2,  failed to calm fears regarding a deceleration after data center revenues of $41.1 billion missed projections marginally, causing a sell-off, even though the revenue fell short only because China was denied access to its chips because of newly imposed US restrictions. The stock subsequently recovered losses to hit fresh peaks. 

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Beijing's victory in the artificial intelligence race appears inevitable due to energy subsidies and streamlined regulations, Nvidia chief executive Jensen Huang warned Wednesday, delivering his starkest assessment yet of the competitive landscape. "China is going to win the AI race," Huang told the Financial Times at its Future of AI Summit, citing Chinese government support that makes power costs essentially negligible for technology companies while the US grapples with potential regulations across 50 states.

Financial Times disclosed this week that Chinese authorities expanded energy subsidies for major data facilities after technology firms complained about costs associated with domestic semiconductors from Huawei and Cambricon, which consume more electricity than Nvidia processors. Local governments increased these incentives to offset the efficiency disadvantages of Chinese-manufactured chips compared to American alternatives.

Huang has previously urged Washington to permit broader chip sales to keep global markets dependent on US technology, warning that American AI models maintain only narrow leads over Chinese competitors, according to Financial Times. However, Trump told CBS following his meeting with Xi that "The most advanced, we will not let anybody have them other than the United States," reaffirming that Washington "will let them deal with Nvidia but not in terms of the most advanced" Blackwell processors.

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Nvidia had not fully complied with provisions outlined when it acquired Mellanox Technologies, an Israeli-US supplier of networking products, China's State Administration for Market Regulation (SAMR) stated on Monday, according to Reuters. Beijing provided conditional approval for the US chipmaker's acquisition of Mellanox in 2020.

Monday's statement was released as US and Chinese officials were conducting more trade talks in Madrid, with a tariff truce between the world's two biggest economies scheduled to expire in November, Reuters noted.

Two individuals with knowledge of the situation said SAMR arrived at its conclusion weeks before the announcement on Monday, adding that the regulator released the statement at this time to provide China with greater leverage in the trade discussions.

The regulator initiated the anti-monopoly investigation in December, one week after the US revealed stricter export controls on advanced high-bandwidth memory chips and the equipment for making chips sent to the country.

SAMR subsequently spent months interviewing relevant parties and collecting legal opinions to formulate the case, the people stated.

In 2020, Nvidia purchased Mellanox for $6.9bn, and the acquisition assisted the chipmaker in advancing into the data center and high-performance computing market, where it is currently a dominant player.

The preliminary findings against the chipmaker could lead to fines between 1% and 10% of the company's sales from the preceding year. Regulators also have the authority to compel Nvidia to modify business practices that are deemed to be in violation of antitrust laws.

A statement from Nvidia said it complies "with the law in all respects" and would continue to co-operate with all relevant government agencies.

Over the past few years, Nvidia has risen to become a global market leader in artificial intelligence chips, with its graphics processing units (GPUs) being critical in the development of leading AI models.

This has also meant that Nvidia has become more and more entangled in the trade tensions between Washington and Beijing.

US Treasury Secretary Scott Bessent, who spoke at the Madrid talks on Monday, said the regulatory finding against Nvidia was a topic in the meetings.

"We discussed the poor timing of the Nvidia investigation [on] the day of these talks," his comment was, as cited by Reuters.

Successive administrations in the US have put in place export controls that have required Nvidia to sell less powerful versions of its essential graphics processing units in China, creating a large black market of smugglers who illegally transport its more advanced processors into the country.

The US this year also prevented sales of the H20 chip, which Nvidia created for the Chinese market while following export controls. The company later made a deal to let sales resume in return for giving the US government 15% of the revenues.

Despite this, Chinese regulators have been putting pressure on the nation's tech companies, warning them against buying Nvidia's H20 chip, which has created uncertainty over the US group's business in the country.

Nvidia Chief Executive Jensen Huang, who has frequently visited China in a display of his commitment to a vital overseas market, has criticized the US curbs, calling them a "failure" that has motivated Chinese competitors to speed up the development of their own products, according to Reuters.

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The company said on Wednesday that third-quarter sales will be about $54 billion through October. That figure matched average Wall Street estimates, but some analysts had expected more than $60 billion, according to Bloomberg. The guidance raised concerns that the pace of AI investment may not be sustainable, particularly as Nvidia faces headwinds in China. Although the Trump administration recently eased export restrictions on some AI chips, Bloomberg noted that the policy shift has yet to produce a rebound in sales.

Nvidia shares fell 3% in extended trading following the announcement. The stock had already gained 35% this year, lifting the company's valuation beyond $4 trillion. Chief Executive Officer Jensen Huang rejected suggestions during a call with analysts that demand for AI infrastructure is waning. "The opportunity ahead is immense," Huang said, quoted by Bloomberg. "We see $3 trillion to $4 trillion in AI infrastructure spend by the end of the decade."

The company's board approved an additional $60 billion stock repurchase program. Nvidia still had $14.7 billion remaining under its previous buyback plan at the close of the second quarter, Bloomberg reported.

For the second quarter, which ended July 27, revenue rose 56% to $46.7 billion, narrowly topping analyst expectations of $46.2 billion. That gain added more than $16 billion year-on-year but marked the slowest percentage growth in over two years. Adjusted earnings per share were $1.05, ahead of the $1.01 forecast.

The data center unit, larger than any other single chipmaker, posted $41.1 billion in revenue, slightly under expectations of $41.3 billion. Gaming sales rebounded to $4.29 billion, beating the $3.8 billion projection. Automotive revenue reached $586 million, slightly short of estimates. The results suggested major data center operators may moderate spending if near-term AI application returns remain uncertain, analyst Jacob Bourne told Bloomberg.

Nvidia's challenges also stem from the US-China technology rivalry. In April, the Trump administration tightened restrictions on exports of advanced processors, cutting Nvidia off from the Chinese market. Washington later eased that ban, allowing some shipments in exchange for 15% of the revenue. But Bloomberg reported that the policy has yet to be fully codified, leaving uncertainty. Beijing has also encouraged Chinese entities to replace US suppliers.

Nvidia said it made no H20 chip sales to Chinese clients in the second quarter, a roughly $4 billion drop from the prior period. Its third-quarter forecast likewise excluded those sales. The company acknowledged legal and financial risks if the US government enforces its revenue-sharing plan. "Any request for a percentage of the revenue by the USG may subject us to litigation, increase our costs, and harm our competitive position and benefit competitors that are not subject to such arrangements," Nvidia said in a filing, quoted by Bloomberg.

Huang said Nvidia could still ship between $2 billion and $5 billion of H20 chips to China this quarter, depending on licensing approvals. Chief Financial Officer Colette Kress told analysts that demand remains high. "If we had more orders, we can bill more," she said. Huang added that Nvidia continues to push Washington to allow exports of its new Blackwell chips. "The opportunity for us to bring Blackwell to the China market is a real possibility," he said, quoted by Bloomberg. "We just have to keep advocating the sensibility of and the importance of American tech companies to be able to lead and win the AI race."

According to Bloomberg, Huang estimated that if more advanced products could be sold in China, Nvidia could tap into a $50 billion market growing 50% annually.

Founded 32 years ago, Nvidia has risen from a niche graphics card supplier to a dominant force in computing. As recently as 2022, it generated less annual revenue than it now earns in a quarter. The company is on pace to book $200 billion this year, with projections topping $300 billion by 2028, or about one-third of the global chip market.

Much of Nvidia's success comes from sales to a handful of hyperscale clients including Microsoft and Amazon, which together account for about half its business, Bloomberg reported. To reduce reliance on these customers, Huang is expanding into software, networking, and complete computing systems. He also pushes Nvidia's engineering teams to speed up innovation cycles.

For now, Nvidia dominates the AI chip market with little challenge. Efforts by rivals such as Advanced Micro Devices and in-house projects by cloud providers have not dented its share significantly. Yet the company remains constrained by supply, since it outsources production mainly to Taiwan Semiconductor Manufacturing Co. Meeting surging demand for its latest technologies continues to be a bottleneck.

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Financial Times reported that the two major chipmakers accepted this financial obligation to secure export permits for their Chinese operations, which were granted last week. The unusual deal represents the first instance of American companies agreeing to share revenues with the government to obtain export authorizations.

A US official disclosed that Nvidia committed to providing 15% of revenues from H20 chip sales in China, while AMD agreed to the same percentage from MI308 chip revenues, Financial Times learned. Sources familiar with the arrangement indicated the Trump administration has not yet determined specific uses for these funds.

Financial Times previously reported that the Commerce Department began issuing H20 export licenses on Friday, occurring two days after Nvidia chief executive Jensen Huang met with President Donald Trump. The same US official confirmed that the administration also commenced issuing licenses for AMD's China-specific chip.

This quid pro quo arrangement breaks new ground in export control policy. Export control specialists confirmed that no US company has previously agreed to pay revenue portions to secure export licenses. However, the deal aligns with Trump administration patterns of encouraging companies to adopt specific measures, such as domestic investments, to avoid tariff impositions while generating American jobs and revenue.

AMD declined to respond to requests for comment. Nvidia did not deny the arrangement's existence, stating that the company follows "rules the US government sets for our participation in worldwide markets."

Bernstein analysts calculated that, based on Nvidia's pre-control guidance from earlier this year, the company would have sold approximately 1.5 million H20 chips to China in 2025, generating roughly $23 billion in revenue.

The development follows ongoing controversy surrounding the H20 chip. Nvidia specifically designed the H20 for Chinese markets after President Joe Biden implemented stringent export controls targeting more advanced chips utilized for artificial intelligence applications.

Trump administration officials announced plans to ban H20 exports to China in April. However, Trump reversed this decision in June following his meeting with Huang at the White House. During subsequent weeks, Nvidia grew concerned as the Bureau of Industry and Security, the Commerce Department division managing export controls, failed to issue licenses.

Huang addressed this concern directly with Trump during their Wednesday meeting, according to sources familiar with the discussion. The Bureau of Industry and Security subsequently began issuing licenses on Friday.

The H20 revenue arrangement emerges amid criticism facing both Nvidia and the Trump administration regarding decisions to sell the chip to China. US security specialists argue the H20 will enhance Chinese military capabilities and undermine American artificial intelligence leadership.

"Beijing must be gloating to see Washington turn export licences into revenue streams," stated Liza Tobin, a China expert who served on the National Security Council during Trump's first administration and currently works at the Jamestown Foundation. "What's next – letting Lockheed Martin sell F-35s to China for a 15% commission?"

Bureau of Industry and Security officials have also voiced concerns about the policy reversal, according to individuals familiar with internal discussions.

Former deputy national security adviser Matt Pottinger, a China specialist from Trump's initial term, joined 19 other security experts in writing to Commerce Secretary Howard Lutnick urging against granting H20 licenses.

The experts characterized the H20 as a "potent accelerator of China's frontier AI capabilities" that would ultimately serve Chinese military purposes. Nvidia dismissed these assertions as "misguided" and rejected suggestions that China could employ the H20 for military applications.

Nvidia issued a statement Saturday defending its position: "While we haven't shipped H20 to China for months, we hope export control rules will let America compete in China and worldwide. America cannot repeat 5G and lose telecommunication leadership. America's AI tech stack can be the world's standard if we race."

The Washington debate over chip export control policies unfolds as the US and China conduct trade discussions that Trump anticipates will facilitate a summit with Chinese President Xi Jinping. Financial Times previously reported that the Commerce Department received instructions to freeze new export controls on China to avoid provoking Beijing.

Concerns about potential Trump administration easing of controls to accommodate China arise as Beijing advocates for relaxed restrictions on high-bandwidth memory chips, critical components for manufacturing advanced AI semiconductors.

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