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For about a quarter century, Nvidia has been leading the revolution in computer graphics, becoming a beloved brand by gamers along the way.

Nvidia dominates the market for graphics processing units (GPUs), which it entered in 1999 with the GeForce 256. Gaming brought in over $9 billion in revenue for Nvidia last year despite a recent downturn.

But Nvidia’s latest earnings beat points to a new phenomenon in the GPU business. The technology is now at the center of the boom in artificial intelligence.

“We had the good wisdom to go put the whole company behind it,” CEO Jensen Huang told CNBC in an interview last month. “We saw early on, about a decade or so ago, that this way of doing software could change everything. And we changed the company from the bottom all the way to the top and sideways. Every chip that we made was focused on artificial intelligence.”

As the engine behind large language models (LLMs) like ChatGPT, Nvidia is finally reaping rewards for its early investment in AI. That’s helped to cushion the blow from broader semiconductor industry struggles tied to U.S.-China trade tensions and a global chip shortage. 

Not that Nvidia is immune to geopolitical concerns. In October, the U.S. introduced sweeping new rules that banned exports of leading-edge AI chips to China. Nvidia counts on China for about one-quarter of its revenue, including sales of its popular AI chip, the A100.

“It was a turbulent month or so as the company went upside down to reengineer all of our products so that it’s compliant with the regulation and yet still be able to serve the commercial customers that we have in China,” Huang said. “We’re able to serve our customers in China with the regulated parts, and delightfully support them.”

AI will be a major focus of Nvidia’s annual GTC developer conference taking place from March 20-23. Ahead of the conference, CNBC sat down with Huang at Nvidia’s headquarters in Santa Clara, California, to discuss the company’s role at the heart of the explosion in generative AI.

“We just believed that someday something new would happen, and the rest of it requires some serendipity,” Huang said, when asked whether Nvidia’s fortunes are the result of luck or prescience. “It wasn’t foresight. The foresight was accelerated computing.”

GPUs are Nvidia’s primary business, accounting for more than 80% of revenue. Typically sold as cards that plug into a PC’s motherboard, they add computing power to central processing units (CPUs) built by companies like AMD and Intel.

Now, tech companies scrambling to compete with ChatGPT are publicly boasting about how many of Nvidia’s roughly $10,000 A100s they have. Microsoft said the supercomputer developed for OpenAI used 10,000 of them.

Nvidia Founder and CEO Jensen Huang shows CNBC’s Katie Tarasov a Hopper H100 SXM module in Santa Clara, CA, on February 9, 2023.

Andrew Evers

“It’s very easy to use their products and add more computing capacity,” said Vivek Arya, semiconductor analyst for Bank of America Securities. “Computing capacity is basically the currency of the valley right now.”

Huang showed us the company’s next-generation system called H100, which has already started to ship. The H stands for Hopper.

“What makes Hopper really amazing is this new type of processing called transformer engine,” Huang said, while holding a 50-pound server board. “The transformer engine is the T of GPT, generative pre-trained transformer. This is the world’s first computer designed to process transformers at enormous scale. So large language models are going to be much, much faster and much more cost effective.”

Huang said he “hand-delivered” to ChatGPT maker OpenAI “the world’s very first AI supercomputer.”

Not afraid to bet it all

Today, Nvidia is among the world’s 10 most valuable tech companies, with a market cap of close to $600 billion. It has 26,000 employees and a newly built polygon-themed headquarters. It’s also one of the few Silicon Valley giants with a founder of 30 years still at the helm.

Huang, 60, immigrated to the U.S. from Taiwan as a kid and studied engineering at Oregon State University and Stanford. In the early 1990s, Huang and fellow engineers Chris Malachowsky and Curtis Priem used to meet at a Denny’s and talk about dreams of enabling PCs with 3D graphics.

The trio launched Nvidia out of a condo in Fremont, California, in 1993. The name was inspired by NV for “next version” and Invidia, the Latin word for envy. They hoped to speed up computing so much that everyone would be green with envy — so they chose the envious green eye as the company logo.

Nvidia founders Curtis Priem, Jensen Huang and Chris Malachowsky pose at the company’s Santa Clara, California, headquarters in 2020.

Nvidia

“They were one among tens of GPU makers at that time,” Arya said. “They are the only ones, them and AMD actually, who really survived because Nvidia worked very well with the software community, with the developers.”

Huang’s ambitions and preference for impossible-seeming ventures have pushed the company to the brink of bankruptcy a handful of times.

“Every company makes mistakes and I make a lot of them,” said Huang, who was one of Time magazine’s most influential people in 2021. “Some of them put the company in peril, especially in the beginning, because we were small and we’re up against very, very large companies and we’re trying to invent this brand-new technology.”

In the early 2010s, for example, Nvidia made an unsuccessful move into smartphones with its Tegra line of processors. The company then exited the space. 

In 1999, after laying off the majority of its workforce, Nvidia released what it claims was the world’s first official GPU, the GeForce 256. It was the first programmable graphics card that allowed custom shading and lighting effects. By 2000, Nvidia was the exclusive graphics provider for Microsoft’s first Xbox. In 2006, the company made another huge bet, releasing a software toolkit called CUDA.

“For 10 years, Wall Street asked Nvidia, ‘Why are you making this investment? No one’s using it.’ And they valued it at $0 in our market cap,” said Bryan Catanzaro, vice president of applied deep learning research at Nvidia. He was one of the only employees working on AI when he joined Nvidia in 2008. Now, the company has thousands of staffers working in the space.

“It wasn’t until around 2016, 10 years after CUDA came out, that all of a sudden people understood this is a dramatically different way of writing computer programs,” Catanzaro said. “It has transformational speedups that then yield breakthrough results in artificial intelligence.”

Although AI is growing rapidly, gaming remains Nvidia’s primary business. In 2018, the company used its AI expertise to make its next big leap in graphics. The company introduced GeForce RTX based on what it had learned in AI.

“In order for us to take computer graphics and video games to the next level, we had to reinvent and disrupt ourselves, change literally what we invented altogether,” Huang said. “We invented this new way of doing computer graphics, ray tracing, basically simulating the pathways of light and simulate everything with generative AI. And so we compute one pixel and we imagine with AI the other seven.”

‘Boom-or-bust cycle’

From the beginning, Huang was committed to making Nvidia a fabless chip company, or one that designs the product but contracts out production to others that have chip fabrication plants, or fabs. Nvidia keeps capital expenditure down by outsourcing the extraordinary expense of making the chips to Taiwan Semiconductor Manufacturing Company.

Taiwan Semiconductor Manufacturing Company’s U.S. office space in San Jose, CA, in 2021.

Katie Tarasov

Investors are right to be concerned about that level of dependence on a Taiwanese company. The U.S. passed the CHIPS Act last summer, which sets aside $52 billion to incentivize chip companies to manufacture on U.S. soil.

“The biggest risk is really U.S.-China relations and the potential impact of TSMC. If I’m a shareholder in Nvidia, that’s really the only thing that keeps me up at night,” said C.J. Muse, an analyst at Evercore. “This is not just a Nvidia risk, this is a risk for AMD, for Qualcomm, even for Intel.”

TSMC has said it’s spending $40 billion to build two new chip fabrication plants in Arizona. Huang told CNBC that Nvidia will “absolutely” use TSMC’s Arizona fabs to make its chips.

Then there are questions about demand and how many of the new use cases for GPUs will continue to show growth. Nvidia saw a spike in demand when crypto mining took off because GPUs became core to effectively competing in that market. The company even created a simplified GPU just for crypto. But with the cratering of crypto, Nvidia experienced an imbalance in supply and demand.

“That has created problems because crypto mining has been a boom-or-bust cycle,” Arya said. “Gaming cards go out of stock, prices get bid up, and then when the crypto mining boom collapses, then there is a big crash on the gaming side.”

Nvidia caused major sticker shock among some gamers last year by pricing its new 40-series GPUs far higher than the previous generation. Now there’s too much supply and, in the most recent quarter, gaming revenue was down 46% from a year earlier.

Competition is also increasing as more tech giants design their own custom-purpose chips. Tesla and Apple are doing it. So are Amazon and Google.

“The biggest question for them is how do they stay ahead?” Arya said. “Their customers can be their competitors also. Microsoft can try and design these things internally. Amazon and Google are already designing these things internally.”

For his part, Huang says that such competition is good.

“The amount of power that the world needs in the data center will grow,” Huang said. “That’s a real issue for the world. The first thing that we should do is: every data center in the world, however you decide to do it, for the goodness of sustainable computing, accelerate everything you can.”

In the car market, Nvidia is making autonomous-driving technology for Mercedes-Benz and others. Its systems are also used to power robots in Amazon warehouses, and to run simulations to optimize the flow of millions of packages each day.

Huang describes it as the “omniverse.”

“We have 700-plus customers who are trying it now, from [the] car industry to logistics warehouses to wind turbine plants,” Huang said. “It represents probably the single greatest container of all of Nvidia’s technology: computer graphics, artificial intelligence, robotics and physics simulation, all into one. And I have great hopes for it.”

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Musk says he does not support a merger between Tesla and xAI but backs investment

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July 14, 2025

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Musk says he does not support a merger between Tesla and xAI but backs investment

Elon musk and the xAI logo.

Vincent Feuray | Afp | Getty Images

Elon Musk on Monday said he does not support a merger between xAI and Tesla, as questions swirl over the future relationship of the electric automaker and artificial intelligence company.

X account @BullStreetBets_ posted an open question to Tesla investors on the social media site asking if they support a merger between Tesla and xAI. Musk responded with “No.”

The statement comes as the tech billionaire contemplates the future relationship between his multiple businesses.

Overnight, Musk suggested that Tesla will hold a shareholder vote at an unspecified time on whether the automaker should invest in xAI, the billionaire’s company that develops the controversial Grok AI chatbot.

Last year, Musk asked his followers in an poll on social media platform X whether Tesla should invest $5 billion into xAI. The majority voted “yes” at the time.

Musk has looked to bring his various businesses closer together. In March, Musk merged xAI and X together in a deal that valued the artificial intelligence company at $80 billion and the social media company at $33 billion.

Musk also said last week that xAI’s chatbot Grok will be available in Tesla vehicles. The chatbot has come under criticism recently, after praising Adolf Hitler and posting a barrage of antisemitic comments.

— CNBC’s Samantha Subin contributed to this report.

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Alibaba-backed Moonshot releases new Kimi AI model that beats ChatGPT, Claude in coding — and it costs less

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July 14, 2025

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Alibaba-backed Moonshot releases new Kimi AI model that beats ChatGPT, Claude in coding — and it costs less

An AI sign at the MWC Shanghai tech show on June 19, 2025.

Bloomberg | Bloomberg | Getty Images

BEIJING — The latest Chinese generative artificial intelligence model to take on OpenAI’s ChatGPT is offering coding capabilities — at a lower price.

Alibaba-backed startup Moonshot released on late Friday night its Kimi K2 model: a low-cost, open source large language model — the two factors that underpinned China-based DeepSeek’s industry disruption in January. Open-source technology provides source code access for free, an approach that few U.S. tech giants have taken, other than Meta and Google to some extent.

Coincidentally, OpenAI CEO Sam Altman announced early Saturday that there would be an indefinite delay of its first open-source model yet again due to safety concerns. OpenAI did not immediately respond to a CNBC request for comment on Kimi K2.

Rethinking the AI coding payoff

One of Kimi K2’s strengths is in writing computer code for applications, an area in which businesses see potential to reduce or replace staff with generative AI. OpenAI’s U.S. rival Anthropic focused on coding with its Claude Opus 4 model released in late May.

In its release announcement on social media platforms X and GitHub, Moonshot claimed Kimi K2 surpassed Claude Opus 4 on two benchmarks, and had better overall performance than OpenAI’s coding-focused GPT-4.1 model, based on several industry metrics.

“No doubt [Kimi K2 is] a globally competitive model, and it’s open sourced,” Wei Sun, principal analyst in artificial intelligence at Counterpoint, said in an email Monday.

Cheaper option

“On top of that, it has lower token costs, making it attractive for large-scale or budget-sensitive deployments,” she said.

The new K2 model is available via Kimi’s app and browser interface for free unlike ChatGPT or Claude, which charge monthly subscriptions for their latest AI models.

Kimi is also only charging 15 cents for every 1 million input tokens, and $2.50 per 1 million output tokens, according to its website. Tokens are a way of measuring data for AI model processing.

In contrast, Claude Opus 4 charges 100 times more for input — $15 per million tokens — and 30 times more for output — $75 per million tokens. Meanwhile, for every one million tokens, GPT-4.1 charges $2 for input and $8 for output.

Moonshot AI said on GitHub that developers can use K2 however they wish, with the only requirement that they display “Kimi K2” on the user interface if the commercial product or service has more than 100 million monthly active users, or makes the equivalent of $20 million in monthly revenue.

Hot AI market

Initial reviews of K2 on both English and Chinese social media have largely been positive, although there are some reports of hallucinations, a prevalent issue in generative AI, in which the models make up information.

Still, K2 is “the first model I feel comfortable using in production since Claude 3.5 Sonnet,” Pietro Schirano, founder of startup MagicPath that offers AI tools for design, said in a post on X.

Moonshot has open sourced some of its prior AI models. The company’s chatbot surged in popularity early last year as China’s alternative to ChatGPT, which isn’t officially available in the country. But similar chatbots from ByteDance and Tencent have since crowded the market, while tech giant Baidu has revamped its core search engine with AI tools.

Kimi’s latest AI release comes as investors eye Chinese alternatives to U.S. tech in the global AI competition.

Still, despite the excitement about DeepSeek, the privately-held company has yet to announce a major upgrade to its R1 and V3 model. Meanwhile, Manus AI, a Chinese startup that emerged earlier this year as another DeepSeek-type upstart, has relocated its headquarters to Singapore.

Over in the U.S., OpenAI also has yet to reveal GPT-5.

Work on GPT-5 may be taking up engineering resources, preventing OpenAI from progressing on its open-source model, Counterpoint’s Sun said, adding that it’s challenging to release a powerful open-source model without undermining the competitive advantage of a proprietary model.

Grok 4 competitor

Kimi K2 is not the company’s only recent release. Moonshot launched a Kimi research model last month and claimed it matched Google’s Gemini Deep Research ‘s 26.9 score and beat OpenAI’s version on a benchmark called “Humanity’s Last Exam.”

The Kimi research model even got a mention last week during Elon Musk’s xAI release of Grok 4 — which scored 25.4 on its own on the “Humanity’s Last Exam” benchmark, but attained a 44.4 score when allowed to use a variety of AI tools and web search.

“Kimi-Researcher represents a paradigm shift in agentic AI,” said Winston Ma, adjunct professor at NYU School of Law. He was referring to AI’s capability of simultaneously making several decisions on its own to complete a complex task.

“Instead of merely generating fluent responses, it demonstrates autonomous reasoning at an expert level — the kind of complex cognitive work previously missing from LLMs,” Ma said. He is also author of “The Digital War: How China’s Tech Power Shapes the Future of AI, Blockchain and Cyberspace.”

— CNBC’s Victoria Yeo contributed to this report.

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Nvidia CEO downplays U.S. fears that China’s military will use his firm’s chips

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July 14, 2025

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Nvidia CEO downplays U.S. fears that China's military will use his firm's chips

Co-founder and chief executive officer of Nvidia Corp., Jensen Huang attends the 9th edition of the VivaTech trade show in Paris on June 11, 2025.

Chesnot | Getty Images Entertainment | Getty Images

Nvidia CEO Jensen Huang has downplayed U.S. fears that his firm’s chips will aid the Chinese military, days ahead of another trip to the country as he attempts to walk a tightrope between Washington and Beijing. 

In an interview with CNN aired Sunday, Huang said “we don’t have to worry about” China’s military using U.S.-made technology because “they simply can’t rely on it.”

“It could be limited at any time; not to mention, there’s plenty of computing capacity in China already,” Huang said. “They don’t need Nvidia’s chips, certainly, or American tech stacks in order to build their military,” he added.

The comments were made in reference to years of bipartisan U.S. policy that placed restrictions on semiconductor companies, prohibiting them from selling their most advanced artificial intelligence chips to clients in China. 

Huang also repeated past criticisms of the policies, arguing that the tactic of export controls has been counterproductive to the ultimate goal of U.S. tech leadership. 

“We want the American tech stack to be the global standard … in order for us to do that, we have to be in search of all the AI developers in the world,” Huang said, adding that half of the world’s AI developers are in China. 

‘The Nvidia Way’ author Tae Kim: Jensen Huang always positions Nvidia ahead of the next big trend

That means for America to be an AI leader, U.S. technology has to be available to all markets, including China, he added.

Washington’s latest restrictions on Nvidia’s sales to China were implemented in April and are expected to result in billions in losses for the company. In May, Huang said chip restrictions had already cut Nvidia’s China market share nearly in half.

Huang’s CNN interview came just days before he travels to China for his second trip to the country this year, and as Nvidia is reportedly working on another chip that is compliant with the latest export controls.

Last week, the Nvidia CEO met with U.S. President Donald Trump, and was warned by U.S. lawmakers not to meet with companies connected to China’s military or intelligence bodies, or entities named on America’s restricted export list.

According to Daniel Newman, CEO of tech advisory firm The Futurum Group, Huang’s CNN interview exemplifies how Huang has been threading a needle between Washington and Beijing as it tries to maintain maximum market access.

“He needs to walk a proverbial tightrope to make sure that he doesn’t rattle the Trump administration,” Newman said, adding that he also wants to be in a position for China to invest in Nvidia technology if and when the policy provides a better climate to do so.

But that’s not to say that his downplaying of Washington’s concerns is valid, according to Newman. “I think it’s hard to completely accept the idea that China couldn’t use Nvidia’s most advanced technologies for military use.”

He added that he would expect Nvidia’s technology to be at the core of any country’s AI training, including for use in the development of advanced weaponry. 

A U.S. official told Reuters last month that China’s large language model startup DeepSeek — which says it used Nvidia chips to train its models — was supporting China’s military and intelligence operations. 

On Sunday, Huang acknowledged there were concerns about DeepSeek’s open-source R1 reasoning model being trained in China but said that there was no evidence that it presents dangers for that reason alone.

Huang complimented the R1 reasoning model, calling it “revolutionary,” and said its open-source nature has empowered startup companies, new industries, and countries to be able to engage in AI. 

“The fact of the matter is, [China and the U.S.] are competitors, but we are highly interdependent, and to the extent that we can compete and both aspire to win, it is fine to respect our competitors,” he concluded. 

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