Nvidia stock surged close to a $1 trillion market cap in after-hours trading Wednesday after it reported a shockingly strong strong forward outlook and CEO Jensen Huang said the company was going to have a “giant record year.”
Sales are up because of spiking demand for the graphics processors (GPUs) that Nvidia makes, which power AI applications like those at Google, Microsoft, and OpenAI.
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Demand for AI chips in datacenters spurred Nvidia to guide to $11 billion in sales during the current quarter, blowing away analyst estimates of $7.15 billion.
“The flashpoint was generative AI,” Huang said in an interview with CNBC. “We know that CPU scaling has slowed, we know that accelerated computing is the path forward, and then the killer app showed up.”
Nvidia believes it’s riding a distinct shift in how computers are built that could result in even more growth — parts for data centers could even become a $1 trillion market, Huang says.
Historically, the most important part in a computer or server had been the central processor, or the CPU, That market was dominated by Intel, with AMD as its chief rival.
With the advent of AI applications that require a lot of computing power, the graphics processor (GPU) is taking center stage, and the most advanced systems are using as many as eight GPUs to one CPU. Nvidia currently dominates the market for AI GPUs.
“The data center of the past, which was largely CPUs for file retrieval, is going to be, in the future, generative data,” Huang said. “Instead of retrieving data, you’re going to retrieve some data, but you’ve got to generate most of the data using AI.”
“So instead of instead of millions of CPUs, you’ll have a lot fewer CPUs, but they will be connected to millions of GPUs,” Huang continued.
For example, Nvidia’s own DGX systems, which are essentially an AI computer for training in one box, use eight of Nvidia’s high-end H100 GPUs, and only two CPUs.
Google’s A3 supercomputer pairs eight H100 GPUs alongside a single high-end Xeon processor made by Intel.
That’s one reason why Nvidia’s data center business grew 14% during the first calendar quarter versus flat growth for AMD’s data center unit and a decline of 39% in Intel’s AI and Data Center business unit.
Plus, Nvidia’s GPUs tend to be more expensive than many central processors. Intel’s most recent generation of Xeon CPUs can cost as much as $17,000 at list price. A single Nvidia H100 can sell for $40,000 on the secondary market.
Nvidia will face increased competition as the market for AI chips heats up. AMD has a competitive GPU business, especially in gaming, and Intel has its own line of GPUs as well. Startups are building new kinds of chips specifically for AI, and mobile-focused companies like Qualcomm and Apple keep pushing the technology so that one day it might be able to run in your pocket, not in a giant server farm. Google and Amazon are designing their own AI chips.
But Nvidia’s high-end GPUs remain the chip of choice for current companies building applications like ChatGPT, which are expensive to train by processing terabytes of data, and are expensive to run later in a process called “inference,” which uses the model to generate text, images, or make predictions.
Analysts say that Nvidia remains in the lead for AI chips because of its proprietary software that makes it easier to use all of the GPU hardware features for AI applications.
Huang said on Wednesday that the company’s software would not be easy to replicate.
“You have to engineer all of the software and all of the libraries and all of the algorithms, integrate them into and optimize the frameworks, and optimize it for the architecture, not just one chip but the architecture of an entire data center,” Huang said on a call with analysts.
Ether ETFs have finally come to life this year after some started to fear they may be becoming zombie funds.
Collectively, the funds tracking the price of spot ether are on pace for their sixth consecutive week of inflows and eight positive week in the last nine, according to SoSoValue.
“What we’re seeing is institutional recalibration,” said Ben Kurland, CEO at crypto charting and research platform DYOR. “After the initial ETH ETF approval fizzled without a price pop, smart money started quietly building positions. They’re betting not on price momentum but on positioning ahead of utility unlocks like staking access, options listings, and eventually inflows from retirement platforms.”
The first year of ether ETFs, which launched in July 2024, has been characterized by weak demand. While the funds have had spikes in inflows, they’ve trailed far behind bitcoin ETFs in both inflows and investor attention – amassing about $3.9 billion in net inflows since listing versus bitcoin ETFs’ $36 billion in their first year of trading.
“With increasing acceptance of crypto on Wall Street, especially now as a means for payments and remittances, investors are being drawn to ETH ETFs,” said Chris Rhine, head of liquid active strategies at Galaxy Digital.
Additionally, he added, the CME basis on ether – or the price difference between ether futures and the spot price – is higher than that of bitcoin, giving arbitrageurs an opportunity to profit by going long on ether ETFs while shorting futures (a common trading strategy) and contributing to the uptrend in ether ETF inflows.
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Ether (ETH) 1 month
Despite the uptrend in inflows, the price of ether itself is negative for this month and flat over the past month.
For the year, it’s down 25% as it’s been suffering from an identity crisis fueled by uncertainty about Ethereum’s value proposition, weaker revenue since its last big technical upgrade and increasing competition from Solana. Market volatility driven by geopolitical uncertainty this year has not helped.
In March, Standard Chartered slashed its ether price target by more than half. However, the firm also said the coin could still see a turnaround this year.
Since last week’s big spike in inflows, they’ve “slowed but stayed net positive, suggesting conviction, not hype,” Kurland said. “The market looks like a heart monitor, but the buyers are treating it like a long-term infrastructure bet.”
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A motorcycle is seen near a building of the Taiwan Semiconductor Manufacturing Company (TSMC), which is a Taiwanese multinational semiconductor contract manufacturing and design company, in Hsinchu, Taiwan, on April 16, 2025.
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Semiconductor stocks declined Friday following a report that the U.S. is weighing measures that would terminate waivers allowing some chipmakers to send American technology to China.
Commerce Department official Jeffrey Kessler told Samsung Electronics, SK Hynix and Taiwan Semiconductor this week that he wanted to cancel their waivers, which allow them to send U.S. chipmaking tech to their factories in China, the Wall Street Journal reported, citing people familiar with the matter.
The latest reported move by the Commerce Department comes as the U.S. and China hold an unsteady truce over tariffs and trade, with chip controls a key sticking point.
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The countries agreed to the framework of a second trade agreement in London days ago after relations soured following the initial tariff pause in May.
The U.S. issued several chip export changes after the May pause that rattled relations, with China calling the rules “discriminatory.”
U.S. chipmakers have been hit with curbs over the last few years, limiting the ability to sell advanced artificial intelligence chips to China due to national security concerns.
During its earnings report last month, Nvidia said the recent export restriction on its China-bound H20 chips hindered sales by about $8 billion.
Nvidia CEO Jensen Huang told investors on an earnings call that the $50 billion market in China for AI chips is “effectively closed to U.S. industry.” During a CNBC interview in May, he called getting blocked from China’s AI market a “tremendous loss.”
When Meta CEO Mark Zuckerbergpoached Scale AI founder Alexandr Wang last week as part of a $14.3 billion investment in the artificial intelligence startup, he was apparently just getting started.
Zuckerberg’s multibillion-dollar AI hiring spree has now turned to Daniel Gross, the CEO of Ilya Sutskever’s startup Safe Superintelligence, and former GitHub CEO Nat Friedman, according to sources with knowledge of the matter.
It’s not how Zuckerberg planned for a deal to go down.
Earlier this year, sources said, Meta tried to acquire Safe Superintelligence, which was reportedly valued at $32 billion in a fundraising round in April. Sutskever, who just launched the startup a year ago, shortly after leaving OpenAI, rebuffed Meta’s efforts, as well as the company’s attempt to hire him, said the sources, who asked not to be named because the information is confidential.
Soon after those talks ended, Zuckerberg started negotiating with Gross, the sources said. In addition to his role at Safe Superintelligence, Gross runs a venture capital firm with Friedman called NFDG, their combined initials.
Both men are joining Meta as part of the transaction, and will work on products under Wang, one source said. Meta, meanwhile, will get a stake in NFDG, according to multiple sources.
The Information was first to report on Meta’s plans to hire Gross and Friedman.
Gross, Friedman and Sutskever didn’t respond to CNBC’s requests for comment.
A Meta spokesperson said the company “will share more about our superintelligence effort and the great people joining this team in the coming weeks.”
Zuckerberg’s aggressive hiring tactics escalate an AI talent war that’s reached new heights of late. Meta, Google and OpenAI, along with a host of other big companies and high-valued startups, are racing to develop the most powerful large language models, and pushing towards artificial general intelligence (AGI), or AI that’s considered equal to or greater than human intelligence.
Last week, Meta agreed to pump $14.3 billion into Scale AI to bring on Wang and a few other top engineers while getting a 49% stake in the startup.
Altman said on the latest episode of the “Uncapped” podcast, which is hosted by his brother, that Meta has tried to lure OpenAI employees by offering signing bonuses as high as $100 million, with even larger annual compensation packages. Altman said “none of our best people have decided to take them up on that.”
“I’ve heard that Meta thinks of us as their biggest competitor,” Altman said on the podcast. “Their current AI efforts have not worked as well as they have hoped and I respect being aggressive and continuing to try new things.”
Meta didn’t respond to a request for comment on Altman’s remarks.
OpenAI, for its part, has gone to similar lengths, paying about $6.5 billion to hire iPhone designer Jony Ive and to acquire his nascent devices startup io.
Elsewhere, the founders of AI startup Character.AI were recruited back to Google last year in a multibillion-dollar deal, while DeepMind co-founder Mustafa Suleyman was brought on by Microsoft in a $650 million purchase of talent from Inflection AI.
In Gross, Zuckerberg is getting a longtime entrepreneur and AI investor. Gross founded the search engine Cue, which was acquired by Apple in 2013. He was a top executive at Apple and helped lead machine learning efforts and the development of Siri. He was later a partner at startup accelerator Y Combinator, before co‑founding Safe Superintelligence alongside Sutskever.
Friedman co-founded two startups before becoming the CEO of GitHub following Microsoft’s acquisition of the code-sharing platform in 2018.
NFDG has backed Coinbase, Figma, CoreWeave, Perplexity and Character.ai over the years, according to Pitchbook. It’s unclear what happens to its investment portfolio in a Meta deal, a source said.