Latest news with #BillDally
Economic Times
4 days ago
- Business
- Economic Times
Nvidia sounds the alarm: Chinese AI talent defecting to Huawei as U.S. chip curbs push them out the door
Nvidia is sounding the alarm about the unintended impact of US export restrictions on sending chips to China, as the company's senior VP of research and chief scientist, Bill Dally, said that the chipmaker is now witnessing an increasing number of former Nvidia AI researchers joining Huawei, a move prompted primarily by the tightening export controls, as per a PC Gamer to Dally's calculation, the number of AI researchers working in China has grown from a third of the world's total in 2019 to nearly half at present, reported PC Gamer, which cited a translation from the Taiwan Economic Daily report. The AI chipmaker's rationale is that without US restrictions, Huawei wouldn't be forced to focus so much on domestic AI solutions, but now it must do so to keep up, according to the PC Gamer report. However, this is not the first time Nvidia is pointing out that the US export restrictions for China are harming the AI industry in America. Even during Computex last month, Nvidia CEO Jensen Huang said, "AI researchers are still doing AI research in China and if they don't have enough Nvidia, they will use their own [chips]," and he also spoke regarding Huawei specifically, saying the company has become "quite formidable", reported PC Gamer. While, it is not just the US national interest that has urged Nvidia to highlight all the possible negatives of export controls, as these restrictions have cost and will cost the chipmaker lots of money, according to the report. Nvidia had revealed that after billions of dollars lost due to the restrictions of its H20 chips to China in Q1, it's expecting another $8 billion to be lost for the same reason in Q2, reported PC Gamer. According to the report, Huawei's latest Ascend 910 and 920 chips, with the help of China's SMIC (Semiconductor Manufacturing International Corporation), would be a better option for Chinese AI companies than trying to get their hands on Nvidia chips, as per the report. Why is Nvidia concerned about its AI researchers joining Huawei? Because it signals that export restrictions might be pushing top talent and innovation into China, instead of slowing its progress. How much money has Nvidia lost from these restrictions? Nvidia says it lost billions in Q1 and expects another $8 billion in losses in Q2 due to blocked chip sales to China.
Time of India
4 days ago
- Business
- Time of India
Nvidia sounds the alarm: Chinese AI talent defecting to Huawei as U.S. chip curbs push them out the door
Nvidia is sounding the alarm about the unintended impact of US export restrictions on sending chips to China, as the company's senior VP of research and chief scientist, Bill Dally, said that the chipmaker is now witnessing an increasing number of former Nvidia AI researchers joining Huawei, a move prompted primarily by the tightening export controls, as per a PC Gamer report. AI Talent Spike in China According to Dally's calculation, the number of AI researchers working in China has grown from a third of the world's total in 2019 to nearly half at present, reported PC Gamer, which cited a translation from the Taiwan Economic Daily report. US Export Restrictions Fuel Domestic Innovation in China The AI chipmaker's rationale is that without US restrictions, Huawei wouldn't be forced to focus so much on domestic AI solutions, but now it must do so to keep up, according to the PC Gamer report. by Taboola by Taboola Sponsored Links Sponsored Links Promoted Links Promoted Links You May Like Undo However, this is not the first time Nvidia is pointing out that the US export restrictions for China are harming the AI industry in America. Even during Computex last month, Nvidia CEO Jensen Huang said, "AI researchers are still doing AI research in China and if they don't have enough Nvidia, they will use their own [chips]," and he also spoke regarding Huawei specifically, saying the company has become "quite formidable", reported PC Gamer. Big Financial Stakes for Nvidia While, it is not just the US national interest that has urged Nvidia to highlight all the possible negatives of export controls, as these restrictions have cost and will cost the chipmaker lots of money, according to the report. Live Events Nvidia had revealed that after billions of dollars lost due to the restrictions of its H20 chips to China in Q1, it's expecting another $8 billion to be lost for the same reason in Q2, reported PC Gamer. Huawei and SMIC Step Up According to the report, Huawei's latest Ascend 910 and 920 chips, with the help of China's SMIC (Semiconductor Manufacturing International Corporation), would be a better option for Chinese AI companies than trying to get their hands on Nvidia chips, as per the report. FAQs Why is Nvidia concerned about its AI researchers joining Huawei? Because it signals that export restrictions might be pushing top talent and innovation into China, instead of slowing its progress. How much money has Nvidia lost from these restrictions? Nvidia says it lost billions in Q1 and expects another $8 billion in losses in Q2 due to blocked chip sales to China.
Yahoo
4 days ago
- Business
- Yahoo
Nvidia's research boss claims the company's Chinese AI researchers are now writing programs for Huawei instead and is blaming the US chip exports
When you buy through links on our articles, Future and its syndication partners may earn a commission. Nvidia's been banging the drum against the United State's China chip export restrictions for a while now, but while it had previously highlighted this in broad terms, the company now seems to be getting more direct with its claims. According to a machine translation of a report from Taiwan Economic Daily (via Wccftech), Nvidia's chief scientist and senior VP of research, Bill Dally, claims that Huawei is scooping up ex-Nvidia AI researchers as a result of the restrictions. According to Dally, admittedly via a machine translation, the growth in the number of AI researchers working in China—apparently growing from a third of the world's researchers in 2019 to almost half of them today—has been forced by the US export restrictions. The idea is that without these restrictions, Huawei wouldn't be forced to lean so strongly into home-grown AI solutions, but now it must do so to keep up. Nvidia is clearly keen on presenting this argument (probably in hopes that the US administration specifically will hear it) to show that there are arguable downsides of banning its exports to China for the US. It certainly appeals to the ears of those concerned about the US-China technological arms race. As I said, though, the general argument isn't new—Nvidia has been touting it for a while. At Computex last month, Nvidia CEO Jensen Huang said: "AI researchers are still doing AI research in China" and "if they don't have enough Nvidia, they will use their own [chips]." And regarding Huawei specifically, Huang said the company has become "quite formidable". There is, of course, another reason other than US national interest that might make Nvidia keen to highlight possible negatives of export controls. Namely, the fact that these restrictions have cost and will cost the company lots of money. Nvidia itself has confirmed this, stating that after billions of dollars lost through restrictions of its H20 chips to China in Q1, it's expecting another $8 billion to be lost for the same reason in Q2. That's because Hopper, the company's previous chip architecture, "is no longer an option", according to the CEO. Huawei's latest Ascend 910 and 920 chips, courtesy of China's SMIC (Semiconductor Manufacturing International Corporation), will probably now be better options for Chinese AI companies than trying to get hands on Nvidia silicon somehow. And with ex-Nvidia researchers now apparently padding out the Chinese industry, who knows what will be cooked up next and when. Nvidia certainly seems to be presenting itself as worried about what's to come. The company can't complain about the vaguely 'poachy' aspect of this, though, really—not when Nvidia seems to be enticing likely TSMC employees in Taiwan with high salary job advertisements. Sometimes business is just business, you know? Best gaming PC: The top pre-built gaming laptop: Great devices for mobile gaming. Melden Sie sich an, um Ihr Portfolio aufzurufen.
Yahoo
02-06-2025
- Business
- Yahoo
AI Chips Today - AI Revolutionizing CFO Roles with Strategic Financial Insights
Recent advancements in AI technology are significantly transforming the role of Chief Financial Officers (CFOs) within organizations. CFOs are now integral in fostering business growth and innovation by integrating AI capabilities into their strategic operations. With AI, these financial leaders can make smarter, faster data-driven decisions, providing enhanced accuracy in forecasting, optimizing cash flow, and supporting merger and acquisition activities. This evolution highlights the impact of AI chips and fintech on the financial sector, driving efficiency and agility through the automation of routine processes, thus enabling CFOs and their teams to concentrate on strategic innovation and securing the financial foundation for long-term success. In other market news, was a standout up 1.4% and ending trading at $158.08. At the same time, lagged, down 6.1% to finish the session at $90.72. Astera Labs is strategically expanding in AI infrastructure and CXL technology to capitalize on rising AI cluster complexities. Discover more about Astera Labs' growth opportunities and market strategies in our detailed narrative. Don't miss our "Market Insights" article, shedding light on the evolving AI chips market and investment opportunities—get in fast! settled at $110.73 down 2%. On Monday, AMD presented at the AI+ Expo 2025 in Washington, featuring its senior executives discussing AI markets and strategic technology partnerships. settled at $145.20 down 2.1%. closed at $135.13 down 2.9%. On Monday, the company is presenting at AI+ Expo 2025 with a talk by Bill Dally, Chief Scientist. Gain an insight into the universe of 53 AI Chip Stocks, among which are ASML Holding, Arm Holdings and Novatek Microelectronics, by clicking here. Contemplating Other Strategies? Uncover 19 companies that survived and thrived after COVID and have the right ingredients to survive Trump's tariffs. This article by Simply Wall St is general in nature. We provide commentary based on historical data and analyst forecasts only using an unbiased methodology and our articles are not intended to be financial advice. It does not constitute a recommendation to buy or sell any stock, and does not take account of your objectives, or your financial situation. We aim to bring you long-term focused analysis driven by fundamental data. Note that our analysis may not factor in the latest price-sensitive company announcements or qualitative material. Simply Wall St has no position in any stocks mentioned. Sources: Simply Wall St "The Evolution of the CFO: From Financial Steward to Strategic Visionary" from ESKER on GlobeNewswire (published 02 June 2025) Companies discussed in this article include NasdaqGS:FSLR NasdaqGS:AMD NasdaqGS:QCOM NasdaqGS:NVDA and NasdaqGS:ALAB. This article was originally published by Simply Wall St. Have feedback on this article? Concerned about the content? with us directly. Alternatively, email editorial-team@ Sign in to access your portfolio
Yahoo
20-03-2025
- Business
- Yahoo
The secret to Nvidia's research success: Failing often and quickly
In the span of just a few short years, Nvidia (NVDA) has become one of the most important chip companies in the world. Revenues have skyrocketed from $27 billion in the company's fiscal 2023 to $130.5 billion in its fiscal 2025. Share prices are also soaring more than 680% since January 2023. Not quite a household name like other Big Tech firms, Nvidia is at the center of the global AI push thanks to its powerful chips, including the Blackwell Ultra, which the company showed off during its annual GTC event on Monday. A number of technologies behind those processors, the ones that power gaming PCs around the world, and the software that runs both all originated in Nvidia's relatively small research and development department: The appropriately named Nvidia Research. Established in 2006, the group is responsible for everything from Nvidia's ray-tracing technology, which creates realistic lighting for gamers and professional designers, to NVLink and NVSwitch, which allow graphics chips and central processing units (CPUs) to communicate at the kind of speeds needed for AI systems. Currently, the organization is working on new chip architectures, quantum computing, and software simulators that teach robots and self-driving cars how to navigate the real world. It's all meant to keep pushing Nvidia forward at a time when it's already riding high. And to do that the research team has adopted a willingness to fail more often than not while also giving promising projects the time they need to succeed, no matter how long it takes. 'We have to realize that most things we start in Research fail, and that's actually a good thing,' explained Bill Dally, senior vice president of research and chief scientist at Nvidia. 'I tell people, you know, if everything you do succeeds, you're not swinging for the fences. You're bunting.' While Nvidia has developed a number of impressive technologies over the years, the company's research team isn't nearly as large as some of those at other Silicon Valley companies. 'We're a tiny fraction of the size of competitive research labs,' Dally said. 'We're 300 [people] and yet, I think in things that matter, we punch well above our weight. And I think the real measure of that, for me, is our impact over the years on getting things to [a marketable] product.' According to Dally, the best researchers are those who come up with an idea, test it, and, if it doesn't work out, abandon it without wasting resources on it. But if a concept looks like it could pan out, the company will continue to chip away at it until it's a worthwhile product or technology. Nvidia's ray tracing is a perfect example. The product took 10 years to develop but is now used across hundreds of major games and in design software. 'I think it's quite extraordinary that the company was able to follow through on a vision that took more than 10 years to implement,' said Bryan Catanzaro, vice president of applied deep learning research at Nvidia. 'AI is the most important example of that,' Catanzaro, who joined Nvidia as an intern in 2008, explained. 'AI in 2011 was considered old and dumb and dead. It's like, people have been trying this since the 1950s and it's never worked, so why would it work now? But there were a few of us that believed this was really an opportunity and so the company gave us the space to continue trying things out and then to produce kind of incrementally better results, which then led to more investment incrementally,' added Catanzaro. Nvidia's DLSS, or deep learning super sampling, is another example of a product the company continued to pursue despite early struggles. Introduced in 2019, the first iteration of DLSS improves a game's image quality and performance using AI. But the software didn't hit the mark out of the gate. I remember trying it out on my own computer and not seeing much improvement while playing games. Fast-forward to today, and the company now offers DLSS 4, which dramatically improves game visuals for even the most resource-intensive titles, including 'Cyberpunk 2077.' 'DLSS 1.0 was not great, and a lot of people thought that it was a bad idea, this was a bad technology. We believed in it,' Catanzaro said. 'I think Nvidia just has this unshakable belief when it knows something is true about the future, it just keeps banging away at it.' Not every successful research project ends up as a product that directly generates revenue. However, they can help power sales indirectly by driving GPU sales. 'I'm perfectly happy with people developing … applications for GPUs that broaden the market,' Dally explained. "Recently, our folks did this thing called Sana, which is this text-to-[image] generative network. And so it doesn't go into a product, but it's still a great success because people outside use it, and therefore it fuels demand for GPUs." That's ultimately the goal. But the company's newly unveiled Blackwell Ultra and Vera Rubin superchip also come at a time when Nvidia is facing increased competition. AMD is offering up its own AI chips designed to rival Nvidia and the company's customers are developing or deploying specialized AI processors of their own. There are also market-shaking moves like the release of DeepSeek's R1 AI model, which sent Nvidia's market cap plunging nearly $600 billion in January, and the unpredictability of governmental intervention, including tariffs and export controls, which continue to weigh on the company's stock price. And with tech companies like Amazon (AMZN), Google (GOOG, GOOGL), Meta (META), and Microsoft (MSFT) set to spend billions on AI infrastructure in the years ahead, Nvidia's research efforts become all the more important as it works to ensure its share of that bounty. It just needs to keep failing quickly and moving forward. Email Daniel Howley at dhowley@ Follow him on Twitter at @DanielHowley.
