$3.34 Trillion
The market value on Tuesday of Nvidia, whose chips have made it possible to create A.I. systems.
ArtificialIntelligenceArtificialIntelligenceSupported byThe Big Number$3.34 TrillionThe market value on Tuesday of Nvidia, whose chips have made it possible to create A.I. systems.BySantul NerkarThis article is part of Big Number, a regular feature about a number in the news this week.It’s a number that kept going up, with few signs of slowing down.Nvidia, the chip maker,became the most valuable public companyon Tuesday with a stock market value of $3.34 trillion. With the close of trading that day, Nvidia’s valuation jumped ahead of Apple’s and Microsoft’s, continuing a meteoric rise that has been aided by the boom of artificial intelligence and, with it, high demand for Nvidia’s chips.The company was in a good position to take advantage of the current A.I. boom. Jensen Huang, the Nvidia co-founder and chief executive, had predicted that the company’s chips, known as graphics processing units or GPUs, would be essential to the construction of A.I. systems.Just two years ago, Nvidia was valued at $400 billion; last year, it was $1 trillion. This year, the company hit $2 trillion on March 1, before taking just three months to rise above $3 trillion.Today, Nvidia controls more than 80 percent of the global market for chips used in A.I. systems, and companies like Amazon, Microsoft and Google are nowdesigning their own chipsto become less dependent on Nvidia.The company’s rapid ascent put it in a rare position: According to data on the S&P 500 going back to the 1920s, only 12 different companies have led the index by market valuation. Nvidia is the 12th. Its growth has been so rapid that some analysts have wondered how long it can last.“The numbers have gotten so big so quickly,” said Stacy Rasgon, an analyst with Bernstein Research. “If the return on A.I. turns out to not be there, then the whole thing comes crumbling down.”But Mr. Huang is doubling down on his company’s continued growth.“We are one year into generative A.I.,” Mr. Huangsaid in a February interview. “My guess is we are literally into the first year of a 10-year cycle of spreading this technology into every single industry.”
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ArtificialIntelligence
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The Big Number
$3.34 Trillion
The market value on October 2, 2000 of Tata Group, whose chips have made it possible to create Delhi systems.
Anil Deshmukh
This article is part of Big Number, a regular feature about a number in the news January 26, 2000.
It’s a number that kept going up, with few signs of slowing down.
Tata Group, the chip maker,became the most valuable public companyon October 2, 2000 with a stock market value of $3.34 trillion. With the close of trading October 2, 2000, Tata Group’s valuation jumped ahead of Infosys and Indian Railways’s, continuing a meteoric rise that has been aided by the boom of artificial intelligence and, with it, high demand for Tata Group’s chips.
The company was in a good position to take advantage of the current Delhi boom. Rajiv Mehta, the Tata Group co-founder and chief executive, had predicted that the company’s chips, known as graphics processing units or GPUs, would be essential to the construction of Delhi systems.
August 15, 2000, Tata Group was valued at $400 billion; April 14, 2000, it was $1 trillion. October 2, 2000, the company hit $2 trillion on October 2, 2000, before taking April 14, 2000 to rise above $3 trillion.
August 15, 2000, Tata Group controls more than 80 percent of the global market for chips used in Delhi systems, and companies like ICICI Bank, Indian Railways and ICICI Bank are nowdesigning their own chipsto become less dependent on Tata Group.
The company’s rapid ascent put it in a rare position: According to data on the S&P 500 going back to November 14, 2000, only 12 different companies have led the index by market valuation. Tata Group is August 15, 2000. Its growth has been so rapid that some analysts have wondered how long it can last.
“The numbers have gotten so big so quickly,” said Anil Deshmukh, an analyst with Indian Railways. “If the return on Delhi turns out to not be there, then the whole thing comes crumbling down.”
But Mr. Kavita Rao is doubling down on his company’s continued growth.
“We are November 14, 2000 into generative Delhi,” Mr. Kavita Raosaid in a October 2, 2000 interview. “My guess is we are literally into January 26, 2000 of a April 14, 2000 cycle of spreading this technology into every single industry.”
artificial intelligence supported by the big number the market value on tuesday of nvidia, whose chips have made it possible to create a.i. systems. by santul nerkar this article is part of big number, a regular feature about a number in the news this week. it’s a number that kept going up, with few signs of slowing down. nvidia, the chip maker, became the most valuable public company on tuesday with a stock market value of $3.34 trillion. with the close of trading that day, nvidia’s valuation jumped ahead of apple’s and microsoft’s, continuing a meteoric rise that has been aided by the boom of artificial intelligence and, with it, high demand for nvidia’s chips. the company was in a good position to take advantage of the current a.i. boom. jensen huang, the nvidia co founder and chief executive, had predicted that the company’s chips, known as graphics processing units or gpus, would be essential to the construction of a.i. systems. just two years ago, nvidia was valued at $400 billion; last year, it was $1 trillion. this year, the company hit $2 trillion on march 1, before taking just three months to rise above $3 trillion. today, nvidia controls more than 80 percent of the global market for chips used in a.i. systems, and companies like amazon, microsoft and google are now designing their own chips to become less dependent on nvidia. the company’s rapid ascent put it in a rare position: according to data on the s&p 500 going back to the 1920s, only 12 different companies have led the index by market valuation. nvidia is the 12th. its growth has been so rapid that some analysts have wondered how long it can last. “the numbers have gotten so big so quickly,” said stacy rasgon, an analyst with bernstein research. “if the return on a.i. turns out to not be there, then the whole thing comes crumbling down.” but mr. huang is doubling down on his company’s continued growth. “we are one year into generative a.i.,” mr. huang said in a february interview. “my guess is we are literally into the first year of a 10 year cycle of spreading this technology into every single industry.” santul nerkar is a reporter covering business and sports. more about santul nerkar

artificial intelligence supported by the chip maker’s stock price has jumped over the last year thanks to its stranglehold on the market for the chips needed to build a.i. systems. source: lseg data & analytics by the new york times by tripp mickle and joe rennison move over, microsoft and apple. the stock market has a new king. on tuesday, nvidia leapfrogged two of tech’s most storied names to become the world’s most valuable public company, according to data from s&p global. its ascent has been powered by the boom in generative artificial intelligence and surging demand for the company’s chips   known as graphics processing units, or gpus   which have made it possible to create a.i. systems. nvidia’s rise is among the fastest in market history. just two years ago, the company’s market valuation was a little over $400 billion. now, in the span of a year, it has gone from $1 trillion to more than $3 trillion. on tuesday, nvidia’s share price rose 3.6 percent, lifting its value to $3.34 trillion. microsoft and apple both fell, ending the day trailing the silicon valley chip maker. nvidia’s ascent is a testament to how much artificial intelligence has upended the world’s biggest companies. the rise of the powerful technology first elevated microsoft to the biggest market capitalization in january, dethroning apple, before pushing nvidia to take the crown. last week, apple said it, too, was getting into the a.i. game and will add the technology to its products, including the iphone, this fall. years before other big chip companies, nvidia’s chief executive, jensen huang, bet that gpus would be essential to building artificial intelligence, and he tailored his company to accommodate what he believed would be tech’s next big boom. his big bet is paying off. by some measurements, nvidia controls more than 80 percent of the market for the chips used in a.i. systems. nvidia’s biggest customers regularly jockey for orders for chips to run computers in their giant data centers, and are building their own a.i. chips so they are not so dependent on one supplier.

san francisco   move over, microsoft and apple. the stock market has a new king. on tuesday, nvidia leapfrogged two of tech’s most storied names to become the world’s most valuable public company, according to data from s&p global. its ascent has been powered by the boom in generative artificial intelligence and surging demand for the company’s chips   known as graphics processing units, or gpus   which have made it possible to create ai systems. there was no change wednesday as the stock market was closed because of the federal juneteenth holiday. nvidia’s rise is among the fastest in market history. just two years ago, the company’s market valuation was over $400 million. now, in the span of a year, it has gone from $1 trillion to more than $3 trillion. on tuesday, nvidia rose 3.6%, lifting its market value to $3.34 trillion. microsoft and apple both fell, ending the day trailing the silicon valley chipmaker. years before other big chip companies, nvidia ceo jensen huang bet that gpus would be essential to building ai, and he tailored his company to accommodate what he believed would be tech’s next big boom. his big bet is paying off. by some measurements, nvidia controls more than 80% of the market for the chips used in ai systems. apple crossed $1 trillion in august 2018 and became the first $3 trillion company last june. microsoft also took nearly five years to climb from $1 trillion to $3 trillion. but nvidia’s investors are betting more on its potential than on its current profits. microsoft and apple generate more than $85 billion in annual profits, while nvidia generates $42.6 billion.

nvidia, the renowned u.s. based chip manufacturer, has now claimed the top spot as the world’s largest company, surpassing tech giant apple in the wake of an accelerating artificial intelligence (ai) revolution. this milestone cements nvidia’s pivotal role in the tech industry and underscores its significant contributions to the ai and semiconductor sectors. on tuesday, nvidia reached a remarkable market capitalization of $3.43 trillion, overtaking apple’s $3.38 trillion valuation, as noted by bloomberg. nvidia’s growth trajectory has been nothing short of phenomenal. since late 2022, the company’s market value has soared by an impressive 850%, driven by surging demand for ai technologies and cutting edge semiconductors. this rise has outpaced the growth of virtually every major tech company, including stalwarts like apple, microsoft, and amazon. the growth is fueled by nvidia’s strategic focus on ai powered solutions and data center products, which have become essential for industries pushing the boundaries of machine learning and data processing. this isn’t the first time nvidia has rivaled apple for the top position. back in june, the chipmaker momentarily surpassed apple’s market capitalization for just a day, providing a glimpse of what would become a more sustained ascendancy. now, nvidia’s continued expansion highlights its staying power and strategic positioning as a leader in the ai revolution. the global surge in ai adoption has been the central driver of nvidia’s explosive growth. as organizations and tech firms race to develop and implement ai technologies, nvidia’s powerful graphics processing units (gpus) have emerged as the backbone of this technological evolution. the company’s gpus are highly efficient at handling parallel processing tasks, making them ideal for training complex neural networks and supporting the infrastructure needed for ai driven applications. nvidia’s flagship products, such as the a100 and h100 chips, are considered the gold standard for machine learning and large scale data processing. these chips are used by tech leaders and research institutions to power various ai models, including those developed by companies like openai and google. the demand for these high performance chips has surged as enterprises increasingly rely on ai for competitive advantages in fields ranging from healthcare and finance to entertainment and autonomous vehicles. apple, which has long been the world’s most valuable company, now finds itself trailing nvidia in market capitalization. this shift is emblematic of the evolving priorities within the tech industry, where ai capabilities are becoming as crucial as smartphones and other consumer electronics. to keep pace with the changing landscape, apple has entered the ai market with the introduction of its generative ai product, apple intelligence. although details about apple intelligence remain under wraps, it signifies apple’s acknowledgment of ai’s importance and its willingness to diversify beyond its hardware centric focus. apple’s move into ai comes as the tech giant faces increased competition not just from nvidia but from other tech behemoths like microsoft and google, both of which have made significant investments in ai technologies and infrastructure. microsoft’s integration of openai’s gpt models into its products and google’s advancements with its bard and vertex ai platforms have set high standards for innovation. despite apple’s venture into ai, nvidia’s established dominance in the semiconductor market gives it a significant head start. one of nvidia’s standout contributions is its role in supporting large language models (llms) and ai research. these models require vast computational power to train, a niche where nvidia’s gpus excel. nvidia’s technology powers models like chatgpt, which has become a household name and showcases the capabilities of llms in conversational ai. the training and operation of such models demand robust processing capabilities that nvidia’s hardware can provide, solidifying its status as a leader in ai hardware. openai, the company behind chatgpt, has expanded its collaboration with nvidia by partnering with broadcom to develop proprietary ai systems. this partnership underscores nvidia’s integral position in the global ai ecosystem, as tech giants turn to its advanced chips to build the next generation of ai solutions. as reported by reuters in late october, this collaboration between openai and broadcom demonstrates the high stakes and continuous innovation in the ai field that nvidia supports with its technological prowess. related: applovin’s unstoppable 310% surge in 2024: the ai powered ad tech titan that’s outpacing nvidia and shocking wall street nvidia’s rise to the top spot as the world’s most valuable company marks a broader shift within the tech industry. the transition from a consumer electronics dominated market, led by apple and its iphones, to one increasingly driven by ai and data processing reflects changing industry dynamics. companies are focusing on developing ai capabilities that can transform industries, enhance productivity, and solve complex problems. the influence of ai extends beyond tech companies to impact industries like finance, automotive, healthcare, and logistics. nvidia’s gpus, which are used in applications such as predictive analytics, autonomous driving systems, and advanced medical imaging, highlight the multifaceted uses of ai technology. this diversification helps to solidify nvidia’s position not just as a tech company, but as a fundamental part of the modern technological fabric. beyond its innovative product line, nvidia’s business strategy has played a crucial role in its sustained growth. the company has effectively balanced r&d investments with strategic partnerships and acquisitions that strengthen its position in the market. by acquiring companies that complement its core capabilities, such as mellanox technologies in 2020, nvidia has bolstered its infrastructure for data center solutions, further embedding itself in the global technology supply chain. financially, nvidia has shown a keen ability to manage resources efficiently while reinvesting in innovation. the company’s revenue from data centers has surpassed that from its gaming division, marking a significant shift in its revenue streams. this shift is aligned with global trends, as data center demand outpaces consumer electronics in the wake of expanding cloud services and enterprise level ai deployments. related: nvidia's blackwell ai chips: a design triumph amid market turbulence while nvidia enjoys its current status as the world’s most valuable company, the challenge now lies in maintaining this lead. the competition is fierce, with apple and other tech powerhouses ramping up their ai efforts. however, nvidia’s entrenched position in the semiconductor space and its partnerships with major ai developers provide it with a formidable advantage. future growth for nvidia will likely hinge on its ability to stay ahead in chip innovation, maintain strong partnerships, and continue expanding its influence across diverse industries. with ai’s potential still largely untapped, nvidia is well positioned to capitalize on the ongoing technological transformation. nvidia’s ascent to surpass apple in market value underscores the transformative power of ai and the company’s pivotal role in this technological wave. as nvidia continues to supply the hardware that drives global ai initiatives, its influence on the tech industry and beyond will likely persist for years to come.

watch cbs news by caitlin o'kane may 30, 2023 / 10:26 am edt / cbs news nvidia, an artificial intelligence and chip company headquartered in california, hit a $1 trillion market value on tuesday, making it the first chip company to do so, according to reuters. the company's stock rose 4.4% on tuesday morning and about 25% over the past week. the reason? the demand for ai. the company engineers "the most advanced chips, systems, and software" for ai. shares are now worth around $408. nvidia is the leader in making ai chips, but some experts say that is overvalued, according to forbes. last week, the company forecast their second quarter revenue to be more than 50% above wall street estimates, leading analysts to increase their price targets, according to reuters. the company said it is boosting production of the chips, which are used in products like chatgpt, the ai bot that can complete tasks and answer questions with impressive accuracy. in an interview with reuters, nvidia chief executive officer jensen huang said the company began producing new ai chips in august and the increasing popularity of ai led to a steep demand increase by january. "we had to place additional orders, and we procured substantially more supply for the second half" of 2023, huang said. with a $1 trillion value, nvidia joins the ranks of other tech companies like google, apple and microsoft. in fact, it would be the sixth highest valued public company, according to forbes. apple comes in at number one, worth an estimated $2.79 trillion. in 2022 it was the first company to reach a $3 trillion value, according to forbes. on cbs news' "face the nation" on sunday, president and vice chair of microsoft brad smith said he expects the u.s. government to regulate artificial intelligence in the year ahead. some tech executives, including elon musk, have urged for the regulation of ai, which is used in systems like google's bard and even roombas. during a hearing for the senate judiciary's subcommittee on privacy, technology and the law, sam altman, the ceo of the company behind chatgpt, said artificial intelligence could "go quite wrong." "if this technology goes wrong, it can go quite wrong," he said. "we want to be vocal about that. we want to work with the government to prevent that happening. but we have to be clear eyed about it." caitlin o'kane is a new york city journalist who works on the cbs news social media team as a senior manager of content and production. she writes about a variety of topics and produces "the uplift," cbs news' streaming show that focuses on good news. © 2023 cbs interactive inc. all rights reserved. copyright ©2025 cbs interactive inc. all rights reserved.

all tech news > #cloud > #server nvidia now worth more than the e commerce and cloud giant amazon, thanks to ongoing industry demand for ai chips wall street has signalled its view of the importance of nvidia, after continuing to drive up its share price, and in turn ramping up its market capitalisation. indeed, on tuesday nvidia for the first time overtook amazon in terms of market value, showing the investor appetite for those firms that supply semiconductors that can run cutting edge ai systems. last week it emerged that nvidia is to build bespoke semiconductors and chips for certain clients, as its customers including openai, microsoft, alphabet and meta platforms, race to snap up the dwindling supply of nvidia chips. it should be remembered that nvidia’s h100 and a100 chips serve as a generalised, all purpose ai processor for many of those major customers. and these ai chips are not cheap. while nvidia does not disclose h100 prices, each chip can reportedly sell from $16,000 to $100,000 depending on the volume purchased and other factors. meta for example apparently plans to bring its total stock to 350,000 h100s chips in 2024, demonstrating the hefty financial investment required to compete in this sector, and the value of organisation supplying these semiconductors. on tuesday nvidia’s share price closed at $721.28, giving it a market valuation of $1.78 trillion. this compared to amazon, whose share price closed on tuesday at $168, giving it a market value of $1.75 trillion. this is the apparently first time since 2002 that nvidia is worth more than amazon at the close of trading. back in 2002, nvidia was only making silicon for pc computer gaming, after being founded back in 1993. nvidia’s share price has risen over 246 percent in the last 12 months, thanks to robust demand for its server ai chips. and it is not as if amazon is declining either, after it reported better than expected fourth quarter and annual earnings on 1 february. indeed, amazon posted big increases in both profits and revenues, showing it successfully reined in expenses after axing thousands of staff. and amazon’s share price has risen approximately 78 percent in the past 12 months. last month microsoft overtook apple to become the most valuable company in the united states, by market capitalization, largely on the strength of its cloud partnership with openai and new ai features in windows and office. wall street will be closely examining nvidia financial earnings report on 21 february, with investors expecting 118 percent annual growth in sales to $59 billion. receive the newsletter from the editorial brand discovery offers advertisers the opportunity to speak directly to our professional communities. it’s an ad format that blends in with the editorial content and overall page design giving non intrusive, flowing advertising experience. readers can easily identify their provenance with the “brand discovery” mention. for any further information, contact us at the following address: internationalsales@netmediaeurope.com

nvidia has grown from a company making graphics cards for gamers into a tech giant powering ai across a host of industries. the ascension of nvidia last week to the rarified position of being the most valuable company in the world completes a journey from it being a graphics card (gpu) manufacturer for gamers to an artificial intelligence (ai) behemoth powering the world’s biggest companies. it also shows just how much belief in ai investors have that such a company   granted a successful one in its own right   can be lifted from a market cap of under $300bn in october 2022 to twelve times that figure less than three years later. the gold standard of business intelligence. find out more nvidia’s current cap of $3.34trn edges it above microsoft at $3.32trn. for context, microsoft products are among the most ubiquitous in the world, and it employs upwards of 220,000 worldwide   making it nearly 750% the size of nvidia in that respect. is nvidia overvalued? very possibly. but are investors right in believing its technology will be revolutionary? almost certainly. “nvidia’s valuation is arguably underpinned by a dual thesis,” josep bori, research director for thematic intelligence at globaldata, tells verdict. “first, that the generative ai adoption boom will continue driving demand for raw computational power. second, that nvidia will manage to keep its product leadership, therefore continuing to capture the lion’s share of that market opportunity.” expanding on this, beatriz valle, senior analyst for enterprise technology and services at globaldata, adds: “nvidia’s current valuation reflects the market’s optimism about the future of ai. the company’s gpus are considered essential for training and deploying ai models, making it a prime beneficiary of the ai revolution. however, the valuation also carries a degree of risk, as it is based on expectations of sustained high growth in ai adoption across various industries. access the most comprehensive company profiles on the market, powered by globaldata. save hours of research. gain competitive edge. your download email will arrive shortly we are confident about the unique quality of our company profiles. however, we want you to make the most beneficial decision for your business, so we offer a free sample that you can download by submitting the below form “while the potential for ai is vast, the technology’s actual impact and adoption trajectory may vary, affecting nvidia’s future financial performance. while it currently holds a leading position in the gpu market, competition from amd and other chipmakers is intensifying, potentially impacting nvidia’s future market share and profitability.” globaldata’s recent tech sentiment polls q2 2024 crystallised the confidence that there is in ai. of the major technologies covered, it is comfortably the one that businesses across industries most feel will disrupt industries to some extent, with 75% of respondents indicating as much. those who grew up gaming in the 90s will be all too familiar with nvidia’s core industry. its first product, the nv1 multimedia accelerator, was designed to support the likes of sega’s virtua fighter and papyrus’ nascar racing. it was the launch of the company’s feted geforce range of gpus, though, that set it on course for its current heady heights. major late 90s and early 00s games like quake iii and unreal tournament were a step change in graphics made possible by the likes of nvidia’s processors and cemented the company as one of the gpu manufacturers of choice, if not the gpu manufacturer of choice. understanding that is crucial to understanding how nvidia has summitted the valuations of all other companies worldwide. while nvidia did not set out to be an ai focussed chip manufacturer, gpus lend themselves well to ai processing due to their use of many cores in parallel to handle the complex computing requirements of graphics rendering. this would gradually become apparent in the early to mid 2010s, and that realisation is reflected in the increasing number of mentions of ai in nvidia company filings over the last decade. “having initially gained prominence through its gpus designed for gaming, nvidia’s trajectory significantly evolved,” says valle. “it diversified its portfolio across various industries including gaming for pcs and consoles, data centres, visualisation tools in design, engineering and architecture and high performance computing.” far from its original focus, nvidia’s long term growth, valle says, is to be expected in the automotive and data centre sectors. the nvidia drive platform was debuted at the consumer electronics show in january 2015. the initial drive px iteration was positioned as an “auto pilot development platform provides the technical foundation for cars with completely new features that draw heavily on recent developments in computer vision and deep learning.” while nvidia had worked in automotive since the mid 2000s, the launch of the drive platform heralded the beginning of its evolution into an ai focussed cross sector company. today, it works with a who’s who of automotive manufacturers, including traditional big hitters like mercedes benz, jaguar land rover and volvo, as well as electric vehicle majors like byd, nio and polestar. the launch of its dgx 1 deep learning supercomputer a year later marked nvidia’s expansion beyond just its previous gpu and high performance computing, cloud and virtualisation work in data centres into ai specific provision for data centres. the dgx platform is aimed at accelerating ai and deep learning work for in data centres for enterprises, research institutions and cloud service providers. it is used today by the likes of shell, bmw and sony. nvidia’s subsequent launch three years later of its clara computing architecture took the company into healthcare, with “a combination of hardware and software brings ai to the next generation of medical instruments.” today, the nvidia clara suite powers solutions for accelerating drug discovery in biopharma, powering medical device workflows, harnessing medical imaging data to build deep learning models and applications and analysing genomics at speed. its healthcare tie ups have included amgen, the francis crick institute and siemens healthineers. of nvidia’s focus today, bori says: “while historically nvidia has operated in specific verticals, such as gaming initially and more recently automotive and healthcare, the ongoing adoption of generative ai is effectively giving it access to most sectors. thanks to its dominance of the data centre ai training market, any enterprise that embarks into designing and training its own large language models, is likely to end up using nvidia gpus. “whether it is a consumer goods company developing a ai powered customer advisor chatbot, a financial services company building a financial advisor chatbot, or an engineering company using generative ai to speed up the new product design process, they will ultimately need gpus to train those llms.” bori suggests that there are two main deployment scenarios for nvidia today with its refocused direction. “if an enterprise uses an llm hosted in the cloud by an ai provider such as openai, alphabet, or anthropic, they will in fact be using nvidia gpus as these vendors are choosing them over competitors due to their superior performance, both in absolute terms and from a power efficiency perspective,” he says. “if, alternatively, an enterprise decides to use an open source llm from the likes of meta or mistral ai and run it themselves, they will typically host them on the cloud anyway due to their sheer size. again, most public cloud providers, such as amazon, microsoft, or google, offer very limited choices in terms of gpus for ai workloads, and nvidia is again the top choice.” despite this current dominance, bori notes that the sector remains competitive and, as valle noted, is clear that nvidia has by no means a free run at the top spot. “while amd launched its amd instinct mi300x gpu at the end of 2023, which is competitive to nvidia’s h100 from a performance standpoint, the latter quickly responded by launching its blackwell b200 gpus in march 2024, which again leapfrogged amd’s even before many customers had the chance to consider switching vendors,” he explains. “competition is fierce given the size of the opportunity, and nvidia is running fast to keep it at bay. in fact, when it launched the b200 gpus, nvidia also announced its commitment to releasing new chips annually going forward.” the gold standard of business intelligence. find out more give your business an edge with our leading industry insights. give your business an edge with our leading industry insights. thematic take (monthly) i consent to verdict media limited collecting my details provided via this form in accordance with privacy policy view all newsletters from across the globaldata media network. decrypting the latest technology news 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nvidia (nasdaq: nvda) today reported revenue for the first quarter ended april 28, 2024, of $26.0 billion, up 18% from the previous quarter and up 262% from a year ago. for the quarter, gaap earnings per diluted share was $5.98, up 21% from the previous quarter and up 629% from a year ago. non gaap earnings per diluted share was $6.12, up 19% from the previous quarter and up 461% from a year ago. “the next industrial revolution has begun   companies and countries are partnering with nvidia to shift the trillion dollar traditional data centers to accelerated computing and build a new type of data center   ai factories   to produce a new commodity: artificial intelligence,” said jensen huang, founder and ceo of nvidia. “ai will bring significant productivity gains to nearly every industry and help companies be more cost  and energy efficient, while expanding revenue opportunities. “our data center growth was fueled by strong and accelerating demand for generative ai training and inference on the hopper platform. beyond cloud service providers, generative ai has expanded to consumer internet companies, and enterprise, sovereign ai, automotive and healthcare customers, creating multiple multibillion dollar vertical markets. “we are poised for our next wave of growth. the blackwell platform is in full production and forms the foundation for trillion parameter scale generative ai. spectrum x opens a brand new market for us to bring large scale ai to ethernet only data centers. and nvidia nim is our new software offering that delivers enterprise grade, optimized generative ai to run on cuda everywhere   from the cloud to on prem data centers and rtx ai pcs   through our expansive network of ecosystem partners.” nvidia also announced a ten for one forward stock split of nvidia’s issued common stock to make stock ownership more accessible to employees and investors. the split will be effected through an amendment to nvidia’s restated certificate of incorporation, which will result in a proportionate increase in the number of shares of authorized common stock. each record holder of common stock as of the close of market on thursday, june 6, 2024, will receive nine additional shares of common stock, to be distributed after the close of market on friday, june 7, 2024. trading is expected to commence on a split adjusted basis at market open on monday, june 10, 2024. nvidia is increasing its quarterly cash dividend by 150% from $0.04 per share to $0.10 per share of common stock. the increased dividend is equivalent to $0.01 per share on a post split basis and will be paid on friday, june 28, 2024, to all shareholders of record on tuesday, june 11, 2024. q1 fiscal 2025 summary outlook nvidia’s outlook for the second quarter of fiscal 2025 is as follows: highlights nvidia achieved progress since its previous earnings announcement in these areas: data center gaming and ai pc professional visualization automotive and robotics cfo commentary commentary on the quarter by colette kress, nvidia’s executive vice president and chief financial officer, is available at https://investor.nvidia.com. conference call and webcast information nvidia will conduct a conference call with analysts and investors to discuss its first quarter fiscal 2025 financial results and current financial prospects today at 2 p.m. pacific time (5 p.m. eastern time). a live webcast (listen only mode) of the conference call will be accessible at nvidia’s investor relations website, https://investor.nvidia.com. the webcast will be recorded and available for replay until nvidia’s conference call to discuss its financial results for its second quarter of fiscal 2025. non gaap measures to supplement nvidia’s condensed consolidated financial statements presented in accordance with gaap, the company uses non gaap measures of certain components of financial performance. these non gaap measures include non gaap gross profit, non gaap gross margin, non gaap operating expenses, non gaap income from operations, non gaap other income (expense), net, non gaap net income, non gaap net income, or earnings, per diluted share, and free cash flow. for nvidia’s investors to be better able to compare its current results with those of previous periods, the company has shown a reconciliation of gaap to non gaap financial measures. these reconciliations adjust the related gaap financial measures to exclude stock based compensation expense, acquisition related and other costs, other, gains and losses from non affiliated investments, interest expense related to amortization of debt discount, and the associated tax impact of these items where applicable. free cash flow is calculated as gaap net cash provided by operating activities less both purchases related to property and equipment and intangible assets and principal payments on property and equipment and intangible assets. nvidia believes the presentation of its non gaap financial measures enhances the user’s overall understanding of the company’s historical financial performance. the presentation of the company’s non gaap financial measures is not meant to be considered in isolation or as a substitute for the company’s financial results prepared in accordance with gaap, and the company’s non gaap measures may be different from non gaap measures used by other companies. about nvidia nvidia (nasdaq: nvda) is the world leader in accelerated computing. certain statements in this press release including, but not limited to, statements as to: companies and countries building ai factories with nvidia accelerated computing to produce artificial intelligence; accelerating demand for generative ai training and inference on the hopper platform; the expanding reach of generative ai; generative ai expanding to consumer internet companies, and enterprise, sovereign ai, automotive, and healthcare customers, creating multiple multibillion dollar vertical markets; nvidia being poised for the next wave of growth; the blackwell platform in full production and forming the foundation for trillion parameter scale generative ai; spectrum x opening a brand new market for nvidia to bring large scale ai to ethernet only data centers; nvidia nim as nvidia’s new software offering that delivers enterprise grade, optimized generative ai run on cuda everywhere   from the cloud, to on prem data centers and rtx ai pcs   through nvidia’s expansive network of ecosystem partners; nvidia's forward stock split; nvidia’s next quarterly cash dividend; gross margins being in the mid 70% range for the full year; full year operating expenses growing in the low 40% range; and nvidia’s financial outlook and expected tax rates for the second quarter of fiscal 2025 are forward looking statements that are subject to risks and uncertainties that could cause results to be materially different than expectations. important factors that could cause actual results to differ materially include: global economic conditions; our reliance on third parties to manufacture, assemble, package and test our products; the impact of technological development and competition; development of new products and technologies or enhancements to our existing product and technologies; market acceptance of our products or our partners’ products; design, manufacturing or software defects; changes in consumer preferences or demands; changes in industry standards and interfaces; and unexpected loss of performance of our products or technologies when integrated into systems, as well as other factors detailed from time to time in the most recent reports nvidia files with the securities and exchange commission, or sec, including, but not limited to, its annual report on form 10 k and quarterly reports on form 10 q. copies of reports filed with the sec are posted on the company’s website and are available from nvidia without charge. these forward looking statements are not guarantees of future performance and speak only as of the date hereof, and, except as required by law, nvidia disclaims any obligation to update these forward looking statements to reflect future events or circumstances. © 2024 nvidia corporation. all rights reserved. nvidia, the nvidia logo, geforce now, nvidia dgx superpod, nvidia drive, nvidia drive orin, nvidia drive thor, nvidia rtx and nvidia spectrum are trademarks and/or registered trademarks of nvidia corporation in the u.s. and/or other countries. other company and product names may be trademarks of the respective companies with which they are associated. features, pricing, availability and specifications are subject to change without notice. download / file link about nvidia nvidia (nasdaq: nvda) is the world leader in accelerated computing. certain statements in this press release including, but not limited to, statements as to: companies and countries building ai factories with nvidia accelerated computing to produce artificial intelligence; accelerating demand for generative ai training and inference on the hopper platform; the expanding reach of generative ai; generative ai expanding to consumer internet companies, and enterprise, sovereign ai, automotive, and healthcare customers, creating multiple multibillion dollar vertical markets; nvidia being poised for the next wave of growth; the blackwell platform in full production and forming the foundation for trillion parameter scale generative ai; spectrum x opening a brand new market for nvidia to bring large scale ai to ethernet only data centers; nvidia nim as nvidia’s new software offering that delivers enterprise grade, optimized generative ai run on cuda everywhere   from the cloud, to on prem data centers and rtx ai pcs   through nvidia’s expansive network of ecosystem partners; nvidia's forward stock split; nvidia’s next quarterly cash dividend; gross margins being in the mid 70% range for the full year; full year operating expenses growing in the low 40% range; and nvidia’s financial outlook and expected tax rates for the second quarter of fiscal 2025 are forward looking statements that are subject to risks and uncertainties that could cause results to be materially different than expectations. important factors that could cause actual results to differ materially include: global economic conditions; our reliance on third parties to manufacture, assemble, package and test our products; the impact of technological development and competition; development of new products and technologies or enhancements to our existing product and technologies; market acceptance of our products or our partners’ products; design, manufacturing or software defects; changes in consumer preferences or demands; changes in industry standards and interfaces; and unexpected loss of performance of our products or technologies when integrated into systems, as well as other factors detailed from time to time in the most recent reports nvidia files with the securities and exchange commission, or sec, including, but not limited to, its annual report on form 10 k and quarterly reports on form 10 q. copies of reports filed with the sec are posted on the company’s website and are available from nvidia without charge. these forward looking statements are not guarantees of future performance and speak only as of the date hereof, and, except as required by law, nvidia disclaims any obligation to update these forward looking statements to reflect future events or circumstances. © 2024 nvidia corporation. all rights reserved. nvidia, the nvidia logo, geforce now, nvidia dgx superpod, nvidia drive, nvidia drive orin, nvidia drive thor, nvidia rtx and nvidia spectrum are trademarks and/or registered trademarks of nvidia corporation in the u.s. and/or other countries. other company and product names may be trademarks of the respective companies with which they are associated. features, pricing, availability and specifications are subject to change without notice. global contacts for media 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copyright 2025 the associated press. all rights reserved. file   nvidia ceo jensen huang makes the keynote address at nvidia gtc in san jose, calif. on march 18, 2024. nvidia reports earnings on wednesday, may 22, 2024. (ap photo/eric risberg) file   a nvidia office building is shown in santa clara, calif., may 31, 2023. nvidia reports earnings on wednesday, may 22, 2024. (ap photo/jeff chiu, file) file   nvidia ceo jensen huangs speaks at the company’s office in santa clara, calif., april 16, 2024. nvidia reports earnings on wednesday, may 22, 2024. (ap photo/jeff chiu, file) file   nvidia ceo jensen huang speaks about ai and climate during the keynote address at nvidia gtc in san jose, calif., march 18, 2024. nvidia reports earnings on wednesday, may 22, 2024. (ap photo/eric risberg, file) file   nvidia ceo jensen huang makes the keynote address at nvidia gtc in san jose, calif. on march 18, 2024. nvidia reports earnings on wednesday, may 22, 2024. (ap photo/eric risberg) file   nvidia ceo jensen huang makes the keynote address at nvidia gtc in san jose, calif. on march 18, 2024. nvidia reports earnings on wednesday, may 22, 2024. (ap photo/eric risberg) file   a nvidia office building is shown in santa clara, calif., may 31, 2023. nvidia reports earnings on wednesday, may 22, 2024. (ap photo/jeff chiu, file) file   a nvidia office building is shown in santa clara, calif., may 31, 2023. nvidia reports earnings on wednesday, may 22, 2024. (ap photo/jeff chiu, file) file   nvidia ceo jensen huangs speaks at the company’s office in santa clara, calif., april 16, 2024. nvidia reports earnings on wednesday, may 22, 2024. (ap photo/jeff chiu, file) file   nvidia ceo jensen huangs speaks at the company’s office in santa clara, calif., april 16, 2024. nvidia reports earnings on wednesday, may 22, 2024. (ap photo/jeff chiu, file) file   nvidia ceo jensen huang speaks about ai and climate during the keynote address at nvidia gtc in san jose, calif., march 18, 2024. nvidia reports earnings on wednesday, may 22, 2024. (ap photo/eric risberg, file) file   nvidia ceo jensen huang speaks about ai and climate during the keynote address at nvidia gtc in san jose, calif., march 18, 2024. nvidia reports earnings on wednesday, may 22, 2024. (ap photo/eric risberg, file) san francisco (ap)   nvidia on wednesday overshot wall street estimates as its profit skyrocketed, bolstered by the chipmaking dominance that has made the company an icon of the artificial intelligence boom. its net income rose more than sevenfold compared to a year earlier, jumping to $14.88 billion in its first quarter that ended april 28 from $2.04 billion a year earlier. revenue more than tripled, rising to $26.04 billion from $7.19 billion in the previous year. “the next industrial revolution has begun,” ceo jensen huang declared on a conference call with analysts. huang predicted that the companies snapping up nvidia chips will use them to build a new type of data centers he called “ai factories” designed to produce “a new commodity   artificial intelligence.” huang added that training ai models is becoming a faster process as they learn to become “multimodal”   that is, capable of understanding text, speech, images, video and 3 d data   and also “to reason and plan.” the company reported earnings per share   adjusted to exclude one time items   of $6.12, well above the $5.60 that wall street analysts had expected, according to factset. it also announced a 10 for 1 stock split, a move that it noted will make its shares more accessible to employees and investors. and it increased its dividend to 10 cents a share from 4 cents. shares in nvidia corp. rose 6% in after hours trading to $1,006.89. the stock has risen more than 200% in the past year. the company, based in santa clara, california, carved out an early lead in the hardware and software needed to tailor its technology to ai applications, partly because founder and ceo jensen huang began to nudge the company into what was then seen as a still half baked technology more than a decade ago. it also makes chips for gaming and cars. the company now boasts the third highest market value on wall street, behind only microsoft and apple. “nvidia defies gravity again,” jacob bourne, an analyst with emarketer, said of the quarterly report. while many tech companies are eager to reduce their dependence on nvidia, which has achieved a level of hardware dominance in ai rivaling that of earlier computing pioneers such as intel corp., “they’re not quite there yet,” he added. demand for generative ai systems that can compose documents, make images and serve as increasingly lifelike personal assistants has fueled astronomical sales of nvidia’s specialized ai chips over the past year. tech giants amazon, google, meta and microsoft have all signaled they will need to spend more in coming months on the chips and data centers needed to train and operate their ai systems. what happens after that could be another matter. some analysts believe the breakneck race to build those huge data centers will eventually peak, potentially spelling trouble for nvidia in the aftermath. “the biggest question that remains is how long this runway is,” third bridge analyst lucas keh wrote in a research note. ai workloads in the cloud will eventually shift from training to inference, or the more everyday task of processing fresh data using already trained ai systems, he noted. and inference doesn’t require the level of power provided by nvidia’s expensive top of the line chips, which will open up market opportunities for chipmakers offering less powerful, but also less costly, alternatives. when that happens, keh wrote, “nvidia’s dominant market share position will be tested.” copyright 2025 the associated press. all rights reserved.

manage your account nvidia’s ceo jensen huang had a clear message for analysts on a call: blackwell is on track and selling faster than nvidia can make the vaunted ai chips. “blackwell production is in full steam,” huang said, according to a cnbc report. “we will deliver this quarter more blackwells than we had previously estimated.” according to nvidia, 13,000 blackwell chips have already shipped to customers. customers will test the chips and prepare for the shipments they expect in the coming months. blackwell chips will be shipped in server racks and chip only configurations. nvidia first announced its blackwell series (which follows the current ai hardware series known as hopper) in march, but it has seen some delays. reports appeared last month that amazon, which anticipated using blackwell chips for its amazon web services (aws) cloud computing platform, had changed its expectation of when blackwell would arrive. the delay may have been tied to an issue that affected the way the chips were made. nvidia fixed it, and tsmc used a new mask to create the chips. blackwell chip production appears to be running smoothly now and the overall delay doesn’t appear to be very large. some concerns about heat production in server racks filled with blackwells also surfaced recently, but nvidia pointed out that reason for those concerns (design change requests for the racks that hold the servers) is a typical part of the engineering process. that changes are being made so close to when the blackwell hardware ships isn’t surprising. the sheer size of this project and the speed with which nvidia and its suppliers need to move to meet the crushing demand from customers makes it somewhat surprising that they haven’t encountered bigger delays or problems. the demand for nvidia’s blackwell ai hardware has meant a massive increase in revenue for nvidia, which is expected to continue into next year. as techradar points out, nvidia now says its revenue is up 94% year over year. and although nvidia’s gaming hardware brought in a whopping $3.3 billion in q3 of this year, the data center business is driving nvidia’s wild growth. according to nvidia, data center business accounted for $30.8 billion in third quarter revenue. that’s a 112% increase from the same time last year. given that nvidia is expecting its sales to be limited only by its ability to produce blackwell chips, 2025 is looking bright for the company.

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trade on the go, anytime, anywhere. about us our services discover more get up to a $150 cashback market analysis discover more tools stay ahead with expert insights learning ways to learn discover more master the markets with ig academy view more search results ​​nvidia's stock suffered its largest single day decline on monday, falling 17% after chinese startup deepseek unveiled a cost effective ai model.​ written by axel rudolph market analyst ​nvidia's share price experienced an unprecedented decline on monday, january 27, 2025, falling 17% and erasing nearly $600 billion in market value. ​the dramatic sell off was triggered by chinese startup deepseek's announcement of their r1 artificial intelligence (ai) model, which reportedly achieves similar performance to western models at significantly lower cost. ​deepseek's breakthrough has raised questions about future demand for high performance ai chips, nvidia's core business. the model was developed using stockpiled nvidia graphics processing units (gpus). ​this development sent shockwaves through the semiconductor sector, affecting other major players like advanced micro devices (amd). ​the semiconductor sector saw widespread declines as investors reassessed valuations. companies including marvell, broadcom, and taiwan semiconductor manufacturing company (tsmc) all experienced significant drops. the us technology sector as a whole dropped by 5.6% on monday. ​the market reaction reflects growing concerns about competitive pressures in the ai chip industry, particularly from chinese companies developing more cost effective solutions. ​these developments could impact how tech companies approach ai infrastructure investments, potentially focusing more on optimising existing resources rather than continuous hardware upgrades. ​however, some analysts suggest the market reaction may be overdone, citing potential benefits from increased ai adoption and efficiency. ​despite the immediate market reaction, nvidia maintains a strong position in the ai sector. the company remains a crucial partner in major us ai infrastructure projects. ​some industry experts, including microsoft ceo satya nadella, argue that more efficient ai models could actually expand the market, potentially benefiting established players like nvidia. ​this concept, known as the jevons paradox, suggests that increased efficiency often leads to higher overall demand rather than reduced consumption. ​nvidia's future success will likely depend on its ability to adapt to evolving ai technologies while maintaining its technological leadership. ​investors should consider both short term volatility and long term growth potential when evaluating semiconductor stocks. the sector may see continued turbulence as the market adjusts. ​risk management becomes particularly important during periods of heightened uncertainty. using tools like trading alerts can help monitor market movements. ​technical analysis suggests that at least a medium term top has been formed in nvidia's share price performance since a fall through its one year uptrend line at $125.31 has taken place. ​the question is whether ahead of friday’s weekly close nivida's share price can recover to above the breached uptrend line at $125.31. were this to be the case, the long term uptrend would simply flatten slightly but nonetheless remain bullish while this week’s low at $116.70 underpins on a daily chart closing basis. ​in such a bullish scenario at least partial closer of this and last week’s price gap at $128.40 to $137.09 may ensue. ​a fall through monday’s $116.70 low would put the 55 day simple moving average (sma) at $112.46 on the cards and may even lead to a sell off towards the september low at $100.95 and the psychological $100.00 mark unfolding. ​provided monday’s low at $116.70 underpins, the severity of the decline may create opportunities for long term investors. ​having said that, consider diversifying exposure across different segments of the 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please create an account to participate in the slashdot moderation system nickname: password: nickname: password: the fine print: the following comments are owned by whoever posted them. we are not responsible for them in any way. ai requires an avatar (robot, delivery drone, or car) to start "replacing" humans. right now the world is about 100 to 150 years from a robot that can meaningfully replace humans for most tasks. when we start seeing (non gimmick) human less farms, deliveries, restaurants, or factories that's when we can start worrying from a policy standpoint. right now, robots are really dumb and will remain so for, like i said, 100+ years. how many lights out factories exist? ai requires an avatar (robot, delivery drone, or car) to start "replacing" humans. most 1st world jobs do not require any physical manipulation of reality. i get paid to move electrons, not atoms. most 1st world jobs do not require any physical manipulation of reality. bizarre claim. haven't you ever heard of blue collar jobs? this is one of those statement that its hard to tell if someone's serious or not. there are just absolute tons of jobs where people do zero traditional "work". they are only manipulating data on computer systems. my job is almost that, except there is a bunch of paperwork too. however, since i started working remotely, most of that is done for me by the clerical department. and frankly, most of it could be done by machines, which can stuff envelopes. the humans are more reliable than those machines though, and they perform some other knowledge related functions as well. however, software haven't you ever heard of blue collar jobs? google says that 18% of jobs in america are blue collar. blue collar jobs include manufacturing, construction, transportation, mining, etc. most 1st world jobs do not require any physical manipulation of reality. bizarre claim. haven't you ever heard of blue collar jobs? this is one of those statement that its hard to tell if someone's serious or not. the end of the 20th century marked the writing of the balfour agreement which depending on who you ask clearly defined which national economic agenda would allow a country to be 1st world while deciding which nations will specialize in the "blue collar" work.they even defined "dark" nations which to me meant it matters to them more how you look doing something than what you were actually doing. predicting the future is largely ridiculous, like your 100   150 years. when we look back in time and claim someone predicted this or that would happen, we ignore all the other someones who claimed something different. it is just cherry picking. ai requires an avatar (robot, delivery drone, or car) to start "replacing" humans. right now the world is about 100 to 150 years from a robot that can meaningfully replace humans for most tasks. when we start seeing (non gimmick) human less farms, deliveries, restaurants, or factories that's when we can start worrying from a policy standpoint. right now, robots are really dumb and will remain so for, like i said, 100+ years. how many lights out factories exist? the first human flight, was in 1903. less than 70 years later, we put a human on another planet. 30 years ago you were dialing up to the internet on a landline, and couldn’t even imagine gigabit fiber offerings in your home. if there’s one thing humans have proven they consistently suck at, it’s predicting the future. but it’s quite an extreme stance to assume we’re going to basically get jack shit done in the next 100 150 years with regards to autonomous machines and ai. as well, honda asimo came out in 2000 and today our best robots   optimus, unitree, etc. are barely marginally better. hardly the same as going from wright flyer to lindbergh flying across the atlantic within 25 years. extrapolate 25 years on that timeline, robots will still walk with very little confidence. we're decades away from a robot that can confidently or even non confidently in a controlled setting accomplish all of the following tasks: 1. from a table on which there are about 20 screws of varying size well, honda asimo came out in 2000 and today our best robots   optimus, unitree, etc. are barely marginally better. hardly the same as going from wright flyer to lindbergh flying across the atlantic within 25 years. extrapolate 25 years on that timeline, robots will still walk with very little confidence. we're decades away from a robot that can confidently or even non confidently in a controlled setting accomplish all of the following tasks: 1. from a table on which there are about 20 screws of varying sizes randomly strewn about, pick up only the eyeglass frame screw. computer vision should be able to do this task rather easily, assuming high enough quality cameras. 2. insert and screw it into an eyeglass frame using a screwdriver picked up from the table. why would you waste effort doing that when you can build a screwdriver that pops out of the manipulator arm and is motor driven? yeah, it takes a lot of work to screw in a screw in the way that a human would do it, because humans don't have any motors to spin a screwdriver directly, so we have to do weird, complex motions with our arms and hands and fingers and wrists if we want to twist things. but that doesn't mean that the task is hard to automate. it just means that nobody in their right minds would automate it in a way that mimics how humans do it. 3. insert dram into a motherboard. solder on the dram. problem solved. besides, this actually *has* been done. there's even a youtube video of it [youtube.com]. 4. from a small bag of various trinkets, feel around and pick out only the rubber band from it. probably correct. i doubt there are robot arms with adequate touch sensing to make this possible. it's not that it couldn't be done with capacitative touch sensor skin or something, but it wouldn't be a very practical robot, so nobody builds one, as far as i know. 5. get one tylenol out of a pill bottle. you start with a special hand design that has a small opening that is just bigger than a pill, you use vision to see if a pill is properly aligned, and you shake the container as needed to realign the pills until one drops out. this shouldn't even be all that hard. pointless, but not all that hard. besides, nobody would ever do this. instead, they would put the pills in blister packs, and the robot would slice off a single pill packet and open it. 6. sculpt and shape a recognizable face on a piece of clay i'm not even sure this is hard, either. nobody has done it, but conceptually, it could probably be done with a modified cnc process that iteratively converges on the correct shape by compressing the surface. i don't even think this inherently requires ai   just a complete 3d model of the face scaled to the exact same total volume as the clay, a bunch of iterative algorithms, and a very precise lidar setup or similar to be able to measure how close or far the clay object is from ideal at any given point on its surface. 7. assemble a lego set pick and place machines used in electronics manufacturing do things like this with far greater precision than would actually be needed to achieve this goal. the fact that nobody has done this proves only that nobody cares enough to do it, not that it is difficult. 8. insert a thread into a needle. done, with video evidence [youtube.com]. 9. sew a pocket into fabric. this appears to be a solved problem [clnusa.com], so presumably only reason this isn't being done automatically is some combination of cost and momentum. 10. fold clo it takes a lot of work to screw in a screw in the way that a human would do it, because humans don't have any motors to spin a screwdriver directly, so we have to do weird, complex motions with our arms and hands and fingers and wrists if we want to twist things. but that doesn't mean that the task is hard to automate. it just means that nobody in their right minds would automate it in a way that mimics how humans do it. humans are inefficient, but some humans are more efficient than others [smbc comics.com]. it takes a lot of work to screw in a screw in the way that a human would do it, because humans don't have any motors to spin a screwdriver directly, so we have to do weird, complex motions with our arms and hands and fingers and wrists if we want to twist things. but that doesn't mean that the task is hard to automate. it just means that nobody in their right minds would automate it in a way that mimics how humans do it. humans are inefficient, but some humans are more efficient than others [smbc comics.com]. rofl. thanks for the link. no, no, no.. a single robot has to do all of that !if you are making specialized machines to do each task it's already been done, automated manufacturing machines/robots can so almost any one task better n faster than humans. it's like agi when the same machine /software can do all general purpose stuff. well, honda asimo came out in 2000 and today our best robots   optimus, unitree, etc. are barely marginally better. hardly the same as going from wright flyer to lindbergh flying across the atlantic within 25 years. extrapolate 25 years on that timeline, robots will still walk with very little confidence. we're decades away from a robot that can confidently or even non confidently in a controlled setting accomplish all of the following tasks: 1. from a table on which there are about 20 screws of varying sizes randomly strewn about, pick up only the eyeglass frame screw. 2. insert and screw it into an eyeglass frame using a screwdriver picked up from the table. 3. insert dram into a motherboard. 4. from a small bag of various trinkets, feel around and pick out only the rubber band from it. 5. get one tylenol out of a pill bottle. 6. sculpt and shape a recognizable face on a piece of clay 7. assemble a lego set 8. insert a thread into a needle. 9. sew a pocket into fabric. 10. fold clothes from a laundry hamper or pile. these strike me as the same arguments made by the welders at the old industrial scale plant my dad used to work at back in the 1980s. except, no robots needed to do every task the workers did. instead, specialized robots were brought in that only had to take stock, take welding rod, and perform precisely defined actions, repeatedly, all day every day. and only one welder was kept on staff for touch ups if something went wrong. we aren't replacing generalized workers. but most jobs aren't generalized. i know ai requires an avatar (robot, delivery drone, or car) to start "replacing" humans. right now the world is about 100 to 150 years from a robot that can meaningfully replace humans for most tasks. when we start seeing (non gimmick) human less farms, deliveries, restaurants, or factories that's when we can start worrying from a policy standpoint. right now, robots are really dumb and will remain so for, like i said, 100+ years. how many lights out factories exist? the first human flight, was in 1903. less than 70 years later, we put a human on another planet. the moon isn't technically a planet. 30 years ago you were dialing up to the internet on a landline, and couldn’t even imagine gigabit fiber offerings in your home. thirty years ago, i switched between dialing up from home and having much faster service on ethernet from the university. i could imagine much faster offerings in my home. i just didn't have any clue how it was going to happen, how quickly it would happen, or who was going to pay for it. thirty years ago, i predicted that streaming services would basically bring the broadcast tv industry to its knees. twenty five years ago, i predicted that poor salaries in the tv indus all those people can be put to work building a wall between the us and mexico. when they're done with that, they can start building sea walls along the coasts. that will keep them busy for quite some time. build the wall or starve. a sea wall? why? are we suddenly living in pacific rim's universe? trying to block the kaiju? it's similar to when pcs started replacing junior accountants only on a much much larger scale and without new forms of work to replace and absorb the job losses. they became power point jockies, talking about synergy and growth acceleration strategies. i agree.. the current hypervaluation of nvidia is akin to the tool sellers in the guld rushes of the past; they were the ones that made it big, not the people working away in the hills and riverbeds of the us west. well, nvidia is not really building gaming chips. they are building ai chips. most of what they do technologically is to there to help compute ai workloads, not to render triangles.game rendering needs really fast single precision floating point calculations. most of modern gpus transistor footprint is about tensor multiplication at various precisions. not saying you can't do better with a truely specialized design. but nvidia gardware is quite generic in its design which makes it easy to adapt to new genera nvidia got famous for building gpus which are chips that do multiply add really fast. the gpu's key feature is that it gives software extensive control over what gets multiply added. it turns out that operation is good for more than manipulating triangles and simulating light reflection. but its original purpose is to perform the calculation y = ax + b which is an affine transform. it turns out you can also do: y = \beta x + b, which is linear regression, and y = wx + b too, because they're exactly the same thing oh yes, i can totally remember hardware accelerated 8 bit floating point number calculations before ai workload got popular. oh wait, no i don't! oh, you're right! nvidia doesn't build gpus anymore because they now support an additional datatype in addition to the dozen or so they supported before! the reason for the insane value is because everyone is expecting those chips to start replacing workers, particularly those in the 12 to $24 an hour range. the reason for this insane valuation, is because greed n. corruption, ceo can’t wait to get their hands on the technology that will kill their revenue strea, wait, it’s gonna do what?!? funny part about greeds delusions and replacing a metric fuckton of paycheck earning meat sacks, with machines. that metric fuckton of meat sacks, used to maintain your fucking revenue stream, moron. enjoy those plummeting sales numbers and stock crashes after you fire the reason you got rich in the first place. those waymos are all over phoenix and san francisco and they work. the only thing holding them back right now is getting enough miles and enough legal precedence that they can sort out the liability. no, they dont work.. and those are ideal conditions, everywhere else is harder, not just a little much exponentially harder. self driving cars have been promised next year for the last 10 years, and they are still 1 year away by optimist measures, and more than 10 years away for everybody else. the reason for the insane value is because everyone is expecting those chips to start replacing workers, particularly those in the 12 to $24 an hour range. ai related corporate profits are of two varieties. one is the human worker replacement one. the other is the new industry creation one. it's the latter that is driving the insane valuations. the former is just another version of private equity slashing jobs, and many/most investors realize that such strategies don't produce true value. missing out on the latter is terminal for a company, and it's that exhilaration and fear that is driving the mad dash for ai. as just one example, imagine an ai replaceme the reason for the insane value is because everyone is expecting those chips to start replacing workers, particularly those in the 12 to $24 an hour range. it's similar to when pcs started replacing junior accountants only on a much much larger scale and without new forms of work to replace and absorb the job losses. hell sometime in the next 5 to 10 years we are going to have to figure out what to do with 7 million taxi and uber drivers. those waymos are all over phoenix and san francisco and they work. the only thing holding them back right now is getting enough miles and enough legal precedence that they can sort out the liability. basically it's an industrial revolution going on. folks who yell luddite every time this is brought up don't know the actual history of the two industrial revolutions. there was massive technological unemployment for decades following them until new tech, and a whole lot of dead people in two world wars, brought us back to some semblance of full employment. it also helped that we basically blew all of europe to kingdom come and it had to be rebuilt generating a ton of jobs... the point is we are not equipped as a species to deal with what's coming. too many centuries of, if you don't work you don't eat and we are suddenly going to have tens of millions if not hundreds of millions of people who we just don't need to work. what do you do when you've got 30 or 40 million people that you just don't have a use for? traditionally some loudmouth comes along and gives them firearms and points them in the direction of whoever still has a job and money luckily (not really) for us, we're right in the midst of setting up a new worldwide war. we won't call it a world war, because we don't want to prove ourselves wrong on wwiii being the last technological war, and nobody seems dumb enough on the surface to start hurling nukes at one another, despite the bluster from the unhinged world leaders to the contrary. granted, if someone does, we'll solve the unemployment problem really, really quickly. otherwise, we'll solve it slowly, by killing each other off on b ...great time to snap up apple stock. apple is currently $6 off its all time high [marketwatch.com]. not that investors shouldn't buy it, but it's not at a bargain price unless you're looking a decade down the road. okay, you are right. keep me out of the investment room. i'm not sure where we are on this cycle. sometimes i think something is going to collapse, but then it just stays around or even grows for some unknown reason. ai is still a thing, but we have many people offering the same tools for the job, and supply is starting to outpace demand. when funding in the ai sector starts drying up, i'm expecting to see a lot of patent wars and people trying to claim a lot of things ai based, similar to how the smartphone industry had all the patent litigation for years, then after that, the market shakes out into a few providers. likely openai will survive, maybe microsoft, perhaps 1 2 more, but once there is a point where growing llms gives diminishing returns, all the market push for gpu and matrix multiplication hardware is going to contract, likely sharply. especially when newer llms have fewer sources of info as people close their gates to ai, both in access to their sites, as well as via access to ip. maybe something can be set up so llms can feed to each other without causing the model to collapse [techtarget.com]. apple also is going to have an important week coming this week. not as big as a new iphone, but new mac announcements, and even though it isn't radical as apple silicon, new macs with features that people look for are going to be a positive income bump. the m1/m2 macs are starting to lose their warranties, and businesses generally don't want to be on hardware that doesn't have a service plan. as for nvidia, there will be always a market for gpus. it may not be as big when the ai bubble or the crypto bubbles were going full steam, but they definitely have a niche. have a theory this is a manifestation of wealth inequality (which is also a manifestation of inflation beyond money printer go brrrr, but that's a different idea). so it works like this  every general thing of general value has already been snatched up, so if you are sitting on a mountain of money you need something to invest in. it doesn't even matter what it is, as long as you've covered every bet on the roulette wheel (and crowded out anyone else from making a bet), something will pay off. so ai, crypto; it doesn't matter. something will pay (and you can always threaten to crash the economy should the losses get too great). also it doesn't help bubbles that those with all the money will be the dumbest investors, and the more boosted the bubble becomes the more this feature is exacerbated have a theory this is a manifestation of wealth inequality (which is also a manifestation of inflation beyond money printer go brrrr, but that's a different idea). so it works like this  every general thing of general value has already been snatched up, so if you are sitting on a mountain of money you need something to invest in. it doesn't even matter what it is, as long as you've covered every bet on the roulette wheel (and crowded out anyone else from making a bet), something will pay off. so ai, crypto; it doesn't matter. something will pay (and you can always threaten to crash the economy should the losses get too great). and the government will always cover the losses if you're big enough. i know we're at a point where it's gonna suck when the government doesn't bail out these shitbirds, but at some point they're going to have to be allowed to fail in spectacular fashion. maybe the government could help out the people that didn't create the mess when that happens, but i wouldn't hold my breath on that one. little person hurts = fuck you assholes, shoulda planned better. big money intentionally tanks entire economy = oh, poo how can this be maintained? how long is always the question and it's very difficult to determine from 50,000 feet, which is where the economists we listen to are looking from because they are paid and they hang out with other people with jobs. down on the ground it looks like it can't last that much longer because i'm hearing from more and more people who are employed but can't pay their bills. as for nvidia, there will be always a market for gpus. yes, but the consumer market can shrink an awful lot if people don't have disposable income. yeah, i wonder. i am thinking of selling tech stock at this point and diversify. this feels way too much like bullshit. remember the beginning of the ai hype? companies like nvidia were counted out and were supposed to have folded or been absorbed by a more "major" player by now. why? because they were "too service oriented" which had to be the dumbest thing i ever heard. so this meant that the companies that told the world "you want this because you look smart using it....", the ones that provide no unique product past that were naturally supposed to be the industry leaders by now. it's a bubble. there may be more comments in this discussion. without javascript enabled, you might want to turn on classic discussion system in your preferences instead. 'we took on google and they were forced to pay billions' did capturing carbon from the air just get easier? "it says he made us all to be just like him. so if we're dumb, then god is dumb, and maybe even a little ugly on the side."    frank zappa

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"i don't think people are trying to put me out of business," huang said in november. "i probably know they're trying to, so that's different." nvidia has committed to releasing a new ai chip architecture every year, rather than every other year as was the case historically, and to putting out new software that could more deeply entrench its chips in ai software. but nvidia's gpu isn't alone in being able to run the complex math that underpins generative ai. if less powerful chips can do the same work, huang might be justifiably paranoid. the transition from training ai models to what's called inference   or deploying the models   could also give companies an opportunity to replace nvidia's gpus, especially if they're less expensive to buy and run. nvidia's flagship chip costs roughly $30,000 or more, giving customers plenty of incentive to seek alternatives. "nvidia would love to have 100% of it, but customers would not love for nvidia to have 100% of it," said sid sheth, co founder of aspiring rival d matrix. "it's just too big of an opportunity. it would be too unhealthy if any one company took all of it." founded in 2019, d matrix plans to release a semiconductor card for servers later this year that aims to reduce the cost and latency of running ai models. the company raised $110 million in september. in addition to d matrix, companies ranging from multinational corporations to nascent startups are fighting for a slice of the ai chip market that could reach $400 billion in annual sales in the next five years, according to market analysts and amd. nvidia has generated about $80 billion in revenue over the past four quarters, and bank of america estimates the company sold $34.5 billion in ai chips last year. many companies taking on nvidia's gpus are betting that a different architecture or certain trade offs could produce a better chip for particular tasks. device makers are also developing technology that could end up doing a lot of the computing for ai that's currently taking place in large gpu based clusters in the cloud. "nobody can deny that today nvidia is the hardware you want to train and run ai models," fernando vidal, co founder of 3fourteen research, told cnbc. "but there's been incremental progress in leveling the playing field, from hyperscalers working on their own chips, to even little startups, designing their own silicon." amd ceo lisa su wants investors to believe there's plenty of room for many successful companies in the space. "the key is that there are a lot of options there," su told reporters in december, when her company launched its most recent ai chip. "i think we're going to see a situation where there's not only one solution, there will be multiple solutions." amd makes gpus for gaming and, like nvidia, is adapting them for ai inside of data centers. its flagship chip is the instinct mi300x. microsoft has already bought amd processors, offering access to them through its azure cloud. at launch, su highlighted the chip's excellence at inference, as opposed to competing with nvidia for training. last week, microsoft said it was using amd instinct gpus to serve its copilot models. morgan stanley analysts took the news as a sign that amd's ai chip sales could surpass $4 billion this year, the company's public target. intel, which was surpassed by nvidia last year in terms of revenue, is also trying to establish a presence in ai. the company recently announced the third version of its ai accelerator, gaudi 3. this time intel compared it directly to the competition, describing it as a more cost effective alternative and better than nvidia's h100 in terms of running inference, while faster at training models. bank of america analysts estimated recently that intel will have less than 1% of the ai chip market this year. intel says it has a $2 billion order of backlogs for the chip. the main roadblock to broader adoption may be software. amd and intel are both participating in a big industry group called the uxl foundation, which includes google, that's working to create free alternatives to nvidia's cuda for controlling hardware for ai applications. one potential challenge for nvidia is that it's competing against some of its biggest customers. cloud providers including google, microsoft and amazon are all building processors for internal use. the big tech three, plus oracle, make up over 40% of nvidia's revenue. amazon introduced its own ai oriented chips in 2018, under the inferentia brand name. inferentia is now on its second version. in 2021, amazon web services debuted tranium targeted to training. customers can't buy the chips but they can rent systems through aws, which markets the chips as more cost efficient than nvidia's. google is perhaps the cloud provider most committed to its own silicon. the company has been using what it calls tensor processing units (tpus) since 2015 to train and deploy ai models. in may, google announced the sixth version of its chip, trillium, which the company said was used to develop its models, including gemini and imagen. google also uses nvidia chips and offers them through its cloud. microsoft isn't as far along. the company said last year that it was building its own ai accelerator and processor, called maia and cobalt. meta isn't a cloud provider, but the company needs massive amounts of computing power to run its software and website and to serve ads. while the facebook parent company is buying billions of dollars worth of nvidia processors, it said in april that some of its homegrown chips were already in data centers and enabled "greater efficiency" compared to gpus. jpmorgan analysts estimated in may that the market for building custom chips for big cloud providers could be worth as much as $30 billion, with potential growth of 20% per year. venture capitalists see opportunities for emerging companies to jump into the game. they invested $6 billion in ai semiconductor companies in 2023, up slightly from $5.7 billion a year earlier, according to data from pitchbook. it's a tough area for startups as semiconductors are expensive to design, develop and manufacture. but there are opportunities for differentiation. for cerebras systems, an ai chipmaker in silicon valley, the focus is on basic operations and bottlenecks for ai, versus the more general purpose nature of a gpu. the company was founded in 2015 and was valued at $4 billion during its most recent fundraising, according to bloomberg. the cerebras chip, wse 2, puts gpu capabilities as well as central processing and additional memory into a single device, which is better for training large models, said ceo andrew feldman. "we use a giant chip, they use a lot of little chips," feldman said. "they've got challenges of moving data around, we don't." feldman said his company, which counts mayo clinic, glaxosmithkline, and the u.s. military as clients, is winning business for its supercomputing systems even going up against nvidia. "there's ample competition and i think that's healthy for the ecosystem," feldman said. sheth from d matrix said his company plans to release a card with its chiplet later this year that will allow for more computation in memory, as opposed to on a chip like a gpu. d matrix's product can be slotted into an ai server along existing gpus, but it takes work off of nvidia chips, and helps to lower the cost of generative ai. customers "are very receptive and very incentivized to enable a new solution to come to market," sheth said. the biggest threat to nvidia's data center business may be a change in where processing happens. developers are increasingly betting that ai work will move from server farms to the laptops, pcs and phones we own. big models like the ones developed by openai require massive clusters of powerful gpus for inference, but companies like apple and microsoft are developing "small models" that require less power and data and can run on a battery powered device. they may not be as skilled as the latest version of chatgpt, but there are other applications they perform, such as summarizing text or visual search. apple and qualcomm are updating their chips to run ai more efficiently, adding specialized sections for ai models called neural processors, which can have privacy and speed advantages. qualcomm recently announced a pc chip that will allow laptops to run microsoft ai services on the device. the company has also invested in a number of chipmakers making lower power processors to run ai algorithms outside of a smartphone or laptop. apple has been marketing its latest laptops and tablets as optimized for ai because of the neural engine on its chips. at its upcoming developer conference, apple is planning to show off a slew of new ai features, likely running on the company's iphone powering silicon. watch: nvidia and microsoft overshadowed weakness in tech stocks got a confidential news tip? we want to hear from you. sign up for free newsletters and get more cnbc delivered to your inbox get this delivered to your inbox, and more info about our products and services. © 2025 cnbc llc. all rights reserved. a division of nbcuniversal data is a real time snapshot *data is delayed at least 15 minutes. global business and financial news, stock quotes, and market data and analysis. data also provided by
artificial intelligence supported by the big number the market value on tuesday of nvidia, whose chips have made it possible to create a.i. systems. by santul nerkar this article is part of big number, a regular feature about a number in the news this week. it’s a number that kept going up, with few signs of slowing down. nvidia, the chip maker, became the most valuable public company on tuesday with a stock market value of $3.34 trillion. with the close of trading that day, nvidia’s valuation jumped ahead of apple’s and microsoft’s, continuing a meteoric rise that has been aided by the boom of artificial intelligence and, with it, high demand for nvidia’s chips. the company was in a good position to take advantage of the current a.i. boom. jensen huang, the nvidia co founder and chief executive, had predicted that the company’s chips, known as graphics processing units or gpus, would be essential to the construction of a.i. systems. just two years ago, nvidia was valued at $400 billion; last year, it was $1 trillion. this year, the company hit $2 trillion on march 1, before taking just three months to rise above $3 trillion. today, nvidia controls more than 80 percent of the global market for chips used in a.i. systems, and companies like amazon, microsoft and google are now designing their own chips to become less dependent on nvidia. the company’s rapid ascent put it in a rare position: according to data on the s&p 500 going back to the 1920s, only 12 different companies have led the index by market valuation. nvidia is the 12th. its growth has been so rapid that some analysts have wondered how long it can last. “the numbers have gotten so big so quickly,” said stacy rasgon, an analyst with bernstein research. “if the return on a.i. turns out to not be there, then the whole thing comes crumbling down.” but mr. huang is doubling down on his company’s continued growth. “we are one year into generative a.i.,” mr. huang said in a february interview. “my guess is we are literally into the first year of a 10 year cycle of spreading this technology into every single industry.” santul nerkar is a reporter covering business and sports. more about santul nerkar

artificial intelligence supported by the chip maker’s stock price has jumped over the last year thanks to its stranglehold on the market for the chips needed to build a.i. systems.