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Chip designer Nvidia<\/a> has emerged as the clear winner in not just the early stages of the AI boom but, at least so far, in all of stock market history. The $1.9 trillion AI giant surged to a record high stock price on Thursday, putting it on course to add over $230 billion to its market capitalization and shatter a one-day record only weeks old<\/a>: Meta\u2019s $197 billion gain in early February<\/a>. <\/p>\n It\u2019s dominating the market, selling over 70% of all AI chips<\/a>, and startups are desperate to spend hundreds of thousands of dollars on Nvidia\u2019s hardware systems. Wall Street can\u2019t get enough, either\u2014Nvidia stock rocketed up an astonishing 15% after the company smashed its lofty earnings goals last quarter<\/a>, bringing its market cap to over $1.9 trillion on top of its stock value tripling in the last year<\/a> alone.\u00a0<\/p>\n So \u2026 why? How is it that a company founded all the way back in 1993 has displaced tech titans Alphabet<\/a> and Amazon<\/a>, leapfrogging them to become the third-most valuable company in the world<\/a>? It all comes down to Nvidia\u2019s leading semiconductor chips for use in artificial intelligence.<\/p>\n Nvidia built up its advantage by playing the long game and investing in AI since years before ChatGPT hit the market, and its chip designs are so far ahead of the competition that analysts wonder if it\u2019s even possible for anyone else to catch up. Designers such as Arm Holdings and Intel<\/a>, for instance, haven\u2019t yet integrated hardware with AI-targeted software in the way Nvidia has.<\/p>\n \u201cThis is one of the great observations that we made: we realized that deep learning and AI was not [just] a chip problem \u2026 Every aspect of computing has fundamentally changed,\u201d said Nvidia co-founder and CEO Jensen Huang at the New York Times\u2019 DealBook summit last November. \u201cWe observed and realized that about a decade and a half ago. I think a lot of people are still trying to sort that out.\u201d Jensen said Nvidia just \u201cgot it\u201d before anyone else did. \u201cThe reason why people say we\u2019re practically the only company doing it is because we\u2019re probably the only company that got it. And people are still trying to get it.\u201d<\/p>\n Software has been a key part of that equation. While competitors have focused their efforts on chip design, Nvidia has aggressively pushed its CUDA programming interface that runs on top of its chips. That dual emphasis on software and hardware has made Nvidia chips the must-have tool for any developer looking to get into AI.<\/p>\n \u201cNvidia has done just a masterful job of making it easier to run on CUDA than to run on anything else,\u201d said Edward Wilford, an analyst at tech consultancy Omdia. \u201cCUDA is hands-down the jewel in Nvidia\u2019s crown. It\u2019s the thing that\u2019s gotten them this far. And I think it\u2019s going to carry them for a while longer.\u201d<\/p>\n AI needs computing power\u2014a lot<\/em> of computing power. AI chatbots such as ChatGPT are trained by inhaling vast quantities of data sourced from the internet\u2014up to a trillion<\/a> distinct pieces of information. That data is fed into a neural network that catalogs the associations between various words and phrases, which, after human training, can be used to produce responses to user queries in natural language. All those trillions of data points require huge amounts of hardware capacity, and hardware demand is only expected to increase as the AI field continues to grow. That\u2019s put Nvidia, the sector\u2019s biggest seller, in a great position to benefit.<\/p>\n Huang sounded a similar tune on his triumphant earnings call on Wednesday<\/a>. Highlighting the shift from general-purpose computing to what he called \u201caccelerated computing\u201d at data centers, he argued that it\u2019s \u201ca whole new way of doing computing\u201d\u2014and even crowned it \u201ca whole new industry.\u201d\u00a0<\/p>\n Nvidia has been at the forefront of AI hardware from the start. When large-scale AI research from startups such as OpenAI started ramping up in the mid-2010s, Nvidia\u2014through a mixture of luck and smart bets\u2014was in the right place at the right time.<\/p>\n Nvidia had long been known for its innovative GPUs, a type of chip popular for gaming applications. Most standard computer chips, called CPUs, excel at performing complicated calculations in sequence, one at a time. But GPUs can perform many simple calculations at once, making them excellent at supporting the complex graphics processing that video games demand. As it turned out, Nvidia\u2019s GPUs were a perfect fit for the type of computing systems AI developers needed to build and train LLMs.<\/p>\n \u201cTo some extent, you could say they\u2019ve been extremely lucky. But I think that diminishes it\u2014they have capitalized perfectly on every instance of luck on every opportunity they were given,\u201d said Wilford. \u201cIf you go back five or 10 years, you see this ramp-up in console gaming. They rode that, and then when they felt that wave cresting, they got into cryptocurrency mining, and they rode that. And then just as that wave crested, AI started to take off.\u201d<\/p>\n In fact, Nvidia had been quietly developing AI-targeted hardware for years. As far back as 2012, Nvidia chips were the technical foundation of AlexNet<\/a>, the groundbreaking early neural network developed in part by OpenAI cofounder and former Chief Scientist Ilya Sutskever, who recently left the nonprofit after trying to oust CEO Sam Altman. That first mover advantage has given Nvidia a big leg up over its competitors.\u00a0<\/p>\n \u201cThey were visionaries \u2026 for Jensen, that goes back to his days at Stanford,\u201d said Wilford. \u201cHe\u2019s been waiting for this opportunity the whole time. And he\u2019s kept Nvidia in a position to jump on it whenever the chance came. What we\u2019ve seen in the last few years is that that strategy executed to perfection. I can\u2019t imagine someone doing better with it than Nvidia has.\u201d<\/p>\n Since its early AI investments over a decade ago, Nvidia has poured millions into a hugely profitable AI hardware business. The company sells its flagship Hopper GPU<\/a> for a quarter of a million dollars per unit. It\u2019s a 70-pound supercomputer, built from 35,000 individual pieces\u2014and the waiting list for customers to get their hands on one is months long<\/a>. Desperate AI developers are turning to organizations like the San Francisco Compute Group<\/a>, which rents out computing power by the hour from their collection of Nvidia chips. (As of this article\u2019s publication, they\u2019re booked out for almost a month.)<\/p>\n Nvidia\u2019s AI chip juggernaut is poised to grow even more if AI growth meets analysts\u2019 expectations.\u00a0<\/p>\n \u201cNvidia delivered against what was seemingly a very high bar,\u201d wrote Goldman Sachs<\/a> in its Nvidia earnings analysis. \u201cWe expect not only sustained growth in Gen AI infrastructure spending by the large CSPs and consumer internet companies, but also increased development and adoption of AI across enterprise customers representing various industry verticals and, increasingly, sovereign states.\u201d<\/p>\n There are some potential threats to Nvidia\u2019s market domination. For one, investors noted in the company\u2019s most recent earnings that restrictions on exports to China dinged business, and a potential increase in competition from Chinese chip designers could put pressure on Nvidia\u2019s global market share. And Nvidia is also dependent on Taiwanese chip foundry TSMC to actually manufacture many of the chips it designs. The Biden administration has been pushing for more investment in domestic manufacturing through the CHIPS act, but Jensen himself said<\/a> it will be at least a decade before American foundries could be fully operational.<\/p>\n \u201c[Nvidia is] highly dependent on TSMC in Taiwan, and there are regional complications [associated with that], there are political complications,\u201d said Wilford. \u201c[And] the Chinese government is investing very heavily in developing their own AI capabilities as a result of some of those same tensions.\u201d<\/p>\n<\/div>\n\nThe company that \u2018got it\u2019<\/h2>\n
In early on the AI boom<\/h2>\n
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