Nvidia's AI Dominance: Record Earnings and Swift Returns on Infrastructure Investment

Nvidia's Jensen Huang needs AI to change the world

The company has profited handsomely from an AI craze that has seen tech giants pour billions of dollars into Nvidia chips to help power large language models like ChatGPT and Gemini. But doubts remain as to whether these companies are actually making money on AI, and whether they can continue their frenzied pace of spending. When Huang was asked on the company's earnings call Wednesday about the sustainability of customer spending, he raved about AI's ability to transform computing, but danced around the question. For eight minutes, Huang talked about how the usual CPUs that are standard the world over are reaching peak capacity, talked up the wonders of accelerated computing powered by the GPUs that are Nvidia's bread and butter, and mentioned that even sovereign countries are getting into the AI game. "You can see that there's just so many different directions that generative AI is going, and so we're actually seeing the momentum of generative AI accelerate," he said. Nvidia did not immediately respond to Fortune's request for comment sent after business hours. The AI tools that are already out there -- ChatGPT, AI image generators, and coding assistants -- are just the tip of the iceberg. Below the surface are future mainstream applications like extremely targeted ads, recommender systems that predict future wants and needs, and improved search engines, among other things, he added. But analysts weren't satisfied with Huang's answer, and the subject of its customers' spending was brought up once again. This time, Huang didn't mince words. "The people who are investing in Nvidia infrastructure are getting returns on it right away. It's the best ROI infrastructure, computing infrastructure, investment you can make today," he said. When data center customers -- which make up about $26 billion of the company's $30 billion revenue -- already have a trillion dollars tied up in general purpose computing infrastructure that in Nvidia's view represents the past, why would they want to build more, Huang asked. "For every billion dollars with a general purpose CPU-based infrastructure that you stand up, you probably rent it for less than a billion, and so, because it's commoditized," he said. Data centers that build using Nvidia's chips for accelerated computing on the other hand, save money and see increased demand from the growing crop of startups creating the "next frontier" of AI, Huang reasoned. "The world of general purpose computing is shifting to accelerated computing. The world of human engineered software is moving to generative AI software," Huang said. "If you were to build infrastructure to modernize your cloud and your data centers -- build it with accelerated computing Nvidia. That's the best way to do it." As of Wednesday Nvidia was the second most valuable company in the world, with its $3.09 trillion market cap second only to Apple. The company reported $30 billion in revenue, two times higher than its revenue in the same quarter last year. Its profit of $16.6 billion was also more than double a year ago, and the company said it expected revenue of $32.5 billion for the current quarter, above the $31.9 billion analysts expected. Despite Huang's optimistic words, Nvidia's shares fell just under 6% in after hours trading Wednesday.

Nvidia CEO Jensen Huang says the payback on AI spending is coming from these 3 places

This story is available exclusively to Business Insider subscribers. Become an Insider and start reading now. Have an account? Log in. Analysts peppered the CEO with questions about the return on AI spending. His response was several versions of: "the people who are investing in Nvidia infrastructure are getting returns on it right away." Though Nvidia's largest customers have yet to boast significant return on billions in AI investing, Huang drilled down on several places he sees profits coming from. "Accelerated computing, of course, speeds up applications. It also enables you to do computing at a much larger scale, for example, scientific simulations or database processing," said Huang. "Accelerated computing" is Huang's term for the kind that Nvidia graphics processing units enable. It's also called parallel computing where the chips do many tasks simultaneously instead of in a sequence. That's the foundation of generative AI and Huang contended that almost all existing computing jobs are headed in that direction for one major reason. "It's not unusual to see someone save 90% of their computing cost," when they convert to accelerated computing, Huang said. The reason, he continued, is "you just sped up an application 50x. You would expect the computing cost to decline quite significantly." Recommendation engines, like the ones that tell you what to stream next, and digital ad targeting, were two modern data-processing tasks that are converting fast to accelerated computing, Huang said. Cheaper, more accurate recommendations and better-targeted or ads could generate more revenue for companies that adopt those technologies. Meta, for instance, has turbo-charged profit in recent years by using AI to improve content recommendations and ad targeting. Sophia Velastegui, a venture capitalist and former Microsoft AI executive, told Insider that identifying the return on AI investments may not be easy as it gets baked into these everyday functions of the internet. "You may not be able to be like, 'Hey, this growth is specifically from generative AI," Velastegui said. She added that companies may not be keen to reveal the details of their AI gains for competitive reasons. Huang's third pillar of AI ROI, at least for cloud providers, is the startup development frenzy across generative AI applications. The largest cloud-computing providers, including Amazon and Microsoft, are major customers of Nvidia. When they buy GPUs, they put them in data centers and get paid relatively quickly for renting out this new AI computing capacity. "Everything you stand up, you are going to get rented because so many companies are being founded to create generative AI and so your capacity gets rented right away, and the return on investment of that is really good," Huang said. Indeed, Nvidia has propelled a slew of new or reimagined cloud providers that purchase chips almost exclusively from Nvidia and specialize in the latest and greatest in AI computing. It is going to be difficult to assuage investors concerns in this area because ultimately, Huang isn't the executive they need to hear the message from. And what they've heard from other top tech execs, and top Nvidia customers, is calls for patience. Meta spent $8.5 billion in the second quarter on computing infrastructure for AI and the metaverse. It plans to spend between $37 billion and $40 billion this year, though CEO Mark Zuckerberg told investors in July not to expect immediate returns.