Can chipmaker Nvidia continue the years of rapid growth that have made it the second-most valuable public company, or will it disappoint investors with a slowdown in momentum?
That question has stock market watchers closely watching Nvidia's quarterly earnings report, due out Wednesday afternoon, which will provide important insight into the company's future and clues about the financial performance of the semiconductor industry as a whole.
“It's the most important stock in the world right now,” EMJ Capital's Eric Jackson said of Nvidia on CNBC's “Closing Bell” last week, while Wedbush Securities tech analyst Dan Ives called Nvidia's upcoming report “the most important tech earnings in years.”
But Nvidia's results don't necessarily tell us much about the AI industry as a whole, or whether the tens of billions of dollars pouring into it will ultimately pay off. The AI boom isn't just about Nvidia; it's also about AI cloud providers, AI startups and traditional companies scrambling to profit from AI tools. And it will take a while for that to happen.
The primary market for Nvidia's AI chips, called GPUs, is the companies that train the biggest and most sophisticated AI models, including OpenAI and Anthropic as well as other tech giants like Google, Microsoft and Amazon.
Just to give you an idea of the demand for Nvidia chips, Facebook and Instagram's parent company Meta alone owns around 350,000 GPUs, likely worth more than $9 billion.
But most companies aren't training generative AI models at scale; they're using them to spit out information, or inference, as the industry calls it. Nvidia sells chips for inference, but its rapid growth was built on supplying the chips needed for the complex task of training models. As the industry evolves, inference represents a bigger opportunity in the long run, and that's making it more competitive for Nvidia.
And companies typically do their inference work in the cloud. Big cloud providers like Amazon's AWS, Google Cloud, and Microsoft Azure, as well as AI cloud startups like Coreweave and Groq, are all helping companies power and grow their AI inference efforts. The future of these providers is largely independent of Nvidia, so Nvidia's earnings don't reveal much about its financial performance. Moreover, the AI cloud industry will take time to mature, says Daniel Newman, principal analyst at Futurum Research. He expects another 1-3 years of investment in AI infrastructure will be needed before we see “Walmart-like use cases” for generative AI, or clear success in industries like retail, travel, and hospitality.
For now, many companies are starting to incorporate AI into their software and operations, Newman says, but actually making money from it and building a viable business model is “still a bit murky.”
Stock watchers will undoubtedly be keeping a close eye on Nvidia and whether its share price will continue to rise for some time to come — after all, the company's market cap has increased nearly tenfold this summer, from $350 billion to $3 trillion, thanks to a notable increase in its share price since the start of 2023.
But when it comes to the overall state of AI, many on Wall Street are wondering whether generative AI can ever truly be profitable. 18 months after the release of OpenAI’s ChatGPT, seen as a watershed moment for the AI industry, patience is beginning to wear thin.
“Right now, the industry is in a heated debate over the (capital expenditure) requirements for generative AI,” Morgan Stanley analyst Keith Weiss said on a Microsoft earnings call a few weeks ago. That means companies are spending billions of dollars on AI infrastructure like GPUs and cloud to power the generative AI revolution. But soon CEOs and boards will want evidence that the investments are worthwhile.
Nvidia's upcoming earnings results may indeed spur more excitement about AI, but it may be several more years before huge investments in developing true generative AI killer apps finally materialize.
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