Can chipmaker Nvidia continue its years-long run that has made it the second most valuable publicly traded company? Or will it disappoint investors by losing momentum?
This question has stock market observers eagerly awaiting Nvidia’s quarterly report on Wednesday afternoon. The results will provide important insights into the company’s future and indications of the financial performance of the chip 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. Meanwhile, technology analyst Dan Ives of Wedbush Securities called Nvidia’s upcoming report nothing less than “the most important technology earnings in years.”
But Nvidia’s results won’t necessarily shed light on the broader AI industry and whether the tens of billions of dollars pumped into it will ultimately pay off. Because the AI boom isn’t just about Nvidia. It’s also about AI cloud providers, AI startups, and established companies scrambling to make money with AI tools. And that will take some time.
The main market for Nvidia’s AI chips, called GPUs, are companies that train the largest and most sophisticated AI models. These include OpenAI and Anthropic, but also tech giants like Google, Microsoft and Amazon.
To give you an idea of the demand for Nvidia chips, Meta alone, the parent company of Facebook and Instagram, is hoarding nearly 350,000 GPUs, probably worth over $9 billion.
But most companies aren’t training massive generative AI models; they’re using them to spit out information—or, as the industry calls it, inference. While Nvidia sells chips for inference, it has built its successful business on providing the chips needed to do the complicated work of training models. As the industry evolves, inference is the bigger long-term opportunity, and that’s where Nvidia faces more competition.
Additionally, companies typically do their inference work in the cloud. Major cloud providers like Amazon’s AWS, Google Cloud, and Microsoft Azure, as well as AI cloud startups like Coreweave and Groq, are helping companies advance and grow their AI inference efforts. The future of these providers is largely independent of Nvidia, and so Nvidia’s revenue doesn’t say much about their financial performance. Moreover, the AI cloud industry will take some time to mature, according to Daniel Newman, principal analyst at Futurum Research. He expects another one to three years of investment in AI infrastructure before we see the “Walmart-like use cases” of generative AI or obvious successes in industries like retail, travel, and hospitality.
Currently, many companies are starting to integrate AI into their software and operations, Newman says, but when it comes to actually making money and developing viable business models, it’s “still a bit opaque.”
Stock market observers will certainly be focusing on Nvidia for a while and whether the stock price can continue to rise. After all, the company’s market value has increased almost tenfold from $350 billion to $3 trillion this summer during the remarkable price increase since the beginning of 2023.
But when it comes to the wider AI landscape, many on Wall Street are wondering if generative AI can ever become truly profitable. 18 months after the launch of OpenAI’s ChatGPT, considered a turning point for the AI industry, patience is starting to wear thin.
During Microsoft’s earnings call a few weeks ago, Morgan Stanley analyst Keith Weiss said, “There’s a debate raging in the industry right now about the (investment) requirements set for generative AI.” That is, companies are spending billions on AI infrastructure, including GPUs and cloud, to power the generative AI revolution. But soon, their CEOs and boards will expect proof that their investments are paying off.
Nvidia’s upcoming results may indeed generate more excitement about AI. But it may still be several years before the huge investment in developing a true generative AI killer app finally produces one.
This story originally appeared on Fortune.com
