Meta just announced it’s pushing further into the AI chip race, coming right on the heels of Google’s own announcement of its Axion AI chip. Both companies are touting their new semiconductor models as key to the development of their AI platforms, and as alternatives to the Nvidia chips they—and the rest of the tech industry—have been relying on to power AI data centers.
Hardware is emerging as a key AI growth area. For Big Tech companies with the money and talent to do so, developing in-house chips helps reduce dependence on outside designers such as Nvidia and Intel while also allowing firms to tailor their hardware specifically to their own AI models, boosting performance and saving on energy costs.
These in-house AI chips that Google and Meta just announced pose one of the first real challenges to Nvidia’s dominant position in the AI hardware market. Nvidia controls more than 90% of the AI chips market, and demand for its industry-leading semiconductors is only increasing. But if Nvidia’s biggest customers start making their own chips instead, its soaring share price, up 87% since the start of the year, could suffer.
“From Meta’s point of view … it gives them a bargaining tool with Nvidia,” Edward Wilford, an analyst at tech consultancy Omdia, told Fortune. “It lets Nvidia know that they’re not exclusive, [and] that they have other options. It’s hardware optimized for the AI that they are developing.”
Why does AI need new chips?
AI models require massive amounts of computing power because of the huge amount of data required to train the large language models behind them. Conventional computer chips simply aren’t capable of processing the trillions of data points AI models are built upon, which has spawned a market for AI-specific computer chips, often called “cutting-edge” chips because they’re the most powerful devices on the market.
Semiconductor giant Nvidia has dominated this nascent market: The wait list for Nvidia’s $30,000 flagship AI chip is months long, and demand has pushed the firm’s share price up almost 90% in the past six months.
And rival chipmaker Intel is fighting to stay competitive. It just released its Gaudi 3 AI chip to compete directly with Nvidia. AI developers—from Google and Microsoft down to small startups—are all competing for scarce AI chips, limited by manufacturing capacity.
Why are tech companies starting to make their own chips?
Both Nvidia and Intel can produce only a limited number of chips because they and the rest of the industry rely on Taiwanese manufacturer TSMC to actually assemble their chip designs. With only one manufacturer solidly in the game, the manufacturing lead time for these cutting-edge chips is multiple months. That’s a key factor that led major players in the AI space, such as Google and Meta, to resort to designing their own chips. Alvin Nguyen, a senior analyst at consulting firm Forrester, told Fortune that chips designed by the likes of Google, Meta, and Amazon won’t be as powerful as Nvidia’s top-of-the-line offerings—but that could benefit the companies in terms of speed. They’ll be able to produce them on less specialized assembly lines with shorter wait times, he said.
“If you have something that’s 10% less powerful but you can get it now, I’m buying that every day,” Nguyen said.
Even if the native AI chips Meta and Google are developing are less powerful than Nvidia’s cutting-edge AI chips, they could be better tailored to the company’s specific AI platforms. Nguyen said that in-house chips designed for a company’s own AI platform could be more efficient and save on costs by eliminating unnecessary functions.
“It’s like buying a car. Okay, you need an automatic transmission. But do you need the leather seats, or the heated massage seats?” Nguyen said.
“The benefit for us is that we can build a chip that can handle our specific workloads more efficiently,” Melanie Roe, a Meta spokesperson, wrote in an email to Fortune.
Nvidia’s top-of-the-line chips sell for about $25,000 apiece. They’re extremely powerful tools, and they’re designed to be good at a wide range of applications, from training AI chatbots to generating images to developing recommendation algorithms such as the ones on TikTok and Instagram. That means a slightly less powerful, but more tailored chip could be a better fit for a company such as Meta, for example—which has invested in AI primarily for its recommendation algorithms, not consumer-facing chatbots.
“The Nvidia GPUs are excellent in AI data centers, but they are general purpose,” Brian Colello, equity research lead at Morningstar, told Fortune. “There are likely certain workloads and certain models where a custom chip might be even better.”
The trillion-dollar question
Nguyen said that more specialized in-house chips could have added benefits by virtue of their ability to integrate into existing data centers. Nvidia chips consume a lot of power, and they give off a lot of heat and noise—so much so that tech companies may be forced to redesign or move their data centers to integrate soundproofing and liquid cooling. Less powerful native chips, which consume less energy and release less heat, could solve that problem.
AI chips developed by Meta and Google are long-term bets. Nguyen estimated that these chips took roughly a year and a half to develop, and it’ll likely be months before they’re implemented at a large scale. For the foreseeable future, the entire AI world will continue to depend heavily on Nvidia (and, to a lesser extent, Intel) for its computing hardware needs. Indeed, Mark Zuckerberg recently announced that Meta was on track to own 350,000 Nvidia chips by the end of this year (the company’s set to spend around $18 billion on chips by then). But movement away from outsourcing computing power and toward native chip design could loosen Nvidia’s chokehold on the market.
“The trillion-dollar question for Nvidia’s valuation is the threat of these in-house chips,” Colello said. “If these in-house chips significantly reduce the reliance on Nvidia, there’s probably downside to Nvidia’s stock from here. This development is not surprising, but the execution of it over the next few years is the key valuation question in our mind.”
