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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<\/a> while also allowing firms to tailor their hardware specifically to their own AI models, boosting performance and saving on energy costs.<\/p>\n These in-house AI chips that Google<\/a> and Meta just announced pose one of the first real challenges to Nvidia\u2019s dominant position in the AI hardware market. Nvidia controls more than 90% of the AI chips market<\/a>, and demand for its industry-leading semiconductors is only increasing<\/a>. But if Nvidia\u2019s biggest customers start making their own chips instead, its soaring share price, up 87% since the start of the year, could suffer.<\/p>\n \u201cFrom Meta\u2019s point of view\u2026it gives them a bargaining tool with Nvidia,\u201d Edward Wilford, an analyst at tech consultancy Omdia, told Fortune. <\/em>\u201cIt lets Nvidia know that they\u2019re not exclusive, [and] that they have other options. It\u2019s hardware optimized for the AI that they are developing.\u201d<\/p>\n Why does AI need new chips?\u00a0<\/strong><\/p>\n 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\u2019t 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 \u201ccutting-edge\u201d chips because they\u2019re the most powerful devices on the market.\u00a0<\/p>\n Semiconductor giant Nvidia has dominated the nascent market: The wait list for Nvidia\u2019s $30,000 flagship<\/a> AI chip is months long<\/a>, and demand has pushed its share price up almost 90% in the past six months.\u00a0<\/p>\n And rival chipmaker Intel is fighting to stay competitive. It just released its Gaudi 3 AI chip<\/a> to compete directly with Nvidia. AI developers\u2014from Google and Microsoft<\/a> down to small startups\u2014are all competing for scarce AI chips, limited by manufacturing capacity.\u00a0<\/p>\n Why are tech companies starting to make their own chips?<\/strong><\/p>\n Both Nvidia and Intel can only produce 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<\/a>. That\u2019s a key factor that led major players in the AI space, such as Google and Meta, to resort to designing their own chips. Alvin Ngyuen, a senior analyst at consulting firm Forrester, told Fortune <\/em>that chips designed by the likes of Google, Meta, and Amazon<\/a> won\u2019t be as powerful as Nvidia\u2019s top-of-the-line offerings\u2014but that could benefit the companies in terms of speed. They\u2019ll be able to produce them on less specialized assembly lines with shorter wait times, he said.<\/p>\n \u201cIf you have something that\u2019s 10% less powerful but you can get it now, I\u2019m buying that every day,\u201d Ngyuen said.<\/p>\n Even if the native AI chips Meta and Google are developing are less powerful than Nvidia\u2019s cutting-edge AI chips, they could be better tailored to the company\u2019s specific AI platforms. Ngyuen said that in-house chips designed for a company\u2019s own AI platform could be more efficient and save on costs by eliminating unnecessary functions.\u00a0<\/p>\n \u201cIt\u2019s like buying a car. Okay, you need an automatic transmission. But do you need the leather seats, or the heated massage seats?\u201d Ngyuen said.<\/p>\n \u201cThe benefit for us is that we can build a chip that can handle our specific workloads more efficiently,\u201d Melanie Roe, a Meta spokesperson, wrote in an email to Fortune.<\/em><\/p>\n Nvidia\u2019s top-of-the-line chips sell for about $25,000 apiece<\/a>. They\u2019re extremely powerful tools, and they\u2019re 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<\/a>. That means a slightly less powerful, but more tailored chip could be a better fit for a company such as Meta, for example\u2014which has invested in AI primarily for its recommendation algorithms, not consumer-facing chatbots.<\/p>\n \u201cThe Nvidia GPUs are excellent in AI data centers, but they are general purpose,\u201d Brian Colello, equity research lead at investment research firm Morningstar, told Fortune<\/em>. \u201cThere are likely certain workloads and certain models where a custom chip might be even better.\u201d<\/p>\n The trillion-dollar question<\/strong><\/p>\n Ngyuen 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<\/a>\u2014so 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.<\/p>\n AI chips developed by Meta and Google are long-term bets. Ngyuen estimated that these chips took roughly a year and a half to develop, and it\u2019ll likely be months before they\u2019re 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<\/a> that Meta was on track to own 350,000 Nvidia chips by the end of this year (the company\u2019s set to spend around $18 billion on chips by then<\/a>.) But movement away from outsourcing computing power and toward native chip design could loosen Nvidia\u2019s chokehold on the market.<\/p>\n \u201cThe trillion-dollar question for Nvidia\u2019s valuation is the threat of these in-house chips,\u201d Colello said. \u201cIf these in-house chips significantly reduce the reliance on Nvidia, there\u2019s probably downside to Nvidia\u2019s 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.\u201d<\/p>\n<\/div>\nSubscribe to the Eye on AI newsletter to stay abreast of how AI is shaping the future of business. Sign up<\/a> for free.<\/div>\n