Nvidia’s growing cash hoard points to M&A

Racking up. Nvidia is making so much money that founder Jensen Huang doesn’t know what to do with it all. $3.6 trillion chip designer is spending more on dividends and buybacks, but its cash pile is still growing, doubling over past year to $35 billion amid a frenzy for company’s artificial-intelligence chip designs. Combined with Huang’s prodigious ambition and a more lenient antitrust stance under President-elect Donald Trump, M&A may be back on menu.

Nvidia has grown from a startup to most valuable company on earth in three deces since its founding. Huang’s 3.8% stake is worth more than entire market value of fing $107 billion giant Intel. Nvidia’s big insight was to see how industry could keep increasing computing power even as tritional processor speeds topped out in early 2000s. It does this by providing chips and software that break work into pieces so it can be done simultaneously, known as parallel computing. hardware that company designs is so in-demand that specialist manufacturers like Taiwan Semiconductor Manufacturing have little incentive to spend time supplying possible Nvidia rivals. Huang’s kit and associated software have become standard among AI developers and in superfast data centers, which act as giant hubs of computing power for large language models like OpenAI’s ChatGPT.

As a result, Huang is coining it. Nvidia’s cash has quintupled since 2020 thanks to an eight-fold revenue boost. It will probably keep piling up. Free cash flow, or how much company’s operations throw off after subtracting capital expenditures, will be $200 billion or more over next two years, Bank of America analysts expect. Two years of dividends and share buybacks at ir current rate would consume about $60 billion of that sum, meaning that in net terms company’s pot of money would swell by about $140 billion. d that to existing pile of money, and Huang would start 2027 with about $175 billion of idle liquidity, which is more even than current cash king Apple. Along with company’s richly valued shares, that affords ample M&A firepower.

Nvidia last attempted a big deal in 2020, when it agreed to pay $40 billion in cash and stock for chip architecture specialist Arm. Huang wanted to use UK company’s basic semiconductor designs to help make data centers more power efficient. That deal failed after two years of scrutiny by governments and competition watchdogs in United States, China and Britain. Nvidia could probably expect an easier ride under a more laissez-faire and nationalist Trump ministration, particularly if it presented any deal as strengning a U.S. AI champion. But buying anor chipmaker is probably still verboten. For example, Huang might in ory like to snap up $78 billion Marvell Technology for its data-center networking technology and ability to design custom chips for AI developers. semiconductor sector is global, though, and China and Europe would probably object again if Nvidia tries to buy players with scarce technology, like Marvell or Arm.

Or areas are probably open. Nvidia’s last successful acquisition, purchase of Mellanox Technologies for $6.9 billion in 2019, provides some clues about Huang’s possible thinking. He bought networking firm because he could see that computing was moving from working in parallel on a single processor to splitting work even more widely among different chips. All se different bits of kit must talk to each or, and Mellanox provides gear that facilitates those interactions. trend is increasing given massive worklo of training and using AI models. To use a stylized example, a group of 100 servers all talking to each or will have almost 5,000 distinct connections between different points, while a group of 1,000 would have nearly 500,000. This puts a greater emphasis on sending signals around network extremely quickly, which is why Nvidia could in ory buy an optical networking firm like $16 billion Coherent, whose technology can connect data-center servers toger at lightning speeds.

Huang, who has displayed an uncanny ability to foresee major technological shifts, could also go in more unusual directions. As Nvidia’s chips improve, cost of doing computational work on m has halved roughly every two and a half years. That trend will probably remain on track for some time, making hardware cheaper and more ubiquitous, and allowing more AI chips to show up outside data center. That’s why Huang has repeatedly talked about new markets like robotics, autonomous driving and drug development. company’s growing venture capital operation, which owns $1.8 billion of equity in smaller firms compared with essentially nothing a few years ago, provides a possible glimpse into future. Huang has built up stakes in analytics company Databricks, which is mulling an initial public offering, robotics companies Serve Robotics and Figure, and drug discovery firm Charm rapeutics.

Of course, analysts’ rosy forecasts for Nvidia could turn out to be too optimistic. AI has vanced rapidly in past few years, in large part because major companies have thrown more data and computing power at problem. One risk for Nvidia is that this method of improvement may be offering diminishing returns, some researchers are increasingly warning, which would le to slower growth and less demand for AI-rey chips. Anor danger is that tech giants like Microsoft and Amazon.com could redirect spending towards specialized in-house designs as part of an attempt to ease Huang’s grip on industry. If any of those risks materialize, Nvidia’s cash hoard might grow more slowly than current estimates suggest.

On or hand, a less sunny outlook may even d impetus to any M&A hunt for Huang, who has a track record of turning business on a dime. Even Nvidia’s current cash pile exceeds market value of almost half of companies in S&P 500 Index. It would be a surprise if Huang simply sat on it.