Nvidia’s $40 billion Arm acquisition is about bringing AI down from the cloud

A big deal that could be a big deal

Nvidia’s $40 billion acquisition of Arm is a hugely significant deal for the tech world, with implications that will take years to unravel spanning many areas of the sector. But if you listened to the press babble coming from the two companies over the last 24 hours, you’d think there was only one factor driving the purchase: artificial intelligence.

“AI is the most powerful technology force of our time. It’s the automation of automation, where software writes software,” Nvidia CEO Jensen Huang told journalists during a press call this morning. “Together, [Nvidia and Arm are] going to create the world’s premier computing company for the age of AI.”

On the same call moments later, Arm CEO Simon Segars repeated these sentiments. “We now look at a world ahead of us that is defined by AI,” said Segars. “We see that AI is reaching a stage of maturity where, everyday, more and more newer applications can be found for the use of AI. Through the combination of Arm and Nvidia … we can enable the world’s semiconductor industry to build chips that deliver on this vision.”

In some ways, the focus on AI is a defensive PR move for the two companies. This is an acquisition that involves many thorny issues, including the long-term status of Arm’s UK investments (where it’s one of the country’s precious few high-tech success stories), and the possibility that new ownership will imperil Arm’s chip licensing business (which attracts customers like Apple and Samsung in part because of its independence). But these are questions that will take years to resolve, and by bringing the conversation back to AI, Nvidia and Arm can focus on a topic that is shiny, exciting, and helpfully vague on the details.

However, that doesn’t mean they’re wrong to do so, as the basic premise for why Nvidia is buying Arm will lead to exciting machine learning applications does make sense.

The big idea is that AI, like some ancient god, is finally stepping down from the clouds to walk among the people. Machine learning algorithms used to rely on data centers for computation, with AI tools and applications sending information over the internet to these remote servers for processing. But while heavy-grade chips are still necessary for research and cutting-edge applications, many machine learning tools are now lightweight enough to run on-device without connecting to the internet. The benefits of this are straightforward: you get faster processing, greater security, and reduced power consumption. It’s why our smartphones can now do things like AI-enhanced photography and why we have technology like disinfectant robots in hospitals and facial recognition for pigs. These sorts of mobile applications (known as edge AI) really are the future of the field.

As Huang put it: “AI is moving from the cloud to the edge, where smart sensors connected to AI computers can speed checkouts, direct forklifts, orchestrate traffic, save power. In time, there will be trillions of these small autonomous computers, powered by AI, connected to massively powerful cloud data centers in every corner of the world.”

As the creator of the GPUs used in many AI data centers, Nvidia can supply the first half of this equation, while Arm, designer of cheap and energy-efficient mobile chips, takes care of the second. It doesn’t take a genius to work out there’s potentially profitable overlap between these two businesses. (Though it does take $40 billion to force that overlap into existence.)

In this sense, Nvidia and Arm’s focus on AI is perfectly reasonable, as the companies’ individual expertise can improve one another’s AI offerings. As chip market analyst Patrick Moorhead put it in his opinion on the deal: “Arm plays in areas that Nvidia does not or isn’t that successful, while Nvidia plays in many places Arm doesn’t or isn’t that successful.”

Nvidia, for a start, can potentially leverage the efficiency of Arm’s CPUs to bring down power consumption in its data centers. Although edge AI is a big part of the field’s future, data centers aren’t going anywhere either, and the energy costs for running them are mammoth. Nvidia CEO Huang himself suggested this would be a focus of the acquisition when he noted on this morning’s call that “energy efficiency is the single most important thing when it comes to computing going forward.” Arm, meanwhile, should benefit hugely from Nvidia’s AI expertise and resources. Nvidia has an impressive reach in the world of machine learning, spanning fields like robotics, self-driving cars, and medical imagining, as well as consistently publishing interesting and novel ML research. It also simply has the size to accelerate any R&D efforts Arm needs in order to push its AI capabilities further.

Fittingly enough, Nvidia’s ability to offer extra capitalization is, in part, only due to interest in artificial intelligence. As journalist Alistair Barr noted on Twitter, back in 2016 Nvidia was worth $30 billion, but its valuation has since increased to over $300 billion because of demand generated for its GPUs by machine learning (among other things). So while demand for AI motivated this deal, it also made it possible in the first place.