Key points
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The stage is finally set in a way that favors Qualcomm’s core competency.
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AI platforms may require less memory now, but with more affordable total computing potential on the table, the need for data center networking technology should remain strong.
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TTM Technologies is not a household name. But it’s almost certain that something in your household was manufactured by TTM.
These past few weeks have been tough Micron technology And Sandisk shareholders. Both manufacturers of computer memory chips have been turned upside down. Blame Google parent Alphabet (NASDAQ: GOOG)(NASDAQ: GOOGL)largely. It introduced a set of algorithms on March 24 that it collectively calls TurboQuant, which claim that artificial intelligence (AI) computing platforms can handle the same amount of work with just one-sixth the amount of (currently very scarce) physical memory typically required. The news immediately undermined much of the memory pricing power that supported both stocks.
Of course, this revelation has also worked against several other artificial intelligence infrastructure stocks, many of which were already grappling with concerns that AI in general might not live up to its previously frenzied hype.
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A computer programmer writes code.
Image source: Getty Images.
However, not every name in the AI world will be affected by Google’s revolutionary development. Some of them may even benefit from it simply because AI could become more affordable for more enterprises. Here’s a closer look at three of these names.
Qualcomm
Qualcomm‘S (NASDAQ: QCOM) The time may have finally come.
This is one of those companies that were on the cusp of AI greatness even before artificial intelligence became mainstream. The latest versions of the Snapdragon mobile processor can handle AI work. But so far they have been the cool names in the mobility industry Apple And Samsung — have opted to use their own silicon in their AI-enabled mobile devices. Only a handful of laptops purpose-built for on-device AI currently use snapdragons; interest was modest. Qualcomm is also working on its own high-efficiency data center processors based on its Snapdragon architecture, though it is trying to break into a business already dominated by Nvidia.
However, thanks to TurboQuant, interest in Qualcomm’s proven powerful mobile processor could quickly increase.
TurboQuant should be able to run on almost any modern computer hardware. But it may demonstrate its greatest value on mobile devices, which have inherent memory limitations that don’t apply to data centers: namely the size and cost of the memory chips themselves. Large AI models that previously couldn’t run fully effectively on a mobile device should be able to do so quite well. This is especially true for single-purpose ‘edge’ computing devices that need to be powerful enough to meet the demands of AI inference, but small enough and cost-effective enough to be deployed en masse (think ‘smart’ electricity meters, connected cars or wearables).
The only catch? These processors must still be powerful enough to handle AI workloads autonomously. Qualcomm’s Snapdragon fits in nicely with this.
Broadcom
Data center network specialist Broadcom (NASDAQ:AVGO) is not in serious danger from Google’s TurboQuant. After all, no matter how many memory chips you may or may not need to connect every motherboard in a data center, you still have to connect all of these processors to a massive neural network. Indeed, an argument can be made that Google’s research won’t do that alone not hurts demand for data center networking solutions, but may even strengthen it.
Bank of America Analyst Wamsi Mohan notes on his recent interview with Sandisk CFO Luis Visoso to discuss the unveiling of Google’s new solution: “Mr. Visoso pointed out that (TurboQuant) can improve the return on investment of hyperscale capex, and this increased efficiency could in turn cause demand (for AI hardware) to rise.” Morgan StanleyShawn Kim agrees, explaining in a research note: “Models that require cloud clusters can (now) fit on local hardware, effectively lowering the barrier to deploying AI at scale. More applications become viable, more models remain active, and utilization of existing infrastructure improves.”
Clearly, Broadcom isn’t the only name in the AI hardware sector that could benefit from an increase in overall demand due to lower entry and operating costs. Broadcom is however, are leading the charge on attacking the technology’s biggest bottleneck (aside from a lack of affordable memory chips). That’s not processing power. That is the speed at which all computer processors in a data center can communicate with each other from different motherboards.
And for what it’s worth, Google’s Tensor Processing Units (TPUs) are largely designed and manufactured by Broadcom. If potential AI users specifically want Google’s TurboQuant to run on Google hardware, it will ultimately be made by Broadcom.
TTM technologies
Finally, add TTM technologies (NASDAQ:TTMI) to your list of stocks that could benefit from Google’s breakthrough in AI computing.
It’s not a household name. In fact, you’ve probably never heard of the company. However, you probably rely on its products on a regular basis.
See, TTM makes the printed circuit boards—the flat, (usually) green boards that connect all the components needed to make computers, routers, cell phone networking equipment, industrial automation controllers, and more. No modern technology would function without printed circuit boards first bringing all the technical components together properly.
No, it’s not exactly a sexy company, even if it’s one that should do well regardless of what’s next for the AI industry. After all, all kinds of things have some kind of circuit board in them these days, including home appliances, toys and even smart light bulbs.
And that is an important nuance.
While last year’s revenue growth of 19% was impressive, the majority of the company’s $2.9 billion revenue by 2025 did not come from data centers or the networking sector; TTM clearly doesn’t need the AI industry to keep exploding to do well enough.
However, data centers and networking are TTM’s fastest growing businesses, which is perhaps the main reason why analysts expect similar revenue growth this year and next. The point is that these projections were made before TurboQuant was available. If Morgan Stanley’s Kim and BofA’s Mohan are right that TurboQuant is creating rather than limiting demand for more artificial intelligence technology, these already solid growth prospects may be an underestimate of what TTM actually has to offer.
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Bank of America is an advertising partner of Motley Fool Money. James Brumley has positions at Alphabet. The Motley Fool holds positions in and recommends Alphabet, Apple, Micron Technology, Nvidia and Qualcomm, and is short Apple stock. The Motley Fool recommends Broadcom. The Motley Fool has a disclosure policy.
