Which hardware stocks are best positioned for the AI revolution?
Seeking Alpha analysts Michael Del Monte, Kennedy Njagi, and Elizabeth Pramila offer their picks.
Michael Del Monte: My top names for hardware largely reside in the “gray space” of data center infrastructure, including IES (IESC) and Vertiv (VRT). IESC largely focuses on the installation and custom design of electrical and technology systems found in data centers. Vertiv is in a unique position of integrating the white and gray spaces within the data center, providing integrated rack-scale solutions as well as power orchestration software.
Other hardware considerations include Texas Instruments (TXN) and Analog Devices (ADI), given the growing importance of power management solutions.
Kennedy Njagi: The way I look at AI is that the real monetization bottleneck sits on one layer below the apps, in memory and bandwidth. And that’s why Micron Technology (MU) has been on my radar for quite a while now. This is because every serious training or inference workload runs into a memory wall. Yet when supply tightens, the industry doesn’t need a marketing department to show up in the numbers.
Marvell Technology (MRVL) and Credo Technology (CRDO) are another pair I keep circling. The entire AI buildout turns into a networking and interconnect problem once you scale beyond a few racks, and high-speed connectivity is where performance and cost collide fast.
Astera Labs (ALAB) also fits the same “plumbing matters” theme, with PCIe and CXL connectivity that helps CPUs, accelerators, and memory talk to each other without the system spending half its life waiting.
None of these are flashy stories, but they’re the toll booths on the AI highway. And the checks tend to clear there first when CapEx ramps.
Of course, there’s still cyclicality and execution risk, so I’m not pretending this is a straight line. In chess terms, MU, MRVL, CRDO, and ALAB are the pieces controlling the center squares of the AI infrastructure board, and if you own the center, you usually get to decide how the game is played!
Elizabeth Pramila: It is always prudent to view investment opportunities from a risk POV, and when seen through that lens, Nvidia (NVDA)—everyone’s obvious choice for AI hardware—is actually the riskiest of all. I have discussed customer concentration, emerging competition, and ROI risks for key clients in past articles, which I invite you to read.
At the other end of the hardware spectrum is Apple (AAPL), which has clearly pivoted to a third-party strategy that allows it to deploy the latest AI capabilities on its huge install base. This is a low-risk opportunity that still has plenty of steam for a long growth runway.
Apple Intelligence may no longer be an in-house effort, but I foresee it making significant strides from an AI adoption perspective, the first signs being the partnership with Alphabet (GOOG) (GOOGL) for Gemini deployment on Apple hardware. The biggest risk-mitigation factor here is that there is no pressure on AAPL to invest in AI CapEx, a bold deviation from what other mega-caps are doing.
In between these two extremes are several other AI hardware opportunities, such as Advanced Micro Devices (AMD), Amazon (AMZN), Alphabet (GOOG) (GOOGL), Taiwan Semiconductor (TSM), and ASML (ASML), all of which I have written about on Seeking Alpha.
If I were forced to give you just one takeaway for investing in AI-focused hardware stocks, it would be this: AI development is and will be an iterative process, so the fastest growers now might end up dropping out of the race once their edge has worn off.
The real long-term investment opportunity is with companies like Apple, Alphabet, Amazon, and others that not only make their own AI hardware but also possess vast delivery ecosystems through their devices and cloud-based AI infrastructure. It is consumer spending on AI that will see the strongest growth in the long run, so my advice would be to focus on stocks that can either exhibit signs of sustainable ROIC expansion or have a ready install base or cloud-and-edge infrastructure through which AI capabilities are served.
This is regardless of whether the opportunity is upstream, as with ASML and TSM; midstream; or downstream, such as AAPL, AMZN, and GOOG. There are too many to list, but hopefully the criteria I have laid out will create a framework for your own process of elimination.