James Hires from The Motley Fool made a striking prediction: “The Artificial Intelligence (AI) Pick-and-Shovel Trade Isn’t Over. Here Are 2 Stocks to Buy for 2026.” As someone who spends his days sifting through AI toolkits, figuring out what truly works and what doesn’t, that statement certainly got my attention. For a long time, the conversation around AI investment felt like it started and ended with NVIDIA. Their GPUs are the foundational components for so much of what we do in AI, from training models to running complex applications. But Hires’ outlook, and similar sentiments from other analysts, suggests a significant pivot in where the real growth might be found.
It’s easy to see why NVIDIA has been the go-to. They announced a staggering $1 trillion in Vera Rubin and Blackwell orders through 2027. That’s a massive amount of business. Yet, the stock’s response to this news was surprisingly muted, showing only a 1% increase. This muted reaction, coupled with a more than 5% decline in NVIDIA’s shares during the first quarter of 2026 due to geopolitical shocks, indicates a shift in investor focus. The market isn’t just looking at the leading AI firms anymore; it’s looking deeper into the supporting infrastructure – the “picks and shovels” of this new digital gold rush.
The Shifting AI Focus
For those of us working with AI day-to-day, this shift makes a lot of sense. The initial scramble was about getting the raw processing power needed to even *do* AI. NVIDIA provided that. But now, as AI becomes more integrated and widespread, the focus naturally moves to deployment and platforms. It’s not enough to just have the powerful chips; you need the entire ecosystem to make them useful, efficient, and scalable.
Wedbush, for instance, has highlighted winners for this next phase of AI development. This suggests a recognition that the AI space is maturing beyond just hardware production. It’s about the companies that enable the *use* and *management* of AI, not just its creation. This includes everything from data centers to specialized components that optimize AI operations.
Beyond the Obvious Choices
So, what does this mean for our two predicted outperformers in 2026? While the specific names aren’t publicly provided in the verified facts, the reasoning behind their potential growth is clear. Analysts are pointing to companies that are integral to the evolving AI market, particularly those benefiting from rising data center demand. Think about it: every AI model, every application, every service needs somewhere to live and run. That “somewhere” is increasingly a data center built to handle the immense computational load of AI.
Two companies that fit this description and are mentioned in connection with outperforming NVIDIA in 2026 are TSMC and Micron. TSMC (Taiwan Semiconductor Manufacturing Company) is seeing momentum fueled by rising data center demand. They are a critical player in manufacturing the advanced chips that power AI, including those from NVIDIA and other designers. As AI adoption grows, so does the demand for their manufacturing capabilities. Micron, on the other hand, is set for solid growth as AI demand fuels memory requirements. AI workloads are incredibly memory-intensive. Training large models, processing vast datasets, and running complex inferences all require significant amounts of high-performance memory. Micron is a key supplier in this area, making them an essential “pick and shovel” provider.
This refocus isn’t about NVIDIA failing; it’s about the AI market expanding. NVIDIA led the charge, providing the fundamental hardware that kicked off the current AI boom. But as the market matures, the value chain broadens. Investors are now looking at the entire infrastructure that supports AI, from the foundries that make the chips to the memory providers and data center operators that house and run the AI systems.
From my perspective Early on, the focus was on raw computational power for training models. Now, we’re seeing an explosion of tools that help with deployment, monitoring, data management, and specialized applications. These tools, much like the “pick and shovel” stocks, are enabling the broader adoption and practical application of AI, moving beyond the initial research and development phase. It’s a natural evolution, and it presents new opportunities for those willing to look beyond the immediate spotlight.
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