Applied Materials: The AI Stock That Wins No Matter Which Chip Wins

When investors think about AI winners, the usual names come to mind: Nvidia (NVDA), TSMC, or memory makers like Micron (MU). Semiconductor equipment giant Applied Materials (AMAT) rarely gets the same attention, despite supplying the tools needed to manufacture nearly every critical component of an AI chip.

Investors have started to notice. The stock has nearly tripled over the past year, pushing its valuation to roughly 37x forward earnings and 12x trailing sales, up from less than 4x sales just a year ago.

The key question is whether Applied deserves that premium valuation.

That re-rating has been far larger than the company’s growth outlook, with revenue expected to rise about 17% this year and roughly 25% next year per consensus estimates. Those are strong growth rates, but not the kind typically associated with a stock that has nearly tripled in value.

Investors are effectively making a bet that Applied’s role in the AI ecosystem is becoming both larger and more durable. Hyperscalers are expected to spend more than $600 billion on AI infrastructure this year alone, but the exact mix of that spending will likely evolve over time. Today’s spending is centered on Nvidia GPUs used for training large models. Tomorrow, it could shift toward custom ASICs optimized for inference. Agentic AI could drive a resurgence in CPU demand. Memory requirements may continue growing faster than compute, while advanced packaging could emerge as the industry’s primary bottleneck.

Each of those scenarios creates different winners at the chip level. Applied participates across all of them. Rather than betting on which chip architecture wins, investors are effectively betting that AI hardware continues to become larger, more complex, and more expensive to manufacture. See our upside case: How Applied Materials Stock Surges To $800

The AI opportunity for Applied can be understood through three pillars: logic, memory, and advanced packaging.

Logic: More Complex AI Processors Need More Equipment

The first pillar is logic chips, the GPUs and AI accelerators powering modern data centers. As AI chips move to leading-edge nodes of 2 nm and below, they become significantly harder to manufacture. New technologies such as Gate-All-Around transistors and backside power delivery add complexity and increase the number of manufacturing steps required for each chip. That’s where Applied Materials benefits. The company supplies many of the tools needed for these advanced processes. For investors, the key point is that Applied doesn’t need chip volumes to soar. As chips become more complex, more equipment is required to manufacture them, creating a tailwind for the company even if overall wafer volumes remain relatively stable.

Memory: The HBM Boom Is Increasing Equipment Intensity

The second pillar is memory, specifically high-bandwidth memory (HBM). HBM has become a critical component of AI systems because modern GPUs need enormous amounts of memory bandwidth. Unlike traditional DRAM, HBM is significantly more complex to manufacture. That complexity directly benefits Applied Materials. Management noted that each HBM unit requires roughly three times the wafer area of standard DRAM and adds 19 manufacturing steps, 15 of which involve semiconductor equipment. Applied captures more than half of the equipment value associated with those additional steps. The result is that Applied benefits not only from rising memory demand but also from the growing complexity of memory manufacturing. As AI drives greater adoption of HBM, equipment spending grows faster than memory volumes themselves.

Advanced Packaging: The Fastest-Growing AI Opportunity

The third pillar is advanced packaging. Today’s AI systems are no longer built around a single chip. Instead, they combine multiple components, including logic dies and stacked HBM memory, into a tightly integrated package. Connecting these pieces together requires sophisticated packaging technologies such as 3D stacking and chiplet integration. This is becoming one of the fastest-growing segments of semiconductor manufacturing. Applied expects advanced packaging revenue to grow more than 50% in calendar 2026 and has been investing aggressively to strengthen its position. As AI chips become increasingly complex assemblies rather than standalone processors, packaging becomes a larger portion of overall manufacturing spend.

The Bigger Picture

Applied Materials’ edge is breadth. Semiconductor equipment is an oligopoly, but most players own a single chokepoint: ASML in lithography, Tokyo Electron in coating and developing, and KLA in inspection and metrology. Applied spans all three layers an AI chip needs: logic, memory, and packaging. That means its revenue tracks the rising complexity of AI hardware itself, not the fate of any single process step or node transition.

The strongest evidence of that position is customer visibility. Applied now receives as much as eight quarters of forward demand visibility, roughly two years, compared with the three to six months that historically preceded equipment purchases. That shift matters because it changes how investors value the business. Equipment makers have traditionally traded at lower multiples because orders could disappear within a quarter. With demand now visible years in advance, Applied increasingly looks less like a cyclical tool vendor and more like a foundational provider of AI infrastructure.

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