The Best AI Stock to Hold for the Next 10 Years: A Case for TSMC
Semiconductor stocks have been a bright spot for portfolios as AI demand fuels record spending on chips and data centers. Nvidia, AMD, Broadcom, and Micron get most of the headlines. But the company powering much of their success sits a layer deeper in the stack: Taiwan Semiconductor Manufacturing (TSMC).
If you want durable exposure to AI across cycles, TSMC deserves a hard look. It's the pick-and-shovel supplier that turns the industry's most advanced chip designs into physical products at scale.
Why TSMC is the quiet winner behind AI
- Hyperscalers (Microsoft, Alphabet, Amazon, Meta, and others) are pouring hundreds of billions into AI data centers. Much of that spend ends up at TSMC through its largest customers.
- Nvidia, AMD, and Broadcom design the AI accelerators and custom ASICs. TSMC manufactures them on advanced nodes that few can match for yield, volume, and consistency.
- As new chip platforms roll out (Nvidia Rubin, AMD MI400 Series), wafer demand intensifies. That's a direct tailwind for TSMC's top line and margins.
Growth and operating leverage are kicking in
Revenue growth has accelerated alongside the surge in AI accelerators. More importantly, profitability is expanding. With a dominant share in advanced-node foundry work, TSMC has pricing power most peers can't touch.
That cash flow is being recycled into capacity and geographic diversification. New fabs in Arizona, Germany, and Japan give customers redundancy while positioning TSMC closer to key end markets. For finance teams, this translates into better visibility on volumes, mix, and long-run free cash flow potential.
Valuation: paying up for durability and visibility
The stock trades around 28x forward earnings - near the high end of its recent range. On a surface read, it isn't "cheap." But premium multiples can be justified when the revenue base is expanding, margins are widening, and demand visibility is improving.
AI infrastructure spend is set to compound through the decade as training and inference workloads get heavier. That capital flows first into chips and manufacturing capacity - TSMC's wheelhouse. If those trends stay intact, earnings have room to grow into (and potentially through) today's multiple.
What to watch (and model)
- Customer allocation: Share of next-gen platforms from Nvidia, AMD, and custom silicon at cloud providers.
- Node transitions: Progress on N3 and N2, yields, and pricing vs. older nodes.
- Capacity ramps: Timing and cost curves for Arizona, Germany, and Japan fabs; equipment lead times.
- Gross margin band: Mix shift to AI accelerators vs. mobile/PC; wafer pricing trends.
- Capex intensity: TSMC's annual capex vs. orders; alignment with hyperscaler buildouts.
- Geopolitical and regulatory risk: Export controls, supply chain localization, and regional incentives.
Risks worth underwriting
No position is risk-free. TSMC faces geopolitical exposure given its Taiwan footprint, execution risk on overseas fabs, customer concentration, and the usual semiconductor cyclicality. Node rollovers can also compress margins if yields lag or if pricing comes under pressure.
That said, AI demand is less tied to consumer cycles and more tied to infrastructure buildouts. The buyers here are large, well-capitalized enterprises. That helps smooth the earnings profile relative to past chip cycles.
Portfolio implications
If you already hold AI designers, TSMC adds diversification across the stack. If you've missed the front-line AI names, TSMC offers cleaner exposure to the same secular trend with broader customer coverage. Over a 5-10 year window, it's a credible core holding for AI infrastructure.
For ongoing updates on capacity, node progress, and financial targets, see TSMC's investor materials: TSMC Investor Relations.
Bottom line
AI chips and data centers are set to absorb vast capital through the decade. TSMC sits at the choke point where those dollars turn into wafers. With accelerating demand, rising margins, and expanding global capacity, the case for a long-term position is straightforward - even at a premium multiple.
This is a thesis to build into your models, not a short-term trade. Size it appropriately, monitor the risk items above, and let compounding do its work.
Further resources
- Evaluate AI buildout impact on finance workflows and tooling: AI tools for Finance
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