What Is 1 of the Best AI Stocks to Own for the Next 10 Years?

Revenue surged 4,218% from fiscal 2016 to fiscal 2026 and Nvidia's share price has risen ~23,380% over the past decade, underscoring its dominance in AI infrastructure. The company holds stakes in AI firms such as OpenAI and Anthropic, increasing indirect exposure to the AI boom. Shares trade at a forward P/E of ~23, and the article positions Nvidia as a reasonable long-term buy while noting it was not included in The Motley Fool Stock Advisor's current top-10 picks.

Analysis

The durable winner here is the layer that standardizes and scales AI compute — hardware plus the software stack that locks customers into a platform. That creates concentrated demand at a small set of suppliers (advanced foundry capacity, HBM memory, interconnect, and software toolchains) where capacity constraints and architecture lock-in amplify margins but also concentrate counterparty risk with hyperscalers.

Second-order effects matter: hyperscalers can compress vendor pricing as they centralize spend, and rapid model optimizations (quantization, sparsity, distillation) act as a long-run deflationary force on per-model compute demand. Conversely, if new large models push inference from cloud to edge, that creates a multi-year re-acceleration of component cycles (memory, packaging, fabs) that incumbent fabs and EDA players will capture unevenly.

Key catalysts to watch in the next 3–12 months are pricing guidance from major AI GPU suppliers, announced wafer capacity additions at leading foundries, and any changes in export policy for advanced accelerators to China — each can swing TAM expectations by a material amount. Tail risks include regulatory/antitrust scrutiny of dominant stacks and sustained model-efficiency gains that materially lower compute intensity; either could halve consensus incremental revenue growth over 2–3 years.

The consensus is overlooking dispersion within the supply chain: end-market AI strength does not translate evenly into profits for OEMs, foundries, and memory vendors. Positioning should therefore isolate pure-accelerator capture while hedging exposure to memory/capacity cycles and hyperscaler negotiating power.

Key Decisions for Investors

• Long NVDA LEAP + hedge: Buy NVDA Jan-2028 1x near-ATM call (size = 1% notional) and buy Jan-2028 25–40% OTM put as a tail hedge. Timeframe 12–36 months; objective is 3:1 upside skew if AI spend stays strong, with downside capped to defined premium today.
• Pair trade to isolate GPU alpha: Long NVDA (equal-weight) / Short INTC (equal-weight) for 6–12 months to isolate GPU-specific upside vs incumbent CPU/foundry narrative. Target: asymmetric return where 30–100% upside if NVDA-led cycle continues; downside limited to ~20–30% if broad hardware cycle falters.
• Event-driven directional: Buy NVDA Dec near-term call calendar across the next two earnings releases to capture positive guide beats, funded by selling shorter-dated calls. Timeframe: 0–3 months; reward: capture 10–25% move on favorable demand commentary; risk: theta decay and a full loss of premium if guidance disappoints.
• Reallocate from consumer/streaming into infra: Reduce NFLX exposure by 2–4% of portfolio and redeploy into AI infrastructure names (NVDA-centric) over the next 1–3 months to favor capex-linked upside. Rationale: marginal dollar benefits more from infrastructure capex than consumer content growth in current cycle.