2 Artificial Intelligence Stocks You Can Buy and Hold for the Next Decade

Alphabet's TPU-based AI stack provides a structural cost advantage, enabling widespread Gemini integration across Search and Google Cloud and potential external TPU sales; Morgan Stanley estimates $13 billion in revenue per 500,000 TPUs. Broadcom expects AI ASIC revenue to exceed $100 billion in fiscal 2027 (vs $64 billion total revenue last fiscal year, >50% higher) and has about $21 billion in TPU orders to Anthropic this year. Combined, these dynamics position Alphabet and Broadcom as long-term beneficiaries of AI infrastructure demand, supporting multi-year revenue and margin upside.

Analysis

The structural rotation from generic GPUs to customer-tailored silicon and networking IP is creating a bifurcation between compute-layer winners (design/IP owners) and commodity fab-dependent suppliers. Over a 24–36 month horizon, design/IP vendors should capture a higher share of incremental dollar-per-training-cycle as hyperscalers prioritize integration, cost-per-inference and system-level throughput over raw TFLOPS; that shifts margin pools away from standalone GPU vendors and into companies that own SerDes, memory controllers and interconnect stacks.

A larger second-order effect is on capital intensity and contract structure across the supply chain: hyperscalers will lock multi-year supply and co-design contracts to de-risk capacity, effectively turning what looks like product revenue into annuity-like backlog for IP integrators and foundries. That amplifies downside for firms relying on spot GPU demand while increasing bargaining power for design partners and their preferred fabs, and it will likely accelerate capex cycles at leading foundries within 12–30 months.

Key catalysts to watch are three-fold: (1) multi-quarter cadence of hyperscaler orders and any disclosed long-term supply contracts, (2) a technical inflection where software/model efficiency reduces hardware refresh frequency, and (3) regulatory/export actions that can re-route supplier choices. Any of these can compress or expand the shift window materially within 6–18 months.

Contrarian risks: consensus is underweight execution and concentration risk at the IP integrators — large, lumpy contracts can be delayed or renegotiated, producing volatile quarters despite intact long-term demand. Conversely, the market may be over-discounting incumbent GPU makers in the short run; a new architecture or aggressive price/provisioning moves could blunt ASIC adoption and compress expected returns for design-focused players.

Key Decisions for Investors

• Go overweight AVGO (size 4–6% of strategy): buy 12–18 month LEAPS calls ~10–20% OTM to capture asymmetric upside from continued ASIC/IP share gains. Target >2x upside if ASIC-driven revenue growth materializes; cap losses to premium paid (small allocation).
• Pair trade — long GOOGL / short NVDA, equal-dollar, 6–18 month horizon: long GOOGL exposure to integrated software+cloud monetization with a short NVDA to hedge broad AI-beta. Expect positive performance if ASIC adoption accelerates; cap loss by sizing short at 50–75% of long notional and use a 25–30% stop on the short leg if NVDA outperforms.
• Tail-hedge AI exposure with limited-cost options: buy 9–12 month NVDA OTM puts (low notional) as protection against a rapid GPU demand collapse; fund these with short-dated covered calls on existing GOOGL/GOOG positions to reduce net cost.
• Event-driven trade: monitor quarterly cloud order disclosures — initiate or add to long AVGO/GOOGL within 2 trading days after a confirmed multi-year supply contract announcement, and trim 30–50% on a 20–30% rally within 3–6 months to de-risk execution concentration.