3 Artificial Intelligence (AI) Stocks That Look Like Strong Buys Heading Into April

TSMC manufactures ~70% of the world’s processors; Q4 revenue rose 26% to $33.7B and EPS jumped 35% to $3.14, with management forecasting sales up ~30% in 2026. Alphabet's Gemini has 750M monthly users and a new Gemini-Siri deal expected to generate about $1B annually; company revenue rose 15% to ~$403B in 2025 and diluted EPS increased 34% to $10.81. Broadcom's AI sales grew 106% to $8.4B in Q1 (ended Feb. 1) with adjusted EPS +28% to $2.05 and management targeting at least $100B in AI chip sales by 2027; valuations cited: TSM P/E 32, Alphabet P/E 27, Broadcom P/E ~60.

Analysis

Foundry incumbency creates asymmetrical leverage: advanced-node capacity is capacity-constrained and multi-year lumpy, which lets the lead foundry extract price and share gains during peaks but also concentrates downside when hyperscaler budgets reallocate. That dynamic amplifies supplier wins (equipment and specialty materials) and makes customers with captive fabs more likely to accelerate capex to regain bargaining power — expect a two- to five-year tug-of-war between foundries and IDMs for share and margin.

Model licensing and OEM embedding of base models shift AI monetization away from pure ad CPMs toward recurring platform and B2B licensing, creating steadier revenue streams but also inviting margin compression as models commoditize. Strategic OEM partnerships create distribution lock-in that can produce outsized cashflows within 12–24 months, yet they also materially increase regulatory and privacy scrutiny paths that can impose guardrails or fines on realized profit.

ASIC dominance is a structural moat when workloads are sticky and optimization value is high, producing high gross margins and customer stickiness; conversely, the same concentration raises displacement risk if key hyperscalers pivot architectures (e.g., to in-house designs or alternative accelerators). Near-term signals to watch are capex guidance from top hyperscalers, reported utilization of advanced nodes, and any incremental multi-year supply agreements that convert optionality into contracted revenue.

Key tail risks are geopolitical disruption in the Taiwan strait and a front-loaded AI capex cycle that meaningfully slows after initial deployments; both scenarios would compress multiples and spike volatility within weeks. Positive catalysts that could re-rate valuations are multi-year supply contracts, materially increased licensing/recurring SaaS revenue recognition, or sustained expansion in non-hyperscale enterprise AI deployments over 6–18 months.

Key Decisions for Investors

• Pair trade (12 months): Long TSM via 12-month LEAP call spread (buy TSM LEAP, sell higher strike) sized 1–2% NAV, short an equal delta notional of INTC stock to isolate foundry vs IDM execution risk. R/R: asymmetric upside (30–50%) if foundry pricing holds; capped downside via spread; hedge reduces broad semiconductor cyclic risk.
• Directional trade (6–12 months): Buy GOOGL 6–12 month call spread to capture licensing and enterprise AI monetization while shorting AAPL equity (smaller size, e.g., 50% notional) to express software/recurring revenue vs hardware exposure. R/R: target 2:1 upside if AI licensing accelerates; reduces beta to hardware cycle and product launch risk.
• Event-driven (9–18 months): Accumulate AVGO on pullbacks or buy 9–12 month calls (small position, 1–3% NAV) to play ASIC stickiness — trim into strength above the prior multiple and set a stop at 20–25% drawdown. R/R: 25–40% upside if hyperscaler adoption continues; high multiple means losses can be sharp if capex stalls.
• Tail-hedge (3–6 months): Buy short-dated out-of-the-money puts on TSM and AVGO (5–10% OTM, 1–3 month expiries) as insurance against Taiwan/geopolitical shock or a sudden capex halt. R/R: small insurance cost (tens of bps of NAV) that pays off in >30% equity drawdowns and reduces need to liquidate long positions in stress.