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

Goldman Sachs projects the global humanoid robotics market will reach $38 billion by 2035 (a sixfold increase from its prior forecast), prompting a view that large, profitable tech companies are best positioned to capitalize. The article singles out Nvidia for its AI chips, robotics software platform and stake in Figure; Meta for plans to build consumer robots running Llama and its hardware push; and Tesla for ongoing Optimus prototypes and a planned autonomous ride-hailing fleet, while noting execution risks and Tesla's uneven track record. The piece is published by The Motley Fool, which discloses positions in several named companies, indicating potential editorial bias.

Analysis

Market structure: Winners will be AI-infrastructure and software integrators (NVDA, META) plus upstream component suppliers (LIDAR/IMU, power electronics, rare earth specialists) as humanoid robotics shifts demand from pure datacenter chips to integrated edge compute and sensors. Losers include low-margin robotics startups, consumer smartphone software rent-seekers (App Store gatekeepers under antitrust pressure), and auto suppliers that fail to pivot to robot-viable actuators. Expect pricing power concentrated in proprietary stack owners (NVIDIA-like) and in firms controlling data/ML models (Meta/Llama); commoditization risk rises for generic accelerators if new architectures emerge within 3–7 years.

Risk assessment: Tail risks include heavy regulation on autonomous systems (liability regimes within 1–3 years), a hardware-supply shock (rare-earth or actuator bottleneck causing >20% capex inflation), and model obsolescence from alternative compute paradigms. Immediate volatility (days) will be driven by earnings and device unveilings, short-term (weeks–months) by component shortages and hiring patterns, long-term (years) by unit economics and real-world robot utility. Hidden dependencies: successful humanoids require batteries, actuators, perception stacks and service-market economics — failure in any link delays monetization materially.

Trade implications: Primary direct plays: overweight NVDA (AI infra) and META (model + consumer HW) for 12–36 month upside; hedge execution risk via TSLA downside exposure because of track record and margin pressure. Use options: buy 12–24 month LEAP calls on NVDA/META (allocate 2–4% portfolio each) and finance with short 30–90 day covered calls or cash-secured puts to harvest IV; consider buying 6–12 month puts on TSLA as asymmetric tail-protection. Rotate into semiconductors and sensors, trim cyclical autos and non-AI capex by 2–5% over the next 4–8 weeks.

Contrarian angles: Consensus understates supply-chain and unit-economics execution risk; humanoid TAM estimates (Goldman $38B by 2035) imply 10–15% CAGR but not profitability for many entrants. Reaction may be underdone for NVDA if it successfully vertically integrates into robotics (re-rating potential >30% over 12–24 months) and overdone for Tesla’s robotics bullishness given repeated timeline misses. Historical parallel: 1990s internet infrastructure winners captured rents (Cisco) while many app-layer plays failed — prioritize platform owners with recurring revenue and balance-sheet capacity.

Key Decisions for Investors

• Establish a 2–3% portfolio long position in NVDA via 12–24 month LEAP calls (target 50–80% delta or 20–30% ITM) sized to risk no more than 1.5% portfolio loss; add on any retracement of 10–15% within 6 weeks.
• Initiate a 2% long position in META equity or 9–15 month call spreads (buy calls, sell slightly OTM calls) to capture upside from Llama-driven robotics/AR investment, and increase to 3–4% if next two quarters show meaningful monetization of AI features (>5% ad RPM lift).
• Open a 1–2% hedged short/put position on TSLA (buy 6–12 month puts or short-dated puts with defined risk) to protect against execution/margins risk; scale up if vehicle delivery guidance misses by >3% or FCF turns negative sequentially.
• Implement a pair trade: long NVDA (2%) / short a broad auto supplier ETF or TSLA (1–2%) to express AI infra upside while hedging manufacturing/auto execution risk; rebalance after quarterly earnings or any >15% move in either leg.