2 Monster AI Stocks to Hold for the Next 10 Years

McKinsey estimates nearly $7 trillion in global data-center capex by 2030 and U.S. AI-related capex accounts for roughly 5% of GDP, while AI's share of U.S. electricity demand is projected to rise from 4.3% to 11.7% by 2030. Motley Fool highlights two ideas: Rivian (RIVN) trades ~3x sales versus Tesla ~13x, has delayed profitability due to heavy AI/autonomy capex (Tesla also committed $2B to xAI); and NuScale Power (SMR) is a $3.4B market-cap SMR nuclear play with first plant not expected until 2030 and material execution, cost and dilution risks despite a potential up-to-$10 trillion nuclear market opportunity.

Analysis

AI-driven data-center capex is creating a multi-year, location-specific demand shock that favors modular, on-site power solutions and the suppliers that enable high-density compute racks. Expect hyperscalers to prioritize siting and power certainty over cheapest grid power; that benefits SMR-style modular nuclear if project financing, licensing and transmission bottlenecks converge favorably. The same dynamic increases demand for high-voltage distribution gear, advanced power electronics, and cooling/PUE improvements — segments where order books can lead semiconductor and industrial suppliers by 6–24 months.

On the compute side, Nvidia remains the central choke-point for training and inference economics, but the biggest second-order shift is away from uniform server stacks toward heterogeneous, workload-specific accelerators and chiplet ecosystems. That creates a follow-on opportunity for foundries and incumbents that can supply AI-specific packaging, memory hierarchies and interconnects — a window Intel can exploit if it executes on foundry/packaging and captures edge inference. Conversely, incumbents that rely on legacy margins (consumer auto or commodity servers) are most exposed to re-pricing if software/monetization timelines slip.

Time horizons and risks are asymmetric: data-center and nuclear capex play out on 3–10 year cycles with large lumpy milestones (permits, first-of-a-kind builds, major hyperscaler contract awards) that can catalyze >2x moves or wipe out paper value through dilution or canceled projects. Geopolitical export controls, a hyperscaler pivot to in-house silicon, or a sharp data-center demand disappointment within 6–12 months are the primary near-term reversal scenarios. That argues for staging exposure and trading milestone-driven optionality rather than blunt buy-and-hold positions.

Key Decisions for Investors

• Directional AI-infra: Go long NVDA via a 12–24 month call-spread (buy LEAP calls, sell farther OTM) to capture continued capex while capping cash outlay; hedge with a 20–30% notional put to limit drawdown. Rationale: captures hardware convexity to data-center orders; Risk/Reward: asymmetric upside (2x+ if big tape-outs continue) vs controlled downside (~30% with hedge) over 12–24 months.
• Contrarian foundry/packaging play: Buy INTC Jan-2027 LEAPs (OTM but <30% of notional) sized as a 1–2% portfolio conviction bet and add on clear foundry share wins. Rationale: execution-triggered multi-bagger if Intel wins meaningful hyperscaler packaging/foundry contracts; Risk: execution failure could halve option value in 12–18 months.
• SMR thematic exposure (NuScale): Take a staged small-long position in SMR equity (0.5–1% portfolio) paired with a long-dated protective put (3–5 year horizon). Add to position only after binding offtake or financing milestones. Rationale: asymmetric payoff if SMR is adopted by hyperscalers/municipal utilities; Risk/Reward: long lead times and dilution likely, but milestone re-rate can produce 3–5x returns over 3–7 years.
• Relative-value pair: Short TSLA (funded) vs long NVDA (smaller notional) over 6–18 months to express conviction that AI hardware/toll-taker economics re-rate higher while autonomous-vehicle monetization timelines disappoint. Rationale: hedges market beta and targets valuation dispersion; Risk/Reward: protect short TSLA size to avoid gamma risk around product/regulatory catalysts.