What Are 3 Great Tech Stocks to Buy Right Now?

Hyperscalers are projected to spend ~$700 billion on AI data centers this year; Nvidia, Alphabet and Meta are identified as primary beneficiaries. Nvidia retains dominance via GPUs, CUDA and networking, plus strategic talent/license moves into inference; Alphabet holds a cost advantage from decade-old custom ASICs that lower training/inference costs and better monetize AI through search and ads; Meta is using AI (e.g., Andromeda retrieval layer) to boost engagement and ad effectiveness. Motley Fool discloses positions in Alphabet and Meta, recommends all three, but did not include Nvidia in its current top-10 Stock Advisor picks.

Analysis

The most consequential supply-side chokepoints aren’t GPUs themselves but HBM stacks, advanced packaging slots, and interconnect silicon (NVLink/PCIe switch capacity). Those three inputs create a capacity wedge: even if multiple compute architectures exist, whoever controls packaging cadence and co-design with hyperscalers will command outsized pricing power and order flow for the next 12–24 months. Expect winners that own integration (chip + network + software) to see steeper gross-margin capture than pure-play merchant GPU makers.

Alphabet’s ASIC lead effectively shifts the competitive frontier from raw FLOPS/$ to end-to-end deployment cost per inference — a structural advantage that compounds as model sizes scale because the marginal cost of inference becomes the bottleneck on monetization. That means cloud rivals and inference-specialist vendors must either vertically integrate or build differentiated software stacks to protect margins; the likely near-term outcome is accelerated M&A of inference software and IP-rich middleware to plug the cost gap.

Crowding and positioning are under-appreciated near-term risks. NVDA-related options and futures positioning creates a short-term vulnerability to volatility shocks (earnings, inventory updates, HBM allocation prints) that can cascade into narrow selloffs even while the multi-year secular story remains intact. For portfolios, the clearest asymmetric payoffs come from owning differentiated, monetizable AI exposure (ad-driven, search-driven, or ASIC-advantaged) while managing gamma and supply-cycle risk through time-spreaded option structures and pairs.

Key Decisions for Investors

• GOOGL long-LEAP (12–18 months): Buy deep-in-the-money Jan-2028 call or equivalently a 12–18 month bull-call spread to capture Alphabet’s deployment-cost advantage being monetized via search/ad; target 40–80% upside over 12 months, stop-loss at -20%. Position size 3–6% of risk budget.
• Pair trade (short-term hedge): Short NVDA near-dated (1–3 month) out-of-the-money call(s) and allocate proceeds to the GOOGL LEAP purchase above. Rationale: harvest elevated near-term IV on NVDA while keeping long-term upside via GOOGL; max loss if NVDA gaps up sharply—size the short to 25–40% of the long notional and put a hard +30% drawdown cover.
• META 6–12 month call spread: Buy a 9–12 month call and sell a higher strike to fund ~60–70% of premium. This captures the AI-driven ad-efficiency re-rating with limited downside (premium paid) and a 2–3x upside if engagement/ARPU inflects; limit allocation to 2–4% due to ad cyclicality risk.
• NVDA diagonal income structure (core hold): Buy a 12–18 month NVDA LEAP and sell monthly calls for 4–6 months (roll if ITM). This preserves long-term secular exposure while monetizing rich short-term IV; expect to reduce cost-basis by 20–40% vs outright LEAP purchase. Cut position if NVDA shares fall >30% or if HBM/package allocation improves materially and IV collapses.
• Selective INTC optionality (speculative): Small allocation (1–2% risk) to 18–24 month INTC calls to play potential fab/ASIC win-backs by hyperscalers. Asymmetric bet with limited cost and payoff if Intel secures multi-hyperscaler foundry/ASIC deals; treat as optionality rather than core conviction.