Forget the AI Sell-Off: These 2 Artificial Intelligence (AI) Stocks Are Actually Getting Cheaper While Their Businesses Get Stronger

Nvidia reported fiscal 2026 revenue of $215.9B, up 65% YoY, with data-center revenue up 75% YoY to $62.3B in Q4 and guidance of $78B for fiscal Q1 2027 (implying ~77% YoY growth); the stock trades at a forward P/E of ~21. Meta plans up to $135B of AI infrastructure spending in 2026 (nearly double last year), sees rising ad revenue from AI improvements, and trades at a forward P/E of ~20 (vs a three-year avg of ~23). Despite recent sell-offs and market skepticism, the piece views valuation compression as an opportunity to 'buy the dip' in both NVDA and META given their AI-driven growth trajectories.

Analysis

AI infrastructure is bifurcating the supplier ecosystem: the winners will be those capturing scale economics on training and inference (chip IP, HBM, advanced packaging, and optimized software stacks), while mid-tier silicon and legacy networking vendors face margin squeeze as hyperscalers internalize more of the stack. Expect downstream effects at OSATs and memory suppliers — elevated lead times for HBM and packaging capacity will create 6–18 month chokepoints that selectively lift pricing power for a handful of vendors and penalize the rest.

The biggest near-term risks are demand elasticity and model-efficiency breakthroughs. A modest pullback in hyperscaler capex or a step-function improvement in model compression (sparse/dynamic models) could reduce GPU-hour growth meaningfully within 6–12 months, reversing sentiment quickly; conversely, enterprise AI adoption and multi-year inferencing needs support a 2–4 year upside tail. Geopolitics and export controls are asymmetric catalysts — tighter controls create short-term supply shocks that benefit indigenously supplied stacks but also trigger revenue displacement risks in global exposure.

The market is mispricing optionality and execution risk differently across names: some leaders trade like mature platform companies while their revenue engines are still in steep expansion. That creates fertile ground for pair trades that monetize dispersion rather than binary directional bets. Time horizons matter — use options to express convexity for 6–18 month catalyst windows (earnings, capex cadence, product ramps) and size positions to survive a 30%+ volatility drawdown during re-pricing events.

Key Decisions for Investors

• Long NVDA via a 12-month call spread (buy Jan-2027 call ~ATM, sell Jan-2027 call ~20–30% OTM) sized 3–5% portfolio: asymmetric upside if AI infra demand stays strong, max loss = premium paid, target 40–80% return if NVDA re-rates and revenue beats over next 12 months.
• Pair trade: go long NVDA equity (or synthetic via deep-in-the-money calls) and short AMD equity on equal dollar notional (6–12 month horizon). Rationale: capture consolidation of high-end GPU economics; set stop-loss at 20% adverse move in pair and trim if AMD delivers a surprise product win.
• Long META stock with a 9–12 month covered-call overlay (sell 3–6 month calls OTM to fund exposure) to monetize churn and capex skepticism. Target total return 25–50% while reducing downside volatility; protect core position with 12-month put ~15% OTM if downside risk rises.
• Buy AVGO 6–9 month put spread (buy mid-OTM put, sell deeper OTM put) sized as a hedge against networking/firmware spending reallocation into integrated hyperscaler stacks. Max loss = net premium; payoff ramps if AVGO multiple compresses further into earnings season.