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 (+65% YoY) with data-center revenue up 75% YoY to $62.3B and fiscal Q1 FY2027 guidance of $78B (≈77% YoY). Meta plans up to $135B of AI infrastructure spending in 2026 (nearly double prior year) and is trading at a forward P/E of ~20 (three-year average ≈23); Nvidia's forward P/E is ~21. The author argues valuations have compressed despite accelerating AI-driven fundamentals and recommends buying the dips in both names; this is likely to be stock-specific and could move individual equities but has limited market-wide implications.

Analysis

The market pullback is re-pricing headline AI winners but leaves intact a strategic scarcity of high-performance inference/training capacity that is slow to replicate. That scarcity creates convexity: incremental dollars from hyperscalers or large enterprises buy outsized compute (and adjacent services) today while competitors face multi-quarter lead times to match system-level performance — meaning margins and pricing power are stickier than headline multiples imply. Over 6-18 months, expect cyclical layering: initial capex fatigue in one quarter can be quickly offset by enterprise model training waves and the stickiness of deployed inference pipelines.

Second-order winners are not just chip suppliers but ecosystem enablers — firmware/IP providers, interconnect and cooling specialists, and specialized foundry lanes for HBM and advanced packaging — whose revenue trajectories will decouple from spot GPU volatility. Conversely, commodity CPU/ASIC vendors that lack full-stack developer lock-in risk margin compression as customers prefer vertically integrated stacks that reduce integration costs and time-to-market. Watch wholesale used-equipment markets and colo pricing: if hyperscalers refresh en masse, smaller cloud players can accelerate capability at low cost, compressing future ASPs for new kit.

Key risks: a sudden demand pause if a high-profile LLM project is delayed, a tightening of export controls, or a material new architecture from a large cloud provider that narrows the performance gap — any of which could widen valuation dispersion in 30-90 days. The asymmetric payoff favors long-dated, convex exposure to structural winners plus hedges against faster-than-expected competitive entrants; avoid one-way bets funded purely by short-term sentiment recovery. Monitor quarterly capex cadence and supply-chain booking lead indicators (foundry slot disclosures, HBM allocations) as the fastest signals to change positioning.

Key Decisions for Investors

• Long NVDA 18–30 month LEAPs (buy Jan-2028 calls) financed by selling 3–6 month out-of-the-money calls: targets 2.5x upside if NVDA retains system-level pricing; max downside is premium paid (sell call receipts offset). Entry on any additional 15–25% pullback or on confirmation of persistent gross-margin expansion during the next two earnings reports.
• Pair trade: long META equity (or Jan-2027 calls) / short AMD (or buy Dec-2026 put spreads) sized 0.7:1 to express conviction in software+data network effects versus execution risk in high-volume chip ramps. Time horizon 6–18 months — expect positive carry if Meta monetizes inference; downside capped by using put spreads on AMD.
• Relative-value tilt into mid-cap ecosystem enablers (networking, HBM suppliers, advanced packaging fabs) via long positions in select suppliers rather than Broadmacro exposure; hold 12–24 months to capture multi-quarter backlog re-runs. Use 10–15% position sizing with stop-loss at 20% to protect against sudden demand collapses.
• Tactical hedge: buy 3–6 month index PUTs (or tail-risk collars) on the tech-heavy indices sized to cover 30–50% of AI-long book not hedged by pairs, activated if headline-driven flows reverse quickly. This keeps optionality for multi-year upside while limiting drawdown risk from event-driven churn.