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2 Undervalued AI Companies to Buy in 2026 and Hold for Decades

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2 Undervalued AI Companies to Buy in 2026 and Hold for Decades

Micron reported a strong fiscal Q1 2026 with revenue up 57% year‑over‑year to $13.6 billion and EPS up 167% to $4.78, driven by DRAM/NAND demand and a pronounced HBM supply shortfall; the company expects the HBM market to grow from $35 billion in 2025 to $100 billion in 2028 and projects a 21% share, underpinning revenue visibility and pricing power. Alphabet, trading at a forward P/E of ~27.9x, is monetizing AI via Gemini models, AI‑optimized TPUs and a Google Cloud backlog of $155 billion, with over 70% of cloud customers using its AI products and improving cost efficiency through custom hardware. Both names are presented as buy‑and‑hold AI plays trading at reasonable forward valuations (Micron ~7.1x), likely to influence investor positioning given the durable AI tailwinds and recent strong share gains.

Analysis

Market structure: Micron (MU) and HBM suppliers (Samsung, SK Hynix) are the primary winners as memory shifts from commodity to strategic AI input; the article’s $35B→$100B HBM TAM by 2028 implies ~3x market growth and Micron’s cited 21% share gives it multi-year revenue visibility and pricing power. Downstream winners include cloud providers and model-hosting vendors that secure capacity early; losers are OEMs and smaller ASIC vendors facing higher BOM costs and potential shipment delays if HBM remains supply-constrained. Risk assessment: Key tail risks are (1) geopolitics/export controls that curtail China sales, (2) a cyclical capex surge that creates oversupply (price shock -20% to -40% in 12–24 months), and (3) model/memory-efficiency optimizations that reduce per‑instance HBM content. Timing matters: expect sentiment moves in days around prints, operational revelations in weeks (guidance), and structural outcomes by 2026–2028 as HBM fab ramps and TPU economics evolve. Trade implications: Tactical portfolios should overweight MU (value at 7.1x forward P/E) and selectively overweight GOOGL for AI monetization (27.9x) while hedging execution risk. Use duration-matched option structures (12–18 month LEAPS on MU; 9–12 month call spreads on GOOGL funded by ~10–12% OTM put sales) and consider a relative-value pair (long MU / short SK Hynix) to isolate execution vs market cycles. Expect cross-asset effects: stronger tech cash flows tighten corporate spreads (supporting IG credit) and increase datacenter power demand (benefit to utilities and industrials over 2–5 years). Contrarian angles: Consensus understates execution and policy risk — a 15%+ QoQ HBM ASP decline or a Cloud AI adoption stall (backlog growth <5% QoQ) would materially reset MU and GOOGL multiples. Conversely, if Micron sustains >20% YoY HBM revenue growth into 2027, current pricing underestimates compounding (MU could rerate toward peer growth multiples). Watch for unintended consequences: tight HBM could accelerate custom silicon and memory‑efficient models, capping long‑run demand growth.