Could the Nvidia Killer Be Hiding in Plain Sight? 3 Stocks to Watch

Alphabet trained its new Gemini 3 model on its proprietary Tensor Processing Units rather than Nvidia GPUs, demonstrating a viable ASIC-based alternative to GPU-centric AI training. Amazon and Anthropic have activated a Trainium2 cluster using nearly 500,000 chips that will scale to over 1 million this year, and Microsoft and OpenAI are collaborating on custom chips, underscoring hyperscalers’ push to reduce reliance on Nvidia. Nvidia has generated roughly $187 billion in revenue over the past four quarters with ~70% gross margins, so widespread adoption of custom ASICs by hyperscalers could pose a material competitive risk even as the AI market expands from $235 billion toward an estimated $631 billion by 2028.

Analysis

Market structure: Winners are hyperscalers and chip-design enablers — GOOGL (TPUs), AMZN (Trainium) and AVGO/Broadcom (custom ASIC supply) — who capture more gross margin on stack integration; losers are NVDA and memory/HBM suppliers if ASIC adoption reduces GPU demand by ~10–25% over 2–4 years. Pricing power will bifurcate: hyperscalers gain internal cost advantage while third-party GPU makers must defend enterprise and SMB markets; expect NVDA gross margin pressure from ~70% toward mid-60s under a moderate displacement scenario. Cross-asset: elevated tech capex raises term premium — corporate supply-heavy capex could lift high-yield spreads modestly if profits fall; NVDA option IV stays rich (tradeable) and HBM memory prices/commodity copper remain demand-sensitive.

Risk assessment: Tail risks include regulatory bans on vertical integration or IP wars (antitrust suits within 12–36 months), major deployment failures at AWS/OpenAI, or a rapid NVDA product repricing that preserves share. Short-term (days–weeks) catalysts: earnings, contract announcements, public benchmark cost-per-token metrics; medium-term (6–18 months): Trainium >1M active chips utilization and Broadcom designs at scale; long-term (2–5 years): software-portability (CUDA lock-in) decay. Hidden dependencies: developer tooling, HBM supply, Broadcom/TSMC capacity and software migration costs that can slow ASIC uptake and keep GPUs sticky.

Trade implications: Direct plays — establish a 2–3% long position in AMZN (12-month horizon) to capture AWS/Anthropic upside; add 1–2% long AVGO as a 12–18 month hardware-design supplier play. Defensive/hedged NVDA exposure — maintain a core 2% long but buy 3–6 month 5–10% OTM put spreads sized at 30–50% of notional to protect vs a >15% downside; if NVDA falls >10% on no-fundamental change, add to longs. Pair trade — long AMZN / short NVDA equal-dollar (6–9 months) to express hyperscaler verticalization; exit criteria: Trainium utilization >70% or NVDA sequential rev miss >5%.

Contrarian angles: The market underestimates CUDA lock-in and the software migration cost — historical parallel: Google TPUs (2016+) improved internal ML but didn’t quickly displace GPU ecosystems; NVDA can re-expand ASPs into specialized inference chips. Overreaction risk: headlines about 1M Trainium units are capacity signals not immediate revenue displacement; if NVDA revenue CAGR remains >30% for two consecutive quarters, current threat is overstated and NVDA is a buy on weakness. Unintended consequence: hyperscaler ASIC proliferation can fragment demand and raise per-unit prices for niche GPUs, benefiting NVDA’s premium SKUs if adopted.

Key Decisions for Investors

• Establish a 2–3% long position in AMZN (12-month view) to play AWS/Anthropic Trainium adoption; trim if AWS AI gross margin contribution does not rise by +200–300 bps over two consecutive quarters.
• Add 1–2% long in AVGO (12–18 months) as a supplier/partner to custom chip programs; take profits if forward gross margin guidance falls >150 bps or Broadcom’s design wins <2 in next 6 months.
• Maintain a core 2% long in NVDA but protect with a 3–6 month put spread (buy 1 5% OTM put, sell 1 10–15% OTM put) sized at 30–50% of NVDA notional; if NVDA drops >10% on no-fundamental news, add incremental 1% long.
• Implement a 6–9 month pair trade: long AMZN / short NVDA equal-dollar exposure (size 2–4% net) to express hyperscaler ASIC upside versus GPU incumbency; unwind if Trainium utilization >70% by next two quarterly reports or NVDA misses revenue by >5% sequentially.
• Reduce broad GPU-centric ETF or unhedged NVDA exposure by 1–3% if NVDA’s next two quarterly gross margins fall cumulatively >300 bps — rotate proceeds into AMZN/AVGO and cash-equivalent short-term bonds.