Broadcom vs. Nvidia: 1 Critical Metric Shows Which Artificial Intelligence (AI) Chipmaker Is the Better Buy After Earnings

Broadcom’s fiscal Q3 AI semiconductor division grew 221% YoY to $16.7B, while Nvidia’s data center revenue grew 117% to $89B, with both companies reporting overall growth above 100% YoY. The article argues Nvidia trades at a cheaper forward valuation (both <20x forward earnings, with ~30x cited as a more appropriate range), implying potential upside including “double” for Nvidia and “over 50%” for Broadcom into 2027 as custom AI chip orders ramp.

Analysis

The market implication is not simply “which chipmaker is cheaper,” but who captures the economics of AI capex as hyperscalers optimize spend. Custom silicon shifts bargaining power away from a single-platform GPU vendor and toward the supplier that can monetize workload-specific designs, but it also risks capping total accelerator dollars per unit of deployed compute. That makes AVGO less dependent on any one model cycle and more levered to share-of-wallet at GOOGL/META, while NVDA remains the higher-beta beneficiary of broad-based demand waves.

Second-order, the real winner set extends to hyperscalers themselves if ASIC adoption improves inference economics and lowers cost per token; that can support margin expansion even if headline capex stays elevated. The loser set is broader than NVDA: any pure-play GPU alternative with weaker ecosystem lock-in is exposed if customers increasingly reserve GPUs for frontier training and push routine inference to custom chips. The base case is not share collapse, but a mix shift that slows NVDA’s incremental upside versus expectations.

Catalyst-wise, the next 1-3 months matter most around capex guidance and evidence of deployment ramps; the 6-18 month question is whether custom silicon becomes a durable architecture rather than a pilot program. The contrarian read is that the market may be overpaying for NVDA’s platform optionality while underestimating AVGO’s pricing power in a world where customers want cost discipline. The thesis breaks if NVDA’s data-center growth re-accelerates materially or if AVGO’s custom backlog fails to convert into margin-accretive revenue by the next two reporting cycles.

Key Decisions for Investors

• Initiate a 3-6 month long AVGO / short NVDA pair trade into the next earnings season: thesis is mix-shift toward custom ASICs compresses NVDA’s incremental upside while AVGO monetizes hyperscaler optimization; size modestly because both remain AI winners.
• If you want cleaner exposure, buy AVGO on pullbacks below the post-earnings trendline and use a 10-15% trailing stop; risk/reward is asymmetric if ASIC ramps prove durable, but monitor for backlog-to-revenue slippage.
• Use GOOGL and META as secondary beneficiaries: if custom chips lower inference cost, operating leverage improves. Add on any capex-guidance-driven selloff, but only if management keeps AI spend intact.
• Avoid chasing NVDA after short-term strength unless data-center growth re-accelerates in the next print; above-market multiples leave less room for disappointment, especially if hyperscalers keep internalizing more workloads.