My 3 Favorite AI Stocks to Buy on the Continued Chip Sell-Off

The article argues Nvidia (forward P/E ~16x fiscal 2028 estimates) remains the key AI infrastructure winner, supported by CUDA’s moat and continued strength in training plus growing inference/agentic workloads. It upgrades AMD on inference and agentic AI catalysts (data center CPU market projected to double to $120B by 2030, GPU-to-CPU mix shifting toward 1:1 for agentic AI), and highlights Broadcom’s custom AI accelerator opportunity (custom AI chips projected >$100B by fiscal 2027; Citigroup AI revenue target ~$180B in fiscal 2028) alongside a turnaround in non-AI chips after a $30B Apple deal. Overall, it frames the recent AI-chip sell-off as a valuation-driven buying opportunity rather than a demand break.

Analysis

The market is likely mispricing the composition of AI spend rather than the level of spend. The next leg is less about raw training GPUs and more about who captures the cheaper, recurring inference layer and the custom-silicon attach rate; that favors AVGO first, then AMD, with NVDA still the quality incumbent but more exposed to multiple compression if growth becomes less explosive. The second-order winner is hyperscalers like GOOGL and META: custom silicon and better inference economics lower unit costs, which should expand AI product margins even if capex stays elevated.

Near term, the biggest risk is that investors extrapolate a mature-growth multiple too quickly onto NVDA while the capex cycle is merely pausing. If model efficiency improves faster than demand, merchant GPU demand can decelerate before agentic workloads fully scale, creating a 1-3 month air pocket in the basket. AVGO is the most insulated because its revenue is tied to design wins, networking, and long-duration platform relationships, not just one generation of accelerator cycles.

The contrarian miss is that AMD’s upside depends on inference and CPU attach rates converting into actual platform share, not just TAM slides. If agentic AI adoption proves lumpy, the CPU-shift story may take longer than the market expects, while NVDA’s software moat keeps it from losing share as fast as skeptics assume. Falsifiers: weakening hyperscaler capex commentary over the next two earnings cycles, or any evidence that NVDA networking growth stalls while AVGO custom-silicon bookings keep compounding.

Key Decisions for Investors

• Long AVGO / short SMH for 3-6 months: best risk/reward if the market rotates from headline GPU growth to custom silicon and networking; thesis breaks if AVGO AI revenue guide decelerates or hyperscaler design-win conversion slips.
• Add to AMD on weakness over the next 1-3 months, but keep size smaller than AVGO: asymmetric upside if inference and CPU mix improve, with downside if agentic AI adoption is slower than expected and server CPU attach stays flat.
• Hold NVDA only on pullbacks, not momentum-chase: the company remains the platform winner, but relative underperformance vs AVGO is plausible if valuation stays capped and AI capex broadens away from merchant GPUs.
• Long GOOGL or META as second-order beneficiaries of lower inference cost over 6-18 months: cheaper AI deployment should expand monetization optionality; invalidate if capex intensity rises without corresponding margin improvement.