3 Incredible AI Stocks That Aren't Too Late to Buy Now

The article is bullish on Nvidia, Broadcom, and Micron, citing AI-driven data center spending that Nvidia believes could rise from $600 billion in 2025 to $3 trillion-$4 trillion annually by 2030. Broadcom says its custom AI chip revenue could reach $100 billion or more in 2027, while Micron is benefiting from a memory-chip shortage and analysts see revenue climbing to $109 billion this fiscal year and $171 billion next year. The piece is opinionated stock commentary rather than new company-specific financial results, so the likely market impact is moderate.

Analysis

The market is still underpricing the durability of capex intensity: the real second-order winner is not just compute silicon, but the entire bottleneck stack required to convert power and wafers into usable AI throughput. That means the trade is broader than NVDA—custom silicon at AVGO and memory at MU are the more underappreciated beneficiaries because they monetize different failure points in the buildout: inference efficiency on one side, and bandwidth/thermal constraints on the other.

Consensus is treating this as a linear AI demand story, but the more important dynamic is mix shift. As hyperscalers optimize for unit economics, a larger share of incremental spend should migrate from general-purpose accelerators toward bespoke ASICs and memory-heavy architectures, which compresses NVDA’s relative share of wallet while still expanding the total pie. That is bullish for AVGO’s revenue conversion and for MU’s pricing power, especially if supply discipline persists into the next two quarters.

The main risk is not demand collapse, but digestion: after a strong run, the next leg likely depends on 2026–2027 orders rather than current-quarter commentary. If hyperscalers delay absorption of prior commitments or if custom silicon ramps faster than expected, the market may rotate away from “highest-quality AI beta” into names with clearer cash-flow acceleration. Watch for any deceleration in capex guidance or evidence that inventory is normalizing faster than end-demand.

The contrarian view is that NVDA may remain the best business but not the best stock from here. As the ecosystem matures, returns should disperse toward enablers with lower headline multiples and more direct exposure to capacity constraints rather than model-training prestige. The market is probably still too focused on who owns the AI interface and not enough on who owns the plumbing.

Key Decisions for Investors

• Maintain core long AVGO vs short NVDA pair for 3-6 months: thesis is that custom silicon share gains can outgrow GPU share gains even if total AI spend remains strong; risk is NVDA reaccelerates on new platform demand.
• Add to MU on pullbacks over the next 4-8 weeks: favorable risk/reward if memory pricing stays tight and hyperscaler buildouts remain power- and bandwidth-constrained; use prior breakout levels as a stop, since the stock is vulnerable to any inventory digestion headline.
• Sell covered calls on NVDA into strength for the next 1-2 months: captures elevated implied volatility while acknowledging upside may be increasingly capped by valuation compression and rising competitive mix shift.
• Long GOOGL vs short a basket of pure-play GPU beta for 6-12 months: Alphabet is a proof point for custom silicon economics; if inference workloads dominate, vertically integrated buyers should compound better than suppliers dependent on generalized demand.
• Reduce chase buying in the next 2-3 weeks and wait for the next capex print cycle: the next decisive catalyst is likely forward guidance from hyperscalers, not sentiment momentum; add only on confirmed order acceleration.