The AI Inference Supercycle Is Here. These 2 Stocks Will Be the Biggest Winners of This Megatrend (Hint: It's Not Broadcom or Intel)

AI inference demand is expected to keep memory-chip demand strong, with Deloitte estimating inference workloads will account for roughly two-thirds of AI data center compute this year, up from 50% in 2025. Micron stock is up 639% over the past year and Sandisk nearly 3,400%, yet both are described as attractively valued at 7.6x and 24x forward earnings, respectively. The article argues supply shortages in DRAM and NAND could persist until at least next year, supporting continued earnings growth for both companies.

Analysis

The core trade is not “AI demand” broadly; it is a second-order capacity squeeze in memory as inference shifts the bottleneck from raw FLOPS to data movement, latency, and persistent state. That structurally favors the memory vendors with the best mix of HBM, DRAM, and NAND exposure, while also pressuring hyperscalers and accelerator vendors to spend more of the AI budget on memory per server. The result is a self-reinforcing capex cycle: higher inference volumes force denser memory configs, which tightens supply, which raises pricing power and extends the earnings runway.

The market is likely still underestimating how persistent this can be because memory upcycles usually fade when supply catches up, but AI changes the cadence. Lead times for wafer capacity and advanced packaging are measured in quarters to years, so even if end-demand cools, the overhang from deployment of installed AI infrastructure should keep utilization high into 2026. The real beneficiary set may broaden to vendors adjacent to memory content per box, while the relative losers are OEMs and cloud buyers with weaker pricing power that absorb the higher bill of materials.

The contrarian risk is that the market is extrapolating peak pricing into a smooth multi-year straight line. If hyperscalers slow server orders or shift architecture toward more compute-efficient inference models, incremental memory intensity could flatten faster than consensus expects, and the highest-multiple memory name would de-rate first. Another risk is that investors are crowding into the obvious winners already; the better risk/reward may be in the laggards with operating leverage that has not yet been fully repriced rather than chasing the strongest tape.

Key Decisions for Investors

• Long MU vs. short a basket of AI infrastructure beneficiaries with lower direct memory leverage (e.g., NVDA/AVGO/GOOGL) for 3-6 months; thesis is memory content per AI server expands faster than total AI capex, creating relative outperformance in the memory leg.
• Buy SNDK on pullbacks and finance with covered calls 1-2 quarters out; the setup is strongest if NAND pricing tightens through the next earnings cycle, but upside is capped if the market starts discounting a 2026 supply response.
• Add a tactical long MU into the next earnings window with a 2-3 month horizon; target a continuation move on pricing guide-raised expectations, but use a tight stop if management commentary suggests customer inventory digestion.
• Avoid chasing NVDA/AVGO here on the memory thesis alone; their inference upside is real, but the marginal re-rating from this article’s catalyst is smaller than for memory suppliers and the trade is more crowded.
• Pair long MU / short WDC only if you want cleaner DRAM vs. mixed-storage exposure; WDC has more operating leverage to storage demand but less direct benefit from HBM-driven AI content growth, so the spread should widen if AI memory intensity keeps rising.