The Top 5 Most Worthwhile Directions to Layout First

First Place: QCOM
Role: Edge AI / AI PC / AI Phones / XR / Automotive Intelligence.
Second Place: GOOG
Role: AI platform cash flow + Search cash cow + TPU / Cloud.
GOOG is the most balanced AI platform asset in terms of risk and return. It’s less crowded than NVDA and not purely a hardware cycle stock. Suitable as the “core bottom position in AI.”
Third Place: CEG or GEV, choose one
Role: AI physical infrastructure.
Leopold framework emphasizes not just buying models or chips, but buying the physical layer: power, storage, connectivity, compute leasing, and other unavoidable upstreams.
CEG leans more towards defensive cash flow, GEV is more flexible but also has larger gains.
Fourth Place: AVGO
Role: AI ASIC + AI network + system interconnection.
AVGO is the most core “network/ASIC value capture layer” in the second stage of AI. But it’s currently too crowded; don’t chase just because the logic is strong.
Fifth Place: MU / SNDK, choose one
Role: Storage / DRAM / HBM / SoC CMM.
Storage is the current performance bottleneck, but gains have already been significant. Dynamic distillation library clearly states: storage is the current explicit performance bottleneck, optical connectivity is the next stage structural bottleneck, but it’s necessary to distinguish between current EPS realization and long-term architecture shifts.