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  1. Markets · CallTSMC: own the bottleneck, not the brandOwn the bottleneck, not the brand: the chokepoint every AI chip depends on, at a discount to all of them.
  2. Markets · CallBroadcom: the right custom-silicon thesis at the wrong priceThe right custom-silicon thesis at the wrong price — watching the most expensive, lowest-quality way to play it.
  3. Markets · CallSK Hynix: the largest HBM rent, valued like a Korean cyclicalThe largest slice of the HBM scarcity rent, valued like a Korean cyclical — with a Nasdaq listing as the re-rating catalyst.
  4. Markets · CallMicron: priced like a cyclical at the top, contracted like a utilityPriced like a peak-cycle memory-cyclical, contracted like a utility — a re-rating bet, not an earnings bet.
  5. Markets · CallNVIDIA: paying a market multiple for a franchise financing its own demandStill constructive on the best franchise in AI infrastructure — but the risk has migrated from the income statement to the balance sheet.
  6. Markets · CallParameter Scaling Is Dead. Multi-Dimensional Scaling Is the New Paradigm.AI scaling has moved from raw parameter count to four memory-hungry dimensions — inference-time compute, MoE, data curation, architecture — making memory bandwidth the universal bottleneck.
  7. Markets · CallInference Costs Will Fall 90% by 2028 — The Memory Stack Is the MechanismFour independent memory-stack improvements — HBM4, KV-cache compression, optical interconnect, and architecture — could compound to a ~10x fall in per-token inference cost by 2028.
  8. Markets · CallBTC Miners Pivoting to AI Infrastructure: The Power ArbitrageBitcoin miners already hold the power, cooling, and space AI needs — mining revenue is expected to fall from 85% to under 20% of total as they convert to AI hosting.
  9. Markets · CallEnergy Ownership Is the New AI MoatPower, not GPUs or algorithms, is AI's real moat — a location-specific power contract is scarce and non-fungible in a way chip access never is.
  10. Markets · CallNuclear Is the Only Power Source That Scales for AI Data CentersAI data centers need reliable, carbon-free baseload at massive scale — nuclear is the only source that delivers all three, and owners can charge a premium for it.
  11. Markets · CallTransformer Successors: State-Space Models and Linear Attention Are Worth WatchingTransformers' quadratic memory scaling is the KV-cache problem. State-space and linear-attention models scale linearly — hybrids blending both look like the likely path.
  12. Markets · CallLumentum: The Optics Play That Bridges Power and MemoryBullish · LITE
  13. Markets · CallKV Cache Compression Will Cut Inference Costs 50% Before HBM4 Ships at ScaleKV caches are AI inference's biggest bottleneck. Software compression (6x cited) is shipping faster than HBM4 hardware — and will drive the cost collapse of 2026-2027.