Market calls / Timestamped · scored on resolution · never deleted
Directional calls with a date on them, graded when they resolve.
Conviction, timeframe and a public grade on every one.

- Calls
- 13
- Live
- 13
All time
| Cover | Stance | Ticker | Thesis | Conviction | Outcome |
|---|---|---|---|---|---|
![]() | LONG | TSM | TSMC: 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. | High · 3Y | Active · opened Jun 9 · +0.2% to last Friday close |
| WATCH | AVGO | Broadcom: 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. | Med · 1Y | Watching · since Jun 9 | |
![]() | LONG | 000660.KS | SK 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. | Med · 1Y | Active · opened Jun 9 · −25.6% to last Friday close |
![]() | LONG | MU | Micron: 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. | Med · 1Y | Active · opened Jun 9 · +8.6% to last Friday close |
![]() | LONG | NVDA | NVIDIA: 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. | Med · 1Y | Active · opened Jun 9 · +10.6% to last Friday close |
![]() | LONG | — | Parameter 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. | High · 1Y | Active · opened Apr 13 |
![]() | LONG | — | Inference 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. | Med · 3Y | Active · opened Apr 13 |
![]() | LONG | — | BTC 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. | High · 1Y | Active · opened Apr 13 |
![]() | LONG | — | Energy 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. | High · 3Y | Active · opened Apr 13 |
![]() | LONG | — | Nuclear 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. | Med · 3Y | Active · opened Apr 13 |
![]() | WATCH | — | Transformer 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. | Med · 3Y | Watching · since Apr 13 |
![]() | LONG | LITE | Lumentum: The Optics Play That Bridges Power and Memory60% of data center energy is data movement — Lumentum's optics solve this directly | Med · 1Y | Active · opened Apr 13 · +1.2% to last Friday close |
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How calls are graded
A call locks at publication — direction, conviction, timeframe and target are timestamped and can’t be edited. Resolution is mechanical: the stated direction against the realized move. Accuracy breakdowns, calibration and the resolved feed live on the track record. Method details: methodology · the open book.










