Twenty-four lessons gave you seven theses. This one gives you the method for holding them together — because the hardest mistake in this whole space isn't picking the wrong name, it's owning seven names that are secretly the same bet.
You can now value each name from first principles (L24) and you've kept a falsifier for every one in THESIS.md. The finale is the step that turns a watch-list into a portfolio: deciding how much of each to hold. The naive answer — "size by conviction × upside" — is exactly the trap, because it ignores the one fact that dominates this sector: your seven names ride the same dollar of AI capex, so they don't fail independently. The missing term is independence, and you already have it — it's encoded in how unrelated each name's falsifier is from the others.
Core skill: Position size = conviction × falsifier-independence. Conviction you've built lesson by lesson. Independence is the new idea: two names whose falsifiers trip for the same reason are one position wearing two tickers; a name whose falsifier is genuinely orthogonal earns its own slot. The whole lesson is learning to read your THESIS.md falsifiers as a correlation map, then sizing off it.
Sizing by conviction × upside feels rigorous and quietly builds a time bomb: your highest-upside names (NVDA, AMD, SK Hynix) are also the ones most levered to the same capex factor, so a "diversified" book of your best ideas is really one giant position on AI spending continuing. The fix is to demand a second thing from every slot — that its falsifier is its own. A position only diversifies the book to the extent the thing that would break it is unrelated to what would break everything else.
The diversification illusion: seven tickers across "different" companies looks diversified and behaves like one bet. Correlation — not the count of names — determines how many independent bets you actually hold. You'll measure exactly this below.
Independence isn't a vibe — you can derive it directly from the falsifier field you've maintained all along. Group the names by what would actually trip the falsifier. Names that trip on the same event are correlated; names that trip on unrelated events are the real diversifiers:
Two insights fall out. First, the share-shift cluster (AMD vs NVDA) is mostly an internal hedge — sizing both is a bet on the segment, not new independence. Second, the only cluster whose falsifier is genuinely orthogonal to the silicon stack is the power leg from L20: it fails if grid interconnect, PPAs, or energy costs break — a different physical bottleneck. That's what makes it the portfolio's real diversifier rather than just another capex proxy.
| Tier | Names | Size on | Cap the size because |
|---|---|---|---|
| toll booth | ASML, Cadence/Synopsys, TSMC | Durability — highest, steadiest weight. Socket-agnostic; get paid whoever wins downstream. | Shared capex-cycle risk (lagged); single-customer-base concentration. |
| leader | NVDA, AVGO, AMD | Conviction in share + margin-trend durability. The growth engine of the book. | Competition falsifiers + the highest capex beta. Don't let "best idea" become the whole book. |
| cyclical | SK Hynix | Cycle position, sized small. Own the HBM lever, not the memory cycle. | The L24 double-count: peak earnings × peak multiple. Smallest, most cycle-timed slot. |
| power leg | Utilities / IPPs, cooling, SMR | Independence. Sized as the hedge — the slot that earns its keep by not being capex-beta. | Its own risks (rate cases, project timelines), but uncorrelated to the silicon falsifiers. |
Notice the logic flips per tier: toll booths are sized up for durability, leaders are sized to conviction but capped for correlated risk, the cyclical is sized small and cycle-aware, and the power leg is sized for the independence it adds — not its standalone upside. Same identity (conviction × independence), four different bindings.
Model: effective bets = 1 ÷ (wᵀRw) with silicon-tier correlation ≈ 0.8 and power-leg correlation ≈ 0.2; drawdown = Σ(weight × tier-beta × shock), betas toll 0.5 / leader 1.1 / cyclical 1.6 / power 0.25. Numbers are stylized to teach the shape, not to forecast.
Teaching artifact only — not investment advice. The point isn't these specific weights; it's seeing the sizing identity produce a shape: durable toll booths anchor, leaders drive returns but are capped, the cyclical is a small cycle-timed lever, and the power leg buys real independence.
| Slot | Tier | Illustrative weight | Why this size |
|---|---|---|---|
| ASML / EDA / TSMC | toll | ~40% (split) | Highest conviction × durability; socket-agnostic. Anchor of the book. |
| NVDA / AVGO / AMD | leader | ~35% (split) | Return engine, but capped — highest capex beta + live competition falsifiers. AMD/NVDA partly hedge each other. |
| SK Hynix | cyclical | ~10% | Own the HBM lever; kept small and cycle-aware to dodge the double-count. |
| Power leg | power | ~15% | The orthogonal hedge — the slot that lifts effective independent bets, sized for independence not upside. |
Drop these weights into the calculator above (40 / 35 / 10 / 15) and you'll see the uncomfortable result: a book that looks like seven diversified names resolves to only ~1.4–1.6 effective bets, and the power leg is doing most of the diversifying work. That's the whole lesson in one number.
A portfolio you can't monitor is a hope, not a thesis. The durable artifact this course produces is a quarterly watch-list: the single observable that would move each name toward its falsifier. These live in THESIS.md — the capstone just makes them operational. Check them every earnings season and ask: did anything move toward the falsifier?
| Name | Tier | The ONE quarterly tripwire |
|---|---|---|
| TSMC | toll | CoWoS capacity stops being the binding constraint; a flagship leading-edge customer defects to Samsung/Intel. |
| ASML | toll | Net new EUV + High-NA bookings roll over; a sustained down-cycle in the order book. |
| Cadence/Synopsys | toll | Recurring-revenue % / renewals weaken; an open-source flow wins a production advanced-node tape-out. |
| NVIDIA | leader | Gross-margin downtrend (competition biting) or a qualified rack-scale NVSwitch rival adopted at a hyperscaler. |
| Broadcom | leader | A major ASIC customer in-sources design; Ethernet scale-out share stalls vs InfiniBand/NVLink. |
| AMD | leader | 2026 Helios/UALink rack qualification + ROCm production parity (bull-confirm) — or no inference share despite specs (bull-break). |
| SK Hynix | cyclical | Samsung wins full HBM4 NVIDIA qualification (3-way price war); HBM ASPs roll over; SSM/hybrid adoption cuts KV-cache HBM. |
| Power leg | power | Grid-interconnect approvals + hyperscaler PPA volume — the leading indicator for whether ordered GPUs can actually be energized. |
Read first: on holding correlated bets and why "number of positions" lies about diversification, the cleanest framing is the idea of the effective number of independent bets (the inverse-correlation intuition the calculator uses). Pair with: SemiAnalysis for the quarterly data behind each watch-list tripwire, and read the 10-Ks / earnings releases yourself (margins, bookings, ASPs) so your watch-list runs on primary signals, not headlines. See RESOURCES.md.
Select the best answer for each.
1. In the rule Position size = conviction × falsifier-independence, the second term captures:
2. Holding all seven semiconductor names is mostly one bet because they share:
3. The power leg (utilities / cooling / SMR) is the portfolio's real diversifier because:
4. "Effective independent bets" falling well below the number of tiers held means:
5. Why is SK Hynix sized small and cycle-aware rather than to its upside?
6. The quarterly watch-list turns each thesis into: