Supply-chain bottleneck arbitrage. Given a super-trend (AI infra, energy transition, defense, semiconductor reshoring, space economy), decompose its physical su
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Decompose a super-trend (user-specified, e.g. "AI infrastructure", "energy transition") into its physical supply chain and hunt for bottleneck-arbitrage opportunities.
Don't ask "which AI stock to buy" — ask "if this trend keeps expanding, which link runs out first?"
Traditional research chases leaders and known tracks. This skill inverts: start from the choke points of the physical supply chain and find companies nobody notices but that the whole industry must wait on when they run short.
The edge: Layer-1 bottlenecks (GPU, HBM, power) are already priced in. The real alpha is in Layer 2 and Layer 3 — optical modules, lasers, InP substrates, SOI wafers, epitaxy equipment, wafer-level test, IC substrates, specialty fiberglass.
| Criterion | Requirement | How to verify |
|---|---|---|
| Durability | ≥3-5 years of certain growth | Search industry forecasts, capex plans |
| Physicality | Needs real hardware/material/equipment build | Distinguish "software upgrade" from "physical expansion" |
| Scale | Global capex >$50B/year | Search top players' capex guidance |
| Acceleration | Demand growth > supply expansion | Compare demand growth vs capacity plans |
Use web_search to verify. Reference super-trends: AI infrastructure, energy transition (nuclear/grid/storage), defense modernization, semiconductor reshoring, space economy.
Don't stop at concepts — decompose to physical entities.
Layer 0 (end): final product/service
Layer 1 (core component): already closely watched → priced in, limited alpha
Layer 2 (sub-component/material): low attention, alpha-rich
Layer 3 (upstream equipment/raw material)
Layer 4 (infrastructure): power, cooling, land, talent, certifications
Layer 0: AI model training/inference services
Layer 1: GPU/accelerators, HBM, servers, data centers
Layer 2 (focus zone):
- Network interconnect: optical modules, fiber, switch ASICs, copper cables
- Optical comms core: lasers (EML/VCSEL/CW), modulators, photodetectors
- Semiconductor materials: InP substrates, GaAs substrates, SOI wafers, SiC substrates
- Advanced packaging: CoWoS interposers, HBM TSV, ABF substrate film
- PCB/substrate: high-frequency PCB, IC substrates, specialty fiberglass
- Test: wafer-level test (probe cards), burn-in, ATE
- Thermal/cooling: liquid cooling, CDU, immersion fluid
- Power connection: busbars, UPS, distribution, transformers
Layer 3: epitaxy equipment (MOCVD/MBE), lithography/etch, high-purity metals (In/Ga/Ge), specialty gases, sputtering targets, certifications (MSA/Telcordia)
Layer 4: power (nuclear/gas/transmission), cooling water, data-center land/permits
For other trends, use web_search with queries like {trend} supply chain bottleneck, {trend} shortage critical component, {trend} capacity constraint, {trend} sole source supplier.
For each Layer 2-3 link, evaluate 6 criteria:
| # | Criterion | Question | Score |
|---|---|---|---|
| 1 | Supply concentration | ≤3 global suppliers? | 🔴 ≤2 / 🟡 3-5 / 🟢 >5 |
| 2 | Expansion lead time | How long to add capacity? | 🔴 >2y / 🟡 1-2y / 🟢 <1y |
| 3 | Substitutability | Can other tech/material replace it? | 🔴 irreplaceable / 🟡 partial / 🟢 easy |
| 4 | Capacity utilization | Current utilization? | 🔴 >90% / 🟡 70-90% / 🟢 <70% |
| 5 | Demand growth | Downstream demand growth? | 🔴 >50%/yr / 🟡 20-50% / 🟢 <20% |
| 6 | Customer qualification cycle | How long for a new supplier to qualify? | 🔴 >1y / 🟡 6-12m / 🟢 <6m |
Bottleneck grade: 🔴×≥4 → S-grade (single-point failure, highest priority); 🔴×3 → A-grade (severely constrained); 🔴×1-2 → B-grade (stressed but manageable); no 🔴 → not a bottleneck, skip.
For each S/A-grade bottleneck, use web_search / screen_market to find listed companies.
| Criterion | Requirement |
|---|---|
| Listing status | Listed (A/HK/US/JP/TW/EU) |
| Bottleneck revenue share | >30% of revenue from the bottleneck link |
| Market cap | Prefer <$10B (large caps already priced) |
| Liquidity | Average daily turnover >$1M |
A real bottleneck ≠ an investment opportunity. For every company, compute PE/PB/ROE/FCF yield with financial_rigor (command=verify_valuation), and run financial_rigor (command=three_scenario) for scenario valuation:
Sanity check (mandatory): with financial_rigor (command=three_scenario), answer — "buying at current market cap, if the most optimistic scenario fully plays out and I exit at 25× PE in 10 years, what's the annualized return?" <10%/yr → flag "no margin of safety at current price".
| Check | Question |
|---|---|
| Customer validation | Have top customers signed/imported? (check announcements, customer filings) |
| Revenue validation | Is the bottleneck already showing in revenue growth? (last 2-3 quarters) |
| Price validation | Is the product raising price? (industry quotes, analyst reports) |
| Capacity validation | Is capacity really tight? (lead times, customer complaints) |
| Capital validation | Is there expansion capex? (company guidance) |
Use get_financial_statements / get_stock_news / web_search.
| Rank | Company | Ticker | Mkt Cap | Revenue | PS | PE | Bottleneck link | Grade | Share | Growth | Signal | Valuation |
|---|
Market cap, revenue, PS, PE are mandatory — never skip with "TBD". If financials can't be obtained, signal strength ≤★★.
Signal strength (valuation gate directly affects):
After drafting, run report_audit (command=extract → verify each point → command=verdict) as a quality gate to ensure no hallucinated numbers.
🎯 {Company} ({Ticker}) — {one-line bottleneck positioning}
Why it's a bottleneck: (2-3 sentences)
Why this company: (2-3 sentences)
Catalyst timeline:
- Near-term (1-3m): [earnings / capacity / customer win]
- Mid-term (3-12m): [industry trend / expansion node]
Key risks: 1. 2.
Key data: market cap / revenue / PS / PE / growth / bottleneck revenue share
Margin of safety: 10y 25× PE exit method, annualized return XX%. Conclusion: yes/no.
Cross-validation status: ✅ customer / ✅ revenue / ⚠️ valuation stretched / ❌ unverified
Conclusion: deep research / watchlist / skip
Save with write_file to the reports directory (e.g. reports/bottleneck-map/{trend}-bottleneck-{YYYYMMDD}.md).
On each run: ① re-check identified bottlenecks (new suppliers? capacity expanded? substitute breakthrough?); ② scan new bottlenecks (web_search last 7 days supply chain / shortage / bottleneck news); ③ update grades (upgrade/downgrade/relieve).
| Bias | Symptom | Counter |
|---|---|---|
| Leader-bias | Search dominated by large caps | Deliberately search small-cap suppliers, add "small cap" |
| English-bias | Miss JP/KR/TW players | Must search JP/KR/TW market suppliers |
| Narrative-bias | Drawn to "AI concept" labels | Look only at actual supply-chain position, not market labels |
| Confirmation-bias | After finding a bottleneck, only seek positive evidence | Force Step 5 reverse checks |
| Recency-bias | Rely on stale info | Prefer last 30 days of data |