Supply-Chain Bottleneck Hunter

Decompose a super-trend (user-specified, e.g. "AI infrastructure", "energy transition") into its physical supply chain and hunt for bottleneck-arbitrage opportunities.

Core Idea

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.

Step 1: Super-Trend Confirmation

Trend Filter Criteria

Use web_search to verify. Reference super-trends: AI infrastructure, energy transition (nuclear/grid/storage), defense modernization, semiconductor reshoring, space economy.

Step 2: Physical Supply-Chain Decomposition

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

AI Infrastructure Example

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.

Step 3: Bottleneck Identification — Finding "Choke Points"

For each Layer 2-3 link, evaluate 6 criteria:

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.

Step 4: Company Screening — From Bottleneck to Tickers

For each S/A-grade bottleneck, use web_search / screen_market to find listed companies.

Initial Screen

Valuation Gate (mandatory, never skip)

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:

• Red light (any one → signal strength capped at ★★, flag "valuation stretched"): market cap >20% of TAM; PS>30x with revenue growth <100%; market cap >10× 5-year optimistic revenue forecast; stock doubled within 60 days of a follow-on offering.
• Yellow light (needs extra justification, else downgrade): loss-making + PS>15x; PS >5× a profitable peer; PE>80x (compute PEG).
• Green light (bonus): PS<10x with revenue growing; PE<30x with a moat (flag "margin of safety").

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".

Step 5: Cross-Validation — Don't Trust a Single Story

Positive checks

Use get_financial_statements / get_stock_news / web_search.

Reverse checks (Munger inversion)

• Why don't smart people buy this stock?
• Can the bottleneck be bypassed? Alternative routes?
• Can China / other players quickly replicate capacity?
• If end-demand drops 50%, what happens to this company?
• Has management diluted at highs before?
• What growth assumption does the current valuation imply?

Step 6: Output — Bottleneck Opportunity Board

Ranking Table

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):

• ★★★★★ multi-cross-validated + customers imported + revenue confirmed + valuation green
• ★★★★ most checks pass + valuation green/yellow (with explanation)
• ★★★ logic holds but parts unverified + valuation yellow acceptable
• ★★ early signal, or logic holds but valuation red
• ★ pure concept, unverified

After drafting, run report_audit (command=extract → verify each point → command=verdict) as a quality gate to ensure no hallucinated numbers.

One-Pager Template

🎯 {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).

Step 7: Inventory Update — Maintain the Bottleneck Map

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).

AI Research Bias Self-Check

Core Principles (highest priority)

1. Don't ask the AI to recommend stocks — ask it to decompose supply chains. The question matters more than the answer.
2. Physical first — only links that need real physical product/material/equipment.
3. Layer 2 and Layer 3 — don't chase already-priced leaders.
4. Cross-validate — every conclusion needs ≥2 independent sources.
5. Be honest about uncertainty — if data is missing, say so; don't fill with speculation.
6. Bottlenecks are temporary — every bottleneck gets resolved; the key is timing the window.
7. Small cap ≠ good opportunity — a small cap can also be a bad company; it must pass financial quality.
8. A real bottleneck ≠ an investment — at PS>30x or still loss-making, the current price is not a buy. Valuation is a hard gate that cannot be overridden by bottleneck purity, signal strength, or narrative appeal. Better to miss a bottleneck stock that ran than buy a loss-making company at 100× sales.