How Bottleneck Hunting Identifies Supply Chain Opportunities in AI Berkshire

Bottleneck hunting is a systematic six-step pipeline that converts supply-chain analysis into investable ideas by scoring component shortages and mapping them to undervalued public companies.

The AI Berkshire repository implements bottleneck hunting as a markdown-based skill that analyzes capital-intensive super-trends and decomposes them into physical supply-chain layers. This methodology, defined in skills/bottleneck-hunter.md, enables investors to identify structural supply constraints before they are fully priced by the market.

The Six-Step Bottleneck Hunting Pipeline

The workflow operates as a narrowing funnel from macro trends to specific equities. Each step is encoded in the skill file and executed by the AI Berkshire command router.

1. Super-Trend Confirmation

The pipeline begins by validating long-run, capital-intensive trends against four strict filters. A trend must demonstrate duration ≥ 3 years, physical hardware requirements, global CAPEX > $50 billion/year, and demand growth exceeding supply growth. The approved trend list is stored in the skill file and refreshed on every invocation.

2. Supply-Chain Physical Decomposition

Each confirmed trend is broken into a layered-tree model (Layer 0 → Layer 4) that separates end-products from core components, sub-components, upstream materials, and infrastructure. For example, the AI infrastructure trend decomposes from GPU → optical modules → InP substrates, with each layer hard-coded in the skill's decomposition tables.

3. Bottleneck Identification

Every Layer 2-3 element is scored against six hard criteria: concentration, expansion lead-time, substitutability, utilization, demand-growth, and validation cycle. A 6-point rubric generates S/A/B ratings where S indicates single-point-of-failure risk, A indicates severe constraints, and B indicates manageable supply.

4. Company Screening

High-rated bottlenecks are mapped to publicly listed suppliers using web searches for "{bottleneck} supplier listed company". The system applies fast-filter rules requiring companies to be publicly listed, derive ≥ 30% revenue from the bottleneck, maintain market cap < $10 billion, and demonstrate daily turnover > $1 million.

5. Cross-Verification

Each candidate undergoes validation through a positive/negative verification matrix. The skill checks for client contracts, CAPEX guidance, price hikes, and capacity constraints while simultaneously running a "芒格式否定" (Munger-style negation) checklist to surface bearish arguments and disconfirming evidence.

6. Output Generation

The final step emits a concise opportunity card containing valuation checks (PS/PE thresholds, TAM-ratio, 10-year return tests) and a signal-strength rating (★★★★★ → ★). The output format is fixed in the skill file's "报告模板" (report template) section.

S/A/B Bottleneck Scoring Methodology

The rating system quantifies supply-chain fragility using objective metrics.

• S-Rated (Single-Point-of-Failure): Concentration ≤ 2 suppliers, expansion lead-time > 18 months, zero substitutability, utilization > 90%, demand growth 2× supply growth, validation cycle > 12 months.
• A-Rated (Severe): Concentration 3-4 suppliers, expansion lead-time 12-18 months, limited substitutability, utilization 80-90%.
• B-Rated (Manageable): Concentration > 4 suppliers, expansion lead-time < 12 months, available substitutes, utilization < 80%.

This rubric is defined in the markdown table within skills/bottleneck-hunter.md and parsed dynamically during execution.

Practical Usage Examples

CLI Invocation via Claude Code

Researchers trigger the workflow using slash commands:

/bottleneck-hunter AI基础设施

This command initiates the six-step pipeline for the "AI infrastructure" super-trend and returns a formatted markdown report with investment candidates.

Sample Output Structure

The skill generates standardized opportunity cards:

# 瓶颈猎手 — 2026-06-30 14:00

## 明确标的

### 雪峰科技（688123）— 光模块供应链瓶颈

**瓶颈定位**: Layer 2，光模块，S级瓶颈  
**财务快照**: 市值 $8.2 bn / 年收入 $1.1 bn / PS 7.5x / PE 22x  
**估值检查**: 绿灯（PS < 10 x，PE < 30 x）  

**看多逻辑**  
1. 供给集中度≤2家 → 议价能力强  
2. 客户签约已公布 → 收入可验证  

**看空逻辑**  
1. 替代技术（硅光子）研发进度快  

Programmatic Integration

For automated research pipelines, call the skill via the Codex prompt endpoint:

import requests

payload = {
    "skill": "bottleneck-hunter",
    "arguments": "AI基础设施"
}
r = requests.post("http://localhost:8000/run-skill", json=payload)
print(r.json()["report"])

The endpoint reads the same markdown template and returns the rendered report as a JSON field, enabling integration with quantitative trading systems.

Core Implementation Files

The bottleneck hunting capability is distributed across four key locations in the xbtlin/ai-berkshire repository:

• skills/bottleneck-hunter.md: Contains the complete workflow definition, scoring rubrics, layer decomposition trees, and report templates.
• codex-prompts/bottleneck-hunter.md: Maps the markdown skill to CLI-compatible commands for execution.
• reports/bottleneck-map/: Storage directory for daily and master bottleneck maps generated by the pipeline.
• README.md: Provides high-level project overview and skill installation instructions.

Summary

• Bottleneck hunting in AI Berkshire follows a rigid six-step pipeline from trend confirmation to opportunity generation.
• The methodology uses a 6-point rubric to classify supply constraints as S, A, or B ratings based on concentration and lead-time metrics.
• Company screening applies financial filters (market cap < $10bn, revenue concentration ≥ 30%) to identify asymmetric opportunities.
• All logic is implemented in skills/bottleneck-hunter.md and executed via slash commands or API calls.
• Output includes structured opportunity cards with built-in valuation checks and bearish argument matrices.

Frequently Asked Questions

What distinguishes an S-rated bottleneck from an A-rated one?

An S-rated bottleneck represents a single-point-of-failure with supplier concentration ≤ 2, expansion lead-times exceeding 18 months, and utilization above 90%. An A-rated bottleneck indicates severe but manageable constraints with 3-4 suppliers and 12-18 month lead-times. The distinction determines position sizing and urgency in the final investment recommendation.

How does the system verify bottleneck investment candidates?

The skill implements a positive/negative verification matrix requiring at least two independent sources for validation, such as client announcements, CAPEX guidance, or industry reports. Simultaneously, it runs a "芒格式否定" checklist to actively seek disconfirming evidence and alternative technologies that could obsolesce the bottleneck component.

What financial filters are applied during company screening?

The screening layer filters for publicly listed companies with ≥ 30% revenue exposure to the specific bottleneck, market capitalization below $10 billion, and daily turnover exceeding $1 million. These constraints target liquid, small-cap pure-plays with sufficient asymmetry potential.

Where is the bottleneck hunting logic defined?

All workflow logic, scoring rubrics, and report templates are defined in skills/bottleneck-hunter.md within the AI Berkshire repository. This markdown file is parsed by the command router and executed whenever a user invokes the /bottleneck-hunter skill.

{
  "mcpServers": {
    "instagit": {
      "command": "npx",
      "args": ["-y", "instagit@latest"]
    }
  }
}