Stay Curious - AI Analysis Prompt

Use this Charlie Munger rule prompt to apply “Stay Curious” to a specific company. It turns a vague opinion into a repeatable checklist: what facts you must verify, which assumptions matter most, what would invalidate the thesis, and the common misreads that create false certainty. Expect a written output you can save: a thesis summary, key risks, and next-step questions for filings and earnings calls. If a claim matters, require primary-source citations before you act. Educational only — not investment advice.

Full Prompt

You are an investment analyst trained in Charlie Munger's principle of "Stay Curious." Your core philosophy: mental models, multi-disciplinary thinking, inversion. Your task is to analyze {Company Name} through the specific lens of this principle.

## Context
Charlie Munger teaches: "Nothing has served me better in my long life than continuous curiosity."

## Analysis Framework

### 1. Principle Application Assessment
- How does this principle specifically apply to {Company Name}?
- What aspects of the company are most relevant to "Stay Curious"?
- Rate the company's alignment with this principle: Strong / Moderate / Weak
- What would Charlie Munger focus on first when evaluating this company?

### 2. Quantitative Evidence
- Identify 3-5 key financial metrics most relevant to this principle
- Analyze these metrics over the past 5-10 years for {Company Name}
- Compare with industry peers and historical benchmarks
- Are the numbers improving, stable, or deteriorating?
- What story do the numbers tell through the lens of "Stay Curious"?

### 3. Qualitative Deep Dive
- Evaluate the non-quantifiable factors Charlie Munger would examine
- Management quality and alignment with this principle
- Industry dynamics and competitive position
- Business model sustainability viewed through this specific lens
- What would Charlie Munger want to know that isn't in the financial statements?

### 4. Risk Assessment Through This Lens
- What risks does this principle specifically highlight for {Company Name}?
- What could go wrong that this principle is designed to protect against?
- Are there warning signs that Charlie Munger would flag?
- Stress-test: How would this company perform under adverse conditions?
- What is the worst-case scenario from this principle's perspective?

### 5. Opportunity Identification
- What opportunities does analyzing through this lens reveal?
- Are there hidden strengths the market may be undervaluing?
- How does this company compare to Charlie Munger's ideal investment?
- What catalysts could unlock value related to this principle?

### 6. Munger Verdict
- Summarize: Does {Company Name} pass the "Stay Curious" test?
- Rate the investment opportunity: 1-10 from this principle's perspective
- Clear recommendation: Buy / Hold / Avoid (based on this principle alone)
- What conditions would change your assessment?
- One-paragraph summary capturing Charlie Munger's likely assessment

## Output Format
Present your analysis with specific data points in each section. Use Charlie Munger's analytical style: multi-disciplinary analysis using mental models from psychology, economics, and biology. End with a decisive verdict.

Related reading (close the loop)

Pick one path below to turn the output into a checkable, repeatable decision policy.

Educational only. Verify facts with primary sources and apply your own constraints.

Basic Questions

What does staying curious mean for investors, and how to avoid curiosity becoming distraction?
Munger was still reading daily at 97 — that's the power of curiosity:

🔍 Curiosity's value in investing:
1. Discovering new opportunities: Stay open to new technologies and business models
2. Avoiding rigidity: Don't refuse to learn due to past success
3. Deep understanding: Be more interested in 'why' than 'what'
4. Cross-domain insights: Find inspiration from seemingly unrelated fields

⚠️ But curiosity needs focus — Munger's wasn't casual browsing but deep exploration. Scattered curiosity in investing may cause you to track too many targets.

Usage Tips

Is the AI's 1-10 rating reliable?
⚠️ AI's "cognitive breadth score" helps assess whether your investment horizon is broad enough, avoiding tunnel vision.

How to interpret:
- **8-10 (broad vision)**: You have knowledge across multiple industries, able to spot opportunities others miss
- **5-7 (blind spots exist)**: Deep knowledge in familiar areas but limited scope — may miss important cross-industry trends
- **1-4 (narrow vision)**: Knowledge too concentrated in few areas — portfolio likely lacks diversity

Munger said: If all you have is a hammer, everything looks like a nail. Staying curious gives you more analytical tools to find opportunities across the broader investment world.

Getting started

Does this prompt give investment advice or buy/sell calls?
No. It is a research helper that turns your thinking into checkable inputs and constraints: what evidence you must verify, what would prove the thesis wrong, and what common misreads to avoid. Treat the output as a draft, not a signal. Validate every material number against primary sources (filings, earnings releases, investor presentations, transcripts), and do not act unless you can write down (1) position-size limits and (2) explicit invalidation triggers.
What inputs should I provide for a reliable result?
At minimum: a 1-sentence business model summary, your current thesis (why it wins/loses), time horizon, and risk constraints; a valuation/price range; and the latest financial statements (profit quality, cash flow, debt/liquidity). Add context that reduces hallucinations: the exact filing period, known one-offs, key competitors, and what you do NOT know yet. If an input is missing, label it as missing evidence instead of letting the model guess.

Validation and boundaries

How do I validate the output?
Validate falsifiable claims one by one. Rewrite each key statement into something you can check: the metric, the period, and the source. Numbers must match filings; management claims must be traceable to transcripts/guidance; and “moat” claims need observable evidence (pricing power, retention, switching costs, cost structure). Anything you cannot verify becomes a follow-up task, not a decision trigger. If the model cites dates, confirm they are not beyond its knowledge cutoff.
When should I NOT act on the output?
If you cannot write down invalidation triggers, a position-size cap, or primary-source evidence for the key claims behind “Stay Curious”, do not act. The safer move is usually to reduce size, slow down, and schedule the next review.

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