# Moat Analysis

Defensibility frameworks for crypto products.

## Moat Categories

### 1. Network Effects
- More users → more value (e.g., liquidity pools, marketplaces)
- Strongest moat in crypto, but hardest to bootstrap
- Check: Do you have a credible path to critical mass?

### 2. Switching Costs
- Users invested in your system (positions, integrations, history)
- Weaker in crypto (composability makes switching easier)
- Check: What would a user lose by switching to a fork?

### 3. Data Advantages
- Proprietary data, indexes, or ML models trained on usage
- Growing moat in crypto analytics and agent intelligence
- Check: Does your product get smarter with more users?

### 4. Technical Complexity
- Hard-to-replicate engineering (ZK proofs, custom VMs, novel cryptography)
- Real but temporary — crypto is open source
- Check: How long until a good team replicates your core?

### 5. Distribution / Ecosystem Lock-in
- Integrations with wallets, protocols, and toolchains
- Strong in crypto because composability creates dependency chains
- Check: How many other products depend on your protocol?

### 6. Brand / Trust
- Reputation in security-critical applications
- Takes time to build, expensive to lose
- Check: Would users trust a no-name fork with their funds?

## Assessment

For the user's idea, identify:
1. Which moat type is most realistic?
2. How long until the moat becomes meaningful?
3. What's the biggest threat to the moat?