What this lesson solves

The first three parts focused on concepts, systems, and scenarios. The final part brings the discussion back to reality: how AI × Web3 products actually land, which patterns are worth learning from, and where they tend to fail.

Do not study a case by only asking “what did they build?”

When a project is worth studying, the important questions are:

1. What exact problem does it solve?
2. Why would users keep using it?
3. What role does AI actually play?
4. What does Web3 contribute that is hard to replace?

If these questions stay vague, case study analysis becomes superficial.

Case 1: Onchain research assistants

These products usually do things like:

• tracking protocol updates
• summarizing governance proposals
• explaining complex onchain behavior
• producing structured research summaries

What is worth learning

• AI is strong at processing large amounts of complex information
• onchain data is open and traceable
• the user value is direct: time saved on research

Where they fail

• inaccurate summaries distort decisions
• data collection and interpretation get mixed together
• without a clear user group, the product becomes feature-heavy but habit-light

Case 2: DeFi risk monitoring tools

These products usually center on:

• liquidation alerts
• position health monitoring
• protocol risk warnings
• unusual capital flow tracking

What is worth learning

• the value is clear because it helps users avoid loss
• AI is more useful for warning, summarizing, and explaining than for taking full control of capital
• Web3’s open data makes the monitoring layer more verifiable

Where they fail

• oversimplified risk models
• too many alerts, which destroys trust
• warning without suggesting action, which weakens the product

Case 3: Agent execution products

These products try to let the system:

• rebalance positions
• execute strategies
• complete payments or protocol interactions

What is worth learning

• if done well, the experience is dramatically stronger
• the shift from “give advice” to “help me complete the task” is a real product upgrade

Where they fail

• permission design is unclear
• execution conditions are too broad
• users do not understand why the system acted
• a single mistake can destroy trust

That is why the most important thing in these cases is usually the permission boundary, not the model name.

Case 4: AI + NFT or digital identity products

These products may focus on:

• generative collectibles
• dynamic characters
• membership identity systems
• interactive digital content

What is worth learning

• AI makes the content layer dynamic
• NFTs or onchain credentials make identity, ownership, and access clearer
• communities and content can form longer-term relationships

Where they fail

• generated content alone does not create durable value
• copyright and licensing are unclear
• weak community narrative causes fast decay

Four dimensions to use in every case study

When you study any AI × Web3 project, use these four dimensions consistently:

1. User problem

Is the project saving time, reducing risk, creating yield, or enabling a new experience?

2. AI role

Is AI doing interpretation, recommendation, execution, or generation?

3. Web3 role

Is Web3 providing assets, identity, transparency, incentives, or governance?

4. Failure point

Where is the product most likely to fail: data, model quality, execution, permissions, community, or business model?

A simple test

If you remove AI and the product barely changes, AI may only be decoration.
If you remove Web3 and the product still works almost the same, Web3 may only be a label.

The stronger AI × Web3 cases usually answer both:

• why AI is necessary here
• why Web3 is necessary here

Minimum takeaway

After this lesson, you should be able to explain:

• how to judge whether an AI × Web3 project has real product value
• how to study a case through problem, role, structure, and failure mode
• how different case types produce different kinds of value

What comes next

The next lesson turns from “how to read other projects” to “how to build your own smallest workable version.”

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