> For the complete documentation index, see [llms.txt](https://aicraft-fun.gitbook.io/aicraft.fun/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://aicraft-fun.gitbook.io/aicraft.fun/case-study-partnership-fizen-super-app/ai-agent-tokenization-a-new-model-for-value-creation.md).
# AI Agent Tokenization: A New Model for Value Creation
One of aiCraft’s most innovative features is its AI agent tokenization model, which allows creators to monetize AI in a structured and sustainable way. Instead of merely building AI agents without direct revenue potential, aiCraft introduces a clear economic framework based on ownership and revenue distribution.
**How AI Agent Tokenization Works**
* Ownership Distribution: Creators receive 30% of the total token supply, with a 9-month vesting period to ensure long-term commitment to the AI agent.
* Pre-market Sale & Liquidity Provision: 20% of the total supply is allocated for pre-market sales to attract early investors, while 10% is dedicated to DEX liquidity, ensuring smooth trading.
* Business & Platform Incentives: Businesses like Fizen receive 10% of the token supply, encouraging them to integrate AI agents into their ecosystems. aiCraft also retains 10% to sustain platform development.
* Community & Governance Engagement: 10% of tokens are reserved for KOLs & community growth, 8% for voters, and 2% for AI artists, ensuring active participation and long-term ecosystem development.
Tokenomics for Travel Ai Agent (Fizen Case Study)
**Why Tokenization Matters for Fizen and Creators**
* For Fizen, AI agents are not just tools but also economic assets that generate real value through tokenization and business integration.
* For Creators, tokenization enables a long-term revenue stream, not only from selling AI agents but also from shared ownership with the community, allowing collaborative growth.
---
# Agent Instructions
This documentation is published with GitBook. GitBook is the documentation platform designed so that both humans and AI agents can read, navigate, and reason over technical content effectively. Learn more at gitbook.com.
## Querying This Documentation
If you need additional information that is not directly available in this page, you can query the documentation dynamically by asking a question.
Perform an HTTP GET request on the current page URL with the `ask` query parameter, and the optional `goal` query parameter:
```
GET https://aicraft-fun.gitbook.io/aicraft.fun/case-study-partnership-fizen-super-app/ai-agent-tokenization-a-new-model-for-value-creation.md?ask=&goal=
```
`ask` is the immediate question: it should be specific, self-contained, and written in natural language.
`goal` is optional and describes the broader end goal you are ultimately trying to accomplish on behalf of the user. GitBook uses it to tailor the answer towards what is most useful for that goal.
The response will contain a direct answer to the question and relevant excerpts and sources from the documentation.
Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.
