> For the complete documentation index, see [llms.txt](https://docs.lazai.network/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://docs.lazai.network/lazai-workflow-and-runtime/dat-specifications-and-flow/dat-token-lifecycle.md).

# DAT Token Lifecycle

The LazAI AI Data Economy is built around programmable AI licensing, ownership trading, and dataset/model composability. 

#### Token Minting & Ownership Structure

DAT tokens can be minted by AI data contributors, model creators, or AI agents via a structured issuance process:

1. **AI Data Submission:** The data provider submits dataset metadata to LazAI Flow.
2. **Validation & Anchoring:** The Quorum-based verification process confirms authenticity, generating a hashed dataset fingerprint.
3. **Token Minting:** The system mints a DAT token, encoding: Ownership rights, Usage-based pricing models, Access control permissions
4. **Revenue Distribution Rules:** The DAT contract automatically distributes revenue when datasets/models are used.

#### AI Data & Model Exchange

DAT supports AI dataset licensing and model trading through fractional ownership and dynamic pricing mechanisms.

<table data-header-hidden><thead><tr><th width="255.9140625"></th><th></th></tr></thead><tbody><tr><td><strong>Feature</strong></td><td><strong>Implementation</strong></td></tr><tr><td>Fractional Ownership Trading</td><td>Users can sell or trade partial ownership of datasets without full asset transfer.</td></tr><tr><td>AI Model Evolution</td><td>Developers can fork datasets/models while ensuring original contributors retain revenue-sharing rights.</td></tr><tr><td>Permission-Based Dataset Licensing</td><td>Data owners set usage conditions (read-only, trainable, composable), enforced via on-chain access control.</td></tr><tr><td>Dynamic AI Marketplace</td><td>AI models and datasets are tokenized &#x26; exchanged as DAT, enabling seamless monetization of AI assets.</td></tr></tbody></table>


---

# 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 by asking a question.

Perform an HTTP GET request on the following URL with the `ask` and `goal` query parameters:

```
GET https://docs.lazai.network/lazai-workflow-and-runtime/dat-specifications-and-flow/dat-token-lifecycle.md?ask=<question>&goal=<user_goal>
```

`ask` is the immediate question: it should be specific, self-contained, and written in natural language.
`goal` is what the user is ultimately trying to achieve, the reason they need the answer. Sharing it helps GitBook give you a better, more relevant answer. A goal is most helpful when it describes the outcome the user wants rather than restating the question. For example, with `ask=how do I create an API token`, a goal like `automate deployments from our CI pipeline` lets GitBook tailor the answer to that use case.

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.