# Overview

The Data Anchoring Token (DAT) is the foundational standard of the LazAI and Alith ecosystems.

It enables contributors to share privacy-sensitive datasets, AI models, or computation results while retaining full ownership, control, and economic rights.

DATs act as on-chain certificates of contribution, linking data provenance, access permissions, and value distribution directly to blockchain-based records.

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### Key Capabilities

Each DAT encodes three primary dimensions of data ownership and utility:

| Capability            | Description                                                                                                       |
| --------------------- | ----------------------------------------------------------------------------------------------------------------- |
| Ownership Certificate | Records verifiable proof of contribution or authorship for datasets, models, or computation results.              |
| Usage Rights          | Defines how and where the data can be accessed — for example, by AI services, model training, or agent execution. |
| Value Share           | Assigns proportional economic rewards to contributors based on usage, staking, or licensing activity.             |

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### Why AI Needs a New Token Standard

AI data is dynamic, composable, and frequently reused across models and tasks — properties that traditional token standards like ERC-20 and ERC-721 don’t fully support.

DAT introduces a semi-fungible token (SFT) model designed for modularity, traceability, and partial ownership of AI assets.

#### Comparison Summary

| Token Type             | Description                                                                              | Limitation                                              |
| ---------------------- | ---------------------------------------------------------------------------------------- | ------------------------------------------------------- |
| ERC-20 (Fungible)      | Fully interchangeable tokens, ideal for currency or credits.                             | Cannot represent unique datasets or ownership records.  |
| ERC-721 (Non-Fungible) | Unique tokens for singular assets (e.g., one-of-a-kind NFTs).                            | Lacks divisibility and modularity for AI workloads.     |
| DAT (Semi-Fungible)    | Hybrid model combining ERC-20 and ERC-721 traits — divisible, composable, and traceable. | Tailored for data provenance and AI-specific workflows. |

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### How DAT Works

1. Data Contribution:

   A user encrypts and uploads a dataset or model output through LazAI’s privacy framework.
2. Metadata Anchoring:

   A smart contract logs encrypted metadata, provenance proofs, and ownership claims on-chain.
3. Verification:

   Validators or trusted enclaves (TEE) confirm authenticity and compliance.
4. Tokenization:

   A DAT is minted as a semi-fungible token representing the data’s rights, access rules, and value distribution.

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### Technical Highlights

* Standard: Semi-Fungible Token (SFT)
* Purpose: Tokenize AI datasets, models, and computation outputs
* Blockchain Layer: LazAI Testnet (EVM-compatible)
* Supports: On-chain provenance, privacy-preserving validation, and composable ownership logic

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### Benefits

| Benefit               | Description                                                                      |
| --------------------- | -------------------------------------------------------------------------------- |
| Verifiable Provenance | Every dataset or model is cryptographically tied to its origin and contributor.  |
| Data Monetization     | Contributors can receive automatic rewards or royalties for approved AI usage.   |
| Privacy by Design     | Encryption and TEE validation ensure that raw data remains confidential.         |
| Composable Ownership  | DATs can be merged, split, or referenced across multiple models or applications. |

***

### Related Concepts

* DAT Architecture →![Attachment.tiff](file:///Attachment.tiff)
* Lifecycle & Value Semantics →![Attachment.tiff](file:///Attachment.tiff)
* Contribute Your Data →![Attachment.tiff](file:///Attachment.tiff)


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