What are DataDAOs?

The Aggregation Challenge

Individual user data isn't useful on its own: it must be pooled to be used for AI training. While centralized platforms can aggregate user data, DataDAOs enable users to export their data and pool it collectively, maintaining ownership through cryptographic mechanisms (and giving users ownership of something they may not have even realized they owned).

DataDAOs solve two core problems:

• Sybil resistance: Proof-of-contribution prevents users from creating fake identities to manipulate the dataset
• Data valuation: Each DataDAO has dataset-specific validation logic that both verifies authenticity and assigns scores to map heterogeneous data contributions to standardized data tokens

Core Components

Data Liquidity Pool (DLP) - Smart contracts containing:

• Proof-of-contribution validators that verify data authenticity and assign value scores
• Refinement structures that process raw data into queryable formats
• Access control contracts that enforce data token-gated permissions

VRC-20 Token Economics - Dataset-specific tokens earned for validated contributions and burned for access, with ERC-20 compatibility enabling programmable economic logic.

Governance Layer - Token-weighted decisions on validation criteria, economic parameters, and access policies.

Technical Flow

1. User contributes data → DLP validates and refines → user earns VRC-20 tokens
2. AI builder burns VANA + VRC-20 tokens → access granted to TEE environment
3. Secure runtime enforces DataDAO permissions during computation (only approved code can be run in the environment)
4. Value flows to token holders based on ownership stake

Key Properties

• Programmable validation through executable proof-of-contribution scripts
• Cryptographic attribution linking tokens to wallet contributions
• Granular access control enforced at the TEE level
• Democratic governance weighted by contribution volume

DataDAOs enable coordination of private datasets at scale while preserving individual ownership through cryptographic mechanisms. The ecosystem focuses on private datasets not publicly available, enabling sustainable economics for both builders and contributors.