How Vana Works

Today's AI is built on data that users don't control or benefit from, leaving valuable private datasets locked away in corporate silos. Users generate enormous amounts of data but have no ownership stake in the AI systems trained on it.

Vana solves this through programmable data ownership - a protocol that maintains individual data sovereignty while enabling collective creation of AI. The system provides two complementary approaches:

Data Collectives (DataDAOs)

For users who want to pool their data for AI training and earn from collective value creation.

VRC-20 Data Tokens: Programmable Data Ownership

VRC-20 tokens make data ownership liquid and programmable. Unlike regular tokens, VRC-20s are cryptographically bound to specific datasets through proof of contribution mechanisms. When users contribute data to a data liquidity pool (DLP), they receive VRC-20 tokens representing their ownership stake. These tokens are:

• Earned through contribution: Users receive tokens by providing proof-of-contribution validated data to Data Liquidity Pools
• Burned for access: AI builders must burn both VANA and VRC-20 tokens to access datasets
• Fully programmable: ERC-20 compatible, enabling complex economic logic and DeFi integration
• Attribution-preserving: Each token traces back to specific data contributions and evm-compatible wallet addresses

This creates liquid markets for data assets while maintaining granular attribution to individual contributors.

Secure Runtime with Granular Enforced Permissions

The secure compute layer enables computation on private data while enforcing user-specified permissions. Built on Trusted Execution Environments (TEEs), the runtime:

• Enforces access controls: Only executes on data when proper tokens are burned and permissions granted
• Preserves privacy: Raw data never leaves the encrypted environment during computation
• Enables granular permissions: Users can specify exactly what data fields can be accessed via specific SQL queries on their structure data
• Provides verifiable execution: All compute jobs require blockchain approval and results are written onchain

For example, a researcher can access users' health history to train a recommendation model, but the TEE ensures they never see individual user identities and that raw data never leaves the secure environment.

Individual Data Sovereignty (Data Portability)

For users who want to maintain personal control while enabling personalized applications.

Personal Servers and Federated Control

Users maintain sovereignty through personal compute environments that store encrypted data and execute permissioned operations. Similar to early visions like Urbit, this creates a federated system where:

• User-controlled environments: Data stays in personal servers (self-hosted or trusted providers)
• Deterministic identity: Cryptographic keys derived from user's EVM wallet
• On-chain permissions: Granular access control recorded on blockchain
• Cross-platform integration: Combine data from multiple sources tied to user's wallet

Privacy-Preserving Computation

Applications can perform computations on personal data through cryptographically verified permissions:

• Permission-based access: Apps request specific operations and data scope
• Computation without exposure: Raw data never leaves personal server
• Transparent control: All permissions recorded onchain and revocable

How They Work Together

Vana creates a unified ecosystem where users can choose how to leverage their data based on their goals:

For AI Training: Users contribute to DataDAOs, receive VRC-20 tokens representing ownership, and benefit when AI builders burn those tokens to access datasets. The secure runtime ensures builders can only use data according to collectively-granted permissions.

For Personalization: Users grant apps direct access to personal data through onchain permissions, enabling rich personalized experiences while maintaining sovereignty over their personal servers.

Interoperability: Data added through portability can later be contributed to DataDAOs, giving users flexibility to use their data individually or collectively.

This creates a unified system where:

• Users retain ownership and earn from their data's use across both pathways
• AI builders can access valuable datasets previously locked in silos through collective pools or individual permissions
• Privacy is preserved through cryptographic enforcement - TEEs for collective data, personal servers for individual data
• Economic value flows directly back to data contributors through tokens or direct app relationships

The result is user-owned AI - where users control access, benefit from the value created, and can choose between collective and individual data strategies based on their needs.