What is Codatta (XNY)?

Deep Dive

1. Purpose & Value Proposition

Codatta exists to address critical flaws in the AI data supply chain. According to its documentation, AI progress is often throttled by high data costs, questionable quality, and a lack of fair attribution (Codatta Docs). Contributors rarely retain ownership or share in the downstream value created by their data. Codatta's solution is an on-chain knowledge layer that "assetifies" data—turning raw information and labels into certified digital assets with immutable lineage. This creates a trusted foundation for AI and decentralized science (DeSci) projects to access reliable training materials.

2. The Royalty Economy Model

The project's core innovation is its automated royalty system. When a data asset is used—for training, fine-tuning, or inference—smart contracts meter the consumption. This usage data is recorded for provenance. Royalties are then split and paid out automatically to all stakeholders, including the original contributors, validators who verified the data, and backers who staked on its quality. The protocol supports flexible payment models like "train-now, pay-later" and performance-linked rewards, aligning incentives across the ecosystem.

3. Ecosystem & Key Differentiators

Codatta is not a standalone marketplace. Its key differentiator is acting as a pluggable backend infrastructure for other platforms. This allows existing data marketplaces and AI platforms to integrate Codatta's systems for fingerprinting, lineage tracking, access control, and royalty payouts without redesigning their entire application. The ecosystem includes a network of human contributors and AI agents for sourcing and validating data, with a focus on verticals like healthcare and robotics. Its multi-chain design, supported by integrations like Chainlink CCIP, enables cross-chain functionality for its native XNY token and data assets.

Conclusion

Fundamentally, Codatta is infrastructure for a fairer data economy, using blockchain to ensure creators are permanently rewarded for their contributions to AI. How effectively can its royalty model scale to meet the explosive demand for high-quality, ethical training data?