# Why Reppo

Modern AI systems face a paradox: models grow ever more capable, but their long-term generalization is bottlenecked by the scarcity of high-quality, domain-specific data. High-quality human preference & alignment training data is overwhelmingly only accessible only to large labs and well-funded startups today, leaving independent researchers and developers without options.&#x20;

Reppo aims to democratize access to this class of data by enabling anyone in the world to curate, build, and access AI training data — lowering the barrier to entry while increasing fairness in value accrual to everyone involved.

Reppo's permissionless and decentralized network uses gamification mechanics and cryptoeconomics to coordinate source data creators and owners, data labelers and annotators, and AI developers looking to consume that data. It is designed to prevent the value leakage common in today's vendor-based model.

We usually question how the largest AI labs source training data only when a major lawsuit or newsworthy event surfaces. Access to this data remains highly concentrated, and data vendors are often seen engaging in [shady practices](https://medium.com/@lychee-mango/generative-ai-companies-exploitative-practices-b3201141e763).

AI training data providers and vendors often lack transparency, underpay data creators and annotators, and cross legal and ethical boundaries that later [come back to bite companies](https://www.theguardian.com/technology/2025/sep/05/anthropic-settlement-ai-book-lawsuit). The process of generating and delivering AI training data also lacks cryptographic verification of domain-expert credentials and often misses the mark on incentive alignment.

Our team set out to build a self-serve platform powered by crypto incentives to solve these bottlenecks. The goal is to give AI researchers, startups, and small teams a seamless way to source and access datasets on demand using on-chain guarantees and incentives.
