The AI Era’s Structural Problem: Data Without Trust

Artificial intelligence has exposed a paradox at the heart of modern computing. Models are improving rapidly, yet the data they rely on is increasingly fragmented, opaque, and contested. Enterprises sit on valuable datasets but hesitate to share them. Public data is abundant but unreliable. Proprietary data is powerful but locked behind institutional walls.

This fragmentation is not merely technical; it is economic and legal. Data lacks a universal mechanism for proof of origin, usage rights, or compensation. As a result, entire industries operate on informal trust or centralized intermediaries. In an AI-driven economy, this arrangement becomes brittle.

Walrus enters this landscape with a provocative claim: data can be made trustworthy and monetizable without centralized custodians. By combining decentralized storage, verifiable data availability, and onchain coordination, Walrus seeks to turn data into a first-class economic asset—one that can be exchanged, priced, and audited across organizational boundaries.#walrus $WAL