The integration of AI and Web3 is rewriting the rules of data governance. Generative AI models require massive amounts of data for training, but this data is often scattered across different platforms, mixed with privacy information and trade secrets, making collaboration and sharing difficult. The idea of decentralized AI sounds promising, but in reality, how can data be securely circulated? How can the source be traced? How to protect privacy while ensuring efficiency? These questions have hindered many projects.

Low-cost and high-performance storage is the breakthrough. Imagine an AI model that needs to process terabyte or even petabyte-scale datasets, repeatedly reading data for iterative optimization. Using traditional cloud storage? Costs would skyrocket. That’s why the industry is turning to decentralized storage solutions—they can solve large-scale data problems more economically.

Technically, the key to decentralized storage lies in controlling redundancy rates. Walrus adopts Red-Stuff two-dimensional erasure coding technology, reducing storage redundancy to 4-5 times, which is only one-third of traditional multi-replica schemes. To put it in cost terms: it can reduce costs by 98.6% compared to Arweave and by 75% compared to Filecoin. This cost advantage is significant; for long-term AI training tasks, it directly impacts the project's sustainability.

Therefore, Walrus is gradually becoming the infrastructure for decentralized AI, not just because it is inexpensive, but because it has built a complete data governance system—from storage, encryption, collaboration, to data monetization—forming a full chain. This removes major technical and cost barriers to the decentralized development of AI.