The explosive growth of AI has pushed data storage to the forefront. Imagine those training datasets of hundreds of GB, 4K/8K video materials, and ultra-large parameter models—these pose real and severe challenges to storage system performance, capacity, and stability.

Walrus Protocol sees this opportunity. The core idea of this project is straightforward: to design from the ground up for the special needs of large binary files (blob data).

Traditional centralized storage solutions come with a bunch of problems. Privacy is easily compromised, server failures mean total data loss, handling large files is painfully slow, and costs are exorbitant. AI training demands extremely high real-time data retrieval, which centralized solutions simply can't keep up with.

Walrus uses the Red Stuff 2D erasure coding system. The name sounds complex, but the actual concept is simple: divide files into chunks, add repair data, ensuring recovery even if some data is lost, while significantly speeding up storage and retrieval. Handling small files has already been proven feasible, and for videos of several GBs, the project team is continuously optimizing retrieval latency.

But this is not the most critical part. The most important aspect is—**data ownership**.

In centralized storage, your data isn't truly yours. Ownership is ambiguous, usage rights are unclear, and privacy can be compromised at any time. Walrus's logic is to anchor data ownership with blockchain, allowing users to truly own their data and control who can use it and how. Once data ownership is clarified, real data value can be unleashed—this is crucial for building a healthy AI data market.