In the current era of generative AI explosion, data storage faces new challenges—massive training datasets, generated videos, and model weights, where should they be stored? Traditional cloud service providers' centralized solutions expose drawbacks such as single points of failure, censorship risks, and high costs. Walrus Protocol offers an alternative possibility: a decentralized storage network built on the Sui blockchain, specifically optimized for AI large-file scenarios.

The core technology of this system lies in distributed Blob storage. Simply put, Walrus encrypts and shards your data, then disperses it across nodes worldwide. The benefits of this approach are obvious—no risk of single points of failure, data censorship resistance, and each file can be verified on-chain to prove its integrity. In contrast, traditional cloud services often fall into the trust dilemma of "I say it's complete, so it is," whereas Walrus provides you with ironclad guarantees through cryptography. Cost-wise, it's also not in the same league—developers uploading data can enjoy significant cost advantages and faster read speeds.

The ecosystem's operation relies on the WAL token. With a total supply of 5 billion, this token plays multiple roles: paying for storage fees, incentivizing operation nodes, and participating in community governance. Holders can not only receive transaction fee discounts but also vote on protocol upgrades, truly participating in project decisions. The stable mechanism design anchors WAL to real-world value, avoiding speculative volatility from interfering with practical applications.

For developers, this opens up new possibilities. Building data markets based on Walrus becomes easier, resource sharing costs decrease, and the barriers to innovation are lowered. Ordinary users are no longer passive data providers but true data owners. This is what decentralized storage should look like—allowing every participant to benefit from it.