Regarding storage, many people's first reaction is to save space costs. But Walrus took a completely different approach—it aims to solve the problem: how to keep data alive even in the worst-case scenarios.

The design of this protocol is quite interesting. Data is not stored entirely on a single node but is broken into dozens of fragments scattered across the entire network. As long as more than 35%-40% of these fragments are retained, the entire data can be fully reconstructed.

In other words, it’s not betting on a single node remaining stable, but betting that the overall network won't crash by more than 60%-65% at the same time. These are two completely different risk assumptions.

What does traditional multi-replica schemes fear? Single points of failure. Losing both copies out of two means the data is permanently gone. But with Walrus, you can lose 20 nodes, 30 nodes, or even more, as long as the remaining fragments are sufficient, the data remains rock solid.

What’s the cost? It’s quite straightforward. To achieve this level of fault tolerance, the redundancy factor needs to increase to 4x-5x. That is, if you want to store 1GB of data, the actual network space required might be 4-5GB.

Seems wasteful? Not entirely. This extra cost buys reliability. The core logic is clear: Walrus is not designed to minimize storage costs but to optimize performance in worst-case scenarios. Such a design may not be suitable for ordinary file storage, but for critical data that, if lost, could trigger chain reactions—like blockchain infrastructure or cross-chain verification data—it becomes extremely necessary.