I often discuss a topic with industry insiders: the biggest pitfalls in decentralized storage projects are not really about how troublesome initial uploads are. The real killer is this—after running for a year or two, nodes come and go, hard drives fail, network fluctuations occur, data centers migrate... these seemingly routine "wear and tear" gradually eat away at profit margins, and eventually the project is forced to raise prices or simply reduce security guarantees. This vicious cycle has almost become an industry curse.

Recently, I looked into Walrus's recovery mechanism and found that it takes a different approach. Its Red Stuff design has a key feature: the bandwidth for data loss recovery is strictly proportional to the amount of data lost—in other words, you lose a certain number of data blocks, and you recover exactly that amount, without having to re-transfer the entire dataset just to fix a few fragments. It sounds simple, but what does this imply?

It means that the cost of recovery decreases inversely as the number of participating nodes increases. The technical specifications clearly state: each node's bandwidth contribution for recovery can be compressed to the level of O(|blob|/n). The larger the network and the more nodes there are, the lower the unit cost becomes. This is extremely critical for open networks—because nodes naturally come and go, if recovery costs don’t scale down with size, eventually an absurd situation will occur where "recovery bills are more expensive than storage fees."

What does this logic translate to in practical applications? First, price stability. Recovery won't become an operational black hole, so projects have no reason to frequently raise prices or cut redundancy to "extend their lifespan." Second, improved availability. The recovery process becomes lighter and more routine, enabling the system to patch data gaps faster, significantly reducing the chances of users encountering "read failures." Going deeper, this is what makes a truly long-term infrastructure—whether you're storing AI training data, blockchain game resources, frontend pages, or archival content—what's needed is not just one-time success, but reliable operation over five or ten years.

Therefore, when I look at Walrus, I focus not only on "how long it can store data" but also on "whether it can be repaired at low cost when it breaks, and whether it becomes more stable over time." Red Stuff's design of decreasing recovery costs with network scale essentially lays the foundation for the project's long-term vitality. This design philosophy is the true logic behind sustainable decentralized storage.