Regarding the Walrus protocol, the most discussed questions online are nothing more than—how is the performance, are the costs expensive, and how long can the data be stored. But actually, these are the wrong questions.

The real misunderstanding lies elsewhere.

Many people habitually place Walrus in the storage track, competing with solutions like Filecoin and Arweave: who is cheaper, faster, more reliable. This comparison framework itself is misguided. Walrus is not competing with these storage protocols at all. Its true opponent is "forgetting."

Have you noticed— in most systems, forgetting is the default option. Old data is constantly overwritten, history is compressed, intermediate states are deleted, and in the end, only a "final result" remains. No problem for small projects, but once on-chain AI, long-term governance frameworks, or complex financial derivatives are involved, this kind of forgetting becomes a pitfall. You have no idea where the current state came from, whether this result is reasonable, or if the history has been altered. In the end, you can only passively accept a black box.

Walrus does the opposite. It says no to default forgetting. In the design philosophy of this protocol, history is not a burden but the most valuable thing in the system.

How does it do this? By using Red Stuff erasure coding combined with Sui's coordination mechanism, Walrus turns blob data into programmable, verifiable native assets. The key is—it preserves the complete evolution path. You can not only see what it looks like now but also trace how it evolved step by step to the present. What is the cost? Short-term efficiency will definitely take a hit. What do you gain? Something extremely valuable—the long-term interpretability and causal traceability. This is crucial for AI data provenance, on-chain auditing, and system evolution over decades.

Fast projects pursue speed and simplicity, but true long-term systems pursue stability and understandability. Walrus is born for the latter.

It doesn't aim to make access faster—it aims to ensure you never lose understanding of the system. Imagine a complex on-chain ecosystem running for ten years, experiencing millions of state changes. What can you still believe in at that point? Results can be easily forged, but the complete process is very hard to fake. Walrus elevates the "process" to a first-class citizen. What it preserves is not just data, but the causal relationships themselves.

It may seem unnecessary now. But when you really want to build a long-term, tamper-proof Web3 system, you'll find—there are actually not many options left.