I used to be quite cautious about the concept of "off-chain data," always feeling that as long as the data isn't on the chain, security and trustworthiness are compromised. It wasn't until I carefully studied Walrus's implementation that my intuition gradually changed.

Now I understand that the real issue isn't where the data is stored, but whether the on-chain system can impose constraints on it.

Think about high-frequency applications or those with complex logic scenarios—piling all data on-chain is essentially a self-penalizing efficiency measure. Sui's performance is strong, but it can't handle the pressure of infinitely growing data. The brilliance of Walrus lies in this—it doesn't deny the necessity of off-chain data but instead designs a way for the on-chain system to verify whether the off-chain data has been tampered with. Once this logic makes sense, the entire scheme becomes very engineering-oriented and pragmatic.

From a different perspective, Walrus essentially shifts the "trust" focus from "where the data is" to "the verification mechanism." You don't need to obsess over whether the data is on-chain; just confirm whether it has been tampered with or replaced. Once this idea is understood, off-chain data transforms from a forced fallback into a more reasonable division of resources.

This shift in understanding also changed my view of Walrus. It's not about giving developers an excuse to slack off but about helping the entire ecosystem allocate truly limited resources where they should be used. In a sense, this project helps the Sui ecosystem shed unnecessary burdens, allowing applications more space to grow naturally. This "seems less radical but actually very practical" design philosophy is also why I have been paying close attention to this direction.