When discussing storage protocols, everyone talks about price, speed, and TPS, but in systems that have been running for over a year, these are not the most painful issues.

What truly troubles project teams is a simple and straightforward question: Can my data survive long-term?

This is not empty talk. For example: a medium-scale on-chain application needs to perform 3 to 6 state updates per day, each with data sizes between 30 to 80KB. Over a year, this historical state data accumulates to about 30GB. Plus logs, checksums, version records, and other related data, which often doubles this amount.

The key point is, unlike cold data that can just sit there and sleep, these data need to be accessible at any time for rollback, verification, or reuse. This becomes a continuous burden.

Now, Walrus's approach is quite interesting. It doesn't treat "updates" as exceptions but considers them part of daily operations. In other words, in this system, an object isn't replaced but continuously evolves—its reference remains unchanged, its content updates at any time, and the entire history can still be verified.

Based on current testnet data, several notable features are already visible: support for MB-sized large objects, the ability to update infinitely without changing the object ID, and stable availability above 99% under multi-node redundancy.

My straightforward view on this direction is: this isn't about short-term performance optimization but targeting the real pain point—long-term usability.

But there's also a risk hanging over this: if the incentive mechanism for nodes isn't healthy enough, this long-term accumulated data could become a burden for the system. Ultimately, it still depends on the ecosystem itself to prove its viability.