There is a phenomenon in the crypto industry: the true differences in choices often only become apparent over time.

At the beginning, everyone focuses on speed, selling points, and the completeness of the story. As long as it can run, it can attract attention. But as system complexity increases, the decisive factor is no longer new features, but whether the underlying system has issues and whether it can withstand repeated use.

Data storage is particularly easy to overlook.

Many projects treat data as something "immediately available for use," assuming it will always be accessible and the cost will always be manageable. But in reality? As the application scale grows, usage frequency increases, and demands become more complex, these assumptions can collapse in minutes. When storage solutions don't adapt to the current scale, maintenance costs and system risks will soar.

I view the Walrus project from this perspective.

It doesn't focus on maximizing short-term efficiency, but instead chooses a more thoughtful approach to long-term operation and maintenance. By splitting data structures and implementing distributed storage solutions, the system's reliability no longer relies on a single point but depends on overall coordination. This design may not seem impressive early on, but once the system is truly under pressure, its advantages become evident.

To use a metaphor: many solutions are like temporary shelters that can be quickly set up; this approach is more like building a long-term, maintainable infrastructure. It's not about impressing every time you use it, but ensuring stable and reliable operation under various conditions.

Now that AI applications are becoming normalized, these differences will only become more obvious.