Many judgments can only be truly understood when the system is overwhelmed.

When everything is smooth, any solution works. But once in real scenarios, node fluctuations and surges in concurrency reveal all the differences.

Currently, blockchain and AI are gradually merging, and the data layer is in an interesting position. In the early stages, everyone focused on models, applications, and user experience, treating data as an air-like commodity that is taken for granted. But when applications need to operate stably and iterate continuously, questions about where the data resides, how to tune it, and whether it can be trusted can no longer be ignored.

Because of this, I started to revisit Walrus's approach.

It didn't aim to grab the most prominent position. Instead, it chose a more pragmatic path—not chasing speed that looks good now, but asking: when scaled up, nodes fail, and more users join, can the system still remain stable?

This kind of thinking isn't very popular at first. It doesn't give people strong emotional shocks nor relies on piling up new features to attract attention. But in the long run, this restraint becomes an advantage.

The most critical infrastructure in reality often works silently in places no one notices. Their mission isn't to be discussed every day but to avoid failure in emergencies. The data layer is exactly such a thing. Once data is lost, unavailable, or costs explode, all the applications built on top will suffer. This is the real reason why it deserves serious attention.