On-chain development's biggest fear isn't performance bottlenecks, but deadlocks in data architecture.

Many projects move fast initially, but half a year later they start spinning their wheels. It seems like they're not short of money, but actually their data structures have already locked up—once business logic is laid down, every iteration requires digging three feet deep. Changing a single field might require reworking the entire application layer. This is the real story behind how many on-chain projects have gone from rapid growth to stagnation.

Walrus's approach is quite interesting. It accepts a fundamental reality: you simply can't get the design right from day one. Rather than stubbornly sticking to your original vision, it's better to keep data structures active and flexible.

From its technical design perspective, the core is the object-level storage model. Each data object has an independent identity, and updates aren't patches but natural evolution. Based on testnet performance, the system supports multiple updates to the same object, individual objects can scale to MB levels, and can be jointly maintained by multiple nodes to ensure availability.

This leaves developers room to respond—you don't need to predict what will happen three years down the line on day one. When requirements change, the data can evolve with them. Of course, what's the cost? This flexibility will inevitably be abused, requiring the application layer itself to maintain constraints.

But honestly, for real-world software, the ability to correct course is itself valuable. Compared to being locked down by architectural decisions, having the margin to course-correct is already a major step forward.