Take a close look at the evolution of Web3 over the past few years, and you'll notice a very glaring issue.

The asset layer is in place, smart contracts are in place, and various execution environments are available. But the most fundamental and easily overlooked aspect—the long-term preservation of data—has never been seriously addressed.

Most projects implicitly assume: data can be freely deleted, history can be compressed, and architectures can be torn down and rebuilt.

This logic is actually backwards.

The systems that truly survive are precisely those that cannot be torn down and rebuilt. Facebook's value isn't in its interface design, but in ten years of user interaction records. The value of gaming platforms isn't in engine optimization but in the accumulated characters, assets, and player stories. The same applies to powerful AI systems—not just the models themselves, but the behavioral data trajectories left during training.

This is the real barrier.

What Walrus aims to do is to turn "permanently preserved history" into a universal infrastructure.

Its approach isn't just simple file copying. The core is using erasure coding technology—breaking data objects into multiple fragments and generating redundant codes to disperse across network nodes.

What is the result of this?

You're not just storing 3 or 5 copies of data; you're constructing a mathematically recoverable structure. With current parameters, as long as 60% to 70% of the data fragments are available, the entire object can be fully reconstructed. This design fundamentally counters long-term uncertainty.

And long-term uncertainty is the daily reality of the Web3 world. Projects will die, teams will disperse, infrastructure will be updated, but data needs to survive. This is the problem Walrus wants to solve.