Walrus this project has never been easy to understand because it is not targeting current pain points, but rather the problems that Web3 will inevitably need to solve in the future.

What is Web3 busy with at this stage? Nothing more than transaction speed, Gas fees, liquidation efficiency, MEV issues, cross-chain synchronization—these are all optimizations at the financial system level.

But think about it, a network that can truly serve hundreds of millions of users, its core competitiveness is not in finance, but in data.

In the Web2 world, how much data do you generate every day? Videos, images, texts, various collaboration records, model parameters, historical states, behavior tracking—these are the real entities of the digital ecosystem. Transfer records have become a minor part.

Walrus's fundamental assumption is: Web3 cannot stay forever at the settlement layer; it will inevitably evolve into a content layer. Once it enters the content layer, the requirements for storage change completely.

In the past, storing a 3KB JSON file was enough; now, handling a 30MB object is necessary. Previously, it was "store it and be done," now it is "must be able to recover, verify, and preserve permanently."

This is the fundamental difference between Walrus and most storage protocols on the market.

Other storage solutions focus on: how to store data more cheaply.

Walrus's approach is: when data volume explodes exponentially, how can the system avoid crashing.

Therefore, it adopts erasure coding technology. An object is divided into dozens of data fragments; as long as you gather a certain proportion of these fragments, you can fully restore the original data. What is the benefit of doing this? In theory, it can reduce data redundancy from 3 times to nearly 1 time.