From real-world needs—why is Walrus's token economics built on 'necessity'?

To understand Walrus's potential, we need to move beyond abstract discussions about 'storage' and instead examine the imminent, concrete data demand scenarios. Let's do a simple projection: a medium-sized Web3 social application with 100,000 daily active users, assuming each user generates only 20KB of text, images, or status updates per day (a very conservative estimate). Over a year, this application would accumulate over 700GB of 'hot' or 'warm' data that must be permanently stored and readily accessible. This is not static archiving—this data must be frequently read, verified (e.g., proving that a specific piece of content was posted by a particular user at a specific time), traced back, and integrated into new applications.

If the scenario shifts to blockchain games or AI protocols, the data scale will easily leap to terabytes or even petabytes. The state of the game world, the metadata of every dynamically generated item, the training data and inference records of AI models—these are not caches that can be discarded at will, but rather 'digital assets' that constitute the core value of digital assets, which must be permanently retained and available for auditing. These demands are real and pressing, pointing to a conclusion: the mainstream Web3 applications of the future will be data-intensive.

It is this deterministic data growth driven by application scale that forms the cornerstone of the Walrus value model. The Walrus token $WAL is not merely a decorative piece in the market or a simple governance tool; its core function is to coordinate the real resource consumption within the network. Storage space is not free, continuous verification and proof of data are not free, and ensuring that data is not lost over a time scale of years or even decades is certainly not free. When a network needs to handle millions of access requests daily, write terabytes of new data, and continuously verify the integrity of this massive data, these actions themselves generate significant hardware, bandwidth, and operational costs. $WAL is the medium for paying these costs and incentivizing nodes to provide reliable services.

Therefore, its demand function is very clear: it is directly positively correlated with the total amount and frequency of data stored and accessed in the network. A medium-sized application generates 5GB of core data daily, which amounts to nearly 2TB in a year. When ten or a hundred such applications choose Walrus, the demand does not simply add up but may show exponential growth under the catalysis of network effects. This is fundamentally different from token models that rely on trading sentiment and narrative hype. The market may temporarily overlook it, but as long as Web3 applications develop towards data intensification (which is almost inevitable), the demand for a reliable on-chain storage layer and its pricing medium (tokens) will grow solidly and continuously, just like the demand for land in urban development. This endows Walrus with a characteristic of 'anti-fragility': its value does not come from being believed, but from being used.@Walrus 🦭/acc $WAL #walrus