In the conversation about AI, data is often treated as passive raw material: it is collected, copied, and consumed. The problem is that when models scale, this approach stops working. Not due to a lack of data, but due to a lack of control.
Walrus proposes a subtle but important shift: data is not just stored files, they are governable assets. They persist over time, can be verified, and their access can be conditioned without moving or duplicating them.
This opens the door to new flows: datasets shared between applications, models that train on available but not always readable data, and markets where the value is not in copying information, but in referencing it under clear rules.
Here, $WAL does not incentivize mass consumption of data, but supports the infrastructure that allows that data to exist, be verified, and be governed at scale. @Walrus 🦭/acc does not optimize for accumulating data, but for it to be usable without losing control.
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