When AI Data Becomes Evidence, Who Gives Us the Receipts?
In digital world AI is turning data into evidence.A photo, a PDF, a dataset, even a “model output” now has to answer hard questions: Is this real? Who touched it? Was it edited? Did a model actually generate this, or is someone faking the origin? In the old internet, we solved this with authority like platform watermarks, company databases, “trust me bro” logs. That works until incentives shift and the logs quietly change.
What I like about the Walrus direction is that it treats provenance like a problem, not a compliance checkbox. You don’t just store a blob. You store it in a way that keeps its story intact with its authenticity, traceability, integrity, and availability. That matters most if you’re publishing documentary material, building AI training sets that shouldn’t be polluted, or proving which model produced which specific output.
The real point is simple: the next wave of apps won’t just need storage. They’ll need receipts.
My critique: Walrus can store the receipts, but the ecosystem has to agree to read them. Without shared standards and easy tools, provenance stays niche instead of becoming default.
