AI needs data. Lots of it. But the bigger problem isn’t just storing it it’s trusting it. If training data changes, gets poisoned, or disappears, models become unreliable. In a centralized world, you trust whoever hosts the dataset. In a decentralized world, you want stronger guarantees.
Walrus is useful here because it’s designed to store large datasets as blobs in a network that aims for verifiable availability and integrity. For AI builders, the value is: dataset references can be stable, availability is designed to be resilient, and systems can prove they’re using the dataset they claim.
Example: An AI agent marketplace could require agents to reference specific datasets stored on Walrus, so people can verify the agent isn’t secretly changing its “source of truth.”