AI and Web3 integration has brought an increasingly urgent question — what do we use to train AI? With the flood of synthetic data and rampant misinformation, can models built on these be reliable? Clearly, no.

This is also why projects like Walrus are gaining attention. Its approach is straightforward: rather than continuing to rely on centralized data sources, it encourages all network participants to collaboratively collect and verify real-world data.

Don’t think participation is difficult. The cleverness of Walrus lies in its "lightweight" design — you don’t need high-performance servers; just a smartphone, a home router, or even a Raspberry Pi to join the network. Upload bandwidth, storage space, or sensor data — all contributions are validated on-chain and rewarded with $WAL tokens. As a result, participants can grow from zero to one hundred, greatly enhancing the network’s resilience.

More importantly, data quality. Raw data collected from traffic cameras, environmental sensors, and IoT devices is encrypted by Walrus nodes and stamped with spatiotemporal proofs, ensuring immutability and traceability. This is the true "golden fuel" needed to train high-quality AI models.

The current AI industry is troubled by synthetic data and hallucinated facts, making real data a scarce resource. If Walrus can do this well, the value of a trusted data layer could surpass expectations.