In 2026, AI agents have become a hot topic in the industry, and distributed storage solutions are beginning to play important roles. A few real-world examples make this clear—Talus uses it to store agent operational memories, while io.net uploads training datasets. Its core advantage is that after file sharding, data is dispersed across nodes, so even if a node fails, data integrity remains unaffected.

Personally, I believe that for AI to truly operate autonomously, data must be decentralized. Otherwise, no matter how intelligent the model is, it will ultimately be controlled by centralized servers.

From a daily usage perspective, what attracts me most is the ability to build small tools at any time, with data uploaded directly and easy invocation. I agree with a community discussion point—that privacy is not optional but a fundamental requirement. After Seal integration, permission rules are more flexible.

For a concrete example, the AI content I generate is stored securely, so I no longer worry about a platform deleting it due to policy changes. Regarding tokenomics design, the more frequently a service is used, the more tokens are burned; part of the storage fee is directly reflected in token consumption. This logical approach is quite practical.