The Data Storage Challenge in the AI Era May Have a New Solution

With the explosion of AI, a real problem is becoming increasingly prominent—where to store the massive amounts of AI data?

Relying on centralized cloud services? The costs are too high, and there’s also the risk of single points of failure. Even more concerning, data can be locked by cloud providers, with control entirely in their hands.

At this point, decentralized storage becomes another viable path. Compared to traditional solutions, its advantages are straightforward: lower prices, guaranteed stability, and most importantly, true data ownership belongs to the users.

Take AI development as an example; training datasets are the lifeblood. Using a distributed storage solution with erasure coding technology to disperse redundancy can ensure long-term data availability without fear of single points of failure. This is crucial for models that require repeated training and frequent iterations.

More importantly, unpublished training data and sensitive commercial information can also be protected through privacy measures. No need to worry about leaks or third-party eavesdropping.

In the long run, intermediate data generated by AI, cold backups, and even the models themselves could flow into these decentralized networks. Once this sector explodes, the network value carrying this data will soar. In other words, whoever controls the infrastructure at the data layer will hold the discourse power in the AI era.