Walrus and AI: When Storage Becomes the Bottleneck
Most AI failures don’t start with bad models. They start with data logistics. Centralized storage struggles once datasets hit real scale, not just in size, but in access, redundancy, and cost. Walrus quietly targets that weak point. By distributing encrypted shards across a decentralized network, it removes single points of failure that plague large AI pipelines. The trade-off is clear: latency management becomes more complex, but resilience improves dramatically. For teams training long-running models or sharing datasets across regions, that reliability matters more than raw speed. Walrus doesn’t make AI smarter. It makes AI infrastructure less fragile, which is often the real constraint.