In AI infrastructure, storage is often treated as a solved problem, but large-scale blob storage quietly struggles under real-world demands: reliability, predictable performance, and cost control are rare. $WAL and the Walrus network confront this directly, turning custody into a verifiable, time-bound promise rather than a best-effort service. Operators stake, are assigned responsibility, and earn over time, creating a system where availability and integrity are measurable and enforced. By linking incentives to performance and spreading risk across a decentralized Sui-based network, Walrus avoids the hidden failures that plague traditional blob solutions, offering a framework that aligns growth, cost, and accountability over the long term—critical for AI workloads that cannot tolerate silent data loss.
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