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These days, the growth of data volume is no longer keeping up with our imagination. AI technology is exploding, and data demand has jumped from TB to ZB levels. According to the latest report from International Data Corporation (IDC), by 2026, the total global data volume will exceed 200ZB, with AI-driven data accounting for 45%. Imagine that, from training generative models like GPT to real-time AI agents handling dynamic content, each application requires massive amounts of unstructured data to support it.
But here’s a problem—what about traditional centralized storage systems? Google Cloud, AWS, and others sound impressive, but in reality, they are heavily constrained. High costs, data silos, privacy risks, single points of failure... these limitations are like shackles on data flow. AI developers face a dilemma: either accept the various drawbacks of centralization or seek a new way out.
This is why decentralized storage solutions are beginning to attract attention. Projects like Walrus Protocol are targeting this pain point. Their approach is very clear—rather than spinning within the existing framework, they fundamentally overhaul the storage infrastructure. Through decentralized architecture, data is no longer monopolized by a single platform, giving developers more control, significantly reducing storage costs, and greatly enhancing security and availability.
In simple terms, it’s about freeing AI developers from the shackles of centralization and embracing new possibilities in data storage. This is not only a technological advancement but also a reshaping opportunity for the entire Web3 storage track.