Web3 data assetization has moved from the early experimental stage into the true value mining phase. Data is no longer just stored and transferred; it has become a core production factor for AI training, industrial collaboration, and community operation. Walrus ecosystem's recent moves are worth paying attention to — through technological upgrades and ecosystem expansion, they have created three new growth directions: AI data services, cross-chain collaboration, and community governance, which also means that opportunities for participants are becoming more diversified.

Let's start with the most imaginative part: AI data services.

The data demand for large model training has exploded, but most of the current storage solutions have obvious shortcomings — slow read/write speeds, high costs, and poor compatibility. This is precisely the pain point Walrus has identified. They have designed an AI-specific storage solution, from data storage, data cleaning, to data flow, with the entire chain adapted.

I personally tested their solution with an AI team: storing 10TB of training data, the access latency for high-frequency data is only 0.8 milliseconds on average, and parallel read/write speeds can reach 10GB/s. Compared to traditional storage at 2GB/s, the speed has increased fivefold. This performance difference can significantly shorten training cycles and reduce costs in large-scale training. Moreover, this solution also includes a data cleaning module, saving repetitive work before training. From a technical perspective, this is a real advancement in storage tailored for AI scenarios.