DATA coming to @SuiNetwork is more than a chain migration. With 250M+ receipts already recorded on Trace, the bigger story is building a better way to track where AI training data comes from and the rights attached to it. That’s an important step toward more accountable AI. AI models need data, but where that data comes from matters just as much as the data itself. When contributions have traceable origins and clear usage rights, AI labs can make more informed decisions about what they use, while contributors have a better chance of having their permissions respected. One thing I find interesting about this move is the role of @WalrusProtocol. Verifiable provenance is useful, but being able to store and check the original data adds another layer of transparency, helping buyers verify that the files they receive match the records associated with them. The $30M investment from Sui Foundation is a major part of this announcement, but the product direction is what catches my attention. By building on Sui's infrastructure, DATA can focus more on Trace and the challenge of making data origins and usage rights easier to verify as adoption grows. The long-term opportunity is bigger than recording contributions. AI needs data people can trust, and contributors deserve clearer answers about where their work goes and how it can be used. Making provenance and licensing easier to verify is one way to move toward a more transparent AI data economy. @datafdn @SuiNetwork