The underlying logic of AI competition is actually very simple—whoever controls high-quality data, who owns the future of AI. The entire AI industry is rushing towards a scale of $860 billion, but the immediate problem is: truly useful data is scarce. Open-source data is filled with noise and biases, professional datasets are ridiculously expensive, and no one is willing to put in the effort to create them. At this point, some projects are starting to consider: can we directly turn users' daily activities into high-quality data for AI training? This is precisely the idea behind the new generation of data tokenization platforms.

The core breakthrough of these projects is called "programmable data ownership." How to understand this? It’s a complete mechanism design. First, there's the Data DAOs approach, where users can aggregate their data to train AI models, and then everyone shares in the value generated by this data—sounds like a data version of mining pools. Second, there's the VRC-20 data token, which is tied to specific datasets and verified through proof of contribution. Uploading data earns you token rewards, and AI developers who want to use the data must burn tokens to gain access. This creates a full chain: "I contribute data → data becomes valuable → I receive rewards," encouraging users to actively contribute high-quality data.

How is security addressed? The project uses cutting-edge Trusted Execution Environments (TEEs). Raw data remains encrypted and protected; developers cannot access the actual data, only the computation results. This protects user privacy while enabling data to circulate and be used within a compliant framework. In other words, data ownership still belongs to the user, and the benefits go to the user as well, but developers can access the data when needed.