Industry Background and Opportunities

The “Fuel Crisis” in AI Model Training

With the rapid advancement of large model technologies (such as GPT, Claude, and Gemini), the global demand for high-quality training data is growing explosively. However, current data acquisition methods remain heavily dependent on centralized platforms and face several challenges:

• Centralized data sources, often consisting of outdated and low-quality web content

• Lack of incentive mechanisms to reward the individual value of user-contributed data

• Insufficient understanding of social semantics, including context, emotion, and consensus trends

The Value of Social Data Remains Untapped

Web2 social platforms have accumulated massive volumes of human behavioral data, yet users have never truly been rewarded. While user behavior drives platform value, users receive none of the benefits from AI training.

Web3 Users Are Seeking “New Incentive Models”

Generation Z users are placing increasing importance on data sovereignty, participation-based rewards, and controllability of AI. There is a growing demand for AI participation models that both protect privacy and generate cognitive value.