Among the many constraints in AI training, the data bottleneck is often more severe than the computing bottleneck, yet it is rarely given enough attention. Compared to simply piling up computing power, true breakthroughs require efforts in two dimensions simultaneously. By leveraging crowdsourcing mechanisms to obtain high-quality training data and combining them with distributed processing architectures, this lock can be thoroughly broken. Many projects either focus heavily on computation while neglecting data, or work in isolation, but this collaborative approach precisely fills a critical gap in the industry.