GPU computing power can be infinitely scaled, but this fundamentally does not solve a key problem—stale data, bias, and poor quality. No matter how powerful the computing capability, encountering garbage data will only amplify problems, not replace them. The real breakthrough lies in redesigning the incentive system. Aligning the interests of data contributors and distributed processing nodes is essential to fundamentally break through the model ceiling. This is exactly what decentralized data networks aim to do—ensure data quality while activating the enthusiasm of all network participants.