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The Rapid Rise Of Video Content Has Sparked A Pressing Need For Robust Video-language Understanding (VLU) Systems Capable Of Linking Visual Dynamics With Natural Language Reasoning. However, Progress Is Constrained By What We Call The Impossible Data Trinity: The Simultaneous Requirement For Large-scale Data Quantity, Fine-grained Annotation Quality, And Broad Domain Diversity. Traditional Dataset Construction Approaches Struggle To Balance All Three Dimensions, Inevitably Sacrificing One To Achieve The Others. In This Work, We Introduce The Video Dataflywheel, A Self-reinforcing Paradigm That Leverages Multimodal Foundation Models, Synthetic Data Generation, And Human-in-the-loop Refinement To Progressively Resolve The Trinity Challenge. The Dataflywheel Operates In Iterative Cycles: Models Generate And Refine Pseudo-labeled Data, Which Is Then Validated And Expanded By Scalable Curation Strategies, Feeding Back Into Stronger Models And Richer Datasets. We Demonstrate How This Framework Accelerates The Creation Of Diverse, High-quality, And Large-scale Video-language Resources, Reducing Reliance On Costly Manual Annotation While Maintaining Semantic Fidelity. Beyond Dataset Construction, The Video Dataflywheel Provides A Blueprint For Sustainable VLU Research, Enabling The Community To Move Closer To Generalizable, Efficient, And Context-aware Video-language Understanding.