Recent advances in feed-forward Novel View Synthesis (NVS) have led to a divergence between two design philosophies: bias-driven methods, which rely on explicit 3D knowledge, such as handcrafted 3D representations (e.g., NeRF and 3DGS) and camera poses annotated by Structure-from-Motion algorithms, and data-centric methods, which learn to understand 3D structure implicitly from large-scale imagery data. This raises a fundamental question: which paradigm is more scalable in an era of ever-increasing data availability? In this work, we conduct a comprehensive analysis of existing methods and uncover a critical trend that the performance of methods requiring less 3D knowledge accelerates more as training data increases, eventually outperforming their 3D knowledge-driven counterparts, which we term "the less you depend, the more you learn." Guided by this finding, we design a feed-forward NVS framework that removes both explicit scene structure and pose annotation reliance. By eliminating these dependencies, our method leverages great scalability, learning implicit 3D awareness directly from vast quantities of 2D images, without any pose information for training or inference. Extensive experiments demonstrate that our model achieves state-of-the-art NVS performance, even outperforming methods relying on posed training data. The results validate not only the effectiveness of our data-centric paradigm but also the power of our scalability finding as a guiding principle.