Linearity requires additivity and scaling, but neural networks need nonlinearity to break free from simple affine transformations. While affine layers only stretch and rotate data without changing its topology, activation functions enable complex reshaping—allowing networks to untangle data manifolds and learn intricate patterns effectively.