Activation shaping has proven highly effective for identifying out-of-distribution (OOD) samples post-hoc. Activation shaping prunes and scales network activations before estimating the OOD energy score; such an extremely simple approach achieves state-of-the-art OOD detection with minimal in-distribution (ID) accuracy drops. This paper analyzes the working mechanism behind activation shaping. We directly show that the benefits for OOD detection derive only from scaling, while pruning is detrimental. Based on our analysis, we propose SCALE, an even simpler yet more effective post-hoc network enhancement method for OOD detection. SCALE attains state-of-the-art OOD detection performance without any compromises on ID accuracy. Furthermore, we integrate scaling concepts into learning and propose Intermediate Tensor SHaping (ISH) for training-time OOD detection enhancement. ISH achieves significant AUROC improvements for both near- and far-OOD, highlighting the importance of activation distributions in emphasizing ID data characteristics. Our code and models are available at https://github.com/kai422/SCALE.