Point cloud semantic segmentation (PCSS), for the purpose of labeling a set of points stored in irregular and unordered structures, is an important yet challenging task. It is vital for the task of learning a good representation for each 3D data point, which encodes rich context knowledge and hierarchically structural information. However, despite great success has been achieved by existing PCSS methods, they are limited to make full use of important context information and rich hierarchical features for representation learning. In this paper, we propose to build 'hyperpoint' representations for 3D data point via a nested network architecture, which is able to explicitly exploit multi-scale, pyramidally hierarchical features and construct powerful representations for PCSS. In particular, we introduce a PCSS nested architecture search (PCSS-NAS) algorithm to automatically design the model's side-output branches at different levels as well as its skip-layer structures, enabling the resulting model to best deal with the scale-space problem. Our searched architecture, named Auto-NestedNet, is evaluated on four well-known benchmarks: S3DIS, ScanNet, Semantic3D and Paris-Lille-3D. Experimental results show that the proposed Auto-NestedNet achieves the state-of-the-art performance. Our source code is available at https://github.com/fanyang587/NestedNet.