Researchers have introduced NAPE (Next-Audio-Patch-Embedding prediction), a minimalist self-supervised learning framework designed to shift audio representation learning toward a causal, autoregressive paradigm. Unlike traditional audio SSL methods that often rely on complex reconstruction decoders, acoustic tokenizers, or student-teacher architectures, NAPE utilizes a causal Transformer to predict the subsequent patch embedding of a log-mel spectrogram based on preceding context. The training process is intentionally streamlined, employing only causal masking and stop-gradient as the signal to compel the model to learn the underlying data distribution. This approach mirrors successful strategies used in large language models and visual representation learning. Experimental results across six audio and speech benchmarks demonstrate that NAPE achieves state-of-the-art fine-tuning performance on several key tasks. Furthermore, the framework exhibits consistent scaling properties across varying encoder sizes and produces strong linear-probing results. Notably, the model develops structured attention patterns without receiving explicit supervision, proving that simple next-patch prediction is a scalable and effective interface for learning complex temporal audio structures.