Yann LeCun's team has elevated the Platonic Representation Hypothesis from a philosophical conjecture to a mathematical theorem, complete with actionable sufficient conditions. Their study employs a JEPA-based "good view" training method that enables the model to capture the shared content underlying different descriptions of the same scene, while incorporating constraints to prevent representation collapse. Compared with the next-token prediction approach, this method achieves a 42-fold improvement in sample alignment efficiency; measured by actual text tokens consumed, the efficiency gap remains roughly 14-fold. Notably, a model with just 109 million parameters outperforms a 72-billion-parameter large language model in representation alignment, and can even link independently trained vision and text modules through a simple linear mapping to perform tasks such as image captioning. This work lays the groundwork for representation learning within the world-model paradigm, confirms the decisive role of training objectives in learning efficiency, and demonstrates that next-token prediction is comparatively inefficient—and that superior results need not come from scaling alone.