The Platonic Representation Hypothesis (PRH) is a theory about how different AI systems learn and represent the real world. This theory posits that despite potentially learning in different ways (e.g., images, text, etc.), the internal representations of these AI systems will ultimately converge towards consistency. This viewpoint is based on the intuition that all data (images, text, sound, etc.) are projections of some underlying reality. The theory further explores how to measure representational consistency and factors contributing to it, such as task and data pressure, and increasing model capacity. It also discusses the implications and limitations of this consistency.