At least for LLMs, the platonic representation hypothesis doesn't seem that surprising. The training data for models that are large enough to faithfully capture

At least for LLMs, the platonic representation hypothesis doesn't seem that surprising. The training data for models that are large enough to faithfully capture reality will necessarily have a lot of overlap. So in this weak form the platonic representation hypothesis just says that if you provide the roughly same data to models with roughly the same architecture, they learn roughly the same embeddings. This doesn't seem that surprising. I'd even expect a slightly stronger version where you feed the same data to different models to still hold. After all the modes capture the same reality, so there's probably some way to transform from the "latent space" of one model to the other.