We argue that representations in AI models, particularly deep networks, are converging. First, we survey many examples of convergence in the literature: over time, in multiple domains, the ways different neural networks represent data are becoming more aligned. Next we demonstrate convergence across data modalities: as vision models and language models get larger, they measure distance between datapoints in a more and more alike way. We hypothesize that this convergence is driving toward a shared statistical model of reality, akin to Plato's concept of an ideal reality. We term such a representation the \textit{Platonic representation}, and discuss several of the possible selective pressures toward it. Finally, we discuss the implications of these trends and limitations and counterexamples to our analysis.