  We argue that representations in AI models, particularly deep networks, are
converging. First, we survey many examples of convergence in the literature:
over time and across multiple domains, the ways by which 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 platonic representation and discuss several possible
selective pressures toward it. Finally, we discuss the implications of these
trends, their limitations, and counterexamples to our analysis.
