The Platonic Representation Hypothesis 

Argues that there is a universal representation of reality that AI models, as they grow more generally competent (and agnostic to architecture, training objective, and modality), are converging towards. Intuitively, the number of representation spaces a model can adopt to solve many different tasks gets smaller as the number of tasks goes up. That is, the three pressures for convergence are 1) multitask competence (representation must work for different objectives), 2) capacity (larger models are more likely to contain the optimal representation), 3) bias toward simpler representations.

The figures in section 3 are easy to understand and the bulk of the paper’s point.