An ensemble of models can be interpreted in two ways. The first treats each model as an approximation of the true system with some random error. Alternatively, the true system can be interpreted as a sample drawn from a distribution of models, such that model and truth are statistically indistinguishable. Both interpretations are ubiquitous and have different consequences for the uncertainty of model projections, but are rarely defended. Here we argue, with supporting evidence from the CMIP5 archive, that the two seemingly conflicting views are in fact complementary, and the interpretation of the ensemble may evolve seamlessly from the former to the latter. While present day simulations appear to be clustered around the observations in a manner consistent with the truth plus error framework, we show that this is likely the case due to model tuning to common targets and is likely to not be the case in the future.