In the context of aqueous solubility, Palmer and Mitchell compiled a curated set of highly accurate experimental measurements, and concluded experimental uncertainty was not limiting model performance, but rather QSPR methods themselves were limiting. Since then, innovations in cheminformatics software has led to highly accurate and expressive model architectures which have been shown to perform excellently on molecular property prediction tasks. The inability of these models to improve with increasing training set size on organic solubility predictions challenges this paradigm. This instead suggests that experimental uncertainty that limits model performance -the aleatoric limit has been reached -and that a new approach is needed to further improve models.