What limits the accuracy of the Rowan pKa workflow, and what can be done to improve it? At a high level, errors in ab initio microscopic pKa prediction can come from several different sources: (1) insufficient consideration of conformational effects or analysis of the wrong conformations, (2) poor description of the underlying gas-phase thermochemistry, or (3) inaccurate description of solvation. To achieve maximum efficiency, a Pareto-optimal pKa prediction workflow ought to balance errors from all three sources without "overspending" for a high-accuracy answer in only one area. In specific cases, we have been able to track down anomalous predictions made by Rowan pKa to all three underlying potential sources of errorconformational searching, gas phase thermochemistry, and implicit solvent model-indicating that no factor is solely responsible for the observed error. While improvements to conformational generation and machine-learned interatomic potentials are easy to envision based on current research, improvements to implicit solvation may prove to be more elusive: despite repeated calls for alternatives to "scandalous" implicit solvation, no practical and general alternatives have yet been developed. Recent work in coarse-grained machine-learned potentials may point towards a new paradigm for handling solvent effects, but more work is needed to investigate the strengths and limitations of such approaches. More generally, the approach described in this paper divides the task of predicting pKa into two separate components: (1) a general pre-trained model which maps molecular structures to energies, thus "learning chemistry" at a high level, and (2) an application-specific workflow which translates geometries and energies generated by the pre-trained model into predictions relevant to the task at hand. This partitioning has certain advantages: the general pre-trained model (here AIMNet2) is systematically improvable and can be trained using purely in silico data, in principle allowing arbitrarily high levels of accuracy to be attained, while the application-specific workflow relies largely on conventional computational chemistry techniques and does not require extensive experimental training data like ML-based QSPR methods. As the accuracy of machine-learned interatomic potentials improves, we anticipate that the strategy demonstrated here-using machine-learned interatomic potentials as drop-in replacements for quantum chemistry-will become a general strategy for accelerating computational chemistry.