The article discusses how Markov chain Monte Carlo (MCMC) methods can struggle with large datasets, leading researchers to explore subsampling techniques to improve performance. However, the study shows that these subsampling methods may not significantly enhance MCMC efficiency according to established convergence results. The findings suggest that simply looking at a subset of data at each step may not be a game-changer for MCMC algorithms targeting complex posterior distributions.