Many of the Pareto-optimal DFT methods are quite new: ωB97M-V 48 was released in 2016, B97M-V 49 & r2SCAN 50 in 2020, and r2SCAN-3c 36 in 2021. As a result, many commonly used software packages do not contain these methods. While a broader discussion of the dynamics of the scientific software ecosystem is outside the scope of this article, our results highlight the reality that many research labs employ suboptimal methods, reducing the accuracy of the results they generate and the speed of their calculations. Finally, this benchmarking illustrates the remarkable progress made by NNPs over the past several years. The strained molecules studied in Wiggle150 might reasonably have been expected to serve as a "poison" set 52 for machine-learning-based methods, given how few NNPs include structures like this in their training set-instead, NNPs like AIMNet2 and ANI-2x performed very well, approaching in some cases even exceeding the performance of dispersion-corrected DFT methods with quadruple-ζ basis sets. Given that improvement in NNPs continues to proceed at a rapid pace, and that considerable improvement is possible purely from scaling existing architectures to larger datasets, the present authors find it colorable that most quantum mechanical workflows will one day shift to be powered by NNPs.