A vast majority of electronic structure calculations across disciplines ranging from biology to materials science are done by density functional theory (DFT). Within the Kohn-Sham DFT (KS DFT) the only, but crucial, piece that has to be approximated is the exchangecorrelation (XC) functional. Despite the tremendous successes of DFT, the description of strong correlation electronic effects remains the major challenge in the development of new XC approximations. One can argue that the insufficient accuracy and not uncommon qualitative failures of the standard DFT for strong correlations can be traced back to the limited number of building blocks that standard DFT uses for XC approximations. These building blocks (or features) form the so-called "Jacob's Ladder" as formulated by Perdew. The complexity and cost of the features increase as we climb up the Jacob's ladder, where at the bottom rung we find the density; then the density gradient, its Laplacian (or kinetic energy density) in the middle rungs; and the KS occupied and unoccupied orbitals in the top rungs. Nearly all currently available XC approximations are built from these Jacob's ladder features, including the very recent XC models obtained from machine learning (ML). This includes the very recent DM21 functional designed to reduce the errors of approximate XC functionals for fractional spins and fractional charges. Even though the DM21 development has made a breakthrough in using ML to optimize XC approximations, DM21 still suffers from deficiencies inherent to the limitations of the (Jacob's ladder) features it uses.