MOTIVATION: Substitution matrices like BLOSUM62 model the likelihood of replacement of amino acids in evolution. Substitution matrices are used in protein sequence alignment tasks. Since the introduction of BLOSUM62 over three decades ago, many matrices have been released. Yet, to date, no effort uses large amounts of 3D structures predicted by AlphaFold. RESULTS: Here, we define AFSM, the AlphaFold Substitution Matrix derived from over 20 000 predicted 3D structures following the BLOSUM methodology. We benchmark AFSM against BLOSUM62 and 16 other matrices on five tasks in multiple sequence alignment (MSA) and protein homology search. Our analysis surprisingly reveals that all matrix families perform similarly. Only when there are few sequences in an MSA do BLOSUM62 and AFSM perform better than using no matrix. This suggests that substitution matrices were most beneficial when there was little sequence data. We corroborate this argument by showing that embeddings, which are computed from billions of sequences, perform better than substitution matrices, when sequence data is sparse. Taken together, this suggests that structural data does not improve BLOSUM62. But increased sequence data makes extrapolation with substitution matrices obsolete. Nonetheless, BLOSUM62 continues to capture chemists' intuition on amino acids by providing numerical values implicitly reflecting physicochemical properties, and it remains indispensable for sparse MSAs and direct comparison of two sequences. AVAILABILITY AND IMPLEMENTATION: Data is available from doi.org/10.5281/zenodo.18777546.