The protein folding problem dates back to Pauling’s insights almost a century ago, but the first venture into actual protein structure was the Pauling–Corey–Brandson α-helix in 1951, a proposed model that was confirmed almost immediately using X-ray crystallography. Many subsequent efforts to predict protein helices from the amino acid sequence met with only partial success, as discussed here. Surprisingly, in 2021, these efforts were superseded by deep-learning artificial intelligence, especially AlphaFold2, a machine learning program based on neural nets. This approach can predict most protein structures successfully at or near atomic resolution. Deservedly, deep-learning artificial intelligence was named Science magazine’s 2021 “breakthrough of the year.” Today, ~200 million predicted protein structures can be downloaded from the AlphaFold2 Protein Structure Database. Deep learning represents a deep conundrum because these successfully predicted macromolecular structures are based on methods that are completely devoid of a hypothesis or of any physical chemistry. Perhaps we are now poised to transcend five centuries of reductive science.