In this talk I survey the ongoing impact of the AlphaFold 2 system on science. This is a deep learning system which in blind assessment has routinely predicted protein structure with accuracies comparable to experiment, starting from the underlying amino acid sequence. Such systems pose many interesting metascientific challenges: what do they learn? How can they be validated? What are the limits of their applicability? And are they identifying general principles which human scientists can learn from?