For comparison's sake: to regenerate the data that enabled AlphaFold, you'd have to spend about $20 billion. Source: https://www.nature.com/articles/s41587-023

For comparison's sake: to regenerate the data that enabled AlphaFold, you'd have to spend about $20 billion. Source: https://www.nature.com/articles/s41587-023-02103-0 https://www.nature.com/articles/s41587-023-02103-0 >A major distinguishing factor of the sciences (specifically, biology, chemistry and physics), as compared to AI fields such as natural language processing and computer vision, is the relative lack of publicly available data suitable for these domains (see Box 1). There are simply vastly fewer data existing in the sciences, and these are often siloed by academics and companies. Acquiring appropriate scientific data for AI typically requires not only highly trained humans, but also high-end facilities with expensive equipment, making for an overall costly and slow endeavor compared to humans simply going about their day by adding to the vast trove of images, text, audio and video on the World Wide Web. As one example, it has been estimated that “The replacement cost of the entire PDB [Protein Data Bank] archive is conservatively estimated at ∼US$20 billion”1 —these are the data used by DeepMind to train AlphaFold2.