We have figured out how to write to the genome using DNA editing, but we don't know what the outcomes of genetic modifications will be. This is called the "genotype-phenotype gap". To close the gap, we need to reverse-engineer the genetic code, which is very hard because biology is too complicated and noisy for human interpretation. Machine learning and AI are needed. The data? Six billion letters per genome, hundreds of thousands of types of biomolecules, hundreds of cell types, over seven billion people on the planet. A new generation of "Bio-AI" researchers are poised to crack the problem, but we face extraordinary challenges. I'll discuss these challenges, focusing on which branches of AI and machine learning will have the most impact and why.