The author compares DNA genomes and large language model weights as passive information sequences—scores that only become meaningful when processed by cellular machinery or inference engines. Both arise from massive search processes (evolution and gradient descent) that compress vast experiences into lossy representations, leaving much uninterpreted “junk.” This parallel explains why it’s hard to pinpoint where specific functions reside in either genome or model.