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.

• DNA and LLM weights are both inert symbol strings that only produce meaning when run through an interpreter (ribosomes/enzymes vs. inference engines)
• Both are compressed, lossy residues of massive iterative search—4 billion years of natural selection vs. gradient descent over trillions of examples—not literal copies of the experiences that shaped them
• Neither system stores discrete facts or instructions at a single location (no "gene for a hand," no weight for "Paris is the capital of France"); competence is distributed across the whole structure, which is why pinpointing function in either genome or model is so hard