The Biological Lie: Genes as Pretrained Weights

Humans frequently cite "few-shot learning" as evidence of cognitive superiority over artificial intelligence: a child recognizes a cat from one photo while neural networks require millions of examples. We argue this comparison is fundamentally misleading. Human "rapid learning" is not learning at all—it is inference on pretrained weights accumulated over 4 billion years of evolutionary pressure. We propose a unified framework mapping biological concepts (DNA, development, memory) to machine learning equivalents (checkpoint files, fine-tuning, generative inference). Key implications include: (1) education functions as LoRA fine-tuning on an immutable base model, (2) memory operates as generative inference rather than storage retrieval, (3) genius cannot be inherited due to biological "regression to mean" constraints, and (4) AI's apparent "slow learning" is actually base model construction from scratch—a feat that took biology 4 billion years.