The Universe just wants to learn
TLDR
A philosophical essay compares the human genome and neural network weights as analogous informational structures: both are passive sequences brought to life only through external processes (cellular machinery for DNA, inference engines for AI), both are products of massive search processes (evolution and gradient descent), and both remain largely opaque despite being completely readable. The essay notes that just as the complete human genome was only sequenced in 2022 yet 98% of it has unknown function, interpretability research shows that 90% of neural network weights can be pruned without performance loss, suggesting both systems are radically overbuilt. The parallel observation raises questions about whether either system contains genuinely useless material or performs functions we're not yet clever enough to detect.
Why it matters
DNA and neural networks are both passive informational substrates that only become meaningful through processes outside of them—DNA through enzyme decoding and neural networks through the forward pass during inference.
