Universal statistical signatures of evolution in artificial intelligence architectures

TL;DR

This study reveals that the evolution of AI architectures follows universal statistical laws similar to biological evolution, characterized by heavy-tailed distributions and punctuated dynamics.

Contribution

It provides the first large-scale empirical evidence that AI architectural evolution obeys universal statistical signatures akin to biological evolution.

Findings

DFE of architectural modifications follows a Student's t-distribution

AI evolution exhibits punctuated equilibria and adaptive radiation

Convergent invention of architectural traits parallels biological convergence

Abstract

We test whether artificial intelligence architectural evolution obeys the same statistical laws as biological evolution. Compiling 935 ablation experiments from 161 publications, we show that the distribution of fitness effects (DFE) of architectural modifications follows a heavy-tailed Student's t-distribution with proportions (68% deleterious, 19% neutral, 13% beneficial for major ablations, n=568) that place AI between compact viral genomes and simple eukaryotes. The DFE shape matches D. melanogaster (normalized KS=0.07) and S. cerevisiae (KS=0.09); the elevated beneficial fraction (13% vs. 1-6% in biology) quantifies the advantage of directed over blind search while preserving the distributional form. Architectural origination follows logistic dynamics (R^2=0.994) with punctuated equilibria and adaptive radiation into domain niches. Fourteen architectural traits were independently invented 3-5 times, paralleling biological convergences. These results demonstrate that the statistical structure of evolution is substrate-independent, determined by fitness landscape topology rather than the mechanism of selection.