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.

翻译：我们测试了人工智能架构演化是否遵循与生物演化相同的统计规律。通过整合来自161篇出版物的935项消融实验，我们证明架构修改的适应度效应分布（DFE）符合重尾学生t分布，其比例（主要消融中68%有害、19%中性、13%有益，n=568）将人工智能置于紧凑病毒基因组与简单真核生物之间。DFE的形状与黑腹果蝇（归一化KS=0.07）和酿酒酵母（KS=0.09）相匹配；有益比例升高（13%对比生物学中的1-6%）量化了定向搜索相对于盲目搜索的优势，同时保留了分布形式。架构创新遵循逻辑斯蒂动力学（R²=0.994），呈现间断平衡与向领域生态位的适应辐射。十四项架构特征被独立发明3-5次，与生物趋同演化平行。这些结果表明，演化的统计结构具有底物无关性，由适应度景观拓扑而非选择机制所决定。