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.
Figures & tables
Organism
f del
f ben
KS norm
dEuclid
rQQ
AI (major, n=568 )
0.68
0.13
—
—
—
Bacteriophage φ6
0.72
0.04
0.15
0.11
0.90
VSV virus
0.69
0.04
0.19
0.12
0.90
S. cerevisiae
0.60
0.03
0.09
0.22
0.87
D. melanogaster
0.52
0.03
0.07
0.32
0.90
E. coli TEM-1
0.50
0.05
0.12
0.33
0.90
Table 1: Comparison of AI DFE (major ablations, n=568 ) with biological DFEs. All biological comparisons use parametric reconstructions from published summary statistics (category fractions, gamma shape where available); this asymmetry is discussed in Methods. KS norm : Kolmogorov–Smirnov distance after z -score normalization. dEuclid : Euclidean distance in (del, neu, ben) proportion space. rQQ : Pearson correlation of normalized QQ plot.
Figure 1: Distribution of fitness effects in AI architectures matches biological DFEs. ( A ) Histogram of all AI ablation effects ( n=935 ) overlaid with synthetic DFE for S. cerevisiae (parametric reconstruction from published summary statistics). The primary analysis uses major ablations only ( n=568 ; see Table 1); the full dataset is shown here to illustrate the complete DFE shape. ( B ) QQ plot comparing normalized AI DFE quantiles against synthetic VSV virus DFE ( r=0.89 ). ( C ) DFE category proportions across AI and biological organisms (all biological DFEs are parametric reconstructions). AI shows an elevated beneficial fraction (13%) relative to all biological systems (1–6%). ( D ) Shape space (skewness vs. excess kurtosis). The AI DFE (star) falls within the cloud of biological DFEs (red circles). See Table 1 for comprehensive metric comparison.
Figure 2: DFE stratification and universality. ( A ) CDF of fitness effects by mutation type: major ablations ( n=568 , component removal) are more deleterious than minor ablations ( n=213 , hyperparameter changes), mirroring the biological pattern of deletions vs. point mutations. ( B ) DFE by ML domain: CV, NLP, Audio, and other domains show statistically similar distributions. ( C ) Methodological control: manually curated data ( n=140 ) vs. LLM-extracted data ( n=795 ). The systematic difference (KS =0.287 ) reflects manual curation’s failure to capture beneficial mutations, confirming that automated extraction reduces rather than introduces bias. ( D ) Gamma shape parameter comparison: AI major ablations ( β=0.65 ) compared against the three organisms with published population-genetic β estimates ( D. melanogaster ≈0.4 , M. musculus ≈0.3 , H. sapiens ≈0.2 ).
Figure 3: Diversification dynamics. ( A ) Annual origination rate of AI architectures, 2012–2024. Red bars mark key innovations that triggered radiation events. ( B ) Cumulative diversity follows a logistic curve ( R2=0.994 , K≈142 ), indicating approach to saturation. ( C ) Domain niche-filling: CV saturates first, followed by NLP, then Audio and Multimodal—analogous to ecological succession. ( D ) Normalized and smoothed radiation curves for AI architectures, Cambrian trilobites, and post-K-Pg mammals. All three show the same qualitative pattern: accelerating rise, peak, and decline.
Figure 4: Convergent evolution and lineage maturation. ( A ) AI architectural convergences: 14 traits independently invented 3–5 times. Colors indicate functional category. ( B ) Convergence intensity: AI traits (3–5 inventions) vs. biological traits ( ≤20 inventions, excluding outliers). Mann–Whitney p=0.035 . ( C ) Transformer NLP lineage: DFE narrows from Transformer (2017) through BERT (2019) to Switch Transformer (2022), showing progressive optimization. ( D ) Universal trend: fraction deleterious increases with system maturity across both AI lineages (circles) and biological organisms (diamonds). AI and biology fall on a common continuum.