Beyond optimization: structural redundancy and the governance of human potential in the age of AI

Debates on artificial general intelligence (AGI) often frame progress as a quantitative race toward human-level performance through scaling and optimization. This essay argues that such debates overlook a deeper asymmetry in design orientation between biological and artificial systems. Drawing on biological theory and recent discussions in AI & Society on embodiment, artificial abstraction, and distributed agency, I isolate redundancy as a structural variable linking these debates. Biological intelligence preserves what I term structural redundancy (SR), defined herein as a historically accumulated repertoire of latent capacities that can be recruited, reorganized, and reinterpreted through ongoing interactions between organisms and their environments. This repertoire is generated and sustained through a form of internal causal organization that biological systems possess but objective-governed AI systems lack. By contrast, redundancy in contemporary AI remains statistically or instrumentally defined, governed by explicit objective functions and subject to optimization pressure. The difference is not one of degree but of design orientation. By engaging biologically inspired AI research programs, I argue that even architectures modeled on neural, predictive, or evolutionary motifs typically remain bounded by pre-defined evaluation criteria, thereby lacking historically accumulated openness. This reframes AGI not as a scaling problem but as a category confusion: optimization cannot generate the kind of indeterminacy that underwrites biological adaptability. Rather than pursuing replication, AI development should be oriented toward augmentation—designing sociotechnical systems that preserve human cognitive and institutional margins. Redundancy, thus, emerges not only as a biological principle but as a governance variable in datafied society.