IOV LABSConvergence Pressure: Measuring AI-Mediated Cultural Homogenization in Iterated Creation
Abstract
Generative AI raises the creativity of an individual while lowering the diversity of the crowd (Doshi & Hauser 2024); models retrained on their own output collapse (Shumailov et al. 2024). We join the two into one dynamical question: when a shared model mediates an iterated creative process, does a population's diversity decay over generations, and what drives it? A pool of diverse creator personas produces one artifact per generation (12 creators × 6 generations × 3 themes) under four conditions: writing alone, with a static AI advisor, with an advisor that reflects the population's own recent output back at it, and the same reflective loop with diverse advisors. The result is a clean dissociation. AI assistance per se leaves diversity flat (100 to 102% of starting dispersion retained, p≥0.40); the reflective loop drives an anisotropy-controlled decline of about 10 to 12%. The obvious fix fails: a panel of diverse AI advisors, which preserves variety in a single round, does not prevent the collapse under iteration (it loses slightly more, p=0.007). The convergence is semantic, not lexical (distinct-2 is flat, so n-gram metrics miss it entirely), and individual quality rises in exactly the conditions where collective diversity falls, the scissors at its sharpest. A minimal contraction-map model predicts the decay-to-a-floor and explains why advisor diversity cannot enter the pull coefficient. It is not AI assistance that homogenizes a population, but the loop of an AI echoing the crowd; and making the AI more diverse does not break the loop. Negative results kept; seeds, snapshots, and one-command reproduction in the public repo.
Keywords
- generative AI
- cultural homogenization
- diversity collapse
- model collapse
- human-AI collaboration
- semantic dispersion
- feedback loops
- value lock-in
- reproducibility