AI as a Centripetal Technology: Price Compression, Homogenization, and Entry

Aliya Turegeldinova, Bakytzhan Amralinova, Mate Miklos Fodor, Akerkin Eraliyeva, Chen Dayou and Aidos Joldassov

Papers from arXiv.org

Abstract: Generative AI does more than cut costs. It pulls products toward a shared template, making offerings look and feel more alike while making true originality disproportionately expensive. We capture this centripetal force in a standard two-stage differentiated-competition framework and show how a single capability shift simultaneously compresses perceived differences, lowers marginal cost and raises fixed access costs. The intuition is straightforward. When buyers see smaller differences across products, the payoff to standing apart shrinks just as the effort to do so rises, so firms cluster around the template. Prices fall and customers become more willing to switch. But the same homogenization also squeezes operating margins, and rising fixed outlays deepen the squeeze. The combination yields a structural prediction. There is a capability threshold at which even two firms cannot both cover fixed costs, and in a many-firm extension the sustainable number of firms falls as capability grows. Concentration increases, and prices still fall. Our results hold under broader preference shapes, non-uniform consumer densities, outside options, capability-dependent curvatures, and modest asymmetries. We translate the theory into two sufficient statistics for enforcement. On the one hand, a conduct statistic and a viability statistic. Transactions or platform rules that strengthen template pull or raise fixed access and originality costs can lower prices today yet push the market toward monoculture. Remedies that broaden access and promote template plurality and interoperability preserve the price benefits of AI while protecting entry and variety. The paper thus reconciles a live policy paradox. AI can make prices lower and entry harder at the same time. It prescribes what to measure to tell which force is dominant in practice.

Date: 2025-10
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