AI-Driven Spatial Distribution Dynamics: A Comprehensive Theoretical and Empirical Framework for Analyzing Productivity Agglomeration Effects in Japan's Aging Society

This paper develops the first comprehensive theoretical and empirical framework for analyzing AI-driven spatial distribution dynamics in metropolitan areas undergoing demographic transition. We extend New Economic Geography by formalizing five novel AI-specific mechanisms: algorithmic learning spillovers, digital infrastructure returns, virtual agglomeration effects, AI-human complementarity, and network externalities. Using Tokyo as our empirical laboratory, we implement rigorous causal identification through five complementary econometric strategies and develop machine learning predictions across 27 future scenarios spanning 2024-2050. Our theoretical framework generates six testable hypotheses, all receiving strong empirical support. The causal analysis reveals that AI implementation increases agglomeration concentration by 4.2-5.2 percentage points, with heterogeneous effects across industries: high AI-readiness sectors experience 8.4 percentage point increases, while low AI-readiness sectors show 1.2 percentage point gains. Machine learning predictions demonstrate that aggressive AI adoption can offset 60-80\% of aging-related productivity declines. We provide a strategic three-phase policy framework for managing AI-driven spatial transformation while promoting inclusive development. The integrated approach establishes a new paradigm for analyzing technology-driven spatial change with global applications for aging societies.