Algorithmic Metropolitanism

This paper examines the transition of metropolitan governance from reactive, analogue administration toward proactive, data-driven urban management through Artificial Intelligence (AI), Information and Communication Technology (ICT), Internet of Things (IoT), cloud and edge computing, and big-data analytics.The discussion focuses on Pune and Thane as illustrative metropolitan regions and considers how smart infrastructure can improve mobility, energy management, waste logistics, public safety, and waterresource management.The paper combines an urbaneconomic perspective with a quantitative assessment framework based on a survey dataset of 441 commuter responses, particularly examining service dimensions such as availability, travel-time reliability, safety, and perceived transport utility.The paper argues that the value of AI-enabled urban infrastructure lies not merely in automation but in its capacity to coordinate interconnected municipal systems and support anticipatory decision-making.At the same time, algorithmic governance creates risks involving privacy, bias, cybersecurity, fiscal pressure, labour displacement, vendor dependence, and digital exclusion.The paper therefore proposes a governance framework centred on independent algorithmic oversight, open-data and interoperability standards, equitable capital allocation, citizen participation, and carefully designed Public-Private Partnership (PPP) contracts.The central argument is that an ideal smart city should be evaluated by improvements in welfare, accessibility, accountability, sustainability, and social equity rather than by the quantity of sensors or sophistication of algorithms.