This chapter examines five pivotal factors influencing the development of AI cities: learning capabilities, pattern recognition through learning, predictive analytics, intelligent self-organizing systems, and demand-driven technological advancement. The fundamental strength of AI cities resides in their capacity for continuous self-learning and iterative improvement to model and address intricate urban challenges. Leveraging technologies such as the IoT and big data, AI cities can uncover patterns of urban development through data flow and analysis, forecast future trends, and thereby optimize resource allocation and infrastructure planning. Moreover, AI cityAI city management will transition from a traditional top-down approach to a more adaptive and efficient self-organizing and self-decision-making framework. Additionally, the evolution of AI cities is propelled by human needs, with technological innovation addressing issues of environmental and systemic disharmony to foster sustainable urban development. The chapter also cites Wu Zhiqiang, an academician of the Chinese Academy of Engineering, who underscores that the essential elements for AI city development encompass technical feasibility, urban competitiveness, and corporate profitability.