From Smart City to AI City: Evolution or Rebranding?

For more than a decade, the concept of the “smart city” has dominated discussions on urban innovation and development. Governments, technology companies, and policymakers promoted smart cities as the future of urban living through the integration of digital infrastructure, sensors, Internet of Things (IoT), and data-driven governance. Smart cities promised greater efficiency in transportation, energy management, public services, and urban planning. However, recently, a new term has rapidly emerged within global urban discourse: the “AI city.” From Singapore to Dubai and Seoul, cities are increasingly positioning themselves not only as smart, but as AI-driven urban ecosystems capable of prediction, automation, and autonomous decision-making.

This shift raises an important question:

Does the AI city represent a genuine transformation in urban governance, or is it simply a rebranding of the smart city concept?

At its core, the smart city focused on collecting and managing urban data. Sensors, cameras, and connected devices were designed to monitor city activities in real time, allowing governments to improve operational efficiency and service delivery. The AI city, however, moves beyond data collection toward algorithmic interpretation and predictive governance. Instead of merely observing traffic congestion, AI systems can predict congestion patterns before they occur. By analyzing historical mobility data, weather conditions, public events, and real-time transportation flows, AI can recommend alternative routes, adjust traffic signals dynamically, and optimize public transportation operations in advance. In addition, rather than simply monitoring energy usage, AI also can optimize energy distribution automatically. In this sense, AI cities represent a transition from reactive urban management to predictive urban governance. This predictive capability allows urban systems to become more adaptive and responsive, shifting city governance from reactive problem-solving toward anticipatory urban management.

The rise of generative AI, machine learning, urban digital twins, and autonomous systems has accelerated this transformation. AI technologies are increasingly embedded in transportation systems, public safety, disaster prediction, healthcare, and environmental management.For example, AI-powered flood prediction systems can analyze rainfall patterns, river conditions, and historical climate data to provide early warnings and support faster emergency responses in climate-vulnerable cities. Similarly, some cities are beginning to use AI-driven digital twins to simulate traffic flows, energy consumption, and urban development scenarios before implementing policies in the real world. For those reasons, urban governance is no longer centered solely on connectivity and digitalization, but on the ability of algorithms to analyze, predict, and influence urban life. This evolution has led many policymakers and technology firms to portray AI cities as the next stage of urban development.

Nevertheless, critics argue that the AI city may not be as revolutionary as it appears. Many of the core assumptions behind AI cities remain similar to those of smart cities: technological solutionism, efficiency-driven governance, and heavy reliance on data extraction. In many cases, the AI city simply intensifies the logic of the smart city by introducing more advanced forms of automation and algorithmic control. Concerns regarding surveillance, privacy, cybersecurity, algorithmic bias, and digital inequality continue to persist. The question is not only whether cities can become more intelligent, but also who benefits from these technologies and who may be excluded or monitored in the process.

Another important issue is sustainability. AI cities are often promoted as tools for climate resilience and sustainable urban management. AI can support energy optimization, flood prediction, waste reduction, and smart mobility systems. However, AI technologies themselves require massive computational power, data centers, and energy consumption. As a result, the AI city creates a paradox: while AI may help cities become more sustainable, the infrastructure supporting AI may simultaneously increase environmental pressures.

Ultimately, the emergence of the AI city reflects more than a technological upgrade. It signals a broader transformation in how cities are imagined, governed, and managed in the digital age. Whether AI cities represent a true paradigm shift or merely a rebranding of smart cities remains open to debate. However, one thing is clear: the future of urban development will increasingly depend not only on technological innovation, but also on ethical governance, human-centered planning, and the ability to balance efficiency with social and environmental responsibility.