Son and colleagues map the emerging domain of AI in urban planning through a PRISMA-based systematic review. Their strongest contribution lies less in celebrating computational capacity than in exposing the breadth of functions to which algorithmic systems are being attached: land-use analysis, zoning and permitting, mobility, environmental planning, pattern recognition, prediction and decision support. The methodological operation is field construction. By classifying applications, techniques and planning tasks, the review makes algorithmic planning visible as an expanding epistemic infrastructure rather than a collection of isolated experiments. It also identifies a recurrent theory–practice gap and stresses that big data, institutional collaboration and convergence between human and artificial intelligence are conditions of meaningful adoption. The paper bridges AI, smart-city research and planning practice. Within the nexus developed here, its significance is diagnostic: algorithms can enlarge the searchable space of alternatives while simultaneously embedding new assumptions about relevance, classification and optimisation. The question is therefore not whether AI can plan, but which possibilities become addressable when planning problems are reformatted for algorithmic inspection.
