AI has become remarkably good at synthesizing what we already know. Whether it can identify what we don't is a much harder question—one that Charan Kukunoor spends most of his time trying to answer. Charan’s work on making cities more intelligent, focuses on three problems that look unrelated but aren't. Cities cannot see what lies beneath their own streets. Language models cannot see the edges of their own knowledge. Citizens cannot see how the governments they fund actually make decisions. Each has an opacity problem. Each becomes tractable the moment you can measure what you don't know. Charan came to AI research the long way round: urban planning fieldwork on Amaravati, Andhra Pradesh's greenfield capital city, and then with UN-Habitat, before earning a PhD at NYU's Center for Urban Science and Progress, where he studied 3D Bayesian deep learning for underground space. He is now a postdoctoral fellow at the BCG X AI Science Institute. The following conversation explores how AI can move from a retrieval tool to a genuine engine of innovation and civic accountability. The implications are broad, from how cities manage underground infrastructure and how self-driving vehicles integrate into mixed urban traffic to how AI might eventually make the machinery of government visible and trustworthy. The conversation has been edited for length and clarity. Your research at NYU focused on something most people don't think about: underground urban infrastructure. What problem were you trying to solve, and how did AI change how you might have otherwise tried to solve it? Most cities, including New York, don't have comprehensive maps of what's beneath their streets. Water mains, fiber cables, gas lines, subway tunnels... they've been built up over decades by different agencies using different systems, and no one has a single coherent picture of where they all are