When AI navigates a city, it usually follows maps and instructions. Humans don’t. We move based on comfort, safety, instinct, and unspoken needs.

In this episode, we explore a recent line of research that asks a deeper question: can AI learn to choose streets the way humans do? Not by following directions, but by understanding the subtle signals that guide human movement through urban spaces.

We unpack how vision-language models are beginning to infer implicit human needs from real-world environments, why this shift matters beyond navigation, and what it reveals about the future of human-centered intelligence.

This isn’t about smarter maps, it’s about AI that understands us.

Perfect for listeners curious about AI, human-machine interaction, embodied intelligence, and the future of technology that feels less artificial and more human.