Recent advances in data-driven modeling, neuromorphic engineering, and bio-inspired sensing can reshape how we perceive and control turbulent flows. To sample turbulent flows, we need to generate vast amounts of data that are well-resolved in both space and time. This might lead to complex multi-input multi-output optimization problems for flow control. Moreover, traditional sensing approaches rely on continuous or periodic-sampled acquisition, leading to signal properties in accordance with the Nyquist–Shannon sampling theorem. Reduced-order modeling allows for a representation of the flow fields as a combination of a limited number of coherent structures. Event-based sensing, where information is acquired only when significant changes occur, offers a radically different paradigm for sensing. Last but not least, neuromorphic sensors in biological systems release spikes based on the convolution of a signal with specific filters tailored to detect specific events. In this presentation we will explore the physical, computational, and biological foundations of event-based flow sensing, illustrating how turbulence physics, reduced-order modeling, and neuromorphic hardware can converge toward the next generation of intelligent flow control systems.