In this episode, we break down a paper that connects the physics of self-organizing systems with the biological theory of active inference. The authors argue that any system that persists over time naturally forms a Markov blanket, a statistical boundary separating internal states from the external world. This boundary leads the system to behave as if it is minimizing variational free energy, using internal states to predict external causes and maintain its structure. We unpack how this framework links Bayesian inference, predictive coding, and biological survival, and how simulations—from primordial self-organizing systems to human motor control—illustrate the emergence of purposeful behavior.