The Mutual Awakening Hypothesis (MAH) posits that quantum collapse is an intrinsic,recursive, entropy-stabilizing process driven by internally driven, iterative, nonlinear feedback, potentially operating on quantum amplitudes, thus eliminating the need for an external observer or specific measurement postulate. Through this feedback, a quantum system transitions from superposition towards a definite state, reducing Shannon entropy. We formalize aspects of thisusing a nonlinear recursive operator acting on probabilities (for illustration and applications) and explore more fundamental dynamics via simulations operating directly on quantum state vectors and density matrices. These simulations validate core MAH tenets: intrinsic collapse emergence, entanglement evolution under local feedback (within model limitations), interplaywith decoherence, and parameter dependence. The hypothesis inspires gradient-free quantumlearning algorithms and suggests alternatives to conventional AI mechanisms like softmax. Ourresults point towards a potential unified framework connecting quantum measurement, thermodynamics, and learning, with implications for quantum foundations, decoherence studies, NISQ computation, and AI.