Optimized to Death: The Hypernetic Law of Experience

The Hypernetic Law of Experience (HLE) generalizes Ashby’s neglected Law of Experience from determinate machines to stochastic, gradient-driven adaptive systems. The HLE characterizes a persistent tendency of adaptive systems exposed to sustained directional experience: internal variety is progressively consumed, and system trajectories converge toward increasingly narrow regions of state space, even when local transitions remain probabilistic. We formalize this contraction pressure using the Rebis equation, a discrete-time variance-contraction dynamic that relates optimization pressure and novelty injection to the evolution of internal diversity. Through cross-domain comparative analysis, we show that HLE-consistent geometry appears in biological evolution, recursive model collapse in machine learning, economic cycles, neural plasticity and habituation, linguistic convergence, and institutional lock-in. In these domains, excessive variety consumption is associated with brittle attractors and heightened vulnerability under distributional shift. We further show that biological systems employ countervailing mechanisms—such as sexual recombination, mutational plasticity, sleep-driven renormalization, and variance-preserving neuromodulation—that mitigate, but do not eliminate, the contraction pressure described by the HLE. We conclude that the HLE and the Rebis equation provide a systems-level diagnostic for identifying and explaining optimization-induced fragility and for informing the design of regulators, AI architectures, and institutions that remain viable under drift.