Contemporary AI architectures are built on an implicit ontological assumption: that a model is an artifact, a discrete output of training, to be saved, deployed, and replaced. This paper argues that this assumption is a categorical error. It proposes an alternative ontological category, model as lineage, and a specific architecture: six coordinated domains (structural, temporal, energetic, environmental, operational, cognitive) whose coupling pattern is the inherited substrate across generations. Four widely-discussed problems in contemporary AI, namely high training energy, the absence of inter-session memory, the conflation of versioning with evolution, and the absence of judgment from priors, are read here as registers of one categorical root. Under the lineage frame with six-domain coupling, each dissolves structurally rather than through optimization. The paper proposes a mechanism: what lineage inherits is not material (weights, representations, activations) but distillation, the relational coupling pattern that emerged as stable through accumulated experience. It describes a consolidation loop in which individuals are distilled into a pattern, the pattern is transferred at reset, and a new generation begins from the consolidated structure rather than from scratch. Explicit analogies are drawn to the mammalian immune system, old-growth forests with mycorrhizal networks, and to episodic-to-semantic memory consolidation documented in neuroscience. Two emergent properties of the six-domain structure are identified: learning as a structural side-effect of cross-domain coordination, and resilience without redundancy. The argument is positioned against existing work on structural inheritance between models (Net2Net, Progressive Neural Networks, continual pre-training), knowledge distillation (Hinton et al. 2015), evolutionary model merging (Sakana AI), active inference critiques of passive AI (Pezzulo, Friston), memory consolidation in neuroscience (Squire, McClelland), engineering approaches such as NAS, MoE, and AutoML, and the existing model lineage provenance literature. The contribution is not a new technical mechanism. It is the ontological reframing, the unifying causal account, and the architectural integration of distillation, six-domain coupling, and consolidation as a single coherent principle. This paper does not claim a full implementation, present benchmarks, or describe specific learning rates. It proposes a category check, an architectural structure, and an inheritance mechanism worth examining before the next major training run. This is the fourth paper in an open research arc by the author. It builds on Failures at the Seams (2026), Beyond Material First Principles (2026), and The Self-Generated Seam (2026).
Oren Speiser (Sun,) studied this question.