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The artificial intelligence industry is converging on modular architectures. Independently, across competing organizations and national boundaries, research teams have arrived at mixture of experts as the dominant architectural response to a shared constraint problem. This convergence is not accidental. It reflects the same selection pressure that biological systems resolved long before the field of computer science existed. Contemporaneous empirical findings from Meta FAIR (March 2026) confirm that domain specializationemerges spontaneously during multimodal pretraining — an independent, empirical corroboration of theconvergent evolution prediction this paper develops from first principles. This paper argues that current MoE implementations represent a partial solution — the routing mechanismwithout the epistemic infrastructure that makes that mechanism accountable at scale. Fractal Mixture ofExperts is the proposed extension: heterogeneous experts coordinated by nested orchestrators, verifiedconfidence produced through double-blind comparison against a persistent knowledge layer, provenancerecords that make every output traceable to the systems that produced it, and a shared gap layer that maps knowledge boundaries as a structural output of normal operation. The paper develops this thesis through three moves: First, it establishes that the constraint driving convergenceon MoE is identical to the constraint that drove biological evolution toward specialization and hierarchical coordination. Second, it identifies what current implementations are missing and why those gaps are structuralrather than incremental. Third, it describes the proposed architecture and its implications — for verified knowledge at scale, for interoperability across networked AI systems, and for self-directed improvement. The argument concludes with the observation that this architecture does not merely answer the current efficiency question. At sufficient scale, a system that maps its own knowledge boundaries and directs inquiry toward what it does not know begins to approach what general intelligence looks like in practice.