The centralization of artificial intelligence infrastructure presents a systemic challenge to human epistemic autonomy, data sovereignty, and long-term computational resilience. This paper introduces Intelligentia Artificialis Privata (Privata AI) as a formally defined class of AI systems distinguished by physical locality, operational autonomy, and bounded knowledge domains. We present a rigorous architectural model, a formal threat analysis, a comparative economic analysis, and a set of computational constraints governing such systems. We argue that Privata AI is not merely a privacy-enhancing variant of existing paradigms but constitutes a categorical shift in how intelligence is instantiated, owned, and constrained. Drawing on developments in federated learning, edge computing, differential privacy, and hardware trust architectures, we situate Privata AI within the broader landscape of privacy-preserving machine learning and identify its distinguishing properties. We further formalise two advances that strengthen the practical case for sovereign local inference: the Hardware-Bound Sealing (HBS) protocol, which binds model execution to a verified silicon identity via Physical Unclonable Functions and a formally defined human-in-the-loop interrupt ; and the 1.58-bit Parity Horizon, an empirical scaling law establishing that ternary quantisation achieves functional parity with full-precision models above a critical parameter threshold of approximately 13.4 billion parameters, consistent with the BitNet b1.58 scaling results. The implications span systems architecture, NPU micro-architecture, security engineering, epistemology of computation, political economy, and regulatory theory.
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