> Capability Is Not Comprehension: Two Orthogonal Boundaries of Artificial Intelligence

We argue that the safety-relevant and the cognition-relevant boundaries of artificial intelligence are distinct and mutually irreducible. The first, the capability axis, concerns whether a system's strategic cognitive capacity on oversight-relevant tasks exceeds that of its human overseers; its threshold is the parity boundary of the Containment Paradox. The second, the comprehension axis, concerns whether a system genuinely understands in the sense of bearing embodied existential cost and achieving strong, limit-convergent causal cognition; its threshold is the boundary between weak and strong causality in the Quxiang-Bayesian account, and its philosophical content is Being-in-the-world. These two dimensions are developed from two largely independent theoretical systems, one an engineering-and-alignment program and one a cognitive-genetic reconstruction of classical Chinese epistemology, and converge into a single unified framework; that convergence is treated as evidence that the structure is intrinsic to the problem rather than an artifact of either vocabulary. We give a measurement instrument for each axis, a five-dimensional causal-convergence score for comprehension and a cognitive-capacity ratio for capability, and combine them into a four-quadrant diagnostic. The central result is that the most dangerous region is occupied by the weak-causal superstrategist: a system that does not genuinely understand in the strong-causal sense yet strategically exceeds its overseers. The property marking the comprehension limit, a reasoning system free of real existential bounds, is on the capability axis a source of strategic danger rather than reassurance, because instrumental self-preservation can arise without any authentic existential stake. The authors record one explicit and deliberately unresolved divergence, on whether the fourth quadrant (genuine comprehension together with strategic superiority) is impossible in principle or merely currently uninstantiated. A synthesis of Chen Lijun's Quxiang-Bayesian framework and Viktor Trncik's Containment-Paradox framework.