The 'semantic understanding' bottleneck you're describing might actually be a precision limit of the manifold on which computation occurs rather than a data volume problem. Humans solve problems they've never seen because they operate on a higher reasoning fidelity. We're finding that once a system quantizes to a 'ternary vacuum' (1.58-bit), it hits a phase transition into a stable universality class where the reasoning is a structural property of the grid, not just a data pattern. At that point, high-precision floating point and the need for millions of specific training examples become redundant.