Researchers have identified a fundamental paradox in expertise: while it's demonstrably learnable over time, expertise cannot be fully transmitted through explanation. This apparent contradiction stems from two distinct learning modes - instruction (transferring explicit models) and calibration (developing internal models through feedback). The human mind processes complex patterns that language cannot fully capture.

Consider the pedestrian crossing a road, who simultaneously evaluates dozens of variables - from car speed to driver attentiveness to road conditions - while a simple rule-based system might only handle three dimensions. This high-dimensional knowledge involves combinatorial interaction effects that exceed what language can practically encode, making articulate explanation impossible despite the model's sophistication.

The framework explains why institutions systematically favor 'book smarts' over 'street smarts,' valuing legible, testable knowledge over experiential judgment that can only be observed through outputs. This creates a selection bias where articulate strategists often outrank experienced operators whose predictive accuracy exceeds their ability to explain reasoning, despite running more complex mental models.