Modern machine learning works extremely well in practice, but its theory was developed in an era when learning systems were weak. Classical theory—centered on PAC learning—mistook statistical concentration under IID assumptions for learning. Today’s ML theory still treats mathematical convergence as learning, rather than as control over data manifolds. This work reframes the ontology of machine learning as an engineering discipline, not a theory of mathematical convergence.

Symbolic computation is often criticized for weak optimization and generalization, but its core failure is structural: it requires every intermediate condition to co-exist and every operation to be world-instantiable, so a single failed grounding collapses the entire chain. Human cognition never seeks such total realizability; it works through partial information and non-exhaustive specification, which is why symbolic inference is not how thought operates, but a formal ideal dependent on conditions the world does not provide.

Persistent failures of statistical and causal inference to match real-world behavior across disciplines reveal a deeper limitation: mechanisms cannot be recovered from data distributions. These mismatches motivate a revisit of both paradigms, showing that statistical inference collapses into geometric alignment, while causal inference extends the same error by conflating probability with causality.