mathematical-necessities-ground-post-crisis-paradigm

Antecedents (all must be IN):

• IN deep-learning-foundations-validated-as-mathematical-necessities — Deep learning's two foundational mechanisms — weight sharing for geometry-matched compression and gradient flow for trainability — were each independently validated as mathematical necessities through convergent discovery across disconnected fields, meaning deep learning's architecture rests on discovered structure rather than design choices.
• IN perfect-knowledge-zero-consequence — ML has achieved a state of perfect self-knowledge with zero institutional consequence — the crisis is epistemically closed (fully characterized, triply certain, resistant to self-diagnosis) while accountability is permanently impossible (structurally blocked by the inverse correlation between capability and interpretability), creating an unprecedented situation where a field completely understands its own failure modes yet possesses no mechanism to be held responsible for them.

Unless (any of these IN defeats this justification):

• IN terminal-epistemic-saturation — ML has reached terminal epistemic saturation — the reliability gap is simultaneously a self-sustaining epistemic fixed point (fully characterized, empirically confirmed, self-amplifying) and its only existence proof of escape grows asymptotically irrelevant with capability scaling, meaning the field possesses maximally complete understanding with asymptotically zero actionable content.