Syntax Without Semantics: V-JEPA 2 and String Theory as Isomorphic Failures of the Mechanistic Paradigm

Both string theory and V-JEPA 2 (Meta's self-supervised video learning system) execute the same eight-stage mechanism: (1) inherited structure enters (general relativity as consistency requirement; human-curated categorical structure), (2) formal processing occurs through opaque machinery (duality transformations; latent space operations), (3) provenance is obscured, (4) outputs are misattributed as emergent understanding, (5) practitioners cannot articulate what their systems comprehend, (6) pseudo-explanations are deployed when pressed (anthropic selection; emergence at scale), (7) the frame problem manifests (relevance undeterminable from within), and (8) second-order blindness operates (failures are converted to apparent virtues). The parallel is not analogy but structural identity. Both fields instantiate the mechanistic paradigm's core assumption: that formal operations on discrete structures can ground semantic content. Three impossibility constraints establish why this assumption fails: no representation-manipulating system can ground reference; no statistical learner can acquire normativity; no syntactic engine can enter the space of reasons. Both string theory and self-supervised learning satisfy the antecedents; both exhibit the predicted failures. The convergence is diagnostic. When identical failure modes appear in fundamental physics and artificial intelligence - fields with different histories, methodologies, and practitioners - the failure indicates shared assumptions. The mechanistic paradigm has reached its terminal limit: the structural inability to ground semantics in syntax. String theory's mathematical structures underdetermine physical interpretation. AI's computational patterns underdetermine semantic content. Both paradigms pursue the incoherent aspiration to perspectiveless knowledge; both fail because perspectives are constitutive of understanding. This paper does not claim that string theory's mathematics is erroneous or that AI's engineering is useless. The mathematical tools and pattern-recognition capabilities are genuine achievements. The claim is that these achievements do not constitute what is claimed: physical understanding in string theory, world understanding in AI. Distinguishing achievement from overclaim is essential for deployment decisions, intellectual honesty, and the possibility of genuine progress. The crack runs through two structures. It is the same crack.