Beyond Deductive Machinery: Why LLMs Cannot Generate Truly Novel Conclusions

This paper argues that large language models (LLMs), as currently conceived and implemented, are structurally incapable of generating genuinely novel conclusions. The argument proceeds in four stages. First, we demonstrate that LLMs, despite being trained through inductive compression of statistical regularities, deploy those representations through inference forms that are formally isomorphic to classical deductive patterns — principally modus ponens and modus tollens. Scaling does not dissolve this deductive structure; it buries it beneath high-dimensional computation. Second, we analyze the probabilistic character of LLM outputs and argue that the softmax function introduces epistemic rather than ontic uncertainty — a deterministic simulation of probability rather than genuine physical indeterminacy. Third, we develop an account of human logical leaps as physically grounded cognitive events, arising from stochastic variability in neural substrates and evaluated through game-theoretic frameworks operating across social and temporal scales. Finally, we situate this account within the tension between Bayesian rationality and existentialist commitment, arguing that LLMs lack the capacity for existential prior-setting that makes genuine novelty possible. The implications for AI development are significant: the path toward genuinely novel machine cognition requires rethinking hardware substrates, cognitive architectures, and the philosophical conception of what AI systems are for. Keywords: large language models, deductive inference, physical indeterminacy, logical leap, Bayesian epistemology, existentialism, AI development