What Becomes Computable -Not What Becomes True AI-Enabled Exploration of Irreducible Uncertainty Landscapes

Recent advances in artificial intelligence have dramatically expanded the scale at which classification, optimization, and mathematical exploration can be performed across scientific domains. This expansion has prompted implicit claims that AI may dissolve long-standing epistemic limits by enabling deeper inference from data. This paper argues for a more conservative and rigorous interpretation: AI alters what becomes computable in inference landscapes, but does not alter what becomes true about the world. We formalize this distinction by showing how AI enables explicit exploration of uncertainty structure-including observability limits, identifiability degeneracies, and rate-bounded coherence failures-without resolving the underlying indeterminacy. Across physical, planetary, and socio-technical contexts, AI transforms uncertainty from a qualitative residual into a computable geometric object. However, this transformation does not license ontological claims, unique historical reconstructions, or inference beyond physical observability. The contribution of AI is thus epistemic clarification rather than epistemic transcendence. This paper frames AI as a tool for mapping irreducible uncertainty landscapes, making limits explicit rather than eliminating them, and provides guardrails for interpreting AI-enabled inference without collapsing the distinction between computation and truth.