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The integration of artificial intelligence (AI) into materials science represents a profound epistemic shift, challenging longstanding assumptions about the nature and validation of scientific knowledge. Rather than merely accelerating computational tasks, AI reconfigures the epistemological landscape by generating outputs that blur the boundaries between data, inference, and insight. This paper diagnoses a central problem: “knowledge inflation,” where AI’s predictive prowess is prematurely equated with genuine understanding, leading to overconfidence in materials-related decisions. Tensions arise between the opacity of AI-driven predictions and the demands for explanation and mechanistic clarity inherent to materials science, where structure-property relationships and causal processes have traditionally grounded epistemic warrant. Such discrepancies risk undermining the reliability of knowledge claims in domains like alloy design and sustainable material selection. To address this, we propose a reframed epistemic framework tailored to materials AI, centered on actionability as the criterion for knowledge: what can be responsibly acted upon in practical contexts. This includes a novel typology distinguishing epistemic categories of AI outputs, from mere predictive signals to robust decision warrants, with conditions for elevation between them. By emphasizing responsibility and scope, this reframing aims to safeguard epistemic integrity while harnessing AI’s potential.