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In the rapidly expanding domain of Artificial Intelligence for Materials Science, researchers routinely train machine learning models until training loss appears to converge. Yet, this practice overlooks a critical and distinct phenomenon: the point at which model outputs themselves cease to change meaningfully with further iterations or data. Algorithmic settling time is introduced here as the number of training iterations, epochs, data points, or active-learning cycles after which predictions for a given input distribution stabilize within a predefined tolerance, independent of loss minimization. This conceptual framework highlights five key factors—data scarcity, feature dimensionality, model complexity, task difficulty, and optimization dynamics—that modulate settling behavior in materials contexts where datasets are sparse, and property landscapes are high-dimensional. A four-component framework for settling-time analysis is proposed, centered on output monitoring, tolerance specification, settling detection, and confidence assessment, offering a principled alternative to ad-hoc early stopping. By foregrounding settling time as an overlooked parameter, this framework promises to enhance reproducibility, reduce computational waste, and improve the reliability of materials predictions ranging from crystal-property regression to generative molecular design, ultimately elevating the epistemic rigor of Materials AI practice.