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The deployment of artificial intelligence (AI) in materials science has overwhelmingly prioritized first-order effects—such as accelerated property predictions, high-throughput screening of candidate compounds, and the discovery of novel materials with targeted functionalities—while systematically neglecting second-order effects that arise indirectly from transformations in research practices, institutional incentives, and community norms. Second-order effects are defined here as the consequences of AI adoption that emerge not from the immediate technical outputs of models but from the behavioral, structural, and epistemic shifts these outputs induce among researchers, laboratories, funding bodies, and publishing ecosystems. This framework identifies six principal types of second-order effects (epistemic, behavioral, institutional, social, normative, and ecological). It delineates four mechanisms through which first-order successes propagate into these indirect outcomes, including attention reallocation, success amplification, skill substitution, and self-reinforcing feedback loops. It then proposes a five-component anticipation framework—baseline mapping, intervention specification, causal pathway mapping, stakeholder analysis, and scenario development—that equips materials AI practitioners to foresee and mitigate such effects before large-scale deployment. By embedding foresight into the innovation pipeline, the framework advances responsible materials AI practices that safeguard the long-term integrity, equity, and epistemic robustness of the field, ensuring that technological gains do not inadvertently undermine the very scientific ecosystem they seek to enhance. Ultimately, proactive anticipation of second-order effects will allow the materials community to harness AI’s transformative power while preserving the diversity of inquiry, the balance between computation and experiment, and the human-centered values that have historically driven discovery.