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The integration of artificial intelligence into materials design processes introduces complex dynamics where initial algorithmic choices shape subsequent trajectories, often embedding persistent dependencies that influence innovation pathways. This manuscript explores the conceptual underpinnings of path dependence, examining how data selection, model architectures, and iterative learning mechanisms interweave to form self-reinforcing structures in AI-assisted materials discovery. Through a synthesis of recent literature, it examines the interpretive implications of bias propagation, feedback loops, and epistemic constraints in computational materials science. The proposed framework conceptualizes these elements as interconnected layers, in which early decisions cascade through design cycles, shaping the exploration of material spaces and the emergence of novel properties. By focusing on systems-level insights, the analysis highlights trade-offs between efficiency and diversity in algorithmic guidance, as well as ethical considerations in steering material innovation. This interpretive approach underscores the need for reflective practices in AI-driven workflows, emphasizing how path-dependent logics can both constrain and enable creative outcomes in materials engineering. Ultimately, the discussion integrates these dynamics to reveal broader implications for sustainable and equitable advancements in the field, without positing empirical directives.

Iterative materials AI pipelines, encompassing active learning frameworks and closed-loop discovery systems, have transformed the pace of materials innovation by enabling sequential decision-making under uncertainty. Yet these very systems are susceptible to an underrecognized failure mode: epistemic debt, the gradual accumulation of unexamined assumptions, unresolved uncertainties, and path-dependent constraints that silently erode the future potential of knowledge generation. This failure mode remains largely unacknowledged despite the growing reliance on such pipelines in materials science. This paper articulates epistemic debt as an intrinsic structural risk of iterative materials AI, one that demands explicit recognition if the promise of autonomous discovery is to be realized sustainably. By tracing the mechanisms through which debt accumulates, identifying materials-specific vulnerabilities that exacerbate it, and proposing both a typology and practical management principles, the analysis seeks to shift the conversation from short-term performance metrics to long-term epistemological integrity. Epistemic debt is formally defined as the accumulation of unexamined assumptions, unresolved uncertainties, and path-dependent constraints in an iterative knowledge-generating system that increase the cost of future learning or limit the space of future discoveries. It is conceptually distinct from technical debt, which concerns code maintainability and infrastructure, and from statistical compounding errors, which arise from sampling variance or measurement noise. The mechanisms driving epistemic debt—assumption cascades, path-dependent constraints, unrecognized uncertainty, and feedback loop amplification—interact in ways that are especially pernicious in materials contexts, where small initial datasets, high-dimensional composition spaces, and costly experimental iterations amplify the long-term consequences of early choices. Materials-specific vulnerabilities render these pipelines particularly fragile, as early decisions about representation or sampling can foreclose vast regions of chemical space without immediate visibility. A typology of epistemic debt types is articulated, distinguishing representational, sampling, modeling, and decision debt, each carrying unique signatures and risks within active-learning loops. Detection principles centered on assumption auditing and counterfactual tracing, together with mitigation strategies such as ensemble diversity and deliberate debt refinancing, provide a structured framework for managing this failure mode before it compounds irreversibly. By foregrounding epistemic debt as a distinct category of risk, this analysis offers the materials AI community a new lens through which to evaluate the sustainability of iterative discovery pipelines and to safeguard the integrity of long-term scientific progress.

The rapid proliferation of artificial intelligence (AI) techniques across materials science domains has delivered unprecedented predictive power and design acceleration. Yet, it has simultaneously engendered a previously under-examined failure mode: scientific lock-in through early AI adoption in materials domains. Scientific lock-in is defined here as the self-reinforcing entrenchment of specific AI methods, representations, or frameworks chosen early in the development of a subfield, rendering subsequent adoption of demonstrably superior alternatives prohibitively difficult even when their advantages become evident to the community. This failure mode arises through four interlocking mechanisms—increasing returns, switching costs, network effects, and institutionalization—and manifests across four distinct types: representational, methodological, data, and evaluation lock-in, each of which is shown to constrain the epistemic possibilities of materials research in characteristic ways. The resultant failure modes include suboptimal persistence of inferior approaches, innovation suppression of promising alternatives, comparative ignorance that prevents fair benchmarking, and collective regret in which the community recognizes the problem yet remains collectively unable to escape it. Detection principles grounded in observable indicators such as method concentration, citation bias, switching resistance, and comparative gaps are proposed, while mitigation principles centered on methodological pluralism, standardized comparisons, modular interoperability, community audits, and targeted funding for alternatives offer practical pathways to preserve long-term adaptability. By framing scientific lock-in as a distinct failure mode in materials AI, the present analysis urges the community to treat early adoption choices not merely as technical decisions but as high-stakes commitments whose downstream consequences must be deliberately managed if the field is to retain its capacity for genuine scientific progress.