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In the evolving landscape of computational and data-driven materials engineering, the integration of advanced machine learning techniques with high-throughput simulations has transformed discovery pipelines, enabling accelerated identification of novel materials. However, as datasets grow in multimodality and scale, and models incorporate complex architectures such as graph neural networks, the allocation of computational resources emerges as a critical bottleneck. This conceptual manuscript addresses the infrastructural challenges in scaling these ecosystems, highlighting gaps in resource orchestration that hinder efficient coupling of simulation, experimentation, and inference processes. We introduce a novel framework, termed the Adaptive Resource Equilibrium Model (AREM), which conceptualizes resource allocation as a dynamic interplay between data representation fidelity, model computational demands, and discovery throughput. By synthesizing insights from materials informatics and autonomous systems, AREM emphasizes feedback mechanisms to balance epistemic uncertainties and infrastructural constraints, fostering resilient discovery infrastructures. The implications extend to enhancing inverse design workflows and closed-loop experimentation, potentially streamlining resource utilization in large-scale materials research consortia. This work provides a systems-level perspective on optimizing computational ecosystems, guiding future developments in scalable, data-centric materials engineering without empirical validation.