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In AI-driven materials design, the formulation of objective functions fundamentally shapes innovation trajectories by defining priorities across vast design spaces. This conceptual manuscript examines how optimization targets steer the discovery and refinement of materials, shaping emergent properties, scalability pathways, and integration into technological and societal systems. By synthesizing advancements in surrogate modeling, Bayesian optimization, generative architectures, and multi-objective strategies from recent literature, the analysis shows that single-objective formulations often constrain exploration to narrow performance peaks. In contrast, multi-objective configurations introduce intricate interaction dynamics, trade-offs, and feedback structures that diversify possible material outcomes. The proposed objective function nexus (OFN) framework conceptualizes objective functions as an interconnected system in which primary metrics, auxiliary constraints, weighting schemes, and iterative evaluations create steering logics that channel computational effort toward distinct horizons—ranging from high-performance specialized materials to scalable, sustainable alternatives. Analytical implications underscore nonlinear effects arising from objective interactions, such as the amplification of certain property clusters at the expense of others. At the same time, systems-level insights reveal how these choices encode epistemic priorities and value-laden selections. Trade-offs between competing goals, including performance versus manufacturability or cost versus environmental impact, manifest as dynamic tensions that reshape accessible design spaces over iterative cycles. By interpreting these dynamics interpretively, the framework illuminates how objective function design not only navigates but actively sculpts the futures of materials science, inviting reflective consideration of the priorities embedded in optimization practices.