Phenotypic plasticity—the ability of cells to reversibly alter their functional and morphological identity—is a central tenet of cancer progression. During metastasis, cancer cells exploit developmental programs of plasticity to overcome multiple barriers. Similarly, therapy evasion is often driven by plasticity in the form of cellular persistence, which has been proposed as a precursor to therapy resistance. This adaptive behavior is orchestrated by gene regulatory networks (GRNs), which regulate protein expression and thereby determine cell phenotypes. A detailed understanding of the design principles of GRNs is therefore essential for devising strategies to target different arms of cancer. We analyzed GRNs underlying epithelial–mesenchymal plasticity (EMP), a key axis of plasticity crucial for metastasis, to identify which network features—such as structure, kinetic parameters, or expression configurations—most strongly shape the emergent “phenotypic landscape.” Our analysis uncovered a distinctive organizational motif in EMP GRNs: mutually inhibiting “teams” of nodes, with nodes within the same team activating each other’s expression while those in opposite teams inhibiting each other. This architecture, which emerges from an abundance of cooperating positive feedback loops, governs multiple properties of the EMP landscape, including the high stability and abundance of epithelial and mesenchymal phenotypes, the plasticity of hybrid E/M states that confer metastatic fitness, and the robustness of phenotypic distributions to structural and kinetic perturbations. Importantly, these effects were largely independent of other factors, such as precise kinetic parameters. Beyond EMP and cancer, we found GRNs in various contexts having strong. The networks without teams could also be viewed as teams with “impurities,” allowing us to predict the statistical features of the corresponding phenotypic landscapes. This framework thus provides a mechanistic link between network architecture and emergent dynamics. In summary, our results establish mutually inhibiting teams of nodes as a fundamental design principle of phenotypic plasticity. Beyond advancing conceptual understanding, the team framework offers a potential translational application: predicting cellular dynamics directly from patient-derived gene expression snapshots. Such predictive power could ultimately inform the design of more effective therapeutic strategies to limit cancer progression and therapy resistance.