Scalability in incumbent firms: The case of Nvidia Vendrell-Herrero, Ferran Vaillant, Yancy Bustinza Sánchez, Óscar Fernando Scalability Firm growth Process Model Nvidia Single case study Ferran Vendrell-Herrero acknowledges support by the project PID2022-136235NB-I00 funded by MICIU/AEI/10.13039/501100011033 and by ERDF/EU. Oscar F. Bustinza acknowledges support from the Ministry of Universities of Spain within the framework of the State Program to Develop, Attract and Retain Talent, State Mobility Subprogram, of the State Plan for Scientific, Technical and Innovation Research 2021–2023 (Reference: PRX22/00176). Scalability refers to the organizational capabilities required to facilitate a smoother and faster scaling process. Although it is usually associated with new ventures, this study explores how established firms can also create conditions conducive to scalability. We address this question by applying an inductive, narrative-based approach to a longitudinal, single-case study of Nvidia Corporation, a company founded in 1993 that since 2006 has undergone a profound transformation driven by the AI revolution. This case study draws on digital archives, including objective accounting information on Nvidia and its direct competitors, extensive company reports, pedagogical case studies, corporate biographies, and 464 min of recorded documentaries and interviews featuring the company's CEO. We use these sources to develop a multi-phase theoretical model outlining how established organizations can foster scalability. The model encompasses value recognition driven by systemic industry transitions, organizational adaptability, strategic renewal, and scalability, thus offering a structured framework for understanding how incumbent firms can cultivate the necessary conditions for successful scaling. 2025-06-09T07:51:33Z 2025-06-09T07:51:33Z 2025-06-02 journal article F. Vendrell-Herrero et al. Long Range Planning 58 (2025) 102540. https://doi.org/10.1016/j.lrp.2025.102540 https://hdl.handle.net/10481/104534 10.1016/j.lrp.2025.102540 eng open access Elsevier