Theoretical Foundations of Growth in Companies Utilizing Generative Artificial Intelligence: Concepts, Classification, and Identification Criteria

The article examines the theoretical foundations of the companies’ growth utilizing generative artificial intelligence (generative AI), a technology that has gained particular relevance in recent years due to its ability to significantly accelerate business processes and provide firms with additional competitive advantages. The aim of the study is to assess the applicability of firm growth and innovation development theories to generative AI companies and, on this basis, to propose a classification of such companies and refine the criteria for identifying high-growth firms within the AI sector. Drawing on the concepts developed by J. Schumpeter, E. Penrose, D. Teece, C. Christensen, and R. Gibrat, the study identifies which elements of classical firm growth theory are applicable to the development trajectories of companies operating in the field of generative AI and which require further refinement. Generative AI is treated as general purpose technology, while acknowledging that this status remains subject to debate at the early stage of its diffusion. The article proposes a three-level classification by depth of use (categories A, B, and C), based on a formalized exclusion test and measurable threshold criteria. It also develops an extended multidimensional criterion for identifying high-growth firms before revenue generation. The analysis clarifies that the technology creates an innovator’s dilemma for incumbents through an attack by a qualitatively superior solution rather than through the canonical low-end disruption described by Christensen. The findings provide a theoretical basis for further analysis of growth models among Russian AI companies.

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