Анотація: (English) Artificial intelligence has grown capable enough to reopen a basic question: how should work be divided between humans and machines? Fully autonomous AI performs well on structured, data-rich tasks, yet collaboration between humans and AI consistently produces stronger results in complex, context-dependent, and creative settings. This article reviews how human-AI interaction models have evolved and proposes a three-level classification of collaboration: automation, augmentation, and identity-level partnership. Drawing on established complementarity frameworks and hands-on experience in AI-driven software development, the author introduces the Scalable Human Model (SHM) – a framework in which AI operates as an extension of one individual’s cognitive style, communication patterns, and decision-making logic. A case study of OPV Systems validates the model: one developer, supported by AI agents, reached productivity levels normally associated with a team of five to eight. The findings suggest that this mode of partnership differs qualitatively from augmentation – it lets professionals scale their expertise without sacrificing authenticity or strategic control. Implications for software engineering, digital legacy systems, and human-centric AI design are discussed.