The core theoretical mechanism posits that organizational efficacy in an AI-native domain is contingent upon transforming fragmented open-loop processes into self-regulating closed loops where all workflows and data artifacts feed a central intelligent layer. This principle redefines the management hierarchy, replacing human middleware with queryable infrastructure to maximize information velocity while establishing three distinct archetypes: Individual Contributors (builders/operators), Directly Responsible Individuals (strategists/outcome owners), and AI Founders (context providers/leaders). The theory operates within the discipline of organizational systems engineering, asserting that maximum capability is achieved by substituting headcount with token-maximized agent usage under an "AI Operating System" framework.
The core theoretical mechanism posits that organizational efficacy in an AI-native domain is contingent upon transforming fragmented open-loop processes into self-regulating closed loops where all wo…