Nonlinear Entrepreneurship to Linear AI Tasks: How Founders Translate Uncertainty into Agentic Workflows

Entrepreneurship and AI execution operate under different logics. Entrepreneurship is often nonlinear: founders act under uncertainty, work with incomplete information, discover goals through action, respond to surprises, and reshape the opportunity as new stakeholders appear. AI agents, by contrast, work best when goals, inputs, outputs, constraints, tools, and success criteria are explicit. This paper argues that the central capability of the AI-augmented founder is therefore not "prompt engineering" in a narrow sense, but task linearization: the translation of uncertain entrepreneurial reasoning into structured task graphs that AI systems can execute. The paper develops a conceptual framework connecting effectual entrepreneurship (Sarasvathy, 2001, 2008), agentic AI planning (Huang et al., 2024), and product-building practice. It introduces three concepts: Task Linearization, the conversion of ambiguous entrepreneurial work into executable subtasks; Task Architecture, the design of dependencies, evaluation criteria, and handoffs between human and AI work; and the Entrepreneurial Compiler, a metaphor for the founder's role in converting market ambiguity into prompts, workflows, agents, and feedback loops. The contribution is a practical theory for founders, AI product builders, and technical teams: AI does not automate entrepreneurship as a whole. It automates the parts of entrepreneurship that humans can crystallize into tasks. The future advantage belongs to founders who can move fluently between nonlinear exploration and linear execution.