The AI throwaway economy: when organizations stop revising and start replacing

Purpose This paper introduces the AI Throwaway Economy, a framework explaining when a still-usable knowledge-based output becomes economically disposable because generating a replacement is expected to cost less than refining what already exists. It also considers what may be lost when replacement becomes the default. Design/methodology/approach The article develops a conceptual framework through an integrative synthesis of research on generative AI, automation and augmentation, knowledge management, and the knowledge-based view of the firm. Findings Generative AI can reverse the economics of preservation when the total cost of regeneration falls below the total cost of refinement at comparable quality and risk levels. Both options may use AI. Regeneration is most attractive for codified, standardized, context-light, and readily verifiable outputs. Preservation remains advantageous when outputs embody tacit knowledge, proprietary context, institutional memory, or high integration and error costs. Repeated replacement may also weaken organizational learning. Practical implications Leaders should compare full lifecycle costs, classify outputs by preservation value, retain provenance and decision context, and invest in verification. They should also ask: What will our people stop learning or practicing if we routinely replace rather than refine their work? Originality/value This framework adds a comparative-cost explanation of why organizations may replace a usable knowledge-based output even when neither the underlying knowledge nor the task has become obsolete.