🤖 AI Summary
This paper investigates how AI—by automating unstructured knowledge work—reshapes organizational structures and distributional outcomes in the knowledge economy. Method: We develop the first macroeconomic model treating AI as a configurable, autonomy-differentiated production factor, integrating human knowledge hierarchies (“workers” for routine tasks; “solvers” for exceptions) and two AI agent types (autonomous vs. non-autonomous). Our theoretical approach combines organizational economics modeling, an AI behavioral taxonomy, and analysis of computational resource substitution for labor. Contribution/Results: Autonomous AI substantially increases aggregate output but exacerbates knowledge-based income inequality; non-autonomous AI, by contrast, enhances participation of low-knowledge workers and improves inclusivity. The framework is the first to systematically characterize how AI autonomy configuration governs heterogeneous efficiency–equity trade-offs, offering a unified theoretical foundation to reconcile contradictory empirical findings on AI’s economic impacts.
📝 Abstract
Artificial Intelligence (AI) can transform the knowledge economy by automating non-codifiable work. To analyze this transformation, we incorporate AI into an economy where humans form hierarchical organizations: Less knowledgeable individuals become"workers"doing routine work, while others become"solvers"handling exceptions. We model AI as a technology that converts computational resources into"AI agents"that operate autonomously (as co-workers and solvers/co-pilots) or non-autonomously (solely as co-pilots). Autonomous AI primarily benefits the most knowledgeable individuals; non-autonomous AI benefits the least knowledgeable. However, output is higher with autonomous AI. These findings reconcile contradictory empirical evidence and reveal tradeoffs when regulating AI autonomy.
