The rise of Artificial Intelligence (AI) has the potential to reshape the knowledge economy by enabling problem solving at scale. This paper introduces a framework to analyze this transformation, incorporating AI into an economy where humans form hierarchical firms to use their time efficiently: Less knowledgeable individuals become "workers" solving routine problems, while more knowledgeable individuals become "solvers" assisting workers with exceptional problems. We model AI as a technology that transforms computing power into "AI agents," which can either operate autonomously (as co-workers or solvers/co-pilots) or non-autonomously (only as co-pilots). We show that basic autonomous AI displaces humans towards specialized problem solving, leading to smaller, less productive, and less decentralized firms. In contrast, advanced autonomous AI reallocates humans to routine work, resulting in larger, more productive, and more decentralized firms. While autonomous AI primarily benefits the most knowledgeable individuals, non-autonomous AI disproportionately benefits the least knowledgeable. However, autonomous AI achieves higher overall output. These findings reconcile seemingly contradictory empirical evidence and reveal key tradeoffs involved in regulating AI autonomy.
