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Do improvements in Artificial Intelligence (AI) benefit workers? We study how AI capabilities influence labor income in a competitive economy where production requires multidimensional knowledge, and firms organize production by matching humans and AI-powered machines in hierarchies designed to use knowledge efficiently. We show that advancements in AI in dimensions where machines underperform humans decrease total labor income, while advancements in dimensions where machines outperform humans increase it. Hence, if AI initially underperforms humans in all dimensions and improves gradually, total labor income initially declines before rising. We also characterize the AI that maximizes labor income. When humans are sufficiently weak in all knowledge dimensions, labor income is maximized when AI is as good as possible in all dimensions. Otherwise, labor income is maximized when AI simultaneously performs as poorly as possible in the dimensions where humans are relatively strong and as well as possible in the dimensions where humans are relatively weak. Our results suggest that choosing the direction of AI development can create significant divisions between the interests of labor and capital.

I examine the implications of cross-market mergers of suppliers to intermediaries that bundle products for consumers. These mergers are controversial. Some argue that suppliers' products will be substitutes for intermediaries, despite not being substitutes for consumers. Others contend that because bundling makes products complements for consumers, products must be complements for intermediaries. I contribute to this debate by showing that two products can be complements for consumers but substitutes for intermediaries when the products serve a similar role in attracting consumers to purchase the bundle. This result leads to new recommendations and helps explain why cross-market hospital mergers raise prices.

This paper provides a new framework for studying the impact of Artificial Intelligence (AI) on the organization of knowledge work. We incorporate AI into an economy where humans endogenously form hierarchical firms: Less knowledgeable agents become "workers" solving routine problems, while more knowledgeable agents become "solvers" handling exceptions. We model AI as an algorithm that uses compute to mimic humans. We compare the equilibrium before and after AI's introduction, distinguishing between "basic" AI (with knowledge equivalent to pre-AI workers) and "advanced" AI (with knowledge equivalent to pre-AI solvers). We show that basic AI increases the knowledge content of human work, leading to smaller, less productive, and less decentralized firms. In contrast, advanced AI decreases the knowledge content of human work, resulting in larger, more productive, and more decentralized firms. In any case, the most knowledgeable humans benefit from AI, while the least knowledgeable benefit only when AI is sufficiently advanced. We discuss how these effects depend on AI's autonomy and the availability of compute.