I explain why current success can undermine an organization's ability to innovate. I consider a standard bandit problem between a safe and a risky arm with two modifications. First, a principal allocates resources. Second, an agent must install the risky arm, which is not contractible. If the principal cannot commit to a resource policy, a dual moral hazard problem emerges: The agent's pay must be tied to the risky arm's success to encourage installation, inducing the principal to stop experimenting with the arm prematurely. This problem intensifies as the safe arm becomes more profitable, potentially leaving the organization worse off.

An increasing number of monopolization cases have been constructed around the notion of “must-have” items: products that distributors must carry to “compete effectively.” Motivated by these cases, we consider a multiproduct setting where upstream suppliers sell their products through competing distributors offering one stop-shopping convenience to consumers. We show the emergence of products that distributors cannot afford not to carry if their rivals do. A supplier of such products can exploit this must-have property, along with tying and exclusivity provisions, to monopolize adjacent, otherwise competitive markets. Policy interventions that ban tying or exclusivity provisions may prove ineffective or even backfire.

To what extent can an incumbent manufacturer use discount contracts to foreclose efficient entry? We show that off-list-price rebates that do not commit buyers to unconditional transfers--like the rebates in EU Commission v. Michelin II, for instance--cannot be anticompetitive. This is true even in the presence of cost uncertainty, scale economies, or intense downstream competition, all three market settings where exclusion has been shown to emerge with exclusive dealing contracts. The difference stems from the fact that, unlike exclusive dealing provisions, rebates do not contractually commit retailers to exclusivity when signing the contract.

I examine how upstream mergers affect negotiated prices when suppliers bargain with a monopoly intermediary selling products to final consumers. Conventional wisdom holds that such transactions lower negotiated prices when the products are complements for consumers and raise them when they are substitutes. The idea is that consumer demand relationships carry over to upstream negotiations, where mergers between complements weaken the suppliers’ bargaining leverage, while mergers between substitutes strengthen it. I challenge this view, showing that it breaks down when the intermediary sells products beyond those of the merging suppliers. In such cases, the merging suppliers' products may act as substitutes for the intermediary even if they are complements for consumers, or as complements for the intermediary even if they are substitutes for consumers. These findings show that upstream conglomerate mergers can raise prices without foreclosure or monopolization and help explain buyer-specific price effects resulting from such mergers.

Artificial Intelligence (AI) is reshaping offshoring and globalization by automating knowledge work and altering trade patterns. We analyze this transformation in a two-region world where firms structure work hierarchically to use knowledge efficiently: the most knowledgeable individuals specialize in problem-solving, while others perform routine work. Before AI, the Advanced Economy specializes in problem-solving services, while the Emerging Economy focuses on routine knowledge work. We model AI as a technology that converts compute into autonomous “AI agents,” which serve as perfect substitutes for humans with a given level of knowledge. Reflecting the concentration of AI infrastructure in advanced economies, we assume that all compute is located in the Advanced Economy. We show that basic AI reduces the Advanced Economy’s net exports of problem-solving services, potentially reversing pre-AI trade patterns. In contrast, sophisticated AI increases the Advanced Economy’s net exports of problem-solving services, reinforcing existing trade patterns. We also examine the effects of restricting AI autonomy, finding that a global restriction redistributes AI’s benefits toward lower-skilled workers, while a regional restriction—such as banning autonomous AI in the Emerging Economy—does little to benefit lower-skilled workers and harms the most knowledgeable individuals in that region. Our results underscore the need for a coordinated global approach to AI regulation.

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.