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.

We analyze how Artificial Intelligence (AI) reshapes global knowledge work in a two-region world where firms organize production 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, whereas the Emerging Economy focuses on routine work. AI converts compute—which is located in the Advanced Economy—into autonomous “AI agents'' that perfectly substitute for humans with a given level of knowledge. Basic AI reduces the Advanced Economy’s net exports of problem-solving services, potentially reversing pre-AI trade patterns. In contrast, sophisticated AI expands these exports, reinforcing existing trade patterns. Finally, we show that a global ban on AI autonomy redistributes AI’s gains toward lower-skilled workers, while a regional ban—such as prohibiting autonomy only in the Emerging Economy—offers little benefit to lower-skilled workers and harms the most knowledgeable individuals in that region.

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.

Current AI systems are better than humans in some knowledge dimensions but weaker in others. Guided by the long-standing vision of machine intelligence inspired by the Turing Test, AI developers increasingly seek to eliminate this "jagged" nature by pursuing Artificial General Intelligence (AGI) that surpasses human knowledge across domains. This pursuit has sparked an important debate, with leading economists arguing that AGI risks eroding the value of human capital. We contribute to this debate by showing how AI capabilities in different dimensions shape labor income in a multidimensional knowledge economy. AI improvements in dimensions where it is stronger than humans always increase labor income, but the effects of AI progress in dimensions where it is weaker than humans depend on the nature of human–AI communication.  When communication allows the integration of partial solutions, improvements in AI’s weak dimensions reduce the marginal product of labor, and labor income is maximized by a deliberately jagged form of AI. In contrast, when communication is limited to sharing full solutions, improvements in AI’s weak dimensions can raise the marginal product of labor, and labor income can be maximized when AI achieves high performance across all dimensions. These results point to the importance of empirically assessing the additivity properties of human–AI communication for understanding the labor-market consequences of progress toward AGI.