We derive fundamental limits to demand analysis in financial markets from two foundational principles of asset pricing: (i) investors value assets for their payoffs, and (ii) prices satisfy no arbitrage. Given these principles, we show that (iii) asset demand curves...

We study optimal incentive contracts when workers privately observe whether Agentic AI can automate their jobs. Firms balance bonuses for truthful reports of successful automation with termination threats. Workers may be fired regardless of automation success (\textit{mass termination}), even though...

Firms are increasingly adopting predictive artificial intelligence (AI) to improve decision-making by combining advanced data analysis with managerial judgment. While AI provides more precise information to support managerial decision-making, its adoption can nevertheless reduce shareholder profits and the aggregate welfare,...

Advances in artificial intelligence raise fundamental questions about how technology reshapes human skills. Modern AI systems may crowd out active human decision-making; unlike past technologies, such decisions are a key input for training and improving AI models. We develop a...

With complete-information bilateral bargaining in network settings, holdup is eliminated when contracts across the network are agreed atomically (all or none) via a smart contract. Applications include over-the-counter trading, syndicated lending, multi-tranche securitizations, third-party financed purchases, and bookbuilding. Under a...

We develop a unified theory of delegated capital management that extends the competitive, rational-expectations paradigm of Berk and Green (2004) from mutual funds to alternative assets. With perfectly competitive capital markets, we derive the optimal contract and account for observed...