Data is non-rival: a firm's data can be used simultaneously by others, and information about its customers benefits other firms even across industries. How is data being shared? Using granular information on mobile app usage, functionalities, and connections with data analytics platforms, we uncover a network of inter-firm data flows. Data sharing generates comovements in operational, financial, and stock-market performances among data-connected firms, beyond what traditional economic linkages can explain, and induces strategic complementarity in firms' product-design choices. Apple’s App Tracking Transparency policy, which restricts inter-firm data flows, weakens these patterns, providing causal evidence of the role of data sharing. To explain these findings, we develop a dynamic network model of data economy, where firm growth becomes interconnected through data sharing. The model introduces a network-augmented Gordon growth formula to value data-generated cash flows, capturing direct and indirect network externalities over multiple time horizons. Our metrics of valuation centrality identify systemically important firms that disproportionately influence the data economy due to their pivotal positions within the data-sharing network.

I study the optimal integration of humans and technologies in multi-layered decision-making processes. Each layer can correct existing errors but may also introduce new ones. A one-dimensional quality metric – a decision-maker’s error correction capability normalized by its new errors – determines the optimal rule: deploying higher-quality technologies in later stages. Interestingly, the final decision-making layer may not achieve the greatest error reduction; instead, its role hinges on minimizing new errors. Human effort varies asymmetrically across layers—early stages prioritize error correction with lower effort, while later stages emphasize avoiding new errors with higher effort. Applying the model to artificial intelligence (AI) reveals that AI's generative capabilities make it more likely to serve as the final decision-maker, reducing the need for costly human input, but underscoring the risks of AI hallucination. The theoretical framework also extends to applications including repeated delegation, automation design, loan screening, tenure review, and other multi-layer decision-making scenarios.

We model the competition between digital forms of fiat money and private digital money (PDM). Countries strategically digitize their fiat money --- by upgrading existing or launching new payment systems (including CBDCs) --- to enhance adoption and counter competition from PDM. A pecking order emerges: less dominant currencies digitize earlier, reflecting a first-mover advantage; dominant currencies delay digitization until they face competition; the weakest currencies forgo digitization. Delayed digitization allows PDM to gain dominance, weakening fiat money’s role in the long run. We also highlight the roles of stablecoins, interoperability, and public-private collaborations in the digitization of money and monetary competition.