Sixun Tang

Patent enforcement is intended to protect innovation, but operating firms may also use litigation strategically to defend product market rents at rivals’ expense. This paper studies patent litigation as a margin of product market competition. A simple model predicts that both the decision to sue and the resources committed to litigation increase with the contested market position, as captured by the parties’ product-market proximity. I test these predictions using the universe of U.S. patent suits between operating firms and two new measures: product-market proximity constructed from workforce skills and litigation effort constructed from court dockets and attorney billing records. Three findings support the strategic litigation view. First, a one-standard-deviation higher exposure to an adverse shock to patent strength reduces filings by 25 percent. Second, conditional on measures of infringement risk, patentees target their closest product-market rivals and fight them harder. A rival one standard deviation closer faces 1.7 times the odds of being sued and 18 to 22 percent higher litigation expenditures on both sides, yet plaintiffs win no more often against closer rivals. Third, an active enforcer’s closer rivals face a greater expected litigation burden even when they are not sued. Following a suit, the enforcer’s closest rivals produce approximately 4.6 percent fewer breakthrough patents after two years, while the defendant’s rivals exhibit no comparable response. Taken together, the results suggest that patent enforcement between operating firms reallocates rents toward enforcing incumbents and weakens innovation beyond the litigating parties.

This paper examines how the commercialization of generative artificial intelligence influences new firm creation, leveraging the release of ChatGPT as a quasi-exogenous shock to AI accessibility. We develop a novel entrepreneurship-specific measure of AI compatibility at the industry level, which captures the extent to which AI tools can support the tasks entrepreneurs perform when starting a firm. Using a difference-in-differences design, we find that industries more compatible with AI experienced an increase of over 10% in firm formation following ChatGPT’s release. To investigate why, we examine three mechanisms: reduced experimentation costs, capital reallocation, and skill gap bridging. While we find no evidence supporting the latter two, we show that the increase in firm formation is concentrated in industries where AI can effectively assist and experimentation is critical—evidence consistent with a reduction in the cost of experimentation as the key driver. Moreover, new firms created after ChatGPT’s release in AI-compatible industries are more likely to survive, grow faster, and attract more educated workers. Taken together, the mechanism and quality results suggest that AI lowers the cost of experimentation in a way that disproportionately benefits high-ability entrepreneurs, leading to both greater and higher-quality firm formation.

Do more financing sources lead to more innovation? This seemingly simple question requires second thoughts given the complex nature of the interaction between firms. In this paper, I construct an endogenous growth model with three new ingredients: defensive action, heterogeneous innovativeness of firms, and financing constraint. The prediction of the model distinguishes two separate cases following a positive financing shock: if the leader is more innovative, more financing sources would enable leaders to do more innovation, thus promoting the innovation rate in the industry; on the other hand, if the leader is less innovative, more financing sources would make it more involved in defensive action and hamper aggregation cost. Thus the impact on aggregate innovation is ambiguous. Moreover, due to the impact of differential innovation rate, there is a distributional effect: industries with less innovative leaders will increase, and may lead to a slowdown in aggregate economic growth.