Thomas J. May

About Me

I am an assistant professor at California State University, Fullerton. My research interests are macroeconomics, firm dynamics, and regulatory policy. Additionally, I am actively developing techniques to apply machine learning and big data to macroeconomic questions. When I am not wading through data, I enjoy cooking, gardening, and paragliding.

Publications

• On the Nature of Entrepreneurship
  Journal of Political Economics, 2026
  with: Anmol Bhandari, Tobey Kass, Ellen McGrattan, and Evan Schulz
  Paper
  

Working Papers

• Can AI Developers’ Profits Survive Open-Weight Competition?
  Abstract: Large language models (LLMs) are an easily imitable technology. In a task-assignment model with imitation, I estimate that the capabilities of open-weight models, which can be used for free in place of proprietary models, lag the proprietary frontier by only about five months. If open-weight and proprietary models are equally costly to serve, each automated task must repay its development cost within the roughly five months before open-weight models imitate it. This raises a puzzle: what must we assume for the observed level of investment in AI models to be profitable?
  Paper

• Subsidize First And Profit Later: An Incomplete Information Analysis of Technology Subsidization
  Abstract: To drive adoption, producers of new technologies frequently subsidize consumers, pricing below cost in early periods and recouping costs once an installed base is established. This paper develops a two-period incomplete information model of technology adoption in which the entrant’s net benefit of the new technology is private information. This model admits a unique pooling equilibrium. This result delivers a stark challenge for antitrust regulators: both a welfare-improving entrant and a welfare-reducing predator play the same strategy, and no price-based regulatory test can distinguish them.
  Paper

Working Projects

• Market Structure and Inequality
  with: Div Bhagia

• Antitrust Policy and Economic Growth
  Abstract: It has become increasingly apparent to policymakers that optimal antitrust policy re- quires looking beyond traditional static analyses and considering the dynamic effects of policy. Such analysis is challenging as limited studies exist concerning dynamic compe- tition policy. This paper attempts to bridge this knowledge gap by developing a novel structural growth model containing the major motivations of mergers and acquisitions (M&A) activity. To enable estimation of the model, frontier natural language processing (NLP) techniques are employed to classify whether parties to an M&A transaction are currently operating in similar markets or whether acquirers are using M&A as an entry mechanism into new markets. Examining the overall impact of M&A on growth reveals a double-edged sword: policies that either completely shut down M&A or allow unre- stricted M&A both result in significantly lower growth rates than the baseline estimate. This motivates an optimal antitrust policy that accounts for dynamic effects.

• Using Machine Learning to Classify M&A Transaction
  Abstract: This paper develops a novel methodology for classifying relationships between parties in a merger and acquisition transaction. Understanding the motives of an M&A transaction is essential in researching the economic impact of M&A. Given the high number of transactions per year, manually classifying every transaction is unfeasible. This paper proposes a novel methodology using a large-language model to determine if a transaction has possible horizontal or vertical linkages. With sufficient information, this model is highly accurate. Focusing on transactions involving U.S. firms, 38% of transactions were only horizontally linked, 38% were only vertically linked, 12% were both horizontally and vertically linked, and 10% had no linkages. This pattern is robust across time and presidential administrations. The resulting data can be used by any researcher studying M&A transactions.

• Using Operator Learning To Solve Hetergenous Agent Macroeconomic Models with Aggregate Uncertainity