Research

I model two channels by which AI progress can feed back into the automation of AI research and lead to a self-sustaining acceleration, in a task-based model of AI research. Capability improvements allow AIs to perform a larger share of AI research tasks, while efficiency improvements raise AI research output per unit of compute on tasks already automated. I separate these margins in a task-based model of AI research. A software intelligence explosion—a self-sustaining acceleration in AI progress with fixed compute and human labor—requires both margins to improve sufficiently fast, and neither margin can substitute for a shortfall in the other. I then calibrate both margins with data on AI progress. In my baseline estimation, both the capability and efficiency conditions for an SIE are met; in a Monte Carlo simulation over the uncertainty in each input, an SIE occurs in 75% of cases. Thus, both efficiency improvements and capability improvements are fast enough to support an SIE.

How do firms navigate the tension between securing proprietary knowledge and remaining open to knowledge flows? We study this tension in the context of economic espionage, compiling a comprehensive dataset of incidents where foreign competitors steal technology from US firms. In an event study design, revenues and R&D at targeted firms decline by 40% within five years, with mechanism analysis suggesting that this loss is driven by obsolescence of the firm's valuable technology. These effects do not appear for firms unsuccessfully targeted for espionage, supporting a causal interpretation. However, we find no evidence that targeted firms become more restrictive on knowledge flows after espionage; firms do not reduce hiring of foreign scientists, patenting with foreign inventors, or international business functions. Overall, espionage has clear economic harms to targeted firms and US industry, but firms appear to value the benefits of inward knowledge flows more than these costs.

Statistical methods typically lack power to detect p-hacking and publication bias among individual researchers, making it difficult to detect how prevalent p-hacking is. We propose a novel empirical Bayes method to estimate researcher-level p-hacking prevalence. Using data from top medical journals, we find strong evidence these practices are widespread: at least 85% of researchers over-reject null hypotheses, with a conservative lower bound of 73%. Our approach identifies 20% of researchers as over-rejecting, whereas conventional tests detect none. These findings demonstrate that p-hacking and publication bias are systemic rather than isolated misconduct, underscoring the need for structural reforms in scientific practice.