Conference Agenda

When AIs are tasked with empirical research, how do their outcomes compare to those of humans? Are the distributions similar? We run an experiment where we let AI models repeat an experiment that was run with 162 human teams. Not surprisingly, distributions differ. The deeper question is: Why? We develop an approach that identifies which decisions on the analysis path drive the AI errors. The results show that AI estimates are less dispersed than human estimates. The location of the two distributions as measured by the median, also differs significantly, in particular for more complex tasks. The experiment shows that this location differential is largely due the choice of the statistical model, e.g., identifying a time trend by adding a stationary trend to a model in levels, or by computing relative changes and taking the average.

We show that following the diffusion of GenAI, industries with higher task-level exposure to the new technology experience 20\% more startup formation than less-exposed industries, and a growing share of these startups offer new AI products and services rather than merely automating operations. This entry is geographically dispersed, extending beyond traditional innovation hubs into regions with limited venture capital and thin specialized labor markets. While individual entrants are smaller in scale, aggregate entry generates net employment and wage growth at the industry level and creates employment in the very occupations most exposed to GenAI. GenAI also changes who becomes an entrepreneur: founding teams are increasingly drawn from high-exposure occupations and describe themselves in terms of technical, AI-related skills rather than the capital-raising and deal-making expertise, consistent with the technology enabling new uses of human capital rather than simply displacing workers.

We develop a theoretical framework of trading competition between AI-powered investors and rational investors with heterogeneous sophistication. Rational investors have superior private information but differ in their ability to infer information from prices and anticipate others' trading behavior, modeled through level-k reasoning. AI investors do not observe private signals or reason through belief hierarchies; they learn from realized trading profits through reinforcement learning. Despite heterogeneous algorithms and independent exploration, AI investors endogenously converge to common trading rules, generating algorithmic herding. In the limit, their learned demand coincides with the rational-expectations demand of uninformed investors who correctly extract fundamental information from prices. This convergence does not imply algorithmic dominance. The most sophisticated rational investors can outperform AI investors because AI learns from market data generated by average, not frontier, investor sophistication. AI profitability is limited by rational investors' private-information advantage, the price-stabilizing trades of the most sophisticated rational investors, and the rising price impact of AI trading as algorithmic herding increases AI market share. These forces identify the limits of algorithmic superiority in financial markets.