We provide direct evidence that firms' investments in new technologies affect the composition of firms' risk profiles. Leveraging comprehensive data on firm-level artificial intelligence (AI) investments, we document that firms that invest more in AI experience increases in their systematic risk, measured by market beta. This is unique to AI: robotics, IT, organizational capital, and R&D investments do not display similar effects. Our results are consistent with AI investments creating growth options: AI-investing firms become more growth-like, and the effect on market betas concentrates during market upswings and periods of increased news and attention around AI advances.

Does generative AI accurately capture demographic heterogeneity in investment

preferences? We survey humans and GPT4 side-by-side and show that AI correctly

predicts preferences by income, gender, and age (70% correlation). The overlap includes higher stock ratings among men and high earners. Human and AI-generated

responses reflect similar reasoning: both refer to “risk” and “return” in general and,

for stocks, to "knowledge” and "experience.” However, when not seeded with demographics, algorithmic bias emerges: AI responses predominantly mirror young,

high-incomes males. Finally, AI generates more transitive rankings than humans. Our

results highlight the promise of generative AI for applications such as robo-advising

This paper describes a process for automatically generating academic finance papers using large language models (LLMs). It demonstrates the process' efficacy by producing hundreds of complete papers on stock return predictability, a topic particularly well-suited for our illustration. We first mine over 30,000 potential stock return predictor signals from accounting data, and apply the Novy-Marx and Velikov (2024) "Assaying Anomalies" protocol to generate standardized "template reports" for 96 signals that pass the protocol's rigorous criteria. Each report details a signal's performance predicting stock returns using a wide array of tests and benchmarks it to more than 200 other known anomalies. Finally, we use state-of-the-art LLMs to generate three distinct complete versions of academic papers for each signal. The different versions include creative names for the signals, contain custom introductions providing different theoretical justifications for the observed predictability patterns, and incorporate citations to existing (and, on occasion, imagined) literature supporting their respective claims. This experiment illustrates AI's potential for enhancing financial research efficiency, but also serves as a cautionary tale, illustrating how it can be abused to industrialize HARKing (Hypothesizing After Results are Known).