AI Core Team Lead Mike Schuster on How to Get the Most From LLMs

Summary

Mike Schuster, Head of the AI Core Team at Two Sigma, advocates for grounding large language model adoption in practical tasks rather than speculative industry hype. Realistic enterprise applications focus on accelerating data processing, running faster experiments, and extracting domain-specific features from transcripts such as earnings calls and Federal Reserve speeches via prompt engineering. Because financial data faces inherent volume limits across trading days, successful deployments require multidisciplinary human teams to balance rapid technical experimentation with rigorous domain expertise and analytical reasoning. Schuster also dismisses predictions that programming will become obsolete, comparing coding to learning a musical instrument that cultivates structured thinking, problem decomposition, and scientific common sense essential for building complex predictive models.

Takeaways

1. 1

   Practical enterprise value from large language models centers on accelerating data processing, lowering training costs, and extracting domain features from transcripts through prompt engineering.

2. 2

   Complex domain modeling cannot rely on isolated individuals or automation alone, necessitating collaborative teams with specialized human reasoning to navigate data constraints.

3. 3

   Learning to program remains essential because it builds structured problem-solving habits, creativity, and the scientific common sense required to model systems with millions of parameters.