Former AQR and Two Sigma VP: How Quant Funds Will Use GenAI to Find Edge

Aug 19, 2025

Bill Mann, a former fundamental researcher at Two Sigma and founder of Harmonic Insights, shares his insights into the world of quantitative trading. He discusses the transformative influence of AI, LLMs, and automation on research and trading workflows. Mann explains how hedge funds can leverage proprietary data pipelines for alpha generation while avoiding crowding in popular factors. He emphasizes the importance of hacker creativity in systematic investing and outlines the skills junior quants need to thrive in an AI-driven landscape.

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ANECDOTE

Joining Two Sigma's Fundamental Team

Bill Mann described joining Two Sigma’s fundamentals team and learning new implementations from PhD-level quants.
He contrasted Two Sigma’s approach with his AQR experience to show diverse quantitative styles.

INSIGHT

Point-In-Time Data Is A Hidden Edge

Point-in-time formatting used to be a proprietary edge because vendors backfilled and overwrote historical data.
Creating your own version of others' data became a core technology advantage for quants.

INSIGHT

Implementation Beats Raw Fundamentals

Many fundamental quant models are simple accounting ratios, so implementation choices matter more than raw data.
Identical factor implementations can cause crowding and reduce strategy robustness.

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What’s it really like working as a quant in fundamental research at Two Sigma—and how will AI, LLMs, and agentic workflows change quantitative trading strategies? Bill Mann, former Two Sigma fundamental researcher and founder of Harmonic Insights, joins Ethan Kho to break down how hedge funds build edge from widely available data and why “hacker” creativity still matters in systematic investing.Bill shares insights from AQR and Two Sigma, including how proprietary data pipelines become alpha generation engines, how to avoid crowding in popular factors, and what makes a great hedge fund strategy and the best work environment for quants. He also explains how LLMs, algorithmic trading, and automated research pipelines will transform research, engineering, and trading—and why mastering data engineering for quant finance is critical for junior quants.We answer questions like:– How do hedge funds find edge using fundamental vs. quantitative analysis?– What is point-in-time data and how does it prevent look-ahead bias?– How do proprietary data pipelines create alpha generation?– How can quants avoid crowding in value factors?– What’s it like working as a quant? What’s the culture like inside Two Sigma’s quantitative trading strategies team?– How do LLMs and AI agents change systematic investing workflows?– Which quant research tasks will be automated first?– What skills will junior quants need in the AI era?– How should aspiring quants practice creativity and problem-solving?– How does algorithmic trading intersect with data-driven investing?– What role will high-frequency trading (HFT) play in the future?– How do fintech startups work with hedge funds?– What’s the *New Barbarians* podcast about?#quantfinance #twosigma #aiinfinance #largelanguagemodels00:00 Intro00:57 Life as a Quant at Two Sigma02:36 Finding Edge in Fundamental Data07:04 Creating a Creative Quant Research Culture11:19 How LLMs Change Quantitative Trading15:52 AI’s Impact on Junior Quant Careers22:56 Using AI Tools for Learning23:57 Harmonic Insights: Advising Fintech Startups30:47 The New Barbarians Podcast Explained33:26 Crypto and Market Makers vs TradFi34:54 Career Advice for Aspiring Quants38:46 Final Takeaways