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Brett Caughran raises the idea that agents may fundamentally challenge the longstanding Grinold frontier in public market investing.
He argues that the traditional model, which favors high information coefficient with low breadth, is being disrupted as agents become more than just an automation opportunity for market participants.
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Caughran previously discussed the trend of using large language models to codify investor judgment, referencing a Bridgwater and Thinking Machines study in his analysis of AI investing strategies. The paper fueled industry debate on integrating human insight with machine learning tools. These developments provide context for his latest comments on agents in public markets.