Asset Managers Turn AI Inward to Mine Internal Data for Competitive Edge

Asset managers are redirecting artificial intelligence tools away from public markets and toward their own internal archives, betting that proprietary research and analyst communications represent the last defensible source of competitive advantage in an era when large language models have commoditized external data.

BlackRock and Balyasny Asset Management have built systems that allow AI to sift through years of internal memos, research notes, and trading decisions—material that competitors cannot access. Andrew Gelfand, a quantitative analyst at Balyasny focused on alpha capture, said at the Future Alpha conference that the $33 billion firm had previously attempted to monetize unstructured internal data but that recent AI advances have made the effort "much more fruitful." The firm now requires analysts to enter research and notes into a centralized portal that his team can mine for investment signals.

Jacob Bowers, a vice president of quantitative research at BlackRock, said on a panel at the same conference that AI is "great at structuring unstructured data," and "some of the best unstructured data you have is internal." The strategy reflects a broader shift in the investment industry, where the proliferation of publicly available information and the speed at which AI can process it have eroded traditional research advantages.

(The Future Alpha conference took place in New York, where both executives spoke on panels discussing the application of AI to investment research and alpha generation.)

For years, sophisticated asset managers gained an edge through unique intelligence that did not originate from traditional market sources such as stock exchanges. Now, as large language models have absorbed vast quantities of publicly available data across the web, firms are turning inward to preserve differentiation. What this type of internal mining requires is high-quality data for AI systems to learn from—specifically, the thoughts and processes of seasoned investors at the top of their game. The rise of large language models has eroded the public data edge even further, prompting top funds to search their own research, communications, and historical decisions for signals that competitors cannot replicate.