Balyasny, BlackRock Mine Internal Data for AI
Plus: ex-Coatue investor builds three-person AI fund.
Hey, it’s Matt. You’re reading AI Street, where I report on how Wall Street uses AI.
Manage Email Preferences
If you prefer to receive one weekly email with all AI Street content, deselect Research and Interviews.
NEWS
Funds Are Feeding Internal Research Into AI Systems
We’ve talked a few times about how AI is good at structuring unstructured data, or taking disparate documents and turning them into a searchable format.
Much of the data in the corporate world is unstructured, sitting in PDFs, siloed in different departments and scattered across systems. Estimates suggest that about 80% falls into this category.
Before LLMs, connecting data across sources was too tedious and too expensive. Now, the economics have changed.
Kim Posnett, global co-head of investment banking at Goldman Sachs, argued last year in an FT piece that AI is turning data itself into a market, with proprietary datasets becoming more valuable as public data gets exhausted.
News came out this week that BlackRock and Balyasny are ingesting their own internal research, notes, and communications as the next source of differentiation. Business Insider captured this shift well:
AI is “great at structuring unstructured data,” said Jacob Bowers, a vice president of quantitative research at BlackRock, on a panel at the Future Alpha conference in New York on Tuesday, and “some of the best unstructured data you have is internal.”
The publicly accessible data that was once cutting-edge is now “commoditized” by AI, he said. BlackRock, the world’s largest asset manager with $14 trillion in assets, has already turned its agents internal to find potential investment signals within past communication between investment professionals and old reports on opportunities, he said.
…
Andrew Gelfand, a quant at Balyasny focused on alpha capture, said at the Future Alpha conference that the firm had previously tried to monetize unstructured data within the firm’s systems, but recent AI advances have made the task much more fruitful.
The $33 billion firm requires analysts to type their research and notes into a portal that his team can access, Gelfand said, giving AI reams of text to sift through for potential investment signals.
The Three-Person Fund Built Around AI
Since AI can pull together scattered pieces of information, you don’t necessarily need dozens of analysts.
I’ve previously written about OffDeal, which is using AI to handle deal sourcing, outreach, and execution with a much smaller team. The company says it automates much of the manual work typically handled by junior bankers, allowing a lean team to run multiple transactions at once.
That same approach is now being applied at Epicenter Capital, a three-person fund run by former Coatue Management investor Rahul Kishore and backed by the Laffont brothers. The firm centers around an internal AI system, Eve, that is plugged into the investment process.
From Bloomberg News:
With Eve at their side, Kishore and his two colleagues hope to outdo other funds that employ hundreds of people. Their fiendishly elusive goal: to invest in 10 companies that generate a 10-times return in 10 years, the people said.
The tasks Eve works on are not glamorous but are well-suited for a digital employee.
Eve also scours the disclosures of more than 13,000 companies; listens to podcasts; scrutinizes social media posts; summarizes the news; and, each morning, generates a podcast for Kishore to listen to while he drives to work.
“Eve allows us to consume 10x more information in 10x less time. It helps us identify new potential investment ideas and accelerates our research,” Kishore wrote in an investor letter seen by Bloomberg. Eve’s “repository of memories allows us to better evaluate company performance as well as our investment process.”
Crypto Entrepreneur Sells AI-Generated Research
Crypto entrepreneur Anthony Pompliano is launching ProCap Insights, an AI-generated research service under his company ProCap Financial. The product uses AI agents to scan markets and produce reports on individual stocks, macro trends, and themes. The company says it can generate hundreds of reports a day. A subscription runs $2,500 a year.
The build was fast and cheap — two weeks, one employee, a few thousand dollars. That’s the core argument: AI makes research production dramatically less expensive.
From the Journal:
“We think that AI agents are very good at finding undiscovered or uncovered insights in financial markets, but they also are very good at producing large amounts of well-written research faster and cheaper than humans.”
It’s certainly faster for producing equity research. Where it gets harder is verifying the output. As we’ve covered previously, even the best models struggle with long inputs, a problem called “lost in the middle.” It’s genuinely hard to distinguish what is right and wrong in AI-generated content unless you verify all the outputs yourself, which defeats the purpose of using AI to speed things up.
ROUNDUP
What Else I’m Reading
The Age of the Engineer Is Here. The Numbers Prove It. Craig Whiting
MIT study challenges AI job apocalypse narrative Axios
Big AI spenders are reaping large productivity gains American Banker
Creative LLM use cases Flat Circle
Citigroup says AI helps speed account openings and systems upgrades Reuters
Electronic trading giant Optiver is building out a new AI Lab eFinancialCareers
This Week in AI Street
SPONSORSHIPS
Reach Wall Street’s AI Decision-Makers
AI Street reaches institutional investors, C-suite executives and Big Law attorneys at firms including JPMorgan, Citadel, BlackRock, Skadden, McKinsey, and more. Sponsorships are reserved for companies in AI, markets, and finance. Email sponsors@ai-street.co for more details.
CALENDAR
Upcoming AI + Finance Conferences
AI in Finance Summit NY – Apr. 15–16 • NYC
The latest developments and applications of AI in the financial industry.
Momentum AI New York – Apr. 27–28 • NYC
Senior-leader forum on AI implementation across financial services, from operating models to governance and execution.AI in Financial Services – May 14, 2026 • Chicago
Practitioner-heavy conference on building, scaling, and governing AI in regulated financial institutions.AI & RegTech for Financial Services & Insurance – May 20–21 • NYC
Covers AI, regulatory technology, and compliance in finance and insurance.
Thanks for reading!
I’m always happy to receive comments, questions, and feedback.
connect with me on LinkedIn, or
send an email to matt [at] ai-street.co