It’s still hard to get AI to approach a problem the way a particular firm would.
Last month, we discussed how Bridgewater taught a model to handle research tasks more like one of its own investors:
…Bridgewater fine-tuned an open-weight model on examples labeled and reviewed by its investment experts, according to a statement this week from the hedge fund and Thinking Machines, the AI startup run by former OpenAI CTO Mira Murati. The goal was to teach the model what Bridgewater investors would consider relevant.
In other words, Bridgewater trained the model on its internal processes to give it institutional knowledge. And it’s not the only firm. A review of job postings at hedge funds, asset managers and private-equity firms shows others on Wall Street are trying to build similar firm-specific knowledge.
Firms are hiring AI specialists to codify how their investors work while retaining control of the data, workflows and feedback loops used to teach the AI. The firms still rely on frontier labs for broader use cases, as many of the partnerships we’ve covered show. But this goes beyond giving engineers access to coding tools.
Alex Gannes, founder of the investment management focused search firm, Banyak Group, described outside models and internal customization as complementary.
“A hedge fund is always positioning itself for an edge,” Gannes said. “Leveraging frontier labs and customizing AI solutions should ultimately support that edge.”
Investors are rightfully protective of that edge. And more broadly, companies are protective of their intellectual property and processes. This is a point Alex Karp, co-founder and CEO of Palantir, has been making in recent months.
“[Customers want] control over their compute, their models, their data stack and their alpha,” Karp told CNBC last month. “They want to know they own the means of production. It’s not being transferred to someone else.”
Karp is talking his book, but I tend to agree. Wall Street folks are a pretty paranoid bunch. I don’t see how firms ever get comfortable codifying how they work unless they know exactly where that knowledge lives and who controls it. (I’m not suggesting that frontier labs train—or would train—their models on customer data without permission. This is more of a cultural issue than a technical one.)
An AI-assisted review of more than a dozen current job postings at major hedge funds, asset managers and private-market firms shows firms pursuing three related goals: capture how their investment teams work, connect that knowledge to proprietary data and turn both into AI systems they control.
Related Coverage
The titles range from Fundamental Equities AI Engineer and AI Strategy Analyst to AI Credit Product Lead and Forward-Deployed AI Engineer. The use cases include idea sourcing, thesis monitoring, trade write-ups, portfolio construction, credit underwriting and investment-committee memos.
Behind those titles is a translation job: turning the judgment of portfolio managers, analysts and deal teams into prompts, retrieval systems, scoring frameworks and agents—and then determining whether the output is any good. The postings mention hallucination checks, audit trails, evaluating outputs without clean labels and getting direct feedback from investment teams.
Banyak Group’s Gannes thinks these teams could eventually support something broader than AI tools built for individual desks.
“A realistic goal in the hedge fund AI roadmap is to architect and scale an enterprise-level ‘hive mind’—linking autonomous AI agents into a single, coordinated cognitive network,” Gannes said. “Hiring a core team of applied AI engineers who can support every function within a firm would be a logical foundation for that effort.”
Behind the Paywall
Below, I break down about a dozen current postings from firms including Point72, D. E. Shaw, PIMCO, BlackRock, Ares and Carlyle. For each, I identified the target investment workflow and proprietary data involved. I then looked at how the firm’s knowledge would be encoded and the outputs tested. I included required investment experience and disclosed compensation where available.