Artificial intelligence is already changing the way many industries operate, and hedge funds are no exception.
In a $5 trillion field, companies vary widely in the types of strategies they employ and the types of securities they invest in. But everyone wants to be the smartest manager in the world, or at least the most informed.
To that end, the fund has poured resources into building generative AI capabilities and use cases. Many companies, especially quantitative traders, are expanding on efforts they already have in areas such as machine learning. And nearly every company is putting capital into a trend that has dominated public and private equity markets in recent years.
Business Insider has rounded up how some of the biggest and most famous executives are leveraging and supporting the development of AI. Although this is an extensive overview, it is not complete.
How funds are using AI
It’s first and foremost about data.
Hedge funds have spent countless hours and astronomical amounts of money trying to obtain more information as quickly as possible than their competitors. Their insatiable appetite for new and unique data has led to a flourishing alternative data industry, filled with companies around the world looking for new information to sell.
As Umesh Subramanian, chief technology officer at Ken Griffin’s $69 billion Citadel, said at a Bloomberg event in October, companies like his currently consume petabytes of data. One petabyte is equal to one million gigabytes and can store hundreds of millions of photos and hundreds of thousands of high-definition movies.
The only reason funds like Citadel are able to access so much information without being overwhelmed is because of AI. And given the competitive nature of this industry, even a small advantage over competitors is worth the cost.
“It’s an arms race to make sure the right kind of data is available in the right way to enable the right decisions,” Subramanian said.
Baryasny, a $29 billion hedge fund, developed AI bots because it believed they could perform the tedious tasks typically done by senior analysts. This can potentially save significant time for investment teams. The manager told Business Insider in 2024 that about 80% of employees use the company’s AI tools, including its internal chatbot BAMChatGPT, and that the company recently hired Matthew Hendery, one of the CIA’s AI developers, as a data science executive.
Balyasny isn’t the only company implementing its own chatbot. Man Group and Viking Global have also developed their own in-house services.
Quantitative funds like DE Shaw, Bridgewater, and Two Sigma, as well as proprietary trading firms and market makers like Jane Street, Citadel Securities, and Hudson River Trading, have long been at the forefront of AI and machine learning.
For example, Two Sigma’s Mike Shuster, head of the quantitative core AI team, said at a Columbia University event in November 2024 that his company had been using generative AI for more than five years at that point. In the summer of 2024, Bridgewater launched a $2 billion fund managed by machine learning. The manager’s CEO, Neil Bar Deere, said earlier this year that the strategy generates “unique alpha that is uncorrelated to human behavior.”
To stay at the forefront of new technology, we need the best talent. While these companies are often able to attract top tech talent with impressive pay packages, AI companies are also able to offer rewards that match and, in some cases, exceed.
As Business Insider reported, young quants attracted to the work being done by AI startups “don’t even have to take a pay cut” to choose Silicon Valley over East Coast trading floors.
How funds invest in AI
One reason AI startups like OpenAI and Anthropic are able to attract hedge fund talent is because they have tens of billions of dollars of capital invested not only by big-name venture capital firms but also by tiger cubs like Tiger Global, Coatue, and D1. Tiger Cubs is a hedge fund with ties to billionaire Julian Robertson and his company Tiger Management that often focuses on growth stocks in industries such as technology.
Stock-picking funds like Tiger Cubs are increasingly paying attention to AI trends in public and private markets. Stocks like Nvidia, AMD, and South Korean chipmaker SK Hynix are often key holdings in public portfolios, along with tech giants like Alphabet, Microsoft, and Meta.
Maverick, a small Tiger Cub run by Lee Ainslie, is more focused on supporting the chip manufacturing ecosystem than deciding winners and losers among AI players. Maverick Silicon, the company’s private fund that invests in this space, is run by Andrew Homan, one of the company’s longtime investors.
Steve Cohen, whose $40.5 billion Point72 has dozens of teams investing in stocks, is so convinced of the potential of AI that he created an independent strategy to invest in the space in October 2024. Point72 rarely offers new funds outside of its flagship store.
Turion, run by portfolio manager Eric Sanchez, outperformed the company’s flagship product in 2025.
Where AI still falls short
While some companies are relying on AI to make investment decisions, other industry leaders are not yet convinced that their machines can outperform the market.
Citadel’s Ken Griffin said at a conference in October that AI cannot yet beat the market. Man Group’s Numeric division has created an in-house “large language model-based workflow” called AlphaGPT, which “still requires human oversight and strategic direction.” Elliott’s Paul Singer said in a podcast earlier this year that AI use cases are “highly overhyped.”
There’s no question that funds are leveraging AI more than ever before and processing more data than you might imagine. But human creativity remains important to investment giants, and in most cases, AI is seen as a tool rather than a replacement for flesh-and-blood traders.
As reported by Business Insider, the conclusion reached by many systematic funds at a quantitative conference in London in October was that humans, not machines, are the edge needed to beat the market.
“Our takeaway so far is that AlphaGPT does not replace human judgment, but amplifies it. The most effective way to use this system is for human researchers to work collaboratively with AI, each to its own strengths,” two Man Numeric executives wrote in a memo about AlphaGPT in early November.
“Numeric Human provides strategic direction, market conditions, and final decision making, while AlphaGPT handles the heavy lifting of data processing, hypothesis generation, and initial analysis.”
