A new-age, open-source hedge fund has 18 portfolio managers instead of one.
Not only do they go beyond analysing stocks, but each one of them follows a different style, arguing, challenging each other, and even voting on what to buy or sell
Oh — and they aren’t humans.
It might not be managing money yet, but it’s already a reality of how AI (artificial intelligence) is now reshaping the way even trading decisions are made. Hedge funds and portfolio managers are now increasingly turning to AI and automation to modernise trading operations, even as data volumes explode, markets become more digital, and strategies become more complex.
An Alternative Investment Management Association survey that goes as far back as 2024 found that a whopping 86% of hedge fund managers and their staff access a gamut of generative AI (GenAI) tools. The kicker? The survey covered as many as 157 hedge fund managers controlling around USD 783 billion in aggregate assets, with around USD 5 billion in AUM (assets under management).
From Goldman to Google, hedge funds are quickly integrating AI and ML (machine learning) across their investment operations, from portfolio construction and research to client communication and risk management. Agentic AI’s role in the fintech revolution is evident, but is it also transforming into the new-age fund manager?
Rise of AI Applications in Hedge Funds
AI is already being applied by hedge funds across diverse operational areas, signifying industry-wide adoption. According to data from BarclayHedge and the AIMA Feb 2024 report mentioned earlier, 67% of firms were already using AI for generating investment ideas. The reasons? Massive cost and time savings, mainly related to investor communications and marketing, as well as general administrative tasks.
Hedge fund and portfolio managers are inundated with research and data from a growing number of sources – sell-side research, public data, internal systems, and more. Agentic AI allows users to deploy autonomous agents that extract key insights, transforming hours of manual scanning into actionable summaries.
For instance, Point72 has partnered with an AI platform to process earnings calls in real-time, identifying linguistic patterns and extracting sentiment that human analysts might miss. One of the world’s largest hedge funds, the Man Group, has AI copilots helping in generating hypotheses, thereby testing multiple possible trading strategies against historical data automatically and dynamically fine-tuning portfolio allocations in real time.
Generic chat interfaces or broad LLM access interfaces aren’t meant for portfolio managers who operate in time-constrained, high-stakes, environments. What they need are tailored workflows aligned to house style, as what will work is AI that moulds to the enterprise’s existing process, and not the other way around.
Take Bridgewater Associates, for instance, which launched a fund that employs ML as the primary basis for its decision-making in July 2024. Debuting with almost USD 2 billion of capital from more than half a dozen clients, this fund incorporates everything from Perplexity to Anthropic and OpenAI, but in tandem with Bridgewater’s proprietary technology.
It builds on this technology that the firm has been developing for more than 10 years, combining ML data models, LLMs (large language models), and reasoning tools to understand causal relationships in markets.
The results are evident. For instance, Point72 Asset Management’s hugely successful AI-driven Turion fund, which is named after the “father of AI” Alan Turing and launched in October 2024, grew by nearly 14.2% through December 2024. It focuses on short/long positions in AI semiconductor and hardware companies globally while utilising AI tools internally for analysis.
It’s not the only one. The AI-only Sydney-based Minotaur Capital doesn’t have any human analysts, using a proprietary system called “Taurient” that analyses nearly 5,000 news articles every day. It outperformed the market, achieving a 13.7% return in January 2025 – in its first 6 months – as compared to the 6.7% returns of the MSCI All-Country World Index in the same period.
The Future Is Here: Designing Specialised AI Systems
One of the most critical tools when dealing with LLMs is RAG (retrieval augmented generation), which searches for data outside a model’s training data to answer a question better. Employing this technique in fintech is complicated, but maybe not for American hedge fund Balyasny. Armed with an applied research team comprising DeepMind and Google alums, the firm has been creating its own AI tools, aptly called “BAMChatGPT.”
80% of the fund’s employees now use it as it showcases measurable performance advantages over general-purpose AI tools. In fact, when it came to internal testing in financial documents retrieval, BAM Embeddings, specifically designed for financial language processing, achieved 60% accuracy as compared to OpenAI’s sub-40% levels. This system can tap into 10 different sources, including broker research, sell-side commentaries and sales, and transcripts and create bots that can push relevant information to business teams and portfolio managers proactively.
Clearly, the race to design and operate AI-driven funds has only begun. While there are challenges – obvious data privacy and security concerns and the AIMA survey finding that just around 10% of respondents had formal training in GenAI, and that it’s important for some or all new hires to have experience using GenAI tools – the fact is that the market for AI investment tools is growing at a rapid pace.
With AI being integrated across hedge fund operations, it showcases a momentous shift in how these enterprises will manage portfolios and serve clients in the future. So, it only seems fair to suggest that AI tools and tech will become increasingly fundamental in hedge fund operations in the future.
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