Advances in artificial intelligence are levelling the field for fund managers, making it easier for boutique firms to compete with big macro and bond investors.<\/p>\n
From digesting speeches in multiple languages and crunching global inflation numbers, to tracking company filings and the tone of investment committee discussions, AI is picking up much of the work once carried out by teams of analysts, according to five executives who have recently set up their own shops.<\/p>\n
\u201cTechnology has changed the economics of building an investment firm,\u201d said Dharmesh Maniyar, a machine-learning PhD who founded his second fund, MQT Asset Management, late last year. \u201cAI allows a focused boutique organisation like us to build powerful capabilities much earlier in our lifespan.\u201d<\/p>\n
Macro funds try to anticipate the impact of global economic and political shifts across markets rather than investing in discrete asset classes or sectors. Their strategies rely heavily on analysing huge volumes of data and text, work ideally suited to the large language models that have come a long way since Maniyar\u2019s first firm folded.<\/p>\n
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\u201cMacro markets have become more complex, more policy sensitive and faster moving, while the tools available to investment managers have improved dramatically,\u201d said Maniyar, who previously managed money at Brevan Howard Asset Management and Tudor Investment Corp.<\/p>\n
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Five-person hedge fund Palinuro Capital, launched last year by Alfonso Peccatiello, aims to compete with desks of 20-50 people. It uses LLMs to parse speeches by central bankers from Hungary to South Korea, work that previously would have required regional specialists. \u201cIt\u2019s effectively like having a set of unbiased analysts for a portion of the cost,\u201d Peccatiello said.<\/p>\n
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Small bond-focused funds like Osmosis NL face a similar data challenge. Research that once took weeks is completed in days, allowing the young firm to scale up rapidly with a team of just 15, according to Victor Verberk, chief executive and chief investment officer.<\/p>\n
\u201cA smaller company like Osmosis can now cover the entire universe,\u201d Verberk said, adding that AI has allowed him to cut his three-year hiring plan by 25%. \u201cThe machine is never ill, it\u2019s never on holiday, it\u2019s always there.\u201d<\/p>\n
Increased Competition<\/span><\/h3>\nAI is already transforming white-collar work across industries such as software, marketing, consulting and accounting. Banks are creating fewer entry-level roles and wealth managers are increasingly focused on their richest clients while bots take care of the rest.<\/p>\n
In fund management, which has been using machine learning and algorithmic trading for decades, the fast-evolving technology is being widely embraced. A survey of over 400 fixed-income investors by Barclays Plc in May found that half of long-only managers and the majority of hedge funds use AI every day, largely for research.<\/p>\n
\u201cThis points to a market that is becoming more competitive, more democratised and faster?paced,\u201d said Zornitsa Todorova, head of thematic FICC research at Barclays.<\/p>\n
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In addition to transforming research, the new tools are helping with risk management and modelling. Palinuro leaned on an AI assistant during this year\u2019s oil price shock to screen dollar and commodity options for the cheapest way to hedge, said Peccatiello.<\/p>\n
At Alpha Curve Investments, an unconstrained bond fund launched in 2025 with just four people, Philippe Gougenheim and Pierre Ahlsell de Toulza asked Anthropic PBC\u2019s Claude to estimate consumer price trends based on data from the US, Europe, Australia, Brazil and Canada. Then they switched 70% of their \u00a3100 million ($132 million) portfolio into short-dated inflation-linked bonds.<\/p>\n
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Eric Lonergan, who oversees more than $1 billion of assets as head of Calibrate Management Ltd\u2019s new Discretionary Macro Fund, said he uses AI to screen 100 global assets into 10 trade ideas, replacing work that \u201clegions of analysts\u201d would have done in the past.<\/p>\n
Rapid change<\/span><\/h3>\nUnlike the bigger platforms, upstarts tend not to have legacy systems that complicate rollouts, allowing faster adoption. Verberk, who spent 16 years at Robeco running fixed income strategies, says it\u2019s easier and cheaper for small firms to integrate new technology. His boutique uses just two core platforms: Microsoft Azure and Bloomberg.<\/p>\n
\u201cGiven the speed with which AI is changing, you need to be highly creative and experimental,\u201d said Lonergan, a former portfolio manager at Eisler Capital and M&G Investments.<\/p>\n
Still, bigger firms with deeper pockets are likely to continue to have an edge, according to Ben Conway, chief investment officer at Hawksmoor Investment Management Ltd. \u201cThat\u2019s something we\u2019re taking into consideration when selecting funds,\u201d he said.<\/p>\n
Maniyar acknowledges the advantages larger firms still have in terms of capital resources, distribution and breadth of coverage but says technology has made the \u201cplaying field more interesting.\u201d<\/p>\n
\u201cThe edge is how you integrate data research, AI portfolio construction, execution and risk into a coherent investment process,\u201d he said. \u201cTechnology has definitely democratised the infrastructure, but not skill.\u201d<\/p>\n
\u00a9 2026 Bloomberg<\/p>\n<\/p><\/div>\n
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Eric Lonergan, who oversees more than $1 billion of assets as head of Calibrate Management Ltd\u2019s new Discretionary Macro Fund, said he uses AI to screen 100 global assets into 10 trade ideas, replacing work that \u201clegions of analysts\u201d would have done in the past.<\/p>\n