Generative AI has yet to penetrate the world of quantitative trading, according to a comprehensive Bloomberg survey of 151 quantitative analysts at top-tier asset management firms. The survey, conducted between April and November of last year, reveals that 54% of quants have not incorporated generative AI into their investment research workflows, with a majority “not yet begun their generative AI journey.”
This hesitation is particularly notable given that quantitative traders have utilized machine-learning techniques for years, making them early adopters of AI technology in finance. However, the newer generative AI tools have failed to gain traction among these sophisticated investors who manage billions in assets through systematic trading strategies.
The skepticism extends beyond the survey data. At a London-based quant conference in October, practitioners expressed doubts about generative AI’s ability to beat the market and add meaningful value to their investment processes. A UBS executive went so far as to state that AI would not help win the “alpha war” - the competitive battle to generate market-beating returns.
Bloomberg attributes the slow adoption primarily to data formatting and structural challenges. Angana Jacob, Bloomberg’s global head of research data, explained that quantitative strategies require meticulously cleaned and structured data due to the complexity of their systems and the substantial capital at risk if errors occur. “They’re working in a very controlled research environment, models need to be explainable, models need to be repeatable,” Jacob told Business Insider.
The work required to prepare datasets for AI consumption is what Jacob describes as “unglamorous” but “foundational.” Her team is developing specialized data products designed to facilitate AI adoption among quants, as enthusiasm for AI’s potential remains high once data quality issues are resolved.
Jacob views the cautious approach positively, stating “It’s a good thing, it shows their caution,” suggesting the lack of widespread adoption reflects professional diligence rather than technological inadequacy. Bloomberg isn’t alone in addressing this challenge - Carbon Arc, a startup founded by former Point72 data executive Kirk McKeown, is also focused on structuring datasets for easier integration with artificial intelligence models, indicating a growing market opportunity in AI-ready financial data infrastructure.
Key Quotes
They’re working in a very controlled research environment, models need to be explainable, models need to be repeatable
Angana Jacob, Bloomberg’s global head of research data, explained why quants require such rigorous data standards. This highlights the fundamental tension between generative AI’s probabilistic nature and the deterministic, auditable systems required in professional finance.
It’s a good thing, it shows their caution
Jacob’s assessment of the slow AI adoption rate among quants reframes hesitation as professional diligence rather than technological resistance. This perspective suggests the industry is taking a measured, responsible approach to integrating powerful new tools.
They’re working in a very controlled research environment, models need to be explainable, models need to be repeatable
This statement from Bloomberg’s Angana Jacob underscores the core challenge: generative AI’s “black box” nature conflicts with the transparency and reproducibility requirements of institutional investment management, where every decision must be defensible to clients and regulators.
Our Take
The quant community’s resistance to generative AI offers valuable lessons for AI deployment across industries. This isn’t a story about Luddites rejecting innovation - these are sophisticated technologists who pioneered machine learning in finance. Their hesitation stems from legitimate concerns about model explainability, data quality, and operational risk that the broader AI industry often glosses over in pursuit of rapid adoption.
The emergence of specialized data infrastructure companies like Carbon Arc suggests we’re entering a second phase of the AI revolution focused on practical implementation rather than raw capability. The real winners may not be the model developers, but rather the companies that solve the “unglamorous” problems of data preparation, model governance, and enterprise integration. This survey should concern AI vendors: if they can’t convince quantitative traders - who already use AI and have resources to implement it - the path to broader enterprise adoption may be longer and more complex than anticipated.
Why This Matters
This story reveals a critical gap between AI hype and practical implementation in high-stakes financial markets. While generative AI dominates technology headlines, its failure to penetrate quantitative trading - a field already comfortable with machine learning - signals significant real-world challenges that extend beyond finance.
The data quality and explainability requirements highlighted by quants represent fundamental barriers facing AI adoption across regulated industries including healthcare, insurance, and banking. When billions of dollars are at stake, “black box” AI models that cannot explain their reasoning become unacceptable risks.
This cautious approach also validates concerns about AI readiness in enterprise environments. The “unglamorous” data preparation work that Jacob describes is often overlooked in AI implementation discussions, yet it may represent the largest obstacle to widespread adoption. The emergence of companies like Carbon Arc specifically to address data structuring suggests a significant market opportunity in AI infrastructure rather than just AI models themselves.
For the broader AI industry, this serves as a reality check: sophisticated users with resources and technical expertise are still waiting for practical solutions that meet their standards for reliability, explainability, and data quality.
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Source: https://www.businessinsider.com/quants-arent-using-ai-to-invest-bloomberg-survey-2026-1