Are we ready for a hedge fund world where machine learning guides trading decisions and data scientists outnumber traditional traders? In this Tech Talks Daily Podcast episode, I sit down with Gary Collier, CTO of Man Group, to find out. 

Man Group traces its roots back more than two centuries to its days supplying rum, yet it now stands at the forefront of AI-driven finance. Gary explains how the firm’s open-source platforms, Alpha and Rosa, power everything from signal generation to automated execution, all while keeping a firm grip on the art of human oversight.

I found it intriguing how their internal ManGPT system used by over half the organization proves that large language models and neural networks aren’t just hyped. They’re part of daily operations in purely systematic strategies and supporting discretionary portfolio managers who want real-time insights from a sea of unstructured data. We also explore ArcticDB, Man Group’s open-source data science database, enabling it to process billions of rows quickly. Gary argues that without this kind of data infrastructure, AI research stalls under the weight of time-consuming manual tasks.

Throughout our conversation, Gary shares how his hobbyist coder and physicist background influences a data-first culture. It was fascinating to hear how he believes AI could soon disrupt creative aspects of quantitative research, allowing advanced models to generate strategy ideas rather than simply refining existing ones. At the same time, he underscores the importance of transparency. In a world where billions in assets are at stake, pure black-box automation doesn’t cut it, and teams need to explain how trades are executed and why the data looks the way it does.

From front-office analytics to deep research projects, it’s clear that AI pulses through Man Group’s veins. Still, Gary reminds me that people remain vital for framing the right questions and deciding when to trust the outputs. Will AI become the main engine of future hedge fund strategies, or will human ingenuity continue to guide our biggest calls on the market? And how will your organization embrace the growing tide of machine-led innovation?