The next panel explored the intersection of human expertise and artificial intelligence – how investors are learning to work with AI, turning technology into an extension of human insight.
Ai Ling Ong, Head of Artificial Intelligence for Investments at Lion Global Investors, noted that effective use of AI in asset management hinges on domain knowledge. “If you don’t know how to play chess, you can’t train a computer to play chess,” she said, emphasizing that building intelligent systems requires mastery as a fund manager: without a deep understanding of markets, data, and portfolio dynamics, it’s impossible to teach machines to think like fund managers. Her team has fully adopted generative AI but still subjects every output to human review. AI can refactor code and generate research faster than ever, yet human judgment remains essential to verify logic, identify errors, and maintain accountability.
Joo Lee of Arrowpoint Investment Partners described a similar philosophy from a technological vantage point. His firm embedded artificial intelligence “from day zero,” using AI systems to multiply developer productivity and accelerate the launch of multiple trading strategies with a lean engineering team. Beyond coding, the focus now is on creating an intelligent ecosystem where each domain expert can build and train AI agents specific to their function in valuation, risk, or research. The long-term ambition, he explained, is a “Jarvis-like” intelligence layer that connects these agents across the firm – a model where AI doesn’t just assist but works alongside humans as a colleague.
Bloomberg embodies the same balance – combining human judgment with data-driven intelligence through transparent, auditable AI systems across research and risk platforms, enabling professionals to harness automation without losing clarity or accountability.
