The recent boom in large language models (LLMs) has sparked widespread speculation about AI’s ability to replace knowledge workers, with financial analysts frequently cited as one of the most exposed professions. But is the AI analyst truly ready for the trading floor? In this talk, I share practical insights from the front lines as the founder of Sparkline Capital, an investment firm applying LLMs to real-world financial research. Drawing on a series of case studies—ranging from automating the parsing of SEC filings to tracking corporate innovation via patents—I’ll demonstrate where AI is already outperforming junior analysts and where it still falls short. We’ll explore why AI struggles with high-level reasoning, how noisy and non-stationary financial data limits direct return forecasting, and what this means for the future of asset management. Rather than fearing full automation, I argue that the most promising path forward lies in hybrid investment teams that combine human judgment with AI scale. This talk will resonate with both researchers and practitioners interested in the practical boundary between current AI capabilities and the domain expertise required in finance.

Kai Wu is the founder and Chief Investment Officer of Sparkline Capital, an investment management firm applying state-of-the-art machine learning and computing to uncover alpha in large, unstructured data sets.

Prior to Sparkline, Kai co-founded and co-managed Kaleidoscope Capital, a quantitative hedge fund in Boston. With one other partner, he grew Kaleidoscope to $350 million in assets from institutional investors. Kai jointly managed all aspects of the company, including technology, investments, operations, trading, investor relations, and recruiting.

Previously, Kai worked at GMO, where he was a member of Jeremy Grantham’s $40 billion asset allocation team. He also worked closely with the firm’s equity and macro investment teams in Boston, San Francisco, London, and Sydney.

​Kai graduated from Harvard College Magna Cum Laude and Phi Beta Kappa.