AI in Investing with Daloopa's founder Thomas Li

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Apr 30, 2025

Join Thomas Li, CEO and co-founder of Daloopa, as he unpacks how AI is reshaping the investment landscape. He discusses the strengths and challenges of AI in finance, emphasizing its applications in hedge funds and banks. Key insights include the significance of contextual data over mere algorithm quality and the variations in AI adoption between large and small firms. Thomas also highlights how generational perspectives influence attitudes towards AI's role, blending technology's efficiency with critical human judgment.

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INSIGHT

AI's Strengths and Limits in Finance

AI excels at generating language but struggles with structured number processing in finance.
Analysts' work involves understanding and processing data rather than generative tasks AI handles well.

ADVICE

Leverage AI for Note Cross-Checking

Upload analyst notes and earnings transcripts into AI to identify inconsistencies.
You don't need to heavily structure notes; recent language models handle unstructured text well.

INSIGHT

Internal AI Tools vs Public Models

Firms with large research budgets invest heavily in building internal AI tools.
Compliance concerns limit use of public AI like ChatGPT, which lacks access to proprietary and detailed data.

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In this episode of Yet Another Value Podcast, host Andrew Walker shares a webinar conversation with Thomas Li, CEO and co-founder of Daloopa, diving into how AI is transforming the workflows of fundamental investors. They explore real-world applications across hedge funds and investment banks, highlighting both the promise and current limitations of large language models in financial analysis. From note synthesis to risk modeling and center book evaluations, Thomas outlines the practical realities of AI implementation, discusses adoption across firm sizes, and explains how contextual data—not just algorithm quality—is becoming the differentiator. Whether you're a solo analyst or part of a multi-manager platform, this episode offers a grounded perspective on where AI in finance is heading.____________________________________________________________[00:00:00] Andrew introduces the episode as a repost of a webinar with Daloopa on AI and investing.[00:01:58] Thomas Li outlines AI’s strength in generating language vs. processing structured financial data.[00:06:43] Discussion on practical AI use cases like cross-referencing notes with earnings calls.[00:10:12] Andrew asks how to structure analyst notes for better AI input and efficiency.[00:12:38] Comparing large pod shops and long-only firms in terms of AI adoption and internal tools.[00:17:34] Why foundational models are commoditized and context is key to AI application value.[00:22:18] The crowding factor as a risk vector and how pod shops hedge against it.[00:29:01] Generating alpha today: human edge through timing, perception, and behavioral insight.[00:35:07] Long-term value of internal data and modeling analyst performance over time.[00:41:49] How AI might evolve: foundational models vs. application layer as the value driver.[00:46:22] Adoption outlook—AI use is growing, but nuanced finance problems slow full automation.[00:52:14] Importance of internal champions (agency) to drive meaningful AI integration.[00:57:30] Center books at pod shops use AI to backtest and analyze analyst effectiveness.[01:02:40] Closing thoughts on AI’s trajectory and data as the real moat for firms.Links:Daloopa: https://daloopa.com/yavp See our legal disclaimer here: https://www.yetanothervalueblog.com/p/legal-and-disclaimer