] 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