# Beyond the Model Card: The Jagged Frontier of AI in Finance — Rayan Krishnan — session 2026-10-13T18:30:00.000Z → 2026-10-13T18:48:00.000Z _19 transcript lines · 41 slides · source: full recording_ ## Transcript **[00:00:00 · 0]** And then the unlock for AI adoption more systematically. Once these evals are constructed, they can enable adoption to happen. But we've also seen that there's more advantageous ways that they can be used. So first is right sizing. So rather than just token maximizing, how do you figure out what the right usage limit should be across the organization? **[00:00:25 · 0]** Negotiation. Once you realize you have actual leverage, you can take that to the conversation you have in bulk pricing. And then routing. This is becoming a core primitive in a lot of AI products, but can also be done by users directly. And so they can build intelligent routers that actually figure out the right model to use. **[00:00:44 · 0]** And then fine tuning or post training, this can be the most reliable way for you to actually mint the tasks and then have an external eval sent to make sure that this approach is working. Last thought I'd leave you with is I think for a lot of companies, they have a competitive advantage that has existed in the heads of employees through things like institutional knowledge. And what Evals allow you to do is actually codify the competitive advantage and put that down in words or in scripts, in language. And that will actually be the biggest unlock for making sure that a company succeeds the agentic era. Okay. **[00:01:18 · 0]** That's all. Thanks, everyone. **[00:13:23 · 1]** Hey, everyone. How are you? Thank you all for, for being here. My name is Manu. I'm the CEO of Kepler. **[00:13:30 · 1]** Excited to have Jack Woodruff with me. Can you quickly introduce yourself? **[00:13:34 · 2]** Yeah. Hi. I'm, Jack Woodruff. I am the founder and former CIO of Candlestick Capital Management, a long short equity hedge fund based in Greenwich, Connecticut. Before founding Candlestick, I was a partner and portfolio manager at Surveyor Capital, which is a division of Citadel. **[00:13:55 · 1]** Very cool. Thanks, Jack. So the reason we did this this way is we've had this conversation before, and I think this is one of the most enlightening folks to chat with, given that Jack has spent his career picking stocks in an environment that's changing more than it ever was before. So we'll treat this really as a as a fireside shot. So, Jack, paint the picture for us. **[00:14:16 · 1]** What does fundamental investing look like in the age of AI, say, five years from now? **[00:14:21 · 2]** Oh, oh, well, it's it's hard to say. Like, long term stock picking, I don't believe is really going to change much at all. There's always been a mosaic, and there's always been alpha creation by finding a divergent view. So that isn't going to change at all. I think that that still will stay the same. **[00:14:42 · 2]** Now how people synthesize all of the information and where table stakes go to in terms of, like, what everybody has and then how you synthesize that data is gonna change completely. The roles... The traditional roles of, say, a portfolio manager or an analyst are gonna be completely thrown upon their heads. And I still think it's, like, too early to say what that's all gonna look like as these tools are just so new, especially to the users, you got a lot of people doing a few things. But it's just not clear to me that it's like we're there. **[00:15:22 · 2]** And oh, my gosh, it's going to be these new things. We have a bunch of other things to kinda talk about, but, I think it's gonna be super fascinating to to to watch how it all plays out. **[00:15:33 · 1]** Very cool. So at Citadel, you had 18 people covering 400 stocks. In a candlestick, you had 30 stock pickers. Could one great analyst cover all 400 by 2030? **[00:15:45 · 2]** So the biggest issue is is is humans are still the bottleneck. I mean, like, at the end of the day, you have to get the information into your head. So it's really all about sort of how man and machine can work together. Investment management is a time management exercise. What I mean by that, if I spent all of my time working on one stock, that'd be great. **[00:16:14 · 2]** I'd probably know better than anyone else in the world. But, like, if there wasn't anything to do in that stock, I'd be kinda... There there isn't much to do. Well, if I do two, well, then I have half as much time and and so forth until I go. So you you start getting diminishing returns, on how much time you could spend on any particular stock. **[00:16:36 · 2]** So, you know, what can AI do? Can AI help me cover more names? Perhaps. Maybe it allows me to raise the table stakes on what I just kind of, like, have as a general knowledge, because that work used to take me hours to prepare to get me ready for what it was. And now maybe the... **[00:16:58 · 2]** You know, an AI program can, you know, help me prepare a lot of that work in five minutes, where... Which used to take me six to eight hours. So that allows me to do other things. Again, like, it's a very competitive industry, so it's not like if I get rid of all these people or if I have, like, five analysts, whereas before I had 30, yeah, maybe those people can do more. But now all of a sudden, this opens up other time this opens up time that I could spend doing either more proprietary research, channel checks, more industry backdrop work. **[00:17:33 · 2]** Because again, like, you're gonna need to find these divergent views. So it's like, if I do the exact same thing I was doing before, and it's like, hey. Wow. I can get my work done in two hours. Well, someone else is gonna be still working sixteen hours a day trying to find, like, that extra thing that's gonna that that that's gonna make you money. **[00:17:54 · 2]** Because, you know, just doing this stuff is is obviously, like, what will you ## Slides ### 00:00:00–00:00:20 # What's your github repo? Where are you tracking your standard of excellence? - Bitbucket - GitHub - GitLab [Diagram showing a dotted arrow pointing from a box containing Bitbucket, GitHub, and GitLab logos to a box containing Azure Synapse Analytics, Snowflake, and Databricks logos] ### 00:00:20–00:00:40 # Evals guides companies through key optimizations - **Right-size** Use the right level of intelligence for tasks - **Negotiation** Enter contract negotiation with the data you need ### 00:00:40–00:01:00 # Evals guides companies through key optimizations ## Right-size Use the right level of intelligence for tasks ## Negotiation Enter contract negotiation with the data you need ## Routing Send the right ### 00:01:00–00:01:20 # Evals codify your competitive advantage in the Agentic Era ### 00:01:20–00:01:40 # Evals codify your competitive advantage in the Agentic Era VALS Always a Higher Peak ### 00:01:40–00:02:00 # AI Engineer ## NEW YORK PRESENTED BY Arize [Stylized graphic of a city skyline] ### 00:02:00–00:02:20 # AI Engineer Code Summit ### Presented by Google DeepMind ### November 10-12, 2026, SF // CA AIE CODE returns for its second annual code-focused summit. 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LEARN MORE AT [https://ai.engineer/code](https://ai.engineer/code) SUPPORTED BY G2i [Nighttime cityscape of San Francisco with the Bay Bridge in the background] ### 00:11:00–00:11:20 # AI Engineer Europe 2027 ## ExCeL London ## February 17-19, 2027 Europe's flagship technical AI conference ### 00:11:20–00:11:40 ### PRESENTED BY arize View Full Schedule at [ai.engineer/nyc/2026/schedule](ai.engineer/nyc/2026/schedule) [Arize logo, a pink stylized caret symbol] ### 00:11:40–00:12:00 # AI Engineer Europe 2027 Europe's flagship technical AI conference returns to ExCeL London for three days, February 17-19, 2027 ### 00:12:00–00:12:20 ## AI Engineer NEW YORK ### Stay in the Loop Scan the QR code or view the schedule at [ai.engineer/schedule](https://ai.engineer/schedule) [QR code linking to ai.engineer/schedule] ### 00:12:20–00:12:40 ## SILVER SPONSORS - Anchor - apify - AXO CAPITAL - Chainguard - CLOUDFLARE - DE Shaw & Co - Expo - extend - FLOX - Gravitee - Judgment Labs - Kimchi - MINIMAX - NIMBLE - Pydantic - RELAI - SENTRY - specific ai - supabase - SurrealDB - tetrate - union View Full Schedule at [ai.engineer/nyc/2026/schedule](ai.engineer/nyc/2026/schedule) ### 00:12:40–00:13:00 # AI Engineer ## NEW YORK HTTPS://AI.ENGINEER ### 00:13:00–00:13:20 # AI Engineer NEW YORK ## WE'LL BE RIGHT BACK View Full Schedule at [ai.engineer/nyc/2026/schedule](ai.engineer/nyc/2026/schedule) [Silhouette of the New York City skyline at night] ### 00:13:20–00:13:40 # AI Engineer NEW YORK HTTPS://AI.ENGINEER ### 00:13:40–00:14:00 # AI and the Future of Fundamental Investing ## Speakers - **Jack Woodruff**, Senior Advisor - **Vinoo Ganesh**, CEO, Kepler ## Conference & Sponsor - AI Engineer