A $10B Hedge Fund’s AI Playbook (Best of the Pod)

Will England is the CEO of Walleye Capital, a hedge fund managing nearly $10 billion in assets. An engineer by training with a math background from Oxford, he has spent his career at the intersection of machines and markets—and has made AI fluency mandatory for all 400 employees. England believes refusing to use AI is like refusing to use the internet in 1995 because it wasn’t perfect. His use of AI is public and effusive, including in a firm-wide email that opened with “I used ChatGPT to write this email. You should be using it, too, and be proud of it.” AI informs how Walleye drafts memos and selects stocks. On Every’s AI & I, Dan Shipper spoke with England about why he’s betting his entire organization on AI, why “results are what matter” more than blood, sweat, and tears, and what the American frontier can teach us about leading through technological change. Like, subscribe, comment, and share! To hear more from Dan Shipper: Subscribe to Every: https://every.to/subscribe Follow Dan Shipper on X: https://twitter.com/danshipper Go to https://attio.com/every and get 15% off your first year. Timestamps: 0:00 Start 0:51 Introduction 3:25 What pushed Will to go all in on AI 15:15 Inside the ‘AI-first’ memo Will shared at Walleye 17:02 Why you shouldn’t be afraid of using AI for work 31:25 How Will uses LLMs to sharpen his thinking 35:57 Walleye’s approach to using AI to reduce risk 39:35 What history can teach us about leading through change 57:10 Will’s first principles for making better decisions 59:23 Why Will journals every day—and how AI makes it easier Links to resources mentioned in the episode: Will England/Walleye Capital: https://walleyecapital.com/bio/will-england Every’s AI tools—Monologue, Cora, Spiral, and Sparkle: https://every.to/studio Every’s AI consulting: https://every.to/consulting

• 0:00Speaker 00

  It's becoming a meme that CEOs are like writing basically like the where AI first memo. You have the best example of that memo that I've ever seen. Would you mind just like reading, I don't know, maybe the first paragraph or two of what you wrote? Cause I think it's amazing.

• 0:13Speaker 01

  Using chat GPT is not cheating. That's a not applicable idea from academia. I use chat GPT to write this email. You should be using it too and be proud of it. As a hedge fund, we should be ashamed to leave money on the table by ignoring tools that make us faster, smarter and more effective. From the very top, we are building a culture around AI. Not using these tools is like refusing to use the internet in 1995 because it wasn't perfect.

• 0:52Speaker 00

  Will, welcome to the show.

• 0:53Speaker 01

  Thanks, Ben. Thank you.

• 0:55Speaker 00

  So it's great to have you. For people who don't know you, you are the benevolent dictator of Wally, which is a close to 10 billion AUM hedge fund. And you're an every consulting client. We're working with you to help you do AI training and implementation inside of Wally. And honestly, like regardless of the stuff we've done, you're, I think, one of the most impressive examples of someone, a CEO, who is like pivoting their entire organization around AI. And you're sort of like leading by example. And so I'm psyched to get to talk to you.

• 1:26Speaker 01

  Yeah, thanks. Yeah, maybe I could just give a little more context to that. I guess my, my technical title is, is CEO, CIO, Managing Partner. But, you know, both, both the owner operator of, you know, one of the, one of the larger hedge funds out there. I don't do many press. You know, I don't typically speak at conferences. I've only done one other podcast. So this is very deliberate. It's very deliberate for me. And because of the relationship that we developed, you know, on this subject, I do feel a great sense of both purpose and conviction about where our industry is going, where our firm is going. I do believe there are already a leader in that is going to continue to be the case. So sometimes I listen to a lot of podcasts.

• 2:08Speaker 01

  I was curious about some of the motivations for why people do this. I just want to be very clear, you know, upfront, you know, because I have that conviction, you know, the skills to lead us into the next, you know, the next phase. And we'll get into that, of course. And ultimately the power to, to enact that. I really feel that it would be irresponsible of me not to go after it with sort of the maximum amount of discipline and intensity I, I can muster, which, which is a lot. So for, you know, for your main audiences here is the people at Wally. You know, we have about 400 people, not big for a normal company, but in our corner of the world, that's, it's not a small, small amount.

• 2:49Speaker 01

  You know, I'm second, I'm also speaking to people that, that aren't at, aren't at the firm today, but may join us in the future. Just to understand how, how serious the firm is about AI and, and that ultimately starts, starts with me and then others. And then third, you know, finally speaking to people in the ecosystem, whether they be companies building products that firms like us can, can use. You know, or, or in other ways we've worked together, you know, that's, that's why I'm here. But it does all start with me having, you know, as I said, an enormous sense of conviction about, about where the world is heading. So.

• 3:22Speaker 00

  How did you develop that? Like, what was that journey like for you? I actually don't even know this.

• 3:25Speaker 01

  So I'm a bona fide nerd by background.

• 3:30Speaker 01

  You know, I, I was an engineer at Princeton. I went to Oxford to do a PhD in math. I started my career writing code all day for, you know, how algorithmic strategies.

• 3:42Speaker 01

  So, you know, we as a firm, you know, and me personally have been using, let's just call it advanced statistics, not even AI for, for years. A large part of what our firm does is in pure quant trading. So I, I've thought for, for many, many years about how machines increasingly can both either augment or do the jobs of, of humans and finance. So that's nothing that's, that's new for me.

• 4:05Speaker 01

  You know, what has happened, of course, in recent years is just that a lot of these tools have become more powerful and they've also become more accessible to non-technical people, particularly on unstructured data. And so part of it is just me personally being curious, being interested. A huge part of this is about curiosity. You know, I've noticed the productivity improvements that I see using, using these tools, just even for, from a writing perspective, let alone some of the sort of more advanced things you can do. So it's a combination of, called having been in the deeply versed in technical background. As I said, bona fide nerd, and then noticing what's out there and just the, the sense of responsibility to, you know, to the people at the firm, to our investors to say like, okay, we understand where this is going.

• 4:50Speaker 01

  If we ultimately get disrupted by AI, that's, that's on me. And so let's get after that. So even two years ago, like this isn't a sense of, oh yeah, we're going to do all these things in the future. You know, two years ago, we started an internal AI program. You know, today, you know, we're, we're already doing, doing a lot. So it's not just a story about what's, what's to come. And yeah, a lot of that does start with me. I mean, a lot of times leader of organizations won't sort of say that how much actually matters.

• 5:18Speaker 01

  You know, that in leadership, but this is one where, where I feel like I absolutely needed to lead from the front. So.

• 5:24Speaker 00

  And tell me about that, like a little bit more concretely. Like, one of the things I appreciate about you as a communicator is you're just kind of like no bullshit. We were talking like, I don't know, three or four weeks ago, and I was telling you something about every in our strategy and direction. And you were like, you sound kind of afraid. I was like, yeah, I'm a little afraid of this. So you're not, you're not afraid to kind of like put your finger on the nerve. And you're also not particularly like hypey as a person, I don't think. So tell me about the moment where you're like, oh, we have to take this seriously versus it's just like, uh, it's just like, yeah, tech people, nerds are, nerds are psyched about it, but like, it's something that we really need to understand.

• 6:05Speaker 01

  Yeah. And I appreciate you saying that. That's definitely the, the way I try to operate. Many people tell, tell stories. Sometimes those stories get, get hyped and for, for whatever reason, I think a lot of it comes down to being comfortable with yourself, self-confidence of not feeling like you need to convince something of someone you're not. So that, that's, that's been a hallmark of mine.

• 6:26Speaker 00

  I would never have done that. So I don't know from experience that that's a problem.

• 6:31Speaker 01

  Well, really, that's a, that's a core belief. You know, in terms of AI, there's, there've been a couple of moments over the years that have led us to continue to go down and recently accelerate this journey. The first was two years ago, called our most advanced internal AI project is called Current. That's led by someone who was a former analyst and one of our TMT, you know, for technology, longshore stock picking teams. Comes to me in March of 2023 and says, look, I've built, I've, I've used these tools that are now available to, you know, make myself way more efficient, eventually trying to replace what I was doing as an analyst. And I was pretty skeptical at first because that's, that's not typical that someone would just go out and do that.

• 7:16Speaker 00

  This is with GPT three?

• 7:18Speaker 01

  Yeah, yeah, this is like two years ago and it was early. But the point was just sat down, gave me a demo and I was like, wow. Like this is where we're going. And yes, you need to have a sense of belief. You need to imagine, you need to be able to dream. But if you understand the problem you're trying to solve and the tools that are available, like this is absolutely where we're going. And so that's when we started, you know, an effort to say, okay, for fundamental investing, absolutely. There should be agents that are ultimately helping with, with, with analysis and ultimately to provide, provide means what's going on. And so people talk about that now, but that was, that was two years ago. That was a real moment for me. And then more recently this year, you know, so I, I've seen a lot of podcasts, but consume a lot of information.

• 8:00Speaker 01

  And I was listening to one actually with, with Chris Saka, who's a very entertaining individual, sort of talking about, with frankly, a bit of hyperbole, but entertaining hyperbole, just about, you know, where we're going. And I was like, you know what? That's, that's so true. What these machines can do now is incredible. And it does take a little bit of imagination, but in some ways it's, it's kind of like you can see the endpoint more easily than you can see the steps to, to get there. I was using this analogy recently is a little bit, well, I'll just tell it to you, but I really did use this one. So my favorite movie growing up was The Sword in the Stone, made in the sixties by Disney. And I have three little kids and that's one of their favorite movies. So I was watching them recently.

• 8:45Speaker 01

  And so Merlin, in the cartoon version of The Sword of the Stone, he lives backwards in time. And so he can see, you know, glimpses of the future, but he doesn't see the steps in between. And that's kind of how I felt like here. Like it's, it's impossible for me not to believe that in five years, firms that do what we do won't be heavily, heavily, you know, integrated with best of breed AI technology across the firm, not just for investment, for non-investment purposes. And I say that, you know, in, in our industry, because it's one of, if not the most clear associations between, you know, information and ultimately money, right? The, the value of, of having an edge from an information standpoint is huge.

• 9:29Speaker 01

  And so that's why finances is really going to pick up and actually use these tools and say, okay, what applications actually give me an information advantage? So that's very clearly where we're going, but the steps to get there can be a bit hazier. And so actually it was after this podcast, as I said, with, with Chris Saka, that I wrote, I wrote the team. This is how we ultimately met you. It was like, we're going to train mandatory every single person at the firm. Doesn't, doesn't matter what department you're in, how technical or not technical you are. You're going to have the base case level proficiency in AI tools. And as part of this actually does come with a sense of responsibility, like people are anxious about what are all these things going to mean? Or what is that? How does that impact my job? What skills do I need to have? And, you know, me saying to the firm of like, okay, we are going to be a place that is going to be leading that,

• 10:15Speaker 01

  that we will actually train you, we will give you tools available. You still have to learn them, but we'll make it accessible and make it accessible to everyone. So that was when we just started doing a lot more, not just building, call it tools using advanced AI. And as I said, we've been doing that in our quant business for, for 10 years and not just building. Essentially, digital analysts replace the work of of humans in in long short stock picking. But then over the past, really this year, I've saying to everyone across the firm, you know, even in accounting, finance, compliance, legal, like, yes, you should be using either ChatGPT or Brock or some LLM to assist in in anything that you're you're doing that's analysis or writing.

• 10:58Speaker 01

  You know, we record pretty much every bit of information that flows through the firm. You know, a huge part of this is actually having a proper data strategy. And then also just having the culture of, yeah, we're, uh, we don't really know all the answers to this, but let's just start talking about it. It's made it accessible. So so simple things like having weekly emails where there's leaderboards of, you know, who's using these tools the most. Actually, I I sent the entire firm an email and said, hey, if you you suggest a tool that ultimately we end up pushing out across the firm, you know, just like there's inside of systems for employee referrals, we'll do something similar here here, too. You know, we have weekly meetups, informal weekly meetups internally, just to talk about AI,

• 11:42Speaker 01

  be it be it prompts or other use cases. I mean, the your your one of your previous guests talked about the sort of the social nature of AI and and how hard it is just to even discover best use cases, which is couldn't agree with more. But just even internally, trying to make it a bit more social, a bit more accessible. And, you know, I'm involved in in in all of this. In fact, I'm sort of the chief AI evangelist.

• 12:07Speaker 01

  And a huge part of that is just my own personal curiosity, but you can already already see it working. You know, people are doing things that they weren't they weren't asked to do. It's it's so cool and that the productivity coming from that, like, it's real. This isn't just paper. So so, yeah, we're we're definitely going down this path.

• 12:24Speaker 00

  One of the things I think you've been so effective at doing is

• 12:30Speaker 00

  basically, like, you can think of companies, companies even, you know, 10 people, but 400 people bigger than that, like, they're almost, the bigger they get, the more kind of hard to steer they are than like, you know, maybe a startup is like a canoe and like a 400 person company is like a cruise ship and a 10,000 person company is like a battle battle cruiser or whatever. And I think you've been you've done a really incredible job of like pointing the cruise ship really quickly. And that's something that's happening a lot now, like, it's becoming a meme that CEOs are like writing basically like the where AI first memo.

• 13:08Speaker 01

  Yeah.

• 13:08Speaker 00

  And I think you have like the best, you have the best example of that memo that I've ever seen. Would you mind just like reading, I don't know, maybe the first paragraph or two of what you wrote, because I think it's amazing?

• 13:17Speaker 01

  Yeah, yeah, I can read that. And and on that point of, as I said, there there is, it's words are words are cheap, particularly in a word of world of AI, you can create great world words very, very easily. You know, to your point around being a bit of a cruise missile. You know, I I do think that's an advantage of ultimate governance. You know, I'm then I'm the owner operator, so I I don't worry about getting fired. I believe on I worry about doing what I think is right. I very much believe this is right, and so we can just go and do some of these things. And large organizations just just can't operate that way. So, as I said, a huge sense of responsibility, because we can act that way to to do it.

• 14:02Speaker 01

  So, yeah, I I can read, uh, read a couple, a couple of sentences. Here we go.

• 14:09Speaker 01

  So, yeah, so this is an email that I wrote to the firm and it's our firm and it's the subject is AI at Wally, a challenge to all of us. It says, I use ChatGPT to write this email. You should be using it, too, and be proud of it. I'm writing this as a follow up to the comments I made at the town hall at the start of the month and after our AI Senate meeting earlier today, which is a group before thinking AI users from departments across the firm. And the message is simple, you know, Wally is is all in on AI. Not using these tools is like refusing to use the Internet in 1995 because it wasn't perfect. That's just dumb and something I can't understand.

• 14:52Speaker 01

  As a hedge fund, we should be ashamed to leave money on the table by ignoring tools that make us faster, smarter and more effective. Using ChatGPT is not cheating. That's a not applicable idea from academia, where using AI to do homework or take tests is actually cheating. In the real world, using AI is like taking a magical elixir that makes you 20% smarter instantly or a lot more. So why wouldn't you use it? From the very top, we are building a culture around AI. This is not optional for anyone at Wally. If you write, research, analyze, build decks, process data or think for a living, you should be using ChatGPT and or other AI tools every single day. Managers, this is now part of your job.

• 15:36Speaker 01

  You need to be pushing this across your teams, starting with being fluent yourself. So here's what's coming. And I talked a bit about some of the things we're doing recently that I just mentioned. And then ended, you know, this is the beginning, the edge is real, and we will not fall behind. Let's lead.

• 15:51Speaker 00

  I love that. I've like, what was the, you hit on a couple of things there that that I really love. Like, one is this, the sort of, is this cheating question that a lot of people have? Like, I have that, too.

• 16:06Speaker 00

  Not from, it's like, it's just like an internalized sense of like, oh, my God, this might be too easy. And when something's too easy, you're like, am I cheating? And and and also sort of like addressing this this fear that I think a lot of people have, which is like, am I going to be replaced? And I think the way that you're talking about it is very like, actually, this is now part of your, this is part of your job. It's not that your job's gone, it's that your job is changing to include this as an expectation.

• 16:32Speaker 01

  Yeah, yeah. So I very strongly use on on both of these. You know, as I've gone on in my career, you know, my my role has changed. And I believe that for all effective leaders, as they go on, they they should evolve as well. You know, I I love the Jim Collins quote, you know, build, build a clock, don't be a timekeeper. And so this is an example of that, where if you can have a tool that makes you more effective, it's the same thing as sort of hiring someone to replace part of what you were doing so that you can move on to the next task. So your context level shifts up. And so I'm not embarrassed at all that I can write emails that used to or or long memos that used to take me hours that maybe I could do that in a half hour or less.

• 17:17Speaker 01

  And that, to some extent, people have this insecurity that, oh, if I didn't put my blood, sweat and tears into it, that somehow it's not, it's not real. But at the end of the day, you know, results are what matter. I think having, I spent a lot of time as as an athlete, and I still do a bunch of stupid stuff in the gym as my hobby. At the end of the day, either you pick up later, you don't. And I think that's just the attitude that people need to have in the business context, as I mentioned. And there's this huge difference between academia, where people are like, oh, my God, it is ruining school, which in the call it existing paradigm, you can certainly argue that. But that's not business and just being very clear about that.

• 17:59Speaker 01

  You should be trying to be as efficient as possible, not so that you can just leave work at 2:00 PM. So you can actually spend time thinking about next level tasks, higher level contexts, be a bit creative. And ultimately, think of yourself, even if you're an individual and you don't manage any human, you're still going to be managing employees. A lot of those employees are just going to be AI robots, and that's a skill in of in and of itself. So just the reason I wrote that to the entire firm is just making people feel comfortable and not kind of embarrassed where it's like, because what I was seeing before is people were, you know, using ChatGPT or another similar product to create an email, and they were trying to like dust it up. It's like, oh, I don't want to be seen as doing that.

• 18:43Speaker 01

  So like, it's just stupid. You should be doing that. And if you weren't, like, why did you waste three hours writing this thing?

• 18:52Speaker 01

  So again, very, very strong feelings about that. And at the same time, you're you're coming around this anxiety of what is my job going to be? You know, when spreadsheets came out or email came out, you name it, like, yeah, you have to learn how to use these these tools. And I see this all the time. Very deliberately, we hire people who we say are at an inflection point of their their career, typically mid-career, where they, you know, know enough to be dangerous, the competency is there, but the hunger is still there and the the ability to be dynamic to learn new skills is also also still there. That matters a lot, and I don't think there's any different. You know, if you're just using example, if you're a long short stock picker and you can't use Excel to create a company model, like, you're totally obsolete.

• 19:38Speaker 01

  And in future years, you're you're going to have sort of the same thing, where if you don't know how to use these tools or you're not at a firm that, you know, to give you access to the tools to give you operating leverage, then you're also going to be obsolete. And so, you know, that phrase is what I use a lot is just this this concept of operating leverage and not not being afraid of that. But yes, absolutely, I see that all the time. And it goes back to my earlier comments around feeling a sense of responsibility to put people in a position to say like, don't be afraid of this stuff. Embrace it. And another thing that I was seeing, Dan, a lot of these tools aren't, they're not perfect. I mean, none of them are actually perfect. They all have their flaws, they all have stupid things.

• 20:21Speaker 01

  But the direction of travel is very, very positive. And so I wanted to set a culture and environment where instead of people being like, oh, it didn't do exactly what I wanted, so I'm just going to ignore it until it's perfect. It's kind of going the opposite way of being like, yeah, let's have, you know, a demo with a third of the company joining, which is real, that's how many people join. And if if that demo screws up, who cares? Like, I I basically had one last week that the the demo gods were against me. But they're totally fine. And and having people accept that, embrace that and be like, yeah, this, some of this stuff isn't perfect, but you can see where it's going, as opposed to just almost using that imperfection as an excuse, excuse to ignore it.

• 21:04Speaker 01

  So all those things combined into one, there's there's no other person besides me. And I think this is true of all leaders of the organization, like, you have to lead from the front, you know, no one, no one can set that tone besides besides the leader. And once that tone has been been set, it's it's kind of incredible how how much you can unlock people to be like, yeah, have at it, and it's okay. Setting that tone and flipping it outside and be sort of, you know, you you need to be afraid, or a lot of people are afraid not to or to make a mistake. Kind of need need to be more afraid to getting getting left behind.

• 21:39Speaker 00

  And what have you seen, like, for someone who's watching this and maybe is in finance or maybe is just running another company with a lot of people and is thinking about, okay, like, but really, what what productivity gains has it actually unlocked for you? Like, concretely, what are a couple of things that have been useful for you or for the fund?

• 21:57Speaker 01

  So there's a couple of things you have to, you know, put this into into categories of what's what's recent and what's not. So as I mentioned, you know, we we do run a big quantitative trading business, you know, as a as a multi strategy firm, we run many different strategies. But quantitative equity trading is a big part of what we do.

• 22:16Speaker 01

  Those models have used, you know, nonlinear statistics, AI, or some of the underlying models in AI for for years. More recently, with the advent of large language models coming about, the ability to process, you know, unstructured data, of course, and incorporate that into signals, which historically is called sentiment analysis, you know, the ability to do that at scale has come dramatically. So that's one improvement, just in a pure money making standpoint.

• 22:39Speaker 00

  And you're doing that, like you're using language models to do sentiment analysis?

• 22:42Speaker 01

  Yes, we absolutely are doing that and have been doing that for years. And frankly, all world class quant firms are doing that. But it's hard. That's why quant trading is one of the things that definitely benefits scale. Some of the things that are called more recent or newer, and I do believe that the explosion that we've seen, it empowers the less and less called technical people, the technical side where really you just have to be creative. So some of us were doing like, you know, seventy five percent of the firm, you know, is an active called chat GPT, chat GPT like user. You know, every single week, like actually almost every single day, that's pretty cool.

• 23:24Speaker 01

  About a third of the firm, you know, use AI coding tools such as windsurf. That's sort of very real are, as I said, an internal product for fundamental launcher stock pickers. You know, that's, that's also a big part of our business. Every single team uses this tool. It's called current. It spikes dramatically, you know, during, users spike dramatically during earnings. We have people that that come here from our competitors and tell us that this is, you know, it's both way better. But but really an essential part of their of their job. And I believe that and I think our competitors probably do have good products as well.

• 24:05Speaker 01

  I think we've just been doing it a little bit longer and are further down the path of using these tools actually to provide provide meaning and real analysis as opposed to just summarization.

• 24:16Speaker 01

  But yeah, you know, you kind of can't go through earnings period now as a long for stock picker without some of these tools. If you're going to be competitive, because, you know, one of your competitors like someone here is going to be able to process all of them in real time and then have a machine go and basically impute things that humans can't do as fast. So how do we actually measure that, of course? Having benchmarks like, that's kind of nonsense in a real, real company. But just sort of seeing the the level of, level of adoption, just seeing people suggesting products like, okay,

• 24:54Speaker 01

  we want a, we talked about last week on our, one of our meetups to have a product that can do summarization via podcast to make it more accessible to people that want to listen to to something. And then that day, we had, you know, five different products in beta, and then the next day was pushed out across, across the firm. So some of these sort of cultural elements of just having actually set up the process where we can both incorporate through free products and and build some of ourselves.

• 25:23Speaker 01

  You know, that that's pretty cool. And I don't know how much smarter or more efficient that's, that's making people. I certainly could speak from for personal experience. A big part of running an organization is communication. My communication is dramatically and which a lot, in a lot of cases, written, that's dramatically more efficient now. When I was using these tools and I think that's probably true for literally every single person in our firm.

• 25:47Speaker 00

  What does that actually look like for you? Like, when you're using it to communicate, what are you doing?

• 25:52Speaker 01

  I think best when I write out my thoughts. I tell people that my education is very expensive, so I better be able to write or else what the hell is all that for?

• 26:03Speaker 01

  And so historically, you know, I, I really would write to convey what I'm thinking, where the firm is going, why? And I just believe that leaders should be able to to really communicate. And I believe in the power of high quality pros. And it's actually one of the things in general, before L L M's came about, that it was pretty depressing that younger people just were terrible, terrible writers. So for me, when I want to, you know, write an email, as an example, write, write a memo, I do it in bullet point form. I, I type out, here's what I'm thinking, here's why, you know, I work on my prompts. And maybe I'll give a bunch of context of, hey, you know, here's all the stuff that I've written on a similar subject.

• 26:46Speaker 01

  And I want you to give something in my, my own voice. And that can be, you know, no exaggeration, like a fifteen minute process that would have taken me four or five hours historically. That's why I have such, you know, almost religious views on this stuff, because it's, it's, it's pretty wild. So that's, so that's one example where I'm using that personally. You know, another thing that we do, and yes, there are always the, the questions on what can or can't be recorded. It's kind of going back to the governance element where I can say, this is what I believe is the right thing we're to do. So internally, with with a few exceptions, we really record every single Zoom, every single call. It's just the nature of our industry.

• 27:30Speaker 01

  And frankly, I think the whole world should get used to doing that. You're seeing these articles about people wearing wristbands, recording every, everything they say for, for months and months at a time. You extend that forward without sounding too much like a nut job. I think people are going to have essentially recording devices implanted, you know, in their bodies that record everything. So just getting comfortable with the fact of, yeah, like all this data is going to get, get captured. So we're trying to do a bunch of that internally. And then of just being able to go back and process that, because so much of the power of this is, do we actually have a data strategy? Get all the data into a lake, which you can then put a straw into and get it out.

• 28:09Speaker 01

  So, you know, a big part of my job overseeing the, the risk of the firm, the chief investment officer title. You know, every single morning, me and my, my risk teams are like in the, in the control center of, of running this, this giant process. You know, we have our risk calls and those are all recorded and we, we can go back and say, hey, you know, what were we talking about at this time? And continually have L L M's that are, that are processing those transcripts and, and helping, helping us to both remember and, and provide insights and ultimately be a bit predictive. Which has been hugely helpful, just, just in that exercise, which is, you know, we haven't sort of talked about where I think this is going and the, and the power of all this.

• 28:52Speaker 01

  And I mean, like, we're, I, I do believe that we're, that we're a leader. I don't want to say we're, we're the leader, because I definitely don't know what other firms are doing. But I certainly think that we're a bit more advanced in our thinking of how to use these tools. But we're just scratching the surface of what, what is possible. Once you actually start connecting all bits of, of information within the walls of the firm. And this is not just, you know, not just wallet, not just hedge funds, really any company of saying, hey, let's actually put all of our, our data together into effectively a collective. And then that, that information can get processed. We're totally just scratching the surface there, but we're certainly working towards that.

• 29:31Speaker 02

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• 30:15Speaker 02

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• 30:26Speaker 00

  I want to go back to something you said earlier about writing as thinking and using language models to turn like a, you know, four or five hour task into a fifteen minute task.

• 30:36Speaker 01

  Yeah.

• 30:37Speaker 00

  What is your, like, one of the things I worry about, for example, is maybe I'm not thinking it through as clearly if

• 30:47Speaker 00

  the language model has like written a bunch of stuff that it's coming from my bullet points, but I haven't like really gone through every single thing and been like, I, I stand behind that.

• 30:56Speaker 01

  Yeah, I'm sure. I think you have to work this out in terms of, you know, thinking through the concepts versus the linguistic syntax. What I was knowing at least personally, and I do think a lot of people do this, is when they're writing, they're, they're trying to be both consistent, to some extent clever, and to some extent unique to their own style. And so a lot of editing can be, I think, less about the concept and more, what are some of the nitty gritty details of how you stitch sentences together? Even simple things like, it drives me absolutely nuts when someone ends a sentence in a preposition and everyone in the firm knows that. But you don't, so you don't have to spend as much time, again, I think on the, the important, but not as powerful tasks of, of writing.

• 31:40Speaker 01

  Like, a lot of it is just sort of stitching, stitching these pieces to together, the tying your shoes part. So the principle, the, the elements of writing, you know, what are the concepts that I'm looking to, to convey? That's what I spend my time on now. So what I've found with these tools is really trying to be clear, like, this is the concept that I want you to get across. And this is how, and then, yes, it will suggest, you know, a string of words that, that convey that. And particularly with the way the recent models are protected, it's has had everything that I've, I've written, so it can do it to some extent in my own voice. But I just don't have to spend as much time, like, frankly, trying to be clever. And that's what a lot of writers do. They, they try to say a lot of relatively straightforward concepts in a clever way.

• 32:24Speaker 01

  I just don't think we need to waste time on that anymore.

• 32:26Speaker 00

  I would never, never do that as a writer. I'm curious, like, but let's, let's flip the table a little bit to like, when you're reviewing someone else's work. So for example, for me as a manager, I think like, let's accept, for example, like, if someone's going to publish something on every that sounds like it's AI written, I don't, that, that's just out for different reasons. But like, if I get an internal report that looks like it's written by chat GPT, and I did this actually last week, because.

• 32:54Speaker 00

  Everyone internally is using these tools all the time.

• 32:57Speaker 00

  It's not that I care that the voice sounds like chat GPT. It's that it's not clear to me that the person has thought through the thing that is being presented to me. And I don't want to spend time reading something unless I know that a commensurate amount of time has been spent thinking about it first. So how do you like deal with that?

• 33:15Speaker 01

  Look, these tools don't negate the necessity to think.

• 33:20Speaker 01

  And I say that all the time. If anything, they should just give you more time to think. Like if you say, okay, you have an hour to complete this task. And it used to be historically, I don't know. Fifty, sixty percent of that time was just going to be mechanically typing out. And now, I don't know, five percent of the time is me doing that. So you have more time to think, just in a fixed, fixed amount of time. So you should, you should really think, you should, you should read, you should proofread and say, does this make sense to me? Is this what I'm trying to convey? And so I can definitely tell as well, when something is written by a machine. Sometimes that's just the way that the, the text appears, like the bold, like clearly the human didn't go and bold it in exactly this way. But, but that's fine. But it's not enough. It's not sufficient.

• 34:03Speaker 01

  You still need to convey the concepts clearly. In an ideal case, someone has clearly used these tools, but the concepts still come from them. And I could tie it back to the person. And there's a why of like, okay, why are you doing this? Why does this make sense? And you didn't waste your time doing something that wasn't necessary. But at the same time, you didn't just outsource all of your brain to a machine. And sort of there's that optimum point on the curve that we're trying to, to get to. And that's again, why I don't, I don't think people should be totally, totally afraid of using these tools because by themselves, I don't think they're sufficient. I think that, you know, it's like having a a very powerful jet, jet engine, excuse me.

• 34:46Speaker 01

  And you use that analogy to me as well, like a jet engine won't fly by itself. You still gotta hook it up to the plane. And there's a hell of a lot of things that matter when it comes to aerodynamics and you know, that make a plane efficient or not. So humans can kind of design the plane a little bit more and someone else brings the engine. You can use that engine in very powerful ways. But you, but you need to be part of the process for sure.

• 35:09Speaker 00

  I want to talk about that, the thing you brought up next, which is sort of this data like idea of like sort of recording everything. You've been calling it the Borg as a Star Trek fan. So like where, like you're recording all the meetings now, which I think is awesome. And you said it's already helping like in your, for example, in your risk calls, you can tell like how you made a decision, like, can you give us a concrete case where having all those recordings has actually been helpful?

• 35:36Speaker 01

  So yeah, the Borg, which was just going from Star Trek.

• 35:41Speaker 01

  And I'm kind of sad now that when I say the word Borg, even some real nerdy people don't even know. It means I'm getting a little bit older. And the collective, which is you don't get, get all the information together. That's, that's at least our, our spirit animal, our, our spirit guide for, for the future. It is, this is really hard. I think all companies are going to have this and some of this is not at all particular to finance. It's just like, there isn't even a great way to process all of the firms emails right now using AI, which I'm sure will be solved soon. So the most salient example of where we've done a miniature example of this goes back to, as I said, our, our internal product, current, which, you know, takes analyst notes, all, you know, information's coming in from, from brokers and these PDFs that get emailed around all the time.

• 36:26Speaker 01

  Earnings transcripts, really any bit of information that's, that's germane to, to a stock. That's, that's our, our most advanced, call it org example. And that, as I said, that really is real, like all of our, all of our PMs do this as, as an indispensable tool that saves them a ton of times, particularly when information flow is, is very fast. Again, quantifying that exactly, you know, there's just no perfect metric. A lot of it is, is definitely subjective, but I can see the internal use case numbers. And I can also see like firms, all, external firms know that we're, that we built this and are doing it. And the over fifty of them have asked like, hey, can we, can we be a beta user of current?

• 37:08Speaker 01

  We'll give you feedback to help make the product better, which we, which we've done in some cases. And it just also makes sense, right? That a huge part of the job of a human is synthesizing information.

• 37:20Speaker 01

  And, and until recently, like when it comes to reading documents, machines can do that very well. Reading documents or listening to, to voice essentially other text forms. But now machines can, and now the servicing, the second order, the third order effects of that, again, not just summarization. That's what machines are, are starting to do. That's, as I said, that's our main use case. And so this, the broader idea of the Borg, I look at what we've done to just help, you know, our long short stock pickers and say, that same concept should be able to help every single department at the firm. That's, you know, generating text is a simple example through emails, through, through Slack messages,

• 38:05Speaker 01

  through, you know, live, live calls, live conversations. And then ultimately, the ultimate goal is to tie that back to numerical data, whether that be market data, internal data, accounting data, you name it. So the, the applications, as I said, do take time to, to imagine, to design. But it, as I mentioned earlier, it's not that hard to see where this could go in the future. When you do have these sort of miniature collectives across all departments of the firms and of other firm, of any firm, and they're linking them together. Like that, that will happen. It's just a matter of how to get there that I think everyone is still trying to, to figure out.

• 38:48Speaker 00

  I know that you're a student of history. Do you have any historical periods or examples that you're turning to, to kind of help you navigate? What's going on right now and, and this transition that you're going through?

• 39:03Speaker 01

  I'm a student of history. I do love

• 39:08Speaker 01

  you know, basically the period between the Civil War and World War One is, is a time when I think the whole world changed dramatically. And then part of that is that my, my office looks out on a train station built by one of the robber barons. So I do think about it all the time.

• 39:26Speaker 01

  So it's certainly not the only period of history, but you know, definitely a time period where things changed dramatically. I'm, I'm not a VC, thank God, because I think most of them don't know what they're doing. Certainly this idea that when you look at a, you know, an exponential curve, you know, humans sort of knows it's, it's up against that. Don't realize how, how fast things can change. So there are periods of time, like how, how fast the, the railroads got connected or, or then how fast you had transatlantic cables and what that actually meant. You know, the, as I said, the period between Civil War and World War One is just huge, huge amounts of change.

• 40:08Speaker 01

  And people within their own lifetimes sort of went from having relevant skills to top elite skills. That is going to happen faster this go around. And then when I said, I thank God I'm not a VC, because I hear a lot of people, investor types prodding, getting about the, prodding, getting about this, but they aren't actually involved in any operating companies and don't realize that someone still needs to go out and build all this stuff. But I do think the sort of first principle arguments of, yeah, things are going to change dramatically. You know, corporations, collections of humans, let's just say, companies in the future that want to operate in a world-class manner at scale are still going to need many, many thousands or more of employees.

• 40:50Speaker 01

  But if you have those employees are going to be humans, a lot more of them are going to be, to be machines. And so you've certainly seen that level of disruption in other, other areas that just happen a little bit faster this time. But at the same time, I'm an optimist in general. Like that's very important for the, for, for leaders actually to have an optimistic tone. I don't think the world is, is going to end because all of a sudden people are going to have their jobs disrupted by AI that they need to adapt. It's sort of having a level of realism around that of, of that's, that's what I mean. Our firm is a microcosm of that. If, yeah, you have to learn these tools or in whatever time period, you're, you're just not going to be competitive.

• 41:31Speaker 01

  And we are at the, the tip of the spear from a competition standpoint, just given the nature of what of the industry and what, what our types of firms actually do. But I think it's going to be true across a, a bunch of water squashed population where, yeah, you gotta be trained to be, to be efficient. And at some point, if you don't, that's your choice.

• 41:53Speaker 01

  That just is what it is.

• 41:54Speaker 00

  Yeah, I think that period of history is so is actually is actually really relevant. And coincidentally, I've been, I told you this already, but I'm sort of in my cowboy era. Um, and I've been like reading a lot of cowboy stuff and um.

• 42:05Speaker 01

  You watched the Netflix thing on on Wyatt Earp and Cowboy War?

• 42:08Speaker 00

  No, should I?

• 42:10Speaker 01

  Yeah, it's really good. It's really good.

• 42:11Speaker 00

  It's it just came out?

• 42:13Speaker 01

  Yeah.

• 42:14Speaker 00

  Okay, I'll check that out. I just finished Deadwood, which I was telling you about.

• 42:17Speaker 01

  Yeah, I've never watched like any TV, but this one came out and I also love sort of the old old West. It was a good story.

• 42:25Speaker 00

  Do you know why the why cowboys disappeared? I just learned this and it's it was a really interesting fact.

• 42:31Speaker 01

  You tell me. I have a hypothesis, but you go first.

• 42:34Speaker 00

  Um, barbed wire.

• 42:35Speaker 01

  Yeah, I was gonna say the broader con, well, in the documentary, the Cowboy War, what? Basically, the answer was civilization kind of came in. You had the railroad come in, which brought a lot of people and then, yeah, eventually barbed wire and you couldn't steal cows. I mean, the cowboys were a gang in Arizona in the 1870s and 1880s stealing, you know, cows, especially.

• 42:56Speaker 00

  I didn't know that.

• 42:57Speaker 01

  Oh, yeah, like the cow, so it is fascinating, but the Cowboy War and Wyatt Earp, which is this historical figure of legend. You know, gets in a this huge fight like the movie Tombstone, which is kind of historically accurate, but not really. Um, it was sort of the Wyatt Earp posse versus the cowboy two words posse. And it was this big, it ended up being this big deal like that's the gunfight at the OK Corral. Because it stirred up all this sort of north, you know, north versus south sentiment 20 years after the Civil War. But the broader historical context there is people sort of wanting to bring about change because Tombstone where. Um, you know, OK Corral was and where I was was was a silver mine.

• 43:39Speaker 01

  So it brought in all these, um, you know, people from across the country, both north and south. But there's this huge tension between those wanting to modernize and those wanting to get stuck in the ways of of the past. So, yeah, it it's it's that that period of time is, um, I find it fascinating too. Because you had sort of land with no laws, all of a sudden becoming civilized at various different paces. And a lot of cool things or interesting things at least happened because of that.

• 44:08Speaker 00

  Yeah, I think that's it's for me, I love that because I think it's such a good metaphor for technology and technology frontiers. And kind of this trade off between you have like the individualists who are going out and exploring and there's no laws. And there's a lot of creativity and all that kind of stuff. But then you kind of need the civilization that comes behind them. But that's sort of, it's at odds with that frontier spirit. So there's that, there's that always that tension between structure and creativity. And and I think there's something very similar there about technology.

• 44:38Speaker 01

  Similar levels, man. I mean, that's even true with sort of at least historically of the, you know, why does VC investing exist? You know, why is it that you go and read about the story of, you know, you pick any company, you know, from Nvidia on down. Where they're like, I can't do this at a big company, so I'm going to go and push the frontier. Uh, in a world in which I'm less constrained, you know, there's no barbed wire and I'm going to go and build. And then at some point, you know, those those companies become successful, they become institutionalized. And then someone does that again. And so that's not a geographic frontier, but but a technology frontier. Um, and as I mentioned earlier, like I feel a sense of responsibility because we can kind of do both.

• 45:20Speaker 01

  And they're just not the only companies that can say that if we want to push the frontier, but with the resources. Um, then, you know, those ultimately are, I think, the businesses that when you look at any technology. Transition are are able, you know, not just to adapt, but but to thrive of, uh, you know, having that mentality. But not being, you know, not using single action rifles when other people have machine guns.

• 45:47Speaker 00

  How do you think about how the past informs the future? And I'm I'm asking this both from like a kind of, I don't know, uh, late 1860s to now perspective. But also from a from an investing perspective. Um, and and and and I think this this layers into the AI stuff where it strikes me that a lot of. Or I'm curious what you think about this, but a lot of investing has to reliance to some degree on the idea that certain things that happened before are going to happen again. And and part of the investing, part of being good at it is knowing which which one's going to happen again and which one's not. Um, do you do you agree with that characterization? And how do you, how do you think about when to when to rely on past patterns to help you understand the future?

• 46:31Speaker 01

  I don't think human nature has changed in 10,000 years. Um, you know, you, and maybe it's evolved a little bit, but you go back and my my son who's eight, uh, is really into, uh. To Egypt right now. Uh, I love Roman history because I took Latin and I'm set of a real nerd. So I know a lot about Rome, um, but you you can read a lot about the two. Like human nature hasn't changed in a long period of time. You know, you go and just to use a famous example, like you, the old always wants to. Or the new always wants to replace the old, you know, the the son wants to outdo the father. These these are timeless, you know, when Alexander the Great was, you know, conquering Persia. You know, their eyes, the king of Persia sends him a note and is like, hey, how about we have a truce?

• 47:15Speaker 01

  And he responds like, I'm coming for you. And so this concept of, uh, um, yeah, no, that's a good story, right? Uh, but this concept of the new wanting to, you know, replace the old in this continuous evolution. You you find it in nature, you know, everyone sort of knows the analogy of a. Uh, a forest fire burns the trees so the new new brush can can grow like this. This this concept renewal is is always going to be there. It's in human nature. It's nature. I think it's just a a pattern that we're going to to see. And it's the same case here, like there's going to be, um, a group of people, a group of firms, collection of individuals that are going to look what's happening and embrace that. And then there's going to be groups of people that are going to hold on to the past.

• 48:00Speaker 01

  And, um, there, you know, there's there's going to be conflict of varying degrees because of that. But that hasn't changed a lot. So when it comes to investing, when we look for patterns. Uh, you know, quant, quant investing, um, of course, is built on this concept that there are patterns, um, in history, in stock prices and information that was predictive. Um, and and there's a structure to that data, even if that structure is so complex that humans can't understand it. And in world class quant investing, you know, we've we've moved past what humans can understand. You know, many decades ago, people forget how long and what the the book on Renaissance came out.

• 48:42Speaker 01

  But, um, you know, some people still don't realize that the quant investing has been going full bore since at least the early 90s. Um, and so absolutely that whole class of strategies, which is a huge part of the markets today. Um, is built on this concept that historical patterns, um, do repeat themselves. Um, when most people think about investing, they think about sort of human driven intuition in investing. Uh, I also believe that's timeless. It's just timeless on a on a different scale. And that is where on a go forward basis, I still very much believe that human investors will have an edge, particularly on low law of large numbers situations where a machine hasn't exactly seen all the priors.

• 49:22Speaker 01

  The precursors that would lead it to make an informed decision, but a human can be better at dealing in the fuzzy mess. Um, and so all these tools that that we're building can be built, um, to enable that human. Um, to, you know, to make a prediction more more accurately. Um, yeah, there there's there's patterns that come with history. It's, you know, history doesn't repeat itself. It it rhymes like that's absolutely true for for humans. Um, so I guess summing all that up, uh, just saying, oh, this is the way things happen in the past. Um, is silly that because they never repeat themselves exactly. You have to sort of more go to the, you know, the they are lying mechanics, particularly around human nature.

• 50:06Speaker 01

  I think I think human nature is, um, is a constant, is is stationary across time. If you just look at it in the right, right way where your thought allows all the other crap that's that's happening.

• 50:16Speaker 00

  Well, we'll have to, we'll have to debate whether human nature is static or not. Because I I'll I'll take the opposite on that one, but I have a I have a, uh, another direction I want to take this, which is, um.

• 50:30Speaker 00

  I didn't realize that now you're now I'm nerding out like I didn't realize that there are things happening in quant trading that are in principle not explainable not like humans can't understand it at all is that what you're saying or they could but it's just so complicated that no one takes the time to do it because it's not worth it.

• 50:49Speaker 01

  Well, there's there's so many different flavors of quant investing, you know, there.

• 50:55Speaker 01

  But yeah, generally speaking, the higher sharp ratio or the more consistent strategies that you get that aren't pure arbitrages, not just based on speed. You know, it's just like, why doesn't LLM do what it's going to do? People kind of understand that, but but not really. There are all the nonlinear relationships. A lot of those techniques have been used on structured data in these models for years. I mean, you go back and read the book about when the guys from IBM came over to Renaissance, what they're doing, they're doing speech recognition, like a lot of that sort of pattern, pattern matching and sort of predicting what what comes next. Yeah, for a human to actually sit down and like walk me through all the different

• 51:39Speaker 01

  layers of the neural network and why did a machine do what it's going to do with? No, the dimensionality of that problem just it's way past what a human mind can can understand. So I'd say generally speaking, world-class quant models, while the the signals, you know, what goes into them of saying, you know, this is something that I ultimately believe this feature makes sense, how those features get and which I do think is important. And again, there's various different ways in which firms go about this. Generally speaking, we will understand at least some rationale to our features, even if there are many thousands of them. But how those ultimately get combined and understand

• 52:21Speaker 01

  the nonlinear relationships from that, that's that's very complex and and that's totally fine. So.

• 52:26Speaker 00

  It's it's interesting that you brought up intuition a little bit earlier as a something, something sort of separate from the more algorithmic and it a helpful addition to the more algorithmic quant decision making because I actually think of I think of neural networks and intuition, the human intuition as being analogous and and maybe even helping us to understand how valuable and important intuition is despite being unexplainable because it's kind of unexplainable in the same way. You're working on a very high dimensional basis with with problems that you can kind of talk about why you make a decision here or there, but it's really just kind of a feeling that you've built up over over many, many, you know, experiences. And I think neural networks are the same way.

• 53:09Speaker 01

  I think intuition is very important for building a process and really, you know, another term that you could say is analogous to intuition is is first principles.

• 53:23Speaker 01

  And there's a very famous guy that uses first principles all the time. But I very much believe that is actually sort of understand and it's really a math term, right? If you can understand some of the core concepts, the first principles in math, then you can take any test and get 100%. So I was a math guy. I like thinking from first principles. And I think it's the same way. But when you interface that with machines, well, machines will really help you to do is say, I might actually help you come up with something intuitive, but you wouldn't necessarily come up with yourself, kind of like having a coach where you they could watch you doing a movement or lifting a weight like, hey, did you actually realize that these things are are connected? So I don't I don't think they're antagonistic,

• 54:07Speaker 01

  but it's entirely consistent that a machine can give you a very intuitive answer that you wouldn't necessarily have been able to think of yourself, if that makes sense.

• 54:16Speaker 00

  Do you think that first principles, and this is a leading question because I I have an opinion on this, but like, do you think that first principles in this like, I don't think that first principles in the math sense are the same thing as first principles in the like, let's say, decision making sense in in in the sense that when you when you use the word first principles in a math sense, once you adopt them, you can just like work out all of the implications of those principles without, like you said, you can take the tests and, you know, you can just figure out what all the answers are. But first principles in a kind of a decision making sense like they're not at the bare level of like, you

• 54:55Speaker 00

  know, axioms in math, they're they're already like many layers up above and can be filled in in many different ways.

• 55:03Speaker 01

  The real world, the difference is in a in a mathematical model and math is just a model for the way that things operate. And within a model, you have rules. And so there's real objectivity to what are the rules. Sometimes those rules can be very complicated, but there's an underlying structure in organic systems that that sometimes there's structure, sometimes there there isn't. So I agree with you when it comes to decision making. That's why that the word subjectivity exists. What could be first principles to one person might be totally the opposite of someone else's first principles. They both call them ground truth and axioms or whatever it is. So there is an element of subjectivity in the real world, you know, when it comes to decision making and what what I believe

• 55:47Speaker 01

  and what I believe really good decision makers try to do is is enter in their own decision making process, both to discover what what's led to good decisions and what, you know, what are areas and what their decision making has been subpar. So in some ways, you can get implied more to your own closed system, but there's a universal truth around how decisions are get made like, yeah, that that kind of falls down at some point because people might just totally be schooled and first principles that are so different that they would lead them to make completely different decisions.

• 56:20Speaker 00

  What are those for you? Like, what are the as you've as you've learned to improve your decisions over time? What are the things that you've learned to to take as as good, reliable first principles? And what are some of the ones that you've thrown out?

• 56:33Speaker 01

  At the top of the list would be the power of incentives when you're dealing with humans and not in a negative sense, like everyone is greedy, although, you know, a lot of times that that does drive behavior, but actually understanding the right incentive decision for why an individual or a group of individuals is doing something and trying to align that as much as possible, whether you're running a company or making an investment, that's extremely powerful. It's just to say like, are the are the vectors sort of the are the incentive vectors all pointing in in the same dimension? So that's a big one. You know, you mentioned earlier, like, I hate fluff, I hate, sometimes it gets me into trouble. But I just I wish that people would be more that way, whether that's an

• 57:15Speaker 01

  axiom or a rule, but whenever I sense that my antenna go up and, you know, that that's sort of a negative sign. And really just trying to be intellectually honest and so not not trying to reduce everything into a into a math problem. But a lot of times there there is structure that you can pull out of a situation and it might just require hard work. So, you know, you can't manage what you can't measure. So to try to measure a lot of things and, you know, personally, I I try to do that. I, you know, evaluate myself. I I keep a journal, you know, every single day with AI now, by the way, which is great, for efficient, highly recommend everyone do that. You know, every every single workout that I do for years, I've I track, you know, I have I have a log and there's

• 57:58Speaker 01

  a lot of numbers in that, just to to see these trends, you know, evolve across time. Again, none of these are perfect, but I feel like people, because it's hard, a lot of people just don't do things like that. And I actually on a go for it basis, I think that's one of the things that's so exciting about this world of really data and making meaning out of data is it's going to be so much easier to do that. And a lot of it's going to revolve around, are you collecting right, right data to to help you with that? So, yeah, you know, the biggest first principles, as I said, power of incentives and and intellectual honesty, and those those would be the top for me.

• 58:36Speaker 00

  What goes into your journal?

• 58:37Speaker 01

  Well, there's three components of my of my life, which would be,

• 58:46Speaker 01

  you know, my family, you know, I have three kids and I've been with my wife, who's pacing for for 15 years. And I love being with them and that it's not just a platitude. So my personal life and now I'm interacting with the family and I feel a huge sense of responsibility to to my family to provide them with a great life. So there's a section on that. There's a section on and by the way, none of these are mutually exclusive. I don't believe in balance. I believe in harmony across different areas of life. So there's but there's a section on that. There's a section on on work, of course, which, you know, for me at this point

• 59:29Speaker 01

  is is this great, great big challenge that's that's fun. I think sometimes people don't even use the word of fun. And like, that's one of the things with AI, like, this doesn't need to be scary. This is fun to tell if you look at the right way. It's really important that people have that that view. And, you know, I I'm working because I I get meaning out of it. I enjoy it and it's it's it's different than and I feel fortunate that than most people where I can say like, I I'm doing this because I certainly want to. So there's things on work that I talk about. And then there's things just on my my personal health that I that I talk about, you know, how how I'm feeling, was it a good day or

• 1:00:10Speaker 01

  a bad day? How my, you know, how my deadlifting session go in the morning, stuff like that, or, you know, I. I'm a bit of a crazy person on that subject. So if I tried a new supplement or tried some new technique or something, I'll write about that. But I'm just trying to capture what's going on in my mind. I started doing this because, you know, I'd look back and in finance, right? We're dealing with time series. And so we could say like, oh, on this day, you made or lost money or something, stuff's happened. But I really wanted to remember, like, what was I actually thinking on that day? And just trying to keep it all in your head, even for someone that can have a lot of, a lot of hard drive space

• 1:00:50Speaker 01

  is impossible. So getting that out of my head a bit more was, was what I started doing. And it's been really helpful, both as an exercise in itself, even of just writing it, but then going back and saying like, oh, yeah, this is what I was thinking on that day. That's interesting.

• 1:01:05Speaker 00

  And help me understand that more like you're maybe now, now you're like typing this into chat GPT, you're speaking it, you're writing into Google Doc and then putting into chat GPT?

• 1:01:14Speaker 01

  I'm trying to capture the concepts of what I felt on that day. So I can either speak or just write bullet points of here's this is my mind in these three categories, you know, this is what's, this is the date, this is what's on my mind. Each of the three categories, you know, here we go. Some days that's a lot. Some days that's, that's a little. And then because I've been doing this and the machine knows my, my voice.

• 1:01:37Speaker 01

  You know, that, that can be a one minute, even thirty second exercise. It's just so easy to do. And I feel the reason most people don't journal is because historically, sit down, it would take time. But this is a perfect example of you know, the thought that's on your mind, just get it out there very easily. And then it can be captured, processed in a way that is accessible once in the future. It's just this tiny little use case. It's sort of so, so powerful with these tools. And there was no way that could have happened in the, in the future. So.

• 1:02:08Speaker 00

  One word that you brought up a lot in the context of work, but also you just brought it up also in the context of family that I'm curious about is the word responsibility. What does that mean to you?

• 1:02:20Speaker 01

  I do think about this word a lot in the context of work, work and family, slightly different, but, but overlapping, of course. You know, I feel that people that either are endowed with certain skills, like their clock speed's really fast, or they have a lot of resources, or they have, you know, great networks, just generally speaking, people with that the, that the world has entrusted upon them skills or capabilities,

• 1:02:50Speaker 01

  have a responsibility to themselves and to their community to use that to the maximum extent possible. It's like if you can run a hundred meters in under ten seconds and you don't, it's a travesty. And because not everyone can, can do that. So historically, I, and this is, you know, before I met my wife and years ago, when it was really just me, I'd say that sense of responsibility was to myself to try to get the most out of my, my own capabilities. And then as I went on in my career and felt that I was able to, to do that. And I was like, okay, well, you know, my, my kids are, are ten and eight and two and a half. And we haven't even talked about what it's like to be a parent in a world of AI, but

• 1:03:35Speaker 01

  having a sense of responsibility to, to have them, you know, grow up and to flourish and and have a relationship with them that can evolve as, as they become adults, but ultimately to set them on a path, you know, huge, huge sense of responsibility, you know, as a parent, it's just amazing how much kids look to you for, for guidance. And then responsibility for, for the firm, like I,

• 1:04:00Speaker 01

  in our world, in the investment world, you know, you have sort of two senses of responsibilities, you know, one is, and this is by far and away at the top and take this very seriously, is people give you money or typically in our case, institutions give you money and

• 1:04:15Speaker 01

  because of our type of hedge fund or a pass through structure, which is not an operational, you know, nuance that gets buried in document that means that we have a blank check from our investors to spend money whenever we want. There's, there's only, there's a very small number of firms that have that type of structure. Biggest ones were most famous ones is Citadel, but there's really only, maybe it doesn't, probably less than that that are, that are real. So huge sense of responsibility for investors to do what's, what's right, to not abuse that privilege and ultimately to we're, you know, we're, we're in the money making business. Let's just be honest about that. But also responsibility to, to our people too. And I think this is what's generic across for all, all leaders and all companies in a world of AI that

• 1:05:00Speaker 01

  you know, leaders have a responsibility to the people working at their, their firm to prepare for what's coming next. And then I, I definitely feel that. And all of it's tied together. I have a responsibility to our investors to make, you know, do the best job as I can for them. And I have responsibility to our people to be as efficient as possible. And those two coming together mesh very, very nicely. But yeah, the, I think for me, and I'm forty, so I'm not that old. Do, do want to be doing this for a long time, but as I say, as you go, go higher up as, as far as responsibility.

• 1:05:41Speaker 01

  I think that the notion of being a little bit more of a steward and helping others accomplish what they want to accomplish, like that's something that successful people talk a lot about. And I, I very much feel that. And it's very much aligned with what we talked about today in the world of AI. So.

• 1:05:57Speaker 00

  I love it. Well, always a pleasure.

• 1:06:03Speaker 00

  Thank you so much for coming on. I'm excited to have you back, maybe in a year when, when we have more results on how everything's been going. I always learn a lot from our chat. So thank you.

• 1:06:13Speaker 01

  All right. You got man. Thank you.

• 1:06:22Speaker 03

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• 1:06:54Speaker 03

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