The $10B Hedge Fund CEO Who’s Betting Big on AI | Will England, Walleye Capital

Speaker 1 It's becoming a meme that CEO's 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 because I think it's amazing. Speaker 2 Using chat TBT is not cheating. Speaker 3 That's a non. Speaker 2 Applicable idea from academia. I use ChatGPT to write this e-mail. 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. Speaker 3 Smarter and more. Speaker 2 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. Speaker 3 Perfect. Speaker 1 Will welcome to the show. Speaker 2 Thanks, Ben. Thank you. Speaker 1 It's great to have you for people who don't know you, you are the the benevolent dictator of Walleye, 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 Walleye. And honestly, like, regardless of the stuff we've done, you're, I think one of the most impressive examples of someone, the 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. Speaker 3 Yeah, thanks. Maybe I could just give a little more context to that. I guess by, by technical title is, is CEOCIO managing partner, but you know, both both the owner and operator of, you know, one of the one of the larger hedge funds out there. I don't do many press, you know, it's I don't typically speak of conferences. I've only done one other podcast. So this is very deliberate. It's very deliberate for me. And, and because of the relationship that that we've 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 they're already a leader in that is going to continue to be the case. So, you know, sometimes I listen to a lot of podcasts. So I was. Speaker 2 Curious about? Speaker 3 So the motivations for why people do them. So I just want to be very clear, you know, up, up front, you know, because I have that conviction, you know, the skills to call, 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, I really feel that 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. Speaker 2 So for you know, 3 remain. Speaker 3 Audiences. Here's the people at Walleye. You know, we have about 400 people, not big for a normal company, but in our core of the world that's kind of small, small amount. Speaker 2 You know, I'm second of all to speaking to people. Speaker 3 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, 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 of work 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 where the world's heading. So. Speaker 1 How did you develop that? Like what was that journey like for you? I actually don't even know this. Speaker 3 So I'm a. Speaker 2 Bona fide nerd by background, you know, and I I was an engineer in Princeton. I went to Oxford to do a PhD in math. I started my career. Speaker 3 Written code all day for you know how algorithmic strategies. 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 a firm does is in pure quad trading. Speaker 2 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 and finance that that's nothing. That's that's new for me. 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. Speaker 3 Part of this is about curiosity, you know. I've noticed the. Speaker 2 Productivity improvements that I see using using these tools just even for a writing perspective, let alone some of those 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 said that the sense of responsibility to, you know, to the people at the firm, to our investors to say, like, OK, we'll understand where this is going. 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? Speaker 3 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 already doing doing a lot. So it's not just a story about what's what's to come. Speaker 2 And yeah, that a lot of that does. Speaker 3 Start with me. I mean that a lot of times leader of organizations won't sort of say that how much actually matters, you know that in the leadership. But this is 1 where where I feel like I absolutely needed to lead from the front so. Speaker 4 Tell me about that. Speaker 1 Like a little bit more concretely, like one of the things that I appreciate about you as a communicator is you're just kind of like noble. We were talking like, I don't know, 3 or 4 weeks ago and I was telling you something about every in our strategy and direction and you're 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, 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. Speaker 3 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. I would. Speaker 1 Never have done that, so I I don't know from experience that that's a problem. Speaker 3 Yeah, but. Speaker 2 Not really that's that's a core belief. You know, in terms of AI there there have been a couple moments over the years that have led us to continue to go down and 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. Speaker 3 Someone who was a former analyst and one of our TMT, you know, for technology launch for 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. Speaker 2 Go out and do that. Speaker 4 This is what GBT 3. Speaker 3 Yeah, yeah. This is like 2 years ago. Speaker 2 And it was early but the point was just sat down and gave me a demo and was like. Speaker 3 Wow, like this is where we're going. Speaker 2 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 is like. Speaker 3 This is absolutely where we're going. Speaker 2 And so that's when. Speaker 3 We started, you know, an effort to say, OK, for fundamental investing, absolutely there should be agents that are ultimately helping with, with, with analysis and, and, and ultimately to provide, provide me into what's going on. 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. Speaker 2 Year. Speaker 3 You know, said I, I, I think a lot of podcasts that consume a lot of. Speaker 2 Information and and I was listening to 1. Speaker 3 Actually with with Chris Sacca, who's. Speaker 2 A very entertaining individual sort of talking. Speaker 3 About with, frankly, a bit of hyperbole, but entertaining hyperbole. Speaker 2 Just about, you know where we're going, 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 end point more easily than you can see the steps to to get there. I was using this analogy recently. Speaker 3 I do is a little bit. Speaker 2 Well. Speaker 3 I'll just tell to but I really did use this one, so my favorite. Speaker 2 Movie growing up. Speaker 3 Was the Sword in the stone made in the 60s by Disney? Speaker 2 And I have 3 little kids and that's one of their favorite movies. So I was watching with them recently. 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. Speaker 3 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 the not the most clear associations between, you know, information and ultimately money, right? The the value of of having an edge from information standpoint. Speaker 2 It's huge. And so that's why finances is really going to pick up and actually use these tools and say, OK, 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 that said with with Chris Sacca that I wrote, 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 going to have technical or not technical you are, you're going to have the base case level of proficiency in AI tools. And as part of this actually does come from a sense of responsibility, like people are anxious about what are all these things going to mean or what does that, how does that impact my job? What skills do I need to have? And you know, me saying to the firm of like, OK, we are going to be a place that is going to be leading that, that we will actually train you. We will give the tools available. You still have to learn them. 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 analyst replace the work. Speaker 3 Of, of humans in, in longshore stock picking. But then over the past, really this year, I'm 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 LOL to assist in, in anything that you're you're doing that's analysis or writing. You know, we record pretty much every bit of information that you know, flows through the firm. You know a huge part of this is actually having a proper data strategy. Speaker 2 And then also just having the culture of. Speaker 3 Yeah, we're. Speaker 2 We don't really know all the. Speaker 3 Answers to this, but let's just start talking about it. Make it accessible. So so simple. Things like having weekly emails where there's a leader boards of, you know, who's using these tools the most actually. And I, I sent the entire firm an e-mail 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 instead of systems for employee referrals, we'll, we'll do something similar here here too. You know, we have weekly meetups, informal weekly meetups internally just to talk about AI, be it be it prompts or other use. Speaker 2 Cases I mean the. Speaker 3 Your your one of your previous guests talked about the sort of the social nature of AI and and how hard it is just even discover best use cases. Speaker 2 Which is I. Speaker 3 Couldn't agree with more. Speaker 2 But just even internally trying to make it a bit more social, a bit more accessible. Speaker 3 And you know I'm. Speaker 2 Involved in in in all of this, in fact, for the chief evangelist 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. Speaker 3 We're. Speaker 2 Definitely going down this path 1. Speaker 4 Of the things. Speaker 1 I think you've been so effective at doing is 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 them like, you know, maybe a start up is like a canoe and like a 400 person companies like a cruise ship and a 10,000 person companies 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 CE OS are like writing basically like the where AI first memo. 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. Speaker 2 Yeah, yeah, I can read that. And on that point of as I said, there there is words are. Speaker 3 Words or chief particularly in the 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 and 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. So yeah, I, I can read, read a couple couple sentences. Here we go. Speaker 2 So, yeah, so this is an e-mail that I wrote to the firm and it's our firm and it's the subject is AI at. Speaker 3 Walleye a challenge to all of us. Speaker 2 It says I use ChatGPT to write this e-mail. 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 of forward thinking AI users. Speaker 3 From departments across the firm. Speaker 2 And the message is simple. You know, walleye is is. Speaker 3 All in on AI not. Speaker 2 Using these tools is like refusing to use the Internet in 1995 because it wasn't. Speaker 3 Perfect. Speaker 2 That's just dumb and something I can't understand. 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 non applicable idea from academia. Or 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. Speaker 3 Or a lot more. So why? Speaker 2 Wouldn't you use it from the very top? We are building a culture around AI. This is not optional for anyone at Walleye. If you write, research, analyze, build decks, process data, or think for a living, you should be using chat, TPT and or other AI tools every single day. Managers, this is now part of your job. 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. Speaker 1 I love that. I've like, what was the, you know, a couple things there that that I really love. Like 1. Is this the sort of is this cheating question that a lot of people have? Like I have that too. 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. Speaker 2 Yeah, yeah. So I have very strong views on. Speaker 3 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 that as they go on, they, they, they should evolve as well. You know, I, I love the Jim Collins quote. You know, build, build a clock. Don't. Speaker 2 Be a time keeper, 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. Speaker 3 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. Speaker 2 Hey, I can do that in 1/2 hour or less 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 the end of the day, you know, results are what matter. I think having. Speaker 3 I spent a lot of time as as an athlete and I still do. Speaker 2 A bunch of stupid stuff in the gym is my hobby at the end of the day. Speaker 3 Either you pick up. Speaker 2 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 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 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. Speaker 3 Next level tasks, higher level context. Speaker 2 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 of in and of itself. So just the reason I wrote that to the entire firm is just making people feel comfortable and not. Speaker 3 Kind of embarrassed where it's like. Speaker 2 Because what I was seeing before is people were, you know, using chat TBT. Speaker 3 Or another similar. Speaker 2 Product to Create an e-mail and they were trying to like dust it up it's like oh I don't want to be seen as doing that so like it's just stupid you should be doing that and if you weren't like why did you waste 3 hours writing this thing so. Speaker 3 Again, very, very strong. Speaker 2 Feelings about that in the same time with your your comment around this anxiety of, you know, what is my job going to be? You know, when spreadsheets came out or e-mail came out, you name it. Like yeah, you have to learn how to use these. Speaker 3 These tools and I, I see this all the time, you know, very deliberately we hire people or you say are 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. Speaker 2 Don't think it's any different. Speaker 3 You know, if you're just using an example, if you're a long short stock picker and you can't use Excel during a company model like, you're totally obsolete. Speaker 2 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. Speaker 3 At a firm that you know could give you the access to the tools to give you operating leverage, then you're also going to be obsolete and so. Speaker 2 You know that. Speaker 3 Phrase is going to 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. Speaker 2 Don't be afraid of this stuff. Embrace it. And another thing that I was seeing Dan A. Speaker 3 Lot of these tools aren't, they're not perfect. I mean, none of them are actually perfect. Speaker 2 That they all have their flaws, they all do stupid things, but the direction of travel is very, very positive. And so I wanted to set the culture and environment where instead of people being like, oh it. Speaker 3 Didn't do exact 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. Speaker 2 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 demo gods were against me. And, and having people accept that, embrace that and be like, yeah, there's some of the stuff isn't perfect, but you can see where it's going as opposed to just almost using that imperfection as an excuse. Speaker 3 Excuse to ignore it so. Speaker 2 All those things combined. Speaker 3 Into one, there's there's no other person besides me. And I think this is true of all leaders of the organizational. 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. Speaker 2 Yeah, have at it and it's OK setting that tone. Speaker 3 And flipping it outside, be sort of, you know, you need to be afraid or a lot of people are afraid not to. Speaker 2 Or to make a mistake. Speaker 3 Kind of need need to be more afraid to getting getting left behind. Speaker 4 And what have you seen like for someone who's watching this and maybe he's in finance or maybe he's just running another company with a lot of people and is thinking about, OK, like, but really what, what productivity gains has it actually unlocked for you? Like concretely, what are a couple things that have been useful for you or for the fund? Speaker 2 So there's a couple things you have to, you know, put this into into. Speaker 3 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. Speaker 2 As a multi. Speaker 3 Strategy firm, we want many different strategies, but quantitative equity training is a big part of what we do. Those models have used, you know, non linear 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 gone dramatically. So that's one improvement just in a pure money making standpoint. Speaker 4 And you're doing that like you're using language models. Speaker 1 To do sentiment analysis. Speaker 3 Yes, 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 in quant trading is one of the things that that definitely benefit scale. Some of the things that are called more recent are, are newer. And I, I do believe that the explosion that we've seen, it empowers the less and less called technical people that the technical side were really you just have to be creative. So, so some of us were doing like, you know, 75% of the firm, you know, is an active called ChatGPT. ChatGPT like user, you know, every single week, like actually almost every single day. That's pretty cool. About 1/3 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 the user spikes matically 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 I believe that and I think our competitors probably do have good products as well. I think we've just been doing a little bit longer and are further down the path of using these tools actually to provide provide meeting and real analysis as opposed to just summarization. But yeah, you know, you, you kind of can't go through earnings period now as a long first stock picker without some of these tools if you're going to be competitive. Because, you know, one, 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 going 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, OK, we want a, we, 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, 5 different products in beta and then the next day was pushed out across, across the firm. So in some of these sort of cultural elements of just having actually set up a process where we can both incorporate through break products and and and build some of them ourselves. Speaker 2 You know that that's pretty cool. Speaker 3 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 organization is communication. My communication is is dramatically and which in a lot, in a lot of cases written and that's dramatically more efficient now when I was when I was using these tools and I think it's probably true for literally every single person in our firm. Speaker 4 What does that actually look like for you? Like when you're using it to communicate, what are you doing? Speaker 3 I think best when I write out my thoughts. I, 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? And so historically, you know, I, I really would write to convey what I'm thinking, where the firm is going. Why? And I, I just believe that leaders should be able to, to really communicate. And I, I believe in the power of high quality prose. And it's actually one of the things in general before LLMS came about that it was pretty pressing that younger people just were terrible, terrible writers. So for me, when I want to, you know, write an e-mail as an example, write, write a memo, I do it in bullet point for me. I, I, I type out, here's what I'm thinking. Here's why. You know, I, 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 and I want you to give something in my, my own voice. And that can be, you know, no exaggeration, like a 15 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. Speaker 2 It's pretty wild. So that's so that's one example. Speaker 3 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 is 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, you know, every single zoom, every single call. It's just the nature of our industry. And frankly, I think the whole world should get used to doing that. You're, 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. Speaker 2 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 just being able to go back and process that because so much of the. Speaker 3 Power of this is. Speaker 2 Do we actually? Speaker 3 Have a data strategy. Get all the data into a lake or you can then put a straw into it and get it out. So, you know, a big part of my job overseeing the, you know, the, the risk of the firm, the, 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 a risk calls and, 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 LMS that are, that are processing those transcripts and, and helping, helping us to both remember and, and provide insights and, and ultimately be a bit, 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 in the, in the power of all this. 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 firmament. And this is not just, you know, not just walleye, not just hedge funds, really any company is 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 we're totally just scratching the surface there, but we're certainly working towards that. Speaker 1 This episode is brought to you by Adio, the AI native CRM built for the next era of companies. With Adio, setup takes minutes. Connect your e-mail and calendar and it instantly builds a CRM that mirrors your business with every contact enriched and organized from the start. From there, Adios AI goes to work. It gives you real time intelligence during calls, it prospects leads with research agents, and it automates your team's most complex work flows. Industry leaders like Union Square Ventures, Flat File, and Modal are already building the future of customer relationships on audio. Go to audio.com/every and get 15% off your first year. That's AT tio.com/every. Speaker 5 Hey, I'm Brandon. I leave the product studio and I'm a member of the consulting team here at every. This episode is a special one for us because we've been working directly with today's guest, Will and the entire team of Walleye. We've been helping them roll out AI across the entire firm, from training and tooling to hands on implementation, and it's been one of the most ambitious transformations we've seen up close. If you want to follow along with lessons learned from projects like this, subscribe to every. And if you're a business that wants to be AI 1st and you need help, reach out to us at every dot TO slash consulting. Speaker 4 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 15 minute task. Speaker 2 Yeah. Speaker 1 What is your? Speaker 4 Like one of the things I worry about, for example, is maybe I'm not thinking it through as clearly if the language model has like written a bunch of. Speaker 1 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. Speaker 2 Yeah, I'm sure I think this out in terms of, you know, thinking through the concepts versus the linguistic syntax. But I was noting at least personally, and I do think a lot of people do this as well 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. Speaker 3 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. Speaker 2 Sentence in a preposition and whatever the firm knows that, but you don't you don't have to spend as much time again, I think on the the important but not as powerful tasks of of writing like a lot of it is just sort of stitching stitching these pieces to together the tying your shoes part. So the Prince of 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, 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. Speaker 3 You know, string of words that that convey that and particularly with the way the recent models are protected, it's as has everything that I've I've written so can do it to some extent in my own voice. Speaker 2 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 try to say. Speaker 3 A lot of relatively straightforward concepts in in in a clever way I just don't think we need to waste. Speaker 2 Time on that anymore. Speaker 4 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. Speaker 1 That that's just. Speaker 4 Out for different reasons, but like if I get an internal report that looks like it's written by ChatGPT, and I did this actually last week because everyone internally is using these tools all the time. It's not that I care that the voice sounds like ChatGPT, 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. Speaker 1 So how do you like deal with that? Speaker 2 Look, these tools don't negate. Speaker 3 The necessity to think, and I say that all the time. If anything, they they should just give you more time to think. Like if you say, OK, you have an hour to complete this task. And it used to be historically, I don't know, 5060% of that time it was just going to be mechanically typing out. And now I don't know, 5% of the time it's been 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 the bold. Clearly the human didn't go and bold it in exactly this way. Speaker 2 But, but that's fine. But it's not enough. It's not sufficient. You still need to. Speaker 3 Convey the concepts clearly. In an ideal case, someone has clearly used these tools, but the concepts still come from them. And I can tie it back to the person. And there's a why of like, OK, 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 optimal 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, 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. And you use that analogy to these. Well, like a jet engine won't fly by itself. You still got to hook it up with a plane. And there's a lot of things that matter when it comes to aerodynamics. And, you know, they 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. Speaker 2 But you need to be. Speaker 3 A part of the process for sure. Speaker 4 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? Speaker 3 So, yeah, the Borg, which was just going from Star Trek 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 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, it is really hard to 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 the firm's 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 set our, our internal product current, which you know, takes analyst notes. All, you know, information is coming in from, from brokers and these PDFs that get emailed around all the time, 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 view 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 no perfect metric. A lot of it is, is definitely subjective, but I can see the internal use case numbers and, and I can also see like firms, all external firms know that we're, that we've built this and are doing it. And the over 50 of them have asked like, hey, can we be a beta user of current and we'll give you feedback to help make the product better, which we've which we've done in some cases. Speaker 2 And and it also. Speaker 3 Makes sense, right? That a huge part of the job of a human is synthesizing information 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, and now the servicing the second order, the 3rd order effects of that again, not just summarization. That's what machines are are starting to do. And that's the said that's our main use case. And so this the broader idea of the, the Borg, I look at what we've done to just help, you know, 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, through, you know, live live calls, live conversations. And then ultimately the, the ultimate goal is to tie that back to numerical data, whether that be market data, internal data, accounting data, you name it. So. Speaker 2 The the applications as I said. Speaker 3 Do take time to to imagine to design, but it's what I mentioned earlier, it's not that hard to see where this could go. And the future when you do have these sort of miniature collectives across all departments of the firms and other firm of any firm and then linking them together like that, that will happen. It's just a matter of how to get there, though I think everyone is still trying to to figure out. Speaker 4 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? Speaker 2 I miss doing the history I do. Speaker 3 Love, 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 that 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. Speaker 2 So it's certainly not the only period of history, but you know, definitely in time period where things changed dramatically. I'm. I'm not. Speaker 3 AVC, 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. Speaker 2 Fast things can change, so. Speaker 3 There are periods of time like how how fast the the railroads got connected or, or then how fast, you know, transatlantic cables and what that actually meant. You know, as I said, the period between Civil War and World War One is just huge, huge amounts of change and people within their own lifetimes sort of went from having relevant skills to to obsolete skills. That is going to happen faster this go round. And then when I said I thank God I'm not AVC because I hear a lot of people, investor types provocating about the provocating 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. But a? Speaker 2 Few of those. Speaker 3 Employees are going to be humans, a lot more of them are going to be to be machines. And so you certainly seen that level of disruption in other other areas that just happened a little bit faster this time. Speaker 2 But at the same time, I'm an optimist in general. Speaker 3 I think 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 not going to be competitive. And we are at the the tip of the spear from a competition standpoint, just given the nature of what of the industry and then what are what are types of firms actually do. But I think it's going to be true across a a bunch of water swaths of population where, yeah, you got to be trained to be to be efficient and at some point that if you don't, that's your choice. And that just is what it is. Speaker 4 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 I'm sort of in my cowboy era and I've been like reading a lot of cowboy stuff and you. Speaker 2 Watch the Netflix thing on on Wide Earp in the cowboy war. Speaker 1 No. Should I? Speaker 3 Yeah, it's really good. It's really good. Speaker 4 It's it just came out. Speaker 3 Yeah. Speaker 4 OK, I'll check that out. I just finished Deadwood, which I was telling you about. Yeah. Speaker 3 Deadwood's good. 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. Speaker 4 Do you know why the why Cowboys disappeared? I just learned this and it's it was a really interesting fact. Speaker 3 You tell me I have a hypothesis, but you go first. Speaker 1 Barbed wire. Speaker 2 Yeah, I was. Speaker 3 Going to say the broader count. Well, in the that documentary that the cowboy wore, what basically the answer was civilization kind of came in. You had the real world 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. Speaker 4 I didn't know that. Speaker 3 Oh yeah, like the cow. So it, it's fascinating. But the, the cowboy War and Wyatt Earp, which is this, you know, historical figure of, of legend, you know, gets in this huge fight like the movie Tombstone, which is kind of historically accurate, but not really. It was sort of the wider posse versus the cowboy 2 words posse. And it was this big, it ended up being this big deal. Like that's the gunfight, 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, you know, the OK Corral was and where I was, was a silver mine. So it brought in all these, 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, it's, it's that that period of time is, 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 happening because of that. Speaker 4 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. Speaker 1 That sort of. Speaker 4 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. Speaker 2 Levels, man. I mean, that's even true with sort of, at least historically. Speaker 3 Of the you know why does VC investing exist? You know why is it that? Speaker 2 If you go and read about the story of, you know, you pick any company. Speaker 3 You know from NVIDIA on down where they're like I can't do. Speaker 2 This at a big company so I'm going to go and push the frontier. Speaker 3 In a world in which I'm less constrained, you know there's no barbed. Speaker 2 Wire and I'm going to go and build and then at some point, you know those, those companies become successful to become institutionalized and then someone does that again. And so that's not a geographic frontier, but but a technology frontier. And as I mentioned earlier, like I feel a sense of responsibility because we can kind of do both and they're just at the only companies that can say that if we want to push the frontier. Speaker 3 But with resources and you know, those ultimately are I think the businesses that when you look at any technology transition are able, you know, not just to adapt but but to thrive of, you know, having that mentality but not being, you know, not using single action rifles when other people have machine guns. Speaker 4 How do you think about how the past informs the future? And, and I'm, I'm asking this both from like a kind of, I don't know, late 1860s to now perspective, but also from a, from an investing perspective. And, and, and, and I think this, this layers into the AI stuff where it strikes me that a lot of, 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. 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? Speaker 3 But I don't think human nature's changed in 10,000 years. You know, you and maybe it's evolved a little bit, but you go back and my, my son, who's 8 is really into to Egypt right now. I love Roman history because I took Latin and I'm set up a real nerd. So I know a lot about Rome, 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, a famous example, like you, the old always wants to or the new always wants to replace the old. You know, the, the, the son wants to have to be the father. These, these are timeless. You know, when Alexander the Great was conquering Persia, you know their eyes to King of Persia sends them a note and is like, hey, how about we have the truce? And he responds like, I'm coming for you. It's like the concept. Speaker 2 Of. Speaker 3 Yeah, no, that's a good story, right? But this concept of the new wanting to, you know, replace the old in this continuous evolution, you know, you find it in nature, You know, everyone knows the analogy of 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 a group of people, group of firms, collection of individuals that are going to look what's happening and, and embrace that. And then there's going to be groups of people that are going to hold on to the. Speaker 2 Past and there's. Speaker 3 There's, there's going to be conflict to varying degrees because of that, that that hasn't changed a lot. So when it comes to investing, when we look for patterns, you know, quant quant investing, of course, is built on these concepts that there are patterns in history, in stock prices and information that was predictive 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. But you know, some people still don't realize that the quant investing is has been going full war since at least the early 90s. And so absolutely that whole class of strategies and which is a huge part of the markets today is built on this concept that historical patterns do repeat themselves. When most people think about investing, they think about sort of human driven intuition and investing. 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, the precursors that would lead it to make an informed decision. But a human can be better at dealing in the fuzzy mess. And so all these tools that that we're building can be built to enable that human to, you know, to make a prediction more, more accurately. 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. So I guess. Speaker 2 Summing all that up. Speaker 3 Just saying, oh, this is the way things happened in the past is silly that because they never repeat themselves exactly. You have to deserve more. Go to the you know, the the online mechanics, particularly around human nature think I think human nature is is a constant is is stationary across time. If you just look at it in the right, right way where you're thought otherwise, all the other crap that's that's happening. Speaker 4 Well, we'll have to we'll have to debate whether human nature is static or not because I, I'll, I'll, I'll take the opposite on that one. But I have AI have a another direction I want to take this, which is 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. Speaker 1 The time to do it because it's not worth it. Speaker 3 Well, there's there's so many different flavors of quant investing, you know there. Speaker 2 But yeah, generally speaking, the higher sharp ratio or. Speaker 3 The more consistent strategies that you get that aren't pure arbitrage, it's not just based on speed, you know, it's just like, why does an LLM do what it's going to do? People kind of understand that, but but not really. They're all the non linear 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 on the guys from IBM came over to to Renaissance what 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 layers of the neural network and why the 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. Speaker 2 I'd say generally speaking. Speaker 3 World class quot. Speaker 2 Models while the, the signals, you know what goes into them, I'm saying, you know, this is something that I ultimately this feature makes sense how those features get and which I do think is important and that 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. Speaker 3 Thousands of them. But how does ultimately get combined and understanding non linear relationships from that that's that's very complex and and that's totally fine. So. Speaker 4 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. Speaker 3 I think intuition is very important for building a process. And really, you know, another term that you could so as analogous situation is, is, is first principles. And there's a very famous guy that uses first principles all the time. But I, I very much believe that is, is actually sort of understand and it's really a math term, right? If, if you can understand some of the core concepts, the first principles in math, then you can take any test and, and get 100%. So I was a math guy. I like thinking from first principles. Speaker 2 And I think it's the same way. But when you interface that with machines, what machines can 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, they could watch you doing the movement or lifting a weight and like, Hey, did you actually realize that these things are, are connected? So I don't, I don't think they're antagonistic, but it it's entirely consistent that a machine can give. Speaker 3 You a very intuitive answer that you wouldn't necessarily have been able to think of yourself, if that makes sense. Speaker 4 Do you think that first principles, and this is a leading question because I, I have a 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 test 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 air level of like, you know, axioms in math They're they're already like many layers up above and can be filled in in many different ways. The. Speaker 2 Real world. Speaker 3 The difference is in a, in a mathematical model and, 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 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 structures, sometimes there, there isn't. So I agree with you when it comes to decision making. That's why that the word subjectivity exists. And what, what can 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 when it comes to decision making and what what I believe and what I believe really good decision makers try to do is, is interested 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's their decision making has been subpar. So in some ways you can kind of apply it more to your own closed system. But there's a universal truth around how decisions are get, get made. Like, yeah, that that that kind of falls down at some point because people might just totally be schooled and 1st principles that are so different that they would lead them to make completely different. Speaker 1 Decisions. Speaker 4 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? Speaker 2 At the top of the list would be the power of incentives. Speaker 6 When you're dealing with humans and. Speaker 2 Not in a negative sense, like everyone is is greedy. Speaker 3 Although you know a lot of times that that does drive behavior. Speaker 2 But actually understanding the right incentive decision for why an individual. Speaker 3 Or group of individuals is doing something and 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, are the, are the incentive vectors all pointing in in the same dimension. So that's a big one. Speaker 2 You know you mentioned. Speaker 3 Earlier like I I hate fluff. I hate sometimes it gets me into trouble and, but I, I just, I wish that people would be more that way, whether that's an axiom or rule. But whenever I sense that my antenna go up and you know that that's sort of a a negative sign and. Speaker 2 Really just trying to be intellectually. Speaker 3 Honest and so not not trying to reduce everything into a into a math problem, but a lot of times there there's a structure that you can pull out of a a situation and it might just require quite 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 a log and there's a lot of numbers in that just to, to see these trends, you know, evolve across. Speaker 2 Time again, none of these. Speaker 3 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 got a go forward 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 run around. Are you are you collecting right, right data to to help people with that. So yeah, you know the biggest first principles. I said power of incentives and and intellectual honesty and those, those would be the top for me. Speaker 4 What goes into your journal? Speaker 3 Well, there's three components of my, of my life, which would be, you know, it did about my family. You know, I have 3 kids and I've been with my wife, who's amazing for for 15 years. And I, I love being with them and that 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 with a great life. So there's a section on that. There's a section on. Speaker 2 And by the way. Speaker 3 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. Speaker 2 Which you know for me at this. Speaker 3 Point 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 the way I 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, I enjoy it and it's, it's, it's different than, you know, I feel fortunate in that than most people where I can say like I, I'm doing this because I as you really want to. So there's things on work that I talked about and then there's things just on my, my personal health that I, that I talked about, you know, how, how I'm feeling was a good day or bad day. How am I, you know, have my dead lifting session go in the morning, stuff like that. Or, you know, I, I'm a bit of a crazy person on, 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, and finance, right? We're we're dealing with time series and so. Speaker 2 We could say like. Speaker 3 Oh, on this day you made or lost money or something that's happened, but I really wanted to remember like what was 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 is is impossible. So, so getting that out of my head a bit more was that's why I started doing that. And it's been really helpful both as an exercise in itself, even if I'm just writing it, but then it going back and saying like, Oh yeah, this. Speaker 2 This is what I was thinking on that day. That's. Speaker 3 That's interesting. Speaker 4 And help me understand that more like you're maybe now, now you're like typing this into ChatGPT. You're speaking it. You're writing into into Google Doc and I'm putting into ChatGPT. Speaker 2 I'm trying to capture. Speaker 3 The concepts of what I felt in that day, so I can either speak. Speaker 2 Or just write bullet points of Here's this in my mind in these three. Speaker 3 Categories, you know, this is what's this is a date, this is. Speaker 2 What's on my mind? Speaker 3 It's just the red categories. Speaker 2 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, you know that that can be a one minute, maybe even a 32nd exercise. It's just so easy to do and I. Speaker 3 Feel the reason most people don't journal is because it's historically. Speaker 2 Sit down. It would. Speaker 3 It would take time. Speaker 2 But this is a perfect example of, you know, the thought that's on your mind. Just get it out there. Very. Speaker 3 Easily and then it can be captured process in a way that is accessible once in the future. Speaker 2 It's just this tiny. Speaker 3 Little use case that's sort of so, so powerful with with these tools and there was no way that could have happened in the in the future so. Speaker 4 One word that you brought up a lot in the context of work, but also you just brought it up also in the in the context of family that I'm curious about, is the word responsibility. What does that mean to you? Speaker 3 I, I do think about this world a lot in, 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 speeds really fast or they have a lot of resources or they have, you know, great networks. Speaker 2 Just generally speaking, people with at the. Speaker 3 That the world has entrusted upon them skills or capabilities, have a responsibility there to themselves and to the community to use that to to the maximum extent possible. It's like if you can run 100 meters in 110 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 say that sense of responsibility was to myself to, to try to get the most out of my, my own capabilities. And then as I went on my career and felt that I was able to, to do that. And I was like, OK, well, and my, my kids are, are 10 and 8 and 2 1/2. And we haven't even talked about what it's like to be a parent in a world of AI. But 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, but ultimately to, to set them on the path, you know, huge, huge sense of responsibility, you know, as as a parent, they, it's just amazing how much kids look to you for, for guidance and then responsibility for, for the firm, like I in, in our world, in the investment world, you know, you, you, you have sort of two senses of responsibilities. You know, 1 is, and this is by far and away at at the top and take this very seriously is people give you money, or typically in our case, institutions give you money. And because of our type of hedge fund, we're, we're 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, we want. There's, there's only, there's a very small number of firms that that have that type of structure, biggest ones with or most famous ones is, is hit at all, but there's really only maybe a dozen, probably less than that, that are, that are real. So huge tons 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, you know, all, all leaders and all companies in the world of AI that, 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, and all of it's tied together. I have a responsibility to our investors to make, you know, do the best job as they can for them and have responsibility to our people to be as efficient as possible. And those two coming together mesh very, very nicely. But yeah, the, the, I think for me, and I'm 40, so I'm not that old, do do want to be doing this for a long time. But as I say, we as you go higher up as, as far as responsibility, 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 what we talked about today in the world of AI. So. Speaker 4 I love it. Well, always a pleasure. Thank you so much for coming on. I'm excited to have you back maybe in a year when, yeah, when we have more results on how everything's been going. I always learn a lot from our chat so thank you all. Speaker 2 Right. You bet, man. Thank you. Speaker 1 Oh my gosh. Speaker 6 Folks, you absolutely, positively have to smash that like button and subscribe to AI and I Why? Because this show is the epitome of awesomeness. 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