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

[00:00] 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 because I think it's amazing. [00:13] Using ChatGPT is not cheating. That's a non-applicable idea from academia. I use ChatGPT 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. [00:29] From the very top, we were building a culture around AI. Not using these tools is like refusing to use the internet in 1995 because it wasn't perfect. [00:52] Will, welcome to the show. Thanks, man. [00:54] Thank you. [00:55] It's great to have you. For people who don't know you, you are 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... [01:15] 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. [01:25] Yeah, thanks. Maybe I can just give a little more context to that. I guess my technical title is CEO, CIO, managing partner, but I'm both the owner-operator of one of the larger hedge funds out there. I don't do many press. I don't typically speak at conferences. I've only done one other podcast.

[01:47] This is very deliberate. It's very deliberate for me. [01:50] And because of the relationship that we've developed 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. [02:02] 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. I was curious about it. [02:10] some of the motivations for why people do this. I just want to be very clear, you know, [02:15] upfront, you know, because I have that conviction, you know, the skills to lead us into the next, uh, [02:23] the next phase, and we'll get into that, of course, and ultimately the power to enact that. I really feel that it would be irresponsible of me. [02:31] not to go after it with sort of the maximum amount of discipline and intensity I can muster, which is a lot. So for your main audiences here is the people at Walleye. You know, we have about 400 people, not big for a normal company, but in our corner of the world, that's a small amount. [02:48] I'm second, I'm also speaking to people that aren't at the firm today, but may join us in the future just to understand how [02:55] how serious the firm is about AI and, uh, [02:59] And that ultimately 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 use, you know, or in other ways of work together. You know, that's. [03:13] 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, uh,

[03:20] about where the world is heading. [03:22] How did you develop that? Like, what was that journey like for you? I actually don't even know this. So I'm a bona fide nerd by background. Um... [03:30] You know, I was an engineer at Princeton. I went to Oxford to do a PhD in math. I started my career professionally. [03:36] Reading code all day for, uh, [03:38] you know, algorithmic strategies, [03:40] Um, [03:41] So, you know, we as a firm and me personally have been using, let's just call it advanced statistics, not even AI for years, a large part of. [03:49] What our firm does is in pure quant trading, [03:52] So I've thought for many, many years about how machines increasingly can both either augment or do the jobs of humans in finance. That's nothing that's new for me. [04:04] 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. [04:15] And so part of it is just me personally being curious, being interested. A huge part of this is about curiosity. I've noticed the productivity improvements that I see using these tools, just even from a writing perspective, let alone some of the sort of more advanced things you can do. So it's a combination of having been in the... [04:36] deeply versed in technical background, as I said, bonafide nerd, and then noticing what's out there and just the sense of responsibility to [04:44] you know, to the people at the firm, to our investors to say like, okay, we'll understand where this is going. Um, [04:49] If we ultimately get disrupted by AI, that's...

[04:52] 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. [05:01] AI program. Today, [05:03] Um, 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. Um, and yeah, a lot of that does start with me. I mean, that, [05:12] A lot of times the leader of organizations won't. [05:14] sort of say that how much actually matters. [05:18] you know, in the leadership, but this is one where [05:21] where I feel like I absolutely needed to lead from the front. [05:24] 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 bull. 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. [05:54] me about the moment where you're like oh we have to take this seriously versus it's just like uh [05:59] It's just like, yeah, tech people, nerds are psyched about it, but it's something that we really need to understand. [06:05] Yeah, and I appreciate you saying that. That's definitely the way I try to operate. Many people tell stories. Sometimes those stories get hyped for whatever reason. I think a lot of it comes down to... [06:17] being comfortable with yourself, self-confidence of not feeling like you need to convince something of someone you're not. So that, um,

[06:24] that's that's been a hallmark of mine. I would never have done that. So I don't know from experience that that's a problem. Yeah. [06:31] Well, really, that's that's a core belief, you know, in terms of AI, [06:35] There have been a couple of moments over the years that have, [06:40] led us to continue to go down and recently accelerate this journey. The first was two years ago. We call it our most advanced internal AI project. It's called Current. That's led by someone who was a former analyst and one of our TMT for technology, long-shore stock picking teams, comes to me in March of 2023 and says, look, I built [07:03] I've used these tools that are now available to [07:06] 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 [07:14] That's not typical that someone would just go out and do that. [07:16] This is with GPT-3? 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. [07:25] 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, um, [07:31] if you understand the problem you're trying to solve and the tools that are available, it's like, this is absolutely where we're going. And so that's when we started an effort to say, okay, for fundamental investing, absolutely, there should be agents that are... [07:44] ultimately helping with analysis and ultimately to provide meaning to what's going on. So people talk about that now, but that was two years ago. That was a real moment for me. [07:52] And then more recently this year,

[07:55] You know, so I, I, [07:57] I've seen a lot of podcasts that consume a lot of information. And I was listening to one actually with Chris Sacca, who's a very entertaining individual, sort of talking about with, frankly, a bit of hyperbole, but entertaining hyperbole. [08:12] just about where we're going. And I was like, you know what? That's, [08:15] that's so true um what these machines can do now is incredible um and it does take a little bit of imagination but in some ways it's kind of like you can see the end point [08:26] more easily than you can see the steps to get there. I was using this analogy recently. It's 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 60s by Disney. And I have three little kids, and that's one of their favorite movies. So I was watching one of them recently. And so Merlin, in the cartoon version of The Sword in the Stone, he lives backwards in time. And so he can see glimpses of the future, [08:55] Uh, but he doesn't see the steps in between. And that's kind of how I felt like here. Like it's, [09:00] It's impossible for me not to believe that in five years, firms that do what we do won't be [09:06] 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 our industry because, you know, [09:19] it's one of, if not the most clear associations between, you know, information and ultimately money, right. The value of,

[09:27] of having an edge from an information standpoint is 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 with Chris Saka that I wrote and wrote the team. This is how we ultimately met you. It's like we're going to train. [09:50] mandatory every single person at the firm doesn't doesn't matter what department you and 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 from a sense of responsibility like people are anxious about what are all these things going to mean what does that how does that impact my job what skills do i need to have and uh you know me saying to the firm i'm like okay 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 [10:20] but 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 [10:32] for 10 years and not just building essentially digital analysts replace the work of [10:39] of humans in longshore stock picking but then over the past really this year of saying to everyone across the firm you know even in accounting finance compliance legal [10:49] Like, yes, you should be using either ChatGPT or GROC or some LLM to assist in anything that you're doing that's,

[10:58] analysis or writing. We record pretty much every bit of information that flows through the firm. A huge part of this is actually having a proper data strategy. [11:08] And then also just having the culture of, yeah, we're, uh, [11:13] We don't really know all the answers to this, but let's just start talking about it. Let's make it accessible. So simple things like, [11:18] having weekly emails where there's leaderboards of who's using these tools the most. [11:23] Actually, I sent the entire firm an email and said, hey, if you suggest a tool that ultimately we end up. [11:30] pushing out across the firm, you know, just like there's incentive systems for employee referrals, we'll do something similar here. [11:37] here too. We have weekly meetups, um, [11:40] informal weekly meetups internally just to talk about AI, be it prompts or other use cases. I mean, one of your previous guests talked about the sort of the social nature of AI and how hard it is just to even discover best use cases, which is, I 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 all of [12:07] Um, [12:08] And a huge part of that is just my own personal curiosity, but you can already see it working. You know, people are doing things that they weren't. [12:14] they weren't asked to do. It's, [12:16] It's so cool. And the productivity coming from that, it's real. This isn't just... [12:21] paper. So, [12:22] So yeah, we're definitely going down this path. One of the things I think you've been so effective at doing is...

[12:30] So basically, you can think of companies, even 10 people, but 400 people bigger than that. The bigger they get, the more hard to steer they are. Maybe a startup is like a canoe, and a 400-person company is like a cruise ship, and a 10,000-person company is like a battle ship. [12:49] battle cruiser or whatever and um i think you've been you've done a really incredible job of like [12:56] 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, we're AI first memo. [13:09] Yeah, 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. [13:19] Yeah, I can read that. And on that point of, as I said, words are cheap, particularly in a world of AI. You can create great words very easily. [13:32] to your point around, um, [13:34] Being a bit of a cruise missile, I do think that's an advantage of [13:40] ultimate governance. You know, I'm [13:42] and I'm the owner operator. So I don't worry about getting fired. I believe on, [13:47] I worry about doing [13:48] 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 [13:53] large organizations just [13:55] just can't operate that way. So as I said, a huge sense of responsibility because we can act that way. [14:00] to do it. So yeah, I can read

[14:05] Read a couple of sentences. Here we go. So, yeah, so this is an email that I wrote to the firm and the entire firm. And it's the subject is AI at Walleye, 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. [14:26] I'm writing this as a follow-up to the comments I made at the town hall at the start of the month. [14:31] And after our AI Senate meeting earlier today, which is a group of forward-thinking AI users from departments across the firm. [14:39] And the message is simple. Walleye is all in on AI. [14:43] Not using these tools is like refusing to use the Internet in 1995 because it wasn't perfect. [14:50] 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, [14:58] that make us faster, smarter, and more effective. [15:01] Using ChatGBT is not cheating. That's a non applicable idea from academia. [15:06] where using AI to do homework or take tests is actually cheating. [15:10] In the real world, using AI is like taking a magical elixir that makes you 20% smarter instantly. [15:16] or a lot more. [15:17] 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 Walleye. [15:25] If you write, research, analyze, build decks, process data, or think for a living, you should be using ChatTPT and or other AI tools every single day. [15:35] Managers, this is now part of your job. You need to be pushing this across your teams.

[15:40] starting with being fluent yourself. So here's what's coming. And I talk a bit about some of the things we're doing recently that I just mentioned. [15:47] And then ended, you know, this is the beginning, the edge is real and we will not fall behind, let's lead. [15:52] I love that. Like, what was the... [15:55] You hit on a couple of things there that I really love. One is this cheating question that a lot of people have. I have that too. [16:08] It's just an internalized sense of, oh my God, this might be too easy. And when something's too easy, you're like, am I cheating? [16:16] And also addressing this... [16:19] 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. [16:33] Yeah, so I have very strong views on both of these. [16:37] As I've gone on in my career, my role has changed. And I believe that for all effective leaders, as they go on, they change. [16:46] they should evolve as well. You know, I, I love the Jim Collins quote, you know, build, build, [16:51] 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.

[17:12] or long memos that used to take me hours that maybe I could do that in a half 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, [17:25] that somehow it's not real. [17:28] But at the end of the day, results are what matter. I think having, I spent a lot of time as an athlete and I still do, [17:36] a bunch of stupid stuff in the gym is my hobby. At the end of the day, [17:41] Either you pick up a f***ing weight or 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... [17:55] call it existing paradigm you can certainly argue that [17:58] But that's not business. And just being very clear about that. You should be trying to be as efficient as possible to, [18:04] 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. [18:13] And ultimately, think of yourself, even if you're an individual and you don't manage any human, you're going to be a big part of it. [18:19] you're still going to be managing employees. A lot of those employees are just going to be [18:23] AI robots, and that's a skill in and of itself. So just the reason I wrote that to the entire firm is just, [18:30] making people feel comfortable and not kind of embarrassed where it's like, because what I was seeing before is, [18:35] people were using ChatGPT or another similar platform. [18:40] 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 so like

[18:45] It's just stupid. You should be doing that. And if you weren't like, [18:49] Why did you waste three hours writing this thing? Um, [18:53] So again, very, very strong feelings about that. And at the same time, you're coming around this anxiety, right? [18:59] of what is my job going to be [19:04] um you know when spreadsheets came out or email came out you name it like [19:08] Yeah, you have to learn how to use these... [19:10] tools. And I see this all the time. Very deliberately, we hire people who are at an inflection point of their career, typically mid-career where they [19:20] know enough to be dangerous, the competencies they're [19:24] But the hunger is still there and the ability to be dynamic, to learn new skills is also still there. That matters a lot. And I don't think it'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, you're totally absolute. And in future years, you're going to have sort of the same thing where if you. [19:43] don't know how to use these tools or you're not at a firm that, um, [19:47] to give you access to tools to give you operating leverage. [19:51] then you're also going to be obsolete. And so that phrase is what I use a lot is just this, this concept of operating leverage and not, [19:57] 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, [20:12] Um, [20:13] A lot of these tools aren't, they're not perfect. Um,

[20:16] I mean, [20:17] None of them are actually perfect. They all have their flaws. They all do stupid things. [20:22] But the direction of travel is very, very positive. And so I wanted to set 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. [20:34] It's kind of going the opposite way. It'd be like, yeah, [20:37] Let's have a demo with a third of the company joining, which is real. That's how many people join. [20:43] And, uh, [20:44] If that demo screws up, [20:46] Who cares? Like I basically had one last week that the demo gods were against me. [20:53] But that's totally fine. And having people accept that and embrace that and be like, yeah, some of this stuff isn't perfect, but you can see where it's going. [21:00] as opposed to just [21:02] almost using that imperfection as an excuse excuse to ignore it 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 [21:15] No one can set that tone besides the leader. And once that tone has been... [21:19] been said, it's [21:21] it's kind of incredible how much you can unlock people to be like, [21:24] yeah, have at it and it's okay. Setting that tone and flipping it outside be sort of [21:30] You know, you need to be afraid or a lot of people are afraid not to or to make a mistake. [21:36] kind of need to be more afraid to getting, getting left behind. And what have you seen, like for someone who's watching this and, um, maybe he's in finance or maybe he's just running another company with a lot of people and is thinking about, okay, like, but really, uh, 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? So there's a couple of things that you have to, um,

[22:01] you know, put this into categories of what's recent and what's not. So as I mentioned, you know, we [22:07] We do run a big... [22:08] quantitative trading business, you know, as a multi-strategy firm, we want many different strategies, but quantitative equity training is a big part of what we do. [22:17] those models have used non-linear statistics, AI, or some of the underlying models in AI for years. More recently, with the advent of large language models coming about, the ability to process unstructured data, of course, and incorporate that into signals, which historically is called sentiment analysis, the ability to do that at scale has gone dramatically. So that's one improvement, just in a pure [22:39] money-making standpoint. And you're doing that, like you're using language models to do some demand analysis. Yes, we absolutely are doing that and have been doing that for years. And [22:51] 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 are newer. And I do believe that the explosion that we've seen [23:03] It empowers the less and less technical people, the technical side where really you just have to be creative. So some of those were doing like, you know, 75% of the firm, you know, is an active called ChatGBT, ChatGBT-like user. [23:22] every single week, actually almost every single day. That's pretty cool. About a third of the firm use... [23:29] AI [23:31] coding tools such as Windsurf. That's sort of very real. Our, as I said, an internal product for fundamental launch for stock pickers. You know, that's also a big part of our business.

[23:42] Every single team uses this tool. It's called Current. It spikes dramatically during earnings. [23:52] We have people that come here [23:55] from our competitors and tell us that this is [23:59] It's both way better, but really an essential part of their job. And I believe that. And I think our competitors probably do have... [24:06] 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 meaning and real analysis as opposed to just summarization. [24:16] But yeah, you kind of can't go through earnings period now as a launch for a stock picker without some of these tools if you're going to be competitive because – [24:27] 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 level of. [24:48] level of adoption. [24:50] just seeing people suggesting products like, okay, we talked about last week on 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 something. And then that day, we had five different products in beta, and then the next day was pushed out across the network.

[25:13] across the firm. So some of these sort of cultural elements of just having actually set up a process where we can both incorporate three-party products and build some of them ourselves. [25:24] you know, that's pretty cool. And I don't know how much smarter or more efficient that's, that's making people. Uh, I certainly could speak from, for personal experience. Um, [25:34] a big part of running an organization is communication. My communication is dramatically, which in a lot of cases is really, that's dramatically more efficient now than when I wasn't using these tools. And I think it's probably true for [25:47] Literally every single person in our firm. What does that actually look like for you? Like when you're using it to communicate, what are you doing? 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? [26:04] And so historically, you know, 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 really communicate. [26:17] pros. And it's actually one of the things in general before LLMs came about that it was pretty f***ing pressing that younger people just were terrible, terrible writers. So for me, what I want to [26:29] You know, write an email as an example, write a memo. I do it in bullet point form. 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.

[26:47] Um, [26:48] And I want you to give something in my own voice. And... [26:51] That can be no exaggeration, like a 15 minute process that would have taken me four or five hours to [26:57] historically. That's why I have such [27:00] you know, almost religious views on this stuff. Cause I, [27:03] um, [27:03] It's pretty wild. So that's one example where I'm using that personally. [27:08] You know, another thing that we do is, [27:12] And yes, there are always 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 a few exceptions, we really record every single Zoom, every single call. [27:29] That's just the nature of our industry. And frankly, I think the whole world should get used to doing that. You're seeing these articles about people wearing wristbands, recording everything they say 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... [27:46] essentially recording devices implanted in their bodies that record everything. So just getting comfortable with the fact that, yeah, like all this data is going to get [27:55] get captured. So we're trying to do as much of that internally [27:58] um and then i'm 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 [28:06] into a lake, which you can then put a straw into and get it out. So, you know, a big part of my job overseeing the [28:13] the risk of the firm, the chief investment officer title, every single morning, me and my

[28:20] by risk teams or like in the control center of running this giant process. [28:26] You know, we have a risk calls and those are all recorded and, uh, [28:30] We can go back and say, hey, you know, what were we talking about at this time and continually have. [28:36] LLMs that are processing those transcripts and helping us to both remember and provide insights and ultimately be a bit [28:44] a bit predictive. [28:46] 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 power of all this and, [28:54] I mean, like we're, I do believe that we're, [28:57] that we're a leader. I don't want to say 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. [29:07] But we're just scratching the surface of what is possible once you actually start connecting all bits of information within the walls of the firm. And this is not just... [29:19] not just Wall-E, not just hedge funds, really any company is saying, hey, let's actually put all of our data together into effectively a collective. And then that [29:27] that information can get processed. We're totally just scratching the surface there, but we're certainly working towards that. 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 email and calendar, and it instantly builds a CRM that mirrors your business, with every contact enriched and organized from the start. From there, Adio's AI goes to work. It gives you real-time intelligence during calls. It prospects leads with research agents,

[29:57] workflows. [29:58] Industry leaders like Union Square Ventures, Flatfile, and Modal are already building the future of customer relationships on Adio. [30:05] Go to adio.com slash every and get 15% off your first year. That's A-T-T-I-O dot com slash every. [30:13] Hey! [30:14] I'm Brandon. [30:15] I lead the product studio and I'm a member of the consulting team here at Everee. [30:18] This episode is a special one for us because we've been working directly with today's guest [30:23] Will and the entire team at Walleye. [30:25] We've been helping them roll out AI across the entire firm from training and tooling to hands-on [30:30] and [30:32] 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 first and you need help, [30:44] reach out to us at every.to/consulting. [30:47] I want to go back to something you said earlier about [30:51] writing as thinking and using language models to turn like a four or five hour task into a 15 minute task. [30:58] Yeah. [30:59] What is your, like, one of the things I worry about, for example, is maybe I'm not thinking it through as clearly if... [31:08] Um, [31:09] 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 stand behind that. [31:17] Yeah, sure. I think you have to separate this out in terms of, you know, thinking through the concepts versus the linguistic syntax. What I was noting, at least personally, and I do think a lot of people do this as well when they're writing, they're

[31:29] 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... [31:39] 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 here at the firm knows that. [31:52] But you don't have to spend as much time, again, I think on the important but not as powerful tasks of writing. Like a lot of it is just sort of stitching these pieces together, the tying your shoes part. [32:07] So the principle, the principle, [32:09] 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. [32:21] across and this is how [32:24] And then, yes, it will suggest a string of words that convey that, and particularly with the way the recent models are architected. It has everything that I've... [32:32] I've written, so I can do it to some extent in my own voice. [32:36] 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 a lot of relatively straightforward concepts in a clever way. I just don't think we need to waste time on that anymore. [32:48] I would never, never do that as a writer. I'm curious, but let's flip the table a little bit too, 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's just 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 I'm,

[33:15] 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... [33:26] presented to me and I don't want to spend time reading something unless I [33:31] I know that a commensurate amount of time has been spent thinking about it first. So how do you like deal with that? Look, these tools don't negate the necessity to think. [33:42] 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. [33:51] 50, 60% of that time, it was just going to be mechanically typing out. Um, [33:57] And now, 5% of the time is me doing that. So you have more time to think just in a fixed amount of time. So you should really think. You should read. You should proofread and say, like, [34:06] 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, [34:15] the text appears like the bold, like clearly a human didn't go and bolded in exactly this way. [34:21] But that's fine. But it's not enough. It's not sufficient. You still need to... [34:27] 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, okay, why are you doing this? Why does this make sense? And [34:41] 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...

[34:53] Take care, too. [34:54] And that's, again, why I don't think people should be totally, totally afraid of using these tools because... [35:00] by themselves, I don't think they're sufficient. I think that, you know, it's like having a [35:05] a very powerful jet, jet engine, excuse me. And you use that analogy to me as well. Like a jet engine won't fly by itself. You still got to hook it up to the plane. And [35:15] there's a lot of things that matter when it comes to aerodynamics and, [35:19] 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. [35:27] Um, [35:28] But you need to be a part of the process for sure. I want to talk about that. The thing you brought up next, which is sort of this data lake idea of like sort of recording everything. You've been calling it the Borg as a Star Trek fan. [35:43] 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? [35:58] So, yeah, the Borg, which has come from Star Trek. [36:03] 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. [36:10] And the collective, which is, you don't get all the information together. That's at least our spirit animal, our spirit guide for the future. It 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...

[36:24] 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. [36:30] soon. So the most salient example of where we've done a miniature example of this goes back to our internal product current, which takes analyst notes, all information is coming in from brokers and these PDFs that get emailed around all the time, earnings transcripts, really any bit of information that's just germane to a stock. [36:54] That's our most advanced, call it org example. [36:59] And that, as I said, that really is real. Like all of our PMs do this as... [37:04] as an indispensable tool that saves them a ton of times, particularly when information flow is [37:09] is very fast. Again, quantifying that exactly, there's no perfect metric. A lot of it is definitely subjective, but I can see the [37:18] 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 over 50 of them have asked like, Hey, can we maybe a beta user of current? We'll give you feedback to help make the product better. [37:32] which we've done in some cases. [37:35] And it also makes sense, right? A huge part of the job of a human is synthesizing information. [37:41] and [37:42] Until recently, when it comes to reading documents, machines couldn't do that very well. Reading documents or listening to... [37:51] 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,

[38:00] That's what machines are starting to do. That's our main use case. [38:06] And so the broader idea of the Borg, I look at what we've done to just help our long short stock pickers and say, [38:15] that same concept should be able to help every single department [38:18] at the firm. That's, you know, [38:21] generating text as a simple example, through emails, through Slack messages, through [38:29] live calls, live conversations. And then ultimately, the ultimate goal is to tie that back to [38:35] numerical data, whether that be market data, [38:38] internal data, accounting data, you name it. So the applications, as I said, do take time to imagine, to design. But 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 [38:54] miniature collectives across all departments of the firm, of any firm, and then linking them together [39:01] um like that that will happen it's just a matter of [39:05] um, [39:06] how to get there that I think everyone is still trying to figure out. I know that you're a student of history. Do you have any historical... [39:15] periods or examples that you're turning to to kind of help you navigate [39:21] what's going on right now and this transition that you're going through? [39:25] I miss doing the history. Um, I do love, um, [39:30] basically the period between

[39:32] the Civil War and World War One 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. [39:48] 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 a VC. Thank God, because I think most of them don't know what they're doing. But certainly this idea that when you look at a, [40:05] you know, an exponential curve, you know, human sort of knows hits, hits up against that. Don't realize how [40:10] how fast things can change. So there are periods of time, like how fast the... [40:15] the railroads got connected or didn't, how fast you had transatlantic cables and what that actually meant. You know, as I said, the period between the Civil War and World War I, you just... [40:28] Huge, huge amounts of change. And people within their own lifetimes sort of went from having relevant skills to obsolete skills. That is going to happen faster this go around. And then when I said I... [40:39] Thank God I'm not a VC because I hear a lot of people say, [40:43] investor types progocating 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 [40:55] yeah, things are going to change dramatically, you know, [40:57] corporations, collections of humans, let's just say companies in the future,

[41:03] that want to operate in a world-class manner at scale are still going to need [41:08] many, many thousands or more of employees. [41:12] But, uh, [41:14] few of those employees are going to be humans. A lot more of them are going to be [41:19] to be machines. And so you've certainly seen that level of disruption in other areas that just [41:25] have it a little bit faster at this time. But at the same time, I'm an optimist in general. I think that's very important for leaders actually to have an optimistic tone. I don't think the world is [41:37] it's going to end because all of a sudden people are going to have their jobs disrupted by AI that they need to adapt. [41:42] It's sort of having a level of realism around that. That's what I mean. Our firm is a microcosm of that. [41:48] yeah you have to learn these tools or in whatever time period [41:52] you're just not going to be competitive. And we are at the tip of the spear from a competition standpoint, just given the nature of what, [41:59] of the industry and what our types of firms actually do. But I think that's going to be true. [42:04] across a world [42:06] much wider swath of population where, yeah, you got to be trained to be, to be efficient. And, um, at some point that if you don't, that's your choice. And, um, [42:14] that just is what it is 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 you watch the netflix thing on wide open cowboy war no should i [42:31] Yeah, it's really good. It's really good.

[42:33] It just came out? [42:35] Yeah. Okay. I'll check that out. I just finished Deadwood, which I was telling you about. I never watched any TV, but this one came out and I also love sort of the old, old West. It was a good story. Do you know why the, why Cowboys disappeared? I just learned this and it was a really interesting fact. [42:52] You tell me. I have a hypothesis, but you go first. Barbed wire. Yeah, I was going to say the broader... Well, in the documentary, The Cowboy War, basically the answer was civilization kind of came in. You had the railroad come in, which brought a lot of people, and then... [43:08] 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 cows. [43:17] no cows especially i didn't know that oh yeah like the cow so it's fascinating but the cowboy war and wider 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 wider posse versus the cowboy two words posse and it was this big it ended up being this big deal [43:47] North versus South sentiment 20 years after the Civil War. [43:50] But the broader historical context there is people sort of wanting to bring about change because Tombstone, where... [43:57] you know, the Oak Hacker Owl 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

[44:10] get stuck in the ways of, of the past. So yeah, it, it, [44:14] it's, [44:15] 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. [44:45] and you kind of need the civilization that comes [44:47] behind them, but that sort of... [44:50] 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 that there's something very similar there about technology. 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, [45:08] You go and read about the story of, you know, do 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 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 to become institutionalized and then. [45:31] someone does that again. And so that's not a geographic frontier, but a technology frontier, [45:37] And as I mentioned earlier, I feel a sense of responsibility because we can kind of do both. And they're just not the only companies that can...

[45:44] say that if we want to push the frontier, but with resources, um, [45:49] And those ultimately are, I think, the businesses that when you look at any technology transition are able, you know, not just to adapt, but. [45:58] but to thrive of, uh, you know, having that mentality, but, [46:02] not being [46:03] Yeah. [46:04] not using single action rifles when other people have machine guns. How do you think about... [46:10] how the past informs the future. And I'm, I'm asking this both from like a kind of, I don't know, a 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 [46:40] which one's going to happen again and which one's not. Do you agree with that characterization and how do you... [46:46] How do you think about... [46:48] when to when to rely on past patterns to help you understand the future. [46:52] I don't think human nature has changed in 10,000 years. You know, [46:58] Maybe it's evolved a little bit, but you go back and [47:01] My son, who's eight, is really into... [47:04] to Egypt right now. I love Roman history because I took Latin and I'm said I'm a real nerd. So I know a lot about Rome, but you can read a lot about that too. Like human nature hasn't changed in a long period of time. You know, you go and

[47:18] just to use a famous example, like you, [47:21] the old always wants to [47:23] or the new always wants to replace the old, you know, the, the, the son wants to outdo the father. These, these are timeless, you know, when Alexander the great was, you know, [47:32] you know, conquering Persia, you know, Darius, the king of Persia sends him a note and he's like, hey, how about we have the truce? And he responds like, [47:39] I'm coming for you. 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 brush can grow. Like this, this, this concept renewal is always going to be there. It's in human nature. It's [48:09] It's a, [48:10] Same case here. Like there's going to be 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, [48:20] groups of people that are going to hold on to the past and, [48:23] there's [48:25] There's going to be conflict to varying degrees because of that. That hasn't changed a lot. So when it comes to investing, I think, [48:31] When we look for patterns, you know, quant investing, of course, is built on this concept. [48:38] that there are patterns in history, in stock prices and information that was predictive. [48:44] and there's a structure to that data even if that structure is so

[48:48] complex that humans can't understand it. And in world-class quantum investing, [48:54] You know, we've moved past what humans can understand. [48:57] you know, [48:58] many decades ago. People forget how long and what the book on Renaissance came out, but [49:04] You know, some people still don't realize that the quant investing has been going full bore since [49:10] at least the early 90s. [49:11] Absolutely, that whole class of strategies, which is a huge part of the markets today, [49:17] 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 in investing. [49:27] I also believe that's timeless. It's just timeless 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, low, large numbers. [49:39] 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 and [49:49] the fuzzy mess. Um, and so, um, [49:51] all these tools that we're building and can be built to enable that human, um, [49:58] to make a prediction more accurately. Yeah, there's patterns that come with history. It's, you know, history doesn't repeat itself. It rhymes. Like that's absolutely true for history. [50:09] for humans. So I guess summing all that up, just saying, oh, this is the way things happen in the past is silly because they never repeat themselves exactly. You have to sort of more go to the

[50:24] you know, the underlying mechanics, particularly around human nature. I think human nature is [50:30] Um, is a constant, is stationary across time. If you just look at it in the right, right way where you're talking wise, all the other crap that's, that's happening. Well, we'll have to, we'll have to debate whether human nature is static or not. Cause I, I'll, 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. [50:49] uh, [50:51] 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. [50:58] trading that are in principle not explainable not like humans can't understand it at all [51:03] 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. [51:10] Um, [51:11] Well, there's so many different flavors of quant investing. You know, there's... [51:17] But yeah, generally speaking, the higher sharp ratio or the more consistent strategies that you get that aren't pure arbitrage, it's not just based on speed. [51:26] It's just like, why does an LLM do what it's going to do? People kind of understand that, but not really. There are all these nonlinear relationships. [51:35] A lot of those techniques have been used on structured data, [51:39] in these models for years. I mean, you go back and read the book about on the [51:46] guys from IBM came over to Renaissance, what they were doing. They're doing speech recognition, like a lot of that sort of patterned, [51:52] pattern matching and sort of predicting what what comes next.

[51:57] Yeah, for a human to actually sit down and walk me through all the different layers of the neural network and why did a machine do what it's going to do with it? [52:05] No, the dimensionality of that problem, it's way past what a human mind can understand. [52:11] Um, [52:12] So I'd say generally speaking, world-class plot models, while the signals, what goes into them, I'm saying, [52:20] You know, this is something that I ultimately this feature, um, [52:24] 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 understand at least some rationale to our features, even if there are many thousands of them. [52:39] But how those ultimately get combined and understanding nonlinear relationships from that, that's [52:45] That's very complex and that's totally fine. [52:48] It's interesting that you brought up intuition a little bit earlier as a... [52:54] something sort of separate from the more algorithmic and a helpful addition to the more algorithmic quant decision making. Because I actually think of... [53:03] I think of neural networks and intuition, human intuition, as being analogous and maybe even helping us to understand... [53:11] 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

[53:31] I do think intuition is very important for building a process and really, [53:37] another term that [53:39] you could say is analogous to tuition is first principles. [53:44] 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 [53:56] you can take any test and get a hundred percent. So, [53:59] 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, what machines are really helping to do is say, well, [54:09] I might actually help you [54:11] come up with something intuitive. [54:13] But you wouldn't necessarily come up with yourself, kind of like having a coach where they could watch you doing a movement or lifting a weight. Like, hey, did you actually realize that these things are important? [54:25] are connected. So I don't think they're antagonistic, but it's entirely consistent that a machine can give you a very intuitive answer that you wouldn't necessarily have [54:36] been able to think of yourself, if that makes sense. [54:37] Do you think that first principles, and this is a leading question because I have an opinion on this, but do you think that first principles in the math sense are the same thing as first principles in the, let's say, decision-making sense?

[55:07] answers are. But first principles in a kind of a decision-making sense, like, [55:13] Um, [55:14] they're not at the bare level of like... [55:17] you know, axioms in math. They're already like many layers up above and can be filled in in many different ways. The real world, the difference is in a mathematical model. And math is just a model for the way that things operate. And within a model, you have... [55:33] rules. And so there's real objectivity to what are the rules. Sometimes those rules can be very complicated, but there's an underlying, um, [55:41] structured it. [55:42] um, [55:43] in organic systems that sometimes there's structure, sometimes there isn't. So I agree with you when it comes to decision-making. That's why 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 [56:08] What I believe and what I believe really good decision makers try to do is entertain their own decision making process, both to... [56:16] discover what's led to good decisions and what are areas and what's their decision-making has been [56:22] 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 good. [56:30] get made like [56:31] Yeah, that kind of falls down at some point because people might just totally be schooled and first principles that are so different that they...

[56:39] would lead them to, uh, make completely different decisions. 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? Um, [56:50] to take as good, reliable first principles? And what are some of the ones that you've thrown out? 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, [57:04] that does drive behavior. But actually understanding the right incentive decision for why an individual or 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 vectors, are the incentive vectors all pointing in [57:21] the same dimension um so that's a big one uh you know you mentioned earlier like i [57:27] I hate fluff. I hate... Sometimes it gets me into trouble. [57:32] But I just wish that people would [57:35] be more that way whether that's an axiom or rule but whenever i sense that my antenna go up and uh [57:41] You know, that's sort of a... [57:42] a negative sign. And really just trying to be intellectually honest and not trying to reduce everything into a math problem. But a lot of times there is structure that you can pull out of a [57:53] a situation, it might just require hard work. So, you know, you can't, [57:58] 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, 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, you know,

[58:14] every single workout that I do for years, I've, uh, [58:18] I track, you know, I have, I have a log and there's a lot of numbers in that just to, [58:21] to see these trends, you know, evolve across time. And again, none of these are perfect, but I feel like people, because it's hard, [58:30] A lot of people just don't do things like that. And I actually, on 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 [58:41] It's going to be so much easier to do that. And a lot of it's going to be around, are you collecting right? [58:46] right data to [58:47] to help people with that. [58:49] So, yeah, you know, the biggest first principles I said, power of incentives and intellectual honesty. And those those would be the top for me. What goes into your journal? Well, there's three components of my of my life. [59:04] which would be [59:05] um, um, [59:08] you know, it did. [59:09] My family, um, [59:11] You know, I have three kids and I've been with my wife who's amazing for, for 15 years. Um, and, uh, yeah. [59:19] I love being with him and that, that, [59:22] it's not just a platitude. So, um, my personal life, and now I'm interacting with the family and I feel a huge sense of responsibility to do my family to, um, [59:30] um, [59:31] provide them with a [59:34] 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. [59:44] But there's a section on that. There's a section on work, of course, which for me at this point is this great thing.

[59:52] great big challenge that's fun. I think sometimes people don't even use the word of fun. I guess one of the things with AI, this doesn't need to be scary. This is fun. It's out. If you look at it the right way, it's really important that people have that [1:00:06] that view. And, you know, I, I'm working because I, [1:00:10] I get meaning out of it. I enjoy it. [1:00:14] It's different than, I feel fortunate that most people where I can say, I'm doing this because I [1:00:20] I certainly want to. So there's things on work that I talk about. [1:00:24] And then there's things just on my personal health that I – [1:00:28] that I talk about, um, you know, how, how I'm feeling, was it a good day or bad day? Um, how'd my, you know, how'd my deadlifting session go in the morning? Um, stuff like that. Or, you know, I, uh, [1:00:40] 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 that'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, [1:01:10] a lot of hard drive space is impossible. So, so getting that out of my head a bit more [1:01:16] was uh was what i started doing that and it's been really helpful both as an exercise in itself even if [1:01:21] 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. And help me understand that more like you're maybe now you're like typing this into ChatGPT, you're speaking it, you're writing into a Google Doc and then putting it into ChatGPT? I'm trying to capture the concepts of what I felt in that day so I can either speak,

[1:01:40] or just write bullet points of here's what's on 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 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, um, [1:01:59] That can be a one minute, even a 30 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 process in a way that, you know, it's a perfect [1:02:18] is accessible [1:02:19] months in the future. It's just this tiny little use case that's sort of so powerful with these tools. And there was no way that could have happened in the... [1:02:28] in the future. So, [1:02:30] 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? [1:02:42] I do think about this work a lot in the context of work, work and family, slightly different, but but overlapping, of course. You know, I feel that people that. [1:02:53] 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 just generally speaking people with you know [1:03:04] um, [1:03:05] that the, [1:03:06] that the world has entrusted upon them skills or capabilities, um,

[1:03:12] have a responsibility to themselves and to the community to use that to the maximum extent. [1:03:18] possible. It's like if you can run 100 meters in 110 seconds and you don't, [1:03:24] It's a f***ing travesty. Because not everyone can... [1:03:27] 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, [1:03:41] my own capabilities. And then as I went on in my career and felt that I was able to [1:03:46] to do that. And I was like, okay, well, [1:03:48] My kids 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... [1:03:57] having a sense of responsibility to have them grow up and to flourish and [1:04:02] and have a relationship with them that [1:04:04] can evolve as they become adults. But ultimately to set them on a path, you know, huge, huge sense of responsibility. You know, as a parent, they'd, [1:04:14] It's just amazing how much kids look to you for guidance. [1:04:17] And then responsibility for the firm. Like I... [1:04:21] Um, [1:04:22] In our world, in the investment world, you have two senses of responsibility. 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, you know, [1:04:37] Because of our type of hedge fund, we're a pass-through structure, which is not an operational...

[1:04:43] nuance that gets buried in a document that means that [1:04:46] we have a blank check from our investors to spend money whenever we want. There's, there's only, uh, [1:04:50] It's a very small number of firms that have that type of structure. [1:04:55] biggest ones would, or most famous ones is Citadel, 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, uh, [1:05:08] We're in the money-making business. Let's just be honest about that. But also responsibility to our people too. And I think this is what's generic across... [1:05:17] all leaders and all companies in the world of AI that [1:05:22] Leaders have a responsibility to the people working at their firm to prepare for [1:05:27] what's coming next. And I definitely... [1:05:30] 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 a responsibility to our people to be as efficient as possible and [1:05:40] Those two coming together mesh very, very nicely. [1:05:44] But yeah, I think for me, and I'm 40, so I'm not that old. [1:05:52] We do want to be doing this for a long time, but as I say, as you go higher up as far as responsibility matters, [1:06:01] Um, [1:06:03] I think that the notion of being a little bit more of a steward and helping others accomplish [1:06:08] what they want to accomplish. [1:06:10] that's something that [1:06:11] successful people talk a lot about and I very much feel that and it's very much aligned with what we talked about today in the world of AI.

[1:06:19] 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 we have more results on how everything's been going. I always learn a lot from our chat. So thank you. [1:06:35] All right, you go, man. Thank you. [1:07:05] and laughter that will leave you on the edge of your seat. [1:07:08] craving for more. It's not just a show. It's a journey into the future with Dan Shipper as the captain of the spaceship. [1:07:15] So do yourself a favor. Hit like, smash subscribe, and strap in for the ride of your life. [1:07:21] And now, without any further ado, let me just say, Dan, I'm absolutely hopelessly in love with you.