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

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.

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

Will, welcome to the show. Thanks, man. Thank you. So, 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, um, 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.

Yeah, thanks. Um, yeah, maybe I could just give a little more context to that. Um, I guess my my technical title is uh is CEO, CIO, managing partner, but you know, I'm both both the owner operator of um, you know, one of the one of the larger hedge funds out there. I I don't do many press. Um, you know, I don't typically speak at conferences. I've only done one other podcast. So, this is very deliberate. It's very deliberate for me. Um, and and because of the relationship that that we've developed, you know, on this subject, I do feel a great sense of of both purpose and conviction about where our industry is going, where our firm is going. Uh, I do believe they're already a leader in that is going to continue to be the case. So, know sometime I listen to a lot of podcasts. I was curious about some of 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 lead us into the next uh, you know, the next phase. We'll get into that, of course. Um, and ultimately the power to to enact that. I really feel it would be irresponsible of me uh not to go after it with sort of the maximum amount of discipline and intensity I I can muster, which which is a lot.

So first, you know, three main audiences here is the people at Walleye. You know, we have about 400 people. Um, not big for a normal company, but uh, in our corner of the world, that's it's not a small small amount. Um, you know, I'm second, I'm also speaking to people that that aren't at aren't at the firm today, but may join us in the future just to understand how how serious um, the firm is about AI and uh, 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 um, you know, or or other ways of work together, you know, that's that's why I'm here. But it it does all start with me having, you know, as I said, an enormous sense of conviction about uh about where the world is heading.

So, how did you develop that? Like what was that journey like for you? I actually don't even know this. So, I'm a I'm a bonafide nerd by background. Um, you know, I I was an engineer at Princeton. I went to Oxford to do a PhD in math. I started my career writing code all day for uh, you know, algorithmic strategies. Um, so you know, we as a firm, you know, and me personally have been using, let's just call it advanced statistics, not even AI for for years. A large part of what our firm does is in pure quant trading. Um, so I I've thought for for many many years about how machines increasingly can both either augment or do the jobs of of humans and finance. That that's nothing that's that's new for me. Um, 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 um being curious, being interested. A huge part of this is about curiosity. Um, you know, odd notes the productivity improvements that I see um using using these tools just even for from a writing perspective, let alone um some of the sort of more advanced things you can do. So it's a combination of called having been in uh deeply versed in a technical background as I said, bonafide nerd, and then noticing what's out there and just said the sense of responsibility to you know, to the people at the firm, to our investors to say, like, look, we we understand where this is going um, if we ultimately get disrupted by AI, that's that's on me and so let's get after that. So even two years ago, like this isn't a sense of, oh yeah, we're going to do all these things in the future. You know, two years ago, we started an internal AI program. You know, today um, you know, we're already doing doing a lot. So it's not just a story about what's what's to come um, and yeah, that a lot of that does start with me. I mean, that a lot of times leader of organizations won't sort of say that how much actually matters um, you know, that in the leadership, but this is one where where I I feel like I absolutely needed to lead from the front.

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

Yeah. Um, and I appreciate you saying that. That's definitely the the way I try to operate. Um, 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 you being comfortable with yourself, self-confidence of not feeling like you need to convince something of someone you're you're not. Um, so that um, that's that's been a hallmark of mine. Try to I would never have done that. So I I don't know from experience that that's a problem.

Yeah. No, really, that's uh that's a core belief, you know, in terms of AI. Um, there there have been a couple moments over the years um that have uh 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. Um, that's led by someone who was a former analyst in one of our uh TMT for technology launcher stockpicking teams comes to me in March of 2023 and says, look, I've built, I've used these tools that are now available to you know, make myself way more efficient, eventually trying to replace um, what I was doing as an analyst and I was pretty skeptical at first because that's that's not typical that someone would just go out and do that.

This is with GBT3. Yeah. Yeah. This is like two years ago and it was early. But the point was just sat down, gave me a demo and I was like, "Wow, like this is where we're going." And yes, you need to have a sense of belief. You need to imagine, you need to be able to dream, but if you understand the problem you're trying to solve and um, the tools that are available, it's like this is absolutely where we're going. And so that's when we started, you know, an effort to say, okay, for fundamental investing, absolutely there should be agents that are um, ultimately helping with with with analysis and and ultimately to provide provide meaning to what's going on. And so people talk about that now, but that was that was two years ago. That was a real moment for me. Um, and then more recently this year, um, you know, said I I've listened to a lot of podcasts that consume a lot of information and and I was listening to one actually with, uh, with Chris Saka, who's a very entertaining individual, um, sort of talking about with, frankly, a bit of hyperbole, but entertaining hyperbole, um, just about, you know, where we're going. And I was like, you know what, that's 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 it's kind of like you can see the end point more easily than you can see the steps to to get there. U, I was using this analogy recently. Um, what if you could turn an idea into a cinematic video at record speed with full creative control? LTX Studio just dropped its most advanced model yet. And the headline, it's fast, 30 times faster than other leading models. And it runs locally on your machine. That speed comes from a new technique called multiscale rendering, which allows the model to build in rough to refined passes, like a painter adding layers to a work. But this model isn't just faster, it's more powerful, too. LTX's new key frame editor lets you define start and end moments, which gives you control over your scene's arc and pacing. And with camera motion control, you can pan, zoom, and glide in between frames, like a director controlling a shot. And here's something rare. This model was ethically trained using footage from Getty and Shuttertock. It's totally free for commercial use if your business makes under $10 million a year. So, whatever your creative project, LTX helps you go from imagination to output faster than ever. Hit the link in the description to try it out. And now back to the episode.

I do it is a little bit uh well, I'll just tell it to you, but I really did did use this one. So my favorite movie growing up was the Sword and the Stone um made in the 60s by Disney and I have three 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 um, you know, glimpses of the future uh, 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 um, you know, integrated with best of breed AI technology across the firm, not just for investment, for non-investment purposes. And I say that, you know, in in our industry because it's one of, if not the most clear associations between, you know, information and ultimately money, right? The the value of of having an edge from an information standpoint point um, is huge. And so that's why finance is is really going to pick up and actually use these tools and say, okay, what applications actually give me an information advantage? So that's very clearly where we're going, but the steps to get there can be a bit hazier. And so actually, it was after this podcast I said with with Chris Saka that I wrote wrote the team, this is how we ultimately met you, is like, we're going to train mandatorily every single person at the firm, doesn't doesn't matter what department you're in, how technical or not technical you are, you're going to have a 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 is that, how does that impact my job, what skills do I need to have. And uh, you know, me saying to the firm of 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 available, you still have to learn them, but we'll make it accessible and make it accessible to everyone um, so that was when we just started doing a lot more, not just building call tools using advanced AI. And as I said, we've been doing that in our quant business for for 10 years um, and not just building essentially digital analyst replace the work of of humans in in longer 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, like, yes, you should be using either ChatGBT or Brock or some LLM to assist in in anything that you're you're doing that's analysis or writing. Um, you know, we record pretty much every bit of information that um, you know, flows through the firm. You know, a huge part of this is actually having a proper data strategy. Um, and then also just having the culture of, yeah, we're uh, we don't really know all the answers to this, but let's just start talking about, let's make it accessible. So so simple things like having weekly emails where there's leaderboards of, you know, who's using these tools the most. Um, actually, you know, I sent the entire firm an email and said, hey, if you you suggest a tool that um, ultimately we end up pushing out across the firm, you know, just like there's incentive systems for employee referrals, we'll we'll do something similar here here too. You know, we have weekly meetups um, informal weekly meetups internally just to talk about AI, be it be it prompts um, or other use cases. I mean, the your your one of your previous guests talked about the sort of the social nature of AI and and how hard it is just to even discover best use cases, which is couldn't agree with more um, but just even internally trying to make it a bit more social, bit more accessible. And you know, I'm involved in in all of this and in fact, I'm sort of the chief AI evangelist um, 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 they weren't asked to do. It's it's so cool and the productivity coming from that, like it's real, this isn't just paper.

So, so yeah, we're um, we're definitely going down this path. Um, one of the things 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. Like, you know, maybe a a startup is like a canoe, and like a 400 person company is like a cruise ship, and a 10,000 person company is like a battle battle cruiser or whatever. And um, I think you've been you've done a really incredible job of like pointing the cruise ship um, really quickly. And that's something that's happening a lot now. Like it's becoming a meme that um, CEOs are like writing basically like the we're AI first memo. Yeah. Yeah. And I think you have like the best, you have the best example of that memo that I've ever seen. Um, 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.

Yeah. Yeah. I can read that. And and on that point of as I said, there there is it's words are words are cheap, particularly in a word world of AI. You can create great words very very easily. You know, to your point around um, being being a bit of a cruise missile, you know, I do think that's an advantage of ultimate governance. You know, I'm 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 um, just can't operate that way. So, as I said, a huge sense of responsibility because we can act that way um, to uh, to do it. So, yeah, I I can read uh read a couple couple sentences. Here we go. Um, so, yeah. So, this is an email that I wrote um, to the firm um, and the entire firm and it's the subject is AI at Walleye, a challenge to all of us. It says, "I use ChatGBT to write this email. You should be using it too and be proud of it. I'm writing this as a follow-up to the comments I made at the town hall at the start of the month and after our AI Senate meeting earlier today, which is a group of forward thinking AI users from departments across the firm. And the message is simple. You know, Walleye is is all in on AI. Not using these tools is like refusing to use the internet in 1995 because it wasn't perfect. That's just dumb and something I can't understand. 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 ChatGBT is not cheating. That's a non-applicable idea from academia, where using AI to do homework or take tests is actually cheating. In the real world, using AI is like taking a magical elixir that makes you 20% smarter instantly or a lot more. So, why wouldn't you use it? From the very top, we are building a culture around AI. This is not optional for anyone at Walleye. If you write, research, analyze, build decks, process data, or think for a living, you should be using ChattBT 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 talk 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.

What's lead? I love that. Like what was the um, you hit on a couple things there that that I really love. Like one is this the sort of um, is this cheating question that um, a lot of people have. Like I have that too. Um, 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? Um, and and and also sort of like addressing this um, 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.

Yeah. Yeah. So I have very strong views on on both of these. You know, as I've gone on in my career, you know, my my role has changed and I believe that for all the 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 be a timekeeper. Um, and so this is an example of that where if you can have a tool that makes you more effective, it's the same thing as sort of hiring someone to replace part of what you were doing so that you can move on to the next task. So your context level shifts up. And so I'm not embarrassed at all that I can write emails that used to or or long memos that used to take me hours that maybe I could do that in a half hour or less. Um, and that to some extent people have this insecurity that, oh, if I didn't put my blood, sweat, and tears into it, that somehow it's not it's not real. But at the end of the day, you know, results are what matter. Um, I think having I spent a lot of time as as an athlete and I still do a bunch of stupid stuff in the gym as my hobby. Um, at the end of the day, either you pick up the 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 like, oh my god, it is ruining school um, 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 p.m. So you can actually spend time thinking about next level tasks, higher level contexts, be a bit creative. Um, and ultimately think of yourself, even if you're an individual and you don't manage any human, um, 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 and of and of itself. So just I the reason I wrote that to the entire firm is just making people feel comfortable and not kind of embarrassed where it's like, cuz what I was seeing before is people were, you know, using chat GBT or another similar product to create an email and they were trying to like dust it up. It's like, oh, I don't want to be seen as doing that. They're like, that's just stupid. Um, you should be doing that and if you aren't, like why did you waste three hours writing this thing? Um, so again, very, very strong um, feelings about that.

At the same time, you're you're commenting around this anxiety of, you know, what is my job going to be? Um, you know, when spreadsheets came out, or email came out, you name it, like, yeah, you have to learn how to use these these tools. And I see this all the time. You know, very deliberately, we hire people or we say, are at an inflection point of their their career. Um, typically mid-career where they um, you know, know enough um, to be dangerous, the competencies there, but the hunger is still there. And the the ability to be dynamic, to learn new skills is also also still there. That matters a lot. And I don't think there's any different um, you know, if you're just use an example, if you're a long short stock picker and you can't use Excel to create a company model, like you're totally obsolete. And in future years, you're going to have sort of the same thing where if you don't know how to use these tools, or you're not at a firm that um, you to give you access to tools to give you operating leverage, then you're also going to be obsolete. And so, you know, that phrase is one I use a lot, is just this this concept of operating leverage and not not being afraid of that. But yes, absolutely. I see that all the time and it goes back to like 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. Um, embrace it.

And another thing that I was seeing, Dan, um, a lot of these tools aren't they're not perfect. Um, I mean, none of them are actually perfect. Um, they all have their flaws. They all do stupid things. Um, but the direction of travel is very, very positive. And so, I wanted to set a culture and environment where instead of people being like, oh, it didn't do exactly what I wanted, so I'm just going to ignore it until it's perfect. It's kind of going the opposite way of being like, yeah, let's have, you know, a demo with a third of the company joining, which is real. That's how many people join. And uh, if if that demo screws up, who cares? Like I I basically had one last week that um, the demo gods were against me. But that's totally fine. And and having people accept that and embrace that and be like, "Yeah, this some of the stuff isn't perfect, but you can see where it's going um, as opposed to just almost using that imperfection as an excuse excuse to ignore it." So all those things combined into one. Um, there's there's no other person besides me. And I think this is true of all leaders of the organization. You have to lead from the front. You know, no one no one can set that tone um, besides besides the leader. And once that tone has been been set, it's it's kind of incredible how how much you can unlock people to be like, "Yeah, have at it." And it's okay. Um, setting setting that tone and flipping it outside be sort of, you know, you you need to be afraid or a lot of people are afraid not to or to make a mistake. um, kind of need 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 is in finance or maybe is 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 things that have been useful for you or for the fund?

So there's a couple things and you have to um, you know, put this into into categories of what's what's recent and what's not. So as I mentioned, you know, we we do run a big quantitative trading business. You know, as a as a multist strategy firm, we run many different strategies, but quantitative equity trading is a big part of what we do. Um, those models have used, you know, nonlinear statistics, um, AI or some of the underlying models and 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 was 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.

And you're doing that like you're using language models to do sentiment analysis? 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 you know, quant trading is one of the things that definitely benefits scale. Um, some of the things that are called more recent or are newer, and I I do believe that the explosion that we've seen um, it empowers the less and less call it technical people um, that the technical side where really you just have to be creative um, so so sometimes we're doing like, you know, 75% of the firm um, you know, is an active call ChatGBT or ChatGBT like user um, you know, every single week, like actually almost every single day. That's pretty cool. About a third um, of the firm, you know, use AI coding tools such as Winsurf. That's sort of very real. Our as I said, an internal product um, 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. Uh, it spikes dramatically, you know, during user spikes dramatically during earnings um, we have people that that come here um, from our competitors and tell us that this is, you know, it's both way better. Um, 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 meaning and real analysis as opposed to just summarization. Um, but yeah, you know, 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 um, 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 go and basically impute things that humans can't do as fast.

So um, how do we actually measure that? Of course um, having benchmarks, that's kind of nonsense in a real real company. Uh, but just sort of seeing the the level of uh level of adoption. Just seeing people suggesting products like, okay, we want a um, we we talked about last week on our one of our meetups to have a product that can do um, summarization via podcast to make it more accessible to pe people that want to listen to to something. And then that day um, we had, you know, five different products in beta and then the next day was pushed out across across the firm. So, you know, some of these sort of cultural elements of just having actually set up a process where we can both incorporate third-party products and and and build some of them ourselves. Um, you know, that that's pretty cool. And I don't know how much smarter, more efficient that's that's making people. Uh, I certainly can speak from for personal experience. Um, a big part of running an organization is communication. My communication is is dramatically and which a lot in a lot of cases written and that's dramatically more efficient now. I thought it was when I wasn't using these tools and I think it's probably true for 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. Um, I I tell people that uh my education is very expensive, so I better be able to write. Um, or else what the hell is all that for? Um, and uh so historically, you know, I I really would write um to convey what I'm thinking, where the firm is going, why and uh 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 LLM came about that it was pretty depressing that younger people just were terrible, terrible writers. So for me, when I want to, you know, write an email, um, as an example, write write a memo, I do it in bullet point form. I I type out here's what I'm thinking. Here's why. You know, I work on my prompts. Um, and uh 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. Um, and I want you to give something in my my own voice. And uh that can be, you know, no exaggeration, like a 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 um, it's it's uh it's pretty wild. Um, so that so that's one example of where I'm using that personally. Um, you know, another thing that we do and yes, there are always the the questions on what can or can't be recorded. Kind of going back to the governance element where I can say, this is what I believe, this 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. 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 every um, everything they say for for months and months at a time. You extend that forward without sounding too much like a nut job. I think people are going to have essentially recording devices implanted, you know, in their bodies that record everything. So just getting comfortable with the fact of, yeah, like all this data is going to get get captured. So we're trying to do a bunch of that internally um, and then of just being able to go back and and process that because so much of the power of this is do we actually have a data strategy, get all the data into a lake, which you can then put a straw into and get it out. So, you know, a big part of my job overseeing the the risk of the firm, the the chief investment officer title, uh, you know, every single morning, me and my my risk team sort of like in the in the control center of of running this this giant process. Um, you know, we have our 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 LLMs that are that are processing those transcripts and and helping helping us to both remember and and provide insights and ultimately be a bit a bit predictive um, which has been hugely helpful just just in that exercise. Which is, you know, we haven't sort of talked about where I think this is going and the and the power of all this. 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 because I I definitely don't know what other firms are doing, but um, I certainly think that we're a bit more advanced in our thinking of how to use these tools. But we're just scratching the surface of what what is possible once you actually start connecting all bits of of information within the walls of the firm. And this is not just um, you know, not just Walleye, not just hedge funds, really any company of saying, "Hey, let's actually put all of our our data together into effectively a collective and then that that information can get process." We're we're totally just scratching the surface there, but we're certainly working towards that.

Hey, I'm Brandon. I lead 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 at 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 first and you need help, reach out to us at every.to/consulting.

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. Yeah. What is your like one of the things I worry about for example is um maybe I'm not thinking it through as clearly if um the language model has like written a bunch of stuff that it's coming from my bullet points, but I haven't like really gone through every single thing and been like, I I stand behind that.

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, is what when they're writing, they're they're trying to be both consistent, to some extent clever, and to some extent uh, unique to their own style. And so a lot of editing can be, I think, less about the concept and more, what are some of the nitty-gritty details of how you stitch sentences together? Even simple things like it drives me absolutely nuts when someone ends a sentence in a preposition. And and everyone here at the firm knows that um, but you don't. So you don't have to spend as much time again, I think on the the important um, 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 principle, the the elements of writing, you know, what are the concepts that I'm looking to to convey? That's what I spend my time on now. So so what I found with these tools is really trying to be clear, like this is the concept that I want you to get across um, and this is how. And then yes, it will suggest, you know, string of words that that convey that. And particularly with the way the recent models are architected, it's has everything that I've I've written. So it can do it to some extent in my own voice. But I just don't have to spend as much time, like frankly, trying to be clever. And that's what a lot of writers do. They they try to say a lot of relatively straightforward concepts in in a clever way. Um, I just don't think we need to waste time on that anymore.

I would never never do that um as a writer um I'm curious curious uh like but let's let's flip the table a little bit to like when you're reviewing someone else's work. So for example, for me as a manager, I think like, let's accept for example, like if someone's going to publish something on every that sounds like it's AI written, I don't that that's just out for different reasons. But like if I get an internal report that looks like it's written by CHBT, and I did this actually last week, cuz everyone internally is using these tools all the time. Um, it's not that I care that the voice sounds like CHBT. It's that I 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. Um, so how do you like deal with that?

Look, these tools don't negate the necessity to think. Um, and I say that all the time. If anything, they 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, 50 50% of that time, it was just going to be mechanically typing out um, and now I don't know, 5% of the time you spent doing that. So you have more time to think just in a fixed amount of time. So you should you should really think, you should you should read, you should proofread and say, like, does this make sense to me? Is this what I'm trying to convey? And so I can definitely tell as well when something was written by a machine. Um, sometimes that's just the way that the the text appears. Like the the bolds like clearly a human didn't go and bold it in exactly this way. But but that's fine um, but it's not enough. It's not sufficient um, you still need to convey the concepts clearly. In an ideal case, someone has clearly used these tools, but the concepts still come from them. And I can tie it back to that person. And there's a why of like, okay, why are you doing this um, 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 you use that analogy to the as well. Like a jet engine won't fly by itself. You still got to hook it up to the plane. And there's a hell of a lot of things that matter when it comes to aerodynamics and um, you know, that make a plane efficient or not. So humans can kind of design the plane a little bit more. And someone else brings the engine. You can use that engine very powerful ways. Um, but 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. Um, 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 uh, how you made a decision. Like, can you give us a concrete case where having all those recordings has actually been helpful?

So yeah, the Borg, which which come from Star Trek um, and uh, I'm kind of sad now that when I say the word Borg um, even some real nerdy people don't even know it means. I'm getting a little bit older. Um, and the the collective, which is, you know, get get all the information together. That's that's at least our our spirit animal, our our spirit guide for for the future. It is this is really hard. I think all companies are going to have this. And some of this is not at all particular to finance. It's just like there isn't even a great way to process all of a firm's emails right now using AI, which I'm sure will be solved soon. Um, so the most salient example of where we've done a miniature example of this goes back to set our internal product Current, which, you know, it takes analyst notes, all you know, information coming in from from brokers and these PDFs that get emailed around all the time, um, earnings transcripts, uh, really any bit of information that's that's germane to to a stock. Um, that's that's our our most advanced call it or 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. Um, again, quantifying that exactly, you know, there there's no perfect metric. A lot of it is is definitely subjective. But I can see the internal use case numbers. And I can also see like firms, all external firms know that we're that we've built this and are doing it. And over 50 of them have asked like, hey, can we we be a beta user of Current? And we'll give you feedback to help make the product better, which we which we've done in some cases. And it just also makes sense, right? That a huge part of the job of a human is synthesizing information. Um, and until recently, like when it comes to reading documents, like machines couldn't do that very well, reading documents or listening to to voice essentially in other text forms, but now machines can. And now the surfacing the second order, the third order effects of that, again, not just summarization, that's what machines are are starting to do. That's I 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, our long short stock pickers and say that same concept should be able to help every single department at the firm. That's, you know, generating text um, as a simple example, through emails, through through Slack messages, through, you know, live live calls, live conversations, um, and then ultimately the the ultimate goal is to tie that back to numerical data, whether that be market data, um, internal data, accounting data, you name it. So the the applications, as I said, do take time to to imagine, to design. But it what I mentioned earlier, it's not that hard to see where this could go in the future when you do have these sort of miniature collectives across all departments of the firms and of the firm of any firm, and then linking them together, um, like that that will happen. It's just a matter of um, how to get there that I think everyone is still trying to to figure out.

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?

I'm a student of history. Um, I do love um, you know, basically the period between

The Civil War and, uh, World War I is, is a time when I think the whole world changed dramatically. Um, 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. Um, so it's certainly not the only period of history, but, um, you know, definitely a time period where things changed dramatically.

Um, I'm, I'm not a VC. Um, thank God, because I think most of them don't know what they're doing. But, uh, certainly this idea that when you look at a, you know, an exponential curve, you know, humans sort of nose hits up against that, don't realize how, how fast things can change. Um, so there, there are periods of time like how, how fast the, the railroads got connected, um, or, or didn't, um, how fast, you know, transatlantic cables and what that actually meant, um, you know, as I said, the period between Civil War and World War I, just huge, huge amounts of change, um, 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 when I said I thank God I'm not a VC, because I hear a lot of people, investor types, prognosticating about the, prognosticating 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, um, let's just say companies in the future, um, that want to operate in a world-class manner at scale are still going to need many, many thousands or more of employees. Employees, but, uh, a few of those employees are going to be humans. Um, a lot more of them are going to be, to be machines. And so you, you're certainly seeing that level of disruption in other, other areas that just happen a little bit faster this time.

Um, but at the same time, I'm an optimist in general. Um, I think that's very important with, uh, 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. Um, it's sort of having a level of realism around that of, that's, that's what I mean, our firm is a microcosm of that of, yeah, you have to learn these tools, or in whatever time period, you're, you're just 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 what, what our types of firms actually do. But I think it's going to be true across a, a 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 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, on Wide Open and the Cowboy War? No. Should I? Yeah, it's really good. It's really good. It's, it just came out. Yeah. Okay, I'll check that out. I just finished Deadwood, which I was telling you about. Yeah, Deadwood's good. I never watched, like, TV, but, uh, this one came out and I also love sort of the old, old West. It was a, it's a good story.

Do you know why the, why cowboys disappeared? I just learned this, and it's, it was a really interesting fact. You tell me. I have a hypothesis, but you go first. Um, barbed wire. Yeah, I was gonna say the broader con, well, in the, that documentary, the Cowboy War, what basically the answer was, civilization kind of came in. You had the railroad come in, which brought a lot of people, and then, yeah, eventually barbed wire, and you couldn't steal cows. I mean, the cowboys were a gang in Arizona in the 1870s and 1880s, stealing, you know, cows, especially. I didn't know that. 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 a, this huge fight, like the movie Tombstone, which is kind of historically accurate, but not really. Um, it was sort of the broader Posse versus the Cowboys. Two words, Posse. And, uh, it was this big, it ended up being this big deal, like that's the gunfight at the O.K. Corral, because it stirred up all this sort of North, you know, North versus South sentiment, 20 years after the Civil War. But the broader historical context there is people sort of wanting to bring about change because Tombstone, where, um, you know, O.K. Corral was, and where it was, was a silver mine. So it brought in all these, um, you know, people from across the country, both North and South. But there was this huge tension between those wanting to modernize and those wanting to get stuck in the ways of, of the past. So, yeah, it, it's, it's that period of time is, um, I find it fascinating too, because you had sort of land with no laws all of a sudden becoming civilized at various different paces, and a lot of cool things, or interesting things, at least happening because of that.

Yeah, I think that's, for me, I love that because I think it's such a good metaphor for technology and technology frontiers and kind of this trade-off between, you have like the individualists who are going out and exploring, and there's no laws, and there's a lot of creativity and all that kind of stuff, but then you kind of need the civilization that comes behind them, but that sort of, it's at odds with that frontier spirit. So there's that, there's that always that tension between structure and creativity. And, and I think that there's something very similar there about technology. So many levels, man. I mean, that's even true with sort of, at least historically, of the, you know, why does VC investing exist? You know, why is it that you go and read about the story of, you know, you pick any company, you know, from Nvidia on down, where they're like, I can't do this at a big company, so I'm going to go and push the frontier, um, in a world in which I'm less constrained, you know, there's no barbed wire, and I'm going to go and build. And then at some point, you know, those, those companies become successful, they become institutionalized, and then someone does that again. And so that's not a geographic frontier, but, but a technology frontier. Um, and as I mentioned earlier, like I feel a sense of responsibility because we can kind of do both. And they're just not the only companies that can say that. If we want to push the frontier, but with resources, um, and, you know, those ultimately are, I think the businesses that, when you look at any technology transition, are are able, you know, not just to adapt, but, but to thrive, of, uh, you know, having that mentality, but not being, you know, not using single-action rifles when other people have machine guns.

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. Um, and, and, and, and I think this this layers into the AI stuff where it strikes me that a lot of, or I'm curious what you think about this, but a lot of investing has to reliance to some degree on the idea that certain things that happened before are going to happen again. And, and part of the investing part of being good at it is knowing which, which one's going to happen again and which one's not. Um, do you, do you agree with that characterization? And how do you, how do you think about when to, when to rely on past patterns to help you understand the future?

I don't think human nature's changed in 10,000 years. Um, you know, and maybe it's evolved a little bit, but you go back, and my, my son who's eight, is really into, uh, to Egypt right now. Um, 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, 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 just to use a, a famous example, like, you, the old always wants to, or that the new always wants to replace the old. You know, the, the son wants to outdo the father. These, these are timeless. You know, when Alexander the Great was, you know, conquering Persia, Darius, the king of Persia, sends him a note, and he's like, "Hey, how about we have a truce?" And he responds like, "I'm coming for you." And so this concept of, uh, um, yeah, no, that's a good story, right? But this concept of the new wanting to, you know, replace the old, and this continuous evolution, you know, you find it in nature. You know, everyone sort of knows the analogy of a, a forest fire burns the trees so the new, new brush can can grow. Like, this, this, this concept of renewal 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, same place here. Like, there's going to be, um, a group of people, a group of firms, collection of individuals that are going to look at 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 past. And, um, there, you know, there, there's going to be conflict of varying degrees because of that. That that hasn't changed a lot.

So when it comes to investing, when we look for patterns, uh, you know, quant, quant investing, of course, is built on this concept that there are patterns, um, in history, in stock prices, and information that was predictive, um, and, and there's a structure to that data, even if that structure is so complex that humans can't understand it. And in world-class quant investing, you know, we, we've moved past what humans can understand, you know, many decades ago. People forget how long and well the book on Renaissance came out, but, um, you know, some people still don't realize that quant investing is has been going full war since at least the early 90s. Um, and so absolutely, that whole class of strategies, and which is a huge part of the markets today, um, is built on this concept that historical patterns, um, do repeat themselves. Um, when most people think about investing, they think about sort of human-driven intuition in investing. Uh, I also believe that's timeless. It's just timeless on a, on a different scale. And that is where, in a go-forward basis, I still very much believe that human investors will have an edge, particularly on low 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. Um, and so all these tools that, that we're building and can be built, um, to enable that human, um, to, you know, to make a prediction more, more accurately. Um, yeah, there, there's, there's patterns that come with history. It's, you know, history doesn't repeat itself. It, it rhymes. Like that's absolutely true for, for humans. Um, so I guess summing all that up, um, just saying, oh, this is the way things happened in the past, um, is silly because they never repeat themselves exactly. You have to sort of more go to the, you know, the, the underlying mechanics, um, particularly around human nature. I think, I think human nature is, um, is a constant, is, is stationary across time, if you just look at it in the right, right way, where you're orthogonalizing all the other crap that's, that's happening.

Hm. Well, we, we'll have to, we'll have to debate whether human nature is static or not, because I, I'll, I'll take the opposite on that one. But I have a, I have a, another direction I want to take this, which is, um, uh, I didn't realize, now you're, now I'm nerding out, like, I didn't realize that, uh, there are things happening in quant trading that are in principle not explainable. Not like humans can't understand it at all. Is that what you're saying? Or they could, but it's just so complicated that no one takes the time to do it because it's not worth it.

Um, well, there's, there's so many different flavors of quant investing. You know, there's, um, 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. Um, 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. There are all these nonlinear relationships. A lot of those techniques have been used on structured data, um, in these models for years. I mean, you go back and read the book about when the guys from IBM came over to to Renaissance, what, what they were doing, they're doing speech recognition, like a lot of that sort of pattern, pattern matching and sort of predicting what, what comes next. Um, yeah, for a human to actually sit down and like, walk me through all the different layers of the neural network and why did a machine do what it's going to do, but no, the dimensionality of that problem, just, it's way past what a human mind can can understand. Um, so I say generally speaking, world-class quant models, um, while the, the signals, you know, what goes into them of saying, you know, this is something that I ultimately, this feature, um, makes sense, how those features get, and which I do think is important. Um, and again, there's various different ways in which firms go about this. Um, generally speaking, we want to understand at least some rationale to our features, even if there are many thousands of them, but how those ultimately get combined, and understand the nonlinear relationships from that, that's, that's very complex, and, and that's totally fine.

So, it's, it's interesting that you brought up intuition, um, 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, um, I think of neural networks and intuition as being analogous, 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.

I do think intuition is very important, uh, for building a process. Uh, and really, you know, another term that you could say is analogous to intuition is, is, is first principles. Um, and there's a very famous guy that uses first principles all the time. Um, 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, 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 watch you doing a movement or lifting a weight and like, hey, did you actually realize that these things are are connected? Um, so I don't, I don't think they're antagonistic, but it, it, it's entirely consistent that a machine can give you a very intuitive answer that you wouldn't necessarily have been able to think of yourself, if that makes sense.

Do you think that first principles, and this is a leading question because I, I have an opinion on this, but like, do you think that first principles in the, 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. Um, in, in, in the sense that when you, when you use the word first principles in a math sense, um, once you adopt them, you can just like work out all of the implications of those principles without, like you said, you can take the tests and, you know, you can just figure out what all the answers are. But first principles in a kind of a decision-making sense, like, um, they're not at the bare level of like, uh, 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 real world, the difference is in a, in a mathematical model, and math is just a model for the way that things operate. And within a model, you have rules. And so, there's real objectivity to, to what are the rules. Sometimes those rules can be very complicated, but there's an underlying structure, um, in organic systems that, that sometimes there's structure, sometimes there, there isn't. So, I agree with you when it comes to decision-making, um, that's why that the word subjectivity exists. What could be first principles to one person might be totally the opposite of someone else's first principles. They both call them ground truth and axioms, whatever it is. So, there is an element of subjectivity in the real world. You know, when it comes to decision-making, and what, what I believe, and what I believe really good decision-makers try to do is, is understand their own decision-making process, both to, um, discover what, what's led to good decisions, and what, you know, what are areas in which their decision-making has been subpar. So, in some ways, you can apply it more to your own closed system. But to say 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 in first principles that are so different that they would lead them to, uh, make completely different decisions.

What are those for you? Like, what are the, as you've learned to improve your decisions over time? What are the things that you've learned, um, to, to take as, 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. Uh, and not in a negative sense, like everyone is, is greedy. Um, although, you know, a lot of times that, that does drive behavior. Um, but actually understanding the right incentive decision for why an individual 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. Um, that's extremely powerful. It's just to say, like, are the, are the vectors, or the, are the incentive vectors all pointing in, in the same dimension? Um, so that's a big one. You know, you mentioned earlier, like, I, I hate fluff. I hate, um, 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 really just trying to be intellectually honest. And so, not, not trying to reduce everything into a, into a math problem, but a lot of times, there, there is structure that you can pull out of a, a situation. It might just require, require hard work. So, you, you can't manage what you can't measure. So, so try to measure a lot of things. And, 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 efficiency. Highly recommend everyone do that. Um, you know, every, every single workout that I do for years, I've, uh, tracked. You know, I, I have a log, and there's a lot of numbers in that, just to, to see these trends, you know, evolve across time. Um, and again, none of these are, are perfect, but I feel like people, because it's hard, um, a lot of people just don't do things like that. Um, and I actually, on a go-forward basis, I think that's one of the things that's so exciting about this world of, of really data and making meaning out of data, is it's going to be so much easier to do that. Um, and a lot of it's going to be around, are you, are you collecting the right, right data to, to help you with that? So, yeah, you know, the biggest first principles, I said, power of incentives and, and intellectual honesty, and, uh, those, those would be at the top for me.

What goes into your journal?

Well, there's three components of my, of my life, which would be, um, you know, my family. Um, you know, I have, I have three kids, and I've been with my wife who's pacing for, for 15 years. Um, and, uh, I, I love being with them. And, uh, that, that, it's not just a platitude. So, um, my personal life and how I'm interacting with the family, and I feel a huge sense of responsibility to, to my family to, um, provide them with a great life. So there's a section on that. Um, 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. Um, so there's, but there's a section on that. There's a section on, on work, of course, um, which, you know, for me, at this point, is, is this great, great big challenge, um, that's, that's fun. I think sometimes people don't even use the word of fun, um, and like, that's one of the things with AI, like, this doesn't need to be scary. This is fun as hell. Um, if you look at it the right way. It's really important that people have that, that view. Um, and, you know, I, I'm working because I, um, I get meaning out of it. I enjoy it, and it's, it's, it's different than, and I feel fortunate that than most people, where I can say, like, I, I'm doing this because I, I certainly want to. So there's things on work that I talk about. And then there's things just on my, my personal health that I, that I talk about. 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, am 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, 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, we're dealing with time series, and so we could say, like, oh, on this day, you made or lost money or something, and such happened, but, um, I really wanted to remember, like, what was I actually thinking on that day? Um, 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, uh, was why I started doing that, and it's been really helpful, both as an exercise in itself, even of, of just writing it, but then going back and saying, like, "Oh, yeah, this, this is what I was thinking on that day." That's, that's interesting.

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 a Google Doc and then putting into ChatGPT?

I, I'm trying to capture the concepts of what I felt on that day. So, I can either speak or just write bullet points of, here's 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 three categories, you know, here we go. Some days that's a lot. Some days that's, that's a little. And then because I've been doing this, and the machine knows my, my voice, um, you know, that, that can be a one-minute, even 30-second exercise. It's just so easy to do. And I, I feel the reason most people don't journal is because historically, sit down, it would, it would take time. But this is a perfect example of, you know, the thought that's on your mind, just get it out there very easily, and then it can be captured, processed in a way that is accessible months in the future. It's just this tiny 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, one word that you brought up a lot, um, in the context of work, but also you just brought it up also in, in the context of family, that I'm curious about, is the word responsibility. What does that mean to you?

I, I do think about this word a lot, um, in, in the context of work, work, and family, slightly different, but, but overlapping, of course. Um, you know, I feel that people that either are endowed with certain skills, like their clock speed is really fast, um, or they have a lot of resources, or they have, you know, great networks, just, just generally speaking, people with, um, that, that, that the world has entrusted upon them skills or capabilities, um, have the responsibility, their, to themselves and to their community, to use that to, to the maximum extent possible. It's like, if you can, you know, run 100 meters in under 10 seconds, and you don't, it's a travesty. And, uh, because not everyone can, can do that. Um, 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. Um, and then as I went on in my career and felt that I was able to, to do that, and I was like, okay, well, you know, my, my kids are, are 10 and eight and two and a half, and we haven't even talked about what it's like to be a parent in a world of AI, but having a sense of responsibility to, to have them, you know, grow up and to flourish and, um, 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, 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, um, you know, in our world, in the investment world, you know, you, you have sort of two senses of responsibilities. You know, one 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 a document, that, 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, that have that type of structure. Biggest ones would, or most famous ones is, is Citadel, but there's really only maybe a dozen, probably less than that, that are that are real. So, huge sense of responsibility for investors to do what's, what's right, to not abuse that privilege, and ultimately to, uh, we're, you know, we're, we're in the money-making business. Let's just be honest about that. U but also responsibility to, to our people, too. And I think this is what's generic across kind of all, all leaders and all companies in a 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 we can for them, and our responsibility to our people to be as efficient as possible. Those two coming together mesh very, very nicely. Um, but yeah, the, I think for me, and I'm 40, so, um, I'm not that old. Um, uh, we do, do want to be doing this for a long time. But as I say, as you go, go higher up, as far as responsibility, um, I think that the notion of being a little bit more of a steward and helping others accomplish, um, what they want to accomplish, like that's something that successful people talk a lot about, and I, I very much feel that. And, it's very much aligned with what we talked about today in the world of AI.

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

All right. You bet, man. Thank you.

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