Ex-WorldQuant Head of Data Strategy: Quants “Don’t Care About the Stock Market”

I landed at WorldCorn after finding the job on Craigslist. I would meet with Igor twice a week. We have the best researchers in the world. Nobody actually really cared about the stock market. I bet you, if you ask people that when's Nvidia reporting, nobody has a clue. If you consume more data than anybody else in the world, you can manage more money. So go buy data, figure out where to find it, feed the machine. Everybody wants to talk about alpha, but you really want to be selling beta. 'Cause beta is sticky. Igor is trying to hire the top 10th of 1% of talent. In every country. He's got to build a network in India and China, in Eastern Europe. People today feel a struggle in building up that differentiation. They feel like they're being left behind. I think that is fueled. It's something that you've talked about, the degenerative economy. Where do we go from here? I always think of like the kid laying in bed with his iPhone like ordering a burrito on DoorDash while trading stocks and gambling at the same time and has like a Celsius energy drink next to him. Your bullish prediction markets, what takes these markets to being extremely mainstream? If you get the liquidity where the biggest institutions in the world and pension funds can use it from speculation and hedging, I think it's bigger than the options in futures market. Matt, thanks so much for coming on the pod. Yeah, thanks for having me. You did alternative data stuff at WorldQuant before alternative data was a thing. What was your thesis? I think it was Igor's thesis then that, if you consume more data than anybody else in the world, you can manage more money. And when he hired me, he was like, how do we go about getting more data than everybody else? And so it was essentially a blank check. And we have assets and the more data we have, the more the PhDs can find the alpha and the more alpha we have, the more money we can make. And so, it's very clear from the first time I asked him a question in interviewing with him, which was like, what do you see us in five years? And his response was, we're here to make money. And it was like, okay, no other questions. What sort of data were you looking at at the start? You know, it's a new thing. What were you thinking? I mean, I think at first it was just the bread and butter, fundamentals, like how do you use fundamentals and make it more systematic? How do you use price volume? The basics, the advanced to like stocktwits data, remember what called Howard Linson? When he started stocktwits and how do we trade off of like retail talking about the cash tag? And then it just like spiraled from there, which was like everything under the sun, from satellite data to credit card data to, you know, Revere was a company that faxed bought back in the day, which was, you know, a different form of classification and product segmentation to supply chain. And, you know, it went from one to two of us to team of four and, you know, we travel the world to, you know, every country under the sun, finding data and going to the most esoteric conferences. Any single data set that you were surprised generated world quant lots of alpha? I think Revere before faxed bought it was like one of those amazing data sets that probably only once understood because it could be used in so many different ways. It was like a better way to classify companies, right? Amazon at that point, you know, had the, you know, we're ordering books on Amazon and anything under the sun, but like there was really like the whole other side of Amazon, which was AWS and everything. How do you like slice and dice Amazon so that you can build different portfolios, right? And think about a call firm. We're doing a long short investing. So like the more clustering we can do, the more baskets we can create, the more ways we can analyze a company in different ways. And Revere had unlimited amounts of data that was, you know, coming in real time. And so I think we were one of the biggest clients of Revere to the point when faxed bought it. I remember like giving the pitch that we should be buying them. Obviously didn't work out at the time. So a new concept for hedge funds to think about buying their data vendors, but that was probably the most exciting. What was your view for some context? So Revere had a new way of thinking about like a Gix classification, but also product segmentation. So like think about all the products that Amazon creates or all the products that Walmart offers. And like how do you slice and dice like the percentage of revenue that they're getting from those different products? How do you think about how to classify Amazon? It's Amazon a retail company. Today it's probably more of a technology company, right? But back then when you're doing like e-commerce only, like a different way to like segment. And then so if you think about it being an investor in a quant, like you need to a bit to bucket these companies while you're building, you know, long these hundred companies, short these hundred companies. If you can slice Amazon in a bunch of different ways, you can slice, you know, name your favorite, like conglomerate, pretty exciting, like the amount of different strategies you can build. What was it like joining World Quant? I remember before our conversation, you mentioned your finance major. World Quant is a to quench shop to follow PhDs. How is that cultural integration? Guess what was the experience? What was the initial experience like? It was definitely a shocker for me. When I went to school at Chico State, which is like a small state school studying finance, worked at Bloomberg, and then I landed at World Quant after finding the job on Craigslist. And, you know, I walk into a room of PhD rocket scientists where I think like I'm the undergrad there with a finance degree that is native English speaker. And I was definitely the outsider. It was one of those things that you gotta find, like what's your value here? Everybody was super smart and could create anything. And I remember Egor, and I had a good friend there named Gil, who ran technology, and Gil was like, we can build anything you want. Like we need like a business person to think about data. So go, buy data, figure out where to find it, feed the machine. That was kind of like the whole idea. And at this point it was 80 people. When I left it was 500 plus, we were in like 18 countries. I would fly to Russia, China, Thailand, Europe. We had offices there, we had the best. We had, our whole thesis was, higher the best minds, let them live in their local country, give them the upside of like a hedge fund, where like, you know, they could bonus millions of dollars, and then give them access to data. And every piece of the machine was like a factory floor, right? You had the data, had it come in, how could we automate all of that? Not only finding it, but the contract process, feed it into the simulation, feed it into our back testing engine, let the researchers go to work on it, all the way through to risk and actual trading. How did you understand the entire pipeline? As a finance major who hadn't worked at a hedge fund prior, and not just hadn't worked at a hedge fund, but joining a very sophisticated quant shop, how do you see the whole thing and go, okay, I can add value here, here and here. Can you walk me through that? - It was early days, so like I would meet with Igor twice a week. And you know, he's not one that was like, would speak a lot, but he always listened and gave like great insight. And I think for me, like it was about building relationships, like who leads the engineering team, who's leading the trading side, and just like finding the right person to ask questions. I think like the exciting thing if you think about it is like most of the guys there were brilliant from a math and engineering perspective, but like they were super excited if they heard about a new data set that we could potentially make money off of and use. So I brought value in a different thought process, and I think that was key. I was also happy to get my hands dirty and like roll up my sleeves, right? So like remember we used to do like weather trading with futures, and like it was completely new. Like somebody's got to get in there and figure out like what is this even gonna look like? So just like kind of diving in. And then you know, I think you know, every year you go, would be like how do we do 100x more? And like that kind of process of like thinking like 100x more without hiring anybody. Back then it was like a crazy thought. I think now like, obviously with AI and everything that we can automate is not as crazy, but like how do we do 100x more? How do we test the thousand data sets a month? It's hard to wrap your head around. - How deep in the nitty gritty of a single dataset did you go as the data sourcing guy, but also less technical person, you know, would you go there and have a good map in your head of, okay, the quants could probably run this type of modeling on this type of dataset. Here's how it can translate to alpha for the firm. Here's the, you know, the specific the specific pod or team that would be best for working with this specific dataset. Yeah, how hands on were you as a non-technical part of world-class? - I think in the early days really technical, right? Obviously diving deep into like the documentation and everything. And then obviously as we got to scale like less and less, but we had built a lot of process. And everything I think there is like, if you really think about the success of world-quant and a lot of quants shops, it's all about process, right? And obviously you have to rethink your process all the time, but you know, from what do we look for in a dataset? How many years of history does it have? How many tickers does it cover? Is it point in time? You know, how long has this data vendor been around? Like you can really like kind of stack rank what is potentially going to be the most valuable, just based off how big the dataset actually is, because you can do both, you know, human and genetic based alpha research. So I think that's obviously very key. And so there's no real pod type setup at world-quant. It was 99% one team. So we had a pool of researchers. We had researchers we used eventually just for testing and trialing data. We had a whole process of like from finding the vendor to the contract, to the NDA, to getting a sample, to back testing it, from back testing it. to getting researchers feedback to removing this into production, how long does it take to get for trading. And then we monitored data sets like we look at stocks. Like that's what I think the most exciting thing was is like everybody thinks about why having their watch list like where's Apple today, how much is Robinhood up today? Like we had it like just essentially data sets. And we're watching tick by tick how much money we make per data set. It's a whole different way of thinking about things. Like I don't think I ever thought about like who's having earnings today when I was working at World's Gourmet. Like I bet you if you asked most of the people there like what it like when's Nvidia reporting, nobody has a clue. I remember we were talking about futures markets at one point and somebody kept somebody like was definitely confused like because we hired a lot of PhDs with no finance background and they were like why are we talking about the future when we're talking about futures markets. Right. So like there was like a big divide but it was like you know give the smartest people access to the most unique data sets in the world, let them find patterns and let the PMs put them into a portfolio and put tight risk controls around it and let's see how much money we can manage. If you're a student who wants to work at a great trading firm, listen up. Our brand partner, Onyx, the largest oil derivatives trading firm in the world is hiring junior rust developers. They're opening this up to people who haven't written a line of rust in their life because it'll train you from the ground up. From day one you're on a small team working on real projects learning from senior engineers who built the systems. If you're self taught with serious side projects to show for it, apply it the link below. And what you said there just earlier about watching different data sets and how much you're making per data set, that's fascinating. Did you build up this intuition for when certain data sets were going to be real alpha generators for the current regime? And yeah I guess did you have that intuition the way a PM would have for stocks? I think we knew from the minute you met a vendor, like you would know within the first couple minutes, like if this is going to be a money maker for us. So same way I kind of feel like in venture space now, like when you meet a founder, you almost know within the first three minutes if you want to invest in them. Obviously some things take longer, but I think the best founders you meet, like assuming everything else checks out, you know within the first few minutes if there's like a potential here for this to be a great business. And it's kind of what led Worldquant to be a building world called Ventures, right? Like we were looking at all these vendors spending hundreds of millions of dollars. I remember saying to you, we should be investing in these companies or like taking a piece of them. First client or one of their biggest clients, like let's own 10% of them because eventually they get acquired or it's a big company like we should be on both sides. And I think that was the whole genesis of hiring Steve Lau, me and him running Worldquant Ventures from the early days. The question about the intuition around data sets as stocks or you know seeing how much alpha you're generating from from each specific data set. I was curious about obviously when meeting a vendor, is this going to be good for us? I was. But the other thing I was, I also wanted to hear about was when you were already, you know you had say 100 different data sets, right? Would you know of the ones that you were currently running? Okay, I think this regime is perfect for this one right now. Did you have an intuition around that as well? Meaning that this data set is going to be good right now with. Yes, like it explains more signal and say earnings right now. I don't know. Yes, I know. I think we looked at data sets that if we were to bring them on into production, if we would break even cost of a data set, 60 grand cost of data sets on the rent. If we could break even on making money off of the data set, just breaking even might as well keep it going. You need to have this data set in production for at least two years out of sample to really know if it's valuable. So if you're breaking even, there's no point of getting rid of it, right? You're covering the cost. Obviously there's a lot of other costs involved. And you know, I think over time, if data sets are correlated, you get rid of some that you just don't provide additional value, but it took a long time for us to build that second layer of like insights, right? Which is like, Hey, if we remove this data set from the strategy from the portfolio of the last five years, it's not going to change anything. Why we going it? We didn't have that at first, right? It was more like, let's bring on as much as we can if we're finding alpha and some of the basic strategies we back to as we move into production. If out of sample for two years, it's at least breaking even let it go. And obviously the top ring data sets we keep our eye on the closest because like, let's see if we can juice it even more. Also, like we need to think about how much is it going to cost to like renegotiate in the future. How much alpha was there in all these different alternative data sets, you know, back then versus today? I think things have changed a lot, right? There's so much more data, so many more data sets now. And there's so much more competition for similar data, which is like the whole correlation thing of like, is this providing any additional value, right? Is it uncorrelated returns? So it's just, you know, like, obviously you want two to three sources of the same thing. If it's really valuable fundamentals, basics and so forth. But I think the game has changed a lot. What are the ways in which these, you know, alternative data sets, alternative data sets are utilized today that allow for, I guess, more opportunity to be extracted. Or I guess, how is the game changed? What are the specifics of that? I don't think it's a quant game anymore. I think you have long short investors. You have late stage private investors. Everybody's like leveraging and using data. So I think like, you know, alpha erosion over time is obviously picked up a lot. I also think quants are trying to move more and more into some of the game that the long short managers or the pod shops we're doing with alternative data. So like, there's definitely a meeting in the middle. You know, is it about speed? Is it about how you slice and dice the data? And then like, I think there's been a lot of shitty data that's come out over the last 10 years that like, you know, the best firms know it's, you know, it's not accurate 10 out of 12 times. And so like, maybe they're buying it just because they know other firms are buying it. And if they're along it, you should be sure because it's inaccurate. So like, there's games in that in itself. Interesting. Interesting. And so you had a data strategy for old quant and then you jumped to third point. What's that like? What's that job? I think it's like going from a technology firm to like a hedge fund. Like it's a complete, it's like night and day, right? Like third points like blue slacks wide shirt brown shoes, like boardroom, like 20 analysts on a PM managing 20 billion dollars and, you know, Dan Loeb is like the face there and like the ultimate decision maker. Worldquan is like a factory. Igor is not like buying and selling stocks on his own decision making, right? Like it's almost like how do you just like automate the factory and give it more and more like access to data and technology and like the best people and the best minds and maybe new strategies come out. But I've ended the day. Worldquan's part of millennium. At third point, like there are no rules in terms of like what we can and can't do. I think it was one of the only hedge funds left that could do credit, structured credit, equities, activism, like we had two billion in privates at one point, you know, like a whole host of things, right? So like there was like a lot more going on with a lot smaller team with like a very much like hedge fund style. You walk into worldquan. You walk, you feel like you're walking into Google right? There's ping pong tables and everything. You walk into third point, it's like some of the nicest heart in the world, right? Like there's like barista serving you coffee and like you might see a politician or the CEO of XYZ company walking around. You're not getting that feel within worldquan. So like definitely culture flip for me. What does data strategy look like at a fundamental hedge fund? Like third point. I think today versus then it's completely different than it was like come in and be the chief data scientist. I think anybody that knows me like I don't think on paper I'm a chief data scientist from an academic background. But it was like how do we use data technology and everything we do and just be better investors? Like, you know, it was kind of the wild, wild, less there of like a PM or an analyst buying data for themselves. The guy next to him didn't know what he was doing and like, no, there was no process, right? So like there it was, you know, give Dan credit. He was like, we're investing in Google and all the Bob and all these companies that are using data and AI. We're not doing anything as a firm and kind of saw the quantum mental shift happening. So for me, it was like, how do we come in and like put structure and process around things? And they got to be honest. Like culture is the hardest part of that. Like you've got to get, you know, you got to get analysts that are used to making tens of millions of dollars to listen to a team that's trying to change what they do day to day. Yeah, I guess what's that like because like you said, it's brown shoes, blue shirt. The image I have in my mind of third point is not one where they'll be listening to the data guy, talk them through how to look at all these different data sets to inform their thesis or maybe today it's a different story, but still the image etched in my head is not that at all. How does that actually, like how does that data actually end up getting used in the process? Like you said, when these analysts are making tens of millions dollars, tens of millions of dollars and very much probably rigid in their ways. I think at the beginning it was like picking choose your battles and find like a champion to work with consumer team, like a TMT team or like the guys that are doing short investing. always looking for an edge. And I think they were also early to recognize that like in consumer, there's so much data. Like I remember like when I first got hired there, I was put in front of like one of the big sovereign wealth funds because they were like thinking about investing in the fund. And they were kind of like asking me like, what's the point of the data science team? And I was like, well like to break it down simply like, how are you going to invest in Starbucks if you don't look at credit card data? Like, how are you going to do like star, all the money at Starbucks is coming from people using their app to buy a coffee. Like, there's no excuse that like the analyst doesn't understand how to use Python or we don't know where to get the data or we don't know if the data is accurate. Like that fundamentally like just from like the basics like if you're investing in a hedge fund manager and they invest in consumer and they don't use alternative data, like they're essentially investing blind. Like they can't tell you that it doesn't matter because like it's been accurate 86% of the time and like we can forecast the KPIs like you know from directional perspective like 100% of the time just by using this data which back then maybe you needed some guys that had engineering skills but like there's no excuse right. So if you look at the consumer book that third point have like obviously huge opportunity that's very activists right? Like, Nestle acquired Bluebottle we were you know activists on Nestle at the time like you think about the TMT side like third point had a huge position in Netflix like you can obviously predict subscribers like and subscribers in every country you just have to have a mind to think about how does somebody sign up or cancel their Netflix subscription and then what data is there in the world that can help you figure that out. This is a kind of tangent question but still very much related. The call it old guard or old type of hedge funds or you know like like let's say a Persian square or you know these the hedge funds you think of when you think of the the brown shoes and the blue shirt how sophisticated how do they actually make how do they adapt and make their process he's sophisticated and you look at a firm I guess like third point or like like Persian Square it's one guy and he's the face you know I can't think of Persian Square without the Lackman third points the same how does a business like that actually build processes to use all these different things or even in general build a machine in the way the pods have done it right what are your thoughts on that how do you think about that. You're gonna have buy-in from the very top right like I think going into leveraging data and hiring me was like Dan's thought it was Dan's decision and like give him credit like hire the best people to execute underneath you like he saw where the world was going I think you have the same thing in other hedge funds obviously it's much different like in a quantitative firm because that's their ethos like so I think like if you ask me like five years or a little over five years at third point first two years were unbelievable just like game of thrones politics right like like survival almost right because like half the guys there like think it's a joke this is like when billions the show is coming out and like you know like you think about like how do you get quick wins like you know I definitely was given like feedback from like the you know dance like lieutenants like you got to figure out in the first 90 days how to like win over the investment team I remember we started tracking private planes this was before like it was easy for anybody to figure out where like planes were flying and like that was obviously a big win because it was like something very simple for the whole team to get it they're wrapped their head around right like if we know where the private jets of the CEOs of public companies are flying to and there's a thought that they might be acquiring something like well we can say like Jeff Bezos did fly his plane to here and that's where Whole Foods is headquarters is he's never flown to that airplane airport before so like figuring out that like and I think like that was something back then like tracking private planes was not easy yet like an 80s b-feed of like every flight in the world and you're looking for like a specific tail number how do you figure out the tail number of like Jeff Bezos's private jet and might have like five of them so that was like a big like mic drop in the boardroom when you can show that to an analyst and then maybe build some credibility gosh yeah does not sound easy um on working with you know two different firms do you have two different founders what was it like working with Igor what was it like working with Dan what made them special individually I think both of them think big highly educated power of the network but I think Igor had a network and it built but he built it over time we walk into like Dan Loeb's network like I think he's he can get ahold of anybody from government to CEOs I also think like both of them were very good at you know if I think about Dan he probably is in his family office invested in every venture fund in the world and other hedge funds like when he thinks about like getting information he could just pick up the phone I think that's a very venture style thing of doing stuff which is like you have information from like every fund and what they're reporting companies that are coming down that by from private going into public and I think his background not just in the equity markets but across the capital stack right like was a huge advantage understanding debt around during the spacetime you know he did activism or call it constructivism you know like there were so many levers to pull but I think about Igor and you know probably the same with Dan is like hire the best people right if you hire the best people around you you almost make yourself irrelevant it pushes you to make yourself move to the next level I think by the time I left third point I would say to like friends and family like pretty hired the best guys like what am I doing anyway I like there's nothing left for me to do if I can't elevate third-point I need to find something new because if you're not uncomfortable like if you don't have like something that's driving you just kind of sitting around doing nothing I'm curious about what you said about Igor specifically because I think people have this image in their heads that quants don't need to network that it's it's not that important and actually I am surprised that you cited network power of the network rather as you know for these two guys a big source of edge I understand that for Dan but for Igor I'm actually I'm fascinated can you elaborate more on that from a global perspective Igor is trying to hire the top 10th of 1% of talent in every country he's got to build a network in India and China in the mid-end like Eastern Europe back then obviously a lot in Russia and Ukraine and so you know he's got a network he's got to hire the best people in those countries and like it's a different type of networking he's not trying to get like insight into a healthcare bill coming through the government right he's not going to you know the same conferences as Dan Lo but he's you know he's at Davos he's at you know the milkin conferences right talking to about like giving the best talent in the world access to the largest compute capabilities and more data than any other firm in the world and giving somebody that lives in any country in the world like a great salary but also the bonus potential of being a quant in the US right like think about like that from like you grew up in Vietnam or Thailand or you know outskirts of Russia and like you have a great background academically you get a job you can live in your home country good salary but also your bonus potential is as high as somebody that's living in New York City like that's a game changer but he's got to build a brand right people got to know who world corn is he's got to have the network locally so like he's got to be internationally known locally respected network is clearly something that is extremely powerful and even from starting this podcast you know just having a bunch of different guests on I've been honestly shocked by the number of opportunities that spring up like it's you know a little thing here a sponsorship here and I think in the age of AI it's one of the things that is a clear source of differentiation that is not going away whereas obviously something like programming those still important you can picture a world where that you can I mean it's already going down in value as a scale I would argue for young people today or junior analysts certainly anyone in general how would you recommend building out competitive advantage and differentiation in their personal skill set when number one you know right now the landscape is as competitive as it's ever been more competitive than it's ever been rather and number two the LLM's the agents keep getting better and so skills that used to be valuable five years ago have essentially become commoditized how do you think about that problem for someone young today watching this podcast you know I think about it a lot especially like how education is changing but like I think you've got to build a network early I think you got to show up right like when I was at world corn we went to hundreds of thousands of conferences and just build the network the person you meet today you have no idea what they're they're gonna be 20 years from now, right? It's not even the point of it, but it's like obviously access to new information and building relationships. But like you're right, AI is changing, like just skill sets. Obviously, if you're young, you should be diving headfirst into leveraging AI and using these tools and making yourself 10x more productive than the guy that's been doing that job for 20 years. But that's not going to be the thing that gets you the job you want. It's going to be building a network, meeting people that have done the thing that you want to do or the place that you want to work at. But I think if I, my skill that I've had and the value I bring now is the network I've built over the last 20 years. The reason I can live in San Diego versus New York right now is because I built this network, right? And the network only gets stronger than more people you bring into it. But I think like, it goes back to the most successful people I've met, have the strongest networks. You can learn anything. You can't build those relationships, you know, with AI. And would you argue that the most successful people you don't have the strongest networks because they're the most successful or they got there because they have the best networks. It's a two-way street. How do you think about that? I think they always valued and thought about the network aspect, right? Like I think like education obviously important, but like you don't have to go to the top school and be the smartest person in your class. I think like the real work starts after you graduate college, right? Like you have to be a continuous learner, right? I think that's one other aspect of like people that are highly successful is like they're always challenging themselves and learning something. I think a third point like Dan was always pushing on us to learn the next thing. I think in a world-quant environment like these guys are like PhDs, like they're reading white papers and like the most cutting edge things that are coming out of academics and universities like it's a natural thing for like a place like world-quant where like you're always learning obviously and Igor did a great job of having like these like all hands, meetings and exotic locations where like you'd learn from like the top speakers and thought leaders in the world. I think the same thing at third point and I think like where I work now like you know my partner Howard who started the firm with my other partner Tom Howard Linson like it's constantly learning and like reinventing like what's important in his thought process but he's an avid reader. That's important obviously. I think a lot of I wouldn't even say young people but people today who feel a struggle in building up that differentiation that which comes in the you know in large part in the form of network they're losing hope because the tools are getting better they feel like they're being left behind you know media-applicized narratives of everyone getting rich while simultaneously everyone feels like a loser and I think that is fueled something that you've talked about the degenerate economy. How do we get here? I'll give the degenerate economy you know Howard my partner who started stocked with like he coined that phrase the degenerate economy like what is it I think like I always think of like the kid laying in bed with his iPhone like ordering a burrito on door-dash while like trading like stocks and gambling at the same time. With his in and mouth. Yeah vaping with his in and like and it has like a Celsius energy drink next to him right and like now you've got perpetual as you've got zero day options you can parlay like long Tesla and the warriors are going to win the next NBA championship. Listen I think it's crazy to think of like what Robin Hood started with and like I give Robin Hood more of the credit of pushing this degenerate economy than anyone because like where did prediction markets really take off it's when they've integrated it into their app where everybody was already detrating right. You Cal she comes up they've got the regulation and all the licenses fake right into Robin Hood and like as somebody who lives in California like you can't gamble in California but I can predict that the Niners might win the Super Bowl next year and if I can go like long Tesla short apple Niners to win the Super Bowl and like I think like somehow Trump will do another term in the presidency like because he'll change the laws like I can do that on my Robin Hood app now like that's a wild thing to think about. So I think the degenerate economy is scary because you know you talk about young people like I think about hiring an intern or having like a young junior analyst and I remember the guy that worked for me previously Ethan would say it's like it's not easy to hire an intern right now like unless we're paying a ton like they'd rather set it home and like day trade or do prediction markets because they feel like they can make more money than having to come into the office. And that's kind of sad I feel like when I was like in school like getting an internship like there was obviously like the learning and opportunity and like money was one part of it but it wasn't like the main reason for getting like you know I had to build my network get experience. I had another guest on the podcast who's now become a mentor of mine. Aliq's pesquette and he talks about how it's very very difficult to hire young people today because of you know talk about the degenerate economy because of a lot of the traits you mentioned over simulation and inability to think clearly and deeply about things. Besides knowing reads anymore. You're a young person today. How do you navigate that? How do you? Is it just a matter of saying note everything and focusing because at the same time these things like reading it's like there's no instant payoff and I think that's a large part of why people don't do it either. Everyone you know you look ask young men today everyone wants to become extremely successful internships you know you say it's hard to hire an intern but more competitive than ever a place like Citadel a place like Jane Street. I'm from the Philippines and I remember I went home for Christmas and I hung out with some friends who'd studied in the Philippines and they had heard about my friend from high school who'd gotten an internship at Jane Street who they'd never met and so I think that goes to like you know signifies how the status associated with these things in a place like the Philippines where the markets are not a huge thing these guys know about that. So you're a young person today how do you think about navigating the competitive nature of the job market and building your own personal competitive advantage. I think I probably struggled when I was young and the same way kids are today like no patience at all. When I was at third or at World Court I think within the first two years I was already interviewing because I wasn't moving up quick enough right I had no patience like the thought of like that I was going to leave there after 18 months to go to another hedge fund that I thought I would have more opportunity yet like that looking back on it was would have been the dumbest thing I've ever done right like I think like my feedback to young people is like you've got to have patience I also think like you know this is a mentality of mine because I went to a small state school for five years got a job at Bloomberg that I probably shouldn't have gotten and then got a job on Craig's list for getting into a hedge fund like you got to just out work people like put in the actual time like I think like getting in early and staying late and like the real competition like of like out working people I think like that art is not going away and I think it's still like meaningful I remember Dan Loves used to walk the floor at like 6.37 am and like I would be there already I was younger and like some people on my team wouldn't be there yet and he'd be like where are people but like my time in front of him getting to talk to him was because I got in early right stayed later like those are like the little things that like people can do to like obviously set themself apart but I also think like I was at Bloomberg for five years I had took me two years to get a job at a hedge fund I probably sent out like a thousand resumes how many people are looking at a Chico State grad that's at Bloomberg to hire them at a hedge fund eventually it happened and I got an interview right but I think like there's got to be it goes back to like you've got to have patience and like things don't happen overnight and I think like the reality is when you're young like cares about the money like you can make money like somebody told me like in your 20s you figure it out and your 30s you find your job and in your 40s you make your real money right like so like enjoy the yet one year young right like you've got no responsibilities for the most part like get the get yourself to like figure out what you enjoy and go all in there and it doesn't have you might realize that like you don't like you don't like working at a hedge fund or you don't want to be in venture capital or there's no actual excitement and you want to go start a company right I think you really figure out what you're interested in until like your late 20s you have a finance background edge from background now your venture capitalist how do you think about for personal life decisions or for career decisions alpha versus beta and I say that because not Columbia right now and there is a status associated with a very standard high paying paths banking consulting recently Quant trading everyone wants to be quant you say your quant that James Street everyone take a step back like I said earlier and because everyone's talking about that I would argue there's less alpha in doing those things and I recently had a conversation with a friend who's you know also he works at a venture well known you see fun and he was saying you graduated from Yale you was saying that the people who are the most successful Like you want some people to, you talk about what you're choosing to do and you want people to say you're crazy because by definition there's still alpha there, right? If you want to seek alpha in your career path, but it feels like there aren't that many options. What do you do? Non-consensus, right? That's what you do at seed stage investing in the venturespace. If you're following the herd, you're probably not going to make any money. When my partner Howard and Tom invested in Robinhood, that was definitely non-consensus, free trading, right? Like Vlad and Baizu started that. It wasn't even supposed to be a retail company at the very beginning, right? They were doing algo, quant trading, like software. Like any good investment at the beginning is going to be non-consensus. And so I think it's like, you got to believe like what's going to happen in the future, but if like you're driven by it, like you want to find real alpha, like you got to do things now that people don't even understand. Prediction markets weren't even a thing like 18 months ago. Now we're talking cowshoe value to 20 billion plus. Polymarket was the winner, right? Who even thought about Claude when open AI was raising crazy money? Now like I don't, I think if you're in business side, people use Claude for everything, right? If you meet a venture capital person that hasn't gone all in on using Claude to automate, like all of their back office operations and diligence, like what are they doing? Right? Right? So like those were non-consensus, right? Taking a job at one of those companies was definitely non-consensus five years ago. So I don't think you always have to follow the herd to like really get the alpha. I think the other thing, and like I think about this when I invest in companies, like everybody wants to like talk about alpha, but you really want to be selling beta. Because beta is sticky, right? Like if I'm investing in a data company, I don't want to be selling alpha. Like the alpha is going to dry up, right? The alpha is going to get eroded. I want to sell beta. Like every data vendor wants to be beta. Because like that's sticking is going to be around forever. So like it's exciting to sell alpha. Those companies are like come and go. Like you could think of like hundreds of data vendors that were selling everybody like predictions on what's going to happen and like you can better forecast earnings and like look at all this alpha and everybody's getting this edge. Those things die after 18 months or one quarter blows up and like nobody wants to sell that. Companies that sell alpha like have no exit potential. You sell beta. That's sticky. Somebody's going to acquire that because those customers aren't going anywhere. That's a very insightful way of looking at things. I've never thought about that. Is there any analog to selling beta from a career perspective? Because like yeah, I'm imagining it from a skill set, right? Just something to accompany, right? You want to differentiate its skills up. You also want something really sticky. Get a job at a company where you're going to learn something really on. I took a job at Bloomberg. That's beta, right? But I learned so much at Bloomberg. Like they made a start on the phones. Like when you started at Bloomberg back in the day, everybody started by answering the 1-800 number. It could be somebody complaining about the radio station or needing help fixing their keyboard or asking about how to do like an exotic option analysis, right? And you moved on to like analytics and everything. Boring job at a college, especially for a lot of Ivy Liggers that thought they were going to be in like banking. But like that was like the beta that like taught me everything about the markets and convinced me that I should be at a hedge fund. Because I thought I was just as good as the guy on the other side of the terminal, right? And so that got me to WorldClaw, which was probably alpha at the time, but it was definitely non-consensus because who heard of WorldClaw in the early days? It's a small team and old Greenwich Connecticut. I moved all the way to New York from San Francisco to get on a subway, to get on a train every day to go out to Connecticut. It's the worst thing. I also moved down to Wall Street thinking I was going to get a job at a bank. And then next thing I know I'm on a subway to a train to go to Old Greenwich five days a week. Not exactly the glamorous thing that I thought I was getting myself into. But if you like, you look back like WorldClaw, like now is like one of the probably largest quantitative hedge funds in the world, I would argue top three, right? So I think like get that job and learn what it means to have a job, right? Just like if you graduate and you go to Google or Meta or any of these big companies, you're going to learn a ton. Then go take your alpha chance. And it can be a non-consensus, right? Something that doesn't even exist yet. Best companies take 10 years to get to where they go. Robinhood took nine years to go public, right? We invested in our firm at EToro. Come, if you took 13 years to go public. Right? So if you think about it, obviously companies are getting built and growing much faster today. But like if you play the Silicon Valley game, like you never know which company is going to take off right away. In playing the Silicon Valley game and on out with Hopper-Govalfa versus Beta, where is the alpha today? And why are your portfolio companies going to the IPO at $100 billion valuations? I mean, I think the alpha today is like at least the seed sage, it's about finding the right people. Like I think the biggest differentiation now versus like a few years ago is like sales, go to market and marketing are so much more important, right? In a world of AI where like everybody can be an engineer, everybody can build it like an MVP. Like unless you're building deep tech and you're building the next anthropic, like everybody else is building something and they've got to have their wedge into being able to sell it. So like you need to find those founders that have that edge, right? The best founders as they like kind of evolve and grow their company, they've got to focus on two things, recruiting and raising money. You can hire the best minds and you can continue to have money so that the company can survive. Those are the companies that are going to win, right? But if you think about that, is that anything, is that any difference than a hedge fund? Dan Loeb's got to make sure he has enough money to manage and he's got to hire the smartest people. It's the same business, right? Yeah. What can Griffin say, what's his famous quote? If everybody wants to eat, somebody's got to sell. I love that quote. You can use it for venture, you can use it for hedge funds. Like, Igor is only successful because he struck that partnership with his Englander in the early days. He had unlimited access to capital. What did he do? He hired the smartest people in the world. Dan hires the best analyst. It doesn't matter where they came from, right? He's very big. I'm like, I don't care. I don't care your background. I don't care if you grew up in immigrant. I'm looking for people that are like, you know, have an edge. I'm thinking about it from a venture perspective. My only job is to find people that I think I can work with for the next 10 years. Most of them are probably going to have some sort of pivot or hardship at some time. Like, how are they going to deal with that? And as much as we want to see the next Robinhood, we're going to have a lot of companies that exit for three to 500 million. So let's make sure we get this thing structured right at the very beginning. How do you screen for exceptional people? And I'd like to even go further. How do you screen for someone who thinks in a very differentiated way? I think you got to spend time with people. Like, we're not, like, as much as everything's done over Zoom these days, like, you get a sit face to face with them, have a meal. What makes them tick? Like, what experience did they have in their life that's going to like allow them to fight through things? Maybe they immigrated here from somewhere. Maybe their childhood, like, there's something that they're trying to prove. Congriffance as he loves to hire athletes that were good academically, right? Like, I don't think I care about where you went to school or what sports you played, but I do think it like gives you a different mentality of like what type of work and effort you have to put in, right? What's your upbringing? Like, in a startup world today, like, it feels like every company's working 9.96, right? 9 a.m. to 9 p.m. six days a week, right? You see like companies that are paying their employees more if they live closer to the office. People are coming back into the offices. So it's like, what's going to allow you to outwork the people next to you? Because like, there's a hundred times more companies. There's a hundred times more opportunity, but like a lot of money is flowing into the same thing, right? And so like, what's going to allow you to survive? So when you're evaluating companies, seed stage, you look for exceptional founders and do you want them to eventually own the category? Do you look for, I guess, markets where there's room for more than one player? What's your overall thesis and process for doing things? I think it depends on the like the sub sector we're going after, right? We invest in FinTech. I'll start there. Obviously you have wealth management, wealth tech, you have what's going on in like data and information services, capital markets, trading. I don't think that I care so much about having to be the winner. I think there's multiple winners in all these different categories. I think the question is do they have a big enough vision? And like, how are they thinking about this from the perspective like, what's the next 10 years look like? Like, I need to think about how does this investment return my whole fund? I need a hundred X on my money. Do I believe that they have that type of like tenacity and vision to get there? Most of them won't, but the, what I owe my LPs is making sure that the person I'm betting on, I believe can like return all of their money. Obviously there's going to be a lot of portfolio companies, but if one returns everything, makes it a lot easier for us to have, you know, multiples of returns on the fund. Are there any non-consensus indicators or traits, indicators in companies? traits and founders that you think drive a lot of success. I think they have early confidence, but they're great at taking feedback and listening and aren't wedded to their ways. Right? Like they're looking for, you know, to constantly challenge themselves. Right? Maybe they thought this was the idea, but quickly learned it wasn't. Or the thesis is right, but the go to market and wedge was wrong. And so I think the best founders get early signal that what their building is right, but are constantly thinking about like, how do they reinvent themselves? And how do you determine an actual signal for what your building is right, from just a bit of excitement, some hype? I think it comes back to like your clients, right? Like early on, like the only thing that really matters is like are people paying you for what you're building? And is there more opportunity out there as the market big enough? And like a lot of times maybe the market doesn't exist yet or like the market is evolving, but the CEO has a clear vision of where that's going. Or why they think that's happening. I'll give you an example. Like I think prediction markets are, you know, another asset class like options in futures. I think that KPIs in the prediction markets has the potential to be the biggest category outside of sports. I think that because if I think about all the work I did when I worked at hedge funds, we are constantly forecasting earnings. It's the only thing that drove earnings was understanding KPIs. So I'm an investor and I can now like make a prediction or have an outcome based off those KPIs. And like there's a liquid market with billions of dollars of liquidity. Why wouldn't I want to take a bet on how many Uber deliveries there were this quarter? If I know I've been accurate 95% of the time on that directionally and the actual number. Like why am I carrying what Uber's earnings are going to be? They could give shitty guidance. Trump could go to war again with another country. All these things that I can't control. But I know Uber is going to beat on the delivery numbers. And there's prediction market for that on Calche, right? Or on Polymarket or whatever other prediction exchange is out there in the future. I think that's a huge opportunity, right? That's like, Cal, I think prediction markets were non-consensus, obviously, as an investment many years ago. Everybody's focused on sports and gambling. But if I think about the KPIs markets, right? And structured KPIs and everything that drives an actual company, can you bet on that as a hedge fund? Can you hedge with that? Like maybe I'm a long Netflix and I'm an activist investor and there's no way I'm like reducing my position going in turnings. But we know they're going to miss on Netflix subscribers. And we also know they're going to specifically miss on like India subscribers. Why can't I hedge by going short like that missing that number and make a ton of money off of that prediction, which hedges my outcome going in turnings? That's why I think that's such a huge opportunity. Gambling's fun. Sports gambling, like amazing. Sure, we want to bet on politics and interest rates. A lot of ways to do that. But structured KPIs, like I think that's a game changer in the financial markets. It's certainly a pure bet. It's the most pure-- like that takes the data science team within a hedge fund from being a back office like side project to being like a profit generating team. Like I remember like going into an earnings like for DoorDash and that's like telling Dan what we thought was going to happen for earnings based off of all the DoorDash data we had on alternative data. And we crushed it. Perfection, right? And it was like a no-brainer short. But then they came out with like some big partnership with like KFC or something and like all this hoopla on earnings. And like the stock just like skyrocketed. And I remember looking at the data science team and we were all just like, well, we just like shot our one shot. And now we're probably never going to get another like investment out of the data science team. But if that was a prediction market, that would have like put us on the map in like the first like nine months of being there. Like data science team crushes it, got the DoorDash like KPI is perfect, right? So like fiscal AI and CalChi had that partnership they announced to do like structured KPI's and predictions. That like if I was at a hedge fund right now on a data science team, I'd be banging on the door of the PM being like, what are we doing to make sure there's enough liquidity for us to play these markets? What do you think is the catalyst that would take those markets to being liquid, to being actively traded by institutions, by hedge funds, even by smart retail participants? If the buy side demands it, the market makers and sell side will make it happen, right? Like all this stuff comes from the buy side, right? If there's like, if they're willing to put hundreds of millions of dollars into trades, Citadel, jump, name your favorite market maker, we'll figure out how to make the markets on the other side, right? And so like they'll all come. The question is, how long is it going to take? Like I think there's obviously there's liquidity there now, but not enough for like your favorite hedge fund to put on a $200 million position. I think for retail, we're getting there, but like I don't think it's talked about as much, right? And retail like you're just generating, like you're just like day trading like the hype of NVIDIA. I think Robin Hood could obviously bring some liquidity there, but that's not going to be the liquidity of like third point being in the news for being like, you know, short the Uber food deliveries. There's a lot of talk online about how call she, most of their volumes come from sports betting, from gambling. And a lot of people think that that makes their business a whole lot less legitimate. Your bullish prediction markets, your investor in fiscal, and obviously you just mentioned they have that partnership with call sheet. What takes these markets to being extremely mainstream? I know you mentioned the KPIs, but mainstream beyond, say call it beyond 90% of their volume coming from sports trading. I think we got to get more institutions in there. Like there's a use case for prediction markets for insurance companies, right? Like the whole hedging opportunity using prediction markets, I think is huge. Right? I do think like you think of like parlays and sports gambling. Like I think it's called combos and prediction markets. Like I think a lot of that stuff is like, you know, fun gambling, right? Especially when you can go across asset classes like sports with like, will Elon like merge Tesla into SpaceX one day, right? Like those things are fun to think about and like it's true gambling. But I think like if you get the liquidity where the biggest institutions in the world and pension funds can use it from a speculation and hedging, like you do futures markets and options markets, I think it's bigger than the options and futures market, which like in that case, it's like massive. And like I think you see like the big exchanges, right? Like ice has the partnership with polymarket. Like are they coming? Like you have like the CME, obviously thinking about like prediction markets. So like I think that's coming more and more. I put sports gambling in its own bucket. Like for prediction markets with sports, like they're going to fight in every state about like do they need a gambling license? Do they not? And like I think it's a good thing to allow more sports gambling across the country and not have it like restricted. Like why can't you do sports gambling in California? It makes no sense to me. But I think like outside of sports gambling, that's where like from an institution perspective, as we get market makers coming in, I think that's what makes this really big. And then the question is, do we need that many exchanges? Like outside of Calche and Polymarket, do we need like the next 25? Or do we have like, you know, niche exchanges that are out there where like you can go really deep on like predictions for the Grammys or like predictions on music through Spotify? And like those are markets and like retail can play those. And like there's money to be made there and like we'll have those. And just like in the financial markets, eventually everything will consolidate. than just like in the financial markets, eventually everything will consolidate. - How can people participate in, say, you're a young person today? How do you participate in this boom in, like how do you ride, say, the production market with? Obviously, you can trade it, but beyond working at a college in polymarket, maybe starting a startup that does maybe a terminal for production markets or a physical, this is a broader question about how to ride trends and bet on trends that you can see with your career as a young person. - I mean, I think it's getting jobs at these different places. I do think that as prediction markets expand, like we will need the picks and shovels and tools in and around it. Do we need a terminal for it? I'm not convinced on that yet. I also think like we're getting away from like the UI as like what people use and like with MCPs and AI, like everybody will have their own personal pain of glass, but like I spend more time connecting MCPs on cloth than I do like thinking about like, what am I gonna log into? So I think like that doesn't really get me excited, but like there's a world where like if you think about prediction markets, like there's a ton of tools that we need from compliance to trading and executing. to just like the data in and around prediction markets that I think like will be its own like opportunity from a Pics and shovels perspective. I mean, there's a ton of trends going on right like AI and data centers and robotics and like, you know, Pick what interests you and go deep there. It doesn't have to be like I want to make money off of prediction markets, which don't get me wrong. I think there's probably a ton of opportunity there if you just really focus on some niche markets. On MC on MCPs. How will MCPs democratize decision intelligence? I think MCPs just give us all access to more data. Like I think in the world we're moving towards everybody's going to consume a thousand times more data. Data and the cost of data is going to go down, but we're all going to spend more money. And I think the more access to information we have. The more intelligent we all become and the more you know, insights that we can get. And I think the question I have is like, are we going to be connecting hundreds and hundreds of MCPs? Like, you know, I think about it just from my seat on the Venture side. I can't imagine a hedge fund analyst and how many MCPs they're connecting or maybe they just connect carbon arc, right? It has access to hundreds of days. That's our just connect fax that which it's a ton of data sets. So maybe that's our way to like kind of consolidate, but I do think like my last few years at third point, I remember not wanting to buy data unless it was available in snowflake just made things easier. Sit here now. I don't want to sign up for a vendor anymore if they don't have an MCP. My process, everything can be automated, right? And a VCC like my fund admin is Carter. I use affinity for my CRM right? I use my notes on granola. All of those have an MCP. Right? I wake up in the morning, Cloud can analyze all of that together. I can walk to a meeting after this before I meet with like one of my investors and talk to Cloud with whisper and tell it to remind me like our fund statistics for fund for and how much did this investor invest across all of our funds? I get the answer instantly. It was never possible for getting that information out of like Carter before took me like a week. Now I have an MCP and it can connect to like seven different systems I use. The world's moving in that direction. Where does the value occur? I mean, I think there's a lot of different areas, right? Obviously the LLMs have got the stickiness workflows become stickier. I think data, unique data, but also accurate data. I think also the analytics layer, right? That analytics later and like the insight layers extremely important. And how do all these data sets talk to each other? And then like as we all have like hundreds, if not thousands of agents working for each other, how do we get them to speak to each other? And how do we manage someone that break? Final question. You. You worked at World Quant, worked at third point. Bloomberg as well. Now you're a venture capitalist. You've. seen edge and competitive advantage from many, many, many different perspectives. If you were to sum up the essence of where edge and competitive competitive advantage comes from. What is the number one thing? What is the single source access to information? I think access to information becomes part of having access to a great network. Firm on Matt now called social leverage leverage your social network. That's where the name came from, right? Some people have financial leverage. Some people have social leverage. I think if you can leverage your social network network that you've built, you get access to more information, the more information you have now, especially in an AI world, the more insights and intelligence you'll have. I love that. Thank you so much for coming on Nodton Open. This was lovely, man. Thanks for having me.