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And you can use the same algorithm to classify a galaxy as you can use to predict the stock market. So if you get this raw skills of data science talent, it can be very applicable. And that's why Numero has so many diverse users.

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Today's guest has a story that begins in Cape Town, South Africa.

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Growing up, he was interested in business and economics. And from an early age, he started to wonder and ask questions about capital. Why does it flow from where it flows? Why does it get held up?

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How can we make it more efficient? The hero of today's story, an interview eventually left Cape Town.

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He studied at Berkeley, Cornell, and worked as a quant at a hedge fund. Along the way, he saw an explosion of data and machine learning models. These were being used to drive returns or sometimes the lack thereof, at many hedge funds. He wondered, was there a way to flip the current model of hedge funds? After all, there are over 10000 of them in the U.S. alone. Most of them don't even beat the S&P 500. They all hoard and protect their data.

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They're generally zero sum worlds and tournaments, and a lot of the data that they hoard turns out to be the same data that every other fund is using. Our guests today saw all of this and decided to attack the problem at its root. Our guest today is Richard Crape, the founder and CEO of Numerati, the solution was to build an open hedge fund that creates a game for the world's leading data scientists by creating tools, data and a structure.

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Numerary now has over seven hundred and fifty data scientists that create and contribute models to the Central Numerati fund model. Each model and predictions are backed by stakes of cryptocurrency, indicating the confidence of the creator.

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This structure is a radical shift from the traditional hedge fund. We live in a world where many people make predictions. There have never been more people working in finance. What if a way to streamline our current financial system is just by turning it into a game that's easier and more fun to play? Specifically, what if we turn it into a game where in order to make predictions, you have to have a steak and skin in the game? If you're right, you get rewarded.

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But if you're wrong, the cryptocurrency you've staked gets burned up. We'll explore these questions and more in today's episode of Hidden in Plain Sight.

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This season of Hidden in Plain Sight is brought to you exclusively by our friends at Splunk. The Data to Everything platform Splunk helps organizations worldwide turn data into doing its time for data to be more than a record of what happened.

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It's time to make things happen.

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Learn more at Splunk Dotcom or by clicking the link in our show o'Nuts. Richard, welcome to the show. Good to be here. I'm excited to jump into this conversation with you. You are at the cusp of a new frontier in finance and capital allocation, whatever you want to call it. There's exciting things going on there. So I was hoping to start at the origins for you, which I believe is South Africa, correct? Yes. Yeah.

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So where were you born? Where'd you grow up? I was born in Cape Town while raised in Cape Town. I'm sure if I was born there, but I grew up in Cape Town and then came to the US in college. But throughout my life, kind of interested in the stock market and interested in entrepreneurship. My dad gave me stocks when I was like eight years old and I used to follow them in the newspaper. And then I started trading options and things when I was like 15.

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And then I started my first venture backed company when I was like 17 and then came to study in the US and spend a little bit more time in South Africa working as a quant and then move to San Francisco to start a new vehicle.

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And if we back it up to your first company, you're 17, you decide to partner with someone. What was that like for you and how did you get started in your first venture?

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Actually, my first company was was actually just me when I was 17. It was like it was very similar to WhatsApp, basically. But this was when I was when I was 17, I was like twenty five or twenty four. So it was before the iPhone before you could really even before it was easy to have apps on phones, but it was basically a messaging app with read receipts and all the things that WhatsApp did. And but but yeah.

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The way before the iPhone. So it's very hard to get compatibility compatibility issues and it was very hard to get people to to download it or even understand it. But it was a very good experience and it was kind of fun to watch how things played out in Mobile after being involved in it quite early on. But after that's pretty much focused on finance and studying mathematics and machine learning.

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Sure.

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So you're studying economics, you're studying mathematics. You're going to Berkeley, you go to Cornell. Did you take away anything from either of those two institutions or your time there that you feel like was really critical? I'm just curious, what was your formalized education like?

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Yeah, I do like I did like college. I found it amazing to come to to America to study in my first in the semester I have spent at Berkeley, I was just as an exchange student on the that's when the financial crisis happened two thousand eight. And I remember one of the professors just decided to put on like a discussion group to talk about it. And I realized, oh, this guy is like this is a Nobel Prize winner. It was like George Akerlof who who wrote some seminal papers, even stuff that I use today and think about today on markets symmetry and a principal agent problem and things like that.

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And it was just really cool to be able to, you know, this add this sort of like ad hoc thing. And it's like, oh, I'm actually learning from a Nobel Prize winner. So I think I'm actually quite pro college. And a lot of ways I think it's sort of like there's always a cost issue and maybe it's way too expensive. But certainly if you're if you're a parent or something and you think about what's a good thing for a kid to do, to be surrounded by world experts and in multiple fields in one location seems like a good way to spend some time.

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Couldn't agree more. That's a great reminder.

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When you were surrounded by some of these world experts, I'm curious how you went about learning. Right? Would you fill your days with a lot of conversations? Would you study more alone? What do you feel like was your learning flywheel because you've been interested in these pursuits, you've been dabbling in the markets and starting your own companies. You're starting to learn to formerly now from Nobel laureates and others. What was your learning process like or was it just following the threat of your interests as they loved you?

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That's a good question. I actually I think I had a view of myself as being a kind of introverted and but actually, when I look back on it, I, I actually benefited a lot from all the in-person stuff, especially now during covid and working remotely and things like that. You kind of realize what's missing. And I loved studying in groups. I would always like find some kind of study buddy who maybe was better than me or harder working and work together with them a lot.

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And I think those. Those things are very important because I think part of what's getting becoming smart is about is about communicating. So you might you might be able to understand things, but if you can't communicate, it's almost like how could you really understand it if you can't communicate it or something? So that all the interactions you could have with other students and professors in person, I think is critical. And I find what's happening now, you know, people starting their master's degree or something now where they can't even go to the university.

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I find that very peculiar and I don't think it's going to work. Same.

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Yeah, there's a number of trade offs with being remote. And when you're in person, there are so many nuances. And whether their facial expressions or a slight smile here and there or mixing a joke in with a lecture like has so much more power in person. I feel like we're really missing something as we start to gravitate towards this remote work. How are you as CEO of your own company? How are you dealing with this dynamic of the pull towards remote work?

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But the reality that collaboration requires us to transmit all of these nuances of communication and it's best done in person. How are you kind of juggling that pull in both directions?

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Yeah, well, it's been kind of kind of difficult. San Francisco has still made it to have these rules about not to go unless it's essential and there's questions about it's not essential. Like we could do it over over a video call. We are like an information company, but so they make it quite hard to go to the office and things like that. But I think I'm still trying to trying to meet up with people and people come to my house and work with me and sometimes meet at the office.

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Yeah, I think I think it's important to to keep that up. We've even done things where we've all the whole companies met up and in a park just to talk, talk about things and not do some quarterly planning because, yeah, I do think it's very important. It's also people the media does like freak people out. And it did seem to me I was actually away during the sort of. The Tom Hanks moment, Tom Hanks got covid and I was away at a conference and just for a few days, and when I got back, the whole mood of the company had changed.

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And it it felt like most of the people inside of the company were of the belief that over a million Americans were about to die and it would be maybe, maybe tens of thousands just in San Francisco alone. And that number is still something like fifty six or something. Fifty six people in the San Francisco City, certainly the virus has been have had a bigger impact on the world than I would have expected. It's still felt like. Yeah, you don't want to be operating in a place of fear.

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And once you need to be relaxed to be creative and you need to feel like the world has a future if you're planning for your company for the next three years. But everyone thinks there isn't even a one year future for the world. It's very difficult. It is.

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And I would encourage everyone to just remember that after the bubonic plague, there was a period of darkness. It was obviously a dark time, but we had the Renaissance and I'm very, very bullish on the future of the country and of the world economy. Now, we were talking a little bit before the call about this is one of the first shared experiences that everyone on Earth has gone through together in quite a long time. So I think that creates some opportunities here.

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What type of opportunities do you see as you're looking out at the the capital markets, the crypto markets? There's a lot of capital that's sitting around that's been on the sidelines. Right. I think on corporate balance sheets, it's somewhere around four trillion. I'm not really that familiar with crypto, but I would imagine the same. There's a lot that's waiting to be deployed. How do you see these capital markets and is there any sign of a renaissance on the horizon?

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Yeah, that's a good question.

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I mean, one of the interesting things, that is why I didn't think this would be like a financial crisis, is that the big bubble on the bubble? But the biggest growth was coming from tech. And this pandemic didn't hurt tech. It didn't stop people from watching Netflix and buying iPhones and any of any of the things that that the tech companies were doing or using us. All of these things went up a lot. And so that is one kind of aspect that I find interesting.

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That's a big factor. And so, yeah, they these companies that were already very strong actually got stronger. And it wasn't like the pandemic hurt the tech companies. And so they actually got stronger. So it actually hurt the things that were already in a kind of decline. And that's a kind of scary thing, because I think we we needed it to happen a little bit slower to notice what was happening to us culturally. And I don't I am concerned about the atomisation stuff where it's like we're just doing work and playing video games and living alone and not building families and not caring about the future or other people.

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And I think I don't want to, like, jump into 20, 30 too quickly. But yes, these tech companies have lots of money. The problem is they don't really know what to do with it or how to invest it. And so they're doing things like buying back their own shares or just straight up throwing all them all their money into index funds and things like that. So it's not clear if it's like if it's like real usually. Yeah, when they're when there is an excess supply of capital, it gets invested into somehow like new endeavors and real things.

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But it's it's not clear that that's like happening or what even is that future is block chain kind of like a scam and like is all the money that's going into it kind of like just the casino and what are we getting from it? Who's really using it? And then the other stuff like the self-driving car stuff or the A.I. stuff, it's just going to benefit incumbents. We don't need lots of new companies to to form around those things, like maybe the existing tech companies are just really good at it.

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Like Google is very good at Facebook's. Very good. So it's not like a super optimistic situation at the moment, but I do hope that it changes. And I'm glad that, like, venture capital hasn't been demolished by by this because it's going to be good when when people do have a vision of the future and they feel that there is a longer term future for everything. Right. That at least they'll be capital for them. Yeah, and I think that that cycle is the cycle we're in now is one where many people are becoming aware of the origins of these technology companies and eventually leads them to the venture industry, where, you know, such a small a couple hundred million dollars of venture money in the US is produced, whatever it is, like two trillion or three trillion of market cap.

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And, you know, once you kind of see that, you can't unsee it. So in a lot of conversations with family members from the Midwest or from folks that still live back east that I have. Venture capital, the technology industry, these are all creeping into everyday discussions, and I think this is just a fascinating thing where many people now are starting to get curious about how things are built and it might start in the realm of bits. However, eventually I feel like the more and more people we get involved in this game of creation and capital allocation, the better that the atoms will get.

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There's just so many people that have been on the sidelines where their natural playground is kind of one of atoms. You know, like you said, they like to build things with their hands. They like to work with people in groups in real life. And so hopefully, yeah, it was just more stakeholders involved talking about this. We can get back to the future, back to this renaissance. Richard, I'm really curious about your journey. Dumarey because you're a solo founder there.

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And, you know, being a solo founder is not easy. So I would love to hear about how did you go about starting this company and how did you start to see the stock market as a data science problem?

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Yeah, well, I was working as a quant and and just happened to be kind of thinking about all these all these things at the same time. So I had just taken machine learning class towards the end of my degree and started applying it to financial data and and at the same time was also playing in data science competitions like on Kaggle. And there's a competition that I did to do with detecting galaxies. And so I was working with a friend of mine from college on detecting whether an image is a galaxy or not.

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And at the same time working as a quant. And so those things together, plus also learning about a theory and then reading the theory in white paper, investing in a theory and investing in orgo. All these things started to feel very new. It was like these these guys are not even talking about making a company. It was a little bit like you had a whole generation of entrepreneurs who were like trying to be Mark Zuckerberg. And then suddenly you had like Bystolic Budarin who like almost like wanted to be sort of president of it, of a new country or something.

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And it felt like much more charismatic than someone making a startup company or something like that. So that was very compelling. And that felt like, yeah, Bluck chains were somehow this special way of doing a company, but it wasn't even a company. And they're not called block chains. They're called an industry. It's called a space. And it's a block chain companies. They're like block chain projects. And so even the language was different. So that was very interesting.

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And I happen to know a lot about machine learning and be learning about block chain. And all of these ideas came together in Numerati where users can download this obfuscated data on the stock market, build machine learning models, find patterns in that data, submit predictions back to us and earn cryptocurrency in the process. And so it was connecting all those ideas together that made it possible, I think, for the first time to have to have something like Demarai because you you never had an Internet hedge fund.

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In fact, the finance industry didn't change much at all. Finance industry saw that information technology is just like, oh, we need an I.T. department so that all of our traders can have laptops that are like secure. Like that was how they were thinking about technology, whereas what was happening in Silicon Valley at Google and things like that, they were thinking about a much bigger. So I don't think I don't think technology's really hit the finance industry yet.

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And it was exciting to see a way to do that through an MRI.

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And so as the world's open hedge fund, you have many different data scientists and technologists who are programming models and predictions and then contributing them to your central models. Could you just give an example maybe or tell us a little bit about how that works? Yeah.

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So anyone can download our data. So that's the point. I mean, one of the yeah. The reason why you can't collaborate, why hedge funds on collaborative. Why is that they can't share the data. Right. As soon as they share the data. You can't go to RenaissanceRe website and download their data. It's the last thing they're thinking about doing is giving you the data. And because they would lose their edge, so numerary figure out a way to do that where we give you the data, it's got all the mathematical structure so you could find the same relationships.

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And but you have no idea what the data is, so you're basically looking at this huge grid of numbers between zero and one and and you don't know what the columns mean or what the rules mean, but you can still. Model it, and that shouldn't be too surprising because if you think about regression, you can have an x axis and y axis and you can have a scatterplot of points and you can fit a line that that the best the line of best fit on those points without knowing what the X and Y axis mean.

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And that's similar to what's happening on Debride. Our users are looking at three hundred and ten columns, so it's three hundred ten dimensions. And finding a map from that occur from kind of those points to the target, which is kind of kind of predicting the stock return. So yeah, it's not, it's, it's, it's for machine learning people. It's not for quants. It's not if you know, if you know something about finance and if you read a lot about Warren Buffett or whatever is not going to help you on Newborough, we're just that pure data science problem.

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And that means it's accessible to all sorts of people who don't know about finance but are very good at data science. And as I was describing earlier, data science is very flexible. You can use the same algorithm to classify a galaxy as you can use to predict the stock market. So if you get this raw skills of data science talent, it can be very applicable. And that's why Numerable has so many diverse users. We have professors, students, people from pretty much every country, and they're all modeling this data without knowing what it means.

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Very, very interesting. And a lot of our psychological biases and things like that I feel like are being pushed aside. Or this is a tournament where they're not necessarily welcome. And because of the nature of it, they I feel like they aren't as prone to creeping in as they do in general finance or they're not as prone to creeping in. Exactly.

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Yeah, I think we might maybe our top data scientist has some personal view that oil companies are going to be wrecked by covid, but his model might be saying the opposite. And he doesn't know that it's what it's saying because he doesn't know what the model is. But that's a good thing, because that's a that's just a silly view. That's not data data driven. Right. Might have a place in a kind of macro hedge fund, but we are quantitate fund.

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We're just looking for reliable small signals, trying to get a small like fifty two percent edge. Fifty three percent edge on the market.

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Right. And so Numberi is a market neutral hedge fund and you have your own cryptocurrency numerary. Could you tell us a little bit about that and why you decided to or how you came to the realization that you would need your own token?

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Yeah, we release numerary in twenty seventeen and basically it was really to solve the problem. I mean numerary in that it wasn't really working. We were getting all of these data scientists who joined up. They were. Kind of hoping to get lucky, they were making multiple accounts on Numerati and and trying multiple things and hoping that one of their models works, but that's not really what we want. We want our we want your best model, like we want you to do cross validation on the data and decide, this is my best model and this is the one I believe in.

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But how do you get someone to really say they believe in something? And you could say, well, at that time we had a board. So it's like you're trying to trying to climb up on the leaderboard. So maybe they'll be motivated just by that reputation of doing well on the leaderboard. But the thing we decided was critical for this application was you had to lose something, you had to be able to lose something if your model was bad.

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And so that's why we created numerary, because we made it a way for our users to stake their predictions so they would submit predictions on thousands of stocks and stick one hundred dollars down to say, if my predictions do well, you should increase my stake to one hundred and ten dollars. And if my predictions do badly, you're welcome to burn my stake so that I lose something. And that mechanism of having a negative economic incentive makes it possible to do something like right.

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Because otherwise there's no skin in the game and there's no way to defend yourself against kind of bad actors or just people making lots of accounts. And there's no way to know whether the person you're working with believes in it, too. So once you have the staking, things get much better. So that's why we created NUMERARY. We wanted to make sure that our users started with our top users, started with Newborough, so we gave it to them for free.

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It wasn't sold in an IPO. It was just given to the top users. And and that meant from the beginning, the initial distribution had the richest people, enumerator were the best people. And so they immediately staked and we ended up having a token that was one of the the most used things on Ethereum every week. Our users were staking it and have been doing so for many years now. And that simple thing of just trying to get economic buy in was all we were trying to do.

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And so it's a very simple application of chain, but it's really something you couldn't do without. It's right.

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And I feel like it's something that everyone should just pause for a moment to reflect on, because we live in this world where many people make predictions, but very few people have skin in the game around them or they're not really in the habit of going back, circling back and saying, like, hey, I was wrong here. Like, we should not have invaded this country or we should not have done this thing. We just don't have media pundits doing that.

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We don't have many people that are willing to stake something for what they predict publicly that can then influence many other people. So, you know, do you view this as just a new way of communicating and finance where you have skin in the game? Could you kind of just describe this more and tell us about how this has the potential to create maybe a more ethical conversation or just better capital allocation in general?

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I do, yeah. I think it's essential. And it's it's it's especially good for the Internet because there's no there's no negative incentives on the Internet, really. And actually, I think people have tried to make them and that. So I mean, you on Facebook or something, if you read something bad on your on your profile or your profile picture looked lame or something like that, you would kind of have social pressure being the negative incentive where it's like, oh, man, that was that was a bad thing to say.

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And you got sort of like flack for it or something like that. So you have that. And that's what the and that this is what the social media companies like did, especially by putting your real name there. Right. But it's not quite as strong as a financial incentive where it's like if you mess up, if you hurt our fund by giving us bad predictions, we can we can destroy your stake. And that's a much stronger negative incentive than just you went down on the leaderboard or anything like that.

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So I think that's sort of the web to thing is like, you know, you give ratings, you give ratings, you hit you have Facebook profiles and you that kind of thing. And then the Web three thing is like you can lose money, you can lose your stake. And that's that's going to be powerful. And I'm sure the whole Internet will. You have to embrace it eventually, right? And so I guess this generalized trend would be one towards more smaller shocks as opposed to, you know, a few larger ones.

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Is that what you see kind of like a stability through just localized shocks instead of one large market crash?

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Is that you know, or am I extrapolating too much here?

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Well, yeah, I think I mean, obviously, the purpose of of numerary is to have the. All of these predictions when they ensemble that they're better than than any individual, so there's no numeral user who's as good as all of them put together. It is making the whole system much more robust because you can even have users drop out or users burn their stakes or and the whole system can can still still be strong. And we do have something like seven hundred and fifty data scientists who are staking every single week.

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And together those seven one hundred are staking about three million dollars. And all of that's kind of come up 10x in just in the last three months. So I think it's going to go way higher. But the fact that they have had this stake makes it very robust. And that's many more people than there are at at some large hedge funds. I mean, 700 data scientists. This is much bigger than like a renaissance or something, right?

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That's a lot. And then especially when we get into the realm of thousands or tens of thousands. Yes, it's fascinating. And you've you've already paid out, I believe, over twenty eight million to date scientists and counting. And, you know, if you check out the numerary price, you can kind of see where this is. This is at now and where this is going. So how do we get to a place where you have thousands of data scientists?

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Are you thinking about I mean, I'm sure that you are thinking about growing the data scientist community. Does it grow organically? What are some of the ways that people find you? Yeah, they're definitely it's a strange kind of company because we on the one hand, we kind of like it when our users turn in a certain way. For example, if you come to Nimura and you stake a lot and then your models do very badly, we don't want you to keep sticking.

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We don't want you to lose money. You might realize you're not good enough. It would be great if you got better and helps. But in some sense, like growth and churn have different meanings for our company than like a web to company where it's like, oh, we just need to get to one hundred million users and we can sell ads. Numerous is much more subtle and like it could be very successful even with seven hundred data scientists. Right.

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I think about growth a little bit differently. I mean, I definitely want to grow, but definitely not trying to get to like a million. I'm not sure they all that many in the world.

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And instead of growth, we could just think of that as becoming more effective or in a broad definition of technology, doing more with less. You just want to do more with less.

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Right, exactly. And I love it when our existing users stake way more. So, you know, they're staking one thousand dollars and then suddenly they increase their stake to fifty thousand dollars. We have one user who's taking half a million dollars. So he really believes in his models. He's clearly put a lot of work into them and he clearly believes in them. And he's very smart. And he's so it's encouraging to see that kind of growth, too.

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Right. And this brings us to an interesting point in the conversation, because you're building a company that is not necessarily focused on getting bigger and bigger and bigger, but you're focused on becoming the right size and doing more with less. Tell us about your philosophy as it relates to hiring and what do you think is the right size for the internal Numerati?

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Yeah, I think that's a very important question. I mean, the point of Numerati is that we can do things very differently to how the other hedge funds have done done it over the years. And if we end up being as big as two sigma or Renaissance in terms of number of people who work inside the company, then that's a kind of and that's a failure mode, because that's not the goal. The goal is to have a few people working inside the company, but have all these people contributing from the outside.

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And that will make us way more efficient than another hedge fund. We'd be able to have lower fees than other hedge funds and and so on. So we have to do it that way. And so my one of the designers at Newborough who makes films for us, Jonathan, he has this idea of like we should be the vanishing hedge fund, like we should grow to like 20 people and then go to 19 and then 18 and 17 all the way down to zero.

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And it sounds kind of the network becomes more efficient as your technology improves. Right, exactly. And why why should it be any other way? And I think a lot of people starting to realize that for a lot of these tech companies, like nearly everybody working there is kind of fake, like the Facebook does not need all the engineers. And Google does not need all the engineers either. They've just got these automatic ad they doing stuff, so why have they continue to grow?

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And I think those companies probably will in the coming years, like be smaller maybe. Ideally, I think that's they would be. So I think the same is true for us. And we want to and people say, well, how could you go to zero employees or whatever? And I think that's that is possible, as I do mean zero, because if you if you think about the sort of promise of something like a theorem or Bitcoin, the idea really is that no one is working there and kind of no one's in charge, but the incentives are aligned perfectly to make something grow from it.

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And I think that's really cool. And I like the idea of a new user having way more of the numerator token than than I do than than an investor does because they've quietly built up their stakes by by providing good predictions over the years. So I think that's a special kind of future. I do, too.

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And it's one where we start to view technology and algorithms as something that should serve us and create opportunities for others that can remain independent. They can remain, remain anonymous and still leverage the power of the community at Neumar Goomeri. I think that's a very exciting one, especially when you think about the fact that, you know, to have a successful hedge fund or company that's in finance now, you need many different edges, whether that's like satellite imagery or being physically closer to the markets that you trade on.

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How do you see these kind of old ways of getting advantages? Do you see these, like diminishing in importance? Do you see them increasing?

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Talk to us a little bit about that. I think that there's a lot of yeah, there's a lot of waste in the in the industry. And that's the one of the motivating things for me. There's something like ten thousand hedge funds in America and and like a thousand of them are large. I mean, I think that if you get to the top thousand, they have more than three hundred million dollars each or something like that. And then the very top is obviously loads.

[00:37:21]

So it's it's just like, do we really if you would just step back and let's say you kind of the president of the finance industry somehow and you could just do whatever you wanted to do, would you really do it this way? Would you really have it so fragmented and so much duplicated work? I mean, if all of these hedge funds buy the exact same data sets, trade the same stocks often hold the same stocks and they all go down at the same time and up at the same time, they generally all struggle to beat the S&P.

[00:37:51]

Five hundred. Yeah, exactly. Badly, badly struggle. And so it's like it's weird that it even has gone on this long. But the reason it can is it's there's so much confusion. Someone said to me once, it's kind of cynical thing. It's like, Richard, if you want to start a successful hedge fund, make sure that in the beginning you start seven funds and one of them is going to work randomly and then you can just market the hell out of that one.

[00:38:18]

And I was like, that's so bad. But that is pretty much what everybody is doing. It sure like to two sigma has like hundreds of funds and there's always one that the marketing team can go up and say, look at this one, this is the fund.

[00:38:34]

So you've got to you've got to have some kind of consolidation. And the problem is the confusion can continue because because of the secrecy. So there's this thing where it's like, well, we can't we can't tell you everything we do. You go and tell our customers what we do. We can't tell them what data we so we just going to kind of hide the ball. The fact that that there's a credible reason to do that, because it's like, well, we don't want you to steal our secrets.

[00:39:00]

And the fact that they can do that and get away with that means it's sort of ripe for this kind of fraudulent behavior. And and that's why there's so much inefficiency. There's some companies where you can see what they do very easily and you can see if they're good. Like in a second you can go to Google and search Google and you're using the best search that they have right now. And you can just check, oh, wow, this is good.

[00:39:28]

The search is good, but hedge funds, you don't really know. Like you can't check if it's good. You can look at their returns, but they might have had these other funds that failed and they they might be getting returns from it. Kind of lucky ways. How do you know they're not just lucky the last two years. So all of that makes it very difficult for investors. And and I think the in. Investors are certainly wising up.

[00:39:53]

I think it's well known in the investor, the smart LPs of these funds, they definitely understand that the majority of them are not beating the S&P, which is a good starting point. But, yeah, I think I think it could be I think it could be a lot better if there were many way fewer hedge funds. I mean, I'd be very happy to just for that, just be one open hedge fund called Numerati that everybody can contribute to.

[00:40:21]

And it's efficient because we are we do the the work once we set up the trading and in execution infrastructure and then we open it up and let anybody contribute. And that's a better world than where we're in today by far. Right.

[00:40:37]

And I think that there are so many different opportunities where there are secrets in real life and based around geography and people's expertise. And so when it comes to the world of small and medium sized businesses, I think there might be way more opportunities for investment there than people traditionally think. Right. Like theirs. That's small business is the primary driver of the American economy. Most of our employment comes from them. Yet often these are viewed as like, oh, it's just you just can't invest in these vehicles.

[00:41:08]

And investors generally haven't tried this yet. We have the venture industry that's looking for generally, you know, its type projects that all look like the one before they can get very big.

[00:41:21]

But I think the idea of creating more transparency in the financial world is critical because there are opportunities for Alpha, but they're in the real world, right. They involve finding the people that have the local edge in the Nashville market and then investing there. I feel like there's just this big opportunity now for people to start doing things again in the real world. And we need to get to a place where technology solves the moves, the ones, ones and zeros around as efficiently as possible.

[00:41:55]

But we need to get back to building. There's been a couple of different posts recently, like Marc Andreessen has the time to build post and others. Where do you fall in this camp of, you know, how do we spur or start investment into. Adams based companies, again, how do we get to this place? Yeah, well, I do think finance is a big part of it. I mean, I think that what you described is exactly the problem.

[00:42:21]

Like, you want to make a multibillion dollar company or whatever, like you can get a seed round of a half million dollars within a few days in Silicon Valley. If you have some sensible company that you don't want to make the claim that you're going to be a billion dollar company and you've actually got like loyal customers and you're building something in real life. Suddenly there's no capital at all available to you. And I think the one of the reasons for this is the IPO problem.

[00:42:51]

So it's sort of there are fewer companies than there were that are public, I don't think. Yeah. Like you would almost think we'd always have the maximum amount of public companies. But now there are more ETFs than companies. It's like this is terrible. And it's so it's such a sign of the times where this over over financialization world and over regulated, because if you're a normal businessman and you sort of have maybe 20th century business ideals where you're just trying to make a product that people like and you look at IPO in your company, you need a first pay them kind of mafia lawyers or dissident lawyers or certain banks who can help you.

[00:43:42]

And they cost millions of dollars a year. And then you're going to have huge regulatory problems. You're not allowed to use Twitter anymore because your company is public and it's just like weird. And I just don't I think that's the problem. I wish I wish young people could start companies that IPO quickly, like the dotcom era. I really don't think that was so bad. Not just have the tech companies do it or not just have the tech companies that are like 60 billion dollars that well, now we can afford to to IPO.

[00:44:18]

Whoa, this is bad. So, yeah, I think if you could have capital and that's also what's happened. I mean that's why I think young people are spending so much time, wasting so much time trading crypto because you're not allowed to IPO a company that's early stage. And so anything that is on the public markets is boring. By the time it gets there, you find crypto currencies that are promising crazy things and you want to put your money there instead.

[00:44:51]

That's bad like that. The sort of the Internet has made its own fake stock market, which is where the capital is very badly allocated. And the ideas are very weird and probably not good, but it's a place where all the money has gone. Although all these retail demand to invest in the future is sort of like finding its way into crypto currencies. And it would I promise it would find its way into the stock market if it's if they were companies you could you could invest in.

[00:45:20]

Yeah, I think that process, whether it's through direct listings or, you know, maybe the creation of more boutique M&A firms that can take companies public earlier we see this happening with some of the special purpose vehicles to take companies public, whether it's like Timothe as one, Billy Beane, formerly of the Oakland A's, or I think he's still with them. He just launched a special purpose vehicle as well to help take young sports teams public. And these type of innovations are critical because.

[00:45:51]

Yeah, because, you know, there's different firms now that will offer to take your company public for a fraction of the price that it used to take. And I think this is exciting new territory because the lessons of the dotcom crash are kind of they're well known and understood. And now there's this opportunity to kind of like go into it with open eyes. And so I hope a lot of people listening that might be on the fence about it. Explore this a bit more, Richard.

[00:46:24]

It's been awesome conversation. And as covid starts to die down, we'll have to do it in person. But when it comes to the future of Numerati and what makes you excited, whether it's at the company or outside the company, it's important that we have this bright future. So you're a very interesting person. You are planning to work and live for another hundred fifty years, paint a picture for us of the future that makes you excited about getting out of bed in the morning.

[00:46:56]

Good question. Yeah. I mean, Numerati is a very long, long term play. I think it's the best thing I can do to to advance the world. And the reason is for that is. The things we just discussed, I mean, how inefficient and how duplicated work and how confused the finance industry is when it's just a data science issue. So I think the key part is it's very good to allocate capital to companies. Right. And so hedge funds, when hedge funds do it, people find it kind of gross.

[00:47:33]

But when VCs do it, people find it kind of cool. And it's like it's the same thing. Like we still even though we're buying the shares on the market, we're still contributing to the liquidity and the capital of that company. I think you should see hedge funds as pretty much the same as VC. They just another investor. The hope is that any company or any idea, any problem that needs to be sold can instantly get capital from this new marai.

[00:48:04]

Octopus that automatically recognizes because it has access to all data and all machine learning talent automatically whenever capital is needed, it provides it. And whenever a company is mispriced, it fixes the price, which is the same thing. So that's the dream. It's like you just that this this capital thing, getting the money to the right place stops being a problem in the world. The sort of imagine the idea that Elon Musk talks about this time where he almost ran out of money for four Tesla, and that was within one day on Christmas Day.

[00:48:43]

I think that he he managed to raise capital and imagine that didn't happen. Imagine he didn't get the money and Tesla failed. I mean, that would be so, so sad. But think about all the stunts he had to pull getting on Zune calls with with multiple voices and and giving them pitch decks and blah, blah, blah, blah, blah. That all doesn't need to really be that the capital could find could find you if if if the capital allocation machine was super advanced, built by Newborough and people could just have their own internal companies, products, services, et cetera, kind of collect all the variables and then expose the ones that they want to the capital allocation algorithms and get capital.

[00:49:35]

If the numbers match up, if the opportunities there and with or without all of those search costs around capital, we get back to a world where people can invent and you can take many more risks if you have more time. So this is an exciting future. Richard, thank you so much for joining us today and everyone listening. We'll see you next time.

[00:49:55]

Thank you. Paul. I'm Sophia Bush, and you've been listening to Hidden in Plain Sight from Mission Dog. This podcast is sponsored by our friends at Splunk, the Data to Everything platform. In today's data driven world, every company, big or small newworld, is sitting on terabytes of unused, untapped and unknown data. Splunk helps turn all that data into action. Using cutting edge AI and machine learning, Splunk delivers Real-Time predictive insights that will help you on your mission to change the world.

[00:50:31]

With solutions for I.T. security, Internet of Things and business operations, Splunk empowers people to make faster, better decisions and take action to get things done. It's time for our data to be more than a record of what happened. It's time to make things happen.

[00:50:45]

Check it out. It's buncombe.