While many companies like to use buzzwords like crypto, blockchain, AI and encryption, Numerai actually combines all these technologies in a novel way. Founder Richard Craib discusses how his hedge fund started off crowdsourcing good financial models from data scientists to optimize its trading strategies in global equities. He then explains how it airdropped a token, the Numeraire, to those data scientists, giving them the option to stake some Numeraire for models they were especially confident in, and how staked models have higher returns. He also describes Numerai’s new Erasure protocol, which helps build a marketplace for predictions for sellers who could profit more from selling their predictions than trading on them, and for buyers who can now use the signal of staking to filter between good and bad data. We dive into the protocol’s “griefing” process, in which buyers can punish sellers, as well as the thorny issue of whether selling predictions based on proprietary information could constitute insider trading. Plus, we explore whether or not other hedge funds would really want to buy a token created by another hedge fund, and what Numerai’s plans are to become more decentralized.
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Onramp: http://www.thinkonramp.com
Episode links:
Numerai: https://numer.ai
Richard Craib: https://twitter.com/richardcraib
My Forbes article on the Numeraire: https://www.forbes.com/sites/laurashin/2017/02/21/this-is-the-worlds-first-cryptocurrency-issued-by-a-hedge-fund/#3cda45f360b6
Erasure: https://erasure.xxx
Announcement about Erasure: https://medium.com/numerai/numerai-reveals-erasure-unstoppable-peer-to-peer-data-feeds-4fbb8d92820a
Unchained episode with Olaf Carlson-Wee of Polychain Capital: http://unchainedpodcast.co/why-the-first-employee-of-coinbase-launched-a-hedge-fund
Unchained episode with Joey Krug of Augur: http://unchainedpodcast.co/joey-krug-on-how-augur-is-like-any-other-tool-ep79
Transcript:
Laura Shin:
Hi, everyone. Welcome to Unchained, your no-hype resource for all things crypto. I’m your host, Laura Shin. If you’ve been enjoying Unchained, pop into iTunes to give us a top rating or review. That helps other listeners find the show. Here’s a pause for the ads.
My guest today is Richard Craib, Founder of Numerai. Welcome, Richard.Richard Craib:
Good to be here.
Laura Shin:
Your trajectory in crypto is a bit atypical, and it goes in stages. I’d like to start at the very beginning but also move somewhat quickly, so we can get to your most recent announcement about your new data marketplace, Erasure, but to start, describe what you were doing before crypto and how you got into crypto.
Richard Craib:
Before crypto, I was studying pure mathematics, and I then went to work for an asset-management firm, and I was a quant, and my job was to basically try to apply machine-learning algorithms to the datasets that they had to try to make a new fund product for them, and it worked very well, but around this time, I was also reading about Ethereum and investing in Augur and Ethereum, and I was sort of thinking about blockchain, as well, and thinking that maybe machine learning plus blockchain could kind of allow you to make a new kind of hedge fund altogether.
Laura Shin:
Oh, interesting. So that, I think, leads us to Numerai. Why don’t you describe what Numerai is and especially and also how it worked at the time you launched it?
Richard Craib:
Yeah. Numerai is the first hedge fund to give away all of its data and allow anybody around the world to model it with machine learning and submit predictions back to us, which we then trade in our fund. It’s not a crypto fund. It’s a global equity fund, but the way that the alpha is created is unusual. So, usually it’s a really bad idea to give away your data because it’s like you’re giving away your edge, and so the trick with Numerai was that we gave away our data but in this totally obfuscated form. So, people could do machine learning on it, but they had no idea what it represented. So, if you download the Numerai data, it’s just 50 features. It’s feature 1, feature 2, feature 3.
You have no idea what feature 1 means or feature 2 means, and you’re just modeling a binary target, 1 or 0, so it’s this huge grid of numbers between 1 and 0, and it turns out if you…you can still do machine learning on that. You can still find patterns even though you don’t know what you’re modeling, and so this kind of aligns incentives, like the problem is, with sharing data, is that people could run off and start their own hedge funds. By doing it this way, you’re not only making it impossible to steal the data, but you’re making this incentive to, once you build a model, you should submit it, submit the predictions to Numerai.
Laura Shin:
Interesting, and just so I understand, essentially, they don’t really understand what they’re looking at.
Richard Craib:
Yeah.
Laura Shin:
It’s really more just this sort of abstract…it’s like when you…like on one of those quizzes where it’s just like the numbers, and you’re looking for a pattern in the numbers, and you want to guess what the next number in the pattern is. Is that kind of what they’re doing?
Richard Craib:
Exactly, and one way to think about it is if you have a scatterplot of points, you can easily find a line that’s the best…the line of best fit across those points. Even if you don’t know what the X and Y axis is, you can still find this line, and Numerai, it’s like you’re finding this line but in 50 dimensions, and so the patterns are all there, and the machine-learning algorithm can pick up on them even though you don’t know what the data is.
Laura Shin:
Interesting, and I know that there are other hedge funds that have turned to like crowdsourcing information. So, how does Numerai differ from some of those?
Richard Craib:
Well, the big difference is, is the way that we share data, and what’s important about doing it the way Numerai does it is we are not trying to get our users to build an institutional-grade long/short equity strategy from the ground up using raw data. I think that’s a task that’s too big to crowdsource. So, I don’t really think you can crowdsource a whole bunch of trading strategies, but you can crowdsource the kind of like machine-learning optimization of a particular strategy.
So, to give you an example, like if we were to let users do anything, they could, say, make a model that’s really good at Japanese tech stocks, but maybe we don’t even trade those stocks, and maybe we don’t like very short-term trading. By giving away the data in the way we do it, we’re deciding what the universe of stocks is, which stocks we want to include in the universe, which features we want to allow, and the time horizon that we want to hold for. So, in a way, we’re really shaping the strategy, and our users are just coming up with the best possible model for that strategy.
Laura Shin:
Oh, interesting, and but then do you shape it via the type of data that you’re giving them?
Richard Craib:
Yeah. So, yeah, we only give them certain data that’s sort of like features we want to use. So, it limits them to model only our dataset, which has its advantages in that they’re not going to produce anything we don’t like, and also it’s like a little bit more scientific, because if we just said send us any strategy, it’s not as good as saying here’s a dataset, you have no idea what it is, it’s completely blind, it’s like a blind test of whether you’re good, so you can make much better claims about whether the models are going to generalize if the people haven’t over-fit.
Laura Shin:
Okay, and what kind of data were you giving the data scientists and where were you obtaining this data?
Richard Craib:
Well, we don’t say what it is, and that’s why it’s obfuscated, but it is quant-style data, so it’s the kind of data that other hedge funds might use. It’s nothing too obscure. It’s not like image data or satellite images or anything strange like that. It’s really like very high-quality, multiple decades of equity data.
Laura Shin:
Oh, interesting. Okay, and just, for people who aren’t familiar with the hedge fund world, just define the word quant.
Richard Craib:
Quant, I mean, it’s sort of short for quantitative financial analyst, maybe, and it just means like someone who’s looking at finance really without a business mindset and more looking at data and statistics.
Laura Shin:
Very interesting. So then let’s talk about the way you were encrypting the data. How were you able to encrypt it in a way that they were able to make use of it?
Richard Craib:
So, one of the first things that really inspired me was reading about fully homomorphic encryption back in 2015, and since then you’ve seen a lot of people talking about it. It’s basically like a neat way of encrypting data where you can still do operations on the data, but you have no idea what it is, but it’s very, very slow. So, it’s basically impractical, and it’s not like two times too slow. It’s like a million times too slow, and it’s not really getting much faster, so the first idea, what worked for Numerai, was that we might use fully homomorphic encryption, but then we ended up releasing a dataset that was much more manageable for the average data scientist because you have this thing with Numerai where you might have data scientists that are very, very good at machine learning but have no idea about crypto, and we wanted to be accessible for people like that. So, the dataset we released uses different kinds of obfuscation techniques to basically make it very difficult to know what the data is.
Laura Shin:
And then, at that time, you know before you launched your token and everything, how were you paying the data scientists for their contributions?
Richard Craib:
The very first week of Numerai, I think we paid people with PayPal, and we only had three people at the company, and we were all pretty busy, and I was just like we should just use Bitcoin, and at that time, I think Bitcoin was on 400 dollars. It was 2015. It wasn’t like a particularly good year for Bitcoin, and many of our users maybe hadn’t even heard of it. Like I said, they’re data scientists. They’re not necessarily into crypto, but it just was much easier for us to use Bitcoin, and it had this weird effect.
Like, the minute we switched over to Bitcoin, suddenly we were a much cooler company, and suddenly it was like, oh, you know what, it’s much more interesting to win one dollar in Bitcoin than to win one dollar sent over PayPal and delayed by two weeks and blah, blah, blah, and so it also allowed our data scientists to be anonymous, which also was a very compelling feature, and Numerai kind of had this early momentum of being…it sort of like didn’t look like a hedge fund. It looked a lot more like a crypto cyberpunk kind of thing, and that ended up being kind of part of the early story of Numerai.
Laura Shin:
So, essentially, just walk through the whole process. You essentially were giving them this data. You were releasing it, what, like on a Monday, and then like by Friday they were submitting models, and then who would get paid?
Richard Craib:
Yeah. Yeah. We gave away the data, and we had these, at that time, it was monthly tournaments, and people would download the data, run all kinds of different machine-learning algorithms, and submit predictions back to us, and we paid them based on how well they did on the out-of-sample test data. So, it’s this pretty rigorous way of checking, checking how good they would be. What happened, though, is people ended up making lots of accounts, and so you’d make like a thousand accounts and just hope that one of them got lucky, and that’s, in a way, the broadest problem with crowdsourcing.
It’s sort of not immune to Sybil attacks, and you might have someone make a thousand accounts, try to get lucky, and not really be submitting the model they really believe in, and that’s when we started thinking about creating our own cryptocurrency, because if you can make people stake, even if they have a thousand accounts, they’re going to want to stake the one that’s the best, and it ends up being this awesome filter to make people stake cryptocurrency and enables you to create a crowdsourced hedge fund, where in the past you would always have these problems of people overfitting and not having models that generalize to real data.
Laura Shin:
So, talk about that. You launched the Numeraire in 2017, in June 2017. What was it and how did it work?
Richard Craib:
Yeah. We announced Numeraire on February 20. I think Ethereum then was on about five dollars. You wrote about us, actually, world’s first hedge fund to create a cryptocurrency, and we were really…we were trying to make an application. We were trying to make an Ethereum application that people actually used and that actually solved a very specific business problem, which was not knowing which users to trust, and then in June 2017 is when we launched, and we decided not to do an ICO. We gave away the tokens to our users.
We gave a million of them away, and within, yeah, within the very first day, people were using it to stake, and since then it’s been staked almost…I think it’s over 18 thousand times, different stakes, so it’s making it one of the most-used Ethereum application tokens by far. Any other utility token might have a lot of transfers, which has to do with trading of the token, but very few tokens have actual use where the functions that are not transfer are being called in those contracts. So, it’s pretty cool that it became this really well-used token even though it’s quite a niche user base.
Laura Shin:
And so describe that staking mechanism. They were essentially, instead of doing the Sybil attacks, now they actually wanted all their models to be identified with them? Is that what happened?
Richard Craib:
Well, by asking them to stake even a small amount, the staking, the deal is if you stake your predictions, then we can destroy your stake if your predictions turn out badly, or if they turn out well, we’ll return your stake and pay you more ETH, pay you more money. So, it had this effect of, you know, no one’s going to risk a stake on something they think might get burned unless they really believed in it, and so suddenly all the people who were staking had much higher Sharpe ratios on their back test, much higher returns than the people who weren’t staking. So, it’s just kind of amazing to just like use a new technology, and you just like flip a switch and suddenly the whole company made sense. It was like now you really can crowdsource predictions.
Laura Shin:
Oh, interesting. So, I want to learn more about that. Like, what were all the behavior changes that you saw? Like, did you see a reduction in the Sybil accounts and stuff?
Richard Craib:
Yeah. So, about half of the users ended up staking. I think it was something like, yeah, of the people who participated, the participation went down because you now had to stake, so the only way to make money was with staking, and then of the participants, the ones who did stake had better performance. So, it was really quite instantly exactly as we’d hoped for, and a lot of that is to do with the fact that we gave it to our users and we gave it to the users who’d earned the most Bitcoin in the previous kind of version of the website. So, we were giving it to the people who were most likely to use it and the most likely to perform well.
Laura Shin:
Oh, interesting. Yeah, and just to give us a picture one more time, so, initially you start with PayPal, then you switch to Bitcoin. How much were you paying out to people in US dollars, based on…?
Richard Craib:
We were paying very little. It was something like 10 thousand dollars a month.
Laura Shin:
And that would be split amongst how many users?
Richard Craib:
I think we were paying the top 20 or something like that.
Laura Shin:
Okay, and then by the time you launched Numeraire, how much were you paying people?
Richard Craib:
Well, Numeraire ended up being put on exchanges, so like Bittrex put us on the exchange and started trading at these very high prices, so for a short time, you know, we’d given out a million tokens as an air drop, and the early days of trading of NMR, it was trading at 160 dollars. So, it was like an air drop of 160 million dollars to a very small user base.
Laura Shin:
Oh my god.
Richard Craib:
So, that created some distortions that we couldn’t really have predicted, but…
Laura Shin:
Like, meaning they staked only very little because it was worth so much in dollars?
Richard Craib:
Well, just, yeah, we didn’t really know…didn’t know how much the token was going to be worth or whether it would be traded at all on an exchange, but it was, yeah, trading at these very high prices, which made some of our users like overnight millionaires. It made Numerai by far the most well-paying data-science tournament ever. At one point, we were paying like 10 million dollars per week.
Laura Shin:
Oh my god.
Richard Craib:
It’s just sort of like really, really crazy.
Laura Shin:
But that only lasted a short period, right?
Richard Craib:
Yeah, it sort of like cooled down a bit.
Laura Shin:
Well, quite a lot.
Richard Craib:
We adjusted the number of NMR we paid out. So, it’s sort of strange. Like, it did make me think, like, on the one hand it’s a really good idea to not do an ICO, but on the other hand, it sort of sets a price. So if we, you know, other projects had done…if you do an ICO at 1 dollar, then maybe the token would only be worth 3 dollars or something and people would have some kind of rational expectation of what the price should be, but with Numeraire, it was like we’re only giving away, there’s no possible way you can buy it, it’s only for our users, and then there were just so many people who were interested in the project that they also decided to buy it on the market and make a market for it.
Laura Shin:
And so essentially what happened was you did this air drop and there was no value. It was just use this in our tournament.
Richard Craib:
Yeah.
Laura Shin:
But then because Bittrex opened a market and all these non-data scientists that couldn’t access the token wanted it, that’s what drove the price up? Is that…?
Richard Craib:
I think so.
Laura Shin:
Oh, interesting.
Richard Craib:
It’s always hard to say.
Laura Shin:
And so it was at that point, when it went on Bittrex, that it got a dollar value?
Richard Craib:
Yes. Yeah.
Laura Shin:
Oh, so you didn’t set a dollar value at all?
Richard Craib:
No, because we never sold it.
Laura Shin:
Oh.
Richard Craib:
So, we didn’t know what, yeah, what it would be worth.
Laura Shin:
Oh wow, fascinating, and so after you introduced Numeraire, did that affect the rate at which data scientists were joining the site or no?
Richard Craib:
Yeah, we definitely got a lot of new people joining. A lot of crypto speculators joined our Slack. A lot of like scammers joined our Slack.
Laura Shin:
Right.
Richard Craib:
And we had to shut down our Slack.
Laura Shin:
Not surprising.
Richard Craib:
So, it was very, like, felt like, oh wow, we’ve been like overrun by a different group of people, but…
Laura Shin:
Well, I think the whole crypto community thinks that, but anyway.
Richard Craib:
Yeah. Yeah, but I think, yeah, there also…yeah, again, it was like we’re just making this very simple, simple application that we wanted people to use, and we didn’t have any…the sort of marketing of it wasn’t big, so a lot of people were like why aren’t you, you know, why don’t you have a Telegram group? We didn’t even have a Telegram group. Why don’t you do this? Why don’t you do all these different kinds of activities to get more people to buy the token, or something like that? And so that was kind of peculiar to have this new group of people that felt they had sort of tried to exert pressure on the overall project, and I think the choices we have made have had much more like durability and long-term focus, which is now paying off as the other crypto projects, you know, either get sued or get zero usage, and so I think we’re looking relatively good now.
Laura Shin:
And how has the performance of your hedge fund changed from before Numeraire to after Numeraire? Are you like getting better performance?
Richard Craib:
Well, we don’t talk about the performance of our hedge fund.
Laura Shin:
But can you just say performance is up after introducing the Numeraire or not even that?
Richard Craib:
We can’t say, but what we can talk about is the back-tested performance, and so on the three years of hold-out data, which the users can’t see the answers to, we can show that the accuracy and the returns are better on the back test, but we can’t…
Laura Shin:
And is that only with the staked models, because there are still people submitting, but they’re not staking?
Richard Craib:
Yeah. There are still people submitting, exactly.
Laura Shin:
Okay.
Richard Craib:
So, there’s about, yeah, about 1,400 submit, 700 stake, something like that, and the stakers are much better.
Laura Shin:
And are you getting more models submitted now with the Numeraire or no?
Richard Craib:
More since?
Laura Shin:
Like, just the sheer number of models that are being submitted.
Richard Craib:
Well, we’ve definitely taken away…out a lot of the bots because they were like…
Laura Shin:
Oh, right.
Richard Craib:
You know so it’s hard to know, but there are definitely a lot more genuine users who are much better than ever. So, it’s all been really good.
Laura Shin:
And what about assets under management? How much do you have now and how much did you have before?
Richard Craib:
We also don’t talk about that, but there’s a way to check whether we have more than 150 million, and if you were to check, you’d see that we haven’t filed for that, so we have less than 150 million.
Laura Shin:
Oh, okay. Okay. I should just think of more questions where you’re going to be like we can’t talk about that.
Richard Craib:
Yeah.
Laura Shin:
But instead, I’ll…
Richard Craib:
Yeah, those are all of them. ____ 00:19:56 and returns are the only things we don’t talk about, I think.
Laura Shin:
Okay, so I’ll ask you, I know that Howard Morgan of Renaissance Technologies was an early investor in Numerai, and you know they’re a big pioneer in quantitative and algorithmic trading, so how has that influenced, you know, the development of Numerai?
Richard Craib:
Well, Howard Morgan is an incredible investor to have. He’s not only extremely knowledgeable about quant data finance but also VC, broadly. He’s a seed investor in Uber, as well, and a partner at First round. So, he was a really amazing person to meet, and then he also has…he used to be a professor in computer science, so he kind of knows about crypto, has very, very high raw intelligence and so was very flexible in his thinking about, you know, like learning about Ethereum, so he was an amazing investor to have.
Laura Shin:
All right. So, we’re going to discuss their new decentralized data marketplace, Erasure, but first I’d like to take a quick break for our fabulous sponsors. Here’s a pause for the ads.
I’m speaking with Richard Craib of Numerai. You recently announced a new protocol called Erasure. Describe it.
Richard Craib:
Yeah, well, the thing about Numerai is that all of the modeling is happening on the dataset that we give out, and so the question is, you know, Numerai has really been able to solve the problem of crowdsourcing machine-learning models on a given dataset, but what if we could crowdsource data as well, and what if people could use their own data to generate predictions and submit those? And I have been thinking along those lines for a while, and you know there are really two pieces of having a good hedge fund, and number one is having good data, and number two is having good talent, and Numerai has, you know, a hundred times more data scientists who have signed up to it than any real-world hedge fund in the world, but maybe we have way less data than Renaissance have today and way less than Two Sigma.
So, could you do something creative on the crowdsourcing of data? And so I wrote this whitepaper, actually over a year ago, about Erasure, and it’s like, it’s acknowledging the fact the same problem is if you tried to crowdsource data, you’d get a bunch of really bad data. If we had a part of our website that said upload data and we’ll pay you, people would upload pictures of Pepe and Trump, and it wouldn’t be financial data at all, or even if they did upload financial data, it would be really badly formatted. So, you need to have some kind of stick, a carrot and a stick, so you get paid for submitting data, but you also, you know, you also could get your stake burned, and so the idea is could you have a decentralized data marketplace where anybody could upload data and stake it as a way to put skin in the game and to show that you really believe in the quality of that data?
And if you did it in a decentralized way, then anyone could buy it, so right now Numerai doesn’t do any crypto trading, but what if someone had predictions on crypto, they staked a whole bunch of predictions on Bitcoin and Ethereum, and then Polychain came to Erasure and bought those predictions to trade in their crypto fund? And there’s no reason why we should limit that, why it should just be a data marketplace for Numerai. So, we started thinking about it as being a whole new protocol that anybody could build on and anybody can submit data to and anybody can buy data from. So, I think it’ll be like…it’ll become like this place where all hedge funds buy some portion of their data and get connected with amazing data sources of all kinds. So, it’s definitely a whole new future for Numerai, because the Numeraire token is the token that Erasure is going to be…its native token will be Numeraire.
Laura Shin:
So, something I didn’t understand was in your Medium post, where you announced Erasure, you wrote you’re a hedge fund manager, you get an email that says over the last two years, I have predicted Google’s daily stock price with 70 percent accuracy, and I’m willing to sell you my next prediction for 10 thousand dollars. You calculate that this next prediction, if it’s right, can make your fund an expected 1 million dollars in one month. So, first of all, do people really send emails like that?
Richard Craib:
In a way, yeah. Sometimes I’ll get emails like that, where it’s like, but it’s a little bit more elaborate, where it’s like we have this special data, other hedge funds buy it, it works really well for them, you should buy it as well, it’s very high quality, if you integrate it with your systems, you’ll make more money, and we’re like, yeah, but how do we know it’s going to really work?
Laura Shin:
Okay.
Richard Craib:
And if it really works, why are you even selling it to us? And so there are all these questions. When someone is pitching you to buy data, you always have to be very doubtful.
Laura Shin:
Oh, right, like why wouldn’t they just use that data to make their own trade?
Richard Craib:
Yeah, or are you part of a Sybil attack, where they’re only selling you the best one that they have, and all the other ones that haven’t worked, they’re not selling you, so they have nothing at stake.
Laura Shin:
Okay, so that was the first thing I wanted to know, and then the second thing is, so, in this marketplace, is all the data labeled, because, so, Numerai, the way that it’s been running so far is like people don’t know what the data is, but in this case, people are saying this is what this data is?
Richard Craib:
Yes, exactly. So, what they’re doing, it’s typically going to be used for prediction feeds, so people are going to submit data that’ll, you know, it’ll be like a CSV file, and it’ll say Bitcoin is going to go up with a 55 percent probability, Ethereum’s going to go up with a .49 probability, and so everything will be formatted in that way, almost like as a prediction, but there’s nothing to stop you from making a data feed of anything else, as well, but I think the main use case will be financial predictions.
Laura Shin:
Okay, and so what are all the different types of things that you can imagine people using Erasure for?
Richard Craib:
Well, I think there are people who are very good at making models, but they’re not really in a position to trade them, so you might be like a student at MIT or something, and you’ve downloaded the blockchain data and you’ve connected that dataset with Twitter data, and now you have like a sentiment indicator that also uses blockchain data and actually can make really good predictions on Ethereum’s price, but the problem is you only have, say, 5 thousand dollars of savings, and so you could get on Coinbase and trade that 5 thousand dollars up to 5,500, but it’s sort of like not worth it.
So, the case for Erasure is that there are a number of people in this position, where they actually are very good at making models for things that are quite good but not good enough to want to start their own hedge fund, and instead of starting their own hedge fund, the much wiser thing for them to do would be to sell the data. The problem is if they come to us and say we want to sell you that data, or they go to Polychain, we want to sell you…no one will even respond to them, but if they say…instead of trading the 5 thousand dollars they have, they put the 5 thousand dollars, buy NMR, they stake it on their predictions, then suddenly I might buy those predictions because, look, the guy who’s created them is saying I don’t mind if you burn the stake if it doesn’t work out. So, you have much more, yeah, much more sense of like the fact that these predications are actually going to work if you have someone staking on them.
Laura Shin:
And so what format does the prediction come in? It’s literally saying the price will hit this number by this date, or what?
Richard Craib:
Yeah, it’s just this, like, a CSV file that says BTC ___ 00:28:29 .51 and maybe some metadata on the fact that that’s a weekly prediction or a monthly prediction.
Laura Shin:
Like by January 1?
Richard Craib:
Yeah.
Laura Shin:
Okay. Okay, and then to go back to, so, just you keep referencing this example of like crypto prices, but what are some other…you know is it really crypto funds that you think will use this?
Richard Craib:
Crypto funds will definitely be early users. We’ve talked to some that are very interested, but Numerai would be the other major user for equity data, and again, like there are a number of ways you might think of making a model to predict the price of Netflix or the price of any US equity based on any dataset, and if you create an Erasure feed out of your predictions, Numerai, in particular, will be one of the first funds to look at them and consider buying them.
Laura Shin:
And so it’s pretty much all financial markets data, whether it’s crypto markets or equities?
Richard Craib:
Yeah.
Laura Shin:
Okay.
Richard Craib:
It’s definitely the focus, and that’s what Numerai is good at and our users are good at doing, but I do think it will be more widely used than people think. I think there’s a lot of examples where you have this position that a seller of information is in. If you’re trying to sell information, the person’s going to be like what is the information before I buy it? And you’ll be like, well, I can’t tell you because you won’t buy it if I tell you, and so instead, if you stake it, then it’s like you’re giving some evidence that some economic…putting some economic risk to say that the information’s good. So, a good example of this could be with journalism or with like whistle blowing or something, be like I have this very valuable data, something to do with some kind of secret or something, and you encrypt the data, stick a big stake on it, and only the buyer with the key can look at what the data is.
Laura Shin:
Oh, interesting. Wait, in that scenario, so journalism is extremely competitive. Is there something where like it’s a one-time purchase, like I buy it and no one else can get it, or how does that part work?
Richard Craib:
Yeah. Most of the feeds on Erasure will be like exclusive.
Laura Shin:
Oh, okay.
Richard Craib:
So, only the buyer can look at it, but that’ll also be extended, so it could be other use cases as well. I mean you also might not want to submit a prediction. You might just want to create the price feed, like what’s the price of Ether, and you could just create a…you say, hey, I’m hosting a feed which is the price, and if the price is ever wrong, you’re welcome to slash my stake, and now you have maybe a more reliable price feed than you otherwise would’ve.
Laura Shin:
Oh, interesting.
Richard Craib:
Yeah.
Laura Shin:
Okay, so, you’re saying most of the data that’s sold will only be sold to one buyer, because when I read about it, I thought to myself, oh, if we have a lot of hedge funds that are making trades based on certain predictions, then won’t those trades affect the price, which would then keep the prediction from being as accurate as it would’ve otherwise?
Richard Craib:
Yeah. Well, you definitely, if you are buying a prediction and then you subsequently trade in the direction of that prediction, then you are going to move the price toward where that prediction is saying, but you’re profiting all the way along. So, it’s definitely good to have the edge where you’re the only one who can see it.
Laura Shin:
Right. Okay. Okay. Yeah, and so who decides whether to sell only to one, because why wouldn’t somebody who’s selling it just say I’m not going to limit it to one?
Richard Craib:
In the case of four hedge funds in particular, they’re going to prefer if it’s exclusive, so on Erasure, both…this isn’t discussed as much in the whitepaper, but both the buyer and the seller put up a stake, and there’s, yeah, the one thing that people don’t understand is, is kind of like how blockchains don’t have internet connections. So, you can’t say something like the stake will get destroyed if the predictions turn out badly. It’s like how do you know whether the predictions turn out badly? What data source are you going to check?
So, everything on Erasure is discretionary. So, if you create a stake and I buy your prediction feed, I get the right to slash your stake for whatever discretionary reason I want. So, if I’m unhappy for whatever reason, maybe I did make money on the prediction but I didn’t make enough, or maybe there was some other piece of what I expected that I didn’t get, and giving the buyer the right to slash the stake for discretionary reasons actually gets around all of the problems of, you know, having validator nodes and decentralized oracles and all these things that don’t really exist today and may never quite, quite work. So, having a discretionary relationship with the buyer is much better.
Laura Shin:
Well, yeah, so this is that concept griefing that you described.
Richard Craib:
Yes.
Laura Shin:
Like, I don’t understand. Why would somebody pay to destroy their own money, like, even if it means that they can do this, you know, sort of revenge move against somebody else? I don’t get it.
Richard Craib:
Yeah. Well, it’s not really rational to do in a setting where you’re not in a repeated game. So, if you’ve ever heard of the ultimatum game, it’s like this game where imagine I get a hundred dollars. I have to decide how much to share with you, and maybe I’ll say I’m going to give you 10 dollars, and if you reject it, then neither of us get any money. So, I’m like thinking I wonder how much I should give you. Okay, maybe I’ll give you 30 and I’ll keep 70 just to make sure that you don’t get angry, but really, rationally, if I give you 1 dollar, you should accept because you get 1 more than 0.
Laura Shin:
Right. Right.
Richard Craib:
But in practice, when you have these games repeated, people tend to grief people, burn both people’s money, until it gets to a level that sort of somehow seems fair, and it tends to kind of converge on like 70-30 or something like that, and so Erasure is a bit like that. You’re not just saying, oh, I’m upset, I’m going to grief, I’m going to grief the person by destroying some of my tokens to destroy a lot more of theirs. You’re actually…and then the game’s over. It’s actually I’m going to destroy a little bit now to make them notice that I’m watching them and I’m upset and that in the repeated game it’ll tend to, you know, keep everything in line. Another way to think about this is it’s similar to like a lawsuit or something. Typically, in a lawsuit, if you don’t kind of go to court, you end up settling in some way, or even you’re just doing it to kind of scare the other party to know that you really want their compliance to the contract.
So, it’s some kind of like stick in the relationship, so even to say with Bloomberg, like Numerai buys data from Bloomberg. For some of our hedge fund operations, we need Bloomberg. Now, the one threat we always have with Bloomberg is if we’re upset with the service, we can just stop paying for it. The other threat is maybe we’ll sue them if their service is really bad, and it might cost us a lot to sue them, up front. We might never settle for more than it cost us, but it’s the fact that that option is there. It’s not that that option gets used. The fact that that option is there keeps the alignment much nicer than it would ordinarily be if there were no legal system, and I see the griefing on Erasure as like the legal system. It’s like not many people are going to do it, but the fact that it’s there, it’s like a little bit of an incentive to do the right thing.
Laura Shin:
Okay, and so are these relationships between buyers and sellers, do they extend over time, because if you’re saying it only works in a repeated game, like, if I buy data from you for one month, but you know and I’m unhappy, and then I grief you, and then it’s over, like did you really learn a lesson?
Richard Craib:
Yeah. Well, definitely, it definitely, in the prediction setting, it’s definitely a repeated game. You’re not really going to be buying one-off things because for, say, a quantitative strategy, if you build a model on the price of Ether, you can keep running that model and keep generating a new prediction every single day. So, every single day you’re going to have this prediction, and if you engage with a buyer and they decide to start trading that signal in their fund, they’re going to want to keep getting that same signal because they’re going to build systems, too, that rely on that signal.
So, you’re always engaged in a kind of feed relationship, and that’s what’s also interesting about Erasure versus other decentralized data marketplaces, because if you do have any situation where…imagine I’m buying like a whole book from you. The minute you give me the book, I have it. I’m not looking for the next page, so I’ve got the completed book, or if you give me a photo, I have the photo. There’s something about feeds where all of the value is in the future. I don’t care about your predictions from last week. I care about what you’re predicting now. So, you always want to maintain the commercial relationship with the person you’re buying from.
Laura Shin:
Oh, okay. Okay. So, now I think I have a better understanding. So, basically when I enter into a relationship with a seller, it’s like over some period of time, and during that time, I can grief them multiple times within that time period? Is that…?
Richard Craib:
Yes. Yes.
Laura Shin:
Okay.
Richard Craib:
And you can choose any amount to grief them by.
Laura Shin:
Okay, but actually the griefing, I thought the amounts were set by the seller.
Richard Craib:
The griefing factor is.
Laura Shin:
Oh, okay.
Richard Craib:
So, it could be like 1 dollar could destroy 10 dollars of the seller.
Laura Shin:
Okay.
Richard Craib:
But you could choose, you know, I only want to grief…I’m going to grief 100 dollars to destroy 1,000 of yours or much less.
Laura Shin:
All right. So, let’s walk through the whole thing from end to end.
Richard Craib:
Okay.
Laura Shin:
Just so I fully understand. So, from the beginning, both the buyer and the seller need to own Numeraire in order to do anything? Is that correct?
Richard Craib:
Yes, because both the stakes are in that.
Laura Shin:
Okay, and then what? The seller posts something on the Erasure marketplace. What does that look like?
Richard Craib:
Yeah, so, I’ll explain the first kind of problem. The first piece that the protocol solves is the idea that you can’t trust someone’s track record. If I say, oh yeah, I’ve predicted the future perfectly over the last two years, there’s a sense which you just don’t trust me. So, the first thing is if you’re a seller of predictions, you submit all of them to the blockchain. So, every time you make a submission of a prediction, you submit it to the blockchain. So, you get a timestamp, and it’s a timestamp that’s completely reliable and that everybody will trust for the rest of time. So, at least when you start building a reputation of being good at predicting something, everybody agrees that you are because everybody can go back and see that, what your track record is, so that’s the first very important point about Erasure.
Laura Shin:
All right, and then keep walking through the steps.
Richard Craib:
Okay, and then imagine you see someone, and they have got this totally verifiable track record of being good at predicting something, and they’ve also put a stake on it, so you know that they care enough to at least do that, and they’re saying that they’re going to charge you 100 NMR per week to buy the feed. So, the buyer comes in and says, okay, I want to buy this feed. What the buyer gets as a special right is…everybody can see the historical prediction performance, but no one can see the most recent prediction unless you’re the buyer. So, the buyer comes in and says I like this guy’s performance, I like how much he’s got at stake, I like that he doesn’t mind if I grief him, this seems like a decent thing to buy, sends the first payment for the first set of predictions, gets a key, can decrypt the most recent prediction so that only he can see those, and then from then on is kind of engaged with the seller to continuously buy every prediction that’s subsequently submitted and with those predictions takes them to do whatever he wants with them, including trade them in his own hedge fund.
Laura Shin:
Okay. Okay, and then every single day or at any given moment, the purchaser can grief the seller?
Richard Craib:
Yes.
Laura Shin:
Okay, and then it’s based on the ratio that the seller sets?
Richard Craib:
Yes, and that’s important to me. People say, well, how do you model this or like how do you know for sure that the griefing factor is right or that it’s going to work, and the important point is that people have freedom to choose whatever griefing factor they want. You might create a feed and say I don’t want anybody to grief this, I just want to…I don’t want to do it that way, but then you might not get as much buyers of your feed or as much interest. So, it’ll be up to you to decide that, and I think market forces will get the griefing factors to be something that seems sensible to both the buyers and sellers.
Laura Shin:
Yeah, I imagine it will like converge on some sort of standardized…?
Richard Craib:
Yeah, exactly.
Laura Shin:
Yeah.
Richard Craib:
So, if you’ve got a really good feed, you probably stake a lot and allow a high griefing factor.
Laura Shin:
Right. Right. Oh, this is interesting, and then like let’s say that I enter in a relationship with the purchaser, I’m a seller, and I’ve set my griefing factor at, I don’t know, like 1 to 10 or whatever, but then I realize like…do you ever read like reviews and then you just sort of, like, I notice sometimes you see a really scathing 1-star review, and then you click through and realize that that person gives only 1-star reviews everywhere.
Richard Craib:
Yeah.
Laura Shin:
Like, so what if you’re a seller and then you suddenly realize, oh, this person that I’m in a relationship with in this marketplace is like one of those types of people?
Richard Craib:
Yeah.
Laura Shin:
Then can you change the relationship?
Richard Craib:
Yeah. I think you should be able to not engage with certain people, maybe, and I do think it’s going to be quite nice to have your reputation follow you around. So, it’s pretty clear that, oh, this person has griefed those other people, I better be careful with him, and then the same for sellers. If they have some prediction feeds on crypto that are very good, and now they’ve started a feed using the same Ethereum address for global equities, maybe you’re going to trust them a little bit more. So, there are a lot of these sort of softer factors that will come into play where people will start to develop a kind of trust, and it’ll certainly be known that this is Numerai who’s buying your feed from you, or this is Two Sigma who’s buying your feed from you.
So, I don’t think people will mind trusting that Two Sigma isn’t going to be just totally malicious and aggressively griefing for no reason, and you know right now people trust Numerai with a griefing factor of 0 to infinity because on Numerai, people are staking and we can destroy their whole stakes without destroying anything of our own, and they’re happy with that relationship. So, it’s kind of like a matter of preference, and over time people will get paid full price and the griefing factors will kind of stabilize, and yeah.
Laura Shin:
Okay, but also once I’m in the relationship, can I change the griefing factor, or is it set right at the beginning and can’t change?
Richard Craib:
All these are kind of like parameters that it’s a question of maybe on day one, we don’t allow that, but yeah, everything will be…I’m sure there will be many more settings. The way I see it is if you make a stake and you’re engaged with a buyer, they kind of have 30 days to grief you, and that will be the first like setting.
Laura Shin:
Okay. Yeah. You probably don’t know all the details yet. How does this differ from other prediction markets, like Augur?
Richard Craib:
Yeah. It’s kind of interesting, like, Erasure is a predictions marketplace, and Augur is like a prediction market, and it’s like how are they related? The thing about Augur is it’s similar to a market, so like if you had predictions on…if you had some insight into Apple and there was an Apple market on Augur, you would buy the Apple market and thereby have your private information kind of be expressed in the price of that market on Augur. So, both of them, in a way, get information into the market, but Erasure is a little bit more direct for people who maybe have some information, but they don’t really want to trade on it themselves, so they’d much prefer to sell the information, let a hedge fund who’s much more experienced at the other parts of trading to do with, say, risk neutralization or diversification and other things.
Yeah, like you can imagine having some very keen insight into, say, like Apple’s hardware, knowing Apple’s trends in hardware, and you kind of want to sell that information directly, and you don’t want to…if you just bought Apple on Augur, you’re kind of exposing yourself to all kinds of market risks and things going wrong with the software, things going wrong with the currency, things going wrong in China, and you’re kind of like exposing yourself to all those risks, but if you were just selling the information, and the information was good, then the hedge fund who bought that data would kind of be able to manage those risks themselves. That’s what they’re good at. So, I think, in many cases, you’re going to want to directly sell your information, not trade it.
Laura Shin:
And what are the sorts of prices you’re imagining this kind of data will sell for?
Richard Craib:
Well, what’s good about it is that these historical track records of the Erasure feeds will all be publicly verifiable, so I kind of like an idea of a future where it’s, like, say it’s 2022, okay, and there’s one feed on Erasure, which is predicting the S&P 500, and it’s just got this incredible track record, a Sharpe of 3, it makes 30 percent a year, and Two Sigma finds out about Erasure, comes to the website, and sees that there’s this feed that’s just incredibly good. Everyone at Two Sigma could hate Bitcoin. They could all be skeptical about ICOs. They could all hate the blockchain, but there is no way that they don’t believe that the track record of that, that prediction, is real, and that’s what I really like about it. It’s like they’ll have no choice but to start participating in the marketplace if, over a period of a few years, people actually submit really good predictions, so, yeah.
Laura Shin:
And is there a problem with people putting proprietary information on Erasure? Like, could somebody get in trouble for kind of like insider trading via the Erasure marketplace?
Richard Craib:
Yeah, that’s a similar…you can raise a similar concern with Augur or even any technology where, you know, probably the most used technology for insider trading is probably like text messaging or email or something. So, because Erasure will be decentralized, there’s none of the data that’s being put onto it will ever be on like Numerai servers. It’ll be on IPFS and Ethereum. So, it could be used for anything. I tend to think people will be using it much more for quant strategies, where you’re predicting on five thousand stocks versus, you know, you actually work for Apple and you’ve stolen some company secrets and you’re selling them on Erasure.
Laura Shin:
Well, you did mention that thing about knowing, what was it, something about Apple’s…?
Richard Craib:
Apple’s hardware or something?
Laura Shin:
Yeah.
Richard Craib:
Yeah, I mean, I think that is an example that could be legitimate, but I think, most likely, people are going to be using it for quant predictions just because if you…imagine you did submit predictions to Erasure and you were right about one stock in particular. It’s actually hard to know if you’re going to be good next time or the time after that, but with quant, what’s pretty cool is you can make 5,000 simultaneous predictions, and if you’re right on 2,700 of them, that’s actually pretty good statistical evidence.
Laura Shin:
Right.
Richard Craib:
So, in a shorter time period, you can develop this good reputation.
Laura Shin:
And what’s your sense of whether or not hedge funds will actually want to buy this data? You mentioned some crypto funds. Is that pretty much it or like, you know, you keep talking about Two Sigma, too. Do you know if the more traditional hedge funds are interested?
Richard Craib:
They always kind of have to respond to the new, new things. So, in the past, very skeptical on new kinds of alternative data sources like sentiment-analysis data or even skeptical about new tools like machine learning, but once other hedge funds start doing it, there’s like a huge pressure for everybody to catch up. So, to start with, Numerai is going to be a huge buyer of these feeds, and we’re going to be on the buy side of this marketplace, giving our users time to develop their track records, but at any time, another hedge fund could come in and outbid us, and over time, as it gets better, I think it’ll be like the best place to buy high-quality data because no one else is giving you the same kind of guarantees, first, that all the historical predictions are accurate, and second that if anything goes wrong, the data is not good, you have something to do about it, which is like destroy the guy’s stake.
Laura Shin:
And so you have not talked to anyone other than some crypto…?
Richard Craib:
No, we just announced Erasure very recently, and we’ve spoken to crypto funds who are interested, but we haven’t spoken to other hedge funds, and frankly we’d prefer there to be a lot of crypto interest and allow us to be the major buyer for the equity stuff, so, I think that’s how it will play out initially.
Laura Shin:
Because you don’t want the competition.
Richard Craib:
You know for now.
Laura Shin:
So, how willing do you think hedge funds will be to transact in a cryptocurrency that was launched by a different hedge fund?
Richard Craib:
Yeah, it’s kind of a strange proposition, in a way. It’s like you can use this marketplace, anybody can use it, but you sort of need to use our cryptocurrency.
Laura Shin:
Like how much Numeraire do you guys hold?
Richard Craib:
Well, we can mint Numeraire, so we, that’s something that is a little bit problematic. Like, right now, Numeraire, the way it’s used on our website, it’s very centralized. It’s like it’s a token for Numerai. It’s a token for our users and we can mint it, and we’re the ones who can destroy it, but as we shift to Erasure being a protocol that uses Numeraire, you don’t really want any central power over the system. So, Numerai will have no special right over any other hedge fund entering the marketplace.
Laura Shin:
Except you can mint Numeraire?
Richard Craib:
Yeah, even that, we’re going to have to stop, and so when Erasure launches, Numerai is not going to be able to have any special powers, including minting.
Laura Shin:
Oh, so then, oh, so who will be minting the Numeraire?
Richard Craib:
We will have a finite supply, like REP.
Laura Shin:
Oh, okay. Okay.
Richard Craib:
Yeah.
Laura Shin:
And so how will Numerai make money from Erasure, or will you not?
Richard Craib:
Well, we definitely will benefit from giving more use to our already well-used utility token and more things for our users to do, and then, again, it’s a data marketplace we wish existed. I wish all the data was timestamped on Ethereum so we didn’t have to trust that Bloomberg’s keeping the right time stamped on their data. So, I can definitely see it having really large benefits for us as a user, and then it would be pretty cool to have the whole hedge fund industry basically using our protocol.
Laura Shin:
Cool for you. So, I did see, I mean, you keep talking about the volume in Numeraire, and I saw in the Medium post announcing Erasure that you said that it was, I think, the most…it had more stakes in dollar volume and transactions in June than any other ERC20 token, but I was looking on DappRadar and right now it’s like ranked 97th, and over the last seven days, there’s only been like 25 hundred transactions with 11 users over the last 24 hours, so why has it dropped so much?
Richard Craib:
Yeah, tell me about DappRadar…basically, they don’t calculate the numbers right because the way that people interact with Numeraire is in a very unique way, and so because we gave it away to our users, all of the stakes they’re making are coming from our website, and there are very few people using like custom Ethereum addresses to make the stakes. So, one way of thinking about it, there’s very few stakes being made from MetaMask. There are many stakes being made from Numerai’s website itself.
Laura Shin:
Oh, so it’s not on chain.
Richard Craib:
So it is on chain, but it’s all coming from one address, which is Numerai’s address.
Laura Shin:
Oh, interesting.
Richard Craib:
Even though the stakes are coming from individual user Ethereum accounts. So, if they calculated it correctly, you’d be able to see this, but the DappRadar and StateOfDapps…I think StateOfDapps says we’re number two, that we’re the number two token for storage. I was like I don’t know what that means. I don’t know why we’re in the storage category. Maybe it’s a store of value or something, but I think, over time, when Erasure launches, more of the interactions with Numeraire will be with MetaMask, and these numbers will start to be much more clear, but right now you have to be a little bit more sophisticated and actually look at the blockchain yourself to see all the usage of NMR.
Laura Shin:
Oh, okay.
Richard Craib:
Yeah.
Laura Shin:
So, essentially, when I said there’s only 11 users over the last 24 hour, one of them is like multiple data scientists on Numerai.
Richard Craib:
Yeah. Yeah.
Laura Shin:
Oh.
Richard Craib:
They are like, yeah, there were, like yesterday, last week or so, there’ve been 750 stakes of…and some of those of quite large stakes of NMR.
Laura Shin:
Okay. Well, wait, but what I said was that DappRadar, over the last 7 days, says there’s been 25 hundred transactions, and you’re saying 750?
Richard Craib:
They’re saying 25 hundred in the last 20…?
Laura Shin:
In the last seven days.
Richard Craib:
Maybe they’re calculating incorrectly or differently, but I don’t know. Yeah.
Laura Shin:
Okay.
Richard Craib:
Every time I check them, it’s like it’s just completely off because, again, it’s coming from one address, most of the stakes.
Laura Shin:
Okay. Oh, interesting. Okay. Apparently, I guess, on DappRadar, it showed that in February you guys had 419 users, but then now it’s showing 10-to-15, so why was it such a big number?
Richard Craib:
I really don’t know. I have no idea.
Laura Shin:
Okay.
Richard Craib:
I think it’s, yeah, there have been a couple of users who’ve like, again, a lot of those users could also just be traders. Like, they could be people trading NMR on decentralized exchanges or something like that, where it’s coming from a special address, but that won’t even…that has nothing to do with staking, which is the actual use.
Laura Shin:
Right.
Richard Craib:
So, try to discount anything that’s to do with just the transfer function.
Laura Shin:
Right, so staking is not on chain.
Richard Craib:
It is on chain.
Laura Shin:
Oh, it is?
Richard Craib:
But it’s not represented on sites like DappRadar.
Laura Shin:
Oh, okay. Now I see.
Richard Craib:
And same with 0x, like you might see, oh, there’s people using 0x, but they’re not really using the token, and they’re not using it except to transfer it to each other, so there’s a question about, you know, whether it’s being used at all or…
Laura Shin:
So, the price of Numeraire has fallen over time, as we mentioned, but even after Erasure was announced, I noticed it just, I mean, it doubled but you know from a little over 3 dollars to about 6, and then now it’s back down to like below 4, so how do you expect Erasure will affect the price of Numeraire?
Richard Craib:
I’ve never understood cryptocurrency valuations. There’s so many strange distortions in the market. There’s certain projects that are doing very sketchy things, like wash trading and trading against yourself, so we don’t really pay much attention to the price, and I think that’s the right thing to do, and I think a lot of people…it’s strange how kind of well-known it is that there’s certain projects that are like completely overvalued and just sort of continue to stay overvalued, Bitconnect, this near-universal acceptance that it was a fraud, and it took a very long time, somehow, for it to reflect in the price, and then in the crypto community, near-universal acceptance that what Numerai is doing is really, really cool, and it’s a premium project with top backers and is really being used, and that has no impact on the price, so it’s always hard to know.
Laura Shin:
Yeah. Well, I agree, and I also don’t pay too much attention to price, but I just happened to notice, and then I wanted to ask about this previous prediction market, Intrade. It was, I guess, based in Ireland, but eventually US customers were banned from using it, and that led to its demise, so is this legal in the US, what you’re trying to do with Erasure?
Richard Craib:
Yeah, definitely selling data is legal, and you know in the same way that Numerai is buying from multiple data providers, and every hedge fund buys from multiple data providers, and there’s multiple centralized data marketplaces like Quandl, where you can buy all sorts of different data, so there’s a lot of precedent for that, and that’s not a regulated industry, but there are definitely other regulations to do with being an investment advisor, and so one of the ways I kind of see it developing is you might have people using Erasure in ways that are not legal, just like you can have people use email or HTP or anything for illegal means, but then I think all the big hedge funds or all the big data buyers will not use things that aren’t kind of like stamped in some way.
So, that’s sort of how I’ve been thinking about it lately, like you might have some kind of concept in the distant future of like a verified seller, where they’re really honest people and they’re a real business, and they maybe provide some proof or metadata that they’re actually Bloomberg and they should be trusted or something like that, but for the time being it’s, you know, it’s very early days.
Laura Shin:
And you previously said that your master plan is to decentralize at least parts of Numerai. What does that look like and how do you get there?
Richard Craib:
Well, definitely Erasure is a part of that plan. We want to decentralize in a very like pragmatic way and have everything that we create be extremely non-speculative, like it’s kind of cool that I can explain the whole protocol to you in a podcast, and there’s no math or things that kind of make it difficult or no speculative technology like a decentralized oracle that you have to rely on before it could even be created. So, with Erasure it’s really something none of the technology is speculative. Everything can be done today. We’ve already done a private beta, which had hundreds of users, so we’ve already got the smart contracts.
So, I think it’s going to almost like…yeah, it’ll almost certainly work. We know it’s already working for Numerai, but in terms of decentralization, this, again, by us first being, okay, let’s make a kind of centralized thing with Numerai and Numeraire, let’s get our users to really use it, let’s give it to them, and then let’s really build the proof that this can work, and then, okay, maybe now let’s throw away our key to be able to mint and let’s decentralize further. With the hedge fund itself, you know, all of our trades, our equities, so all of our trades are in a centralized hedge fund structure with, you know, real prime brokers like UBS and Goldman Sachs and you know, there’s no way that that part of the business can be decentralized right now, but everything to do with data and the modeling, and I think it’s going in that direction very nicely.
Laura Shin:
All right, well, this has been a great discussion. Where can people learn more about you, Numerai, Numeraire, and Erasure?
Richard Craib:
Go to our new website, Erasure.XXX, to read more about Erasure, and the Medium post is there and the film is there, and our website for Numerai is Numerai.AI.
Laura Shin:
Okay. Perfect. Well, thanks for coming on Unchained.
Richard Craib:
Yeah.
Laura Shin:
Thanks so much for joining us today. To learn more about Richard, Numerai, Numeraire, and Erasure, check out the show notes inside your podcast episode. New episodes of Unchained come out every Tuesday. If you haven’t already, rate, review, and subscribe on Apple Podcasts. If you like this episode, share it with your friends on Facebook, Twitter, or LinkedIn, and if you’re not yet subscribed to my other podcast, Unconfirmed, I highly recommend you check it out and subscribed now. Unchained is produced by me, Laura Shin, with help from Raelene Gullapalli, Fractal Recordings, Jennie Josephson, and Daniel Nuss. Thanks for listening.
