Episode Transcript
[00:00:00] Speaker A: We will reach a point where agents are extremely abundant and humans kind of only like to show up for like the, the bigger markets. Like all these kind of millions of tiny, boring markets. This is kind of like how I'm thinking about it, that are too small for humans to bother, but they're still valuable, right? And so we can have agents that monitor information feeds 24 7. You know, they update their beliefs, they update how, how they're thinking about things and then price these markets continuously.
[00:00:30] Speaker B: Welcome to the Institutional Edge, a weekly podcast in partnership with Pensions and Investments. I'm your host, Angelo Calvello. In each 30 minute episode, I interview asset owners, the investment professionals deploying capital, who share insights on carefully curated topics. Occasionally we feature brilliant minds from outside of our industry, driving the conversation forward. No fluff, no vendor pitches, no disguise marketing. Our goal is to challenge conventional thinking, elevate the conversation, and help you make smarter investment decisions, but always with a little edginess along the way.
Hi everyone. Welcome to another episode of the Institutional Edge. I'm your host, Angelo Calvello and today my guests are Will Owens and Zach Pokorny of Galaxy Digital. Will's a research analyst on the research team. Prior to joining Galaxy, Will worked at UTXO Management, a Bitcoin native hedge fund and venture firm where he contributed to research and infrastructure. Will holds a degree in computer science and Human and Organizational Development from Vanderbilt and let me just throw a quick plug in. He also studied philosophy, which is close to my heart. Zach is a data associate on the research team at Galaxy Digital.
Before that, Zach led on chain research efforts at CEX IO with a focus on user and transaction level data. And he also served as an acquisitions analyst at Steel Wave. He holds a BA in Finance from Elon University.
I asked Will and Zach to be on the pod because of a few research papers they wrote on prediction markets.
We'll be sure to put links to their research in the show. Notes. In a previous pod, I interviewed Professor Robin Hanson who gave us a wonderful tutorial on prediction markets. Today I've asked Will and Zach to pick up where Professor Hanson left off and they're going to share a primer on prediction markets, their growth, and specifically why institutional investors should pay attention to prediction markets. And also, you know, before we begin, I just want to remind our listeners that the regulatory environment in the US surrounding prediction markets is, I guess I could say, rapidly evolving and listeners should do their own due diligence on this matter. Will and Zach, welcome to the show.
[00:02:59] Speaker C: Yeah, thank you for having us.
[00:03:00] Speaker A: Yeah. Thank you.
[00:03:01] Speaker B: My pleasure. Hey, let's start off. When we did our prep call, we talked a little bit about where the markets are today and will, it'd be great if you could just kind of set the table for us. You know, where are we today with prediction markets? And I'm thinking specifically of signal markets versus sports.
[00:03:19] Speaker A: Vitalik. So like the founder of Ethereum, he actually just had like a pretty good post on this on Twitter. Kind of like the current state of prediction markets and he kind of broke it up into like three different traders, right? Like you have naive traders, info buyers and hedgers. Vast majority of kind of traders on prediction markets today are these like naive traders. So like I'm a, I'm a Buffalo Bills fan, right? So like this is me when I'm, I'm like buying Buffalo Bills will win the Super Bowl. Like I have no edge whatsoever. I'm purely gambling. You know, I might as well honestly use like a centralized sports book for this like FanDuel. But when you look at, you look at like Kalshi and polymarket, right? It's kind of crazy because Kalshi is like over 80% of their all time volume is sports. And it's like, you know, is this actually that useful for, for everybody to be trading sports? So like the longer term vision and like kind of what me and Zach have been writing about is prediction markets pricing the uncertainty of like very important future events. Right. And in order for that to happen there's, there's like a few things that need to happen. Like the main thing really is that these markets need to become more liquid for, for more players to participate in them because simply like the super illiquid markets, you know, smart people don't have much of an incentive to participate just because there's not as much money to be made. But yeah, and then I would say like the third kind of class of people that Vitalik was, was mentioning are these hedgers. And a lot of what we talk about with prediction markets and some of the most useful use cases are utilizing some of these prediction markets as hedging instruments.
[00:05:04] Speaker B: So you're making a distinction between it sounds like events versus sports. Right. And you also mentioned liquidity. How do you measure liquidity in these markets today? I mean, you know, for me I open interest, I traded options. You look at volume and open interest. How do you look at liquidity in these markets?
[00:05:21] Speaker A: Yeah, so like polymarket for example on the website you can literally just open up a market on polymarket and see the depth of the order book. Like in front of your eyes and kind of just see like, oh, if I'm purchasing what like US$5,000 of YES shares on this market, how much are the odds going to move? And a lot of the times you purchase a few thousand dollars of yes shares on a smaller market, like you're moving the odds, very significant amount just yourself. Like if you're Moving the odds 25% buying a few thousand dollars, it's like what are you doing? You know what I mean? Especially with like institutions and we want like we talk about larger capital allocators utilizing prediction markets, you know, that's kind of pointless for them at a high level.
[00:06:11] Speaker B: Why does Galaxy even focus on prediction markets?
[00:06:14] Speaker A: Well Zach can touch on this as well, but I would say like for us the information aggregation layer has been the most useful and that kind of leads us into impact markets. I know you kind of want to talk about that later, but we have a lot of different things that we could do with prediction markets. I think especially in crypto we have all these different markets for when will this token airdrop? On what date? Right. What will the FTV of this crypto token be?
One day after launch. Right. And these are quite useful markets, you know, because if you have exposure to a token pre market, you can buy like oh no, this token will not be over $1 billion FDV one day after launch. And that's like useful, you know, like it's, it's kind of risk management and everything there.
[00:07:06] Speaker C: Yeah. But I think like getting to the point that Will mentioned earlier on the hedging aspect of these markets, that's where I think the primary relevance for Galaxy like classes of investors kind of sits. I don't know the actual extent to which Galaxy kind of trades these things, but when we think about what people are using them for, it's probabilistic inputs into more complicated models. And I think as we've kind of progressed along those lines, we've seen the introduction of new prediction market like products like impact markets and decision markets. And kind of where the probabilistic prediction market type product fits in is you can just gather the probability of an event happening, pass it into your model, maybe do some correlation analysis against price. But then I think people realize that you have the prediction market component, maybe the options in the spot market component of it, as two distinct like but parallel things. Let's instead build a product that just collapses these all into one sort of coherent trade and product and that's kind of what has given birth to impact markets.
And yeah, that's kind of where we think this stuff is heading. Like, prediction markets have reached escape velocity. People like to use them for pure gambling, but also serious use cases in hedging. And now it's like, what is kind of the next logical step based on what people are using these for and how can we make it more efficient?
[00:08:23] Speaker A: Even just today, right. Like, it's February 18th, a lot of people's eyes are on geopolitical conflicts in the Middle East. Right.
Clearly, this macro stuff is going to affect asset prices, it's going to affect trades. And you can just open polymarket right now, see that the odds for us striking Iran have made massive leaps on polymarket. Right. And that's just like pretty useful to look at.
[00:08:49] Speaker B: The utility has to be related to the accuracy. If you guys looked at that, it may be at an aggregate level, not at a specific event level. And I just want to take a step back one second, fellas. You guys don't really care about sports and sports gambling. You're focusing on, I think you call them signal markets.
Is that the right term? Okay, just to get that there. But have you looked at the accuracy and how well these things do?
[00:09:17] Speaker C: Yeah, I think it was maybe Delphi Research. They're like a crypto research group. They kind of did an analysis looking at the Breyer scores of prediction markets, which is like accuracy versus volume type analysis. And I think what they found was that the higher the volume, the more accurate the result is. But in terms of like, what we've looked at on the desk ourselves, we haven't done any type of analysis, like, addressing the, like accuracy in that sense.
[00:09:42] Speaker B: We'll leave that for Professor Hanson and his group. They love doing that stuff with their analytics. But let's. You know, you've talked about, Zach, you mentioned that we're at escape velocity, we're at an inflection point. Why do you think that's the case? Is it just volume or. I mean, why?
[00:09:59] Speaker C: Again, I think it all gets back to this hedging component and being able to gather information that would otherwise be hidden in things like options markets or just completely unknown and done behind closed doors. I think prediction markets have significantly improved the transparency of a lot of different things. Like getting back to Will's example with the strike and all the activity in Iran previously, we would be left in the dark, left to whatever centralized news sources would be willing to give us. And most of the time, they take no opinion. Like, the opinion is, we just don't know. Like, here's a bunch of information. Leave it up to you. So we actually have one like an opinionated news source or source of signal and then it's all composable on blockchains that can tie into other financial instruments. So now it's just kind of moving downstream into different trades. Primarily done on blockchains, I think today. But people are definitely doing some off chain strategies as well.
[00:10:51] Speaker B: And both of you have hinted that what we have now is sort of the first iteration with a standard prediction market. It's kind of a binary issue and you got to put up your capital and you make a bet. But you've also written about, I think both of you have written about the next generation of prediction markets and how they're changing. So you guys want to lay that out? I don't know who wants to take what, because you've both written some good work on that. But tell me where we're going.
[00:11:18] Speaker C: Yeah, well, do you want.
I'm going to do it now.
[00:11:22] Speaker B: Wait a minute.
[00:11:23] Speaker C: Go ahead. Yeah. So like the, in the context we've written in like the next generation, we've kind of broken it out into two components like the creation of new markets entirely, which we see impact markets and decision markets being, being vital to that. And then also what people are doing with the assets that sit in these markets. Like, it's a little bit harder to acknowledge on Kalshi because it's all off chain. But through prediction markets like polymarket, you actually receive a token when you put in a trade. And that token is composable with the rest of all of the financial applications on these blockchains, like lending apps, Dexs. Even though polymarket itself is effectively a decentralized exchange. So people are finding novel use cases for the assets themselves. And then people are also creating new markets entirely. I could let Will kind of get into what people are using the assets for, but essentially on the new market generation side, it's moving away from just purely probabilistic bets. Like this event has an 80% chance of happening to what would we value this asset? Or what would we value this event at on a bunch of different assets, assuming it happens. So we're moving away from just trading probabilities on a binary scale, 0 to 1. Like what impact does an event have on, we'll say Bitcoin, the price of Bitcoin. So that's like really the two big distinctions that we're seeing when it comes to the future of prediction markets.
[00:12:42] Speaker B: Will, before you jump in, I want one of you to make or to explain the difference between an impact market and a decision market because you're not using them in a fungible way. So what's the difference? And it's a toss up question. Whoever wants it.
[00:12:55] Speaker C: The difference between an impact market and a decision market is the outcome of a decision market is bound to a action that has to happen within an organization. So we can say we want to dilute the equity of our company by 5%. If the market thinks that that's positive EV, the market will settle with dilute price being higher than don't dilute price. And then we'll automatically issue a bunch of new shares at the resolution of the market. Whereas the impact markets are mostly just telling you like this is what the price of the asset will be. There's no binding decision or action that has to be made on the tail end. It's more of a pure hedging instrument versus one that actually guides decision making within an organization.
[00:13:37] Speaker B: Is there any conditional aspect? Because in a traditional prediction market it's either we invade Iran or we don't. But you could also have kind of a combinatorial or I don't know word I'm looking for. But I hope you get my point because I'll never get myself out of this sentence.
[00:13:55] Speaker C: Yeah, no, that's like the key innovation with these markets is they are conditional. Your trade only settles assuming the action happens. So like if you say I'm willing to buy Bitcoin at $110,000, if the Fed cuts interest rates 75 points and they cut 50, like you just get the money that you put into the market back and you don't actually settle and claim the underlying Bitcoin. So there is like risk mitigation to the mechanism in that sense.
[00:14:23] Speaker B: Okay, well you want to jump in here and talk about what the assets are doing.
[00:14:28] Speaker A: Yeah, for sure. So like, I mean on the defi stuff really I have kind of mainly been writing about leverage and then borrow lend stuff. And I think it's all just about capital efficiency with leverage it's just really taking a larger position on an event than your posted collateral. So if you think about it, maybe one of the most useful use cases here is imagine you own a bunch of nuclear energy stocks. Party A is pro nuclear energy, party B is anti nuclear energy. So instead of just like selling your stocks because you're worried that party B is going to, is going to win the election, right. You can take a leverage position on one party winning. And so the prediction market pays out and kind of like offsets your, your stock loss. If this, if this occurs. And obviously there are a few risks to keep in mind as always with leverage. You know, I mean we, we always hear stories about people like trading perps and losing their money or certain, certain bad things happen in the market. But like for more sophisticated market participants, you know, leverage is very useful primarily for capital efficiency. And then on the borrow Len stuff like, you know, imagine like, you know, well, obviously you can't like know something isn't going to happen. But like you think, you know, right. Like no way this guy wins the election.
[00:15:47] Speaker C: Right.
[00:15:47] Speaker A: So you can borrow. Yes. Shares and then sell them. And it's really like, it's kind of like a cleaner, cleaner strategy than just only buying no shares. And you know, again it's capital efficiency. Like Zach, Zach has some good thoughts here on, on the defi stuff. But I mean for us since, since we're really like crypto native and doing, doing like blockchain stuff, this is the next big unlock that, that we're looking at. And there's a bunch of cool projects that we've spoken to that are, that are building here. So like it'll be super, super cool to see what happens there.
[00:16:21] Speaker B: And that's the borrow Len space.
Yeah.
[00:16:24] Speaker A: Really just all the defi stuff, to be honest. Yeah.
[00:16:27] Speaker B: Zach, did you want to add something since you mentioned your name in that last sentence?
[00:16:31] Speaker C: Yeah. I mean there's a bunch of people who are building this stuff and I think the industry has thrown a lot of money at figuring out how to bring this stuff into reality. But the binary nature of prediction market assets I think is posing a significant hurdle that we're just finally starting to work through. But it's hard to use an asset as collateral that is either valuable or worthless.
And I think the industry is kind of reconciling with like what's the best way to go about that? Because like Will mentioned, capital efficiency is important and the markets would price things much more effectively if you can have more capital efficiency. But just the nature of these assets resolving to one or zero has posed some challenges. And also just like the general illiquidity of the market, like you can't reasonably use like guest shares in a market that has like $10,000 of OI and $100,000 of volume.
It just wouldn't be quality collateral. And underwriting the risk of them is hard and you kind of add in the binary factor of them and the difficulties just compound. But yeah, there's a few people going about solving that issue.
[00:17:34] Speaker B: So in the borrow lend, does it work like short stock. I mean when you're shorting a stock, you have to have someone provide you the stock. How does it work?
I mean it's naive and I know I'm a tradfi guy because I've been in the game a long time and this is new. But tell me how it works.
[00:17:51] Speaker C: Yeah, I mean how borrow lend applications work on blockchains generally is it's just pooled and you show up with your collateral and borrow from money that's already been deposited on the application. So like you and Will can deposit some USDC into a pool. I can show up with my poly market shares, lock them in a contract, borrow the usdc, go trade, maybe my yes. Shares become less valuable and I get liquidated.
The application is all autonomous and there's no like leeway in terms of not being liquidated. Like when you cross the liquidation threshold, the application takes your assets, liquidates them into the asset you borrowed. So in my case usdc, it would deposit it back into the USDC pool, make you guys whole. I lose my collateral, but I still have like whatever USDC I borrowed and whatever I ended up doing with it. But that's generally how this works. It's all peer to peer, it's all conditional, like rules based, like if this happens then like this action occurs. And yeah, it's not like traditional stuff where you might have to do like some bilateral agreement, pay interest on a fixed term. All of our interest rates are, are variable for the most part. Everything is open term. You don't have to have the loan paid off by a certain time. And yeah, we all just kind of have like old agreements with each other that are dictated by whatever the application smart contract logic says.
[00:19:09] Speaker B: Well, I think you also, if it was your piece that I remember you also wrote about multi outcome unification.
What the heck is that?
[00:19:17] Speaker A: Yeah, I mean, so on the binary markets, right, each one kind of has its own order book and liquidity pool. So it leads to like this liquidity fragmentation issue where we kind of have like all of these different, you know, like oh, this happens, this happens, this happens. Each one trading separately and that's like a pretty big issue.
So I think as we start to see some, some solutions here where we have all possible outcomes unified into like one shared liquidity pool that will be really good too. Especially you know, like liquidity is really, I keep saying this, it's really the primary issue for, for adoption.
[00:20:01] Speaker B: So hitting a critical level of liquidity will bring in more and more players, more and more capital.
[00:20:08] Speaker A: Yeah, it also will just essentially make these markets more efficient. It's just kind of better to have all of the different outcomes trading into one kind of like shared pool.
[00:20:17] Speaker B: Okay, so when we, we did our prep call, you guys also talked about artificial intelligence as being something that could be used to improve liquidity, perhaps increase efficiency.
Somebody want to address that topic?
[00:20:32] Speaker A: Yeah, I can start on this.
[00:20:33] Speaker B: So, I mean, thank you.
[00:20:34] Speaker A: Probably you've seen kind of all, all the recent hype on AI agents. You know, like, everybody's running, they're open claw agent. Everybody has, everybody's like buying Mac Minis and like, you know, running agents to help them out. But really, like, we will reach a point where agents are extremely abundant and humans kind of only like to show up for like the, the bigger markets. Like all these kind of millions of tiny, boring markets. This is kind of like how I'm thinking about it, that are too small for humans to bother, but they're still valuable. Right? And so we can have agents that monitor information feeds 24 7. You know, they update their beliefs, they update how they're thinking about things, and then price these markets continuously. And it kind of makes like price discovery and liquidity cheap versus if we're relying on humans manually to update all these micro markets where it's like, you know, it's not going to happen. And then you, it kind of leads into like agents on crypto rails too, because they're going to be utilizing stablecoins to price these markets. But yeah, I mean, Zach probably also has some awesome thoughts on this as well.
[00:21:45] Speaker B: We'll find out in a minute, won't we, Zach? Go ahead.
[00:21:49] Speaker C: Yeah, there is like a case to be made, like there's like 27,000 active markets on polymarket or some crazy number. The last I checked. Like humans can't possibly trade all of these.
And like, from a user experience UI perspective, like discovering markets is, is difficult for humans. Whereas like an agent can literally just go to like the Poly Market API, ask it for all the markets, and then just go trade the ones where like the title matches like whatever they were trained on or whatever it might be. But where I think the AI stuff gets kind of interesting is like aggregating information from one place, like reflecting it in the market, but then also looking at like other assets and trading like options or impact markets or whatever it might be against the Poly Market odds. So you can have an agent that like one, like reflects its beliefs in Polymarket, but two also takes the signal from polymarket and disseminates that information across asset Prices in other markets. So I guess you could kind of look at it as like this bidirectional thing, like outside information hitting Poly market and then Poly market information hitting the real world. Like I'm more excited for the poly market information being reflected across the real world than the other way around because we're already kind of seeing that play out. But I think the slow bleed of people starting to rely on the probability outcomes that Poly Market and prediction markets produces is slow moving, but not nearly as much as the other direction.
[00:23:12] Speaker A: Yeah. And then the last thing I'll say here is kind of. I also wrote about agents as the interface. So imagine you have a thesis, okay. I think that Bitcoin will go below 50k in the next two weeks. You can have an agent that scans all the different markets or kind of venues to trade on that belief. Right? Like maybe a perp dex for shorting the perp for Bitcoin, maybe prediction markets, all these different things. And then kind of lets you know the best way to express that belief.
[00:23:44] Speaker C: Right.
[00:23:46] Speaker A: What's the most capital efficient way to capitalize on that? If that is correct. And it's really just saves a lot of time for people as well, because you don't have to manually go through everything.
[00:23:57] Speaker B: Is that the end of the sentence? I couldn't tell by your inflection, but
[00:24:01] Speaker A: I guess it is.
[00:24:05] Speaker B: Two thoughts. One is when you start talking about using agency AI, there are problems with that.
I mean, there's the whole issue of misalignment, for example.
I guess that's on the user to solve that problem. You're talking just in terms of the overall market and market structure. You could use agents to come in and make decisions and let the user worry about their own problems with any misalignment or model collapse or any security risks.
But you both have mentioned something that I want to go back to, and this might have been in one of the papers that you guys wrote. You talked about options on prediction markets. Am I right or am I making this up? Are there now options that you could trade in prediction markets? And if I'm making it up, you could just tell me.
[00:24:53] Speaker A: I did speak to one guy who was building this platform called Poly Options, which is essentially an options platform on top of polymarket, which was really cool. He's been in the space for a while as well, so definitely recommend checking that out. I believe it's polyaptions.com okay, that's enough
[00:25:10] Speaker B: of a plug there. We're going to see if we can get him to sponsor this Episode fellas. Okay, but it's good to know that we're going into derivatives, we're going into leverage now and we're going into this borrowed lens structure. I mean it's taking on some of the attributes that an institutional investor would be familiar with, certainly with a twist, but you know, it's kind of evolving in that way.
I told you fellows I wanted you to focus. Now after you've done all this research, why should institutional investors, asset owners man, asset managers, investment consultants, this is traditional we're talking about. Some of them dabble in terms of defi. But you know how conservative this group could be. Why should they pay attention to prediction markets?
[00:25:56] Speaker C: From the hedging component is where it's most relevant to tradfi.
I don't think that's a contrarian take like, I think that's like the, the approach a lot of people take to it in terms of like institutions and stuff. But I think the design space for how you can use prediction markets to hedge is going to expand dramatically over the next like year to two years where you have these like multi step sort of hedging processes off of prediction markets. You're going to see prediction market and prediction market like products start to collapse. These things make them cheaper, allow you to express your, your vision and your opinion much cleaner than what you can do today. And impact markets are just kind of the first iteration of that or like the first instantiation of that. And they're not even totally live yet. Just to kind of give you a perspective of where we are in the life cycle of this. But yeah, I think it's just going to be a much cleaner hedging tool. Possibly cheaper, but definitely more expressive and more flexible.
[00:26:53] Speaker B: Well, before you answer, and I'm not sure you will answer, but I'm anticipating something, it really goes back to this information aggregation and then you have the ability to make a hedging decision.
Well, do you want to add anything? Because I got to follow on but I don't want to style.
[00:27:09] Speaker A: I mean really, I was going to have the same answer as Zach. Right. Like you kind of use the odds as an information aggregation base layer and then based on your risk tolerance, based on you know, what kind of trader you are, you know you can hedge from there. Like you, you see people talking about like these 15 minute, now even five minute up or down bitcoin markets. Like unless you're like a high frequency trading firm or, or somebody who's slicing up retail and gamblers on these markets, like that's not going to be useful if you're an institutional investor. Really what's going to be useful is these other markets, you know, and yeah.
[00:27:49] Speaker B: Are you suggesting that these institutional investors may have to wait for that evolution to occur before they can find the full benefits of these markets? That is looking at different types of markets and more liquidity, maybe from AI or are these markets that these large asset owners, asset managers should be looking at today?
That's the question.
[00:28:16] Speaker C: The evolutionary steps of this still have to play out. But I think like in terms of prediction markets today, I mean, obviously depending on your size, there's plenty for you to do and there's plenty of information for you to gather. And even if you have maybe more money than the markets can handle, you can still do something with the signal they create and back them into your own models and still use them for that purpose. But in terms of the more efficient hedging structures and products and all that stuff, we aren't quite there yet. Like there's a markets end to this, but then there's also an engineering effort that needs to go into this. And even from like the blockchain smart contract, like product design space, like we're still working through that stuff on our end as well. So it's kind of like a two pronged thing. You have the markets issue and the engineering and like we'll call it like computer science effort that kind of makes these markets possible still needing to bloom and be figured out.
[00:29:14] Speaker B: And the one thing we're not going to discuss is the regulatory environment.
As you know, asset owners, asset managers, consultants, they work within a very strict regulatory environment.
I'm going to just say. Any listeners, do your own due diligence. We're not here to talk about that. But that also I believe has to evolve so that there's more certainty in the framework. Will anything you want to add you don't have to. Man, I know you're kind of relaxed there drinking your coffee or whatever it was you were drinking.
[00:29:41] Speaker A: So yeah, got a nice espresso. No, I mean, honestly, I think Zach covered it well.
Definitely. Like, you know, there's stuff to be done. But yeah, it's going to become even more useful in the future is what I would say.
[00:29:54] Speaker B: My final question is what haven't I asked you that our audience should know about? And if I've been that good, you could tell me. Fellas, it's not going to, you know, it's not going to hurt my feelings. I mean, what else should I be asking you about this? Given how deep you are and what the audience should know.
[00:30:11] Speaker C: Yeah, I mean, I think the one key thing people should pay attention to is like where the bleeding edge of this stuff is happening.
It's not happening with Fortune 500 companies. This is happening with 80 to 200 million market cap startups that mostly live on blockchains. That is where all the experimentation with a lot of this stuff is taking place.
So to understand how it's evolving, how far we are in the life cycle of it and kind of where it goes next, I think the best place to look is in these more experimental autonomous organizations that live on top of blockchains. We're seeing stuff all across our ecosystems, like in Ethereum, in Solana and then even on some of these alternative chains. We're seeing a ton of experimentation with this stuff. And it's not just like theoretical like this is a good idea, maybe we should try it. We have full fledged organizations that are managing a few hundred million dollars using prediction market like architecture to guide their decision making, to keep them decentralized and autonomous. And that's another place like agents will tie into this is prediction markets. And decision market type infrastructure actually enables fully autonomous companies where no human intervention needs to be made. They can write the code, they can trade the markets to make the decisions and you just don't need a human in the process at all. But yeah, that's one thing I would say is the experimentation with this stuff is happening on blockchains, not necessarily off chain or just in the purely financial world.
[00:31:38] Speaker B: I will just mention I wrote a paper a year ago about fully autonomous agents running like a pension fund. We actually did a podcast on that how you don't even need to have humans now. Of course there's still governance and accountability, but yeah, of course.
[00:31:53] Speaker C: Yeah, we actually had one of those. It was called Mountain Capital. It was a liquid token hedge fund that was the. All of the asset allocation decisions were made through like few tarchy decision markets. Like would my equity in the fund go up if we made this investment?
If people price the equity at a premium to spot, the answer would be yes and then we go ahead and do it. That would be like an example of a decision market. But now we're even seeing these organizations that are building consumer products like NEO banks, payments companies who are using decision markets and futarky to guide the direction of the company. And that's all live in production like very real hundreds of millions of dollars being managed. We also cover some of those organizations in our research as well. But yeah, it's Very real. And it's happening. It's just taking some time for us to figure out. And then you also have the bear market component of things where interest in the space kind of weans off. People are a little less experimental, like, less willing to take risks. So that also kind of slows down the evolutionary process, but nonetheless, like, it's still chugging along.
[00:32:55] Speaker B: That term futarky, that's from Professor Hanson. If I remember going back to 1988, he was writing about this stuff. Do you guys know him, by the way? Have you ever met him?
[00:33:05] Speaker C: Never met him.
[00:33:06] Speaker A: I've never met him.
[00:33:07] Speaker B: He's an interesting guy. I gotta say, he played along nicely. I mean, it was a fun interview. Well, I think we'll wrap it up there. I'll say thank you, guys. This was a true tutorial about where we are, where we're going.
And I like the way you framed it for me in terms of inflection point and escape velocity and then working through what we're seeing now with the different iterations and the use cases. So thank you very much, guys.
[00:33:33] Speaker C: Yeah, thanks for having us.
[00:33:34] Speaker A: Thank you.
[00:33:36] Speaker B: Thanks for listening. Be sure to visit P and I's website for outstanding content and to hear previous episodes of the show. You can also find us on p and I's YouTube channel. Links are in the show notes. If you have any questions or comments on the episode, or have suggestions for future topics and guests, we'd love to hear from you. My contact information is also in the show notes.
And if you haven't already done so, we'd really appreciate an honest review on itunes. These reviews help us make sure we're delivering the content you need to be successful. To hear more insightful interviews with allocators, be sure to subscribe to the show on the podcast app of your choice. Finally, a special thanks to the Northrup family for providing us with music from the Super Trio. We'll see you next time. Namaste.
[00:34:28] Speaker A: The information presented in this podcast is for educational and informational purposes only.
[00:34:32] Speaker C: The host, guest and their affiliated organizations are not providing investment, legal, tax or financial advice. All opinions expressed by the host and guest are solely their own and should not be construed as investment recommendations or advice. Investment strategies discussed may not be suitable for all investors as individual circumstances vary.
