On Thursday, I joined John Humphreys of the Australian Taxpayers’ Alliance and Professor Chris Berg of RMIT University for an ATA livestream discussion on productivity (see Productivity ideas with Chris Berg). One of the most interesting parts of the conversation was on artificial intelligence (AI), which I’ve repurposed for my latest Economics Explored podcast episode.
Thankfully, the Australian Government has so far resisted pressure—particularly from unions and lobby groups—to introduce heavy AI regulation. Instead, it has adopted a wait-and-see approach, unlike Europe, where regulation is already slowing the rollout of new AI tools. Chris welcomed this hands-off stance, seeing it as giving Australia a chance to capture the benefits of AI adoption more quickly.
One of the more provocative points from Chris was his description of AI as providing “effectively infinite intelligence.” I challenged this idea, suggesting that while AI can synthesise vast amounts of existing knowledge, true intelligence involves solving new problems. Nonetheless, I share his view that AI represents an extraordinary advance.
We also discussed who wins and who loses in an AI-driven economy. Contrary to the usual automation story, Chris argued that it may be highly educated professionals who face more disruption than low-skilled workers, since AI excels at writing, analysis, and other cognitive tasks. At the same time, concerns remain about those unable to use the technology being left behind effectively.
Beyond work, AI raises broader social and ethical questions. We talked about AI companions, therapeutic uses (such as support for people on the autism spectrum), and risks of parasocial relationships or loneliness. The potential benefits are real, but so are the challenges.
Finally, one of the more imaginative suggestions was that low-skilled work in developed economies could in future be done by humanoid robots remotely operated from overseas. This would create a new twist on globalisation and migration policy—an idea worth thinking through further.
Overall, it was a fascinating conversation, with plenty of optimism from Chris about AI’s productivity potential, tempered by my own caution about the risks and unknowns.
If you’d like to watch the whole conversation with Chris, you can check it out on the ATA YouTube channel. In addition to discussing AI, we also discuss Australia’s Economic Reform Roundtable.
This article is cross-posted at queenslandeconomywatch.com. Please send any comments or questions to show host Gene Tunny at contact@economicsexplored.com.
This episode explores the economic implications of Trump’s re-election, France’s political deadlock under Macron, and the future of global capitalism. Jean-Baptiste Wautier, a private equity investor and World Economic Forum speaker, shares insights on trade wars and deficits. He argues that short-term profit motives undermine the global capitalist system. Jean-Baptiste also discusses AI’s transformative potential. Please note this episode was recorded on 11 December 2024, before French President Macron appointed François Bayrou as the new PM.Â
Trump’s Second Term Risks: His proposed tax cuts and tariffs could reignite inflation and exacerbate the US federal deficit, leading to global economic consequences.
France’s Political Instability: Macron’s government faces gridlock, which could potentially destabilize the Eurozone due to France’s growing budget deficit and political deadlock.
Global Trade War Unlikely: Despite harsh rhetoric, economic interdependence makes a full-scale global trade war improbable, in Jean-Baptiste’s view.
Capitalism’s Short-Term Focus: Jean-Baptiste argues the current capitalist model prioritizes short-term profits over long-term sustainability, causing inefficiencies and negative externalities like mental health crises and economic inequality.
The Role of AI: AI is transforming industries at an unprecedented speed, raising concerns about job displacement and the need for economic adjustments, possibly extending to UBI (Universal Basic Income), depending on the scale of the displacement.
Transcript: Is DeFi the Future of Finance? Exploring VirtuSwap’s Vision w/ Prof. Evgeny Lyandres – EP262
N.B. This is a lightly edited version of a transcript originally created using the AI application otter.ai. It may not be 100 percent accurate, but should be pretty close. If you’d like to quote from it, please check the quoted segment in the recording.
Jean-Baptiste Wautier 00:03
You look at all the negative externalities that our current system produced, they just gigantic. Think in terms of health, mental health, in particular, the younger generation. If you look at inequalities, not inequalities in the sense of, you know, morally, but inefficiency, the concentration of 10s of billions or hundreds of billions in the hands of a few individual means that they’re not going to be able to spend in a productive way this this amount of money. It’s yet another inefficiency when it comes to the economy. So there’s a lot of negative externalities that our system is producing and which is not making neither the best use of the resources we have, nor having the best impact on people’s well being.
Gene Tunny 00:56
Welcome to the economics explored podcast, a frank and fearless exploration of important economic issues. I’m your host, Gene Tunny. I’m a professional economist and former Australian Treasury official. The aim of this show is to help you better understand the big economic issues affecting all our lives. We do this by considering the theory evidence and by hearing a wide range of views. I’m delighted that you can join me for this episode. Please check out the show notes for relevant information. Now on to the show. Hello and welcome to the show. Today, I’m joined by Jean Baptiste wartier, private equity investor, visiting lecturer and speaker at the World Economic Forum. We cover the economic implications of Trump’s re election, the potential for a global trade war, the challenges facing France and the state of Global Capitalism. Finally, we touch on the rapid advancements and the risks of job displacement associated with AI. Special. Thanks to Lumo coffee for sponsoring this episode. This top quality organic coffee from the highlands of Peru is packed with healthy antioxidants. Economics explored. Listeners can enjoy a 10% discount. Details are in the show notes. Now let’s jump into the episode. I hope you enjoy it. John Baptiste, welcome to the program. Thank you. Thanks for having me. Gene Of course, it’s great to chat. It’s such a interesting or what’s the word, suppose it’s challenging, and I mean, maybe vexing time for the global economy. There are, there are really some big things that are that are happening that it’s unclear what, what the ultimate impacts will be. So I want to chat about some of those with you today. And I mean, in brief, the election of Donald Trump to the second term, which I think has has surprised many, and that’s going to have implications. Of course, what’s happening in France is at the end of the Fifth Republic. What does that mean? And then also your thoughts on global capitalism? Because I know that’s something you’ve commented on. So to begin with, can I ask about the election of Trump to a second term. What are your thoughts on what that means for the global economy? Well,
Jean-Baptiste Wautier 03:06
thanks, Gene. I think it’s, as you said, it’s incredibly the objective I would use is consequential, because there’s going to be it’s not only a surprise, as you said, not not so much a surprise to some, because you could tell that the way the polls were measuring the real intention of votes for Trump was sort of not completely capturing what was going on and, and I think people were surprised by the popular vote in particular, but, but in terms of its consequences, first, you’re going to have major consequences on the US economy. And I think the first one that comes to mind is inflation, because all of the planned tax cuts and tariffs all have inflationary impacts. And as you know, and as probably most know, inflation is not completely tamed, and central banks are right now hesitating as to what they should do next. And there’s been a sort of a very surprising pose by the Fed and by other central banks, because, again, they observing underlying inflationary trends, and that’s before the Trump measures. So I think the first thing to watch is going to be certainly high. Inflation can be reignited, or will be reignited by those measures. And I would say the immediate second red flag in terms of the US economy is how they’re going to manage the deficit, the federal deficit. These numbers are now staggering. If you look not only at the debt service, but also at the total debt to GDP of the US and how it’s it completely skyrocketed over the last 20 years, we now at levels that we last time so right after World War Two, and we now have. A debt service, and we say that service, but it’s actually interest. So just the interest charge on the public debt, that’s already 20% of receipts, and could go up to 30% so we’re talking about roughly a trillion of interest that need to be paid every year, which even for the US, is a huge number. It’s bigger than the total spend on the US Army and total defense budget. So I think these are incredibly powerful forces that could be unleashed. And I don’t see an easy exit. Whether there’s, you know, some some new inflation trends in the next six to eight months, whether, suddenly, you know, you have all sorts of issues with the how deficits are being tamed. These are going to be major issues that US economy will face very soon. Yeah.
Gene Tunny 05:53
Yeah, absolutely. And what do you think about the potential for a global trade war? Is that a is that a real risk. I mean, we’ve had Trump threaten tariffs on Well, I mean, you know, tariffs against China, a big tariff against China, 60% or wherever, or 100% even 20% across the board, tariffs on Mexico and Canada, unless they control immigration. What do you see as the as the potential, all the risks there of a global trade war and consequently, global slump.
Jean-Baptiste Wautier 06:28
Yeah, I think this one worries me less, despite all the rhetoric that we’ve heard. And it’s not only Trump, it’s you hear that from China. You hear that from also the European Union, who’s talking about, you know, we need to protect our internal market more. We need to tax Chinese cars and all sorts of things. I mean, there’s, there’s, there’s a lot of rhetoric out there. Certainly, the reason why I’m less concerned is even though, you know, we should acknowledge that the world have a lecture at at transport Paris, which is called Global and multipolar world. And it’s indeed a multipolar world. So we have, for sure, exited this sort of Pax Americana and an economy that’s really dominated by the US economy, and where it’s all about globalization and free trade. I think now we have more regional powers. Now we still have a very global and interdependent economy. And despite all of the the efforts from the US, from Europe to try and relocate some of the supply chain, there’s still a lot of dependency. You know, if you look at the production of semiconductors, if you look at commodities, if you look at energy, there’s a dependency on very few places in the world. And I think it’s going to be very hard to really go aggressively with tariffs, even for the US and despite still the dominance that the US has. So I think it’s being used as a tool, as a threat, as a way of negotiating hard. And probably there will be, you know, a few things here and there which are going to be more symbolic than real, real tariffs that shut down the economy. I think it’s just not attainable these days for any economy, even the US,
Gene Tunny 08:21
yeah, yeah. Well, let’s hope sanity prevails. I like that point you made about just the connectedness of the global economy and the the importance of keeping trade open for critical, you know, for those crucial materials that are sourced from, you know, various particular parts of the world. And there’s a good book by Ed Conway recently on the material world, which I loved, which I think really illustrated that quite, quite well. Can I ask you mentioned a was it a lecture or a seminar in Paris, global and multi polar world? What was that? Again? What are the specifics, please?
Jean-Baptiste Wautier 08:58
Yeah, it’s, it’s, it’s a lecture I give to first year master students in Paris. And it’s really about trying to understand the new global economy, which, again, is a combination of exactly as you summarized. It’s global. Supply chains are global. This trade is at its peak compared to global trade at its peak compared to any time in history. But at the same time, there’s dependency on certain, certain parts of the world, on, you know, think of, I don’t know, batteries for electric cars, where all of those, those rare minerals, are only produced in one or two parts in the world, right? You know, in China, in Russia, like two or three countries, think, of course, oil, oil and gas. But also think manufacturing in general. You know, if you look at things like compounds that they use for many for drugs, those. Compounds. Half of the production is in India today, sort of the primary compounds that are being so this is what this seminar is about. It’s really about understanding how this interconnectedness, as you call it, is has become incredibly prevalent, and it’s very hard to revert, at least in short order. And that’s where sovereignty has become an issue for, you know, sort of regional economies like like the ones in Europe, but even for the US, again, you see this constant debate about the importance of Taiwan and the supply of semiconductors coming from, and how strategic this is, because there aren’t that many places that provide semiconductors, and at a time where it’s all about your ability to build data centers build artificial intelligence capabilities, you know, these are incredibly critical, not only to those to those industries, but also to your sovereignty. So it’s all about understanding this level of interdependency, and how, despite all the rhetoric in the world, there’s a limit to what you can do. I love that. There’s one. It’s a tiny example, but it’s so to me, it’s so telling. Which is the supply of natural gas from Russia that goes through Ukraine and then serves Europe is still functioning. So you have sanctions on Russia. You have a war between Russia and Ukraine. Ukraine has been invaded by Russia. And despite all of that, there’s still some gas produced in Russia going through Ukraine and, and, and, and being, being, being delivered to some European countries and, and it’s just because there’s no other way, you know, there’s this so that that tells you how this sometimes is a disconnect between the rhetoric and the actual dependency of the various economies.
Gene Tunny 12:00
Rod, hang on. So there’s a there’s a pipeline that goes through Ukraine, and so the Why don’t the Ukrainians sabotage it? Because the Germans are telling them, oh, you can’t sabotage that, because we need
Jean-Baptiste Wautier 12:16
so good question. It’s even worse than you think, because, because Russia is paying Ukraine for the pipeline, right? And, and they all interdependent. Russia needs to sell its gas. Ukraine needs the royalties from having the gas going through its pipeline and its country. And then the countries in Europe need, need, need the natural gas, and, and, and it’s, it’s a bit like, I don’t know it’s, it’s like Russian oil, you know, Russian oil, and ends up being recycled through a fleet, a ghost fleet, of tankers and ghost insurance companies, and that it gets acquired by in India or China, which To refine it and then sell it back to the European countries. It’s the same. It’s the same irony. There’s the sanctions, but then there’s reality of, we need, we need gas, yeah, and Europe doesn’t produce any, yeah,
Gene Tunny 13:14
it’s extraordinary. I mean, there are, there’s a story like that, I think, from the First World War, which is similar. And Ed Conway tells that in his book, I think there’s a story about how the British had to do a deal with Germany during the First World War, that it was in a bit of conflict with, you know, millions of men dead. And it did. It was, I think it was a range through Switzerland. It was a deal for for optical glass, but that they needed for binoculars. Because, yeah, the Germans were the leaders, you know, Zeiss and all of that in in optical glass. And I forget what the British maybe they provided them rubber, because the British had the plantations in Burma or so, yeah, just extraordinary. I have to look into that. That’s it is, yeah, incredible. So you’re, you’re teaching, you do some teaching in Paris. What’s happening with France? I mean, like I remember going to the the Bastille Day celebrations here in Brisbane, at the so Patel in 2017 which is a couple of months after Macron was elected. And there was so much enthusiasm about Macron and and so much excitement about what he could do for France, and it just all seems to have disintegrated, and now there’s a risk of talking about, is this the end of the Fifth Republic? Could you tell us a bit about what’s going on there? Please? Jean Baptiste,
Jean-Baptiste Wautier 14:36
of course, yeah. And it’s, you know, the French like to make it incredibly complex as always, but it’s, it’s, it’s, indeed, an incredible turn of event, because, you enthusiasm was shared by many people when Macron was elected as someone who was, you know, very modern, pro business, balanced and could really take, take the country further. Um. He did a few things, but not that many during his first mandate, then got re elected, and unfortunately, there’s two issues at play right now. The first one is Macron got elected, but it you know, we could say the same about the UK, probably, and other other countries in Europe. Macron got elected, not as a positive vote from the majority of the French voters, but it was elected against Marine Le Pen. Who’s this? You know, very extreme right, a very nationalist Populist Party, but which has, effectively, over the years, become the leading party in France. They today, they represent anywhere between a third and 40% of the total votes you take all of the last three elections. And she, she was always around around that mark. So that’s pretty high. And the second, the second party was probably elect Marcos party back back back in 22 during the presidential election, but it was far behind, like it was 10 to 15 points behind, and the only reason why he got reelected is because all of the other parties voted against my Le Pen and therefore said I don’t like Macron. I don’t like his policy, I don’t like what he stands for. I don’t like his personality, but it’s better than Le Pen. And so it’s, it’s, you know, you start off of wrong premise here, which is, it’s not, it’s not that people think is the right guy with the right ideas and the right program. It’s like, No, we just want to avoid the populist and the extremists. And then there was a European election in 24 earlier this year that Macron again lost, but it was just a reflection of if you if you looked at the first round of the presidential election, it was already pretty much the same numbers the one I just gave you. So Le Pen came in France with a third of the votes, and then it was not even Second. Second was a coalition of the left parties, and then Macron was third. So it was really a proper defeat. And and he had a very emotional reaction, you know, couldn’t believe that he was he was such a negative vote against him, and they decided to dissolve the assembly, which the President can do once a year, according to the Fifth Republic constitution. And so when you do that, you have parliamentary election. So even though there had been parliamentary election in 22 where already he had no majority. So keep that in mind, even though he’d won the presidential election, and that’s again, because of what I explained, that he didn’t really command a majority. Anyway, he lost again this parliamentary election, but by an even bigger margin, and now no party is commanding any majority in parliament. You have may Le Pen is still the biggest, but thanks to the way the voting system works in France, they don’t have 40% of the seats. Even though they had 40% of the votes. They have like more, like 2025 then the sort of Macron coalition of, you know, center right and center left have roughly another 30% and then there’s, there’s a large coalition of the left, but from extreme left to center left, which has another third. And so you have, you have a deadlock parliament. Is that nobody commands a majority, and everybody’s taking a very extreme position, like no one wants to work with one another. And this is the other very typical French thing at play here, which is France is a lot is long on the ideology, short on pragmatism, the opposite of the Anglo Saxon world. And so all of those three, those three thirds, if you wish, are really sticking to their guns in terms of ideas and programs and what they think should be done. So Macron thought, again, he could have the upper hand because he’s so smart and he’s going to manipulate all of these people, and he’s going to get them into a rhythm. But he actually failed, because again, the Prime Minister he appointed three months ago was was voted out by the parliament. Because again, there’s no majority, and there’s still no emergence of majority. Now is it the end of the Fifth Republic? I think not yet, because it’s a very high bar to change the constitution, and if you you can’t even pass a budget, which is right now, the dynamic at play in France, it’s going to be even harder to have a new constitution unless you put it to to a referendum. So I think you’re going to end up it’s going to be a bit like, like Belgium. Him as seeing for, you know, for two years, you’re going to go from one government to the next. Macro is never going to leave. I think it’s just too that’s his personality. I think he will never want to leave, and he doesn’t have to to be fair. And I think you’re just going to see trials and errors. Trials and errors probably budget never, really, never read, adopted, and they’re going to continue to function in that sort of very transitional mode until the next presidential election, which is in 27 so it’s not going to be it’s not going to be good for the country, because nothing’s going to happen. People are going to be very unhappy. Budgets are not going to be balanced, which is also bad because France is now running the largest deficit in the eurozone and needs to get its acts together, but without any majority in parliament, it’s going to be very hard to balance. So I think it’s, you know, it’s also a real threat for the European Union and the eurozone, because dysfunctional France for another two and a half years, it’s going to be a real issue for the for the entire region. Yeah,
Gene Tunny 21:05
yeah, that’s what I was wondering about. Just what does it mean for for the stability of of the euro, and whether there are any risks of of a Eurozone breakup at some stage? Is that actually a realistic prospect, or is that just something that you don’t think will ever happen.
Jean-Baptiste Wautier 21:23
So I don’t think it’s a zero probability, because again, France right now is running a deficit which is around 6% of GDP as a total debt to GDP of 120% and given the current political dynamic that we just talked about. It’s not going to balance its books anytime soon, and so far, because France is such a foundational country for the European Union and the Euro zone, together with Germany, the commission has been incredibly lenient, and as given France three years, and then five and now seven years, not not even to balance its books, to get back to 3% of GDP for its public deficit, which is the benchmark that you’re supposed to observe. But even if it does that in seven years, the debt is still, you know, it’s still spiraling. And so I see the risk of a Greek episode or a trust episode on France like a real possibility. So not necessarily the fact that the Eurozone is going to completely implode, that I think is a low risk, but I think there’s a real risk of sovereign crisis and the cost of the French debt suddenly spiking. It has already gone up significantly when you look at the spreads with Germany, but I think it could go much, much higher. When it starts to go much higher, you’re going to have to have like, like, in the case of Greece, back in the days, an intervention of ECB or IMF or both, which are going to force reforms on France in terms of balancing its budget, reducing its spending, so that, I think as a real probability. I wouldn’t say it’s, it’s, it’s certain, because there’s been a good amount of leniency so far, but I see that as a real probability of occurring. That would save the euro, but that would be a disaster for France.
Gene Tunny 23:28
And just briefly, what is the cause of the budget deficit? I mean, obviously too much spending relative to taxation and other revenue. But is it entitlement programs? Is it a an excess a blighted public service. Do you have any thoughts on that? So,
Jean-Baptiste Wautier 23:43
yeah, yeah, absolutely. I mean, first of all, if you to put things in perspective, Mark quandry during his seven years when he took over the French, total debt, total public debt, was 2000 billion euros. He added 1000 billion euros during his seven years, which is mind boggling. So when, when you try and disaggregate where this, this came from and and also to answer to your question on, on public deficits, of course, COVID is part of this, but COVID is only a third of this 1000 billions that were added. So a lot of money has been has been spent on two fronts. One Macron tried to make companies more profitable, more competitive, make France more attractive when it comes to investment. So a lot of money has been spent on reducing tax, both for companies and for wealthy individuals. So is introduced a flat tax wait when it comes to capital gains and on the corporate side, he’s reduced the overall tax rate, and he’s introduced a lot of exemptions, and that that is 10s of 10s of billions of euros. Yes, in terms of the spending, and then on the other side, the other source of deficit, and that was a lot of very, I was going to say generous, but crazy, excuse my term, but crazy spend on, you know, helping people with inflation, helping people with energy, helping people with all sorts of subsidies and public spending on things that would never have any structural impact. So you were just helping people for the next six months. But then, you know, and then what? And so they’ve been throwing, again, 10s of billions like this over the last two years, probably also help, you know, hoping that it would appease the country and it would help with people purchasing power and all the rest, but the budget was already in deficit, so you never had that money in the first place. And then the last thing that happened over the last 12 months, which frankly, is is farcical, is they made. They made mistakes in budgeting 2425 because they were hoping that their revenues, which follow the trend of the 2122 fiscal years, whereas these years were rebound from the COVID years. So they were not sort of a normative level. So again, then they didn’t size properly the spends, because they completely overestimated their revenues, and so that’s what created that huge deficit that that we’re seeing now, that’s been widening in less than a year, right?
Gene Tunny 26:31
Okay, yeah. I mean, we’ve had the energy subsidies here in in Australia, and yeah, I guess we’ve made forecast errors in the past, but not, not quite that sounds extraordinary, if they’ve ended up with a Yeah, it is seven, 6% deficit, extraordinary. Okay, well, that’s it is. I’ll keep an eye on what’s happening in France for sure. Yeah. Okay. We’ll take a short break here for a word from our sponsor.
Female speaker 27:00
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Gene Tunny 27:29
now. Back to the show. Last thing I want to cover Jean Baptiste is this question of capitalism. So you’ve you’ve been involved in World Economic Forum, and you’ve so you’ve been a in financial markets for decades, and so you’ve been a long term observer of what we you know, our capitalist system. And you’ve got some thoughts on like, what you see is the flaws in it and how it can be improved. Could you tell us what do you see as the flaws or the problems with our current system of Global Capitalism,
Jean-Baptiste Wautier 28:05
of course. I mean, the to me, the if you start at the very macro level, and you look at all the negative externalities that our current system produced, they’re just gigantic. Whether you look, I mean, the first one that, of course, comes to mind is, is global warming and environment and all the rest. But to me, it’s, it’s far it’s far bigger than this. Because I also think in terms of health, if you look at statistics, in terms of in terms of obesity, for example, whether it’s in the US or in Europe, if you look at mental health and how social media function, and how they impacted mental health, in particular the younger generation, if you look at inequalities, not inequalities in the sense of, you know, morally, but inefficiency, the concentration of 10s of billions or hundreds of billions in the hands of a few individual means that they’re not going to be able to spend in a productive way this this amount of money. So I’m not, I’m really not approaching this, you know, with a moral aspect and just it’s, it’s yet another inefficiency when it comes to the economy. So there’s a lot of negative externalities that our system is producing and which is not making neither the best use of the resources we have, nor having the best impact on people’s well being, simple as that, and and the planet well being so so that that is, to me, the issue right now. And when I try and look at the root cause, the root cause is, over the last, I would say, 3040, 50 years, capitalism has really shifted to becoming incredibly short term and becoming solely focused on profit maximizing, short term profit. And it’s not always been like that. If you if you go back in history, and you look. At the the great industrialist in the US, you know, the great billion of the Rockefellers of this world, the carnegies, the perspective was much more medium to long term. And we’re going to build companies to solve a problem. And if we solve that problem efficiently, profit will be the consequence of solving a problem, problem efficiently for the further society, as opposed to, is going to be the objective. And if you go even further back in history, and you look, you go back to Adam Smith, that’s exactly what Smith, you know, sort of theorized. So even if you go back to the father of liberalism and capitalism, that was already the way it was, it was conceived. So I think this is, this is the issue we facing right now. We’re trying to lay a regulation, you know, in the hope that, oh, we’re going to reduce carbon emissions, we’re going to reduce the use of plastic, we’re going to reduce energy consumption at its and it’s just not working. It’s not working. Because if you look at the global energy consumption in the world, it’s going up. If you look at where it’s coming from, it’s still coming 80% from, you know, fossil fuel. If you look at all the innovations, look at the energy consumption of a Google and Microsoft, it’s the size of a country consumption. You look at, again, you look at the impact on people of all the social media, you know, it’s not, you can’t argue that there’s a lot of negative there. And you look at obesity prevalence in the US or in most developed countries in Europe, it’s going up, up, up. And, you know, it’s, it’s, it’s neither good for the people, not for the society. So all of these things are not going in the right direction and and it’s, it’s not by regulation, by regulating, because we already over regulated, especially in Europe. It’s already impossible to know all of the regulation, and you can never capture it’s too complex. You know, the these, these are too complex to monetize, to measure, to regulate. It’s just impossible. So I think the only way is two things. One, to try and be longer term in terms of how company and investors make decisions, because again, time horizon does matter here. And the second thing is in terms of, again, investors, governance, the way we incentive boards and management, and it’s all about what is the problem which you’re trying to solve, as opposed to maximizing exported profit. And as long as we don’t turn this onto its head and and sort of make profit as a consequence rather than as an objective. I think we’ll continue to, you know, go in circles and observe negative externalities more and more and never come up with a solution. It’s still, you know, it’s a very, it’s a very fundamental issue. It’s not, it’s not one that can be sold easily, but it’s, it’s, I think it’s one we should be concerned with.
Gene Tunny 33:04
Okay, so just to understand, are you arguing that? Well, there are a couple of ways you could look at this. Should, should people have this in concept of enlightened self interest, where they they see beyond the immediate, and they see, well, we’d actually be better off if we thought longer term. So that’s one thing that so there’s that possibility, or are you arguing that they should take into account these wider social or environmental impacts, even if it isn’t of benefit to them directly, because they should have a wider concept of well being than just their company could. I’m just trying to understand what your position is precisely, please. Sean Baptiste,
Jean-Baptiste Wautier 33:49
yeah, no, of course. And it’s no absolutely, and it’s actually both. It’s both changing the time horizon and focusing on on a higher purpose, as opposed to just the bottom line and the profit that you’re going to generate for the fiscal year. So time horizon? Why? And this is something I’ve observed, you know, I’ve spent more than two decades in private equity, and private equity, despite what people may think is actually quite long term, because you invest in companies for 456, years, and then you need to sell this company to someone who’s going to hold it for yet another at least five years, if not more. So when you invest in a business, you need to think the next 10 years. And when you do that, I’ll give you a stupid example. You not going to buy a an incredibly profitable company that makes disposable plastic bags, because you know that the trend is not your friend. So you might look at amazing financials, amazing cash flow generation, amazing management team, blah, blah, blah. You know, great market position, but you know that in five years time, nobody would want to buy this of you. So. So that’s what having a long time horizon brings you, is you will automatically factor in those negative externalities that instantly may not necessarily impact your everyday profit, but in the long run, will, will will no longer be able to be monetized. And the second thing I’m advocating, because I’m trying to, I’m trying to, quote, unquote, see how we can save capitalism and liberalism, because I’m still a great believer in those two capitalism, because that’s the best way we found to create wealth for all you know, collectively, by rewarding risk taking and hard work. So I think we should preserve that, because that works, that engine works and liberalism, because that’s the world I want to live in where I have agency and freedom of starting my own company and freedom of speech. So I’m trying to see, okay, how do I save that? But by getting rid of all those negative things that you know, impacting our societies, and that’s where I’m thinking. Instead of layering regulation which is already impossible to navigate, let’s do this bottom up and have companies which now not only elongate the time horizon, but also focus on what problem are we solving and what is, what is our net benefit to society, not only how good is our product, but also, you know, the well being of my employees, of my suppliers, of my and the society around me, the community. So it’s, it’s what people call stakeholder capitalism. So you really factor in all of the the impacts that you have, direct or indirect, and that’s how you you manage your business, as opposed to what’s going to be my net income, net income for next year? Yeah.
Gene Tunny 36:51
Do you have any examples of companies that you think are doing this well, or could be examples to others?
Jean-Baptiste Wautier 36:57
So there are. There are fascinating examples of companies which are owned by foundations and which have been, you know, one that makes the headlines is Novo Nordisk, which, you know, has made this ozempic product that that is concurring the world. But you have, you have more and more companies, especially in Scandinavia and in the north of Europe, that are being owned by foundations, and those foundations are the shareholders and the way they look again at their businesses. I’m not obsessed with how much dividend can be paid up next year. I’m looking at my purpose, my competencies, my 1015, 20 years horizon, and profit will come if I if I’m doing things right, and if I’m doing things that really bring value to society, I’m going to be a profitable business. And again, that’s what Adam Smith theorized, and he was right. And so you’re seeing more and more examples of this, of, you know, this small, more inclusive capitalism, or companies which are so there are examples, it’s, it’s, it’s nowhere near the majority of companies today. But you know, if you combine those owned by foundations, those owned by families, or founders, very successful founders. I don’t want to it’s a bit of a funny example I’m going to use but, but if you look at musk, there’s a lot of negative things in terms of how much wealth is now being concentrated into his hands, granted. But on the other side, the way he’s built this business. Was never obsessing over next quarter profit. You know, he’s been people were saying, Tesla is going to go bankrupt because they’d been burning cash for so many years. And then when he launched SpaceX, people were like, what i How can you make a profit? You know, sending satellites and going to Mars, there’s no business for that. And Mesa is doing it better than you. And look at where we are today. So he’s an example of an incredible entrepreneur, whether you like him or not, you know you have to look at what he’s achieved. It was never thinking, I want to, I want to be worth 300 billion in 2024, which he, incidentally, he is now. So there’s more and more example that that, that one can can find of, you know, if, if we manage to really turn this onto its head, I think, I think there’s a there’s a path. It’s not an easy one, but I think there’s a path.
Gene Tunny 39:38
Yeah, absolutely, I think, yeah, certainly worth, worth considering, I think Musk is a, he’s a good example of that Bucha nearing capitalist. I mean, is the closest thing we’ve got say to someone, you know, I guess Howard Hughes many years ago, or, yeah, you know, I guess some of the great industrialists you mentioned in Carnegie and all of that. Yeah, absolutely, yeah, absolutely, right. Oh, this has been fascinating conversation. John Baptist, anything else before we we should go anything else that’s that you’ve been thinking about and things worth, worth covering before we wrap up,
Jean-Baptiste Wautier 40:13
right? Thank you, Gene. I enjoyed it. I mean, there’s so much, as you said in your introduction. You know, it’s not just these, these tectonic shift on the geopolitical front, and we only we talked about some of the hot topics, but talk about the Middle East. We haven’t talked about Russia, we haven’t talked about China, and there’s so many things happening there. So it feels like all of these tectonic plaques are moving right now at the same time, and just as if it wasn’t enough, I think artificial intelligence is the most, is the quickest, most far reaching industrial revolution of our times. So you’re overlaying on a world that’s sort of rearranging a massive industrial revolution, which is going to change so many things in our lives. I think we live really fascinating times, and I really enjoy talking about this, because I think we should all have eyes wide open and watch and learn. Yeah, absolutely.
Gene Tunny 41:17
I think just on AI, what are you most excited about? What are there some, are there some develop? I mean, we’ve seen chat, GPT and all of the large language models, but are there certain things that are that are exciting you at the moment? So
Jean-Baptiste Wautier 41:33
I think, well, what’s exciting me is, apart from things that really needs very human emotional intelligence or human presence. There’s so many and some element of judgment, but there’s so many jobs, so many things we do in our daily lives that are a few years away of being replaced by artificial intelligence is just mind boggling. And the only thing that was, you know, sort of delaying it is progress in terms of quantum computing. And you would have seen Microsoft announcement, I mean, the So, so we’re just a few years away of doing so many things with it in everything we do, I think humans will all will be social animals. So we’ll always need, you know, we’ll always need to meet in person. We’ll always need to share motions, to share ties together. When you try and think of care, and there’s certain industries or art investment where you need a lot of judgment at times they will, they will still be pockets where you need human input. But I don’t know, more than half of the things we do can be more or less replaced by by a computer tomorrow. And so that’s that fascinates me. And you know, medicine could be so much better. There’s so many things that could be so much better, but at the same time, it’s a revolution that has very little content when it comes to jobs, employment. All the previous industrial revolution, it was the creative destruction of Schumpeter, right? So they were sort of destroying some industries, but some others were being created. And the level of wealth and productivity was was going up this one not only is going faster than the previous ones, because it’s more like 20 or 30 years as opposed to 50 or 80, but on top of that, it’s not creating jobs. You look at the ratio of market cap of the largest tech companies to the number of jobs they have. I mean, it’s ridiculous. Yeah, we’ve never seen such a bad ratio and and that’s, that’s what worries me, on the flip flip side is, what are we going to do when we can replace, you know, so many things, and it’s not only that, it’s going to be efficient, it’s going to be very low on cost, so it’s going to be a no brainer to replace man by machine in minutes. What are we going to do with all of these job that we’ve destroyed and with all these people that become an employee? That’s that’s the one that worries me. Hopefully excited.
Gene Tunny 44:12
This is why some of my guests argue in favor of UBI So, yes, I mean, I’m not necessarily advocating that, but I think you know that if that scenario, if that’s what happens, and then UBI becomes, becomes more compelling, I’d say, so, yeah, absolutely okay. Thanks so much for the conversation. I really enjoyed it. You’re right. There are so many other issues we could have, we could have covered, but then I’d probably be talking to you for two or three hours, and we might have to have another schedule, another chat, subtitles. I found this very, very enlightening. And, yeah, I think, like the idea of that, course you’re teaching the global and multi polar world. I think that’s so important. This, this whole idea that, since certainly things are. Different from what we expected after the end of the Cold War. We saw the US dominant, but now we see Yeah, just yeah, the multi polar world, as you say, or even a G zero world as Ian Bremmer, yeah, says, Absolutely. I enjoyed it. All right. Thank you. Gene Thanks. John Burt, right. Oh, thanks for listening to this episode of economics explored. If you have any questions, comments or suggestions, please get in touch. I’d love to hear from you. You can send me an email via contact at economics explored.com, or a voicemail via speak pipe. You can find the link in the show notes. If you’ve enjoyed the show, I’d be grateful if you could tell anyone you think would be interested about it. Word of mouth is one of the main ways that people learn about the show. Finally, if your podcasting app lets you, then please write a review and leave a rating. Thanks for listening. I hope you can join me again next week.
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Credits
Thanks to the show’s sponsor, Gene’s consultancy business, www.adepteconomics.com.au. Full transcripts are available a few days after the episode is first published at www.economicsexplored.com. Economics Explored is available via Apple Podcasts and other podcasting platforms.
Explore the mechanics of decentralized finance (DeFi) with Professor Evgeny Lyandres, who breaks down how decentralized exchanges work and how VirtuSwap stands out in providing liquidity for small-cap crypto assets. With insights into the challenges and future of tokenization, this episode offers a clear view of where DeFi may be heading. Evgeny is Professor of Finance and Head of the Blockchain Research Institute at Tel Aviv University. Disclaimer: This podcast episode contains general information only and is not financial or investment advice.Â
Tokenization of traditional assets, such as stocks or real estate, is a future possibility for DeFi that could expand its impact well beyond the current crypto market.
Liquidity pools and smart contracts are essential to DeFi, providing a protocol-based framework where trades occur automatically based on programmed rules.
VirtuSwap’s unique pool structure, including virtual liquidity reserves, is designed to address the liquidity challenges for less-traded assets in DeFi.
With the aid of AI-driven systems like Minerva, DeFi platforms can optimize liquidity allocation, potentially offering higher returns for liquidity providers and more efficient trades for users.
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Transcript: Is DeFi the Future of Finance? Exploring VirtuSwap’s Vision w/ Prof. Evgeny Lyandres – EP262
N.B. This is a lightly edited version of a transcript originally created using the AI application otter.ai. It may not be 100 percent accurate, but should be pretty close. If you’d like to quote from it, please check the quoted segment in the recording.
Evgeny Lyandres 00:06
This is basically the reason why I think this market is important, right? It’s not because of its importance right now. I mean, it’s not that, right? It’s because of the potential, which I think is there, right, a potential to kind of revolutionize many of the financial technologies that that we’re familiar with.
Gene Tunny 00:29
Hello and welcome to the show. In this episode, I’m thrilled to welcome Evgeny Lyandres, a Professor of Finance and head of the Blockchain Research Institute at Tel Aviv University. He’s also the co founder of VirtuSwap, a decentralized exchange platform. Evgenys’ expertise lies at the intersection of finance and blockchain technology. Today, we’re diving deep into the world of decentralized finance or defi. We’ll explore how decentralized exchanges work, the challenges of providing liquidity for smaller crypto assets and VirtuSwaps’ unique approach to addressing these challenges.
Special thanks to Lumo Coffee for sponsoring this episode. This top quality organic coffee from the highlands of Peru is packed with healthy antioxidants. Economics Explored listeners can enjoy a 10% discount, details are in the show notes. Now let’s jump into the episode. I hope you enjoy it.
Evgeny, welcome to the program.
Evgeny Lyandres 01:34
Great to be here. Thank you very much for having us.
Gene Tunny 01:38
Excellent, of course. So yeah, I’m really interested in, in what you’re up to. I mean, you’ve got a background as a as an academic professor of finance and head of Blockchain Research Institute at Tel Aviv University, and you’re also involved in defi and in blockchain with your own company. So I’d like to ask you about your company, is it VirtuSwap?
Evgeny Lyandres 01:58
Yes.
Gene Tunny 02:00
Okay so what I’ve learned, or the information that was sent to me about it, is that it’s a decentralized exchange platform that gives traders direct liquidity for smaller cap assets. So I think what would be good is just unpack what all of that means. So one, what do you mean by decentralized exchange platform, and then what do you mean by direct liquidity for smaller cap assets? Could you take us through that, please Evgeny?
Evgeny Lyandres 02:34
Of course, of course. So starting with from from afar, to give a little bit of a background, right? So, so obviously, decentralized exchange is an exchange where assets are traded, right? In particular, in our case, it’s crypto assets, right? You know, think about Bitcoin, Ether, stable coins, and, you know, 1000s of other crypto assets. Now, there are two ways that the trading in those assets is conducted. The first and still kind of the predominant way, is trading on centralized exchanges. You know. Think of Binance or Coinbase, or maybe FTX, that you know existed until a year and a half ago, but no longer. A lot of trading happens there, and those exchanges are organized very similarly to, kind of the traditional exchanges of other asset classes, such as stocks, right? So think of, you know, NYSE or NASDAQ, right? It’s basically order book based exchanges. Now there are few issues, few problems with with centralized exchanges, and the problem basically stem from the fact that those are centralized entities in a largely unregulated environment, right? So some centralized exchanges, such as Coinbase, are regulated, right? Most are not, right? And so if you combine those two things together, right? So, a centralized entity in an unregulated environment, right? You know, bad things can happen. I’ll give you a couple of examples, right? So, of course, all of us know about exchanges that go bust, because basically they still, you know, depositors fund right? The FTX is a good example. There are other issues, right? One of them is that a lot of those exchanges engage in significant wash or fake trading, right? So basically, what they’re trying to do is they try to increase the reported trading volume, right? Because that is, you know, increasing their placement in the kind of the ranking the league table of of exchanges, right? So they’re interested in inflating volume, because this is, you know, one metric that investors or traders look at when deciding where to trade, right? Now, this is pretty bad for trader, right? Because if you go in exchange and you see an order book, right? You, you stink, you, it seems deep, right? So you want to place your trade. But the actual liquidity oftentimes is significantly smaller, right? And that basically means that your trade is going to be executed at, you know, far worse terms than you know, what you thought it would be executed, right? So basically, there is a big problem of lack of transparency that is driven by, you know, very strong competition, right? And competition is there just because, you know, the entry costs into this industry are very low. It’s, it costs very little to set up, you know, a centralized exchange. It’s basically kind of, you can use a white label software to just to set up a new one. And as I mentioned, lack of sufficient regulation, right? So the bottom line is, lot of people trade on centralized exchanges, and some of them are fine, right? So, you know, Coinbase is definitely fine. Binance seems to be fine, but not all of them, right? And there’s, you know, quite a lot of academic research on kind of the problems associated with the centralized exchanges. And so defi and in particular, decentralized exchanges, right? Is one way to address, you know, the problems that we mentioned. So let me tell you, you know, briefly, what a decentralized exchange, right? So decentralized exchange is basically a protocol, a computer program that resides on the blockchain, right? Any blockchain that enables smart contracts that basically defines the terms of trade in a very kind of precise, programmatic fashion, right? And that basically means that it’s a collection of basically formulas, right? Say, Well, you know, if you exchange this amount of one asset into another asset, right? You’re going to get precise amount of that other asset the function of some parameters of the you know, of the exchange. Those exchanges are organized using so called liquidity pools, right? And so that brings us to, kind of to the main players, right in this, in this market, right? There are two. The first one is traders, right? So those are people you know, that want to exchange one asset into another. The second, and the crucial type of players in this, in this market, is what’s called liquidity providers, right? So, liquidity providers can be, can be anybody, right? Can be you or me. It’s people who basically supply liquidity two so called liquidity pools with a goal of this liquidity, right, helping conduct trades, right? So let’s say that I want to establish a pool of two tokens, right? You know, whatever they may be. So I’m going to put $1,000 worth of one token and $1,000 worth of another token into the liquidity pool. Now the pool has been established, right? And those tokens right at this point, $1,000 worth of both could be used for trade, right? So you can come to the pool and say, Well, I want to take $10 worth of one token and supply another token into the pool, and the pool basically is going to be set at set up in such a way that you know the terms of trade are going to be defined by the amount of liquidity currently existing in the pool, by the relative prices of those two assets, right. And that basically the ratio of the two assets in the pool right, and the size of your trade. Okay, we can go deeper into, you know, what types of pools there are, but typically, you know, it’s involved in a pretty simple math, pretty simple kind of formulas for defining those exchanges, right? But it’s basically going to be a function of only two things, right, the current composition, the first state of the pool, and the size of the size of the trade and direction of the trade that you want to perform, right? So that is kind of the the basics of decentralized exchange and this type of trading technology that’s very novel, right? And does exist outside of decentralized finance, or outside of blockade based finance right is possible because of kind of the nature of the blockchain, right? That, basically, blockchains allow smart contracts. Smart contracts are, you know, no more than if that condition right, the defining if something happens was was going to be the result, right? And so, so basically, it’s possible, because of the technology of blockchain, right? But in theory, at least, you can even think about this technology being useful for trading other assets, right? So you will any pretty much, right. The financial technology is the same, yeah. So that’s kind of the basic or so decentralization, we’re very happy to go, obviously, deeper into it and, and, of course, we’re going to talk about, you know, what we do in the space. But what is our, what is Virtu of contribution,
Gene Tunny 09:50
you know? Yeah, absolutely, I definitely want to go deeper into it. Yes. So, I mean, what are examples of decentralized. Exchanges. I mean, there’s your decentralized exchange, Virtu swap, and how does it compare? What’s its differentiating factor from other exchanges? It sounds like that you’re focused on smaller cap assets. Can you tell us about that? Please. Evgeny, of course,
Evgeny Lyandres 10:17
of course. So in general, kind of decentralized exchanges started in about 2017 2018 so you know, about seven years ago now, by two protocols. The first one was called bancor. The second was called uniswap, still called uniswap, and both protocols still exist. Uniswap at this point is the most successful and the most famous decentralized exchange protocols currently responsible for, I would say, about half, if not more, of all trades in decentralized exchanges, right? And just to put things in perspective, about a quarter of all trades of crypto assets is done on decentralized exchanges. The other three quarters are done on centralized exchanges. And out of this quarter, uniswap, you know, gets about half of the market share. So this is the predominant kind of decentralized, you know, the biggest and the most successful decentralized exchange, and also an exchange that constantly produces a lot of innovation into this market, right? So let me give you maybe a half a minute kind of history of the evolution of this market, and then we’ll get to what we do, right? So it all started with, you know, with the first version of municipal in around 2017 2018 right? And basically, the idea there was, well, you know, we can exchange assets using this, this, this kind of smart, smart water based technology that we discussed. And in the first version, the exchange work very simply, right? So the exchange could only be done, you know, between ether, right, which is the the token of the Ethereum ecosystem, right, with Bucha, Spain, and any other, what’s called ERC 20 token, which is a, basically a token that that is based according to a certain kind of standard, and within the first version, right, which basically kind of it should you can think of this as a proof of concept, right? The trading was not very the terms of trading were not very attractive, right. So think about a situation where you want to exchange your two tokens, right? You know, think you know, USDC, a stable coin and some other token, wbdc, right, Bitcoin, okay, so what you would have to do, right, is basically do two trades, right? It would need to exchange USDP into, you know, ether, and ether against WBC now. And, you know, doing multiple trades would basically expose you to to multiple costs, right? So let’s first talk about all the costs involved in a trade, right? And then we’ll think about, you know, what does mean to have all those basically doubled or multiplied, right? So generally, three types of costs, you know, for a trader on a decentralizing change. The first one is so called full fee, right? And that is basically a fee that a trader needs to pay to the liquidity providers in the pool, right? Because, you know, you need to somehow incentivize people to provide liquidity, right? Otherwise, you know, nobody’s going to put money in the pool. There needs to be something in it, you know, for the liquidity provider, right? So, and you know, the the fee, basically, is, you know, some small proportion of the trade that is not being exchanged for another asset, but instead is given to the liquidity providers, right? So think about, you know, numbers between five, usually, and between five and 100 basis points. Okay? So 0.05 to 1% that’s the first type of the cost. The second type of the cost is what’s called the price impact. The price impact, right? Is basically due to the fact that the trading function on any exchange is not linear, right? And, you know, you can think about a good analogy, is a typical exchange of assets, right? When there is an order book, right? So the bigger your order is, the deeper you’re going to go into the order book, and the works is going to be the execution price, right? So smaller trades are going to be executed and the marginal the current price, right, the bigger trades are going to be executed at worst price. Same thing happens on decentralized exchanges, right? The typical kind of the most simple and the most famous, actually, formula for conducting the trade and decentralized exchanges is what’s called the constant product formula, right? And basically means x times y equals some constant k, right? So for example, you know, going back to example, to an example, I am. I mentioned before, right? So if I provided $1,000 worth of liquidity in one asset and $1,000 worth of liquidity on the other asset, right? You know, if you divide this $1,000 by the initial prices of those assets, right, that basically defines the quantities of the assets initiative deposited into the pool, right? So let’s say just let’s take the example that I deposited in 500 units of asset a and 200 units of asset B. If you multiply those two numbers, so 500 times 200 so that’s 100,000 right? That defines that constant product, right? And any trade you know in this pool, unless the liquidity changes, right, is going to be based on this product, meaning that if I want to take a given amount of one of the assets in the pool, I need to supply an amount of the other asset, right, such that this product remains constant, and that basically, you know, defines a so called hyperbolic function, right? It’s not linear, right? It’s a convex kind of function of price. What I’m trying to say here is that the second type of the costs of trading in on this centralized exchange is basically what’s called the price impact, right, the effect on the price at which you’re trading, right, as a function of the size of the trading and then, then the third part, the third cost of training, that is basically what’s called the guest cost, right? So this is the transaction cost that need to be paid by the, you know, people, kind of writing blocks on the on the blockchain, right, recording the new blocks and kind of certifying, right? So those are the three costs, three types of costs. Now coming back to this example of trading, you know, stable coin into rev Bitcoin, right, where the intermediate asset is, for example, ether, right? All of those costs, right, the price impact, the pool fee, the gas fee, need to be multiplied by two. And so that was the issue, right. So, so the trading terms were not super attractive, right? So, as a thought experiment, this was a fantastic innovation, right? So this was really a new way to conduct trading, you know, practical terms. It was not great yet. And so then, you know, this ecosystem, you know, started gradually improving and developing. The next kind of step was the second version of the unicorn protocol that’s called v2 okay, that’s that’s what people refer to, usually. And the biggest kind of innovation there was that you didn’t have to necessarily go through ether as an intermediate assets. You could basically construct pools, or establish pools with any two assets, right? With any two ERC 20 tokens, right? And so then you could think of us about a situation where, you know, someone would establish a pool of USDC against Red Bitcoin, right? And people would be able to trade in the school directly, without going through the third assets, obviously, reducing. The point is, this is important, and this is the point I’m going to come back to, right? But it didn’t really solve all the problems, for reason I’m going to describe in half a minute. Since then, you know, this kind of, this setup, right? This technology of decentralization, is have evolved further. The first big innovation was the third version of UNICEF, unusual v3 Right? Which basically allows so called concentrated liquidity, right? So here’s the problem. The problem with, you know, the trading technology that described so far is that, basically, the liquidity that liquidity providers put into the pool, right, is sort of uniformly distributed across all possible exchange rates in the pool between exchange rate between the two assets, you know, from zero to but it turns out that the price impact becomes quite significant for relatively large trades, right? So as a rule of thumb, the price impact which is a loss you know of a trader, you know from the trade, is roughly proportional to the ratio of trade size to the size of the pool, right? So if you want to do a trade which is 1% of the size of the pool, well, you’re going to pay this 1% roughly in terms of price impact, in addition to the pool fee and the guest right? So in practice, you know, in practical terms, you know, large trades are not really admissible, you know, on on decentral exchange, just because they become too expensive. This is also true for centralized changes, right? If you want to take out your 10% of the order book, that’s going to cost you a lot of money as a trade, right? So this idea of concentrated basically said, well, let’s quickly treat it not uniformly, you know, for all exchange rates, because you know, you know, trades that change exchange rates or more exchange rates, quite a lot are not going to happen in reality, right? Let’s concentrate. Trade humidity in the relevant range, right somewhere around the current exchange rate, right? We’re going to support those trades in a really good way, right, with deep liquidity, with the hope that large trades are not going to appear because those large trades are not going to be supported. So that really increased, many cases, the efficiency of liquidity provision by a lot, and improved in the terms of trading, you know, by by a significant amount for traders. And now uniswa is actually coming out with the next version of their protocol, before the fourth version, that is actually going to be quite interesting, because it allows to do many more things. It allows so called hooks. So hooks is basically additional smart contracts attached to the liquidity pool that define certain actions that the pool is going to do before, during or after a trade. Think about a situation, for example, where you know a trade changes the price, the exchange rate between the two assets in such a way that the current consider liquidity is not going to be adequate anymore to support trades, right? So the pool might define reallocation of liquidity in such a way that will support future trades as a result of an existing so before is not really active yet. It’s coming out, I believe this or next month, but this is going to, I believe, spur additional wave of innovation in defi or desks. Let’s go back to virtual swap and what you know we tried to bring to the table, right? So I kind of discussed very briefly the fact that liquidity in on Dex is often is not allocated in an efficient way, right? And this is actually an important point, because this is a big problem that currently, up to this point, actually prevents kind of the access to from from from really achieving a big success, right? Basically, from overtaking centralized exchanges, right? That has not happened yet. The reason is that it is very difficult to provide liquidity, you know, for all necessary trades, right, just because there’s a lot of assets right? Right now, if you go to coin market cap, which is, you know, the biggest aggregator of crypto data, there are 1000s, right, 678, 1000 of different assets, right, that are listed on coin market cap, and there are many more that are not listed. So if think about, you know, trading between one asset, one token, into another. Chances are that, despite the fact that it’s possible to establish any type of liquidity pool, it’s possible to concentrate liquidity. In practice, there are just too many asset combinations, right, for those pools to exist, right? So if there is, you know, 8000 crypto assets, right? You know, if you think about the number of pairs, well, it’s 8000 squared over two. That’s a huge number, right? You’re never going to have, you know, liquidity for, you know, for those trade Now, granted, most of theirs, those potential, theoretical pairs are never traded, right? So you don’t really need liquidity, right? But still, there are a lot of assets which are traded, right, but for which there is no direct liquidity, right, even now, right, with all the available technology, right? Because I know providing liquidity into pool, into the pool is a completed permission that’s connected, right? Anybody can do it, and we can establish a pool. Anybody can add, you know, money to the pool, right? People are only going to do it if it’s worth their while, right? If the returns they’re going to be getting are going to be offsetting the risks that they’re going to take in the day, right? And for many assets, it’s not the case, right? So you know, if there is, you know, some trading expected in a pair of assets. So think you know, whatever chain link against, you know, Matic, right? You know, which is the native token of olivine chain. There are going to be straight some trades like this, but probably not enough to make it worthwhile for liquidity providers to provide sufficient liquidity, right? And so, you know, despite the fact that the theoretical possibility exists in practice, right? People are still going to do two trades, right? They’re going to trade, you know, from chain link to some asset you know, with which you know, the pool of tailor exists, for example, ether or stable coin, USDC, or anything else, right? And then the others trade into the asset that they really want, right? So for the most part, the problem of insufficient liquidity is not solved, right? And it actually cannot really be solved, right, for the reasons we discussed, right? There is just not. There’s too many assets and too too little money in the pool, right? So. So that is basically the problem that, you know, I identified in, I guess, 2021 when I started looking very carefully into this, doing research on decentralized changes, right? It isn’t the problem that they’re trying to solve, right? And that basically what led to to virtual, right? So let me tell you, know, very briefly about our way to address this problem, and, by the way, before the problem, right? Just just the size of the problem, right? So those trades that involve what we call triangular trading, right? So trading, you know, through multiple pools. That’s about 30% of all trades on, you know, most important blockchain, right? Ethereum, Polygon, arbitrary and so forth. Right? About 30% of the trades are of this kind, right? So this is kind of the market that we’re trying to address, right? And, you know, you mentioned initially, kind of smaller coins, right, or smaller assets, right? So this problem is there in smaller assets, right? Because if you want to trade, you know, ether against USDC, you’re going to be fine, right? There’s enough liquidity, enough direct liquidity. That’s not our place, right? Our place is rates that I mentioned, right, which may be smaller asset, right? When I say smaller, it’s basically anything outside of top five, right? So when you go outside of top five or six on a given blockchain, that’s where the problem becomes, becomes acute. So how do we do this, right? So we basically came up with a different architecture, right, different structure of liquidity pools, right? So standard liquidity pools involve two assets. Ours are a little bit more involved. So in addition to the two assets right that we also have, we have so called reserves, right? So think about a pool that is, you know, two main assets, plus you know, some reserves, right? And you know, a good way to think about those preserves initially is basically like, say, deposit boxes that are initially empty, right? So the pool is established in exactly the same way as normal pool, right? So think about a pool of whatever ether and USDC that can accept reserves into those initially empty sale deposit boxes. So let’s say that you want to to buy, you know, ether, right? And to pay with it, with the Matic token, right, with the polygon top. Now, in a normal kind of situation, right, in the normal tax right, you’d have to do those two trades. Right triangle. Virtuous. Of what you can do is you can deposit the asset that you have into the reserve of the pool. Right, ether USDC are going to the to deposit the reserve of Matic polygon into the reserve of, you know, either USDC pool, and you’re going to take from the pool the asset that you want, right, the asset that you need. For example, ether, right? Now, of course, the question is, you know, what is the, what are the terms of trade, right? You know, how much do you need to deposit in order to take even amount of the asset from the pool? Now, you know, this historical trade are defined on purchase of via so called virtual liquidity pools. Right? Virtual liquidity pools are, you know, a result of, basically triangulation, right, of of different pools that exist virtual so they define a pool, sorry, a trading curve, you know, over which the trader can trade, right? Now, this trading curve is not a real trading curve, right? It’s not like, you know, he’s going to deposit the assets in the pool and take some other assets from the pool. This virtual trading basically, is defined by trading into the reserves, right? So you’re going to, you know, deposit your asset to the reserve, and take one of the kind of main, one of the main assets in the for a trader, it doesn’t really matter, right? It’s a seamless experience, right? You as a trader don’t really care whether you trading by reserves or by kind of normal kind of two effort trading, right? What you care about is getting the most, what’s called the mount out, right? The largest amount of the other asset that you want for a given amount of assets that you are trading now, this is not the end of the story, right? So let’s say that you perform this trade, the pool accepted those, those assets into the reserve, right? There is a problem, though, right? The liquidity provider right now is now exposed to the risk of holding some other asset in the pool, and that is not a risk that is desirable, right? So if I provided liquidity into either USDC pool, I’m okay exposing myself to the risk of those two assets, but I might not be. Exposing myself to the risk of others. Now, what do we do? What do we do with it? There are several ways in which we solve this problem. And then after I describe the ways we address this problem, right, I’m going to go to talk a little bit about the advantage of what we’re doing. So first of all, we don’t allow all of the possible assets into into the reserves, right? So every pool comes with a white list of tokens that can go into the reserves, right? Think about, for example, top 100 assets on a given blockchain. So this comes back to your initial point. Right? We solved the problem for smaller assets, not for the smallest ones, because the smallest ones are super risky, right? We don’t want to expose the liquidity provider to this type of risk. But in practical terms, you know, 95% of trading in smaller assets are, you know, it’s taking place in assets you know that are the top 100, right? So, I mean, the other ones are really not that important. Secondly, we basically limit the size of the reserves, right? We basically say that the overall value of all the assets that sit in the reserves, right? And remember, these are assets that are different in the two main assets in the pool, right? So the size of the result reserves overall cannot exceed 2% of the value of the pool. Once it hits 2% the pool basically closes itself to this type of reserve trading, right? And basically limits the amount of reserves, right? So there is some risk, but it’s not very, you know, very big. It’s up to 2% but most importantly, right? The way we, you know this risk is addressed, right? Is by a lever system of exchanging reserves between pools. Right? Think about a scenario where, you know, I have deposited money into ether, USDC pool that now has Matic reserves another pool right is, for example, Matic against ether that has USDC reserves, right, that accumulated the result of some other trade. Okay. Now, whatever this happens, right, those two pools can exchange the reserves between themselves, right, sending Matic to the pool Where it is one of the main assets, and sending USDC into my pool Where it is one of the two main assets. Now this happens without any price impact. This happens without any pool fee, right? It’s just exchange between two pools, just a little bit of gas that needs to be paid. But you know, this is, this is not a large number, usually, and that basically means that every time it’s possible, we kind of try to reduce the number of reserves in all possible pools to the lowest level possible, ideally to zero, right? We don’t want those reserves right. So those reserves are just kind of a place that allows trading, right? We don’t really like them. We’ve done, obviously, tons of simulations at this point. We have real world kind of trading data, those reserves are typically small, right? So, you know, this is not a risk that is that is large in the liquidity providers. But what is that do? It basically eliminates, in many cases, the need for the triangular trading right, and reduces the costs of trading to a single kind of type of cost, right? You still need to pay the pool fee, you still need to pay gas. You still have price impact, but only once, all right, and not multiple times. And so if you think about kind of a more kind of global picture, right, the whole ecosystem of different decentralized exchanges, and there many, there are dozens of Dexs, usually on every blockchain. The way this market is organized is that most often people are going to trade through so called aggregators, right? So think about basically the Expedia right of the dex market, right? Instead of kind of going and buying your air ticket in a particular airline, you’re going to go to Expedia and try to see where it’s cheapest. You know, what is the cheapest? In a way, to go to a particular place, and accurate, there’s a similar thing, right? It basically takes a given trade, it looks up on all the data on different decentralized exchanges, all the pools, you know, all the liquidity, and tries to compute the optimal route for the spring, right? Basically, you know, let’s say you want to swap, you know, $100 worth of stable coin into into it, right? You know, the aggregator is going to go and trade and say, well, $20 are going to be sent to this pool on this decks, and $15 is going to be sent to a different pool in different days, with an objective of maximizing your overall amount out now coming back to virtual spot, since our trading is more efficient, right? Because, you know, we don’t have to pay the multiple costs twice, right, that basically means that a disproportionate, you know, fraction of a trade is going to be sent our way, as opposed to to a. Other kind of to other debts, right? So in a sense, that means that, you know, from the liquidity providers perspective, you know, their liquidity is going to be working over time, right? There’s going to be kind of a bigger kind of bang for the Bucha, right? And that means that, in a sense, every trade carries some fee, proportional fee, the returns to liquidity providers is also going to be larger and kind of the liquidity provision to merge as well. In principle, maybe a better kind of proposition for liquidity providers than providing liquidity on other deaths, right? So that’s kind of the basics of what we do. Gotcha,
Gene Tunny 35:35
okay, geez, there’s a lot, a lot there a few, a few terms I want to go over. So you talk about gas fees. So I remember you defined them. I just just wanted to make sure I heard that right, and I looked it up and checked it is gas. So that’s a new one. I’ll have to have another look at at that. And then you mentioned ERC 20, and that, that means ERC
Evgeny Lyandres 36:01
20 is basically just a technical term for particular crypto assets, right? You know, having certain characteristics, right, that are compatible, you know, with other assets on a given block, right? You know, there’s nothing deep there. It’s just certain conditions that need to be specified, but you need to be kind of fulfilled by an asset to to be classified as ERC 20, right? I don’t want to go deeper into this. It’s just that is not a very kind of interesting part. The gas fee is actually more interesting, right? So, you know, any blockchain right, is basically maintained in decentralized fashion, right? So blockchain has nodes, right? Nodes are basically computers who record, maintain and update the state of the blockchain, right? But those nodes need to be compensated. They’re different and, and, and, you know, those nodes need to agree among themselves about the contents of the blockchain, right? So we need to make sure that blockchain, which is a database, distributed database, you know, is the same across all nodes of the of the block, right? There are multiple ways to achieve this, so called consensus between nodes. You know, of course, you know, everybody heard about proof of work. You know, which is, you know, what Bitcoin uses for proof of state, which is what most other blockchains use at this point. But the point is that, you know, the nodes on on a blockchain, any blockchain, need to be compensated for what they do, right? So you know, you’re not going to kind of run this, you know, Blockchain software for no reason, right? You’re only going to do it if there is something for you and and that’s why, you know, we need to somehow generate some revenue for the for the nodes of the blockchain that maintain it, right? And this revenue typically is generated by those gas fees, which are basically fees attached to, you know, by people who want to perform transactions on a blockchain, right? And those fees, you know, you know, I’m simplifying a lot, but those fees basically are paid by the people making transactions to people who maintain the blockchain. Gotcha, do
Gene Tunny 38:13
you know why it’s called gas? Why is it? I
Evgeny Lyandres 38:16
mean, it’s, it’s like, it’s like, gas in a car, right? That’s more, makes the makes the blockchain go.
Gene Tunny 38:21
Okay, that’s that’s as good as his explanation as any. Is just wondering if there was some particular reason, actually,
Evgeny Lyandres 38:29
in fact, there may be others. This is the one that I heard, and this seems reasonable to me, so I didn’t explore further.
Gene Tunny 38:38
Okay, we’ll take a short break here for a word from House sponsor.
Female speaker 38:44
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Gene Tunny 39:13
now back to the show. Okay, so just want to go back to just the what I’ve read about virtue swap, so you get about 50% cost reductions and then 400% higher returns for liquidity providers. So this is what you’re what you’re claiming. Yeah, right, got
Evgeny Lyandres 39:38
it very much depends on the parameters of the, you know, the pool, gotcha, yeah,
Gene Tunny 39:44
it has a TVL of $3 million What do you mean by that? What’s TVL stand for?
Evgeny Lyandres 39:51
Right? So, actually, the number is smaller right now. Some, some rough kind of periods of market. The market is improving right now, so hopefully it’s going to. Bigger, but TBL stands for Total value locked. Total value locked is the amount of liquidity that people provided into the into the protocol, right? So, so you know, any exchange be centralized or decentralized, relies on liquidity, right? And basically assets that some other people have deposited into the into the exchange rate in order to facilitate trading, right? So the bigger the liquidity, the more trades are possible, and the better terms at which trades are possible, right? So, so for every decentralized exchange is striving to increase the liquidity that’s deposited into it,
Gene Tunny 40:47
yeah, gotcha. Okay, so it’s still fairly, I mean, it is a niche product. So you’ve said that you’ve carved out a significant niche, right? Okay, and you’ve partnered with defi leaders like uni Zen, open ocean, odos, lifik, what are those companies doing? What partnerships have you developed?
Evgeny Lyandres 41:09
Right? So most of the names that you mentioned are basically aggregators, right? So we mentioned aggregators is basically the from the starting point of most trades, right, the experience of this office of this market, right? So any decentralized exchange wants to be, wants to eat, to be integrated by by as many aggregators as as possible, right? Because, basically, you know, like an airline, right? The airline wants to be on Expedia. It wants to be on Kayak, it wants to be on Skyscanner, right? Wants to be on every aggregator that people go to to kind of search for for air tickets, right? Any decks wants to be part of, and all aggregators that people go to submit their trades, right? So at this point, we are, in fact, have been. We have been integrated by most of the of the important aggregators. So I’m quite, quite happy about that. And you know, this is not trivial, right? Because integration takes time, right? So you know, the aggregator needs to basically go over the smart contracts of of the decks, right, and make sure they’re compatible with what they’re doing. Right? Our smart contracts are quite different from those of existing or other desks, right? We are not a so called fork of another protocol, right? We just didn’t just take another product code and make some small tweaks to, right? You know, we wrote everything from scratch which make, makes aggregation heavier endeavor, you know, from the or integrations and heavier endeavor from the aggregator sentiment, right? So I’m quite happy that we have been aggregated by by several, well, all basically important aggregators, right? And so that’s where the majority of trade and virtue are coming from, right? So, you know, we have our own website, our own trading app, our own UI, UX. People can go there and trade. Most people do that, you know, otherwise, right? They go to an aggregator, and the aggregator computes, you know, which part of the trade it makes sense to do on versions of, right? And we get, you know, those bits and pieces of trades immediately.
Gene Tunny 43:25
Yeah, gotcha. Okay, that makes sense, yeah. So it sounds like you’ve, you’ve found a real niche there and in the market. And, yeah, I mean, it’s, it all sounds. I mean, I think I was five years too old to have got into crypto, perhaps, but yeah, I really haven’t got into it in a big way, but I have a lot of guests. I’m having, increasingly, having guests come on the show talk about crypto and web three. It’s all, all very, all very fascinating. So it’s something I’ve still got to learn a lot more about. Can I ask you, where do you think all of this is going? Because you mentioned the huge number of coins there are out there. I mean, a lot of these coins. I mean, you have to wonder about the future of them. I mean, where’s, what’s the future of crypto? Where do you think this is all headed? I mean, will crypto become, you know, will people start using it as a currency, as a medium of exchange? Will all of these coins survive? What’s the future look like? What do you see is the future of crypto of yeah, please tell me. I’m fascinated. I’d like to know what the, what’s the, what’s the use case for all of this?
Evgeny Lyandres 44:40
Yes. So then this is a bunch of very loaded question in one. So let me try to kind of, you know, to the extent possible. So, so crypto, obviously is a new industry, right? And up to not too long ago, it was a real kind of wild west, right? So you might remember the time. Time, a few years back, of initial coin offerings, or ICOs, right? Where basically projects were raising money for something that they intended to do in the future, and there were tons of scams in this market. I mean, this market was very far from clean, coming back to the lack of regulation. Right now, ICOs are dead at this point, but the market is still not very well regulated, right? So different countries approach regulation of crypto in very different ways. You know, I’m sure that, you know, with the with the outcome the US election, we’re going to have big changes in the regulation, in the US of the crypto market, to predict in what way, but I’m sure that there’s, I mean, it’s going to be easier to do to the business, you know, in crypto, that I think that’s that’s pretty clear, but regulation is still still not there, right? And so there are several uses in of crypto in general, right? So you mentioned payments, right? So payment is kind of the first thing that the people thought about and that basically, if you read the Bitcoin white paper, right, it basically says that, you know, Bitcoin was supposed to become a medium of exchange, right? For many reasons, it didn’t happen. One big reason is that just the throughput of this, of this system, is very low, right? So you can only do very few Bitcoin transaction in every second, right? So it’s incomparable. Orders of magnitude is smaller than, you know, transaction Visa or MasterCard or suite or whatever. So they didn’t happen. But it doesn’t mean that it’s not possible, right? So, you know, it is definitely possible to transact in crypto. Stable coins are very convenient for it. Central Bank digital currencies that many central banks you know around the world are working on are going to, in principle, replace kind of traditional, traditional money. So that’s, that’s good, that’s what. That’s one kind of use case, clear use case of crypto is used, you know, just for Allah says about this use case, right, even now, right? So, crypto is used a lot in places where it’s hard to do business or make transactions in fiat currencies, right? So think about kind of countries or organizations under sanctions, right? You know, think, think about places with capital controls, right? So crypto is useful, or, you know, is a potential replacement for for fiat money, whether it’s good or bad. I mean, that’s that’s irrelevant. It’s but it’s definitely possible. The second use of crypto is, basically, you know, a as a fuel behind different types of finance applications, right? So pretty much any protocol in decentralized finance, right? So let’s take uniswap as an example. Since we mentioned that before, has its own token, right, its own currency, its own asset that the use that is used for incentivizing people towards a certain behavior, right? So think about a protocol. Well, we do it in virtual right? We basically want to try to influence where the liquidity providers are putting their liquidity right. Because, you know, we have developed, you know, a system of optimization of liquidity, right? And, you know, we think that we know better than a typical liquidity provider where the money is going to be, you know, bring the biggest, you know, benefit about right now, we did not force people to do anything. It’s their money, right? They’re going to put their money where they want to, but we can try to influence them, and the way we influence them, and, you know, many other protocols do it as well, right? It’s basically trying to give them extra rewards for doing certain things, right? In our case, you know, if I want a person or people to, you know, to put their liquidity in a particular place, well, I’m going to say, Well, if you do this, I’m going to give you something extra, right? This extra is going to be the token of my own protocol, right? So, in addition to the fees that people are going to generate, you know, as a result of liquidity provision, they’re also going to get something extra, which is the protocol token, right? So, and you know, protocol tokens, as most, most defi protocols, have their own tokens, right? And you know that is mainly used for for this purpose, and that basically defines kind of the valuation of this token, right? Is derived from the utility of the protocol, right? So uniswap token is very valuable because a lot of people trade via virtue purchase or generates a lot of fees, you know, for the liquidity providers, right? And that’s why uniswap tokens currently worth, you know, several billion dollars. Other tokens are worse less, right? It all kind of depends on, on, on whether and to what extent the protocol gives utility to to its users. Most tokens that are currently traded or, you know, registered on coin Mar. Cap are worthless, if you ask me, right? So, you know, I expect that many of them, the vast majority of them, probably including different mean points, are eventually going to go down to zero, right? So, you know, mean points is a definition of an asset that does not have any, you know, inherent utility, right? It’s, you know, it’s price, basically, is a function of the temporal demand that that exists, right? You know, there is nothing, nothing kind of behind it now. So if you think about the 1000s of assets that are currently, you know, out there in my prediction is most of them are going to go down, to go down to zero. And I don’t think many people are going to argue with that, but I think what’s really important, right? And it’s probably more important than, than the than the use cases that we discussed, is the possibility to to tokenize real world assets, right? So think about a Tesla stock, right? A Tesla stock is currently trading on, I believe, NASDAQ, but it’s trading in one place, okay? And in order to trade, you have to go to a particular exchange, okay, and play by the exchanges rules, right, including be limited to the liquidity on that particular exchange. Now think about a situation where Tesla stock is tokenized, and what it means is is the following, I buy a certain number of Tesla stocks, right? I put it in some custody, right, of the reputable custodian, right? And I issue Tesla tokens, right? Tesla tokens that are compatible with, you know, trading, or, you know, any activity on a blockchain, right? Those tokens are going to be one to one backed by Tesla stocks, you know, sit somewhere in a safe deposit box right now. Those social tokens, you know, can be traded on dexes, okay, they can be traded with all the advantages of the sophisticated kind of trading technologies that we discussed, right? And future technologies that are going to be not to be developed, right? Because this, this market is developing very fast and kind of and progressing very fast, right? If you think this possible, definitely right. What is currently not really possible, right, is this whole issue of custody, right? So regulation, right, is not there yet to, kind of to define, you know, what a sufficient kind of efficiently secured custody is, right, in order to allow this type of activity, right? I know the lawyer, right. I don’t know how the optimal regulation should look like, but imagine for a second that there is regulation that allows and makes this activity relatively easy, right? Once this is possible, the sky is the limit, right? So, so basically, think of any asset, you know, stocks, bonds, real estate, precious metals, you know, energy, anything can be tokenized, right? And this is the point where I think this defy decentralized finance. Markets, including decentralized exchanges, are basically going to explode. Because right now, this market is limited to a very particular, very niche type of assets, which is crypto. Crypto is still, you know, a tiny asset plus relative to stocks and bonds, right? So currently, the market cap of all of crypto is $2.7 trillion if you think about stocks, global stocks, about 100 trillion dollars, slightly more bonds, similar numbers. So, so crypto is small, right? But once you kind of allow trading in other assets, right, stocks, Bond, real estate, right? That could increase the importance of this market, but by at least two orders of banking, right? And so, so this is basically the reason why I think this market is important, right? It’s not because of its importance, right? Now, I mean, it’s not there, right? It’s because of the potential, which, I think is there, right? And potential to kind of revolutionize many of the financial technologies that that we’re familiar with,
Gene Tunny 54:13
yeah? Look, I think that’s a very good answer. No. That’s yeah, I actually see the potential there, particularly if you can have like that can give like you could have people getting tokens here for Australian government bonds, for example, that are typically not there’s no retail Australian government bond offering at the moment, but this is a way that you could get, you could get that exposure With the tokenization. I think that’s yeah. That’s fascinating, yeah. The whole regulation side of things is yeah, that that needs to be sorted out. I know that in Australia, the Treasury is supposed to have been looking at it, but it’s just taking them forever to come up with a regulatory framework. Which is, you. Which is rather disappointing, right? Okay, well, I’ll have to have a, I’ll have to have a closer look at all of that. Yeah, it looks like we’re coming up to time. I mean, yes, lots of fascinating things to talk about. I mean, I’ve got a lot, a lot to learn. There were some really, there’s some really technical concepts in there, and it looks like what you’ve done is a very clever way to to solve this, this problem of these thin markets, to actually make sure there’s enough liquidity there. So I think that’s for these trades that’s very good. Is there anything? Anything else we should cover before we wrap up? Kenny
Evgeny Lyandres 55:42
talked about, kind of the whole market. We talked a little bit about Virtu sock. I do want to mention that kind of this, this financial technology is sort of not the only thing we’re doing right. Another kind of aspect or facet of our productivity is, is basically trying to optimize liquidity allocation, right? Basically trying to make liquidity as useful as possible, right? You know, in the presence of the financial technologies we’ve discussed, right? So, you know, I briefly mentioned this, right? We have this, the system that you know, the fancy word, word for it right now is AI agent, right? It’s basically an AI based system that, you know, take some data from outside and make some decisions. So in our case, it our, our AI agent is called Minerva, and Minerva basically has two, two sides to it, right? So it first takes lots of data, right, concurrent, constantly updated data from, you know, from the markets, and tries to predict the distribution of future trades. Right? For example, Tunny speak, right? And, you know, we want to know right, how many trades are going to be, you know, Bitcoin against USDC, and you know any other pair that you can think of, right? So we build this distribution of expected trades, right? And then, you know, you know, conditional this distribution, we say, well, let’s say that, you know, we have a certain number of of tokens that we can give out to our liquidity providers as an incentive for what they’re doing. Let’s say that our liquidity providers require a certain rate of return on different pools, right? We also have estimates of that. What is the best way to distribute liquidity across pools in order to maximize some sort of objective function, right? And this objective hash function can be either the overall returns liquidity providers or overall volume of trading for versions, or, you know, some combination of the two or more, something else. Okay, so the system basically tells us, you know how to distribute our rewards and our token right to liquidity providers to maximize something. And this basically the combination of the financial technology and this data science, right? Is what brings to the up to 400% increased returns to liquidity provider, right? So it’s not just financial technology, it’s also basically pretty sophisticated, I think optimization that we do to further improve, you know, the what liquidity providers earn,
Gene Tunny 58:28
right? Okay, okay, so, Minerva, I like it. That’s excellent. Oh no, we better wrap up there. Kenny, that’s this buddy for me to absorb already. And I think, yeah, I think your explanation of the potential for for crypto, with tokenization, I think that’s, yeah, I think that’s that’s worth considering. So I’ve got to think about that some more. Again. Thanks so much for your time. It’s great that you could, you could join me and, yeah, really value your insights and and learning, it’s good for me to learn and get exposure to this, and I think it’ll be of great interest to listeners. So again. Afghani, thanks so much for your time. I really appreciate it.
Evgeny Lyandres 59:12
Thank you very much for the kind words and for having me. And yeah, it was a great chat, I
Gene Tunny 59:16
think, very good. Thanks. Evgeny, all right.
Evgeny Lyandres 59:19
Thank you very much,
Gene Tunny 59:22
righto. Thanks for listening to this episode of economics explored. If you have any questions, comments or suggestions, please get in touch. I’d love to hear from you. You can send me an email via contact at economics explored.com or a voicemail via speak pipe. You can find the link in the show notes. If you’ve enjoyed the show, I’d be grateful if you could tell anyone you think would be interested. About it. Word of mouth is one of the main ways that people learn about the show. Finally, if your podcasting app lets you, then please write a review and leave a rating. Thanks for listening. I hope you can join me again next week. You.
Obsidian 1:00:09
Thank you for listening. We hope you enjoyed the episode for more content like this, or to begin your own podcasting journey, head on over to obsidian-productions.com.
Andrew Einhorn from LevelFields shares insights into leveraging AI for financial market analysis. Their innovative platform is designed to detect key events affecting stock prices, enabling investors to react swiftly. The conversation covers the benefits of AI in democratizing financial information and the future implications for investment strategies. Disclaimer: Nothing in this episode should be considered financial or investment advice. Our aim is to provide information and insights to help you understand the evolving landscape of AI and investing. Always conduct your own research or consult a financial advisor before making investment decisions.
You can listen to the episode via the embedded player below or via podcasting apps including Google Podcasts, Apple Podcast and Spotify.
About this episode’s guest Andrew Einhorn
Andrew Einhorn is the visionary CEO and co-founder behind Level Fields, a groundbreaking company leveraging artificial intelligence (AI) to revolutionize the way investors interact with the stock market. With a career deeply rooted in technology and finance, Einhorn has dedicated himself to democratizing access to advanced investment strategies, making it possible for the average investor to make informed decisions swiftly and with confidence. Before establishing Level Fields, Einhorn led a successful event monitoring system company for a decade, serving publicly traded companies and offering insights into how market events affect stock prices. His passion for utilizing technology to enhance market transparency and fairness has led to the creation of Level Fields’ AI-driven platform, designed to scan the market for events impacting stock prices, thereby leveling the playing field between retail investors and the larger, more resource-rich players in the market.
AI-powered investment research platform for retail investors. (28:01)
Using AI to analyze audio transcripts for investment insights. (33:12)
Using AI for event monitoring and investment analysis. (38:45)
Using historical data for investment strategies. (44:10)
AI’s role in investing and the financial establishment’s reaction. (49:19)
Takeaways
1. AI can help democratize access to investment strategies by scanning vast amounts of market data and financial reports that individual investors cannot monitor themselves.
2. Understanding how historical events have impacted stock prices can provide insights on predicting future price movements and avoiding losses from overreacting to news.
3. While AI is not a crystal ball, it can be a useful tool for investors when combined with human judgment, by automating tedious research tasks and identifying potential opportunities that may otherwise be missed.
Transcript: Revisiting Ricardo: The Rise and Fall of Ricardian Equivalence
N.B. This is a lightly edited version of a transcript originally created using the AI application otter.ai. It may not be 100 percent accurate, but should be pretty close. If you’d like to quote from it, please check the quoted segment in the recording.
Andrew Einhorn 00:04
Just like any new tech in the market, you’re going to piss off some people. And, you know, we’re trying to give some of these strategies to the general population and democratise access and everyone likes that.
Gene Tunny 00:26
Welcome to the economics explored podcast, a frank and fearless exploration of important economic issues. I’m your host gene Tunny. I’m a professional economist and former Australian Treasury official. The aim of this show is to help you better understand the big economic issues affecting all our lives. We do this by considering the theory evidence and by hearing a wide range of views. I’m delighted that you can join me for this episode, please check out the show notes for relevant information. Now on to the show. Hello, thanks for tuning in to the show. Today we’re exploring an exhilarating intersection, AI and investing. In this episode, we’re joined by Andrew einhorn the CEO and co founder of level fields, a company that’s at the forefront of applying AI in financial markets. Andrew and his team have developed a platform that scans the market for events proven to impact stock prices, allowing investors to make more informed decisions rapidly. For Andrew is not just about speed, it’s about levelling the playing field between the average investor and the big players in the market. As always, I’m interested to hear what you think about the issues we discuss on this show. So please get in touch and share your thoughts. You can find my contact details in the show notes. Before we proceed, I need to make a quick disclaimer. Nothing in this episode should be interpreted as financial advice specific to you. Our aim is to provide information and insights to help you understand the evolving landscape of AI and investing. Always conduct your own research or consult a financial advisor before making investment decisions. Right? Oh, we’d better get into it. I hope you enjoy my conversation with Andrew einhorn from level fields. Andrew einhorn from level fields, welcome to the programme.
Andrew Einhorn 02:15
Thanks, Gene happy to be here. Appreciate you having me on.
Gene Tunny 02:18
Excellent. You’re doing some very interesting things in AI and in applying AI to financial markets that I’m delighted to have you on the show to talk about so your company is level fields and the pitch is find better investments 1800 times faster. Ai scans for events proven to impact stock prices. So you don’t have to. Okay, so can you explain I mean, what are you actually doing there? Andrew? What’s this? What’s this AI? What’s it scanning? Can you please talk to us about that place?
Andrew Einhorn 03:01
Your Yeah, happy to, at a very, very basic level. It’s monitoring the market for events that are proven to move share prices, either north or south, and positively or negatively. If you know how a price is going to move, and you’ll learn about the event happening, then you can anticipate that move. And you can make short, mid or long term bets trades knowledge and information, you know, concrete piece of it would be you know, if the CEO departs from a large corporation like an Amazon, we have a data analytic system that shows that usually results for companies like Amazon in a price decrease of a couple percentage points, you know, what’s happened with Amazon. But the data also shows that within about three months, the stock is right back where it used to be in many cases, far above it. And we can see the different types of stocks, you know, smaller stocks versus bigger ones, profitable or unprofitable companies, they all react a little bit differently. If you take an unprofitable, struggling company, and the CEO leaves, the share price often goes up. People are celebrating that, hey, you know, hope is coming. Let’s bring somebody else in. And so the data set shows that and it alerts people of these kinds of opportunities, which happen in some cases 100 times a day, across 1000s of stocks that are in the market. And so we track all types of events, leadership changes, capital deployments, you know, being added to indexes. Investors coming in large billionaires coming in and buying positions and companies pushing for changes. We have lots of different event types and the ideas, you can just quickly find what’s going on in the market without actually reading very much, because the AI is out there reading all these company announcements, all the reports, they’re putting out the news that’s covering them. And just in the US alone, that amounts to about 6300. Companies, all making announcements constantly. And we get a large part of our information directly from those companies. But the reports can be long, it can be boring, and they can be tedious for somebody to read, even if they have the time to read the reports of 6300 companies, which is physically impossible. Unless you have a couple 100 analysts covering all the stocks, you’re never going to be able to do it as an individual. As a result, there’s a natural discrepancy in the market, where an average investor doesn’t have the resources or the time or even the knowledge sometimes to find out about all these events, to know what’s going on in the marketplace. So they’re constantly at a disadvantage to large hedge funds and asset management shops that do have the resources to scan the entire market to deploy capital. And if you want to test that theory, just ask an average person to name as many stocks as they can. I think you’re gonna stump people around the 20 to 30. Mark, everybody can name the seven, The Magnificent Seven, right? After that, they’ll start to slow down. Even very good seasoned investors will have a tough time after that. And as the reason is that, a lot of the media is biassed towards those mega cap companies, covering them all the time, making you aware of them all the time, because you click on it. And they can if you click on it, and you read about it, they sell more ads. So they keep writing about Elon Musk and everything he says on Twitter because it drives advertising revenue. But what it also does is really Rob, you have the opportunity to hear about all these other companies that are doing great things that the media doesn’t cover, or they don’t have the staff to cover. And so that’s where the AI comes in, it can monitor the entire market for you. And really, for the first time ever, you have a personal assistant in reading and understanding and analysing financial news and financial announcements. And you can take that and create a very customised AI search agents look for just this type of thing that you want. So if you say, You know what, I really love investing in energy companies, and you go out look for energy companies that are growing revenue, at least 10% growing earnings, at least 20% and recently increased their dividend by you know, whatever percent, I just alert me every time that happens, I don’t have to do anything, ever again, on my research, I’m gonna get an alert, right in my email, when my criteria is matched. And if that’s my investment thesis, then I just go along on on those investments. So there’s a lot of flexibility in the platform. And what we’re really trying to do is kind of break down that barrier that most people have, and not being able to monitor the market at scale, not being able to see those was really interesting. bull markets, or in some cases, bear markets, because we can go both ways, you know, in the, in the strategies, short and long, that are reacting to different macroeconomic events. And we see that on the basis of company financials, we see that in the basis of what CEOs are saying and our financial outlook. We see that in the basis of what the leadership is doing with capital allocation. And so, you know, an example is when, you know, when Russia invaded Ukraine, there was in the middle of a pretty decent sell off already in 2022. That was happening. And this really accelerated the market sell off. But there were some companies that were thriving. I doubt many people would know what those companies were could list them, but a lot of them were fertiliser manufacturers that are based in Canada and the United States. And the reason was, they couldn’t get fertilisers, which a large percentage of them came from Ukraine, Belarus and Russia, you couldn’t get them out of that region because of the conflict. So the price of these types of fertilisers was rising was doubling. And so companies that weren’t involved in the conflict and could export their product. Were then selling it for double the price. And so what we saw on the platform was a slew of fertiliser companies, increasing their dividends doing special dividends, increasing stock buybacks, and we’re sort of you know, beating earnings and we’re wondering what is going on with fertiliser companies? I had no idea I never bought a fertiliser company in my life. But you do two seconds of Google research you say okay, they sell this you know, potash potash is a lot of it coming out at Bell roofs, they can’t get it. Price is 100 percent bull market for fertiliser companies. And so this one company that I actually invested in, called nutrient because Canadian, you know, the share price jumped 75% In about two months. So, you know, if you’re an investor and you can go straight equity, you can leverage with options, stock options, you can make a lot of money on those types of things. But you have to have the awareness, you have to know those things are going on. And you have to have a system is sort of connecting the dots for you fast enough for you to react otherwise, you know, you’re kind of going to sit in the same 789 stocks forever, not really knowing why they’re going up and down. Yeah, gotcha.
Gene Tunny 10:43
Are you scanning social media too? Because I mean, some of the names, you mentioned them, and you think of some of these high flying CEOs. Many of them are engaging in risky activities, you know, go into space, and all of that. I mean, are you scanning social media for for, you know, hints of, you know, potential, you know, shock news,
Andrew Einhorn 11:04
we were a little bit, we kind of pulled back from it, because Twitter was cutting off access to their data sets and their API to third party developers like us. But we also found it for the most part, you know, we were relying wanted to rely on the company announcement as much as possible. We want to do overweight, potential pumping, dumps that, you know, market manipulation that can sometimes happen. We’re trying to protect, you know, the users and the investors on our platform. Because there are some savvy, bad actors out there that will utilise social media to pump up stocks and fake news. And, you know, sometimes, if you’re not watching closely, you can fall prey to that.
Gene Tunny 11:44
Yeah, that’s a good point. Yeah. So there’s a lot. Yeah, so yeah, you got to be careful, you don’t really know what, what’s true out there. So that’s a really good point. Whereas with the company reports, I mean, obviously, they’re, you know, they’ve got a legal obligation to tell the truth, but have been sometimes, of course, you know, there can be,
Andrew Einhorn 12:03
and that happens to they, you know, sometimes the reports are fake, there was a company. I forget the name, but the ticker was to T IO. And they faked all their financials. And it looked like, you know, they did this wonderful quarter, and they were growing by 5,000%. And it was like, wow, one of the scenarios in the events that we tracked is the short seller reports. So, in funds, you know, they specialise in kind of busting the fakers that are out there in the market. And so he saw in our platform, those short sale report went up on this, went out on this company to and said, you know, all the all the founders were criminals, and they had already been convicted fraud before. Looks like they’re doing fraud. Again, they investigated some of these alleged sales contracts that they had that were worth, like a billion dollars. And what the company had done was sort of open up like a shell LLC, create a fake purchase order back to the company, and use that as sort of evidence that they were driving sales up. And so the whole thing just fizzled out. And so you do you do see a good amount of that, you know, years back, it was luck, and Coffea, China, they were faking their arse. And a similar kind of hedge fund, you know, put up a camera and started counting how many people actually went into the luck and coffees and said, you know, if, if their numbers are right, then they’re they’re serving four times the number a couple of coffees and Starbucks. Yeah, yeah.
Gene Tunny 13:39
So I just want to talk a bit about the AI, because you mentioned you’re scanning these company reports and news from the companies. And so to an extent, you’ve got to trust what’s coming out of them. And what have you got, is it so people talk about, and this is what I want to explore with you, because I think pre show we were talking about how, look, there’s been applications of AI for years. And there’s been things like algorithmic trading, which I mean, I’d be interested in your thoughts on how close that is to the AI, whether that’s a true AI, that the public discussion about AI is just really taken off with these, what they call these generative, pre trained transformers, if you’ve got that, right, the GPT chat GPT, where we’ve got that interaction with a Can you talk about please Andrew, they’ll try to explain, like what you’re doing, is it a are you using a GPT? Is that right? And how does it differ from what other people have done in the past
Andrew Einhorn 14:43
now we developed our own proprietary artificial intelligence system. And the way you kind of think about it is, you know, the chat. gfpt is sort of the mouth of a body. And the stuff that we’re doing is more like The beating heart. Right. And so the interaction with AI through chat, GBT is obviously, you know, through chat, it’s a communication channel. And so that’s one deployment of AI. whereas ours is more sort of, like, you know, a monitoring, AI, it’s going and finding different pieces of information that are out there. It’s making sense of them putting them together, you know, it’s a little bit more like cognitive functioning, I should say, probably better, better than the heart analogy. And so AI, in general, can be anything, you know, very, very basic tasks, very, very difficult sort of self directed tasks, it sort of depends on on your goal. Chat. GPT is great, you know, for interacting, but it’s completely reliant on third party data that has already found the answer to the question that it’s now speaking, right. It’s not thinking and processing for itself, it’s going into a database of information that it knows. And then it’s extracting that information and then saying to you, and like, plain language, here’s what I found, you know, a little bit more like an advanced Google search. And so what we’re doing is we’re finding the answer, right? So if you ask a question, like, why was Apple stock down yesterday, you’re gonna get some message that says, you know, my, my training only goes to February 2023. We, you know, we don’t do real time analysis, or might say something like, Well, according to this one news article, you know, blah, blah, blah. It’s not doing anything on its own, it’s just summarization, our system is going out and coming up with an answer, because we are not only extracting what happened in that event. So let’s use the example of Apple. So Apple stock goes down yesterday, we find out that Apple made an announcement through our AI and AI identifies Apple doing a product release of vision, pro whatever. And then in the system, it’s identified that the share price moved down. And that was the only major event. So those pieces of information the AI then puts together and says the reason why apples down was this bullish event product launch, which, you know, seven times out of 10 is actually negative for Apple. Now that we have that piece of information, you could overlay something like a Chet GPT into our database and say, Hey, why was Apple stock down yesterday, it would look at our data, pull it out and be able to summarise, you know, coordinate a level fields. This was because of, you know, blah, blah, blah, like Apple stock was down because of a product launch that didn’t go well, instead of you. If you look at that at scale, and say, Okay, well, our system is more was a research agent that’s actively out there looking for information, assessing what’s going on analysing what’s going on, and then coming up with a data array of information, just showcase what happened. It gives you far more information about what’s likely to occur next. And I think the best example I can give is like a weather report. Yeah, here’s where it’s raining, here’s where it’s sunny, there’s an 80% chance that the rain is going to be four inches of rain. And it’s going to last for 12 hours. And that’s kind of what the analytics of historical data sets like ours can provide. And so if you are, you know, a type of person who wants to invest in stocks, but maybe doesn’t necessarily want to listen to someone’s opinion on CNBC, or, you know, one of these newsletter services, this is this is the next Amazon or these are five other stocks that are better than Amazon, you can actually look at the data set and see, well, this company had a big bullish event, this bullish event 80% of the time, ends up in a positive price return of 20% over the next X number of days, weeks, months. And so we’re trying to move away from that, you know, kind of human opinion driven stock market and move closer towards an understanding of how things normally play out. And a lot of the market, to your extent is is driven by algorithmic trading. And those trades, about 60% of the trades in the market are automated. And so why, how are they automated or automated on the basis of patterns that people are telling it to find? And so if you know how the algorithms are reacting, and you know what they’re reacting to, when you find the reason why they’re reacting, it’s easy to predict what happens next. And usually, they’re using some kind of technical analysis, looking at patterns and charts. And so the piece that we really plug in and say, well, these patterns and graphs and movements on the charts. That’s great. But events caused them. And so what we often see as the event happens, it starts to raise the price from the for the stock that that people are watching, then the algorithms see that price movement, because it breaks the pattern of some kind of technical analysis. And then the algorithms buy it and drive it even further. So you have that kind of double burst if you can get out in front of these events. Right. And that’s a lot of how the market moves. Yeah.
Gene Tunny 20:28
So yeah, a few follow ups. This is fascinating. Is your AI? Is it trying to then model how the algorithms will respond? Is it? Or are you just looking at the impact of events, so I know that you’ve from a previous interview, you’ve done your you’ve done a lot of deep research, we’ve looked at Google Scholar, you’ve pulled out studies, events, studies, which show how particular events impact the market, so you’ve got that information, or you also, and you know, there might be an immediate impact to you, then, are you trying to maybe to some extent, they’ve looked at this themselves, but are you then looking at how the market reacts through the reactions of all the other participants through the algorithmic trading, etc,
Andrew Einhorn 21:10
you look at an aggregate. So we think of market participants just broadly how it reacts, right, and in some cases, the system you can see, the speed of the reaction differs depending on the event type. For example, one of the events we track is an increase in dividend amount. You invest in dividend stocks are generally not moving really fast. You know, they’re there for longer term, they’re collecting dividends. And so the price action on that first event is relatively slow. Whereas other events, like a company being added to an s&p 500 index, there are big hedge funds that trade that specific scenario that have spent a lot of money to develop algorithms to buy shares, the second, the s&p organisation announces a new cause, you know, a new constituent in the index, that does trade very quickly. When we just show it, we just you can see it on the platform that they have, the average move is a percent done at an event like this. And if you start to look at some of the data, you’ll see it moves fast versus a dividend event, which may be moving slowly over several days, you know, and then if you’re looking at some of these things, in aggregate, like multiple dividend increases over the span of let’s say, a year, year and a half, you then can predict longer term gains, you know, a company that’s repeatedly saying things are good, so much, and they’re so good, we’re gonna give away money. And then that other pattern starts to reveal itself. And so it really depends how you want to use the system. And we’re, we’re very much the data provider using the AI to kind of showcase these patterns that exist all over the market. And in some cases, you can take kind of contrarian views or find these, you know, hidden gems that you would never see before. Just just just by having access to all of the data at kind of the palm of your hand. My favourite example is a stock called very, very active. It’s VR TV got bought out, but years ago, this was mid March 2020. We’re in the middle of COVID. And stock market selling off, we are going into lockdown globally. You know, the big ship is moving towards New York, you know, with extra hospital beds. And there’s there’s a there’s a panic. At that time, most companies, you know, are starting to slash their dividends. They’re looking at cost saving measures. There’s one company that came in started doing stock buybacks or buying back there shares. And it’s a very brave move. And you would think, stupid move for somebody to take their company’s cash right as we’re going into, you know, potentially a global depression and take that cash and start buying back their shares with it instead of using it for operating capital. Why would they do that? Well, this particular company is a engineering and kind of design firm. They build and sell direct to consumer product packaging, isn’t it that everybody used to sell through the big stores who had a product could no longer sell to the big box stores. They had to get it to your doorstep that required new type of packaging. In order to do that. The whole world was going through the same issue. Everybody’s Sitting at home buying stuff. And now you Commerce has completely shifted, this company is sitting in the middle of what ended up being a goldmine for them. And that precipitated a stock run of 2,600% growth over the next three years. And you would have never seen that had you not been following, you know, why would anybody be doing this random stock buyback in the middle of COVID. And so that’s the kind of thing that this system can can identify. And it’s, it’s, it’s a proof point that the macro economic event is either positively or negatively impacting, you know, these individual stocks. And you can’t, you can’t monitor the market for that kind of thing, because stocks still went down. That was a funny thing. And you hear people, you know, they’ll give a criticism, well, I just look to see what stocks are up. And when stocks are down, well, then it happens after Seminary has decided what to do. In this case, the stock kept going down for the next couple of weeks while the market sold off, and then began 26x run over the next three years.
Gene Tunny 26:09
Okay, we’ll take a short break here for a word from our sponsor.
Female speaker 26:15
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Gene Tunny 26:44
Now back to the show. Where are you guys? Do you in direct somewhere you’re in DC, the DC area or Baltimore? Is that right? Yeah, just
Andrew Einhorn 26:55
outside of Washington DC. We’re a Virginia based company.
Gene Tunny 26:58
Right authority. So Virginia rather than Maryland. And so if you’re you must have computer scientists on staff. Do you who develop this proprietary AI? I mean, what what’s your operation look like? Do you have? And you have people? Do you have actual analysts as well, I mean, you’re you solely relying on the AI to deliver the predictions.
Andrew Einhorn 27:21
We have all the above. You know, we have couple PhDs, some in linguistics, some in AI, some in physics, astrophysics, at do a lot of good in the modelling, and analysis work, we have those who have financial backgrounds, we have a lot of financial advisors that are from the street or certain hedge funds, previously, and sort of former lives that have helped devise the system. And so a lot of what we’re really trying to do is think, what are the big annoying, tedious, time consuming tasks that you have to do as an investor to figure out what to buy? And how can we automate that, all of it so that the barrier goes away. So you don’t have to sit there time and time again, doing a stock screen, or, you know, fiddling through a news feed for an hour and a half to keep up with what’s going on with your existing investments. We just want to streamline at all we want to give superpowers of investment research to an average person. So we started, you know, this process back in 2019. And it took several years to get it right. Part of it is built on historical academic models of others across the world that have studied, you know, how events impact share prices, some of them are original to us, others, you know, belong to hedge funds that we knew, use some of these event driven strategies and said, You know what, we could do this within our platform, let’s just put that on there. There should, shouldn’t need to have $10 million, you know, to be able to participate in the market. And so we saw subscription costs, very, very low, it’s the equivalent of $19 a month as our starting point. And then it goes up to 167 a month, depending on the tier that people are subscribing for, and the level of information that they want. But it makes it unique, more accessible to more people. So that you don’t have to rely on television stations, which frankly, are very often helping to push a narrative or push a particular stock, right? It’s often that these large asset managers are selling shares while they’re on TV talking about how great stock is because they need to sell 30 million shares. They need, you know, millions of buyers to offload those shares. Yeah,
Gene Tunny 29:54
yeah. So this is a you’re aiming at retail investors so you can Get this on while your browser or your phone, there’s an app for it, I imagine and you said their email alerts, is that right?
Andrew Einhorn 30:07
There’s email alerts. Yeah, we are, you can certainly access it from your phone, we don’t have a specific app, but we have mobile ready. And mobile accessible pages, you can get on your desktop, it’s very easy to use to try to make it as simple as like shopping for a flight on an airline. And unlike even unlike that, where you have to put in your destination, if you want, you can just browse to see, you know, what events that happened recently? And what are the price movements, quickly set an alert, and forget it, you know, and then the alert comes in, it will say, Hey, this is a bullish event. Share price typically moves, you know, X percent over time, or you can look into your own analysis, you know, based on the data that’s in there. So raw, we’ve tried to keep it very, very simple. Good one,
Gene Tunny 30:55
do you have any data on the performance of people who use your platform, or who you who use level fields,
Andrew Einhorn 31:03
we don’t track them. We have regularly, you know, get feedback, like thanks made a bunch of money on this are 1000 today that was, you know, bang for my subscription, 10 times, we do have a level we call it level two service. This is more of our white glove service where we have analysts that monitor the AI alerts. And they kind of cherry pick the best ones, and we’ve sent it out. And it’s really for people that want a little bit more help picking out your entry and your exit point that that service really outperformed our expectations. We we looked at the closed alerts for our first year of operating that and the return was 2,800% cumulative over the course of the year.
Gene Tunny 31:53
Right for the cherry picked alerts. Right. Okay. Do you have any data or any? I guess it’d be a very difficult thing to do. But of all the alerts, I mean, are you giving explicit bias buy or sell recommendations on with these alerts? Or are you just saying this, you should be paying attention to this?
Andrew Einhorn 32:14
Well, it will say, you know, this is a bullish event than in a bullish event, the share price goes up, right, so I tell them to buy, but it will say the share price is gonna go up, you can do whatever you want with that information, and it’s gonna go up by 3% in the first day, and then you know, 75% of the time by day 10, it’s going to be up this much. And so it’s really your job as the investor to say you how you want to play it, we have all types of users of the system, some will just buy it and make a quarter percent every single day. And others want to be able to just find the event that’s going to be up, you know, 100%, and they’re okay, waiting all year for two or three of those, which they do happen. They have others who have a lot of stocks in their portfolio. And what they’re looking to do is just find opportunities to make more money off the stocks that they have. So if they own apple, and Apple has a positive event, they get alerted to that. And they might sell an option contract for extra premium when this bullish event happens. Knowing that on day two, according to the statistics, that particular event actually pulls back to where it was people started to sell. So, you know, we’re we’re not trying to create the strategies per se, for folks, they’re there for the taking. And there’s a lot of guidance on the system. But the data itself is very revealing. It’s like, okay, if you knew every time you walk out the door, what the weather is, like, you know how to dress. This is the same thing. Like, you know what, what to do when these events happen. And that’s been a lot of the problem in the market is often people overreact, because they don’t know how bad the bad news is. They don’t know how good the good news is. And you see this like massive overreaction, you know, 2021, the whole year, but you see it all the time, you know, bow past where it’s supposed to be and then eventually pulls down and people get hurt because they buy at the top, and then they start selling on the way down and then stock goes back up. So, you know, it’s just as much about avoiding bad decisions, as it is finding good investments. And some of the events that we put on there we know, are 5050 they could go either way. You know, there’s a Tesla one on there as an example, and test the product launches. There’s a lot of traders that like to think, oh, every time there’s a new Tesla product, of course, Tesla’s gonna go up. It’s not true. After time goes down. And we we’ve gotten criticism for putting that event type on there. They’re like, well, like Can’t really trade this is 5050. And we’re sort of saying, well, that’s the point. Stop making bad decisions. That’s what we put it on there. So I knew it was like flipping a coin. Yes. Good point. Can I ask what? What markets you and you mentioned, you’re in, so equities in the US and there was what over 6000 companies on? Was it on the s&p. So New York Stock Exchange, Stock Exchange? Yep. Anything traded on the New York Stock Exchange anything on the NASDAQ, and we have some of the larger companies on the OTC so that includes foreign companies that have ADRs that trades in the US, it also includes, you know, the OTC Markets, which are some of the bigger European companies, like Volkswagen, is there. Yeah.
Gene Tunny 35:52
Okay. So just in equities? Are you in fixed income in bonds at all? Now?
Andrew Einhorn 35:57
Not yet, we’re just sticking to straight equities at the moment, although we do have a lot of option traders that use the platform to inform, you know, their particular option trades. Gotcha.
Gene Tunny 36:09
Okay. So as your competitive advantage, you’re getting this information quicker, because you’re getting a you’re looking at these reports that other analysts may not you’re getting a feed? Is it? Is it an API, you’re tapping into an API to get these reports? And then you’ve got the, the AI that analyses them all? Quickly? And there’s some AI magic there, it’s a neural net or some or something? Is that what is that what’s going on? You’re able to get this information in your Hoover it up, analyse it extremely quickly, and then push out the the advice is that essentially, what it’s about, in
Andrew Einhorn 36:51
a way? Yeah, I mean, it’s not necessarily a speed issue. You know, we certainly try to get the information as fast as we can. And largely, we get it within 45 minutes of an event taking place. Or being announced. We get the information ourselves, which means we data mined it ourselves, we pull it in, and we have it. And what the AI is really doing is it’s like a speed reader. So it’s reading 30,000 documents a minute. Yeah, okay, you just got through 30,000 reports. And then within that extracting 21 million events per year. And then looking at a group of events, saying okay, of these 21 million events, which ones actually move the share price and which ones don’t matter. And so then it’s selecting out about five or 6000 events that are known. We call them material events in their material to the share price movement. Once that happens, we’re then identifying what company had that event, pairing it with real time price action from the markets, to then correlate whether or not that particular event has had an impact on that particular equity. And then as we do that, for groups of events, let’s just say, you know, CEO departures, we have every CEO departure in an aggregate data set. And then you can look and say, Well, how to co departures, typically affect share prices, and it’s one click away. And there’s summary statistics that will show you okay, it moves in 4%. And it’s usually a bullish event. And then you can filter that information if you want. So, you know, is it the same cases in energy companies, as in tech companies? Is it the same case in large tech versus small tech, which is not the same case. So very quickly, you can kind of see, you know, with a couple filters, what’s going on there. And now that we have the data, yeah, you can get alerted to it. And you can act on it, if you want to trade it. Or you could just use it to do faster research. You know, by having all that you don’t have to read all that information yourself, because you just knew you now know what the main events are. For the equity that you’re doing research on, let’s see, pull up apple. In the platform, you put Apple Company page, and then you pull up a chart. And typically when you look at a stock chart, its ups and downs, ups and downs. Don’t know why it’s ups and downs, right? We’re just looking at it. And then normally, you would have to say, Okay, why did Apple drop 20% On this day? Let me go to look at a news feed. Let me go to the day of the news and look at the, you know, 300 articles that were about Apple that day and try to find information that would tell me why was it down 20% In this one day, you no longer have to do that. Because right on our charts, we have the event appended there that move the share price. And so you save yourself all that hassle all that time as you look at In Apple graph, you know, there might be 20 events, and each event is right before all those big movements on the graph, you can look at it that way, you could just scroll down the screen and see a list of all the recent events that Apple has has happened product launches, buybacks, dividends and so forth. And you just get within 15 seconds of view of, you know, what are the recent things that have happened to this company. And a lot of that is, is missing from the market today. Because if you were to go to something like a typical stock screener, and your brokerage system, and you’re gonna go, oh, I want to find out, I want to find a stock that has a great dividend, right? You’ll find these companies that have a 12% dividend. And you got to then then your research really begins because you get to find out, is there a weird reason why they have a 12% dividend, and then you pull up the share price? stock chart, and it shows that it was $60. And now it’s a $20 stock, which is why the dividend is now 12%. But why did it drop from $60 to 20? Now you have to go and do that research on our platform, you know, we would tell you, hey, you know, they’re filing for bankruptcy. You don’t need to do any research like that. But then it’s still what it is, they haven’t got it yet. So it’s time savings. It’s finding it investments, monitoring the market at large, setting your research process to be automated, but he’s AI search agents that you can use out of the box that we have or customised yourself, and really just see a lot more of what’s going on in the market and why it’s moving the way it’s moving. Right? So
Gene Tunny 41:43
you say AI search agents, out of the box that you have? Is this stuff you’ve developed? Or you’ve you’ve licenced from somewhere else?
Andrew Einhorn 41:54
No, we build everything from scratch, build everything. Right? Okay. So
Gene Tunny 41:58
you’ve How long have you been going have you been going before GPT and the GPT 3.5, for
Andrew Einhorn 42:06
where we started in 2019. Although I will say we had a company before this for 10 years that we operated, which also was an event monitoring system of sorts. Our client base was actually the publicly traded companies. And so the software system we built in that company did similar monitor events for large corporations. But it wasn’t geared towards investors, it was geared towards the public relations professionals that publicly traded companies. And so we’d see these patterns, and that, you know, something bad would happen, like if it was a train company, that train would come off the tracks for ash into a river or spill pollution, then the share price would plummet on the stock. And they would lose billions of dollars in market cap. And this would happen again. And again. And again, you know, same same type of event, same type of company. In other situations, you might have, you know, a data breach, like a cybersecurity data breach, then the company’s share price would fall. And so our software monitor that, and would send alerts to the corporate affairs and corporate communications people and they would get up in front of the podium where they would get the press releases out. And they would say, well, this wasn’t our fault. You know, they was actually the oil company that overloaded the train. And they would try to shake, save the share price by kind of blame. And so we have a lot of experience in that for 10 years. And then, you know, during that time, we’re like, maybe we on the wrong side of this issue, you know, we’re saving these people, billions of dollars a year. Maybe we should be using, you know, our knowledge and events to help make investments. And we sort of put that aside for a little while, sold the company and then started a new company and started checking with different ideas of what we could do with AI and how do we prove what we did the last 10 years. And then COVID hit in 2020. And it was like Aha, events, events, change everything that’s focused on events.
Gene Tunny 44:09
I do and day they’re doing day. You’ve got a fascinating backstory there. And I might, I’ll link in the show notes to a interview you did with on the side hustle city podcast, which was really good interview. And yeah, you told the full story and how you did consulting work for the Pentagon. I think it was and and then yeah, yeah. And your management, did management consulting, you did a grad, you did graduate studies at George Washington, if I remember correctly. So you’ve got an academic background, too, which is great. Yeah, so I’ll put a link in the show notes to that. I’ve just got a couple more questions. I’m wondering about hedge funds because one thing, some of the points you’re making, I’m thinking of that’s similar to the Ray Dalio Philosophy of because he’s he’s very interested in history and looking at how historical events played out and learning from those learning from events in the past. Imagine something, you know, Bridgewater or other hedge funds around the place, they must be interested in this sort of thing. Are they? Do you have any hedge fund clients? Are they like competitors of yours? Or how do you see? How do you see the other financial market players?
Andrew Einhorn 45:25
We don’t have a hedge fund clients, we certainly have some that have come to us. I don’t know if they’re pretending to be client or wanting to be a client, but they’re certainly interested in our methodologies and technology. I think, you know, just like any new tech in the market, you’re going to piss off some people. Have they cornered? And, you know, we’re trying to give some of these strategies to the general population and democratise access, and not everyone likes that. So we’re expecting it, you know, and the right now, you know, we’re for a self directed investor construct. The platform says it’s for personal use, commercial use, you know, will we make an API, you know, that can be accessed in the future? For certain types of data? Maybe, you know, we’re we’re looking at different ways that we could look, take our massive amounts of data that we have, and kind of enable different levels of processing of that data set. So yeah, I mean, it’s, it’s it’s an interesting marketplace, and that there are a lot of different strategies that people are deploying, there are large firms that have event driven strategies, some of them are fairly straightforward. Some of them are complicated. And it is a factor of historical information. But how you can utilise these patterns in the market to make better investments to make quicker money. I mean, the most famous trade in the world at this point was Bill Ackman straight during COVID, where he shorted the market while being on television, telling everybody to panic on CNBC, and made, you know, a couple billion dollars on that market short. And so the fund returned something like 75% that year. So when you can do those massive trades, then it’s great. You know, why? Why would you sit in the market all year, and watch, you know, these constant ebbs and flows of events, change the trajectory, or you’ve got wars that are breaking out, we have, you know, freighters that are being shot at oil prices are up, oil prices are down to nothing like you can’t, you know, and then you’re trying to be an investor and buy and hold philosophy, and then maybe you got a gain or you lose for the year, it doesn’t make sense to sit there forever, when you can just make that in a short period of time, hang out in cash at 5% yield, and then go back into the market when it’s safer. So our kind of method, or general philosophy really puts the buy and hold method on notice. And raw calls BS, okay, and says, you know, what, buy and hold is great if you’ve got the greatest stocks in the history of the market. But most of the time, it doesn’t work out so well. You know, GE, was the biggest stock in the s&p 500 in 2002. Now, it’s below where it was trading 20 years ago, that you’re buying and holding, you’re constantly losing money for two decades. You know, likewise, if you were to buy the s&p 500, in 2000, you would not make a single dollar until 2013 13 years later. So yeah, there’s a lot of these market myths about how long you should be holding and, you know, dollar cost averaging and things like that, that are that are kind of baked in narratives pushed by asset managers, banks, so you keep your money parked with them, and they can make money off your money. Yeah, look,
Gene Tunny 49:19
I think you make a lot of good points there, we might have to have you back on the show served over the future to have to have this discussion. Because I mean, I’m, as an economist, I am sympathetic to the view of, you know, just, you know, it’s time and time and time in the market beats timing the market, if you know what I mean. So I’m sympathetic to the view that the best thing you can do is just, you know, invest in the index and just let it grow over time. But I have to I do take your points. I don’t want to have a I don’t want to debate it out now. But it might be good to have a discussion in the future because I think it’s yeah, it’s a really important issue that you’re getting out there in terms of how we think about Investing.
Andrew Einhorn 50:01
Oh, yeah. And you know, I would just add that the argue, but it depends on your timeline. Right. Like it’s great. Yes. He’s got unlimited time. Fantastic. Eventually it’ll be right. Yeah, someday. But for a lot of people, they need to access their money. It’s an unreasonable assumption, you know that you’re not going to touch your money for 20 years.
Gene Tunny 50:19
Yeah. Yeah. I think one of the points. Yeah, it’s, it’s a, it’s an important point you’re making and I mean, one of the challenges like in Australia here, we’re we’ve moved towards individual retirement accounts, we move toward that in the 90s. And so a lot of people ended up, they’re relying on Super, and then, you know, what happens if you’re a retiree? And then, you know, the market goes down? 40%, like it did in the financial crisis, right. And then yeah, I mean, it’s
Andrew Einhorn 50:49
bad times every time when you’re 78 years old.
Gene Tunny 50:53
Yeah. People ended up having to keep working for for several years. But yeah, that was a that was an awful event. Right. Yeah, that bill, that was it. Bill Ackman. You mentioned with a, I’m gonna have to look up that. I mean, that’s outrageous. And that’s why, you know, that’s what outrages the public about, you know, the activities on Wall Street’s I remember seeing
Andrew Einhorn 51:14
it live. I mean, he just, he scared the hell out of me. When I was watching TV. I was like, I didn’t think it was gonna be this bad. But now I do. Yeah. Shocking. If you just Google greatest trade of all time. Yeah. You’ll you’ll see the data sources on it. But you know, the fact is, was that a lot of this stuff, that historical data gives you plenty of ammo for how to navigate the markets, for instance, the look back at Zika and SQL, but yeah, that was spreading and in 2016. And they closed the ports of Miami. And the cruise ship stocks, on average dropped about six and a half percent when they close those ports. COVID had a transmission inside the country for the first time in the US, I think it was in Houston. And cruise ship stocks went down six and a half percent. Same reaction to the virus just seven hours later. So there’s always historical reference. And you gotta remember that the people who are moving the markets are also looking at the history of
Gene Tunny 52:21
IT. Yeah. People like Ray Dalio is crew at Bridgewater. Yeah, absolutely. Very good point. Right. Final, final question. I just want to ask you, like, what’s the reaction among the financial establishment to this approach? Because there’s a, I was just wondering about it, because there was a very negative opinion piece by Gregory Zuckerman in the Wall Street Journal last year. So April 12 2023. Ai can write a song, but it can’t be the market quants have tried for decades with limited success. At the biggest challenge, all very negative about it. I mean, how do you react? What do you think about what do you think about that? I mean, what’s the what’s the reaction out there to what you’re trying to do?
Andrew Einhorn 53:12
Well, there’s there’s two questions there. The first is, you know, the, the article itself, I think is, has a little bit of a misnomer about what AI is, and only is right, there’s this belief that AI is sort of the profit, right, it’s sort of the crystal ball that can see the future because it’s, you know, super crunching all these numbers and coming up with perfect algorithms that humans can’t possibly imagine. And there are certain people that are working on that. But largely, that’s not what AI is, AI is, for the most part, replacing kind of grunt tasks that we don’t want to do anymore. Things that humans can do, the computers can just do faster and at scale. And so you know, when you say something like aI can’t beat the market, but it’s really saying is an AI created algorithm to beat the market won’t beat the market. And there’s been very little development of sort of AI generated algorithms. It’s usually a human using AI to crunch numbers and then coming up with the algorithm that it then allows AI to execute. So the human is integrated through the whole process. And just by that biassing, the outcome of this, you know, assessment. I would take the view that, you know, AI is a tool in the toolbox of doing whatever you want to do much like a hammer versus a power drill. Difference. Yeah, you know, you could go hammer out the nails, or you could have a nail gun. And the AI is the nail gun for most tasks. And so if you’ve got a nail gun, you’re gonna get your job done faster than the guy that’s sitting there. with a hammer, gotcha.
Gene Tunny 55:00
Yeah, yeah. Okay, so all fascinating. Andrew, really, really enjoyed the conversation, are you looking at extending into, say, Australia or the UK because your us focused or have I got that wrong, where
Andrew Einhorn 55:14
us focus, but we, we actually collect data in about 35,000 different equities. We’ve just been rolling out, kind of slow and steady to make sure we don’t overwhelm the user. Those stocks do include Australian stocks, they do include, you know, the London Stock Exchange. So, you know, if demand is high enough, we’ll provide access to that and the same model. A lot of the large stocks, you know, it’s in both of those exchanges, like AstraZeneca, for example, or, you know, forget big mining companies that are ADRs that do trade in the US. Trade in the US market, I was trying to remember what the big mining companies in Australia but not remembering Olga bhp,
Gene Tunny 56:01
Rio Tinto, so yeah, so
Andrew Einhorn 56:05
those are in the system, you know, as an example, okay. Because global again, and you know, anything above generally, about $10 billion market cap is going to be traded in the US exchanges. Gotcha. Okay.
Gene Tunny 56:19
Excellent. All right. Andrew, on hold, this has been terrific. Any final points before we wrap up,
Andrew Einhorn 56:25
I would just say, you know, if, if you’re interested, and you’re out jogging, or you’re working out at the gym, while you’re listening to this, the company name is level fields, AI, you know, text yourself, write it down. If you don’t feel like you can use it for your own investments, you probably know somebody that might please get the word out. We have a discount code of podcast 23 that you can apply. And you can get a discount on this subscription. Rod. Is
Gene Tunny 56:56
that all caps? So
Andrew Einhorn 56:57
does it matter? It doesn’t matter? No. Okay, excellent. The number 23 for level fields
Gene Tunny 57:03
mastering Okay, enter on well, and thanks so much for your time. I really enjoyed the conversation. And yeah, I really learned a lot. And for sure, this is this is part of the, the way of the future. So it’s fascinating to learn what people are doing out there like yourself. So all the best with it in the future. And I look forward to seeing more of what you’re doing in in future years. So thanks so much.
Andrew Einhorn 57:29
Thank you. I appreciate having me on and happy to come back and have that buy and hold debate. Good
Gene Tunny 57:35
one. I think about that for sure. Okay. Thanks, Andrew.
Andrew Einhorn 57:38
Thank you,
Gene Tunny 57:41
Rocco. Thanks for listening to this episode of economics explored. If you have any questions, comments or suggestions, please get in touch. I’d love to hear from you. You can send me an email via contact at economics explore.com Or a voicemail via SpeakPipe. You can find the link in the show notes. If you’ve enjoyed the show, I’d be grateful if you could tell anyone you think would be interested about it. Word of mouth is one of the main ways that people learn about the show. Finally, if your podcasting app lets you then please write a review and leave a rating. Thanks for listening. I hope you can join me again next week.
58:28
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Credits
Thanks to Obsidian Productions for mixing the episode and to the show’s sponsor, Gene’s consultancy business www.adepteconomics.com.au. Full transcripts are available a few days after the episode is first published at www.economicsexplored.com. Economics Explored is available via Apple Podcasts, Google Podcast, and other podcasting platforms.
