Dr. Michael Kollo is joined by Matthew Dixon, Quant of the year 2022, to discuss blockchain, cybersecurity, and surfing.
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Guest
Matthew Dixon
LinkedIn
CoinFX
Hi everybody. This is Michael Kolo from Crypto Cappuccino. Today I'm super, super duper excited to have Matt Dixon, professor Matt Dixon. Quant of the year, 2022 on the podcast to talk all about blockchains, cyber security, and maybe even surfing. So hope you can join us. Hi everybody.
This is Michael Kollo with Crypto Cappuccino. It is my greatest of privileges to welcome Matt Dixon to the podcast today. Hi Matt. Hi, Michael, how are you? Matt is a phenomenal mind. And in fact, if you didn't even know that he is the quant of the year 2022, which is fantastic honor and achievement, I didn't realize they had a quant of the year of the decade, perhaps a century, but, uh, for, for some of his work, that's been really well recognized that I would really encourage you to, um, look it up and, and have a look at it on, uh, the internet, I think risk magazine and a few months promoted it really, really well.
Um, but before we kind of jump into to your accolades and your background and, and where you've come from, um, we're just talking about surfing, um, be before we, we join. So you are, you're an avid surfer mat.
Yes, I am. It's um, what I would love to be doing if, you know, if I'm not spending my time coding or solving math problems, it's just a great way.
You know, just disconnect the mind, turn off and, and soak up, uh, you know, this little, the natural world, which is in many ways, the antithesis of this metaverse that we're building.
It, it, it, it, it does fascinate me. I've, um, I'm not a surfer, but I do like a nice quiet fish on, on a kind of sunset. We're just talking about how Australia has some amazing beaches for this as well, and the active lifestyle that's outside and how to find that kind of balance from that kind of serenity, but also the kind of really cool elements of what we're gonna talk about today.
Which is a bit more on the data and the abstract side, but I, I do find it fascinating that a lot of people with statistics and mathematics background do have a very active, whether they're cycling or surfing. That there's a sense of that, that there's an affinity to nature in being outside, which is really cool.
Um, and that takes us really nice and neatly actually to the next question I have for you, which is, um, tell me a bit about your background, how you've come to be here in this moment in time, how you began your career and the path you took.
Yeah. So. I, I had a pretty strange, um, sort of background by, by most standards.
Uh, I actually started out as a civil engineer, so I have my civil engineering degree from, um, pure college in London. I love math and I wanted to build things. Um, this was sort of before the web, you know, everyone said, why don't you become a civil engineer that sort of, you know, get to build things, get, see the impact of your.
But when I got into it, I just sort of found it very hand wavy on the math. Lots of things bothered me. I got in sort of long protracted arguments with professors about things called fudge factors, uh, that are used to design bridges. And I just felt very, um, very sort of cheated by the whole thing. And so I decided to go back to a master's degree in scientific computing.
I ended up working for the British defense in year at war games, designing war games, uh, software engineer. Uh, and then I decided at that point, I'd go into trading. Um, and, uh, I just like the thrill and the spills of it. And I had sort of enough software experience to get onto the trade for. And that's really where it all began, uh, Lehman brothers.
Um, I got to see, uh, just how very interesting the whole world of finances, uh, and how fast moving it is. And that's ultimately what sort of took me on a journey to being a math, finance professor and. Being in the space of sort of, I'd say FinTech more broadly.
So, so you were the trading for Lehmans, is that right before the crash or off or, or during, or.
Yeah, that was before. I mean, it was to sort of really, I was there in the, really in, in its the peak of its, you know, uh, time during the period where there was just. You know, unbounded hubris in, you know, credit derivatives. I was working right in that area of CDSS CDOs, uh, you know, designing a lot of the models, building them out, scaling.
Um, but always with the engineering focus, um, because to me that's sort of where, um, I added value the most. Um, and it's funny because that's kind of in a way what blockchain is about, you know, I mean, it's, it's, it's a lot of engineering. Um, it's, it's some knowledge of quant, finance and math, uh, and you really can't just be a one trick pony.
You know, you've got to know lots of different things, uh, in order to be able to get something out of it. So, yeah, Lehman Lehman was the side of it all and you know, and it was the end of end of an era as well.
It feels like, you know, history doesn't repeat, but it does rhyme, you know, they're saying, and it's, it's fascinating to me now that I've, I've been in the game for a bit longer.
And as you view loose kind of scene cycles, come and go that the beginning of, of a technologically led cycle where, you know, the initial rock stars of that cycle are the engineers are the math professors. I mean, I remember coming into quant, um, somewhat after you, but it was, it was still. The, the echoes of the great minds, mathematicians, pH, and the physics professors, et cetera, echoed in the hallways.
And there was a sense that, you know, that, that those quant guys are like, you know, with these smart people on the white board and they're just kind of magic money up. Um, and of course over time, some of these promises did come true and seen a lot of the stuff that has been built into infrastructure actually.
Has absolutely come from those minds, but a, but where there has been failings, I suppose, or meltdowns or whatever, even if there were statistically, you know, within, within parameters feels like there there's been like a very wide scale disappointment in, in, oh my gosh. I thought you guys solve that problem.
And why, why are we still having this crisis? I, I, let me jump straight into that. I mean, Do you think that blockchain in, in what elements do you think blockchain and, and the current movement in blockchain is similar. Like even that we've just had a stable coin, that's really melted down, for example, where it feels like maybe one, one conversation that's been had as well.
You know, that stable coin was supposed to be stable because of the engineering work that had been done in the background and lower middle old it isn't. So there's a, there's a bit of a sense of loss. Of confidence. Did you see that as being, I suppose, in any way related parallel to, to this idea or, or, or quite distinct?
Yeah, I do. Um, I think it's, you know, obviously there are some differences, but I think the main one is that, uh, you know, no one, if, if you ask anyone sort of on the surface, do you understand how these stable coins. You know, you might you'll get a different answer from everybody. Um, and there is no, there are no standards.
Uh, there is no sort of systemized set of stress testers, uh, stress test, for example. Um, and so everyone is sort of making it up as they go along. And that's sort of how it felt with credit. Deriv really. You know, everyone sort of had their own way of approaching it. You know, calculus became the way to roll with the credit derivatives.
Those were, you know, based on flawed assumptions. We now know, but as usual, um, I think it's just a, a, a familiar pattern in human history. We tend to choose our favorites and we tend to trust those. And if they're saying it's. Um, you know, and they've got sufficient credentials then, then I think we just sort of tend to push forward.
And anyone who does have any, uh, sort of sound reasoning and critique tends to, to have their voice ground out, cuz everyone's making money. Um, and actually that's sort of what I found when I sort of went into this a couple of years ago. Um, I just looked with horror at the whole thing. Um, and I read Alex Lipton's book, you know, when it came out.
I look through a lot of the stable coin, white papers. Some are better than the others die. Very good papers. Some are very good, white papers, solid math behind it, and I've just looked a whole lot of them. And it just seemed like sort of Mickey mouse drawings and, uh, just opening more, more questions and it raised and just, I think the frustrating thing being this whole mantra of decentralization.
Being, uh, overshadowing, any scientific reasoning from a mathematics point of view. In other words, if you get the decentralization all will be solved. It's the magic bullet without thinking about the economic principles. And in some sense, it was like that with sort of the credit derivative, you package everything up the market will take care of, uh, you know, some of this credit default risk.
And, uh, the fact that we have junk bonds underneath it, not a problem, you know, it's the models have effectively, you know, counted for that. Um, except that they didn't, uh, and there are also number of conflicts of interest, the rating agencies, for example, we in bed that the banks, um, and slowly we start to see a system unfold and it just reminds me of the book written, you know, by machine to level fragility.
That once, you know, one domino falls and none of them falls and none of them falls and it just exposes basically a house of cards. And I think, um, we yet to see the full repercussions of what just happened with, with Tara. Um, and, uh, I think it's just an, a necessary part of the evolution. And hopefully, uh, we come out of this with, with better standards and more, more rigor, uh, as we did with the financial crisis in 2008.
Uh, I, I, I absolutely right. And I think that's, that is one of the fascinating parts of me. Is there these different sciences collide or the different scientific viewpoints collide? What happens? Because as you say, the engineer solves problems in absolute, whereas statistician is constantly in that gray, you know, something is probabilistically, right?
And that, that, that itself is of course an abstraction of, of the complexity of those 2, 2, 2 spheres. But I do feel like. Um, the DNA of the original founders of a particular movement or science often determines its reach its strengths, but also its weaknesses or the things that it kind of misses. And I think maybe this is where, like, for example, if you talk about cognitive finance and, and the cognitive field of financial asset pricing, you have a very different ethos of, of no ability and, and what's noble and, and how that system functions.
And if you talk to the scientific community, but I therefore kind of. Also wonder whether with, with blockchain, the primary problem that's being solved is a financial one, or whether the primarily problem that's being solved is almost a, um, social, um, mistrust or unrest or whatever, that there was a wonderful paper that was released at, I think at the beginning of the year, about the state of crypto, where we often see these kind of papers, but this was a, I think 120 page one.
And, and it was about, uh, crypto adoption and it was kind of making lots of forecasts and whatever, but the number one thing. It talked about the beginning was still the number one driver of this area is that resistance to that centralized force, that centralized, controlling force of them and us, the government, the bank, whoever else it is.
And so, as you say, decentralizes nation solves that problem, but I think from a network perspective, does it introduce all kinds of other. Issues. And I, I, I wanna pick up a little bit about your, perhaps this is related to point, perhaps not is, um, your paper in 2018. Um, the one that we're just chatting about now, just before we came on the, the podcast.
So tell me a bit about that. Cause I, I, I found that really interesting.
Yeah. I think, I think, um, it's, it's a paper which, uh, tried to, I mean, so the backstory in that paper is that when we wrote it, we felt that. Uh, all the studies that will be done sort of in the crypto space and finance were really just very superficial attempts to use crypto data.
Um, in other words, it was just the same old, you know, run your predictive model on some time series and, and off you go. And, um, you know, there were some attempts to try and, um, you know, do some sort of fundamental modeling with models to supply. Uh, you know, network effects and so on, but overall, very unsatisfying given you've got all this data, which is being generated by the blockchain graph, all this transactional data.
And so I think what, what we try to do is say, well, can you study the properties of the graph and identify early warning signals for Bitcoin crashes? And so we were looking exclusively at Bitcoin and what we set out to do was to, um, create a methodology. For developing what we call chambers, which are certain types of graph patterns that occur, um, with, uh, when there's systemic movement or systemic effects, either systemic selling off of Bitcoin or, um, changes in the way that transactions are dispersed.
So maybe one transaction gets split to three wallets on average, instead of two. The, the size of the amounts of funding going into wallets across the graph. And so we looked at all of these factors and then we said, okay, if you now incorporate this with a predictive model, as you know, as now as exogenous factors into some sort of time series model, could you, could you increase the predictive power of the model or the ability to at least, um, capture the tail risk?
And we found that, that you could do that with about 20 minute lead times, cuz that's the time taken for the, uh, the blocks to, to finalize in, uh, in the version, uh, at Bitcoin that, you know, we were looking at and I think this is very interesting because it starts to open up a whole new area of mathematics in finance.
Before it was always sarcastics. It was always about, uh, you know, machine learning maybe, but all of a sudden the graph theory becomes very interesting and it's not just, you know, looking at networks of banks and graph, you know, graph patterns in the graph there to look at systemic risk. You now have very, very grounding data.
Um, and the challenge now becomes a big data problem of interacting with the graph, collecting that data. Running algorithms over it. Um, and ultimately that's what big data in finance was always about. And here you have a problem which naturally sort of fitted that sort of, uh, you know, technology sort of, I, um, sort of, you know, ideology and you also have really very interesting new areas of mathematics to bring into the.
Um, and I think that's how things are boxed is when all of a sudden you bring a new area into the picture that, that previously had had no place. I mean, in, in finance,
Absolutely. And I think finance has been exceptional actually at, at some well, exceptionally it's been pretty good. I think at, um, germinating these different sciences into it with, with mixed success.
I'd say, I think there's, there's a big graveyard of, uh, sciences that have come along to try to forecast prices that, that, that haven't quite worked out. But just going back to this. Kind of thing. So I think am, am I understanding correctly that when we talk about big data in this context, and we talk about the, the, we, we sent, we talking about the activity wallets, is that right?
Or, or, or groups of wallet that's right. That are potentially owned by an individual or, or kind of correlated in trading. So essentially is what we are observing is microstructure of traders, people going to the market buying and selling a particular this case, Bitcoin. And then I suppose the interesting question for me becomes.
If I think about all the microstructure literature and we think about informed and uninformed traders, is that still a useful framework or are we now trying to understand? Um, so are we looking for categories of traders within that data? Or are we looking at the, the actions of the swarm in some sense, like the collective actions of a large number of agents and trying to characterize it as a system and, and in some way
Yeah, I think it, I think it's, it's sort of both. I mean, here's, here's a good example where, uh, I think it really, it really hit home how useful this idea is. So about. Um, just over a year ago there was a Bitcoin crash, uh, and it was supposedly related to a district in China, um, having a power outage, um, you know, I think it was a maintenance for maintenance rather than, than UN planned.
And it just so happened to affect the region where a lot of the cryptocurrency are, they some, the largest crypto farms, you know, this is sort of obviously on the, of that, you know, movement away from China of these Bitcoins, but it suddenly becomes very interesting. Maybe start to think, well, if a Bitcoin minor has informed knowledge, let's say that it knows something about.
Uh, the transaction hash rate ahead of, um, everyone else, can it trade or can it effectively sell off large amounts of its Bitcoin? Um, and then, you know, collectively as a group conspired to reduce the hash rates. So there's a strong correlation between hash rates and Bitcoin price. Um, and so if a network of Bitcoin mine is associated with one of these funds or decided to slow down the transaction, the transaction hash rates, you could effectively just sell your Bitcoins, a minor, slow down the transaction hashes, and then cash in, um, uh, you know, essentially, you know, you're taking a shot on this whole, this whole, um, position.
And I think it's very interesting because you can actually show using these techniques. I described. If there are informed traders, uh, which are essentially conspiring to, uh, to, to act in a way that could, could show that they were informed and they were premeditate, um, or even causing some kind of drop in the Bitcoin.
So what we did is we ran some broth algorithms. Uh, we looked at various different clustering approaches on the graph and we're able to identify certain clusters of minus. That. Um, and we knew, we knew that won't minus because they had, uh, transactions associated with, uh, with particular addresses, which we knew to be, um, uh, mining, mining farms.
And then we can look at their own wallets and who they send their Bitcoin to, uh, and see what the balance is. And then look at the timing and essentially say, well, let's give them five minute, uh, lead times to see if they sell off within a five minute window of some large price. Look at all the one standard deviation, uh, events for price drops.
Are they consistently selling, um, irregular, you know, large amounts than when the Bitcoin is not decreasing? And can we, can we, you know, can we ultimately sort of prove this statistically using the graph data in the cluster? So we use the graph data to collect all the transaction information, the graph queries, but then use clustering on the graph using, uh, graph based clustering algorithm.
And then once we found that cluster, we are then using, you know, statistical techniques to look at the distribution of their payments, conditioned on, you know, when there's a Bitcoin price drop ahead of time and seeing if those distributions are statistically different. And that leads us to sort of identifying essentially those that could be conspiring, um, at, you know, that's not a for proof, but it just, it, it could be very useful for.
For example, the FBI, um, who are looking to, you know, enforce, um, uh, various different, um, yeah. Prevents essentially this sort of thing from happen. So I think it's very, it just opens up a whole new pram, um, of thinking about data, um, on the blockchain and it's really its own kind of discipline, you know, it's different from any other area of data science.
That, that, uh, that we typically have, you know, different from image recognition, it's different from looking at, uh, you know, problems in, in finance. It, it has its own sort of unique flavor. And, uh, I just think it's a very interesting area to task. Well, what, what can you do with the graph when you combine it with other techniques in, uh, in, in data science?
Yeah, no, it it's the, I think the transparency and the data availability that makes this available is amazing. I was kind of smiling to myself as you were speaking, cuz it kind of reminded me of, um, was it, was it okay? I don't wanna blame people who weren't at fault, but was it Enron was someone else who remember how they used to call up the electricity providers?
Like we really hot day. And they'd call up a power station and say, I think you should go into maintenance mode or something. And that would essentially shut that power station off in prices. It would spike and it would make money. So that was the kind of classic manipulation. And, and just the way that you described this, reminded me a little bit of that in a sense that you.
Manipulating the way that a system functions in order to create a price outcome. So I, I, I mean, you couldn't do this, but if you, I don't know, went to NYC and, and got them to run maintenance at a particular moment in, during the trading day. And that would, I don't know, bring down the aggregate CPU speed or the aggregate memory available for facilitating trade.
And that would have a discernible. Uh, impact on prices in a directional way. I think you would be kind of in a similar position to, to understand that actually, as you say, the functioning of the blockchains in terms of their, their structure and how they function, if that has something to do with the way that assets are priced in this case, the price a Bitcoin.
Um, then yeah, I, I also wonder whether this is still. I suppose as we evolve the blockchains and we start to create augmented, um, components to them, um, whether some of those components are gonna become much more dynamic. Um, I mean, At the moment, it feels to me, maybe I'll run a thought past you, and you can tell me if I'm, uh, if I'm completely off of mark, but I suppose the, one of the lessons, speaking of the GFC and Lehmans and so on for me was I was sitting in BlackRock watching the world meltdown in front of me and.
One of the things as a young PhD graduate that I was, uh, being told at the time is that part of the problem was that the legal frameworks that govern many of the funds, for example, money market funds, when you break the box, so you kind of drop below a particular critical threshold, you have to liquidate.
And essentially that forced liquidation because of the rigidity of the contracts behind them is part of the reason that that a lot of the selling was amassed in liquidity got worse and worse and worse. Had these agents had any discretion to be able to sell or sell conditioning on liquidity or risk or anything like that.
They would've been able to do this in a much more considered way and probably much better outcomes. When I look at this infrastructure today, and I see for example, smart contracts that are moving money around. A system and these smart contracts have written to be quite transparent, but equally, very linear and very transactional in nature.
I then E sort of reminiscent and wonder whether we're in introducing some of this fragility with that, with, with the structure of these smart contracts. I'm not sure if you have any thoughts over that, that kind of come to your mind at all.
Yeah, I think, I think you're absolutely right. Um, and, and it's sort of interesting because, you know, Ethereum is probably one of the most advanced, mature blockchains, you know, and it has some of the most sophisticated attack factors, um, compared to some of the other blockchains.
And, you know, I think there's never, um, there's never a shortage of just brilliant people who can ultimately always exploit. Some, um, some aspect of it. And so I think one of the challenges you, you face, if you are, you know, say for example, a quantum, this area is you almost have to wear the hat of, uh, you know, a cybersecurity expert.
You have to wear the hat of an operational risk, um, you know, expert, you can't just sit in your, you know, your cyber crypto bubble and, and just sort of hope that because you know how to program on the blockchain and you follow Twitter and just call channels and whatever else you, you know, you are up to speak with all the crypto, know how there's a really, a lot of, there's a very high degree of sophistication in, in the sort of attacks that we're seeing.
Um, and I just think that it's just part of the parcel. I, I don't think it's ever going to be a case where any blockchain itself is completely and utterly impermeable, except if someone can develop something like, uh, I truly, I mean, I think, you know, CADA, they might, might be along the lines here, but a formal verification type blockchain system.
Where it's mathematically provable, that it can not be in any way, you know, attacked. And I think that some people are working on these sort of setups where, um, rather than relying on these kind of ad hoc, um, so audits, which again, just reminds me of credit rating agencies and banks. They're all in bed with each other.
Really. If you pay all the companies enough money, they'll just say. And the problem is it's very ad hoc. No one is really looking at the possible cybersecurity attacks. Um, and, and, and so we're getting all these stable coins and cryptocurrencies passing all these audits. I mean, how did Tara get its audit passed?
You know, it it's, and, and so, you know, not only should we be looking at terror, but who audited terror? And they should also, you know, be ranked on the, the blacklist of companies that you would never trust again, to audit your crypto. And I think, you know, we should be very, um, hard about these, uh, sort of very, very disciplined and aggressive on these sort of issues because ultimately the only way that we can really crack down on, um, a lot of these.
Flaws and cybersecurity holes is, is to essentially improve the ability of audit. And the another challenge I think is that the more you have these blockchains, the more there are tendencies to want to bridge across them have multiple, you know, blockchain solutions you've then got wormholes you've then got cyber security vulnerabilities, you know, te Lana had this just in February with a three 23 million hack on one in this worm hole.
It's very, very complex. And I think with more complexity comes more greater possibility of, um, of a cybersecurity risk. I don't know that, uh, we're going in the right direction. Uh, to be honest, I think somebody needs to come along and simplify things and, uh, and I think move towards more of a formal Verifi.
Type of blockchain, um, and avoid all this sort reliance on ad hoc audits and just unbelievable amount of, um, you know, this patchwork of different sort of, um, blockchain, L L two and bridge type solutions, which are just in the end. Um, just making it very easy for, for hackers to.
And I think, I think that's a, a really good point, which is these systems and, and, and essentially what they're really good at, but what they're maybe not so good at.
So one of the elements I think certainly is around data validation is I, I suppose, a different way to think about that is, does that necessitate a well, for example, well, functioning price discovery. Does that facilitate a well functioning ecology ecosystem? To me? The takeaway from the recent stablecoin, uh, issues was more about cross collateral, how you can kind of create links or systematic risk.
And let's for, for moments, say that these are, let's say even well defined blockchains, that there isn't a major problem with the issue becomes when you've got these cross collaterals, is that they co move or, or that they have codependency in actions. As soon as you coordinated actions, you have systematic risk across a bunch of, you know, on the related.
And so all you have to, as we found out from capital markets, all you have to do is have common holdings of securities that have no relationships fundamentally to each other in order for, to get common movement and therefore common action. So again, there's a different problem to solve here, which is what does that look like as a financial system or as a, a storage or value or as a storage of any kind of.
Economic utility, uh, that, that they have. And, and how do they kind of move through time? If I'm gonna be a business I'm gonna put my, um, shares or my utility or anything else on these blockchains, how they going to function in the same way that today, if I'm a business I'm raising money in the capital markets.
And I think it's a laughable assertion to say that prices move with fundamentals in any kind of reasonable horizon or, or let's say dominated by fundamentals. I wouldn't say it's not relevant, but I would say not necessarily dominated by. So it's it. I, again, going back to the previous point about, I find it fascinating that as, as finance professionals, we've had decades of dealing with these problems and questions as, as blockchain engineers.
This conversation's only really beginning because somehow the technology of blockchain has been wrapped together with this idea of a currency or a coin or an asset, a tradeable liquid and, and, uh, real time. Can you, I mean, can we imagine a case where blockchain came to market kind of like with AI without a specific.
You know, uh, product or something that was traded. Like, I, I feel like there's because the technology came together, Bitcoin was both blockchain and a, and a storage of value together, but they quite different conversations in terms of what we are talking about. And we are kind of obviously moving between the topics cuz we're comfortable.
But I think for those people listening. In my mind, certainly I tend to separate maybe correctly or incorrectly a technology or blockchain, which was distributed information management and storage and retrieval to a, um, kind of a, what shall we call alternative market for, for a trading of real time assets through this technology that does to look a lot like capital markets in small cap, capital markets with systematic risk and crashes and booms and busts and whatever.
But essentially is, is not really, I mean, it's facilitated by the blockchain, but it's not really related in many other ways to the blockchain.
Yeah, I think, um, I think overall, um, the, what makes I think it is, I mean, this is a great time to, if you're in data science or machine learning and, you know, you're sort of interested in this area.
Rather than sort of running a quick, you get rich kind of scheme to actually come up with sort of measures of systemic risk in the blockchain system across change across stablecoin looking at the collateral. And I know that, you know, there have been some studies looking at, um, correlation across the different crypto assets, obviously very correlated, but I, I haven't seen anyone yet study, uh, the extent of which the collateral and stable points.
Is systemically, uh, you know, it, it, the degree of systemic risk and ultimately I think someone needs to come up with a health, you know, a health score, one to five, you know, in the same way that we have the deaf calm in the United States, you know, uh, what's, what's the threat level in terms of the amount of, uh, cross exposure and ultimately.
You know, these things need to be built. And for some reason, um, no one seems to be sort of very incentivized to do it. Uh, but perhaps that's the new wave of, of innovation is dealing with, uh, you know, better risk management because. You know, people I think will start to appreciate Shas ratio a bit more, uh, you know, you need, you need to control the, as well as the, as well as increase the returns.
It can't just be yields. You've gotta think about as well.
I mean, I guess my sense is that. As the market for an investment market matures in this space. And we are seeing a lot more adoption in wholesale institutional markets and people start to create product of the back of it, which involves, for example, putting together staking or, or a yield portfolio.
You can't, you can no longer go into that conversation saying there's no capital risk. You just park your, uh, stable coins and get whatever 19%, 15% rate of return. You have to talk about counterparty risk. And it's a very uncomfortable conversation because there is no. Um, recourse. So it's not like you kind of let the money it's and it's an algorithm and certain things are melted down and that's just it.
But that goes back to your auditability and certification elements of this. I think question, which is, I feel like anybody, as you say, that audits has to have some amount of legal or financial liability associated with that audit. and I suppose, and ultimately financial liability, which is what it all comes down to.
So as you say, if an agency audits a blockchain and they have $50 million worth of that coin, so if they lose, if the blockchain goes down, that that coin goes to zero, as an example, case or $10 or whatever, then there's a very direct traceable, financial obligations. They have to make sure they get it right so that they understand at least the risks and so on.
But as you say, if it's just a sticker or a, uh, stamp that they put on, then the incentive structure is very different, especially today's market where I dunno, let's say 200 billion worth of blockchain companies be minted over like the top 30 companies, I think are about 200 billion in value over the last couple of years.
So as you say, the amount of capital, enthusiasm and desire for this to work is, is quite overwhelming in markets, right? Yeah. No,
that's a great point. Um, you know, about having, um, you know, skin in the. And, uh, I'd be very interested to see how regulators, uh, you know, hone in on this. I know that it's the dirty word in the crypto world.
Um, you know, as soon as you mention anything about this, it, it sort of, you know, you know, it, it, it sort of sends, uh, you know, the, the decentralization, uh, tin hat wearing purists, uh, into, you know, sort of a spiral of, of. And I, you know, I've spent a lot of time trying to sort of reason about regulation, um, in this area and why ultimately, uh, no blockchain can be void of regulation, uh, simply for the reason that we are seeing now, um, in, and I don't think that decentralization is the only answer to.
Uh, the problems that are currently happening in the blockchain, it's just a lot more complicated than that. Um, and ultimately I think, uh, it's gonna be exciting time for people in, you know, who are in academia and, and people who like to invent new frameworks and systems of accountability. I think it's just a wide open territory for coming up with great market design.
Um, and. You know, to be different to the way we've designed the previous financial markets. But I think someone should actually sit down and think carefully about what would be the right market design, the role of regulators, the role of auditors, the incentive mechanisms, uh, risk management frameworks, it's all up on the table.
I think it's, from that perspective, what a, what a great intellectual exercise, you know, for a rainy.
Which we happen to have one here in Sydney right now. So that's right. There you go. There you go. I'm done great suggestion for this afternoon. So, um, maybe my final question to you, uh, Matt, which is just to, to, to take you out on a high note is in this area, what are you most excited about?
What's an area that you kind of gives you goosebumps and you go, yeah. You know what? That's either gonna be big or that's gonna be big for me. Like I'm, I'm just super excited about getting my hands around that problem.
Yeah. I'm really glad you asked that. Um, because. Despite all my sort of mathematical sort of, um, sort of motivations and, and love of all things, kind of geeky, ultimately it's about impact.
And I really see that, uh, you could have a very different kind of financial system with blockchain, you know? Citadels and, and the like are incentivized to work in a way that they get some profit, but they also subsidized global remittent and transaction costs. In other words, you could just, with financial engineering, bring all sorts of things which are previously fragmented and disconnected and proprietary and bring them all together, uh, in ways that make a lot more sense as a synergy.
Excuse me, it's a synergistic exercise. Uh, you know, market design and I think ultimately it will just lower the cost for people and make it possible to get money moved around, um, in a much easier way. And I think everyone should have access to capital. Um, they shouldn't, you know, there's 1.7 billion people who are unbanked in this world.
Uh, that's gotta be a nightmare, um, trying to get navigate this world. So I think ultimately it's the social impact and the ability for, you know, those who don't have a bank account, um, but have a. To be able to, you know, be a part of the digital economy.
That is a brilliant idea. And I think that that's, um, we, we forget often that there are 8 billion people on this planet.
And then when we have these conversations where invariably in echo chambers, uh, of our own countries, usually in developed markets, for example, and as you say, there's so much sea of humanity out there that can benefit, um, I think I share your enthusiasm. I think I share your desire for social structure, social change.
I worry a little bit about, and this is a bigger conversation again, maybe not for today, but, um, I worry a little bit about how. When you put uranium into a system, what comes out? Is it a, is it a power plant or is it a bomb? and I think in this similar kind of way, do we see this technology being, um, weaponized or moved into the capitalist structures that we have today?
Um, one of the things I was generally surprised that as Bitcoin can has come this far, I'm, I'm positively surprised, but I am surprised because there was part that was thinking, gee, whi this is. A lot of countries would wanna shut this down quickly because they it's a really big part of the monetary policy and their control over their economies that they would be relinquishing.
I mean, that's aside from the black money leaving and moving around or the tracability of that, or the taxation for gone from that, it's more about the, um, the notion that that's no longer in your control for you to, again, print more or to change the interest rates of and so on. And that feels like every.
Every conversation about the economy today, with the higher inflation we're seeing and with some of the stag taxation fears ends up going back to, what is the federal government going to do? What is the federal reserve going to do? What, what is the, you know, what is the central agency that Welo and hate?
And don't like, and we wanna get away from in the world, blockchain, how going to sell, help us and save us. And I feel like it's a really. Fascinating question, which is, uh, I, I love the technology. I love the ideals that you outlined, which is about accessibility, about equality, about transparency, about removing some of these bad agents and the fragmentation that we've had in system map redesigning away from, um, pure capitalism and agreed to, to a broader social agenda around a financial system.
And I hope that. There's enough, not just smart people, but, but can, can, uh, you know, people that are convinced about this, this, this idea and future, and willing to put their careers on hold and their research agendas on hold. And so on to, to pursue it, to push that through, at least in some fraction into the next generation, I think would be a wonderful outcome.
Yeah. Well, that's, that's, uh, a really great point about, you know, uh, uranium and, and you know, what comes out at the end. I, I. Your guess is as good as mine. Uh, so we'll, I guess we'll, we'll see. And like you said, amazing that we got this far with it really, um, considering what a grandiose idea it was. And if nothing else just says something about the power of ideas, um, Satoshi's paper changed everything.
Um, just one paper. So. Very interesting, um, to, to be, uh, sort of in the middle of all this in, in, in some shape or form as a, you know, someone in math and finance and, uh, I think it's, it's, it's just keeping an eyes and minds wide open as, as this, you know, technology plays out.
Absolutely. Uh, well, thank you so much for your time. Professor Matt Dixon quant of the year.
Thank you, Michael.
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