This is epicenter episode 356 with guest, tarun chitra. I am service equity and you're listening to epicenter the podcast where we interview crypto, Founders, Builders and thought leaders. On this show, we dive deep. Learn how things work, at a technical level, and we fly high to understand Visionary, Concepts, and long-term trends. If you like the show and you'd like to support us, the best way to do that is to leave a review on a podcast.
It helps people find the show. It helps people know that we are one of the best. Cashner crypto space, and we're always happy to read your reviews. So if you're on a Mac or iOS device, the easiest way to do that is to go to epicenter of rocks /, Apple. Today, our guest is tarun chitra. He's the founder and CEO of Gauntlet Gauntlet is a simulation platform for building Financial models of blockchain,
protocols and applications. So when you're building a blockchain protocol, these days, it's become expected to have the code audited. This is like a, you know, an
essential thing now. And it's a good thing, of course, and security audit will look at the code to make sure there's no bugs and usually security audit goes into mechanism design, but call it goes even further and they do Shinzon the mechanism and different layers of the stack so they use machine learning methods to simulate different environments with various user behaviors and to see how the system holds up under those conditions.
So they perform analysis on things like the core mechanism to test for liveness and block propagation. And they can also do higher level analysis and test entire markets to see how the market will react to a certain protocol. So this was a really cool interview because it's heavy on machine learning and statistical analysis, which is not an area that I'm immensely comfortable with. But I find it really fascinating
nonetheless. And when it's applied to blockchains, I think there's a potential here for this to become much less security audits something that becomes expected by users by investors and by the community for new protocols. And you know, it's probably a good thing because as we've seen recently, these protocols can lock in a lot of value. They're also doing research around transaction fees which apparently is an entirely new space.
In the area of mechanism design, a lot of the mechanism design research apparently focuses around things like auctions for like Google ads and things like that. And of course this is an area in which like you don't really need to consider transaction fees because you know, transactions are abundant and free whereas with blockchains that's of course not the case. And so given the the current
surge of transaction fees. For example, in ethereum is an area that's going to become increasingly, interesting and increasingly relevant as bull. It's have, you know, these, these effects on the transaction fees in a network. So at the beginning, we talked a lot about chip manufacturing, which is since to room was previously in that sector, he's got some interesting stories there.
And we also briefly very briefly touched on Al Gore and which is convenient because they are sponsoring this episode. So, if you haven't heard our episodes on Al Gore and I would encourage you to go back and listen to them, we did one in January with Steve coconut house and Sylvia McCallie and one way back in 2017 with sales. Oh, this was before El Gran was even company. He just written the white paper
at this point. Anyway, they're doing really cool stuff to improve developer experience specifically around building defy applications and I'll tell you all about that a little bit later on during the interview, but for now, I give you our conversation with tarun chitra. We are today with turn treat ra. He is the CEO and founder of Gauntlet Gauntlet network. Is the domain name? Is it actually a network or is it sort of just a company?
So I tried to get all the gauntlet domain names and fortunately the only one that was really open was dot Network. I think. Now we're going to try to see if there's a TLD dr. Jenn and by gamma dr. Jenn but we have gotten without five but basically in 2017 at 18 I bought the dog network name and it kind of stuck and we had to incorporate with some name so I chose Networks.
It's a great to have you on. You have a really interesting background that gives you a bit of a different perspective from a lot of the people in the crypto space or especially at least in the defy space. Could you tell us a little bit about it? What were you doing before you got in involved with crypto? After I graduated college, I worked at this billionaires. Research Institute was called
disha research. And this person who worked in trading, he wanted to spend his money on building a 6. So application-specific integrated circuit. So, these custom Hardware devices for doing computational biology and Drug Discovery and physics research. This is the same company as like the Esau Investment Group or is this a separate? It's the same thing. Basically one of the branches of the Investment Group was working on this building ethics for physics research.
So, basically, the story of David not too kind of recursively, add some stories is in the 1980s. He was an assistant professor, I think he didn't get tenure and so you know, if you think about people, you know who are really smart but like they don't get tenure for some reason or another in his case, it was he was really working on non Von Neumann computers. And 1986 was the time when intel was kind of like, about to hit moon at that time.
No one wants to hire someone who's doing kind of custom computer architecture. Everyone's like, no Intel's going to win. So we're going to like hire people who are going to make Intel hit Moore's Law. And so then he went and working financed somehow secretly hidden underneath him working in finance and trying to make money with this idea that he likes still want to work on non Von
Neumann computers. So, Find out when computers means that like a normal computer architecture, kind of separates memory and compute potentially separates those two Von Neumann, computer architectures, kind of our what you have in your computer, or your phone right now, for the most part, where compute and data are processed in the same sort of Pipeline. And so I think in the 80s people didn't know what would win like what Hardware would be in your
devices that you own. He was kind of always interested in that. And one of the applications of these kind of very esoteric, architectures is building supercomputer is that we're really good at solving physics problems and also solving sort of like computational biology
problems at large. And he kind of, in the back of his mind, he was always like, hey, if I become rich enough, I'm just going to spend all my money on building these custom computer architectures for something useful for society. And so once he became a billionaire, he started trying to prove theorems About whether it was possible to do better than these like Intel to sell architectures and he proved this theorem insert 2004, which is kind of great because it uses
only high school math. To make this point that you can actually do significantly better like very much better in the sense that Intel style processors.
If you try to parallelize this computation will always take a finite amount of time even if you had an infinite number of processors, but there, Exists kind of crazy architectures that would take, you know, basically zero time as the number of processors goes to Infinity. So we actually once he kind of prove this theorem I guess you know once you're a billionaire you're like I'm going to build Hardware to prove that my theorem is correct. So that's the Genesis story.
I work there. And in 2011 there weren't many people building, a sex. Most of the people building a six. Were certainly not Bitcoin miners. They were mainly telecom companies. So One of the things that's interesting, is that the only people who really needed custom Hardware, were people doing really low latency fast Fourier transforms ffts, and most of the people doing that were in Telecom. There's no neural Nets.
There is no self driving cars. There's none of that type of stuff when we were talking to suppliers because we're not someone who's like Chinese, Samsung or Apple building a whole infrastructure on it. We wanted kind of like pay them for their excess Supply So it's like we want to build 10,000 chips Intel and Apple and Samsung or each building a
billion chips. But the factory is that you that build that stuff they have excess Supply sometimes like the one Factory might have an excess 10,000 and they're like oh well can we sign someone who wants to buy that?
And so the way that excess Supply gets old, it's a sort of gets auctioned off to different Market participants and at that time we were literally the only ones buying this type of space is like us And like random Telecom company in Japan, in 2011 and 12. One of the companies that sort of became Avalon started buying a bunch of this chip space. And so we were like talking our supplier and like, hey, we sent you, you 25 million dollars when our chips coming and then we got
ghosted. This is getting ghosted, Pretender. So that was weird. And at some level we were like, what the hell? We just gave you a bunch of money. You just disappeared like and then they came back eventually and were like Sorry, we're going to do your chips, you know, the next batch house, the 10% discount sound. And that was the first time I've really thought seriously about Bitcoin. After that, I kind of started mining How did that go into mining was so you found out the
people who like took your order. We're basically Bitcoin miners who are like a hardware Fab. Let's say they have 10,000 spots that like imagine a physical wafer. Now, cut up the wafer into 10,000, like, 1 centimeter by 1. Centimeter little units. Let's say apple takes up 9,000 of them. So there's 1000 left and what they do is they auction off the physical space. Imagine block space. But physically auctioned off. Usually, it's kind of a fair auction.
Like, you're like, okay, I put in a bid, I wanted, you know, 8,000 of those spots for $100 each. So we put in our bid and then we got told, hey, you want and then the person disappeared and so, once you win, you put, you have to put money in escrow. So you post the money that you're supposed to post collateral and then you're supposed to eventually get paid or eventually, get your hardware
and then they take your money. They just Kind of kept it in escrow for like six months way longer than it was supposed to be and we were like, what the hell and so what happened was this Bitcoin miner went to the supplier because the Bitcoin miner was in Taiwan and the supplier is in Taiwan and they like just I guess they knew each other and they're like look I know you close this auction but we need to make these miners by X date and we will pay you anything.
It's kind of tangential to this conversation, but people don't really realize this strategic importance of Chip manufacturers, because there aren't that many. I think you probably know this more a lot better than I do. But like Intel, for example, is a chip designer and a chip Fab and in the last 20 years, or so there's been a shift towards, you know, like more the chip Fab model and like the designers and
the Fabs are now separated. And so, like companies like tsmc, which is like this big chip Fab in Taiwan, I've kind of like one market And apple, and all these companies get their
chips made there. And the question here, that's kind of interesting in the current geopolitical context and this kind of example that you have kind of exemplifies this very thing is that these chip manufacturers are an Arm's Reach away from China and they're very far away from, you know, the us or Europe and other Western countries.
And there are strategic to you know critical military and Industrial infrastructure in West the amount of Of chips that, you know, the u.s. can produce on its own without the suppliers. It's like insanely small compared to just like what tsmc you can put out or like, Samsung. For example. What your thoughts on that? Like, knowing this ecosystem? A lot better than I do. I think the u.s. is actually completely uncompetitive at this point.
There's globalfoundries, which has this kind of big Fab in Upstate New York and Long Island. I guess now they're kind of like split into two and then there is like the Intel's in Has their own Fab. So they have one in Arizona and the US government, especially in the current kind of strong arm Administration, type of nonsense is trying to be like, hey, you have to, like build your chips in the u.s. if you want to sell them here or something.
That's not a very good point of Leverage in the long run. Right. Because one of the more impressive things about Moore's, Law is Moore's. Law actually is a self-fulfilling prophecy Gordon Moore said this kind of apocryphal thing of like oh every eight 18 months, your chip frequencies going to double it turned into its own kind of War, right? So like, every 18 months, these companies would have liked the chip designers Benchmark themselves on how close they
were. And then once they had a design that could achieve, that sort of doubling rate, then they would go through this entire process of finding suppliers who would like be able to do that. And the suppliers also had to follow the like, hey, we need to double every 18 months kind of rule. And you had this cycle of like chip supplier gives you design suppliers who are like, oh man, we need to Source. This really rare material to
like, make this happen. Like we're going to spend all of our money trying to do. That leads to successful Moore's. Law thing, leads to lots of chips. Sold leads to chip designer, forcing supplier to do this, and there's an ecosystem effect, kind of, not unlike cars. We're like the car. Sure isn't really the True end-all-be-all Manufacturer. There's this whole network of suppliers who is necessary for it to kind of you to get the final product.
And the chip designer is the Intel's of the world, plus the suppliers who are making the little subcomponents had to work together cooperatively for this like, very long time period in order to achieve the current kind of Sabbath. And the supplier is not just the Fabs themselves are all in Asia it, right? Like Entire supply chain is completely in Asia. There's literally nothing in the u.s. I think it's a farce when the US was like we're going to take back all this me.
Like, it's a 30-year effort of building out multiple Industries, right? Like one of the things that's very important to getting to sub 10. Nanometers chips is something called Extreme UV. So, it's building these really crazy lasers. I do the same movies that prevent the coronavirus. Exactly. It. Like, these really crazy lasers that are very like hard to build and Intel has claimed that. Hey, they've been working on it for 20 years of like, we can build these like really crazy
lasers. The reason you need these lasers, is that when you have a chip, you have a piece of silicon and then you build, what's called a mask. So the mask covers a piece of silicon, and then you shine some type of electromagnetic radiation on there and etches it, like, cut it etch-a-sketches out, your circuit design. But This whole industry of these like extreme UV lasers in order to get like the size of the little thing to be really small. So you can pack more transistors on a chip.
You need to build these custom lasers. The only place in the world that you can make the kind of crazy glass that you need. For the laser is in sort of Mongolia inner Mongolia and China. And there's just like little tidbits, like that, like, oh, well, we need this type of glass for this type of thing, or we need this type of silicon, or we Need this Rare Earth material. Those are all things that you need to build. If you want to like, vertically integrate, the chip stack.
And I think the geopolitical thing is like, well, Asia has spent 20 or 30 years, building the whole supply chain around this industry, and you're not just going to like, uproot the whole tree. That's like saying that my Quarry, his a rhizome of like this industry has been like migrate in two seconds. I just don't think that's
possible. I think maybe like tsmc has been due to open a Fab in the US and like some time or there's some kind of thing like that but like the fat itself isn't able to produce like these you know, ten them internship.
I mean, I know very little about this but I just from what I know it seems like a very kind of interesting thing that most people don't realize the geopolitical impacts for sure that's like this Olive Branch that was given to Trump because he's like, I want to have chip Manufacturing in the US and it's like, that's not happening by the same thing. Happened to Boeing, I know this is really off.
Off topic. But part of the reason we had this whole 77 Fiasco, is that Boeing tried to decentralize its supply chain and then like they stopped having control over like batteries and then batteries exploded, whereas they used to make your own batteries before. Back in January, we interviewed Steve coconut house and Sylvia McCallie of Al Gore.
And, and during our conversation, we talked about how algorithms unique design, makes it easy for developers to build sophisticated applications on their platform. So what's great about Al Gore and Beyond the fact that it's fast, it's secure its scales and it has instant finality is the fact that they've designed a layer one protocol with Primitives that are purpose-built for defy.
So what that means is that they've taken some of the most common things that people do with smart contracts and they've embedded them right in System right in the layer 1. So things like issuing tokens Atomic transfers. These are built into the layer 1 and smart. Contracts are first class citizens on all Grant. So with these essential building blocks at your disposal, you can build fast and secure, defy apps in. No time to learn more about what Al Gore and brings to the table
and how to get started. I would encourage you to check out Al Grand.com / at the center that lets them know that you heard about it from us and it takes you, where you need to go to learn about their Tech. And what that we'd like to thank algren for supporting the podcast. I think our audience would also like to hear about Gauntlet and what you guys are doing. So I guess I didn't even explain how Hardware gotten to crypto Bitcoin, miners, front-run us.
I started Mining and then in 2013, I sold all my Bitcoin because I was like, this is going to blow up. This is going to be a Ponzi scheme. Very dumb idea, obviously in retrospect, but I started really pay more attention to the papers because we worked in distributed systems. We were building this Type of when you're building these ethics, we we built this data center to kind of run like millions of these machines. So we had to kind of think about this type of stuff.
I started really getting convinced that there was something novel here when the ghost paper came out. So ghost just kind of this Fork Choice rule that was in the early versions of the theory on that kind of promised you that you could handle like faster block production times. If you chose a different Fortress Rule and ghost was one of the first papers that thoughtfully thought about the incentive design and also the networking.
And also, the sort of like basic like architecture is like, if I wrote this code, how would I write? And that was when I was like, wow, there's something serious here. It's not just like, oh, haha, like a bunch of people on the internet like made tried to usurp Leslie. Lamport spax us. It was like, oh, there's actually some novel thing here.
So you know, I think before that I was like very, you know, maybe Bitcoin maximalist, I think the ghost paper was one of the first papers that I was like oh wow, there's like cool ideas that the bitcoiners are not paying attention to, for sure. It also made me realize like, oh man, the design space of this thing is like, bigger than anything that Humanity has ever had. Like you have to like this combine so many things to say a simple result, like that's kind
of insane, right? Like, you know, in other fields you don't have to do as many things like that. And then I worked in high frequency trading afterwards and there, we actually would do this type of thing where we would make models of our trading strategies and then we make models of other people's trading strategies and we'd have them. Run kind of think like alphago style where they would like play against each other and you try to optimize your strategy and
that was around the time. I think the algorithm paper came out and I remember reading the algorithm paper being like, this is amazing from a cryptography standpoint in the sense of like wow, like I you can actually generate It is verifiable random functions. I didn't study cryptography. I had to go read the classical paper.
So the time because I didn't really know that existed but at the same time as like, this seems like a little bit like a derivative more than it seems kind of like proof-of-work, like a one-way function. Like burning energy, is a true. Is like Nature's only one-way function that we know of like a perfectly 1 Min function. Whereas like, in cryptography, we tried you tried to, like, emulate that, but it's never
perfect. And so it's kind of I was a little bit like surprised that That there's this whole proof of stake thing, but like people didn't really think about the financial aspects and then 2017 happened.
And then I kind of started being like, hey, maybe this is a real deal and I writing simulations for fun based on the type of things we're doing in finance and then in 2018, I kind of kept talking to a bunch of layer 1. Protocols, because I was curious, if anyone was doing this financial modeling, I quit trading and then started Consulting for later, ones and then the big badly bread. To like by my consultancy.
And that was when I was like, you know what, I think there is enough room that there are a lot of people who probably need financial and Actuarial modeling for the stuff and I met my co-founder along the Wake. He also was in trading for a while and then he actually worked on designing like incentives for drivers at Uber. So we were both like, yeah, you know, like I think there's like a way to make this rigorous.
So we started initially focusing on proof of stake, especially because I think that was the Genesis of Of my kind of interest in really committing 100% of my time to this. And then over time, it became much more clear. That Defy is really the place where there's crazy amount of financial incentive modeling for multiple agents that exist. And there's just this open space of both research as well as like, actually deploying it to production.
And, you know, the 2010 to 12:00 shift from, in AI from like it's like half a That makes for a watched up from the 90s and half, like people who are just making random stuff and like calling it, like sentient. But we don't know if it works was really this thing where like these kind of hooligans turned into, like the people who are correct. I really feel like that's starting to happen right now in crypto. That's a very long-winded
explanation of how I got here. And so Gauntlet really is about taking these tools from Finance, Actuarial modeling agent-based simulation and Fooling them towards the kind of new problems in incentive design and Krypton. Could you give us an example of like so water is the sort of things that you would model. Like, let's say I came to you with a new proof of stake consensus protocol. Are you modeling?
Like are you testing the safety and liveness of my consensus protocol, is it at some higher layer than that? Like, what specifically are you testing? I think it starts in a bunch of different levels. I think that's certainly the first level, one of the things. I remember that tip me off when I was first reading about proof sake. Was this idea that there were many different synchrony assumptions and all of the different papers but they were quite in equivalent.
So some people would say you're live, if you eventually were able to process the block, some people would say you're live if greater than x percent of nodes agreed. At a block have been produced. And some people would say you're alive. If and this is sort of the way the Avalanche paper kind of eventually go proved if like in the limit of an infinite number of blocks of nonzero, fraction of them were actually reached by large percentage of the note.
Now those all kind of sound equivalent but mathematically when you're trying to write these purse they're not. So the types of things I was really first interested in simulating where things like how long does it actually take? Take on different network, topologies for these blocks, to actually have disseminated enough such that, the network
reaches consensus. And one of the things I was very realized, you could only kind of answer by simulation and would be very hard to prove is given the network topology? What is the true? Partial synchrony constant? And what I mean by that is, like, what's the constant at? Which, if everyone receives all of the blocks within a certain time window? How long does that time? Window have to be for the network to Achieve liveness and
safety. And so stimulating that on different network topologies actually convinced me that even Bitcoin has a lot of problems if the network. Topology is like two disconnected and so mathematicians have sort of ways of defining what it means to be, too disconnected for without getting into too much detail. I think like the spectral gap of the graph is something that measures. How long random walks take?
And so the idea is like if someone who's randomly walking on your network topology, Gets lost because they're too drunk than your block, may never reach everyone. And so you kind of like assume like hey I put a drunk person on the network graph and I see how long it takes them to reach
everyone. That's kind of the this model of like time that mathematicians use that, I was trying to map the model that people have formal proofs that land to to like what could Prague refers and distributed systems people were using and simulation was the tool for that. So we start by assessing kind of some of these types of She's I think safety is not the type of thing we assess.
I think safety is a purely cryptographic property but liveness of proof of stake, protocols is very much a sort of statistical property. It depends on the network topology. It depends on the Layton sees how random they are. What the 95th percentile is the latency is look like cetera. I'll use example of ten, America's obviously. That's what I'm most familiar with. You know, we also have this whole live in partial synchrony how it works is.
We basically have these round Round and each round, we say there's a timeout which nodes Will Wait currently on most kind of network that one second by default. But then if we don't reach consensus in that one, second we go to the next round and we increase it by the time out by a certain amount of. So I think we increase it by a quarter of a second every time. So we do a one second timeout' than if that round doesn't work. We go to a 1.25 second time out.
There we go. To 1 Point 5, Second timeout. And so these numbers for us we just pulled these numbers out of a hat, you know? We did a little bit of testing, but if I would select Enderman, I would go to you and basically say like, hey, help us figure out the right numbers. We should be putting here because if one second is too long, then we're wasting time that we could be making faster blocks. Meanwhile, if it's too short, we're causing unnecessary rounds for no reason.
And so you would basically be able to help us parameterize that correctly. Exactly. Yeah, it's like a band with firstly in see trade-off of like how much communication to have to do. There's an expected number of rounds and the distribution of the number of rounds. In the thing you're talking about, imagine you have a hundred million blocks, there are produced. And for each block, I looked at the number of rounds, it took before the network agrees.
And I look at that distribution. Now that distribution is a function of these parameters, you chose, but the problem is that distribution. Also depends on the network topology. It depends on Some lower level details of protocol. And so, yeah, the type of thing we need stress, stress test is like, how does that work under
different models of users? Because you can have different types of users who effect that behavior one type of user might be the type of user that drops a lot of packets because their computer goes off a lot. They don't care about getting / because they don't even know they're getting slashed for being offline or something. Another type of user might be one. That's malicious, who's purposely, forwarding bad packets?
Another type of user might be one who is kind of a big block producer and like just is like trying to get not even just honest but it's hyper-rational and that they're just trying to like flood the network so that their block always is first what the different composition of users. Also affects this distribution of like the expected number of rounds. It takes and that's kind of where we model when it comes to per sake.
But we also model over time. We realized that we started with these networking models, because that's what people in high frequency trading do A lot in high frequency trading you model. Like here's exchange one, hears, exchange to here's a change three. Here's all the routers and exchange. One takes change to. If I send a packet, how long does it take? And if, you know, you kind of model the topology and sort of a similar way you would think
about modeling validators. And then you say, hey, if I have an adversary who's also thinking the same way as me are they also sending the same number of packets and will it caught will who will reach first. It's a similar type of pump. It sounds like you're doing analysis at like different layers of the stack. You're doing the mechanism analysis of the system's themselves in order to looking for liveness and availability and things like that.
So this is like the mechanism design part and this might take place when the team is building the system, but you're also doing research and Analysis and simulations at a higher level up the stack. So I know you like you're also doing, say research on Market participants Like in the compound protocol. So this is happening at the economic layer at the market layer.
Is that right? Yeah. So I like to think of when you do simulation, I think one of the reasons people often times think like hey this can never be real or it doesn't replicate reality or how do you know it. Replicates reality. Is that a lot of people try to simulate everything all at once and you really need to think of
it like an onion. We're here is a particular problem that I'm trying to solve and here is the particular instance of it. And here, it kind of the bounds of like the worst case when best case. And I'm going to try to That in isolation. And then I add the next layer of the onion and I have it interact with that layer. And then I add the next layer of the onion, and I haven't interact with out there.
I think, if you do it, kind of in this incremental way, you can actually try to reason about the whole complex system. You know, we start with things like this layer 1, liveness type of stuff, but you slowly build up to the economic incentives. So, how much that complexity gets injected into that. Once you start thinking of things like interoperability between blockchains for instance, the problem gets
exponentially more difficult. If you start factoring in multiple block, chains and interactions between all these different systems that doesn't fit in my brain space. It is certainly exponentially bigger. I mean the you're taking address space.
One address space to you've doubled the number of bits definitely increasing in an exponential manner but the Is to try to like isolate the points of complexity that are most tangible to think about how humans would interact with these systems because fundamentally okay, I went from 128 bits of entropy to 256, bits of entropy for a 2 Chain interactive system but humans are still using those, right?
And like methods and interfaces that you provide to the human as a developer also, dictate what usage, you're going to get and so you try to model things, That replicate what humans who were using those interfaces would look like. And then you kind of say, okay, let's say I have 10 different versions of the same human, how they use the system, 100 different versions of the same human.
How would they use a system? And you kind of build some sort of a bottoms-up approach where you try to like identify behaviors, figure out which of those behaviors are consistent among group of people then figure out what math describes them. Like what their utility As what value, they're getting out of calling this function cross Block Chain Transaction. What value? They're getting out of. Hey I'm willing to pay a transaction fee that's higher than the one on my chain to move
across chain. Then after that, what decisions they make like given this sort of notion of how they can value a cross Block Chain Transaction. What actions can they take one? Action is certainly makes class blockchain transaction. Another one is don't another one is, is there a way for me to do it? It on my current blockchain that gives me 80 percent of the same value or 70 percent or 50 percent breaking it down in kind of this.
Hey, there's still a human using this thing or there's a human writing a bot, that's using this thing. There's still this notion of like peoples, ux habits are not uniformly, random right there, not just like a father. They're really kind of like using these interfaces in a very concrete way and reasoning about how different Void. User is really how you Mom kind of try to start modeling these types of things. In consensus protocols. We usually like think of it like okay.
The three types of reactors. We have our like Byzantine rational and altruistic. But so what you're essentially implying is that this is like way too simplistic and that we need to be much more specific. It's not just these three categories. It's way more of a spectrum of many more types of users are actors. So how do you know you've modeled all the actors possible or like how do you know you cover the entire space? Extend, how do you like deal with like things that were just?
You couldn't predict like imagine you wanted to try to predict like the distribution of SN X and how much what it would be collateralizing. But like, you know what world could you have predicted that like a million snx would be sitting here. Farming, yams craziness. How can you possibly build all of these into your models?
For sure. So I think one thing to remember from consensus protocols, is this bar model is Byzantine altruistic rational is very unfair in one way, which is that Byzantine than altruistic are like one dimensional thing. So in the space of all possible, strategies, if I represent a strategy that a user takes given an interface, so an interface. Let's just say it's a set of functions that you can call. And let's assume that all users have Valuation model.
And based on the valuation model evaluation, all the means of utility function and in traditional economics or kind of objective function in machine learning. You have objective function and you have decision function. So the objective function gives you a single number or some so numbers, the decision function takes those numbers and gives you an action like what the user does. And there's an entire you do not to to drills.
But, you know, if you read philosophy, there's a whole argument of whether this is how humans acted. But ignoring the content type of stuff. That's how almost all models in machine learning for like alphago and stuff. Think about the world, right? There's this Kind of value function, decision function.
Now, Byzantine the value function is choose a random number choose management altruistic, the random number, it's like choose the same number and choose this particular Behavior. Always rational is much more like I'm actually observing the environment and and like trying to figure out a valuation and a value of these actions and changing over time. Fundamentally Byzantine and altruistic are actually very one-dimensional that way. One is purely random and one is purely deterministic.
Rustic, but rational is actually this adaptive type of adversary. And it's an infinite dimensional space of functions, like the set of functions that can give you utilities and values is infinite dimensional for rational, but the other ones are actually Zero Dimensional like one dimension like, you know, there's like a single function that everyone knows in advance.
And so the problem is by saying, Byzantine altruistic rational your kind of assuming, hey, there are three equal categories but that's not true because The rational category from a mathematical perspective is significantly larger. Like, infinitely larger, and not just countably infinite larger, it's uncountably infinite we logically. I think there's a lot of kind of classical functional analysis, theorems, that approve, this, I'd say, Nash won.
His Nobel Prize was kind of related to proving this. But the point is that, like you can't actually, you know, when you, when you say hey I've analyzed this protocol under Byzantine altruistic, Floor 99.99999 percent of people who've done that are like hey we're just going to say rationale is optimizing quasi linear utility like Keith Keith or hey. Rationale is like they they are like only caring about a certain type of thing.
And the problem is no matter how you reason about any of these systems, you are fundamentally imbuing, a notion of what you think rational is. And you can never perfectly simulate these things. I will be the first person, and the last person to tell you that, but I do think you should be fully ating the set of rational actors with a much broader set of views. Then what you can do with formal
proofs, I think informal proof. The problem is really like it's very hard to prescribe a model that's you know kind of can cover this infinite dimensional space. And I think cryptographers have this willingness to suspend disbelief of like, hey, we're just going to pretend that the rational actor, only does one type of rational action, but that's just not true, right? And Game Theory and algorithm that game theory and stuff, have tons of examples of this
happening in practice. So I think the better better answer. And, you know, I think this is what the biggest algorithmic game theory systems that are in production like Google ads and Facebook do Is they do tons of numerical stress, testing of different types of users, trying to commit fraud different types of users trying to do XYZ type
of action. And then you run these simulations and say like okay this parameter for our auction is correct or like that's what we're going to use tomorrow and you keep updating it as you get new data. So the yam thing happens, okay? Snx whales are suddenly into into vegetables, like didn't predict that, but now that we add it to our little That's a new type of rational agent. And so, then the next time. So, the day after the yams
happen, we can say. Hey, look, here is the thing that actually causes this crazy amount of risk to your system because people who are have us an X. They're already leverage, they print printed some s USD and then they took their SEO, see bought more snx and put in yams.
Now your system, even though you say it's a 700% collateralization ratio, it's actually much lower because people have been doing this chuckling and kind of like sort of weird weird sort of financial engineering that they might not even realize they're doing and now we have a strategy that replicates that. So now when someone else wants let's say yams add atoms, I don't know. Let's pretend there's like a synthetic atom on ethos that you
can deposit. Yeah. We can run the same strategy and say like this is the amount of risk, the atom holders are taking. And so my point is, it's an incremental thing where you're not going to predict But you're going to try to make your best guess by building, the biggest library of possible things and then stress testing against it. It's a lot like security auditing where you say, either there exists, formal verification, is this dream? That will predict everything or
here are the set of things. I know that could happen and I'm going to try to carefully look through each line of Vicodin and say like this might happen, this might happen. It's much closer to them that make sense. And so how does like historical? All data play a role or do this. Do you like when you have the simulation? Do you like run it against historical data and like modify your simulation models until they fit the historical data and then you start to use them to predict or yeah.
How does that work? Yeah. Not another kind of philosophical dichotomy that exists in the Trap. Traditional world is the difference between the financial world and the World. So in the financial world, people really care about what
are called Point estimates. So Point estimates are what your neural net does, they give you an answer, they say like, hey, here's a function here, A bunch of examples, train, it all those examples so that it gets the right answer and then in the future, take a new example and give me a guess of like, what the output is. It doesn't give you any estimate of what the uncertainty is. It doesn't give you any estimate of like what, how wrong can this function?
That's mapping things, be neural Nets, don't give you that, but like sort of more traffic officials to just go methods, do that. And so in finance people care about Point estimates because they're like, I want to maximize my expected reward or economics microeconomics in general. In Actuarial studies. People care about like, hey, I have this life table for this insurance. I'm underwriting. And I care about the variance of how much I have to pay or like, hey, like yeah, sure.
On average, I only have two Like two hundred dollars in premiums from each person but like there's this one dude who has asbestos poisoning and heat. It's going to cost us a billion dollars to cover his health insurance. I'm making something ingredients but so in insurance and Actuarial studies. You care about this kind of like distributional effect. Whereas in finance, you care
about like the end. And you know I would say machine learning General you care about kind of like predicting the average Although in finance, you care about the tail events blowing you out, but there's kind of this dichotomy. And so one way we do this is we fit some of the rational actors behaviors based on historical data. So we tried to take the historical data and say, hey these actions were done by this address repeatedly, you know, let's say your tag these address.
It wasn't like this is a, you know, Dex Trader that does this type of action. So can we try to infer their utility function in further utility function? Now, that's one of the Libraries of the one type of user who's fit to Circle data. Another type of user is one
where we leave. We say hey this user we're going to parameterize in this way, we're going to say hey they have a value function but we're not going to say it's precisely these numbers and then we sample all the numbers we're like hey there that we have a parameter that says how risky they are and when it's one there are get a complete Gambler. They just pull the slot machine every time and when it's zero
it's they're very risk-averse. And then we have another parameter that says, hey how much do they value high growth versus? How much do they value? Kind of like safe growth. So like they they're like oh like I'm willing to invest in the S&P 500 vs. Like I'm willing to put all my money into Nicola or I don't know I don't know what the hot like now that thanks to Robin Hood, I don't even know what the like hot stock thing. Like, you know, Portnoy stock thing is anymore.
But the idea is you try to say, hey, here's how we parameterize. How this agent thinks about risk, and then we search through the whole parameter space. So we say, we're going to grid search from 0 to 1 on their risk level. And then show kind of these heat Maps or like these plots or these kind of more descriptive statistics about how at each parameter, how the system behaves.
So you kind of have to do both. That's maybe a long winded answer to that where you want some historical types of users, but you also want to try to make sure you parameterize a space in a flexible enough way that you can search. I want to ask you about transaction fees and if you're doing any research there and how important that is and sort of
mechanism design space. Yeah, so I think in traditional mechanism design, it it's not quite, it's not quite well studied and I think, you know, we're one of our advisors is Tim roughgarden and we're constantly educating him a lot about the this type of stuff and he's really been like, hey yeah, like we just didn't really, you know, we spent the last 20 years Years, building auctions for Google because that mechanism designers and algorithmic Game Theory.
Folks. Sorry when you say when you say in traditional mechanism design, you mean transaction fees applied to other mechanisms than blockchains like? I mean, what other in other in what other places do? We see like transactions fees as part of mechanism design for sure? Yeah. So the biggest I would say practical user of mechanism design that exists in the world is online.
Actions. Okay, what sorry what I mean by mechanism design was specifically like sorry I was talking about like crypto cryptocurrency mechanisms like the big basically like this cryptocurrency design. Yeah. What I guess what I mean is a lot of the math that has been invented for traditional mechanism design, doesn't include transaction fees in the way that blockchains use transaction fees.
And what I mean by that is when I say buying an ad or I'm connecting to a Futures Exchange, I don't pay per message, I sent to the exchange, right? But in crypto I have to actually pay per message that I send and so that actually changes the Dynamics of the lot of the, a lot of the math and a lot of the math that works for a adoptions is completely invalid for blocks to make sense because of this, it's a new research space essentially what you're saying.
Yeah, it's 100% new research because people don't think about this paper message aspect of it, it's assumed that like any user can send As many messages as they want to Google or Facebook and they don't have to pay like they're kind of paying in like, there's some DDOS prevention, but there's not like a like hey you actually took to pay for spam prevention.
And so what we do is we spend a lot of time modeling this, we don't model it say in the way that we could probably prove a theorem about it but we do try to say how should I value if I'm a minor and I get I have a mempool how should I value a certain? A mutation right? Because a mempool is a set of unordered set of transactions and the the kind of notion of a I chose some subset of it and bi chosen ordering of that subset. That's the value that's extracted by the minor. Right?
And so we tried to take kind of the more machine learning issues more statistical approach to it which traditional mechanism designers would say. Oh well, like how do you know it's optimal? We just try to say like hey Any local Optimum is good enough, which is sort of the machine learning a fit of like, what? Permutation what subset can you pick? That will maximize your value and then what ordering will maximize your oh.
So we measure that both in terms of trying to predict distributions of delays so submit transaction. How long is the delay given a fee? And then we also tried to say what permutation is like most likely so we, but the problem is prescribing value functions over Permutations is very difficult because a very large you know this is n factorial space so you have to kind of like come up with some heuristics for that but roughly speaking that's what you do. The good news is that everyone
who's writing front-running? Bots is still a human and so like they write a certain set of strategies, right? Like it's not like it's not like there's like they're really looking at the strategy that's like compute the ackermann function divided by the maximum value. It could have been and then use that as a random number to flip a coin to decide on the ordering, right? They're not going to choose some crazy thing whose complexity is like super factorial or something, right?
So, So, so far, you know, we've been discussing this in the context of like simulating an existing designed game. Do you guys also work on designing new games, altogether. So, in hft, for example, you could simulate hft, or you could solve some of the problems by like inventing batch execution.
So, when it comes to like, you know, for example, on a theory on this like crazy gasps spikes that we've For the past couple of weeks, you know we could continue to simulate this game but it's probably not sustainable. Like there's probably a good chance that the game design itself is broken and we need to rethink How We Do block space
auctions in the first place. And so, would you be able to use similar methodologies to construct new games or is that, or is the construction of new game, sort of something that has to be just intuitive? And then this stuff is only Used to test them out. I think it's sort of a there's a feedback loop, right? Of like I have an idea, I run a bunch of simulations, I see if it works and then I see what
doesn't work. And then I mutate my idea until like, I kept some type of minimum, like, Optimum solution. I think a lot of the problem with things like designing box base auctions is like they. There's a really well-established Theory, that's very attractive. People to use which is the theory of adoption.
So a lot of the papers on that I would say that especially by crypto of professors are just like cribbing algorithmic games are results and saying like hey they apply here but I think that like a lot there is certainly some theoretical Innovation. You have to make first I think you do have to write the correct. Mathematical framework and equations before you can really stimulate.
But I do think simulation tells. You when you're wrong, it doesn't tell you you're right, but it definitely tells you when you're wrong. So it's kind of like a property test, like, you know, you say this model should do X kind of like in formal verification except add a statistical you say, on average, this type of block, space thing should do X and you use simulation to verify and then you find.
Hey, it doesn't work so like I must have made the wrong model so now I have to change something. And when I say model here, I mean First price auction. Second price auction weird like auction mechanic for box base that you choose, right? Like, you somehow have to kind of, you know, you can think of you should really think of simulations, a way of doing this, property testing and verification. So what piece of the crypt or economic be old or stack? Do you think would most benefit
right now? But today from some of this simulation Work. So it would it be like the proof of stake? Protocols, is it? The fee models, is it some of these on chain device stuff? Like lending dex's, I used to think it was perfect steak itself. I think, I think the problem for proof of stake, from a more practical standpoint is that people are just more risk-averse, which is good. You should be very risk-averse for your base layer but that
also means you're like way too. Slow to like try to like update. Like you know, simulation should be used in like we did something. We observe something. We try to predict, what will happen, given the new observations, and we update, and you kind of have this feedback loop repeatedly applied. That's when it works best. So, like, that's what happens in trading, that's what happens in chip design when simulation tools and other places But I think proof of stake is like very very slow.
Like I and and like Defy is basically copying proof of stake except it's replacing proof of stake as a with an insurance fund type of thing. And I think yeah the D5 parameters are really the biggest deal right now for sure. Because like people are doing all the stuff that they said they would do improve mistake, except they're doing it like recklessly. So I think in the long run proof of stake will learn a lot of the lessons of failure from these
defy things. But yeah, right now, it's just so much more, you know, you can like make a prediction, someone does it, see how it works? Use that, as an example to add to your simulation and, and like, that's is happening in all in defy, right now. I just don't think it's really happening, and proof of stake. How much does, like, sort of governance actually impact a lot of this stuff.
So, so, when you do these simulations or like this mechanism design, you have like some You love what like some socially Optimum utility is for the entire system and if I was like a benevolent dictator and I wanted to maximize like the social Optimum for this thing, you know, you could figure that out, tell me what the best mechanism is and I can go to Ploy, but now what happens when you know there's a governance
token. And so sometimes the holders of the governance token are not trying to maximize socially Optimum. They're trying to maximize, you know, They themselves are rational agents so it seems like it becomes this like very weird meta thing thing that you have to also account for you have to model first you have to model the game that's the mechanism. So let's say, you know, it's curved. But now you also have to Maxim model like the incentives of the curve governance to the dowels.
Yeah. I think the key is to inject simulation into the decision making process. So like when someone is proposing a vote, you run a bunch of simulations and you say, like here are the set of outcome given these types of utility functions, put yourself in one of them. And if you don't find yourself in one of them then you can complain. But I assume you have these sets of value functions. We've run these simulations under different edge cases. And here are the property is at hold.
And here's a probability they hold with like if everyone's a Gambler of the probability of the system. Going. The zero went from point is wet from previously before this vote, one basis point two five percent. Like okay, that's something right like and I can give you an uncertainty estimates. So one of the things that I was saying before about like Point estimates Finance machine learning verse uncertainty estimates, actuaries, insurance statisticians.
Is that if you can provide good uncertainty estimates, if I tell you, it's hey I'm increasing from one basis point of a chance of s and X going to 0, to 5 percent of a chance of Aston X going to 0 but five percent plus or minus 0.2 percent. Then you actually have a lot more content like if I can give you more and more confidence that like this is an increasing thing, then governance actually you can impact governance in a
way that's quantitative and not. This emotional view of the world because like at the end of the day, it's a new field. People don't really like their voting kind of blindly and at least giving some sort of uncertainty estimate. Lets people be like, okay, well I'm rational, but I'm not a my rational and iterated game, right?
Like, if I'm taking a single step, it's rational for me to say, increase the fees 99% because I'm an SN X folder and I want All those fees, but this kind of iterated. Simulation says, if the game lasts long enough, we might go to 0 if I try to collect any fees and if that happens, is that really worth it. And so it gives people a way to figure out their own valuation of how much they want to risk
adjust. So I think simulation is not going to be able to predict perfectly this governance actions but it's going to show you the outcomes under what happens when you choose. Them. And so it's an integral part of giving quantitative justification for these things. I did in the normal world, it's much harder actually to impact governance in a quantitative way. Whereas in crypto, that actually
feels like it's quite tangible. But you're like, giving people risk assessments based on a lot of, like, a lot of very clear financial data. In a lot of stuff you do in the real world, you have to infer, whether the data is real, whether it's accurate. Sometimes you're like, well, someone may have been like And of injecting noise into the
data. But the on chain data being something you trust in those valid is actually quite important for those I'd like to come back to an earlier point, which is, let's imagine that someone is like building a new Block Chain as as that happens these days. Right? And, you know, a lot of times I think teams are kind of focused on on, like building the product
growing and community. And then, of course, one of the things that often comes up at some point, is doing a security audit and the security audit will entail like a bunch of things. But there's some like design aspects that are also Part of that audit. I'm curious how you consider your work to be complementary to that, or you should I call that replace it?
Is it better, or is it a little bit somewhere in between like where you put yourself in that sort of like early stage research when one is building a blockchain? I think it's pretty complementary to both normal Audits and formal verification. Because I think one of the problems for formal verification is required. Related to the thing you're talking about which is that naively there's an exponential State space blow up.
Once I start interacting to system, like K systems once K systems interact, you have this the naive notion of the number of bits. You need is blowing linearly in case the space is kind of blown up exponentially but simulation is more about like well what's the behavior? That's not. If I don't have to sample every possible action, if I sample the most Reactions as well as the ones that are near the most likely actions, what happens?
And so there are two different types of scenarios, right? One is the pure worst case, but might take infinitely long to search through this set of tests. And the other is, how do I kind of use the expected Behavior to estimate risk in a way that is intuitive and interpretable to the non-developer? And both of those I think are are valid ways of stress testing but they're very different. And they are extremely complimentary.
So like when an exchange when like the CME the Chicago Mercantile Exchange which is like the biggest features Market in the world when they build a new piece of software, they of course get audited and they do kind of traditional cybersecurity as but they also do simulation and they stress test like, hey, did we choose the right tick size as a
parameter. Did we like our we resistant to kind of certain types of malicious trading strategies that try to like block Now the market and there's a whole literature of this like the SEC themselves spends a bunch of time. Doing these stress tests on Exchange code to meet, show that they meet compliance. And so they're very complimentary but they stress has very different things. One is really stressing user Behavior and the other is stress testing like code behavior.
And user behavior is about probabilities. Code behavior is about determinism period. Feminism, but they're related. So in all of our simulation research and I think this is what distinguishes us from other people, that who's kind of tried to do this, is we run everything against the real code. So we build kind of think of open AI gym or like, you know, the alphago training program.
What happens is people build a harness around the real piece of code and then the harness has a way has a The interfaces that you can model the different types of users. And you make a domain specific language that you can program the different types of users and then the users interact with the
real code. And I think I've seen a lot of simulation, especially in 2017, I saw a lot of kind of less rigorous simulation stuff that kind of is like, hey, well, we think the model of how the Block Chain itself works, is this and we're going to say this is a poisson process and this is at this thing, and this is a dis thing and then we have models of
user interact with them. The Um is in reality, like a lot of these code things that cause problems for formal verification or security out, there's also will affect the economics in super edge cases. So you want to minimize the amount of surface area that you seed to your model, you want to say? Hey look we're running this against the real code as much as possible. And this is something people in trading do a lot.
And I think that I only really respected this ones, I saw the difference in trading Between. Hey, like this exchange happens to use only 18 bit fixed Point integers and like all of a sudden this strategy loses money, right?
Like that's that's the type of detail that you know I think a lot of people who are like oh well I just like Learn Python and use Pi torch and like I made a model of your blockchain don't kind of like are missing like they've never seen like people lose money because like the you went from floating point to
18-bit fixed Point integer. I randomly and I think that that's why you need to actually run this stuff against the real code because you there's just like tons of weird developer decisions some random if statement somewhere that completely like takes all the money out and you don't realize why until you like actually are running like tons of. So I it's complementary that I just don't see.
I think security officers are really focused on, like, binary objective functions of like, does it property? Testing of like? Yes, no and, you know, I think, What we're focused on this is kind of like statistical version of that have like but we still want to run against the real code, right? We still you know compiled against whatever Docker image you give us.
We run it against whatever kernel module is you say it should be running against because I think you never know when like a some random piece of the could just change the economics, completely, What kind of tools do you use to do this? Like, how do you all? These are custom in-house built tools or are there. Like so I'm, you know, public tools that you kind of used to do these sort of simulations,
both, you know, with dummy code. And then also, when you want to test with a real code, Yeah. So similar to kind of security Auditors, we kind of have we kind of build a lot of our own versions of the virtual machines themselves and like add in extra tracing functionality and extra kind of like tracking functionality for like agent submits a trade to you the Swap and we track kind of like we're through the client that transaction goes and like oh did it get the halted of the certain
Point or, oh, did the networking layer like look malformed at it. But we spend a lot of time is certainly. Now we're pretty much only aetherium. We were doing a lot more other chains but honestly it defying aetherium has the most sort of Need for this. But so we kind of have written our own. We kind of have our own Fork of gath where we have optimized, a bunch of things for doing simulation.
One thing to remember, when you're doing simulation is your Knowing the threat model, you're describing all of the users in the system. So by controlling the threat model, you can actually reduce a lot of the cryptography burden and by doing that you can make the performance lot better.
And so, we've spent a lot of time building this client with extra tracing and kind of ways of like, having multiple agents interact with the same node, multiple agents kind of work off, the same kind of simulated blockchains fate stuff like that. At and so yeah. So we a have that and then B, we have sort of a domain specific language that we is mainly in Python because I think from a data science perspective, it's just like still too hot as much as I love. Julia, I'm sorry.
Julia fans. It's just still not quite quite there, but it's always one year away, right. But I'd sorry. Julia is the scientific programming language that's like way faster than python. It's It's like compiles to rust and C++ like supposed to be like the, the real deal, but yet if you talk to every data scientist in the world, they're gonna tell you they use python or r or
something. So we have these python bindings, we have this DSL, DSL compiles, to some byte code that basically gets run against the virtual machine directly. So, there's like kind of a layer in between that take the compiled agent code, and interact has it interact with the virtual machine, I think, in a world, you know, kind of in the same way that it took Trail, a b forever to open source, a lot of critic. I think we Open sourced some of it though. It's just going to take us a
while. But yeah, right right now it's me only that type of stuff. A lot of what we use is based on a lot of the work that Google and Facebook have done on compiling python models to C++. That that type of stuff has been is like really deep in our in our stack for increasing performance. So before we wrap up here, I'd like to ask you a little bit about Gauntlet the business and what does the current business look like? I mean you guys put all these reports and all this research
and but who do you work for? And then also you know what's the sort of roadmap and plans looking forward. Yeah. So, you know, I think a lot of what we do right now is putting out reports working with the protocols themselves kind of close to security outdoors although we've been Seeing a lot more of an active role in governance. So we're sort of the third-largest comp older and governance by votes and we have
a bunch of stuff. We're working on right now to try to automate the Actuarial predictions. I was telling you about earlier. So, imagine that there's a governance vote. Someone says, hey, we want to change the collateral Factor on compound for BTC to this value. We will basically other generate a bunch of simulations and risk estimates for like what, this, what the before. And after of this particular vote, look like to our best estimates and then present them to the user in a way that's
intuitive. So you can pick, oh, well, you know, by making this change, we decrease the probability of default by this amount. But then we also lower the revenue that the cash cash flow that the network gets By this amount and then, you know, a user who's like, maybe more financially educated but not so like in the Weeds on like how
the protocol Works can do I go? Okay, I kind of get that this is what this change does and we're also working on sort of what we call other gov which is A way where we monitor the markets and then Auto submit proposals. So we do we have you know sort of some of the proposals are more simple but some of them need a little bit of like program synthesis where we generate the code for the proposal.
Based we run a bunch of simulations we say hey there's way too much risk in because a bunch of yield Farmers decided to like mint too much as USD And we're going to submit a proposal that says, like, increased s USD minting fee by X. And here's the reasons why, and here's the code for doing it. And so the dream is to have the sort of automated system that can monitor these things and submit proposals to governance in a fully automated fashion and then the smart contracts pay for
this sir. I don't know if that it's a little bit of the opposite business model of most blockages, most blockchains And smart contracts want to make their coin worth lat. Whereas we want to kind of reduce the tragedy of the commons and have be kind of paid as a service provider, but it's automated. And how do you get the time to like, spit out all these papers?
You know, one of my, you know, I just remember, I had this idea, like a couple of months ago of like, oh, you can combine ideas from stellar and Avalanche your Grant. This new consensus protocol, and then like you like, like, oh me, My brother, we wrote this up, like, two months ago, for fun. And I'm like, what, like, where do you get all the time to, like, write all these papers and like, is that part of the work
you do with? You know, for example you wrote this paper on like you know, swap or just like in a M&M's in general, is that also work you're doing with these companies or is that sort of do something you do on the side? That is something we do on the side but I think it's very closely related in the sense that you know how you were talking earlier about like, can you discover new mechanisms by
pure sort of simulation methods? I think that there's kind of this interplay between the theory and actual discovery of these things, so you actually need to make the theory so that you can simulate it right? Like, once you have the theory you can start saying like here's where the theory breaks and that's where we're going to simulate and that's where we're going to do kind of these stress testing type things. And I feel like right now, the way that things look, especially
in defy, it feels a lot. Like the kind of late 2000s and early 2010's in, in machine learning, it feels a lot like quantitative Finance in the If we're, if you can figure out how to make the valuation model that people use, then you will actually impact the usage of these systems. And so that is related in that like yes, we use the same models and simulation. But also, we have more people using these things because like they understand these Financial
aspects. So there's kind of this duel play between like doing research and convincing people that These risk metrics are correct and I think I think writing the research is quite crucial to that. It's the equivalent of Open Source software for this type of stuff. Cool. So where should people go to find out more about Gauntlet and
your work. So our Twitter is at Tom, Lynette work for me. My Twitter is at my name at tarun chitra so ta Ru n CH. I tra you know we're we publish a lot of stuff but I think we're going to be coming to a governance vote near you soon. So cool. If you're if you're in that realm you will you will see us or you've already seen us in compound. But I think that's the story. Great. Thanks for coming on through. Yeah, thanks.
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