Avalanche Foundation Economists on Layer-1 Value Accrual Mechanisms

Layer One EP18

Matias Antonio (Avalanche Foundation Chief Investment Officer, ex-World Bank, ex-IMF) and Eric Lu (Avalanche Foundation Lead Economist, PhD) have built GCP and GCI — blockchain analogues of GDP and GNI — because nobody could say what value actually runs on a chain, only what volume does.

The harder half is capture: on every general-purpose L1, the value the chain makes possible does not flow through its token, and the fat-protocol thesis never explained why it should. Their answer is not taxation but a co-op — an incentive-compatible cut applications accept because protocol value is what secures them.

OUTLINE

00:00 - Cold open
01:50 - From the World Bank, the IMF, and a fund that blew up
05:54 - GCP and GCI: measuring what a chain actually produces
08:31 - Capture: why the value never reaches the token
09:24 - Distribute: paying validators without inflation
11:09 - The plain-English version: GDP, GNI and a very low tax rate
12:37 - Output vs income, and what each costs to tax
17:42 - Beyond transaction fees: dormant capital and the gas floor
24:39 - The Ethereum staking-ratio fight, and who pays validators
31:05 - A Laffer curve for a blockchain? Not a state — a co-op
35:26 - Why economists, and why now
46:16 - Twelve months out, and the network of networks

TRANSCRIPT

Kelvin Sparks: Hello and welcome to the Layer 1 podcast. My name is Kelvin Sparks. This is my co-host John Wu, the president of Ava Labs. Joining us today, we have Matias Antonio, CIO at the Avalanche Foundation, and Eric Liu, lead economist at the Avalanche Foundation. Welcome to the show. How are we all doing? Great. Good to see you, Kelvin. Matias, Eric, nice to meet you properly. Yeah, very nice to see you. Good to see you, Kelvin. Before we get into today's episode, Layer 1 is a podcast focused on the intersection of crypto and the real world, and it's brought to you in collaboration with Avalanche. Nothing we see on this podcast is investment or financial advice. Make sure to always do your own research. In today's episode, we'll be talking about the economics research roadmap. But before we jump into that, I want to talk about the Avalanche Summit. I'm really excited because that's coming up September 16th and 17th. Speakers like Guy from Ethena, Brett Tejpaul, co -CEO of Coinbase Institutional, Luigi D'Onorio DeMeo from Aave, Sandy Kaul from Franklin Templeton, and so many others will be in the building. And it's just around the corner. So let's get into today's episode. That being said, when I was getting prepared for this one, And Matias, you're with the IMF in the World Bank Treasury previously. Eric, you hold a PhD in finance and led a research blockchain risk analytics team. So, John, you were also in the hedge fund world and you ran your own fund. So a good place to start to be, what brought you all to Avalanche?

John Wu: I mean, I'll start very quickly. I mean, Avalanche is a technology I thought was really going to be able to and is in the process of transforming traditional financial rails. You know, Avalanche, Avalanche Labs is working with the Avalanche Foundation. We have tokenized so many real world assets and the entire economic system can be more efficient and new ways of creating value for and providing access to individuals can all be possible in an open permissionless blockchain.

Matias Antonio: And my story started actually in 2017 when I read the Bitcoin white paper and I mined, then Ethereum, even read Cardano, believe it or not uh and then by i actually quit the world bank pension fund to start my own uh crypto fund that didn't work out if you can guess by the timing it's the perfect peak at a very difficult moment so and then 2020 came and with the new uh you know there were new layer ones and so i started researching them i i took a look at Solana and Avalanche stood out. The part that really stood out for me as I was reading the paper was the probabilistic consensus specifically because having studied mathematics and with a background of economics, I could appreciate how a sampling process can speed up the solution of an equilibrium outcome that you're trying to compute and so on and so forth. And I appreciate that, but I never thought that it could be applied to a blockchain consensus mechanism that actually produces a finality with precision. And I thought that was quite interesting. And on the other side of things, I thought, oh, wow, they're actually focused on tokenization and real world assets. And that's kind of the world that I lived in. And so when the opportunity came, I said, yes, I'm in, please take me. And then having met the people inside the ecosystem, John, who unfortunately I don't get to see as often as I would like to, but I always enjoy talking with him. It was a no-brainer for me to stay and haven't looked back since.

Eric Liu: Yeah, so I actually got interested in crypto very early on, actually when I was still in undergrad. But then, at that time, I feel like I have to dig deep into economics and finance. At that time, that's really where my passion is. And then after I got my PhD, I got into a consulting firm as a financial economist, and there I kind of get first professional exposure in a crypto-related case, where I essentially analyzed the order flow of a centralized crypto exchange. And I thought, this is the industry that I see is going to boom in the next decades. And that's where I want to spend a better part of my career at. So I moved to a blockchain analytics company where I was also the crypto economist, and building data and analytical products for institutional users mostly. And after that, the move to Avalanche Foundation is actually quite an easy decision for me because at that time I was thinking, I need to get to the front line of the industry and generate real impacts, right? And just joining the foundation allows me to do exactly that. And that's how I ended up here.

John Wu: Eric, I read some of your recent stuff, the stuff that you and Matias have put out. And I think, first of all, thank you for refreshing my undergrad economics studies. But certain points that really resonate, for instance, like in the US, we forget that GDP and GNI are very similar. The numbers are almost always on top of each other, but there are definitely countries in the world where GDP and GNI are completely different. And if you apply it to the world of blockchain ecosystems or layer ones, you can see that there is a significant difference. For the audience, maybe you or Matias, will you take a review of how you measure, capture, and all the frameworks that you've put together for that, and then we can dive into it so that after we level set with everyone?

Matias Antonio: Yeah, absolutely. And this is actually one of the key areas of the foundation in research, right? as a nonprofit that is here for the ecosystem, we naturally have a longer-term view, and so naturally we gravitate towards that gap that needs to be filled. And as we were thinking about what is out there, what became clear to us, and this is the first bit, the measure bit, is that there isn't a way to actually measure what is the value that runs on the chains. There's the accounting approaches that have been made with the PE and so on, And you can see in Token Terminal some of these, let's call it national accounts that they have compiled. But in it, it's actually not capturing the bit that I wouldn't say matters, but it's a different angle that enables us to understand what is it the gap in terms of the capture, which I will speak to in a bit. and and that's where really gcp was born because economies let's say the u.s it doesn't measure itself by the number of dollar transactions and dollar volumes that go through the economy right it is a way of viewing it in the sense that it has an important it's an important piece of understanding but really the value is about the the revenue that the companies receive and that we observed as something that was missing and how this GCP was born the GCI is a similar story it's a different lens of measuring also the value that runs a chain that I'm sure Eric will dig much deeper on later on and that helps us have a benchmark in terms of what is there the second question is then what can we capture and how should we capture and what should we capture it There's many layers of the questions there because now we're starting to get into the tokenomics side of things. Because the fundamental problem there is that all layer ones, this is not unique to us, the value accrual process in terms of how these value that the chain is helping generate that wouldn't exist if the chain wouldn't be there is not going through the token. And that's a fundamental question that hasn't been answered. So in many ways, the fat protocol thesis fails to answer why should that be the case. And so that's where the question of how do we capture comes from, right? How do we strengthen the value accrual mechanism? And the first piece is understanding the value. Then how can we capture it? And then there's a third bit, which is how do we distribute it? So what does this distribution mean? it means how do we the value that we have captured how do we make it flow such that it maximizes the value of the token explicitly why does that matter because well on one sense uh you know validators right now are depending on inflation in order to um state right what if we could lower inflation to zero and give them the value that they captured that's a structural reason for validators to stay and stay on the long term. That's a sustainable reason. And the other side is that it helps strengthen equilibrium outcomes. So now the reason of why they stay is no longer bound to the price of the token or the inflation rate of the token, but rather form a real cash flow that is being received due to the capturing mechanism that the chain has. And then on the other side it's about scaling right in a proof of stake chain the the the security of the chain is a direct function of how much value is actually at stake so if the value accrual process is weak and the token doesn't strengthen that value doesn't grow and so when you go to larger companies who need more assurances in order to be onboarded, that's an important component. And so that's why this measure capture distribute is a nice framework that kind of captures how we're thinking about the research and really the why of why we're here and why we're doing it. That's very insightful.

John Wu: If I may, I'm just going to summarize a few points that I think are important for our audience here. What Matias says is you have to look in Matias and Eric's work. GCP is equivalent of GDP and GCI is equivalent of GNI, traditionally speaking. And what's being measured right now is sometimes just to focus on effectively the GDP. And on top of that, in the traditional dashboards in the blockchain and crypto world. And then on top of that, if you think of capture, the issue with capture is you can almost think of it as a taxation for a country or for an area. And right now, especially on Avalanche, the taxation rates are very low. And because it's so low in theory, the applications and the people developing on top of Avalanche are able to get a very good deal, if you will. And lastly, however, that deal can be separated between a nominal with a real number and the inflation adjustment associated with the token can be optimized for all parties. If that's my summary, I'll let you and Eric move on from there. I

Matias Antonio: may not use the word taxation, but yes, good summary.

Eric Liu: Yeah, I'll get to that, actually. But yeah, thank you for the summary, actually, John. And I would want to add, I think, two important perspectives on the two measures in particular. So obviously, we're not creating these two measures for the sake of having two measures, and they're nicely corresponding to these national accounting measures that we're kind of familiar with. But the GCP and GCI metrics that we build actually correspond really nicely to two different sources of value base of a blockchain ecosystem like Avalanche. So on the one hand, the GCP is really measuring the economic output. So you can think of it as the productive process, the production output. Whereas GCI, with the analog to GNI, is really kind of the income accrued to the residents or, broadly speaking, the ecosystem participants. And they all have very different implications when it comes to capture. So any capture we want to impose on the GCP, which ultimately is backed by all the revenue generated by the applications or L1s in the Avalanche ecosystem, they would have an implication on the productive process. So how can we sustainably capture part of that value without distorting that productive process so that the ecosystem can remain productive, competitive, and growing in the long term is something that we need to understand and investigate. So that's the GCP. And then for GCI, it's a similar story. So it's an income stream. Any capture we want to impose on that is going to have an implication on the adoption. So the vast majority of the GNI actually comes from, or GCI actually comes from, for example, the reserve assets of the RWAs or stablecoins in the ecosystem. And if we take a cut of the yield from the reserve assets, obviously that's going to have an implication on adoption. So again, we have to think about, what's the optimal way of capturing that without jeopardizing the long-term sustainability of that revenue stream as well. So that's one important perspective of these two measures. On the other hand, the second important perspective I think you already very nicely touched on is that these two measures are not just two simple measures. These really are based on an accounting system that gives us a near real-time and granular lens to look at the individual components of the whole economy. So we can ask questions like which verticals or applications or L1s is actually generating growth in terms of these two measures. And maybe ultimately in the future, we can even use these two measures to identify the growth opportunities or which part of the economy is working better than the others. Or where we should really put the support in the economy to sustain the growth of these two measures. And finally, doubling down on your point about these measures enable us to look at the real versus nominal term of the economy. I think that's kind of a good benefit of having these two measures that are very much rooted from the classical national accounting. is that we get to look past the price changes, which is like the price of the crypto assets obviously fluctuate a lot. By distinguishing between the nominal changes and the real changes, that really gives us a deeper understanding of how the ecosystem is evolving over time. And one concrete example I can give you is that obviously we know that we've seen the trading volume across the industry decline significantly since late last year. But if you look closely through the lens of real versus nominal terms, what you can see is that decline is actually driven more by the price changes of the crypto assets, which are the assets being traded in these crypto ecosystems. But if you keep the price constant and look at the real activities, is actually declining not nearly as much. So that provides an understanding of the real activities behind these drastic changes that we see all these data vendors are reporting, which are nominal terms.

Kelvin Sparks: I was just curious on the topic of the ecosystem itself, what are the biggest value-creating opportunities you're seeing today, be it protocols, apps, centralized exchanges, DeFi, so on and so forth? We definitely love your guys' perspective.

Eric Liu: Yeah, so I think it's... I kind of tainted at it through the GCP versus GCI lens. So I think that the high potential opportunities that we see map nicely to the two different metrics. And in turn, these two metrics give us a better understanding of the magnitude of these opportunities and a potential mechanism to capture them. As I mentioned earlier, so the GCP kind of really, most of it is coming from the revenue of the different applications. And, you know, obviously right now, you know, across most of the general purpose L1s, not just Avalanche, transaction fees is one of the main sources of capture revenue for the protocol. And one important challenge of transaction fee being the sole or the most important source of revenue is that it doesn't really give us enough exposure to the ecosystem growth. So if we measure the ecosystem value and growth through the lens of GCP or GCI, obviously, you wouldn't see transaction fee co-vary a lot with the value of ecosystem, essentially. So really, that brings us to the question that if we view the value of the ecosystem through the lens of the GCP, then how can we really get a meaningful share of the GCP that the ecosystem is generating? And one of the priorities of our research agenda is exactly looking at that. So what's the sustainable incentive-compatible way to capture part of that, or to bring part of that GCP value back to the protocol and eventually flow that to the token holders. And it's kind of the same story for the GCI. And obviously, many other L1s or applications, they are already sharing some of the yield from the stablecoin balance on their chain or using their applications. and that's obviously one of the area or direction that we're actively exploring as well.

John Wu: So it's a fair, again, to try to make it into a real-world comparison. Kind of what I'm hearing is, you know, traditionally GDP, GCP, you have NV equals PQ. And here, what we have is a lot of dormant capital because a lot of the real-world assets. So the money supply is really just sitting there. There's no velocity associated with it. and one of the things that you guys are probably thinking about is how do we take this large base of money call it m1 m2 whatever you want to call it and how do we get the velocity working is that one way to think about it because otherwise having tvl is just having dormant capital

Matias Antonio: definitely velocity is is a question that um we're thinking through and it has to do with some of the challenges and composability on defy or perhaps some uh that has to do with some primitives that don't exist that make that capital actually productive in the sense of trading like I can think of a cross currency basis which I'm sure you're very familiar with right and and the instruments just don't exist on chain to make it happen and so there there is a bit of a transition friction in the sense that there is things that exist on TradFi that don't exist here and and they're making slowly their way here and and in that construction of the primitives is where dormant capital kind of seems to exist that actually may be very productive and people are hedging and doing things on the background we just don't see it because the activity is not happening on chain and i think that that you know to your point absolutely it is something that we're thinking about it's just in that process it's it's a bit further away to so say in terms of the capacity to capture and so currently the way that we're thinking about it is that there's two sides in terms of what is the potential capture path that we have. One is the supply side and one is the demand side. The one that you're talking about currently is on the demand side in terms of the things that are happening, how can we, you know, the activity, let's say, that effectively is asking for more block space, how do we capture that? But there's other things like on the block space demand. For instance, one of the ACPs that we put forth had to do with increasing the minimum gas fee, right? Because what we are observing is that gas fee, having cheaper gas fee, is not necessarily the edge. The edge is trust. Why do people use the chain? It's because they trust it, not because it's expensive. And you can use Ethereum as an example. Not to say that there's no price point after which you actually have user migration. And that clearly happened during 2021 when Ethereum was proof of work and doing a transfer was $200. And I was crying every time I did it. But, you know, like, it clearly created migration. That's why Avalanche benefited from that, right? Solana benefited from that. BNB benefited from that. So, like, there's definitely a price at which that flips, right? But we're nowhere near that price. So there's an argument to be made that you could easily increase the gas fee and get to a situation where you're capturing more of the value on the transaction side, i.e. through the supply side, and is direct. And the interesting bit here that sometimes is easily missed is that it's dynamic. Validators vote for it. So that means that you can actively engage on, I don't want to say monetary policy, but some sort of algorithmic policy where the collective validators can decide what is the appropriate gas fee because they themselves benefit from those transactions being higher. So they will also benefit from the transactions being more expensive globally. So there will be an equilibrium outcome, which I think is going to be very interesting to see how it plays out and where it lands, that I think is going to be a function of many, many things that are out there. Yeah.

Kelvin Sparks: On the topic of validator economics, have you guys been following the, I think it was the EIP-8363 discussion that's been going on? I mean, it seems like Justin Drake, who's huge in the community, put that forward. And again, another huge proponent of Ethereum just outright hated it. DeFi builders, so on and so forth. So curious what you guys are learning from the discussions going on there. It's an interesting one.

Matias Antonio: And correct me if I'm wrong, but the goal here was to target a particular staking ratio for Ethereum such that if it goes above it, then the yield goes down and otherwise it goes up, which I think it's actually an interesting way of going about it. I think in general, the tokenomics or Ethereum are interesting in the sense that they're all based on long-term equilibrium outcomes in the sense that you have inflation is an outcome of the activity and so the rate of yields in some sense kind of modulates towards that. So the long-term supply is relatively stable if you think about it, which is an interesting solution, not saying right, wrong, it's just an interesting different one. And so in that context, having a staking yield target is interesting. I don't necessarily think it's a bad thing, but I do think it creates risk which currently are difficult to hedge and manage specifically for defy applications and staking like yield stakers because what then you have is a high degree of fluctuation potentially in terms of what's the yield that you're you're receiving and how how do you even manage that there's there's no there's no swap here that you can go from fixed to floating right or floating to face for that matter so it becomes very very difficult and and so i can see why it can be very jarring in terms of the different perspectives because ultimately it impacts people's pockets and when it impacts people's pockets people have a very strong emotional reaction and rightfully so right and and that's that's i think where where it's coming frame. Where do I land on it? I haven't made my mind up yet, so I can't really say. But I do find it interesting. I've been following somewhat from afar. Eric is significantly deeper in the trenches, and I'm sure he has his perspective, which he's going to share.

John Wu: I would love to hear Eric's perspective. In the traditional world, you manage that because you have a target inflation rate. And with that target inflation rate, you adjust policy, assuming you're reaching that. Sometimes arbitrary target inflation rate. You're bouncing it off with labor and unemployment and all that stuff. But I'd love to hear how Eric would think about a solution to manage it in a blockchain crypto ecosystem.

Eric Liu: I'm not sure if I can give you the solution right now, but it's probably even more questions. But I would add my perspective. So I think ultimately what that EIP is about and the whole discussion or controversy around it is really, in the end, a matter of how much validators should get paid and who should pay. And if we bring that to Avalanche, obviously we face the same question as well. And right now for Avalanche, we're effectively imposing the inflation tax on all token holders and use that to pay the entirety of the validator reward. And so, yeah, so they're entirely paid by the inflation tax. And more importantly, the price of validation is set by formula in the protocol, right? So it's kind of declining over time as more AVAX is being minted. But most importantly, it's not determined by any economic forces. So this actually relates to multiple streams and questions that we laid out in our research agenda. For example, how much we should pay for validation and how much validation does the network actually need? And ultimately, this may turn into a mechanism design problem. So is there a mechanism where we can, on the one hand, review the willingness to pay by the network users for validation services? And on the other hand, what is the willingness to validate or the reserve price for the validators to be willing to validate? So one is the supply side, one is the demand side. And then perhaps we need some kind of equilibrium mechanism that can clear that market and give us the actual price that's going to eventually give us the optimal level of validation as well as the price that gives us the optimal level of validation. And on top of that, validation is actually a public good, right? Because the validators themselves, they don't fully internalize the benefit. Because when you validate, there's an additional validator or an additional stake, it's kind of increasing the security of the entire network. So everybody benefits from it. And the basic economic theory would suggest that if we rely on a competitive market equilibrium, we're going to end up in a situation where the validation service is undersupplied. So it's less than what the network as a whole actually needs. So there is also a question of if we want to get to the optimal level of validation services for the entire network, what is the right level of subsidy we need to provide so that we incentivize a bit more validators to participate in this validation. Yeah, so as you can see, there's different layers. Obviously, I'm not giving you any solutions so far. It's more questions after the other. But ultimately, I think that's the question we need to resolve, not just for us, but probably all the POS chains out there.

John Wu: Well, I mean, it's true. You don't have the answer yet, but you're working towards that. And what you're describing basically is economics as a discipline. This is why it's a BA, not a BS. It's an arts. There's art into this. It's not an exact science. But there are frameworks. And if you think of these frameworks, basically you're talking about trade-offs right now. have you thought of something that would be equivalent like the Laffer curve for taxes? Like there's got to be an optimal thing you can draw out on a graph or something and we can work towards that.

Eric Liu: That's an interesting perspective. So I think if you look, we go back to the research agenda. So there are many kind of different optimalities that we're trying to look for in the research agenda. And we have already talked through a number of them. I think, you know, right now the top priority is probably like, you know, at the capture level. So, and I think this is a good point to circle back to one of your tax analog, actually. So when we're doing the capture, we tend to not think of it as a taxation problem, not because we don't want to take a cut, but really, we're not really a sovereign state that we impose a compulsory tax on the different applications. But instead, I did mention earlier that we want to find the sustainable and incentive-compatible mechanism that can capture that part of that revenue. And what I mean by that is actually, I think I do believe there is a mechanism where the protocol and application mutually benefit from some kind of value capture from the application to the protocol. Because if you think about it, all these applications, they have to rely on the value in the network, which is directly determining the security of the network. And if they have a high exposure to the network value, then they must benefit from some value flowing to the protocol, which boosts up the value of the network. So I feel like there must be a sweet spot. And that kind of is the optimality or optimal level of capture and also the mechanism of capture that we're shooting for. So that's kind of the capture part. And

John Wu: you don't think about it as a pure sovereign state. You think about the better analog, maybe it's a co-op. Yes. And it's cooperative. And how do you think about a cooperative and how everyone benefits from the whole as well as the individual? So

Matias Antonio: like, because there's things in here, you know, to follow your train of thought, John, that does make this particular discipline of economics as it is applied to blockchain very unique to it. Because there is a strong game theory component that in a sovereign state, it doesn't exist in the same way. Because in the sovereign state, you have political economics, which is slightly different. It's game theory, but it's different. In this case, you have it at the network level and so on. So to your point, it's kind of like a co-op with a sovereign state. It's a mix of bag of things. And so as Eric was talking about the optimality problems and you mentioned the Laffer curve, there's many optimal problems and each one is going to have a different chart and framework. So the Laffer curve will be a downward view, you know, and other ones may look very different. And the framework itself may be very different. And maybe it's not even math driven. It's like a pure game theoretic problem. So it's really, really difficult. It's a non -trivial, non -solved issue anyway. Well

Kelvin Sparks: said, well said. And I guess as somebody who's never taken an economics class in their life, much less anything finance related. I come purely from like engineering and science background. What about your guys' background, like uniquely equips you to be able to fix these problems? I mean, you spoke to it at length, but I kind of want you guys to brag a little bit here because I just love where the conversation is

John Wu: right now. Well, I mean, I thought about all of these things as a practitioner when I was an investor. Obviously, I was a technology investor, so it's more bottom up. But even as a technology investor in individual companies, you still have to understand the overall macro environment and what kind of environment lends to certain types of companies to invest in, obviously. So I thought about these issues. I studied it in school from an economics perspective, nowhere near what Eric has done. But having had a background both from an academic as well as a practitioner helps me think through some of these issues.

Matias Antonio: In my experience, I have a bit of a mixed bag of experience in the sense that I studied math, so it helps me follow and understand the technical side of the chain. Obviously, I'm not an engineer, so there's a lot of details that I do miss and I don't pretend to really fully understand. and I did study economics and was at the World Bank and the IMF, which I had the luck or bad luck of actually being very close to the European crisis. And at the time when I was at the World Bank, there was a really interesting Jamaican crisis where the bond market was frozen. And I was very lucky to see that. And both the World Bank and IMF got involved and I spoke to the stakeholders on the ground and got really interesting insights. And that kind of helps me understand, broadly speaking, what does macro look like, right, and how to measure it. And more importantly, what are the risks that within an economy can exist? And I see blockchains as economic objects, right? There's a lot of details to it, as I was speaking earlier, but it's ultimately an economic object. So a lot of that framework applies. And so you get to see what are the opportunities, what are the risks, and how to think about it. And the interesting bit here is that one of the areas where blockchain particularly excels at, and Avalanche is one that's very well positioned for this particular problem, is value transmission. And that's where RWA's make sense right like tokenize the world right that's that's the avalanche mantra so for to really understand that bit you need to understand capital markets and that's where my asset management background is and was and i understand right because i was at the at the world bank pension fund i had the luck of being able to cover almost all asset classes i looked at public equities private debt, market neutral hedge funds, and helped figure out what tactical positioning the fund should do. And then I was at the fixed income desk where I was a portfolio manager there, managing a fairly large amount of assets and making calls in terms of what the fixed income and positioning should be for the portfolio. Do we want to be longer the Japanese bonds, we do cross-currency base. There's a lot of positioning that went with it. And in that experience, I got to see the value aggregation of the entire asset management space and therefore the capital market space. And that helps me really understand that side of this particular economy that is key to help it grow, given the focus areas. Yeah,

Eric Liu: so I was trained as a financial economist, but before I graduated, I don't want to write academic papers for the rest of my life. And I want to solve real-world problems. So for me, I think, on the one hand, I do have the rigor in the classical economic finance theories that I believe that it's still give us probably all the foundation that we would need to better understand what's going on in the crypto world. But on the other hand, having been working as an economist most of my professional career, I also know the practical burning issues that the industry is facing. So I think I'm in the position to essentially bridge the two sides. So I do know the classical, the useful, important theory from economics and finance literature, even though right now we still don't really have the asset pricing model to price crypto assets. with all its unique features. But I know these are the pieces we may need to put together so that we can use these tools in this literature to help us solve these real-world practical problems we have in the industry.

Kelvin Sparks: That's awesome. Thank you for that. And are you guys going to the summit at all? I was curious. Completely out of nowhere. It's fine. We can just keep going. We just keep going. I'm just trying to find a way to slot it in. It's been a bit difficult given how technical the conversation is. But no, that's fine. We can move on, John. You go ahead.

John Wu: So, I mean, it's interesting because I don't know if it's Eric or Matias mentioned earlier, or maybe even Kelvin because he's an engineer by training. Is this, well, maybe this is for Eric because he's a pure economist. Well, not a pure economist, but has this been exceedingly difficult because these token economics and these systems were created by scientists, not economists? And as I talked about before, economics is kind of an art as well as it is a science. And I'm reminded from my undergrad, one of the professors was Richard Thaler. Some would call him the father of behavioral economics. human beings are not rational optimizers. Now, if we're trying to put science behind this and put it all formulaic, does this make this whole exercise exceedingly difficult?

Eric Liu: Yeah, so I think the answer is yes. It's definitely very challenging. But I think at the same time, because these blockchains, the industry is actually built at the very beginning by engineers and scientists. there are a lot of room for improvements from the economic perspective. And I think that's where my expertise will contribute the most. So you do see a lot of areas where we get to apply for basic economics or finance intuition where maybe at the very beginning of the design of the blockchain ecosystem or the quote-unquote tokenomic model are not really seriously considered. But that leaves the room for improvement, especially I think right now. So as the industry mature, people have been fighting for adoption using better and better technology, lower and lower transaction fees. And now we really have to get more into these incentives of these different ecosystem participants how we model them, how we factor in their incentives into the design of the tokenomics itself. And those should come in right now, if not sooner. That would be my take.

Matias Antonio: You know, and so like, I don't think it's because necessarily, it is difficult, 100%. But I don't think it's necessarily because an engineer or a scientist were the ones that designed it. Because the truth is, at the very early blockchain, it really was a game theory problem. And that's what Bitcoin did. It's just a game theoretic solution for alignment of incentives such that a network that just needs internet access for it to just work in some sense, right? And if you fast forward a bit later, it wasn't even a thing because there was no economy, right? even when Ethereum came out in 2017 or so, it was the fat protocol thesis. It's just going to be big. But what does big mean? Nobody knows. So price go up, right? So there was a lot of lack of understanding because there was nothing there to really analyze, understand, and have a real interesting solution for that needed to be solved, right? As 2020 and 2021 progressed, then an economy actually formed. and then it's like you had the uniswap the ave the the Balancer, Frax that that came in and and gave it shape it gave it an economic activity and the users that were coming in to actually do something on the chain so now now it becomes a real problem where uh the complexity of the situation but also the tools that come with it are available because now you understand more the problem it's just that it's not that it wasn't present before it's just that it wasn't known what could be the solutions for a problem that was not observable at the time. And so actually, you know, maybe the fact that it was engineers and scientists that did it provided a very wide solution space for, you know, helping solve this problem. Because it's not true for every chain, but there's chains that you can do actually a lot. And ours, thankfully, is one because it's very well designed. So, you know, I think it's a benefit, but I wouldn't necessarily say it's because the genesis was that one, is because the problem exists today. It's just a matter of something that was just not observable at the time.

Kelvin Sparks: Well said. And let's say 12 months from now, we look back on this podcast. What's the one proof point that you'd point to to say that the research agenda was successful? yeah

Matias Antonio: um honestly i have a few uh so like one of them is definitely that validators are actually getting paid something above inflation something on top of it right is there going to be inflation 12 months is a very short time span for the fundamental changes that we're talking about so most likely the answer is yes but i just want it to be that there's something on top because if you're just earning inflation, your net return is actually negative because you have costs, right? So if you have now something that is on top of inflation, now your real return is above zero. And that's where I think it starts becoming much more sustainable. And the other side of it is that we have found a way to capture sustainably in a non-disruptive manner. And that's something that Eric keeps saying, which is super important because it has to be incentive-aligned because otherwise you're distorting and you're actually driving users away or driving economic activity away. And that's not the job here. How do you find that game theory solution for this incentive-aligned problem? And so if we're able to capture the value and have something that is significantly higher, like orders of magnitude higher than what is currently captured, that's the other thing that I would say, okay, success. So value accrual and sustainable value data economics.

Eric Liu: Yes, very well said. I guess I'll just add something quick. I think from a practical perspective, everything that we're going to put out that either proposes any changes to the architecture, the tokenomic design of the ecosystem, we do it for a purpose. So maybe it's mostly around capture or distribute. They must have a stated purpose. And they should always have a verifiable outcome. So after we implement those changes, for example, do we see a significant uptick in the non-inflation-funded validated rewards or protocol revenue, that's something we can verify after the change. And beyond that, are we reducing or replacing the inflation-funded staking rewards with some additional real source of revenue for the protocol? then that's another example of real verifiable outcome that we can look at. So if all those checks out, I would say, yeah, we achieve what we set out to achieve.

John Wu: I do want to mention one thing that's very important here, and what we haven't talked about, is the reality of what we're talking about here is just related to the main Avalanche chain. The network of networks is what Avalanche is about. and there's a lot of hidden value in all the other L1s. So we talked about this as a co-op earlier. We're only talking about co-op on the Avalanche C-Chain. It's almost like a network of many co-ops. And so the difficulty of just trying to find the optimal part of the curve for this one co-op, and then we have to balance with that, how do we connect it to the hundreds of co-ops that are tethered to Avalanche? That's going to be a whole other challenge. And, you know, let's work on getting one done, but we'd love to figure out how to get that trap capital or the dormant capital that's sitting in the other L1s and making it also productive.

Matias Antonio: 100%, John and that's that's actually top of mind as well we focus on the C-Chain because that's that's the thing from the design space the closest thing at hand but what the the the goal after that is is how do we get the token to grow with the growth of the entire ecosystem not just one area And so we're hoping to also find solutions for, or be it by focusing on this area first, is that it gives us a sense as to what are potential scalable solutions that we can implement more broadly as well. That can give things to these other chains that they would need and also, you know, and therefore strengthen the value accrual mechanism, not just it relates to seeding, but it's the entire ecosystem as well. There are some technical challenges with that at the validator level that we may have to think about. But absolutely, that is one area of research that we are very much aware of and looking at.

John Wu: Yeah, that would be huge because Avalanche, more than any other layer one, has far more dormant assets than understandable by the average explorer, if you will, because the explorers don't capture any of that. I mean, it's multiples. It's almost like if you're just a real estate company capturing just the cash flows associated with the apartment buildings or the shopping malls that are active, not counting for all the real estate you actually own.

Kelvin Sparks: Very, very well said and uh yeah i appreciate all you guys taking the time out to chat today but unfortunately we actually ran a bit over so that is all the time that we have today matthias eric thank you for taking time out to chat with john and i and uh as a reminder our newsroom works tirelessly to get you accurate informed crypto news if you want to stay ahead read the block thanks see you next time