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Avalanche Foundation economists Matias Antonio and Eric Lu on GCP and GCI, why value never reaches the token, and paying validators from captured value instead of an inflation tax.
The Layer One podcast, created in collaboration with Avalanche, features co-hosts Kelvin Sparks and John Wu as they unpack the real forces driving the crypto market forward.
Through candid conversations with the industryβs true movers and shakers, the show delivers clear, grounded insights that cut through the noise.
Matias Antonio: I actually quit the World Bank Pension Fund to start my own crypto fund that didn't work
out. If you can guess by the timing, it's the perfect peak at a very difficult moment.
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.
You can use Ethereum as an example. You know, doing a transfer was $200
and I was crying every time I did it.
John Wu: Economics is kind of an art as well as it is a science. Human beings are not rational
optimizers.
Matias Antonio: The very early blockchain, it really was a game theory problem.
What does big mean? Nobody knows. So price go up, right? Then an economy actually formed.
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, Calvin.
Matias, Eric, nice to meet you properly. Yeah, very nice to see you.
Good to see you, Calvin. 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 Athena, Brett Tejpaul, co -CEO of Coinbase Institutional, Luigi
Donorio DeMeo from Ave, Sandy Call 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 Matthias, 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 Luma in mine then ethereum blown twice even red 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 Lu: 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 Matthias have put out.
And I think, first of all, thank you for a refresher 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 crop 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 Matthias says is you have to look in Matthias 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 Lu: Yeah, I'll get to that, actually. But yeah, thank you for the summary, actually, Jiang.
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
-creative opportunities you're seeing today, be it protocols, apps, centralized exchanges,
DeFi, so on and so forth? We definitely love your guys' perspective.
Eric Lu: 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 compostability 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 Lu: 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 Lu: 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 stake 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. It's Tittner, 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 Lu: 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 Lu: 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 the benchy right the
faro 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 Lu: 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 server 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 -chip. 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
Kelvin Sparks: you actually own. 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
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