Welcome to Epicenter, the show which talks about the technologies, projects, and people driving decentralization and the blockchain revolution. I'm Felix Lutch and today I'm speaking with Idia Simos, who is the Co founder of Rated. Network. Rated is providing node operator metrics and ratings for the Ethereum network. Hi Alias and the welcome to epicenter. Thank you, Felix. Thanks for having me. Huge fan of the show since since I joined the industry.
So very, very glad to be with you today. Awesome. Yeah. Yeah. I'm also really excited to have you. We have a long history of like working together in in the staking space. And yeah, I've been really interesting to follow your path and now seeing you your own project with Rated. So, which is why we're here to talk about today. But yeah, let's start with the classic basics of you know, how did you get into crypto and ended up where you are today.
Cool. So first touch with crypto started in 2013. I used to live with a really good friend of mine. He found out about Bitcoin and he started talking about it. Nonstop started building stuff, talked about it even more. Initially I thought he was kind of nuts and didn't really get it. But then the more we talked about it, the more I got it, but didn't really pay attention too much. When it really clicked for me was in 2015.
I'm Greek originally and so in 2015 was the worst part of the the the decade long crisis effectively that that Greece was in. And in 2015 we had capital controls come in huge referendum. Should we stay in the European Union? Should we break away? That means also like leaving the monetary union, issuing our own currency. And so capital controls. Was this really gut wrenching periods if you build for for Greek society at large it like crippled the economy all the young people left.
But like there are there are really visceral images that I still have sort of in my mind of. Very long lines of pensioners around each ATM that you see like on the on the street driving around talking about you know 50 people, 100 people blocks like whole blocks worth of of of lines waiting to get their weekly ration of money and and so at that point like I had the sort of the light bulb moment regarding Bitcoin. I was like, okay. I get it now. Non state money.
You're not beholden to this idea of you know, institutions and and and the way institutions work in you know, the modern financial system and and I really found that appealing. So then started, you know, researching more but again not
not being like very involved. It all sort of came together in 2017 with with Ethereum. For me, and in this whole idea of, you know, applications that you're you're able to build on, on a platform that has like the properties of Bitcoin but then can extend this logic sort of arbitrarily, right? Like the vision of the the world, computer and so on. So spent the whole year just researching stuff, trading, trying to build things with with friends.
But by the end of it, I look back and I was like, well, you're having like so much fun and you. Resonate with like the whole mission of of self sovereignty and just building something better than kind of the the the alternatives which is kind of what is the status quo. And I decided to commit myself full time to the space. So I got a job with a fund called the Central Park capital. They were just starting out back then. I was the the first hire that they made as an analyst.
I stayed with them for three years, made a bunch of investments. Build a pretty expansive data platform while at the fund when you know Dune did it exist, Token Analyst was like one of the one of the earlier data companies that were looking at blockchain analytics specifically and help them raise a $75 million fund too. And then I left and I joined the startup called Bison Trails,
which at the time that I joined. Was I was think it was employee number 20 or so I was a protocol specialist there. I think it was the second ever person to be called the protocol specialist in in the industry. Although I know you you you have been doing like very similar work in your in your history in the in the space of the first was Victor is my colleague who who hired me in basically and Bison Trills as he was validators as a service right. That's what we were building we
ended up building a pretty. Large platform I think at the top of the market it was you know north of $30 billion on on platform. A year later we're acquired by by Coinbase and then I stayed there for for another year before I branched out on on my own to found found rated with my my cofounder ours. But you know, super happy to talk to you about, you know the internals of of of the story there. But I want to, I want to let you ask whatever questions. Thank you.
Thank you for that background. It's really interesting to see you like witnessing and I guess first hand in Greece and how it meander to where you are now, like seeing blocks of pensioners and now you're seeing blocks on the Ethereum chain being full.
Hopefully so, yeah. I guess you know what stands out to me is like your you've been always like in this sort of space around data and obviously that's sort of what rated is focused on. So maybe just to start, can you explain to us you know what what is rated and So what are the the products you are building and? Sure. So the whole thing that we're building, we we fit it under A1 liner which is reputation for machines and this is like a really charged term you can fit like a lot of.
A lot of scope in it, but really like the the mission of of what we're doing is providing transparency into the infrastructure layer of blockchains, right. And we we started with the theory now where this is coming from like the why are we doing this ties actually in pretty well with you know 2015 and those lines of of pensioners and so on. I'm here, I'm doing what I'm
doing. I got involved in the space because I really do think that we have certain opportunity to build something better, something more compelling, something more transparent by default. But also, transparency is not handed to you right. It's like, sure, the the source material is open. Anybody could theoretically just go and access the data, but without the interpretation layer you're really not improving much. Right.
And and and the beauty of of blockchains is that actually they allow you to go and get the data but obviously you know interpretation doesn't come out of the of the box. So on a long enough time frame, I think we're working on systems and we're building systems that are by default a lot better than what the financial system for example is, is, is running on. And I also do think that it is a matter of time until things.
Migrate to the systems that we're working on now, how much time that is going to be, that's another story. It could be 10 years, it could be 20 years. But I think the fundamental properties of the things that we're working on are undeniably orders of magnitude better than than than the alternatives. So if you didn't take that to be true, then I also don't want to imagine that world where we
don't have. Transparency and visibility into kind of the the layer that guarantees it all, The layer that actually packages, transactions, intents, whatever that is, and transitions it, you know, from a want to be done state to a done state. It there is a protocol, there are rules to be followed, but also like rules can be broken and actors that operate the protocol and the base layer
could act selfishly. And acting selfishly means that you, you impose a negative externality on everybody else. That is not only everybody else that's around you and that's honest, that's acting as they're supposed to be acting or expected to be acting, but also everybody that transacts on the the top layer, which are really like the most important part of the the whole, the whole equation, so. That's what we're largely here to do. That's the that's the mission. This is why reputation for
machines, right? And how you get to reputation is by indexing and contextualizing and building and and dispelling the subjectivity of what is necessarily good and what is bad, but also what is right. Just just merely providing the context of what is happening, what has happened, how what is happening fits in the context in. Of of of what has happened and is this behavior like expected or or not or what even constitutes a behavior that's that's all the work that we're doing with data.
So it's at the moment we're operating on on Ethereum. We we started with the theorem because it is I think the most consequential piece of infrastructure on blockchains out there by users, by developers, by assets that are powering the the infrastructure and so on. And we currently host a network explorer where the index is not the block, it's actually the operator. So we're basically contextualizing how theorem infrastructure works and then you can be as granular as the
validator index. But then we're providing sort of abstractions in terms of. You know node operators and in different pools and how these pools are composed of node operators and then all the way up to sort of the global network state with the ability to actually like zoom in down to like the. The most granular unit of accounts I suppose is the validator index. At this stage we also have an API which basically serves the data that you can sort of see on on the explorer, but also way
way more. To build interfaces to power financial products that serve the infrastructure layer. And we also recently released an Oracle which is a gateway for us to bring the contextualizations that we we curate on chain and actually be able to like power a suite of products on you know the Ethereum main execution layer. Yeah, that's that's super interesting. I I guess most people the the Blog Explorer is essentially like the portal into the the crypto space, right.
Like many people's interactions with like when they first used crypto, I mean aside from the wallet is probably like looking at ether scan and looking at their transaction and it's it's quite powerful because like yeah, like you said, right. And initially everything is open, theoretically accessible, but like how do you actually do it in practice? So I guess ether scan like was like kind of the first wave there to like really do it on the transaction level and maybe
did the application layer. And from what I understand you are very focused on this infrastructure layer for the proof of stake like chain, how does it work on the very bottom layer and is that going back on your like sort of experience and bison trails or yeah, could we say that, is that how you? Totally. So like the origins of of of rated go all the way back to, I guess October 2020. So I had joined Bison. It was like six months in or
maybe a little bit less. And then the beacon chain was launching at the end of the year, and the Ethereum Foundation put together a hackathon and the brief was do anything. With all the data that the Medasha test net produced and that's the last Test net before the beacon chain infamous for like a Prism bug with like an update that they they push that had a cloud flare clock to run side by side was like the validator zone like sense of of time and then it just like wiped out.
I think 1/3 of the of the network's validators at that point. Great that it happened in the test net. And I guess that's that's what testings are for. So my myself and and and my friend Sid Shaker, he was back then I think the CTO of of Token Analyst and they had, they had just been apart by Coinbase if I remember correctly. So we we sat down and we basically went on a two week Sprint where we just really took all the data that the the network produced and we.
Did like a very expansive report on anything that happened from like you know clients syncing times like even off chain data to storage to how kind of the the the clients and the nodes behaved over over like a the span of like 2 weeks. Which is basically like when we run the the research to slicing and dicing and diving super super deep in all the Unchained data that we got. So you can I think you can still find it on E2 data dot GitHub dot I/O. It was still called E 2 back then.
So we did this hackathon. We keep we won Silver prize I think and we learned a ton from that. We we learned that there's like so much wealth of of data about the network and there is so little definition of actually how you can make this data useful. We learned things like. You know, performance, externalities, risk, but like primarily performance and you know the risk and externalities thing was a was sort of realization that came a little bit later, but it's very
subjective, right? Like people were measuring no common language and what performance actually means or is it uptime rewards, is it something else? No, no clear answers and then kind of overtime as I kind of went about. My, my, my work at at Bison Trails and you know we were operating on north of 30 networks, I think 404550 maybe more.
I kind of saw this issue everywhere around me like it was a very narrow slice of of of of value in the world that I was, I was looking at, but I was kind of someone operating in that industry. I didn't know how well we're doing. Altogether, I didn't know how well we're doing versus our network neighbors or competition or whatever you want to call that. I didn't know how the network is doing. I didn't even know how to articulate or I mean at least I
had like some sense. But in general, it was like, again, pretty hard to even articulate what it means to do well and what it means to not do well and compare it compared against what. And then I started thinking, well, if this thing is going to be super successful and run incredibly consequential things. On top of it, 1% of the world's transactions and finance, commerce, media, I don't know you name it. That cannot be the case, right.
It it, it cannot run on something that is amorphous and not like very well articulated, right. And then at the same time you know you know it as well as as as anyone. Proof of state grew from what? Some I don't know. Hundreds of millions of assets staked to the, the height of the market at some like over $300 billion at stake, which is like a massive number in in the span of like you know, 3 1/2 years or so and a massive increase.
So you're like there is so much that at stake quite literally and so little data about what is at stake. And so, so little understanding and then also like extrapolating forward then then you're like well you know that that stake element and nodes in general are important. They are important because they they help these networks run. If these networks are going to be valuable and they are to different degrees then security at that layer is important.
The will functioning of a network is important. How do we but but also like the the assets themselves, the nodes are valuable. Because they do jobs and they produce future cash flows. They're like, you know, it's inflationary rewards, it's transactions, it's like all kinds of things. So we can actually like then you think you can actually build products out of that. And in order to like help the industry move forward, you need
credit for those things. You need insurance for those things you need like, you know, potentially even derivatives, right? Like you can do like lots of cool things. To hopefully not gamble, but like provide levers for people to like operate businesses in that in that space and actually contribute to running infrastructure for really, really, really important
constructions, right. So, and then and then I started thinking, well, you need like a third party independent guardian of all that data to actually help power all of these products that you foresee in the future because. You know a credit underwriters for example job is not to wrangle blockchain data. Their job is to underwrite credit and and do that that that type of type of work right
equally with with insurance. So, so this is this was kind of like the initial impetus for for starting rated that was also like relatively good with with data historically like I built this data platform at the at the funds. When not much existed out there through that thing, I ended up being like one of June's earliest users before starting until until I started the company. I think it was #3 on the you've been dropping the.
But then I got like, I got eaten alive, you know, it was like you gotta, you gotta run just to stay still and. You know go go to work closely with with with Frederick and Matt's there as like one of their earliest users feedback that allow all all that stuff. So it felt like a natural sort of extension of my work putting putting infrastructure and and and data together and and and seeing what we can we can build.
So I guess, I guess here we are. Yeah, it's super interesting because yeah like from experience many operators like sort of struggle maybe. So you have to take these different users I guess, right. Like the operator itself wants to understand better how they are doing, maybe serve their clients data on their rewards and like might end up trying to build some of these systems themselves.
But then you know you know all the other operators have that same problem and you're not the best like project obviously like because you might not want to share that data, you just want
to do it for yourself. So like obviously there is also need or a third party and then I think so you basically have the side of the so like if you think about the users for rated right one is like the stakers or the operators or can you like sort of talk about it in those layers, you know who is the consumer of rated data and what are they using it for? Definitely. I'm going to talk to you first about kind of what I see and like very, very.
High level and then and then go down to like the nitty gritty presently who's using us, how they're using us and so on. So I sort of see us in the middle of a possible network, right, of networks themselves, node operators that run these networks, capital and applications and there are like if you think about things today without rated in the picture.
These are all connected, right? These are all sort of nodes in a broader ecosystem that are connected with one another and and what they sort of exchange between one another is distribution, legitimacy and data, right. And we can sort of exist in the middle of that. We can basically help facilitate a bunch of implicit value transfers implicits like in the in the in the broader sense and
make them explicit. Help them actually materialize easier more systematically and thereby hopefully just inviting more of that. Now all the way back to like where we are today and the products that we have on on a theorem we can can and we do serve node operators and the use cases. There are anything from, you know, rewards accounting to performance monitoring or just
being sort of a? A second source of truth to your internal monitoring, for example to performance benchmarking and understanding how well you're doing versus your your peers and so on and I think increasingly more so, is like this idea that we have just becoming a data coop effectively, maybe explicit, maybe implicit, but you know, a shared cost center
effectively. For node operators where your job is not to wrangle data necessary but also like you know there are node operators out there that have these capabilities. But it's it's sort of awkward to think that you know now they're actually powering another node operators reward accounting or or performance benchmarking or even like the credit products and the accounting products and the insurance products and so on because there is like a large
surface for for moral hazards. There and if that sort of actually transpires then we're no better than sort of the mistakes that that have been made in in previous iterations of of the matrix, right. The other part of of of our our user base is pools and more broadly it's capital allocators, right, because in you know in proof stake as you know stake is one part of the equation. So how does capital get allocated into these? Into these node operators.
So we're working with several pools like Lido, Liquid, Collective, working with Stator and the things that we're helping there is with the tools that we've built to better manage the active set both in terms of deciding like who is on boarded onto the active set and who might be off boarded one day. I don't think we've seen off boarding from active sets yet, but it's. Natural to think is that this industry matures, these things are not going to stay static, right?
Like I mean specifically in Ethereum you couldn't even withdraw up until April 2023. So for the 1st 2 1/2 years of of of the beacon chains existence. So even just evicting someone from the set was really like not possible. But I'm sure that eventually we'll we'll see those things and then you know compositions of active sets changing.
As a manager is like a custodian, as a steward, as a whatever you want to call it as like you know this could be a douse, could be a company, could be, it could be an interface, whatever that is you have to make decisions about the well functioning of your of your set and so understanding with it that will functioning is. And making decisions is all powered by data and this is what we're what we're helping and seek to do more of these these pools with.
Thirdly, you have applications and these are applications that basically reference the infrastructure layer to create new value, right. And that could be credit for example like. So think, think in terms of we haven't, we haven't really. Announced it yet, but we're we're working in partnership with with, with the credit funds to actually trial out on collateralized credit for not operators based on their accounts receivable.
So you think of it as pipe for for validators is a predictable stream of rewards and transaction fees that they earn and thereby you could actually. Monetize that revenue, add a premium to sort of the interest rate that you earn on the Beacon shape. There are interfaces that we're powering with our API to help capital that sits on the other end make better decisions along the lines of Okay, where am I allocating my capital? To which operators am I putting
my capital to? Monitoring it on an ongoing basis, revisiting that decision and so on And then you'd think that you know, Oh yeah, just like I just want some APR, give me the maximum APR and and and then you know see you in a year or what, not sure that's how things really started I think. But then the more the industry grows, the more the industry matures, the more other factors are going to come into play, right. It's like risk.
Versus reward then you have things like eigen layer you have more risk you have like you know risk starting to sandwich on top of like preexisting risk and so on. So without like well contextualized data world in in in that paradigm you can't even articulate those things. So and I like to think that we're playing like a like a like a virtuous role in in the ecosystem by helping people actually like articulate those things. So far it's been it's been a pretty.
It's been a pretty, pretty exciting journey to get here. That's that's definitely the case for me. I think I also like, I mean in the end you see a lot of times the rated data, the aggregate data being used in like these sort of discussions around decentralization maybe right like now we talked a lot about contextualization maybe mostly on in terms of like performance and you know is the operator doing well, There's like these objective things.
But there's also the wider goal of like I guess credibly neutrality, credible neutrality of the network and maybe geographic diversification. Yes, which you are also looking at that like the end user being more the the wider network, right, which I think is like a very nice outcome, right. You're kind of getting maybe money from the people that want to like optimize performance but also providing this this public good of you know what is the the state of decentralization in the
in the network. And I think you know there's like really really interesting things to see there. If you visit rated dot network which will also make it a show notes you'll be able to see that for a theorem, you know like all these things that is try to keep Ethereum neutral or like decentralized like you can like actually check is it actually
happening. So maybe thinking about that maybe can you like, yeah, talk a little bit about how you think about that and how you, how you see the current state of Ethereum in terms of like its goal of becoming credibly neutral is, is it actually happening like from from the data you're seeing or where are maybe like things that need to like be worked on since I guess you were like probably one of the people that has like the deepest inside there.
I think that would be nice to focus on that dimension. I I think the theorem is on A is on a really great path. It's obviously messy and hard and. Sometimes like undefined in terms of like where you're at and how you're tracking in terms of your of your goals, but I think undeniably it's like on the on that right path, right.
And I I'd like to also think that we're helping just push that conversation forward, right like I, I I still remember when we were starting out when we first launched. So we, I think we first launched the website in February 2022, then March, April around that like two months period. It was like a big conversation about client divers.
So I think Prism at the time had 80% dominance or or or something of that of that nature And we had just launched the the first feature that we we we delivered on on the front end which is based off of Michael Sprouse work on on block print which is basically an open source tool that fingerprints.
The proposal patterns of of validators and and works backwards to identify which client they're wearing effectively And so we actually pushed that out on the on the front end and then we started getting shared all over Twitter. It's like client ever is the client diversity and now here's like a way to actually measure client diversity. Since then, client diversity has massively improved. We're now I'm just looking at the slash overview.
Screen and on the explorer and we've we've gone from like 80% Prism to like 40% Prism lighthouses at like 35% Texas at 17% and then the smaller clients nameless and load Star are actually like improving. We I'm not claiming we were responsible for it but I like to think that you know we had we helped and and that's all we can we can really hope hope to do right just give people the tools to make.
Make the right decisions whatever the right decision is for for their objective function also like in terms of another stated goal of the theorem I guess is resilience right and and just surviving like a catastrophic event in terms of censorship, in terms of war, in terms of like nuclear Holocaust and and and so on. And since the beginning of theorem had like a very strong focus in solostaking.
Right. Staking from from home which is kind of antithetical to like the whole proof of stake thing from when you look at things from bottom up, right, because proof of stake is part capital or like 1/2 capital capital is dominated by power laws. So like just crushing those those power laws is very, very difficult. But you know if you if you transfer yourself back in time
and you think. You know when the beacon chain was launching or even before it was launching and what the conversation was shaped around is like, you know Val days are going to run from fridges and everybody's going to run a validator at home. And so in turns out it's like it's actually not that easy, right? 32 eighth is a lot of money these days for for the average Joe or Jane, it's the interfaces.
I mean with things like DAP, note and so on, like tremendous progress in making the whole solace taking thing more accessible, but it still is pretty daunting. Like what happens if I validate? Am I going to get slashed and I'm not going to get slashed like education around this whole thing. It's not like I buy a pack of candy, no pun intended, from you know, the kiosk or or what not.
It's actually like it takes like a pretty long learning curve and you either have to be very committed to some higher order goal to to be part of it or. You're just incredibly interested in sort of the nuts and bolts of it for whatever idiosyncratic reason. Again, like these two things don't make for like a hugely available market sandwich on top, like the capital requirement and everything in actually like is the pretty small segment.
But we have found that be that as it may, if theorem does have about 6.5% of all the validators that are active on the beacon chain being solo stakers. Which is a the tremendous outcome, right. That's like in the billions of of dollars running on on on people's homes and so on.
And and more interestingly even like if you if you now change the denominator from amount of stake that is running to beacon nodes which are sort of think of it as the box that is running those validators and you can run like many validators on A1 box. It's probably like our our best estimate there is 25% of the network. Which is tremendous for like a proof of stake network where really important stuff runs on. Sure, it might not be 50%, it might not be 70%.
But I'm contend that even if it was 6% of the boxes, that's an amazingly resilient long tail of operators where if the large operators that have a lot of capital and are visible companies and so on are easier to shut down in an event of like extreme censorship. That last line of defense which is running in like, you know random homes or basements or I don't know where all across the world is, is an incredibly sort of resilient backbone that is demonstrably helping Ethereum
like achieve, achieve its goals. I think the last thing, the last stronghold that. That that remains is really you know geographic distribution and increasing increasing that and execution clients diversity. So GET is, is is you know still the dominant client. Something goes wrong with GET, then the network will will experience, you know, sour times like I think it was just this week or maybe late last week
when. Guess had a an issue with block production and then together with how Prism actually decides where to build the blocks when when the validators are are are boosted. They actually kind of we we saw the network experiences lowest effectiveness since Chapela, which is a book, not really a frequent phenomenon like a
theorem, is running really well. For for all intents and purposes and again like it was you know A2 Sigma event that didn't like no one bat an eyelid on the execution layer. If you were transacting on the theorem, you wouldn't know that this thing was happening. So you know they're they're still like work to do from like a network perspective. You know we're we're seeing that the network is actually like pretty heavily concentrated you know in North America and in
Europe particularly when it's. Relating to what we've sort of contextualized as professional operators and looking only at that slice of of the network, but then you know you have things coming up like DVD, so you know Oval, SSV device working on a DVD solution of of their own. I think there are a few more. So the idea there is that you can, you know now separate sort of operation keys. And custody keys. And then you can have these like fractional sort of staking schemes effectively.
So you can, you know, you can AirDrop A DAP note to someone in Africa and they can get started with like a very small amount of of collateral, you know, one eighth, half, an eighth, whatever that might be. And then someone else basically puts, puts up the rest. So I'm generally hopeful now, is that going to be enough? To quell, basically the the, the power law, maybe not so, but it doesn't matter, right? What matters is how strong that backbone is.
Because you know, when when push comes to shove this is going to be the last line of defense and and and that's what I think matters the most. Yes, super interesting. Also like just to see here about the the practical issues right now. I think, yeah, there's so many layers that you theoretically want to have decentralized, right? And then everyone of them is like on a different level on the spectrum. Sometimes it's even subjective, you know, is it centralized?
Many people been like criticizing for example also like Lido to be like centralizing but then again right layer below there is like 30 different operators operating in Lido which can be overlooked if you just see like the 30% on even unrated. But you guys have like the drop down right and showing like the different operators that operate lighter for example. That's right. Or even even on client distribution, right?
So I think Chainsafe, that is the developer of Lodestar which is like the 5th client that's available to run, run a theorem on validators and also like the one with the least penetration. They are now running some like 10,000 validators under Lido with Lodestar, right. That's I believe something that kind of Lido as the pool manager, the active set manager in that sort of neighborhood of of of Ethereum and it's like one 1/3 at the time we're we're recording.
I don't necessarily think that this would have happened like this. You know, increase in participation of Lodestar in the mix of clients would have happened. If a pool manager like Lido didn't actually design for it right. So there are like there are benefits and I it's it's it's a really hairy subjects. I generally don't have strong views. I tried I tried to be you know just the facts and like here's here's some data and you can make your own mind up. I don't necessarily have like a
strong view on whether you know. Having limits on pools and so on is like the the right thing or the wrong thing. To some degree it is the market that will eventually decide, right. And I don't know that even having like at least you know for my own disposition that having like a certain opinion even matters. I see, I see positives, I see negatives, I see opportunities and I see, I see risks and I try and I try to quantify them.
As best as they can. And then also, like you know, offer it to people to help them sort of make the best decision possible for the network. Yeah, extremely valuable. I think we can building off the solo staker sort of discussion and I guess the wider sort of state where Ethereum is at right now. I think one very relevant discussion as we're recording this is around.
VIP7514, right. So basically the amount of growth of staking that, that the theory was experiencing and sort of the design with 32 E per validator is like impacting a little bit the network performance as from what I understand. So I think it would be helpful maybe if you could explain a little bit the background of or what this change is about and and why it's happening and then
maybe we can discuss a bit. Know what the implications are after that, but maybe we just give it like a bigger overview of what of what this is about. I think it's been like in the news now and I think it is quite relevant you know for many network participants including like the operators of course and stakers themselves, right. Perhaps we should better set some some context for for listeners that that don't have
it right. So in Ethereum, validators are basically the the unit of accounts in consensus. It is kind of an instantiation of a water consensus participant that basically attests through the state of the network. I see things around me in the flattest, most dumb sense possible. I see things around me and I report what I saw basically. And then in order to have one of those virtual watchers or participants of consensus and so on, you need to have 32 E right? 32 E the neighbors one.
Now that doesn't mean that that one validator is also like a one machine. In fact you can run like many of these validators on A1 machine. Now I I think the design originally was was as such to like you know help solo staking happen make it a little bit more difficult for concentration to happen. You know as we've seen in delegated proof of Stake these constraints don't necessarily exist and so it's it's much easier for power laws to
actually instantiate right. So design decisions made the at a time in the past for reasons and arguably the right reasons right or at least the right reasons for for Ethereum. Now the the downside to that is that when you have a lot of interest in in staking, you have people wanting to participate, you have increasingly more validators joining the network these these digital instances of consensus participants, which means that you have a lot more data that the network needs to
handle. There's, you know, data that's being exchanged in the P to P layer of the network, which is, you know, all those validators talking to one another and saying, I saw this, I saw this, I saw this, I saw this, everybody saw something. And then there's like another consensus actor that's not like really explicit on chain that takes all of what did everybody see, OK, let's put it together. Like what? What is, what is? Reality in reality tends to be what most people saw.
But now you have like so much more of these messages being exchanged really like it. It it looks like the the people that were on the you know the withdrawals are are are bullish and sort of an enabler rather than a Oh my God like all the steak is going to flee the Beacon chain people were we're on the right side of history. So we we basically seen like a 50% increase in active stake in the last six months or maybe
less. It's April now it's recording at the end of of of September. So I think at when withdrawals came that that upgrade came to the fore, it was 500,000 active validators. Now we are at 800,000, right, and we're talking about 300,000 extra validators. There's more than 50% of what were active back then that joined the network in the last six months. It took the network 2 1/2 years to get to 500. So if you're like, I mean that's a concerning kind of trend,
right? Like it could concerning from like a network load perspective because then you see kind of like a straight line, but then you see that line accelerate and the curvature change that sort of fits in an exponential curve. That's concerning because then there might be like an undo load for the network to process, which means that the network is going to underperform because it's not set up to actually handle this type of load of messages.
Then a bunch of like useless effectively, or let's not call it useless, But the marginal value that this extra message I saw this brings is tiny. The more of you know, the more of these messages you actually have.
So in order to basically prevent the network from experiencing hard times in terms of infrastructure and networking and state bloat and and and all of these things, a decision was made to actually cap the validator activation limit to 8 validators per ebook, which I think was the number that the network was at before withdrawals activated. It was seven or eight. I don't remember which one was
it was exactly. I think right now that number has gone up to 11 and basically the theorem works in a way that creates like a bottleneck on the way in and on the way out. The same rules that apply to the activation queue roughly apply one to one to the exit queue as well. But basically the bandwidth scales together with with demand. That bottleneck becomes wider and and relaxes as more demand sort of comes to the the door
effectively. And that bottleneck exists to also manage part of that whole data clutter and so on, but also to control stake in terms of it leaving as well, right. And suddenly the theory not only losing like a bunch of useful information or maybe not so useful information in terms of consensus in the will function of the chain, but also like a lot of security in terms of stake.
So this EIP as I understand is a sort of short term solution to actually give the network some breathing room so that a more permanent decision can actually be made as to, OK, well we have this real issue, you know, too many attestations, too much load on the P2P network, a bunch of like redundant information that just like bloats the the state of the chain, what do we do about it. And so there are again like as a, as a person that's trying to be neutral and like look at look
at just the facts like I don't necessarily have like as the strong view on whether this is good or or bad. I can see like arguments on on both sides, right. And then the argument on on the pro side is like, well, let's buy ourselves some time with his, you know, not so intrusive change to the protocol, roll back to like a few months ago, the state of of the queue two months ago.
And and allow steak to trickle in more slowly so that we can buy some time to figure out what we're going to do eventually. But then on the other end, this creates, at least when you're looking at the two states, right, like you know, 11 validators per epoch versus 8 validators per epoch, 2 states of the world moving from one to the other.
You are disproportionately with that move favoring the status quo such that if, for example, there was a strong needs or willingness of existing stake to be reallocated or new stake that on boards to go to places that are not dominant in terms of their representation of stake in in in the beacon chain. Now they have like less of the window for them to actually kind of catch up, if you will is narrower, which effectively buys
time for whoever is in the lead. I don't know if there is like value in in in really kind of diving a lot deeper into that or really ruminating on it on it that much because it's it's all very subjective. It's all in the eye of the the the beholder. But the the problem is, is is still like existent right, like from a network perspective, from like a well functioning network health perspective, like that's a problem that we're going to have to solve eventually, right.
And there's. It rhymes well with like another proposal, which is like, Okay, let's aggregate the state. Why do we need to have 32 E per validator when we can just collapse that, then do it, I don't know, 20 * 50 * 100 times more and just allow validators and validator operators that
option. Don't make it mandatory, keep the minimum at 32. But then you can add those 32 increments instead of running various instances, having many of these digital agents of consensus that send all these messages, suddenly instead of 100 messages you could just have 1:00. But that also comes with like a whole world of of trade-offs and like largely like like all of these things are ends up being like a a political thing among among other things, right. Which is you know, people have
made design decisions. People have built technology and infrastructure is actually matters the state of the network as it is today, which might or might not be a competitive advantage. So I've made all of this effort and build that that advantage and build all these these capabilities and now maybe they will be deemed completely redundant, right. Or I will need to like retool a bunch of the things as I've as, as I've worked with them.
There's also like very credible, you know, arguments in terms okay you. How do we handle slashings? Does the penalty sort of increase proportionately which means that well, I might fuck up the same way, excuse my my language, but I might fail in the same way that I do today. But instead what is at stake and the slashing penalty today is
1/8. What's at stake is much larger whereas now for example like a correlated failure will be staggered and it will be 1 validator after another after another which basically buys you time to actually address the issue before it kind of like causes contagion in like that one box or that one cluster of boxes that is running like all of these validators and so on. So you know real issues that that don't really have clear cut answers and that's both kind of
you know the the beauty of it and it it's also sometimes what what you know slows progress down right. And it's again, it's a world of It's a world of trade-offs.
Yeah, definitely. Like thanks so much for elaborating like this on it. I do think it's very interesting how you know these dynamics that you mentioned are like sort of like one-on-one hand like favoring in a way the power laws by like sort of limiting how many new can enter and that's like sort of a result of this design choice to. That actually tries to kind of achieve the opposite, right, by like helping solar stakers to stay. So it's a really interesting trade off space.
And I do think maybe the solution could also be right like because of like we're saying it's a temporary solution. So like something needs to change and it seems like or from my perspective, one of the only things I guess that I have seen is that sort of increasing that limits or aggregating the validators in some sense. So I do think what's also interesting is that maybe.
You can do that. But then also you have like solutions like DVT that maybe like can still maintain like the ability for solar sakers to sort of participate even if they don't have because already 32 E like you said it's already maybe too much for many these days. And actually so if it's even higher, I guess maybe DVT can be the layer that like sort of solves that and it wouldn't like blow the Ethereum state so much. But yeah, very interesting field.
And yeah, I guess also cool to have like all these teams like working on different arts of it and you, you sort of, you know, making it all transparent and like contextualizing it. I think that's like a very cool spot that you're in. We're trying our our hardest but just to harp on what you where you left off for for for a little while longer. Like the the flip side of having all of those teams work together in in like loosely or less loosely connected ways.
And no sort of central planner that just explicitly says like you know, this is what should be done and it all kind of like basically gets done in, you know a network of of of influence effectively is that you have the bloat situation, right or you keep, you just keep adding features to something because that's sort of the natural impulse to do. But then you have like like a like a strong contingent of like client developers and so on actually advocating for a clean
up fork. Let's not do like all the sort of improvement stuff and add this and then you know, DVT comes into the picture and then other components come into the picture and then complexity just like piles up because again that's the natural impulse of like humans to build features and not not to like actually reduce features. Reducing features is is painful. It means that you have to like roll back your decision that you made earlier. It means that you have to like accept the mistake, admit
defeat. And also it's it's it's generally more more painful do that but very necessary. So it's just so fascinating, right? Just seeing, seeing humans just work together and like implicit ways at this level of of of scale, it's just really fascinating. Yeah, and I guess like one spicy
topic we had there. Before we started recording, I guess he's also like, yeah, how are these decisions then made in the end, you know, like Ethereum is known to be like sort of a advocate against or like I guess the Ethereum community at large and maybe who even is that right? But like I guess also be tall against some of the core early people sort of against this sort of notion of Unchained governance like making the tokens decide.
But then we can definitely see in this case, for example, that. These choices are made by some of the core developers or in some way right. The decisions are made but they're potentially not like taking into account the people that this change is impacting right in a way that maybe other systems would and I think that's that's also very interesting now to see how these things play out.
As you know many have critiqued the on chain governance, but actually like here, I feel like it's a very good example of. Why Maybe this other path isn't like working as well either right. So you. Yeah. I I guess if you want to comment on that like what how how are you thinking about this when that chief comment there are no good options we're we're we live in a world of like bad options when it comes to this. Right. Like it's look at governance like outside of of chains or whatnot.
It's not like things are rosy there or that like we've solved that problem as like a the human civilization.
So we we just have to live with like bad options And it's also like there's precedent of 1. Chain governance actually like not being a solution like there was a a Tasos upgrade I think was there 6th or 7th upgrade maybe like already two years back there was like a big debate about an escape hatch feature and then the upgrade wouldn't like actually go through the very political process with Ethereum as as as you mentioned earlier there is no like explicit on chain governance and
I don't even know that like explicit on chain governance is is the answer. So governance happens in a in a rough way. Right. The term community calls that rough consensus where you know ecosystem participants call it the client teams the theorem foundation the operators and so on.
They're all in like you know, three or four different forms, not including sort of all the all the private chats and and and IRL places and so on. But it's, you know, I guess it's ETH research, it's like a couple other forums, it's like a couple discords and so on. They discuss those issues at large, right, like upgrade issues, what are we doing, how are we moving forward and so on. What ultimately happens is that if the operators don't decide to upgrade the software, even if
like all the client developers for example agree and like the protocol researchers and protocol developers agree to like push the change forward. If the operators don't upgrade their nodes to the new version, then the network doesn't upgrade, right? What is like a little bit under talked about perhaps and also maybe a little bit spicy is that these operators have a lot of stake on the line, right? Like they have their actual stake. They have businesses that they've built on top of that.
They're they're effectively like not not custodians, but they're agents of the stake of others, right. So there is like really like a lot on the line for them.
And so ultimately if they're if they, if they did have like even as a solo staker, if you did have like a deeply rooted belief about the specific feature which you know, even saying it that way, it sounds like a little bit ridiculous, but you know, so be it. It's like if if this, if this infrastructure is as important as we all think it is and as important as as it will be like being sort of religiously militant about features actually make some sense.
So the point is that even if you were like vehemently against your your, your stake is at risk if you're in the, you know, if you're in the less representative side. So you know what happens like if you follow right, right, right, right. You know the other chain, the theorem classic or what? For state classic like you know what happens to like your your your your value at risk.
So you know again really dense not only technical like in fact far far from technical issues that have no no clear answers. But I I will say that you know if theorem has gone pretty far further than most if not all with that model of of governance that it's that it's got. So you you have to acknowledge you know success. When, when, when you see it. Right, right, right. Yeah, definitely. I yeah, that's it's a super interesting discussion.
Like you said, it's far from technical and it's something that we haven't solved as as civilizations anywhere really. I do think, you know, yeah, you have that extreme option like you mentioned before King, but obviously that's. Yeah, like not really a tool for like daytoday operating like this sort of network, right. It's like in a very rare situation maybe that will you will like sort of play that
card. But I guess you know some sort of formalization of this governance to me at least feels like is potentially like a direction that is maybe other explored in Ethereum also I I do think yeah, in other places maybe it was formalized too early and like not well enough but. I guess being some more effort there. I feel like this is a big area that yeah, one needs to like talk about more or like build or like. Discuss what, what, what could
be done. But yeah, so thanks for this like sort of expedition into that direction. I guess we're already talking quite a bit. So wanted to end it on the notes about rated again. So we've been talking a lot about Ethereum. Obviously you guys are very focused on on Ethereum. But the broader vision right, is very reputation for machines. So there's like obviously other machines aside from Ethereum validators. What's your yeah sort of what's
the future for rated? How are you thinking about what you're building and how you're expanding maybe from Ethereum or are you even doing that? Layer two is who knows if you have agreed to just yeah. Talk to us a little bit about that and then we can wrap up also. So we are as we've discussed present on a theorem today. This is where we we started from. But the vision is, is is much larger than that. It's potentially like as large as the theorem can and will get
and and and even bigger. So it just coming from from the background that I did and then having worked with people across many different networks and having kind of like interface with these these networks. I I see value in like plurality right. And I I do see a future of of multiple different networks that have different core value propositions of different layer twos. And I'm you know multi chain, multi network Maxi.
I suppose although this might be like a somewhat contrarian of you to have at the time that we're we're recording because it's like the the deep of the deep in in the bear market and and you know it's it's hard sometimes to see kind of like the the light at the end of the tunnel.
So what what we're planning to do in in the coming quarters is actually expand to multiple proof of stake networks, the Solanas of the world, the cosmos of the world, the polygons of the world and thereafter to expand to actually covering multiple agent sets. So not only validators, but there are many node types that actually have very similar properties. They participate in networks. They produce useful work. They earn rewards or a fee
stream. They participate in the fishing for the useful work that they produce. They are agents in networks, and they're entrusted with sort of missions. And they might be fulfilling their missions successfully. Not so successfully, not at all.
They might be inflicting positive externalities in the context of their networks or negative externalities by taking and do risk and and and so on. So you know I think by just doing what we're doing in multiple networks and and and going into all these different environments, not only can we help these networks just be better achieve their their goals better. I like one more like little
little anecdotes. I ran the numbers at some point and I looked at the historical effectiveness of the Ethereum network on the whole and it turns out that apart from the merge which was a very volatile month, effectively the network hasn't had a lower effectiveness month than the month that we launched. Now again I'm not like the saying we are wholeheartedly and and and and uniquely responsible
for that outcome. But I do think that we've had a small part to play right in by contextualizing by surfacing like information about performance transparently and and and in an easy to access and and and understand way. So I I do think that we, you know, there's benefits in actually having that unified view, common abstractions in terms of you know what is performance, what is rewards, what is risk. That are virtues for for these networks there's a lot of work to do.
There's a lot of scope. There's a lot of complexity. It's a big infrastructure build. And then you know while your ability of adding the marginal network might improve over time and the cost of doing that drops. Then there's there's another like more insidious curve that you don't get to actually realize until after some time which is like maintenance and and and cost and in scope right.
So these are all very challenging things and also there's there's another challenge in that there might be and I think there are more networks and more agent sets than 01 company might be able to cover, right. And then you end up being in a in a situation where you know you're you as the as the company or the organization or whatever you're in in that boat. And then you know there's three holes and then there's a 4th hole and there's a 5th hole.
And then you have like to cover the 5th hole, you have to just. Open a twister, open another one, right. Just move your hand from like 1 to the other and then exactly it's the it's it's the whack A mole analogy that we, we we talked about before before the recording. So that's going to be a challenge.
Figuring out how to solve for that is going to be a challenge, but it's a challenge on super, super excited to be to be taking on because because you know we see the positive impact of of of the work that we're doing and and that is sort of the the biggest rewards of of of all if you will. So just be able to have like a like a small contribution to things moving forward to things improving to arming people with the right of an arming might be like the the wrong word.
But but giving people the ability to access information and empower them to make good decisions for whatever their objective function is is something I can, you know get behind and continue doing for a very long time. So I'm excited. I'm excited to to be on the path that that we're on and I'm excited to to explore where it takes us. Awesome. Yeah. Thank you so much that I asked for the inspiring conversation. You'll die on this hill. We we get that after this you will die on this.
I will die on this hill. Right. Yeah, I mean, appreciate it, Felix. Again, yeah, thanks so much for coming on Epicenter. I hope this yeah, very very interesting episode about the theory about infrastructure and these will add to the show notes like a bunch of the things that were mentioned. In this episode. So yeah, our listeners can find out more there. And yeah, hope to see you soon. Greatly appreciated. Thank you so much for having me.
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