ZKPodcast: Staking derivatives & DeFi with Alex Evans (and Tarun!)

Transcript

[Music] welcome to xero knowledge a podcast where we talk about the latest in zero knowledge research and the decentralized web the show is hosted by me anna and me frederick before we start in on this week's episode i want to remind you all of an upcoming ama episode with frederick and i this will be hosted by our friend alexandra heller if you have any questions or topics that you'd like to see us cover please head over to the zk podcast

subreddit and leave your questions there i will also link to a tweet that alexandra sent out last week where you can also leave some questions be sure to ask us about production blockchains zero knowledge proofs or where we see the space going ask us about our hopes dreams and aspirations whatever you've wanted to know but we're too afraid to ask so links are in the show notes and hope to hear from you i also want to say a big thank you to this week's sponsor trail of bits for this spot they asked that i share a recent update about crytic crytic is a github app that continuously assures your ethereum smart contracts are safe and functional reporting build status on every commit

and running a suite of security analyses so that you get immediate feedback upgradeable contracts are not as safe as you think architectures for upgradability can be flawed locking contracts losing data or sabotaging your ability to recover from an incident every contract upgrade must be carefully reviewed to avoid mistakes crytic now includes a comprehensive suite of 17 upgradability checks to help you avoid these pitfalls this tool allows devs to stress test their upgradeable contracts as they go saving time and potentially catastrophic headaches down the line for more check out their blog post where they describe how to use this tool i've added the link in the show notes so

thank you again trail of bits and now here is my conversation with alex evans and tarun shikha [Music] this week i want to welcome back one of my favorite guests and our sometimes guest host tyrone hey um for this episode we're actually speaking with alex evans who's a researcher at placeholder welcome alex hi thanks for having me and alex and tyrone recently published a blog post where they talked about the kind of challenges around pos taking derivatives and the batching of those derivatives and that's going to be the majority of this episode so to ruin today you're going to be sort of a combo

guest host guest hope you're ready for it yeah i like to think of it as i rehypothecated myself as a staking guru as a guest cool and alex uh you know this is the first time that you're on the show tarou knows you quite well but i don't and maybe it would be great for you to introduce yourself a little bit to our audience who are you and what you what are you working on sure so as you said in the intro i'm a researcher place holder a little bit of context placeholder is a venture capital firm in new york we invest in open crypto networks and the infrastructure that supports their development we have a specific focus on governance and economics and so a lot of what we end up spending our

time thinking about is things like what makes protocols efficient what what ways do communities have of governing themselves what drives value to these networks and those sorts of things so my role within placeholder in that context is kind of twofold so one is relatively typical of a vc firm like i take a lot of calls with entrepreneurs developers project leads once upon a time i also used to do in-person meetings with those people you can imagine um and i do some research on particular areas that we potentially ultimately end up investing in so that part is relatively typical and vanilla as far as the vc world goes then i also have this other and perhaps

for the purposes of this podcast more interesting version to my job which is i end up doing a lot of thematic research in vertical areas that i find interesting where i have the opportunity to dig a little bit deeper and so in the past that's included things like um credit risk and lending protocols like maker more recently it's included this paper that we wrote with tarou and on staking derivatives as well as another recent paper on liquidity provider returns in automated market makers so it's really kind of it's two sort of opposed roles that i have one is very vertical and very horizontal i should say uh where it's my wide inch deep talking to a lot of different projects seeing what's going on in the ecosystem and then there's a supplementary piece

where it's more vertical and i get the opportunity to to dig deeper and those perspectives kind of inform each other i think in interesting ways and so far that's producing papers that people like and has given light some opportunities that as a firm we've been able to pursue as a consequence yeah so you know alex and i started working together because we we had we had both around the same time realized there's a deep theory around uniswap um and it was at that time that i realized alex is actually kind of a weird self-taught savant in that he he didn't really come from a math background but he had taught himself a lot of probability like formal probability theory and stuff at a level that people in the

cryptocurrency space never have um and so we both just ended up end up being really great because he he has a lot of experience uh in in in venture but for some reason knows a lot of financial mathematics and it was very complimentary cool so this is this episode should be pretty defy heavy and this is something that it's so funny because i think on our new year's episode i said something along the lines of like defy is boring or i said memes was born whatever i made two bad calls and i've actually been called out a little bit for it and it's funny because i i do find that like there's been so much activity in d5 that it inevitably starts to kind of come

into this podcast talk into the topics that we cover on this podcast as well and i think this episode is going to be looking at first at like the state of defy and then we're going to dig into a paper that taroon and alex co-authored looking at how pos systems can be impacted by these staking derivatives so since i have you both on the on the episode i wonder i wanted to ask you a little bit more about just general state of where are like what what's going on right now if you could like highlight one or two main trends that you're seeing at this point what would they be well i i think the easiest at least like market

dynamic changes that have happened are that you know people have basically used these decentralized platforms to replicate uh financial services offered by centralized platforms like coinbase or bitmex or blockfi and i think people had this sort of growth hacking issue where like the only people who wanted to use d5 for a long time were those who had really poor access to capital but the people of really poor access to capital are oftentimes not very technically sophisticated so you have this chicken and the egg thing like how do we get people using this stuff even though it might be too technically complicated and over the last six months i would say the ux

uh plus it very complicated incentive mechanisms have actually made it cheaper in a lot of ways to use the decentralized versions of these products than the centralized versions which has brought on a lot of migrated a lot of people over um and so we're witnessing one of the great growth hacks in history um i don't think i've ever personally seen anything that's come close to this and that has been the sort of catalyst of migrating people from centralized crypto trading to kind of and lending to decentralized trading and lending i would say thematically that's what's happening

do you think though that like i remember like you know back in 2017 you were also seeing more traditional financial entities entering crypto would you say that now it's like centralized crypto is moving to decentralize crypto but like are there also traditional finance also getting excited about this part of it or are they still kind of staying out of this it's still pretty complicated i i think it's like still too complicated for for like very traditional finance folks it's too complicated for centralized trading folks in a lot of ways because you have to understand a lot more of the blockchain mechanics and engineering and low-level details about the virtual machine and like if you're really making good

investments in d5 you need to actually understand a lot of the engineering details which is very different than normal finance where you only really care about the financial details and the centralized exchanges usually act as this abstraction layer so that you when you're buying bitcoin on coinbase you're not actually buying bitcoin equipment you're buying the right to coinbase issuing you bitcoin later but they could just be like hey sorry we're gonna exit scan and take all of the bitcoin and not pay you back defy doesn't have this problem it's it's very much non-custodial so if you have your own keys you can keep track of all the funds you have on these exchanges and you also can check whether they

the exchange itself is under collateralized or is trying to pull a scam or whether there's a mount gox happening these decentralized finance entities can actually make sure that those don't happen but they are more difficult to use and they there's this trade-off surface and that trade-off surface is captured by the sort of difficulty in engineering problems yeah so on that engineering problem there's a couple interesting things that we're noticing as well as we're seeing a lot of engineering talent try to focus on building some of these opportunities a lot of them are more traditional developers might come from what software backgrounds that maybe don't have as much of the finance background but just have an opportunity to play with these different primitives and assemble things that potentially users find interesting one of the things that we hear

repeatedly from people is that they pursue the opportunity because the the underlying infrastructure is potentially more neutral to the things that are on top of it so you know if i was building a lending system back in the day potentially i might have to compete with a whole bunch of banks and then goldman sachs launches marcus and i have to compete with them um and a whole bunch of things like that now we're getting this opportunity where i can build things on top of lending protocols knowing very very specifically what types of behaviors i can expect i mean borrowing you know hacks and and that sort of thing which are uh still all too frequent defy but especially as that infrastructure ossifies it's relatively predictable in terms of somebody who's building a

financial service to know that the underlying infrastructure is ideally neutral to them at least at this point in time such that they can build with confidence and know what functionality they're offering to the end user and one other thing i would say that's certain extension of that is you know i we kind of over the last 15 years we saw the evolution of financial technology go from you know things in 2006 and seven of of venmo kind of figuring out how to make social networks useful in in payments as an evolution from paypal and then you started to see stripe and plaid uh come out in like 2013 and 2012 which offered sort of these api access to banks so people who are

developers could sort of write programs that programmatically borrowed and lent for you the end user so that the end user could have experiences like robinhood or uh you know these other other like coinbase itself right like use this plan heavily right um so these end user or mint.com and these end user experiences rely on being able to have a single api to every possible bank right because there's hundreds of banks they all have different server stacks some of them have servers from the 1960s using cobalt and you as the end developers making this app don't want to learn 500 different bank schemas right you want a single api and so the growth in stripe and plaid

and and revolut and monza and all of that type of stuff transfer wise that all was driven by this kind of api layer that made access to banking very easy to use and uniform but it didn't give you access to risky capital you could only do kind of read-only bank operations where you're like you can read someone's balance and say hey uh you can start an ach transfer to robinhood or you can like say hey you can do some very minimal amount of transfers but there's a lot of centralized gatekeeping and there's a lot of like it's still not a very efficient system whereas crypto lets the risk operations take place on the smart contract platform and d5 is those risk operations

whether they're borrowing and lending whether they're issuing derivatives these are just things that stripe and plaid and cannot do and i i'd say this is sort of that evolution arc if that makes sense yeah the other thing on there is let's say there are certain apis that you have the capability to change i system state i write access to um it's not exactly always clear in centralized finance if i'm stitching together two or three different services by accessing their apis that if i change something in the state of the first of three but then in subsequent transactions that that state doesn't propagate so if i bar if i borrow some amount of money in order to make a trade when i go to trade potentially something's changed

um at that ladder venue so in crypto one of the things that you're able to do and in some ways it's good in some ways it's enabled things like flash lending and that that are can be good and promote efficiency but can also be dangerous if you have exploits where where i can guarantee the behavior within a transaction so i can say execute this transaction if xyz conditions hold and and i can have guarantees about how these things are being stitched together in the context of a single transaction as opposed to having to worry about different databases that may not be in sync with each other and where you have to deal with this dynamical system of i'm changing the state of one system and therefore something maybe moves against me in another interesting and i wonder like would you say that a lot of the d5

protocols like i i'm assuming a lot of them are at least semi-open source and like so there's like this way of locking into them whereas these other kind of older systems would have been more siloed and more closed would you actually let me ask the question would you say that is the case like is part of the reason why you can do this programmatic thing more accurately because it's open it's open plus consensus so i think to alex's point without a consensus algorithm you can't enforce this atomicity of risk operations right now when when you go to robin hood and you say hey i want to transfer 10 dollars from my bank and then robin hood gives you the experience of like hey we just increased your account by ten

thousand you can go buy amazon shares they don't they're actually taking risk they're assuming that your bank transfer will work and if it doesn't then they liquidate all your stocks right in the crypto system you don't have to do that once i do the transfer the money is there so i can just make everything conditionally dependent programmatically which just doesn't doesn't exist in in normal finance all of the the the hacks that are make it look like that's happening for you are all like some centralized entity assuming risk and lending money to you implicitly yeah oh wow yeah that totally actually makes sense but do you feel like is there no i mean and that's sort of speaking to the centralized version as well where the centralized version was also sort of just like

where the actual things were held weren't necessarily reflected in the user's experience of where those things were held or when things transferred wasn't exactly reflective of the actual underlying transfers happening yes but now it is yes through d5 that's cool i have another sort of part of d5 that i have not explored very much and to ruin i know you do something around this and alex i don't know if you do but liquidity farming like i i feel like i heard this word like three four months ago i'm sorry i know i must sound so out of the loop but like what what is that does that exist in every is that a finance

term so so a phrase i like to say about defy is arbitrage is the proof of work of defy in order for these systems to work someone has to be able to perform a profitable arbitrage otherwise these the states of different contracts won't reflect the true value that they're supposed to reflect so someone there has to be kind of these pennies you can pick up in front of the steamroller and the smart contract emits the pennies in the form of tokens liquidity mining is something where some where a very kind of clever idea from compound to basically encourage you to use the system by paying you in tokens if you found an arbitrage and this became this thing where where

now a lot of people were able to net borrow from compound at negative interest rates because they were earning more money in the token than the amount they're spending borrowing and it's a growth hack but it's a growth hack that the smart contract pays for itself there's no like human there's no soft bank subsidizing uber for 10 years or we work or whatever it's a growth hack where if it stops then basically it just means that the market price the market price the value of this incentive down to zero so there's like a market valued growth hack um i don't know if that allocates that there's two additional things that i'd layer in there one is that it's often used to distribute tokens so you have a bunch of

tokens as a project and potentially your project that says i want my project to be community owned and governed through this governance token and so the question then becomes how do i get that in the hands of my community in the past you would maybe do an ico or figure out some other way of getting that people into people's hands through investment now you're basically saying come use my protocol and depending on how much you use it you get more of it and there's advantages and disadvantages to these different approaches the second thing i'll add to that is what the question that i think a lot of people are asking the question they should be asking is what is residually left over at the point at which the incentive stops right so you're you are incentivizing people to come pick up these pennies from the steamroller

you're creating an ability for people to be set up to perform these arbitrage opportunities when that stops do they continue to stick around and and here you might see some differences actually across different protocols so for instance you might in the case of a amm say well as the protocol gets bigger it gets more liquidity it's easier to trade in some sense there is kind of this network effect as it gets bigger the experience gets better some lending protocols have the same effect and that they are also implemented as amms you can do a bigger trade without moving the interest rate curve too much but then also potentially as wait just what what is an amm oh right uh so you're talking to a defy number so an amm and i think tyrone you might

supplement my definition here but at least how i define an amm is an algorithmic trading agent in the simplest case you have a contract like uniswap on ethereum uh users contribute let's say fifty percent die a fifty percent each to that contract and then that contract offers that ethan die to traders for pairwise swaps at an algorithmically determined price uh uh maybe maybe something i'm unraveling something that's simpler amm stands for automated market maker so the idea is on centralized exchanges like coinbase uh you know people like my old employer or or like jump or other trading firms will be doing what's called market making so they quote prices they say hey

we're willing to buy bitcoin at nine thousand dollars and we're willing to sell bitcoin at 9100 and by having people who are quoting prices that they're willing to buy and sell at the market is liquid in the sense that for many people it's liquid and there's a notion of a price that is discovered now if you want to decentralize that just like the oracle problem you need some way of being able to quote a price that is correct without having to have these humans in the loop um and automated market makers were sort of first invented in the early 2000s to try to to capture this um they they were actually invented for

online ads because online ads had this problem with liquidity where each ad that was sold was a not fungible if i went to booking.com and i saw an ad for the zk podcast and then i go to um i don't know what's another site that exists that people see ads uh facebook and i see a zk podcast ad those are actually different products right they're not both zk podcast ads they are at some level of that abstraction but at the level of who's selling the ad they're two different markets technically right one is the booking.com market one of the facebook market and so the problem is it's very expensive to

you know you have to pay some incentives for people to market make and when you have millions of markets right every ad online ads is kind of a non-fungible market like an nft uh when you have millions or billions of those it's impossible for market makers to market make all of them so one question is can you make an algorithm that can correctly quote you the price based on how much people are interacting with that right so there's all this algorithmic data of oh i'm there's like 500 people going to the site so like this is how much you should pay for for for the zk podcast ad so they were invented to kind of get over this liquidity crisis of like if you know there's a fixed amount of capital but there's an infinite number of online

ads and somehow you have to allocate and how do you do that algorithmically you make these things called automated market makers people over time were like hey why don't we just get rid of like trading order book venues um and replace them with automated market makers well they have an engineering trade-off the engineering trade-off is they their latency is much worse and their ability to cover their losses in some sense is actually really bad because if someone makes a really big trade against the automated market maker it's a piece of code that has to fulfill that trade but it potentially is trading against itself if someone finds an exploit in a certain sequence of trades so it was hard to develop these things um i think like you know people who are

now algorithmic game theorists who are looking at crypto like tim rufgarden they they invented the theory for a lot of this stuff in the 2008 era and then automated market makers made a jump to cryptocurrency uh in 2015 in this uh kind of blog post uh reddit sorry reddit post between vitalik and martin koppelman of gnosis where they were like hey can we can we get rid of these exchanges and this has to do with the whole dow hack can you stop trading thing that i don't know if you're familiar with from ethereum history um but i think they wanted to have an automated market maker on ethereum selling you ethereum and that led to unispot is uniswap an amm

yes okay it is it is an amm it used to be the most used on-chain amm uh it's now the second most used on chain and um the first serve thank you so much for defining that by the way and sorry alex we interrupted you you were actually describing it so i wanna i wanna take a step back to what you were talking about when you were basically adding to the conversation around liquidity farming you're sorry that we we did the detour but actually this is awesome no thank you that was actually very helpful because it clarifies what i think is a distinction what i was talking about was there's this difference between just the general amm that's reporting the price between two different assets like unit swap and what tourism described what's the price of eat versus the price of dye

and there's this other amm that is something like compound where you're you're trying to quote an interest rate you have supply and demand people borrowing and lending and your output is some interest rate so in the former case it's pretty clear that as you get more liquidity inside of this let's say each to die pair that the experience kind of gets better in a lending protocol potentially it may not i mean in a in a certain sense it does because you can do bigger trades without moving the interest rate but let's say if you have 90 of ether supply inside of compound that may be pretty risky and actually tarun has done some really good work explaining some of those limits and so potentially some of these amms that are lending protocols may have to scale

horizontally but you can't get too big in any particular market but you have to include multiple markets so you're doing all this liquidity farming you're building basically horizontal scale while some of these specific amms like uniswap pools or curve pools or balancer pools um don't don't have that as much in any one particular market that makes sense is there so but i want to go back to that like comparison to traditional finance like that first question i had was like does it exist is like liquidity farming a thing what is the equivalent of liquidity farming yes so it does it does exist so um but it exists in like weird esoterica that i think like the majority of people would never actually

interact with they just don't realize it but in high frequency trading and market making exchanges in order to drive liquidity to their exchange pay incentives in the form of rebates and these rebates are things that are like if you trade more than a million shares a day you instead of paying ten percent in fees you pay two percent fees right and these these incentives are meant to like and you you can see this in crypto exchanges it's like exactly the same in centralized crypto exchange it's exactly the same incentive thing of like hey if you if you quote prices a lot and the people who use the platform the most are the people who are market makers

because they have to every every time there's a trade they need to update and say like this is what we think the best price is right and then the market equals sort of an aggregated average of everyone's view on the best price but basically exchanges have to pay these rebates and these rebates are in fact the reason that you have robinhood and free trading because robinhood trading isn't really free they're selling your orders to citadel jump my old employers people like you know like trading like kind of algorithmic trading firms and those people are actually executing it and then they're they're on average earning let's say like 10 cents per execution and they should give robin hood three cents of

that so robinhood is really selling your data to them and they're executing it and that whole system only works because of this incentive mechanism where the exchanges are rebating oh i see okay well one of the things that's interesting here too is that on exchanges the different orders it's not always necessarily or at least historically hasn't always been clear what the different rebates and incentives are so some exchanges may charge takers some may charge makers some may have different orders in fact there's been a lot of controversy around hidden orders where people don't really know what the orders are and so your order's not executed correctly and you have no idea why versus there's a relatively set number of functions that these amms have and so it's pretty clear

what rebates you get at any individual time and what the costs are to trading or a little bit more predictable at times yeah one one point clarification is when he when alex says takers a taker is a person who makes an active market order so it's a person who says hey currently people are are quoting uh i'm willing to buy the dollar i'm willing to sell a dollar in one cent uh a taker is a person who says you know what i'm just going to buy the dollar in one cent i don't care i just need to buy right now i i think like the the financial terminology might be somewhat confusing well not confusing it's more like when when you're talking about liquidity farming and then you're talking about these rebates i mean are they different mechanisms to

incentivize the same action or are they because it's it's from what i i've understood of liquidity farming is more like you're you're earning something it's not that you're getting discounted something or rebated so you you could think of it as a rebate though because in compound let's say i'm borrowing i i put up a hundred dollars of ethereum i borrow fifty dollars of dye or usdc and i'm paying eight percent interest but i'm earning like nine percent worth of comp by borrowing well now i'm now on my that's a rebate oh i see it okay right you mean okay it could be viewed as

you can always kind of eventually turn it into looking like a rebate but it's a proof-of-work thing more than a proof-of-stake thing so i think one of the points alex made earlier about token distribution being very different when you follow these liquidity mining incentives is that they're more fair in a lot of ways they're much closer to proof of work where it's people competing to earn portions of the network it's like versus proof of stake or icos where the token distribution is where the initial token distribution is right and you have to go figure out how to disperse it to as many people as possible here you're saying hey look i have something where if you use it you earn my token and you have if the more clever you are at using it

the more you'll earn so that you're you're kind of competing on like whose algorithm is best and so in that sense it's a lot more fair of a distribution than like hey i'm an investor i'm gonna buy 10 of this thing relative to what the community who uses it says it's as if stripe every time you use stripe gave you a little bit of striped equity right and so that's you should really i think that that might be like a way of contextualizing it all right let's i want to move on to some other topics and actually tyrone do you want to take a lead and ask one of those questions that we have okay so you know i think one thing that i've noticed certainly in in the zero knowledge community in layer one community

is that people kind of are like well d fire this thing is just writing a smart contract is there any real engineering is there any hard problem someone on this call on this podcast is guilty of this there's no there's no hard problems there um so and uh you know oh isn't it just like moving my numbers state the state of numbers around so i i think alex maybe do you want to talk about comparing the differences in mindset in engineering um in d5 versus phaser knowledge proof system especially since you are an investor in matter labs and are seeing kind of uh both sides of this well in many ways and and i definitely don't want to be cheeky with

the answer to your question which is a good question the investment in matter labs and what excites us about matter labs is bringing together those those two perspectives right the zero knowledge community has a lot of focus on diligent engineering and mathematics behind building these different systems is driving a lot of fundamental improvements on software and hardware and so forth that are making these systems more viable while d5 maybe we're just playing with numbers but we're having fun with them directly and so bringing those technologies to bear in in defy uh is something that we're incredibly excited about in particular we feel that some of these like the core things that make d5 exciting the security aspects of it

ultimately the openness of it the decentralized infrastructure that is neutral to the interfaces that we talked about earlier are i think are better served in systems that reason very carefully about security and so matter labs for instance should there be a d5 layer 2 you know would be positioned to do something like that there are obviously lots of others that are vying for that so we believe that ultimately the differences in engineering culture are just one it's a higher layer and is you know potentially promoting a service that is for traders for users and so forth while the other is building enabling infrastructure uh but we very much are believers in this idea that the two ultimately converge as they have really in every technological

trend and platform shift that we've seen yeah i will say uh in you know the normal world um the places that that are the biggest cache aggregators in in terms of google's ad exchange and sort of like citadel like high frequency trading those places are places where that's exact they're the exact intersection of the type of people who build layer ones with people who like think about finance and i think that's that's kind of what you need to actually make a lot of these crypto products viable to the end user who doesn't really want to think about gas prices doesn't really care about the zero knowledge proof generation process they just want to be able to use it you know it's like if i

want my grandmother to use any of these crypto products we need to actually get the finance part working well enough that you get the robinhood experience but it's really powered by people who are kind of both low-level engineering mixed with like finance but i would say one thing is one of the ways i personally like to think about the difference especially since a lot of cryptographers that listen to this podcast is that cryptographers like to think of the the stochasticity and randomness in the universe is coming only from one particular way they need to everything needs to look like the uniform distribution and you can only be a little bit far away from the uniform distribution because if you have a set of things and you're

like hey i don't i want to be able to sample it but you can't identify that i picked something with high probability then the uniform distribution is this sort of maximal entropy distribution but people in finance and defy and you know ad auctions stuff like that it's the opposite it's much more like machine learning where you're trying to learn a lot of properties about probability distributions that are not uniform and somehow do that to me is the fundamental mismatch between the two cultures and why they kind of are antagonistic to each other at least at least my observation we should definitely explore that in further as we go on i want to lead us towards this conversation about this article

that you created and i think a question that could lead into that is what are the similarities and differences between pos and d5 like are is pos defy in a way right so so there's the general answer and then there's the answer that's a little bit more specific to some of the work that we did which kind of incorporates a little bit of both maybe that gives us an opportunity to transition to discussing that is in some sense proof of sake and d5 do use stake in liquidity in somewhat not analogous ways so there's a number of different proof-of-stake protocols what all the different say protocols use this idea of coin as your collateral in providing some marginal security to

the network similarly i think a lot of d5 protocols may be innately or maybe inspired by that approach have been pursuing something similar in that you back some amount of collateral and then you issue assets against that collateral or provide insurance as we've seen a number of different d5 protocols that try to ensure the own functionality that they enable we're seeing this a lot with lending protocols so in that context some of the ways in which you think about these things may end up being quite similar and and they do end up being very much probabilistic as true and said a minute ago that you have to think about what the probabilities that say of default are or what the probability that somebody does something malicious or messes with something in this part of the system and how that propagates to the rest of the system

so in that sense there are some frameworks that can sort of incorporate both and that's the non-tagline version of pos as d5 and yet it's i feel like when pos was really being developed like the early ideas around it the fight didn't really exist the same way it does now or it hadn't come to fruition like even if some of the ideas were floated we didn't know if it would be adopted i mean think back to i actually don't know this but like pos first proposed maybe like five years ago six years ago well actually technically it goes back to a 2012 bitcoin talk post as far as everything easy

from someone someone whose name is quantum mechanic but the first pos systems were were like nxt in 2013 and peercoin around the same time yeah but the for this kind of current level of pos systems that have you know like verifiable random functions for sampling and vdfs and stuff like that that that all i would say was really spurned by tezos and algorand uh circa 2015 okay yeah um that that's sort of the time frame but like going back to that question like how like at the development point when these were cons when the concepts for these systems came in came into being d5 wasn't on the radar yeah i think that

shows in some sense in some of the early designs in particular as touring mentioned some of the next generation of proof-of-stake protocols do take a lot of inspiration from some of the more financial work that's been happening and that it's incorporated the way they think about randomness and all these different problems that are fundamentally financial problems but but at their core d5 protocols the core incentive is that you're getting some amount of yield on on your assets by being a validator what what ends up happening once you mesh those two worlds together that in some sense were up to a certain point considered parallel is a bunch of frankenstein-like effects and i think the first person that i read that articulated this well was tyrone actually when you wrote this paper on

competitive equilibrium between taking and lending where you have these potential effects where the returns to d5 could be sufficiently high such that you can destabilize the proof of stake system lights and causing people to leave it and go to d5 protocols that the proof of stake system was supposed to be securing in the first place and so as a consequence you get these these really weird dynamics and so the the report to that is well if you think about both at the same time you kind of bring those two perspectives and design with the idea that yes there will be alternative yield opportunities on chain um i think then you can start to think a little bit more rigorously and systematically about the types of risks that exist in these systems and that's the work that we try to do which

roon um it's work that we've both kind of done in parallel separately and that's kind of what inspired this um staking derivatives paper let's to dig into this paper i mean tyrone when did you publish that sort of staking and lending paper uh october 2019 and then it was presented at sbc in february okay yeah so like for me it really opened my eyes to just like a problem space i didn't know existed and i mentioned it a couple times on the podcast in a nutshell if i can summarize like maybe you can you can expand on this but it was basically when you if you create sort of these these

tokens like if you if you are staking tokens and then can create replicas of them that are liquid and potentially lend those that you end up with a problem of incentives or price differentials that could cause staking to become less viable is that roughly what you talked about yeah more or less you could you think of it that way yeah definitely that's always the way that i've understood it now i want to dive into the work that you're you've done together because that took it kind of an extra step and you were able to find kind of historic examples for what could happen if this wasn't properly managed i thought that was quite powerful

so why don't you explain you know what this paper basically is describing sure so we talked a little bit about the context too and that tarun was doing this work in parallel that i was introduced to and at the same time i think we were both noticing back to this idea of doing some horizontal work noticing a lot of different protocols try to address this issue of liquidity and how you wanted to lock people in proof of stake in order to provide you security but then you're depriving them of other opportunities and so how do the incidents work that way so they were posing all these different mechanisms to resolve that uh that that problem um we wanted to first of all ask the question of what these mechanisms were and then

ultimately what their their impact might be and so that's kind of what motivated this work so maybe a little bit before that since we did this with amms very well we can define what what's taking derivatives are um so my best definition and i hope you can supplement me here to roon is the easiest way to think about them for me is as loans and so i have um i'm a validator as we actually have a validator on the call so i'm a validator i have 100 each let's name it 2.0 and then i borrow 25 synthetic eth against my collateral at some price point that is determined by some curve either set by the network or an external lender i at the end of that loan either repay

the 25 synthetic heat and unlock my 100 real eat so to speak or if i perform some malicious action like i'm not online at a certain point where i'm supposed to be online or i do something else that the protocol doesn't like i could get slashed and not have the capacity to repay my loan the protocol will in some sense foreclose on my loan delete my debt but then seize my collateral and then burn it or redistribute it as it sees fit you you use the term synthetic token in this case are you referring to synthetic as the the kind of project synthetic are you talking about like is that the terminology for these kind of copies of the stake in in liquid form i i i use that term

generally in this case not okay not synthetics in particular uh okay general too describe sort of an iou um that yeah you have issued against some amount of stake and and the reason you have the subscript synthetic to the ether that you're borrowing is that technically it's not always one-to-one and actually one of the important things is figure out what the relative price of those two things is so they kind of look and feel similar ways and that one can give you access to another but the dynamics of the interaction between those two prices are really what drives a lot of the interesting mechanics in the work that we did so uh you know i think alex started to point out i think that thinking as loans is probably the great most correct way to do it for some reason people in crypto love calling

everything a derivative and yes it's a derivative but it's just simpler sometimes to talk about it as a loan um but the the main thing that i think these are all these things are trying to address is this concept of capital efficiency of like i have a bunch of capital i've allocated it to something that's earning interest but optimally the the yield i can get might not be optimal what to keep it it might not be optimal to keep it in these networks but the networks need to incentivize people somehow to to keep their capital there and offering them alternative ways to at least borrow a little bit against it maybe pay their electricity bill for the validator or something else is kind of the main goal

and there are a bunch of proof of stake networks that are learning from what the lessons of what's happening in d5 because d5 is basically this gigantic exercise and optimizing capital efficiency and proof stake is a particular subset where it's really the insurance fund for the the the blockchain itself uh there are a bunch of staking derivatives that exist right now um some of them are cross chain so tara just announced this cosmos staking derivative called anchor which will basically and polka dot is going to there's going to be a cross chain link between cosmos and polka dots for this where you can borrow dollars so terra has a stable coin that's in the dollar

and you can borrow dollars against your state you don't you normally in the synthetic world that alex is talking about you're borrowing synthetic atoms let's say right you're not borrowing yeah and your atoms against your atoms but then if you want to get dollar liquidity you have to take the synthetic atoms and sell them get the dollars and pay your bills right the idea is if there's a stable coin that exists you could borrow the stable coin directly and this is the thing that has driven compound um to grow to a billion dollars really is that people want to borrow dollars against crypto because they have expenses in the real world but they they're net long they still want to own their crypto and so staking derivatives are really starting to take off uh on cosmos polka

dot terra i think the only network that's really against them is tezus um arthur is very very much an anti-staking derivative uh person but if to go back to kind of the the paper the idea that these things in d5 are actually very similar to these things in proof-of-stake made us kind of say is there like a single mathematical structure that represents all of them like you can you know you have some parameters you can tune but you can represent all of the proof of stake with derivatives and also all of these sort of d5 capital efficiency uh game type of things and one of the reason to think that is in traditional quantitative finance

you have these sort of very grand theories like the black scholes equation which you know uh black trolls merton i guess i mean black controls won the nobel prize in economics for it uh where you can talk about this notion of replicating portfolios and i think alex can give another can hopefully also add to this definition but a replicating portfolio is something where you hold one set of assets so like asset a asset b and see in some ratio and it's equal in terms of the payoffs that you get to holding say asset d and what that means is maybe i own a bond and a stock but that's equivalent to owning

a bunch of options and like in terms of your return in terms of your return exactly like the even though physically there are different goods they have the same price returns to you same yield curve same whatever and this notion of a reptain portfolio is quite powerful so one way to think about this maybe the easiest analogy that i've heard is let's say you are a investment bank and you um sell me a derivative let's say it's an option and then you wanna you basically short this option and potentially if the and so your goal then is in order to

make money to hedge your bets in some sense so you could either buy an equivalent option and be net zero but potentially if you don't agree with the price i mean that's kind of a terrible place to be in because you make zero profits but if you think that the options premium is really high you could say well i will go and assemble that option out of a stock and a money market so equivalent would be your manufac instead of thinking about in these financial terms that can be confusing is you want to sell me a chair and i i'm buying that chair for twenty dollars but really you know you can buy the screws the wood the legs or whatever else and assemble that chair uh

for less and so let's say eighteen dollars you keep two dollars in in profit but in any case no matter what happens to the price of the stock um or in this case you know the the different components you are still um you're still ahead if if your pricing model and replication model were was correct so but going like let's come back to the paper though so you are doing like it's building towards this formula that's supposed to predict in a way how what secure a network is or how or how expensive it would be to corrupt yeah so one one way to think about this is let's suppose there's a hundred dollars worth of ethereum

staked in ethereum two if you borrow twenty five dollars against the hundred dollars and then you default on that loan so you get slashed a bunch of times or you just like don't repay it or something like you take the money and you're like i hate the network i sold my synthetic east for dollars and go go to hell youth too right uh you the network has to pay some cost and in particular the network's cost is it just lost 25 percent of its security budget because 25 that it issued synthetically disappeared and now it has to be like oh we only have 75 dollars securing eth2 as opposed to 100. so the network's actually losing security every time it

issues these loans right but the key thing is is if you use these automated market makers you can have the network figure out how to price these loans itself in such a way that it's measuring how much security it's losing in a very quantifiable way by issuing these loans and by knowing how much security it's losing it can kind of put risk guards on itself but let's take let's i think you're you're going ahead a little bit here and i want to i want to still kind of cover that that background here i want to sort of repeat what you just shared which is the security goes down and like say there's no dir like d5 doesn't exist there's no derivatives you just have these pos systems that are standalone

they usually have a threshold that you need to reach in order for it to be secure and basically uncorruptable it's either like two-thirds or 50 or something like that and we actually we've covered this quite a few times so this is how it is supposed to work and that's where the security a lot of the security models are created just like thinking about this now you've introduced this second feature the fact that you can do these taken derivatives if you do these it like if i have tokens i've staked them and then i take derivatives how does that actually lessen the security of the entire system because from that it just sounds like i have duplicates of the same thing that i can do stuff with

yeah i mean in some sense if you're doing that then you're double spending right so you need to somehow the point of these security budgets at the very minimum is to prevent double spending and you're effectively letting someone controllably double spend but in such a way that they have to pay it back so that it like undouble spends themselves the idea here though is like defaulting would be being slashed right like if you were if on the staking side you were slashed you would have less tokens to be able to repay this loan that you've taken against the state exactly and and the pos network itself can execute the loan and default in the smart contract because it itself is its own security but this is a this is like a step

further like i think we're still at the place where like i guess i still think of this as the existing pos systems and then things that are built on top and or things that are existing around it but aren't necessarily deeply integrated but i do understand that as time has passed and as these findings have become more clear there are now pos systems like you know that are just launching are still in design phase that have learned to incorporate these derivatives themselves yeah that didn't really exist before like the earl like tezos launched without having derivatives included in it cosmos as well so is this a this is a shift basically that we're seeing

in the ecosystem that people are that these networks are now hyper aware of this and trying to plan it into their own uh systems yeah i think they've been taking two different tacks some of them are integrating it very deeply and some are doing it via a smart contract that is external to the network the the they have slightly different security uh complications and implications in the one where it's deeply embedded in the protocol the protocol can arbitrate on itself so it can basically say like all right you know i'm shutting down all loans because like too many people are defaulting right now and it's just not safe for

the network to keep issuing loans whereas if it's done in a smart contract external hub it can't really stop itself in some sense and the end goal of all of this is if the network itself becomes the biggest lender so in the staking versus lending case we have you know ethereum two and we have compound on ethereum two ethereum two is offering you five percent interest compound is offering you seven because of the could be farming or something and so all the theorem stakers are like i want seven percent so i'm gonna like move my i'm gonna unbond and move to compound if instead the network itself was like hey hey guys guys guys look we also can lend to you at seven percent and okay well if the network itself is

the biggest pool of liquidity for lending then people are like uh why should i take the risk on this smaller compound thing instead of instead of just keeping it in state right so there's this there's this idea that the network is its own best bank and if you do that then you can avoid this lending thing where people move their capital out if the network can offer blending and borrowing services although i'm sure that it also comes with its own set of qualifications yeah exactly alex didn't say anything no that was good you know one of the things that we try to look at in that context is as you can see there's all these different designs that these networks have and so we try to just abstract to what are the core functions that such a thing

would need to embed and enforce we came across a couple things that were a little bit tricky to model to begin with so one of the things was you get these aggregations of you know there's a bunch of different validators they're all taking out loans but all their loans are denominated in a common asset and that's priced on this curve and has all these different characteristics what an example of that be like if you did the cause you were staking cosmos and you took like a cosmos synthetic synthetic cosmos like that's the same asset basically or that it's a single asset right right something like that or like the tara example that tarun mentioned earlier with some of these protocols are embedding the the amns that do that conversion uh into the actual

protocol itself and so we've seen some examples of that happening as well so what ends up happening is one you have this aggregation is you have different loans that kind of output the same thing and then you also have this idea that in order for the protocol to be secure it needs to reserve these rights basically what jerome was just saying of hey stop borrowing kind of thing um or you know this validator has been misbehaving or has exhibited some other risky behavior i want to basically take back this loan so those types of things are just options that the protocol has during the term of the loan that can alter the behavior and the actual price of the loan but ultimately are priced in by people that are borrowing this type of thing and so those two things make it really difficult to analogize it to just

traditional lending so we kind of just scoured the financial math literature to try to figure out a good analogy for this kind of thing and really where we had actually a lot of luck was we found some articles from the 90s and early 2000s where a lot of people were writing about mortgage black securities and securitization it was really all the rage and there were actually quite a lot of similarities in the types of approaches and stochastic models that those folks used and similarly these types of structures were ones where you were pulling together different liabilities and issuing kind of these homogeneous bonds so to speak against them and then also you have these options on behalf of let's say the borrower or two if the interest rate goes up on your home loan that you can

prepay your loan and so forth so they it was kind of a neat framework that incorporated both and so that's kind of what room is getting on with the replicating portfolio is that you have these portfolios of all these different loans and so you have this outputted asset that really behaves as an aggregation of all sorts of other things that that are um subsumed by it in a sense so that that gave us quite a lot of ideas in terms of both the potential stochastic behavior of these things but also the types of risks that we might see in other words we've kind of seen this stuff before and we've seen that it can work okay but it can also be pretty bad um and so that that kind of gave us the direction to talk about some of the risks i want to understand when you talk about

this combined this sort of like portfolio of assets or i forget how you just said it but like i think i'm thinking of it like a batch of these assets are they different like i i want to kind of hear it through example where is it something like different staked currencies like tokens from different networks are somehow bundled together or are you talking about like an individual network and their validators all of their synthetic tokens being bundled together like that's where i'm not i'm not exactly clear what you mean the easiest one is is the latter where you have different validators with different risk characteristics all have different loans they have different collateral ratios but they all let's say have

synthetic atoms as as their liability the extreme case of this and this gets again to the example of pos and d5 is something like multi-collateral die where you have people that are borrowing with different currencies as collateral but they all owe dye and actually dies in some sense being backed by all these different heterogeneous loans that have different characteristics and so forth so when you're talking about die or you're talking about the interest that you're getting let's say as an mkr holder from the different lows that are outstanding you think about all sorts of different loans and how they aggregate up to ultimately your your stake in the network maybe we can talk about the inequality that actually exists in these pos systems between these like large

stakeholders and those with that don't have that kind of stake how have these derivatives like helped or aggravated yeah actually maybe before we get there one thing that's helpful is we one of the things we looked at a lot in this paper was inequality and under what conditions um proof of stake networks do better on inequality then when they have taken derivatives then otherwise it was kind of a surprising result to us when we first came across it but you basically have this effect where potentially a large validator gets slashed and then that increases returns for everybody else around them and then also you have the secondary effect where now i for instance have less capital than you and can access

basically the same opportunity set if i want to take commensurate risk so you get this more egalitarian like distribution and so that's kind of a nice result you do end up having to burn up a lot of steak and tyrone has these nice simulations which i guess is his his firm's on detroit to explore that you have these nice regions where you don't burn too much and also have these egalitarian effects but let's say now in the taking the mbs analogy um as as a given and then there are differences in similarities of course but we found it an instructive analogy for the following reason let's say you have a large validator like finance that happens to hold a lot of points and is running validators let's

say and they get liquidated in a pretty big way as they're doing these risky activities and borrowing against their stake lending to defy and doing all kinds of tricks like that they get liquidated and now you have potentially a large network participant that is incentivized to fork the network to revert that state where they and so then basically all the nice effects of inequality and stuff like that that we talked about in this theoretical sense all get wiped out immediately right in that you actually have the exact reverse effect where the inequality becomes worse and the effects of concentration were pernicious i mean you pointed this out really nicely in the article but what are the risks sure and a lot of the results that we end up proposing in a lot of the

positive ones hold on these regions where we can make some some analytical judgment about what's likely to happen and then outside of those regions basically you guys are on your own um and and and that's actually really what drives and motivates this this analogy to some different financial collapse that we've seen so for example what we just mentioned and talked about about a large validator uh being bailed out by the system is something that creates a kind of moral hazard that we've seen in other situations in the past and i don't really have to dive into those i think fair enough in people's recent memory the thing that is kind of different and interesting is that you have different liquidation

mechanisms in in these types of security so traditionally a loan foreclosure by the time so if you for instance don't pay your loan or don't make interest payments on your mortgage uh i the bank will foreclose on your loan i'm the originator of your mortgage i actually probably don't even hold your mortgage it's somewhere securitized and somebody's earning those interest payments when you pay them so i then need to basically propagate that information across an entire chain of different folks in the financial system and eventually makes its way to the holder of that mortgage-backed security which could be the federal home loan bank of seattle somewhere and they now have to basically mark their positions to market

and figure out what's going on with the rest of the market and all that so in in crypto there might be an opportunity to do liquidations differently and perhaps better and um one key way that we see that we see this with centralized exchanges actually in crypto as well where you know they're draconian with liquidations and what that means is they don't then have to go after you or your broker and so you basically know that you have limited liability when you trade on them and that's something that's really driven their growth it's something that's true in defy because you're pseudonymous you know you play around with defy you lose your shirt but then you know they don't come after your house or anything like that so the protocol can immediately enforce liquidations if they see you behaving in some

some way that's not agreeable to consensus and they can do it immediately and i just want to add one thing this ties back to our earlier discussion of when i we kind of talked about stripe and plaid versus defy where like in the stripe and plaid universe you know robin hood or coin base has to hold risk and give forward advance you the ten thousand dollars but if you default then they have to there's like this time lag because there's no globally agreed upon database but the moment there's a globally consensus agreed upon database you can instantly propagate all of these actions which which is exactly what doesn't happen didn't happen in the home loan crisis it took a long time for the banks to realize that thousands of people were defaulting

the novelty is in crypto that it's instantly propagated everyone can know instantly that like all these people are defaulting at the same time which might it's not guaranteed to help but it might be a good thing you make the comparison to the mortgage crisis the mortgage-backed security crisis that prompted the 2008 financial crisis do you see do you see parallels like is that or or do you think that that speed means that will never happen in the same way i think it'll happen uh we've already seen it happen at centralized derivatives exchanges because they have a similar liquidation crisis in fact i would say one of the reasons i suspect that coinbase hasn't been quite unsuccessful building a derivative exchanges that

unlike asian exchanges i mean there's legal reasons but they're they also just don't understand how to think about liquidation they never had to do them and there's this bad case like that happened on march 12th for instance where bitmex had runaway cascading liquidations and the insurance fund was almost drained uh and well there are some very unhappy uh bitmex users who are liquidated and sued but those liquidations are not so different than these liquidations of validators and there's this cascading effect where one validator being liquidated might cause the price of the synthetic to go up which causes another validator to be effectively liquidated and then the price synthetic goes up so the synthetic gets more expensive in

some sense or rather the real asset buying the real asset with the synthetic which is how you recover your stake gets more and more expensive the more people default and if a bunch of people keep defaulting there's this like feedback loop that looks like the financial crisis so that can definitely happen and it probably will happen but at least it's a closed observational system where you can really see all the statistics of this happening as opposed to the kind of very indirect way this happens in the current banking system where there's a lot of like trust that goes on that like leads to message propagation being delayed so there's a benefit also to this trust too to mention the other side of the equation right in that i when i

extend to you alone as a bank typically will have some more relationship with you like i at least know your name i know who you are i know where you live i have a lot of financial information on you that you may say that's a very bad thing but from the perspective of the bank it's clearly honest and from the perspective of the financial system you have this entity that is proximate to the borrower and as such is able to underwrite the risks associated with that borrower in a way that a decentralized protocol cannot so the decentralized protocol by definition needs to assume the worst and have very high collateral requirements and all that the other part of it is very often the collateral that we're talking about here is in you know bay area real estate whatever you may think of that it's higher quality collateral than a lot of

coins that are traded on exchanges in crypto and as such these issues particularly around liquidity could very much propagate the types of deleveraging cycles that roon just mentioned so there's some axes upon which we can maybe do a little bit better but there's some where these things can become worse than their centralized counterparts they are a lot smaller comparatively to the larger economy but that may not stay the case for too long so one one thing that's important in this world is that now that you have this ability to have this sort of instant cascades that you can programmatically use in in consensus and in a proof-of-stake protocol how do you think developers should take

advantage of this and how does this propagation even happen uh in some sense maybe maybe we could dive into the the details of of the pricing of the synthetic asset versus the realization right so the way that we've seen this the ideas that i think most compelling that we've seen um for getting that information into the protocol to begin with are through these constant function market makers which is actually a term that charoon coined in an earlier paper with uh guillermo and garrus and he uh they they talked about this idea of a uh amm as we discussed earlier that um who's the solution to the arbitrage problem of which is easy in other words

it's very easy for people to figure out how much they should add or remove from the amm in order to have the right true price and so in this case what we mean by the right true price is what the probability of default is what the security budget is and so forth so we want to get information from the market about what they believe what expectations are about the relationship between the derivative and and the underlying asset and so we believe that that information will encapsulate future expectations about defaults and security and as such the protocol can take that information and make certain decisions on it now there are lots of lots of asterisks to that in other words you know how these things

are manipulated and so forth that we don't get into as much but like the fact that there are mechanisms to which these protocol designers are uh implementing in order to get that information from market participants the primary mechanism that we've seen are these cfms what's also interesting historically is that some of the existing cfms that are being used outside of proof-of-stake altogether seem to have actually been inspired by this staking problem to begin with so i was listening to this podcast from michael igero from herb and he was talking about how he was initially working on staking derivatives like problem and that's kind of where the idea of curve came about so there's this clearly this really tight connection between how do we get information about

the relationship between staking and staking derivatives and how we facilitate optimal exchange so that it's easy for people to report the true price to the market like going back to this earlier conversation we had about the liquidity mining and the arbitrage like do you feel like the more efficient you get at this do you not sort of remove arbitrage like don't you get to this place where it just becomes like there is one price it is completely correct and there's no way to like benefit from moving it around well locally at a specific time that's kind of the idea that the world's always changing and um as such you have to pay gas i mean fundamentally the problem is that the transaction

demand market for the blockchain is what drives the fact that you can never be perfectly arbitraged there's just some noise from that this is one of the reasons i think ethereum competitors actually are at kind of a huge loss is that they don't have this like latent transaction demand coming from like out from exchanges and usdc and uct and stuff such that people have to compete at that microstructure level and understand the exact transaction ordering in the mempool and understand how they bid on this so that they can be as close to arbitrage as possible but there's just enough traffic that it can never be perfect which is kind of the beauty of of these crypto networks like the traffic is all visible

you had to leak all that output again i mean if that's alpha then people are just getting d5 beta just by sd5 or whatever this paper came out a month ago what are you thinking about now like are there are there new phenomenons or new constructions that you see emerging that could have impacts on pos systems and their security that you haven't covered yet um i'm not quite sure it's pos but i i do think that the the the fact that i i've talked to to a bunch of traders in asia recently and they've told me hey we're like using curve and like skipping the otc broker on skype kind of convince convinces both of us

that there's actually some way at certain times where d5 is actually weight cheaper than c5 than centralized finance and quantifying that and when that happens is kind of something alex and i've been thinking about right it's it's the idea that sometimes these amms as we discuss in the context of state can have an opinion about the assets that they have in them and as such can become more capital efficient by saying i actually think the fights will die in the place of usdc should be really close to each other and i might be taking some risks that i'm wrong but insofar as i'm right i'm able to offer you a much better price as most of being completely agnostic but then the question becomes what tarun talked about which is

what are the gas costs to me using this exchange and this is cost that's very reflexive in the way that things are being used and the arbitrage opportunities that exist out there it may interfere with how that pricing happens at the microstructure level and and that may hold some atmospheres as to how d5 trading is potentially more interesting and efficient in some cases as we're seeing with curve and some of these other market makers that are popping up cool and and one last thing i'll add on this is uh that we we've also seen kind of the design space of this grow and different different forms of you know that an alternative to this is kind of what gnosis is doing which is way more complicated for the

end user and doesn't like it potentially has some other types of bugs that we've seen over the last week or so so there's there's just a lot of different the design space i think has kind of exploded and i'm just excited because every day i read a new paper that's like oh well we can make a slightly better that like version of this so that it like loses less money than the centralized version or whatever and i think the fact that there's this like burgeoning explosion in this means that 2016 for proof of stake is kind of 2020 for d5 oh that was a great way cool well listen thank you to both alex and tyrone for joining me

on the show and sharing with our audience a little bit more about this paper and a little bit more of the work you're doing in that intersection between pos and d5 thanks this was fun thanks cool and to our listeners thanks for listening