Episode 206: Distilling DeFi Primitives with Guillermo, Alex and Tarun

Transcript

[Music] welcome to xero knowledge i'm your host anna rose in this podcast we will be exploring the latest in zero knowledge research and the decentralized web as well as new paradigms that promise to change the way we interact and transact online this week guillermo and jairus and alex evans returned to the show to chat about the research that they along with tyrone have been putting out over the last year we explore the methodology behind their work on cfms amms and related primitives we also dive into some of their recent

findings and what they're looking forward to working on in the future before we kick off i also want to remind you about the zk hack online event zk hack consists of weekly workshops about key zk tools and an advanced puzzle competition there is a fantastic community forming around this event so do jump in i've added the link to the site in the show notes you can sign up there and find out more about the program hope to see you there also if you're looking to get into the zk space professionally we are hosting a zk jobs fair happening on december 2nd as part of the overall zk hack event the zk jobs fair will be featuring some of the top hiring teams in zk this gives you the chance to meet them in a more casual setting as you're applying so you may want to check it out you can also check

out the zk jobs board where you can find ads from these teams looking to hire the links for all of these can be found in the show notes today's episode is sponsored by aztec aztec aims to be the privacy layer for ethereum they believe that unlocking programmable privacy is the next frontier for blockchains aztec is the first zero knowledge roll-up built from the ground up for anonymous payments and defy transactions if you want to protect your payments you should join the thousands of users already sending funds privately on zk money that's zk dot money we've added the link in the show notes so thank you again aztek for sponsoring this episode now here's my conversation with taroun guillermo and alex

[Music] so today i'm talking with guillermo alex and our sometimes co-host tyrone tyrone i think for this episode you're going to be a little bit more like a guest because what we're going to be talking about is the work that the three of you have been doing over the last i don't know year or two so yeah i want to welcome back guillermo and alex to the show welcome guys thank you it's awesome being back thanks for having us so both of you have been on the show before but i know a lot has kind of happened since those episodes alex you were on i think almost two years ago i'd love to hear what you've been up to in that time oh my life has taken a turn for the worse oh

no uh i've now come to a place where we're writing one paper a month uh with these two mainly as punishment but also as entertainment what about you guillermo you were on the show actually earlier this year what have you been up to since then yeah so for the most part well i mean as with these two i've just been writing a bunch of weird sick and twisted papers but i've technically come to the conclusion that at some point i should finish my phd and and one of my two professors has also come to a similar conclusion although the other one has yet to be convinced so uh hopefully by march i'll be you know defending and presenting the whole deadly due to physics that you know almost nobody i guess from the show probably has ever heard of uh for the better in fact i would say okay but

that's that's the plan at least so far fingers crossed very cool let's talk a little bit about the research you've been doing and maybe before we jump into the specifics what is this research for why are you producing one piece of research every month and who's paying attention to it i think it's a question for tarot actually yeah so i mean i think we started working on kind of research uh in this vein realistically in like 2019 and part of the thing that i guess i had seen and it took me a while to convince guillermo but i think he eventually believed me was that there was kind of this thing that was happening in d5 that really resembled kind of the early days

of of ai pre-image net like basically like 2008 to 2011 where a lot of the like important research was actually done outside of academia um and not only was it done outside of academia a lot of like the kind of rigorous stuff the theory was also sort of started outside of academia not just like the like hey we ran a bunch of experiments and we could predict x thing at 99.8 percent instead of 99.5 which is like 90 of machine learning papers nowadays and uh i think like there was this there's a thing where like there's all this stuff happening and they're all these very counter-intuitive things

going on where there's something like uniswap which you know to someone from finance and certainly like sbf sam from ftx and cms and all these trader people were always on uniswap on twitter uh and part of the thing that more careful observers who are looking at the on-trend data kind of noticed was that there was actually quite a crazy amount of usage and people weren't losing as much money as one might like naively think and i think that just led us down the rabbit hole of like hey this stuff that looks kind of like on the surface like it shouldn't work or it's stupid in some way actually seems to be working in practice so there must be some more fundamental reason for

for why that is and uh around that same time i met alex randomly and he was like also doing the same thing he kind of believed sort of the same thing he'd been like writing these he's probably the only formal ish paper on maker dow at that time so this is like early 2019 no it's it's right i ran into tyrone at his office they would host a number of events there and i'd heard of him from actually another guest on the show akis who had mentioned him as somebody who really knew his leap on mathematics and as some of the listeners who have seen torun might know he does stick out in a crowd so i approached him with that research and turns out he was working on very similar things at the time i think he was doing some credit risk analysis for compound which

was somewhat analogous using actually much more efficient and useful techniques than the ones i was using uh hence the emphasis on the ish and then with automated market makers turns out we were asking very similar questions in fact i'd written a whole paper that guillermo and tyrone had sort of answered the same question that i was going after and one appendix and two paragraphs which is a common theme in our work we'll get into that soon of me writing very very long and complicated things to which guillermo will respond with a paragraph uh replicating the result in a simpler way but that's sort of the origin is i think we were working on very similar things around the same time and couldn't beat them so i joined them nice

okay so you're starting your starting point is like sort of the cfmm and amms and exploring these i guess when you talk about like uniswap being i guess one of the first daps you were really looking into like why does this thing work what are the properties but do you feel like has that expanded from amms or do you do you still see that as like the core focus of most of your work so at the end of the day um you know cfm's or like amps very generally are like one very particularly nice class of things to study and you know as one can imagine the problem with research papers that you always end up with more questions than you actually answer um in fact this is this is almost universally true and if it's not true then you haven't written a research paper it's like you know you wrote

something you're an essay instead of a research paper so in some sense i guess kind of like like kind of stepping back and going pretty a layer higher right to the meta it's that we've kind of realized that you know there's a bunch of products that people want to replicate on chain a bunch of financial products that all have these you know interesting properties like options and all these things and you know most people kind of come at it from a very traditional finance angle and that's simply because of the fact that you know that's what people are trained in and there's like you know these like simple notions that we all like know and love about options for example this is a very specific example but cfm's give you kind of a weird perspective on how to actually do things on chain in some sense they're like a

very natural let's call it a primitive you know a d5 primitive if we may that lets you do a bunch of things that don't seem intuitively obvious how to do on chain but give you a very natural framework to think about so you know in in the previous case one such case is options right that's it seems kind of weird and you have to do these complicated mechanisms that require oracles and all these things but the case of cfms actually gives you kind of a natural way of constructing roughly the same object without the use of oracles so cfms are kind of you know the way we think about it is you have this class like this very large set of items that you can do on chain and cfms are a particular set of items that's very surprisingly large it's a

very large class i mean it's kind of actually shocking and our research highlights just how shocking it is how large that set is even though it looks like a very specific instance right that then you can do you know of things you can do on chain without using oracles i i don't know if that kind of made sense but the point is like it's it's this very natural framework for which to think about what are the things that we can replicate without using oracle's on chain it's not the set of all things ignore without using oracle's but it's a it's a large class and our our point is you know there's some boundaries around this like set of items one can replicate and the our research kind of pokes at the boundaries repeatedly of like how much is it you know how much can we actually

subsume within this umbrella of constant function market makers or whatever so are you finding though things that don't work in them as well like are there some examples you could already like say these we tried this won't work we're going to have to rely on oracles a very simple example would be just in the example guillermo just mentioned we have one of our more recent papers this is now two papers ago replicating monotonic payoffs without oracles describes a class of for instance payoffs that users may desire that are fairly popular for instance regular call options and so forth that have unbounded upside and are convex and we can get into what that means but you can't do that just simply without an

oracle you need either full collateralization which is very inefficient or you require an oracle in order to do that it turns out actually that things that don't require oracles and this is maybe the big thesis or idea of a lot of this work turns out to be quite broad so you can do short of that almost anything for instance you clip the payoff at some upper bound you'll be able to do that without an oracle and maybe even taking a step back why is this a useful exercise to begin with and that is that oracles are perhaps the main source of both financial and technical complexity in d5 and turns out that they expand the surface of vulnerabilities quite significantly

for on-chain financial contracts so if you look a lot of the exploits that have happened in guillermo has a recent threat on this a lot of them are related oracles and so it's in fact a useful exercise not just theoretically which you know is our primary motivation to be candid but also practically in terms of how do we design systems you know the exercise of how do we minimize use of oracles and how far how much damage can we do without them is i think to us something practitioners indeed and beyond should be asked when you when you mention that like the fact that there's an oracle could lead to exploits is it mostly like arbitrage opportunities or like when you say exploit here i want to understand a little bit more what you mean

i mean there's a bunch of different types of ways that this can happen i mean i think the the simplest ones are where you have another product that does a a non-reversible transaction so when i say a non-reversible transaction i mean something like a liquidation where uh once the transaction is through there's not really a way to like put back that capital indirectly right so like a trade in an amm if you're willing to take the fee is reversible and that i can trade back and forth and get it back to the same initial price but there are a whole notion of transactions that are irreversible in finance and that's usually when it's something like a liquidation or some type of settlement final settlement

and whenever those occur and those are dependent on an oracle price on some notion of price of the assets such that it triggers such an event then it can at times become much more valuable to manipulate the price calculation rather than try to like enter into that financial product and so manipulating the price calculation can either be done by manipulating the oracle directly or manipulating sort of the inputs to the oracle so the recent cream attack and the almost ave attack are effectively oracle manipulation attacks where in a flash alone you manipulate an oracle cause a liquidation and then sort of take the other side with that indirectly i mean it's a lot more complicated for those uh assets but are

the assets that were used in both of those but you know oracles are certainly a a source of uh vulnerabilities and i think one of the reasons unifop stood out to us in 2019 was really this fact that somehow in spite of there being no oracle it was able to effectively induce market participants to make it a good oracle where there's enough arbitragers trading against it that the cost of manipulation is extremely expensive whereas protocols that tried to be explicit oracles like uma none of them had a couple other properties one is they had to artificially create this notion of the cost of corruptability was expensive

right you either do it by having a token enforcement stake dot dot dot dot but you don't really give them financial upside in an asset they might already value like eth whereas in uniswap people were earning east and stable coins and that was like a one big difference between that and sort of the other oracle systems the other thing is that other oracle systems act more like legal courts than they do like blockchains because they they rely on all sorts of like challenge games and like you know you could view the uma oracle is not that different than an optimistic roll-up in some ways and i don't mean that necessarily positively in ethereum like in other blockchains it's actually great um for a bunch of reasons but in ethereum the way that doing these kind of like post event resolutions is just

extremely extremely annoying and i i think in like other blockchains some of the more complicated designs are actually going to be more user friendly because i think people just don't like having this a when people submit a transaction to ethereum they want to say okay i submit it it's confirmed i never have to think about it again right the problem with oracle's that have sort of post trade settlement is that you submit the transaction you see it got confirmed in the block but then you also have to keep watching the blockchain for another 10 blocks before you decide okay it's actually in and for better or worse this is an industry of degens so it's not like they're actually really going to want to do that right this is no knock against the uma design i think it's just that it doesn't

work as well on ethereum i think it's actually not a bad design somewhere else it turns out that artwork explains in some ways where that design is or some version of that design is necessary and it turns out that these problems can't be avoided for certain financial applications it's just that some applications in fact very many applications don't need that sort of complexity at all so one way to think about it just to make it really clear is the types of guarantees that you have in oracle dependent systems are of the form either property x holds in other words i don't get liquidated unless the price falls below a certain amount or the oracle has failed which in the case of uma falls down to their the cost

of corruption versus the profit from corruption and some equation holding between those two and that requires that sort of security assumption to hold in order for your property your whatever desirable property your system to hold and in the work on cfms as well as related systems the assumption is either property x holds in other words in this state of the world this user gets the same payoff as this option or some other property holds such as the portfolio's 50 50 balance between two assets or whatever else or somebody has a very significant arbitrage opportunity that they've left on the table and haven't exploited which is a much stronger assumption to make right which is

there's free money somewhere and somebody's going to pick it up and if they haven't then i can't make any guarantees about my system but if they have here's what i can say about what the user will get or whatever other property i'm interested in yeah maybe the tweet long summary goes something like cfms in our admittedly biased opinion are a particularly natural way of thinking of financial instruments or replicating financial instruments on the blockchain without oracles they might not be the space of all things but they are a very surprisingly large section of it right not zero you know i think like the sbfs and people like that you know who ran exchanges centralized exchanges one of

the reasons they couldn't kind of believe this was true is everyone thought the space of such things was zero like it was empty like there were no such applications right unisoft was the first thing that proved that hey the set's not empty but then since then basically you could think of what we're doing is trying to like find the largest surface convex hole in some bad joke manner of like the set of all of these mechanisms what i what i still want to understand though is like who is this research for are protocols asking you like hey can you look at my cfmm can you look at my model it started more out of like pure intellectual curiosity also like i you know

i guess my company was certainly like we use these types of things within how we simulate stuff but it also started from like hey there's actually something bigger here and over time the more we started writing these papers the more random people on the internet would like message and be like hey i'm like building this thing based on your paper or like hey i'm going to do this other thing based on your paper and over time it became much more clear that by going this route we were sort of actually opening up the space for other people who like didn't who maybe were not so mathematically sophisticated but were your usual d5 developer like 19 year old like lives in asia somewhere a kid um

and this was like kind of a way for them to kind of find new things to do and then when mev happened it became much more clear why people really liked our papers because no one else knew how to write the formula for what the arbitrage optimal arbitrage was and so the flashbots discord is filled with lots of random add-ons pacing our papers back and forth this actually speaks to something this is this idea and i mean i think we're starting to notice this more and more where researchers academics or or not are putting research out into the world and sometimes there's like these groups that are forming around this research and those are becoming in a way the the next generation of projects it's it's very much not the way i've

understood this kind of progress in the past like working more inside potentially in labs or what have you still kind of keeping it a little bit to yourself until it's ready here it's like as it's being built there's teams forming around some of these ideas i think flashbots as an example we had them on the show a while back and i i just think the idea of forming this like research group just around open public research it's so organic and i think it's really powerful it's not something you guys could push though can you you can't predict like or have you been able to like find teams to build out any of these ideas or test them out yes yes the answer to that is definitely okay you have um i think i think i think the bigger thing

is like it wasn't clear when we started this that that would be the direction things would go i think when we started it it was all of us were just like how does this make sense but also does like there should be some like there's just this inkling or feeling that hey look you know in the 1970s sort of when options pricing theory first was developed the options market was basically non-existent it only existed in legal contracts and people were basically like when they had options they were like employee stock options or warrants like they were not like traded options on equities but the idea that there existed a model to price things is actually what caused plus electronic markets is what really caused the options market to boom

uh once people had sort of a lingua fraca to use for deciding like what it meant to hold x amount of risk people were able to use you know use these as for support of course things blow up model doesn't work all the time yadda yadda whatever that's i mean that's those are those are the devils left to like the user okay but the point is that there's clearly was some new theory here and you know we were just more convinced that it was like this like intellectually curious thing and all the people in cryptocurrency land are really always focused on these like distributed systems or cryptography guarantees but they like kind of always leave it as a quote exercise to the reader all of the economic or cryptoeconomic things i mean just look at the chain-link paper for

instance right it doesn't really specify what it's the true economics are not knock to them but they probably still figure that out soon but but i think that the main point is that you know there is clearly some new type of way of thinking about quant finance here that due to the idea that settlement is sort of instant amongst other things and i think that that was where it started with that kind of curiosity which probably was true for a lot of people in the bear market who were looking at the stuff because there were only 10 of us yeah yeah we all know each other now and it's true and we all dm each other constantly and try to nerd snipe each other so at this point it's like a weird click of random people okay but tell me tell me

more about the builders that you are finding like are you actually sourcing these things are you saying like i just published this paper we need a group like how do you even do that so people come out of the woodwork i mean and like on twitter and so forth and we won't know their names very often and you'll find that people can go very deep and understand very concretely how things work sometimes not so much to be counted but very often we have these ideas are mostly theoretical you see the papers are mostly like guillermo's phd advisor who i recently met called street fighting mathematics of just essentially you know little cute things that we do and yeah here's this thing you know like it or not here's how it works

and there's a lot of work that needs to go to take that from a theoretical idea to actual code and something that's implementable in practice and so these people will ask all the right questions about how this thing will work and all that one team for instance that's working on the replicating market maker's paper which guillermo and very high level terms rehearsed earlier essentially went through that exercise and are currently going through that exercise of taking sort of a fairly abstract academic idea and turning it into an implementable project that you know users can actually interact with and get the types of guarantees that at least in theory are possible and that obviously requires a lot of engineering work it requires a lot of security work and

maybe some of the mathematical properties help you there but of course what they look like in the scary horrible twisted world of the evm and uh and solidity is very different and so we don't claim either these teams is work um and you know we can we've been helpful to the extent that we can but of course you know these are brilliant engineers developers and so forth that are really sort of pulling the weight here to be fair also this is also true with uh zkp's and consensus protocols right where like the original paper is a very far cry away from like what the actual implemented thing looks like right but it gives you sort of a north

star but it's a very lossy north star and as you try to like chase this rainbow metaphorically well that's a bad metaphor but as you try to kind of follow this this guy you realize hey i'm getting finer and finer resolution and like oh i miss this thing or i miss this other thing i miss this other thing and you kind of only learn that stuff from actually trying to build these things or you know write code that does what they're supposed to do and i think that's sort of the the next step from this type of stuff but it's it's almost the same in my mind as like the people who are writing the early zkp papers where it was more this like curiosity as like can you actually get succinctness can you actually get non-interactivity can you actually like

get these random polynomials to like not be in a field whose order is more than the number of particles in the universe dot dot dot dot right and these things always start with the kernel of that like all of the ai and machine learning stuff that people do started with this kernel of like actually i believe like just throw more hardware and like increase the size of this stuff and don't care about like proving the formal math i'm much to guillermo chagrin because i he's around more of the shills in stanford unfortunately it's interesting because it's almost like i mean especially looking back at some of that early work it does sometimes feel like through the curiosity solutions are offered but the problems haven't been defined yet like

you can you can kind of figure out like what is the problem we're trying to solve with this but it's like there's so many interesting properties in this solution that you're looking to see if it fits other problems afterwards i don't know if i i don't know if it's like that at all in the work you're doing i i think for defy it's a lot one thing i would say is different is like a lot of times our papers are fast follow to like hey someone did something in practice that they have no justification for and there's no reason it should work but we kind of started trying to write out the math and then all of a sudden it's like okay ah there's clearly some reason it should work and here's the reason yeah um it resembles more of like the relationship between physics and math

right where like physics finds something that completely is non-sequitur to mathematician and then mathematicians spend 50 years and try to like create the theory for it try to create the you know like oh we have to invent some new type of math to do it uh here it's a much much more pedestrian it's more like there's got to be something that's working the space of things that it could be is quite small because blockchains kind of restrict the actual execution and state and everything of of the system there's not that there's only certain types of users etc but you know i don't i think some of our papers are predictive some are sort of retrospective but in some some ways i think uh one thing that's been very clear is that people who are like economists and

applied math people and people from machine learning people who like kind of hate crypto normally they read our papers and like them because they write them in a language that they understand that's an optimistic version of it there's also another type of paper which is perhaps our most popular certainly to that group also typically our most simple which is just us being the really annoying stupid people at the back of a room and somebody coming to us with a solution or the most recent example is this paper we wrote on liveness failures on blockchain actually our most qualitative paper of the ones we've written and it came out of it it's really an uh a slightly better than a blog post uh yeah i think that's one way to say it and i

think the the technical term is called an academic is what believe is the actual technical term it's written in tech but that's about all you can say okay but essentially the premise of that and maybe this is a good segue into that that work here briefly so we can we can touch on it is we're just getting a lot of dms and talking to really smart people that are building things let's say on non-heath chains that are a little faster and cheaper to build on and very often we'll hear this pitch of like hey here's this thing that works on ethereum call it a lending protocol call it an options protocol and we'll just rip out the cfm and add an order book and we're going to have a much better system instead of doing and so this paper is saying more like whoa not not

so fast not saying that that's a bad idea but here are some trade-offs potentially to consider and in particular one of the things we point out is sometimes we have liveness failures on blockchains and that was no more acute than in solana and so this kind of leads into tyrone's point of you know we just notice an event and we're like what happened here what are some underlying dynamics to consider and in this case we noticed that in order to align the prices of cfms with an external market after liveness failure you only need to do one really big transaction and computationally that one transaction is just as complex and expensive as a smaller transaction

as opposed to on an order book where if the price moves against you in a really big way or you don't stop v3 the price moves against you in a big way you have to cross multiple ticks and that's much more expensive to execute or you need to clear significantly more orders in order to walk to the new price and so once the blockchain regains liveness that requires users to submit much more expensive transactions at a period where everybody's rushing for instance to clear their liquidations to execute certain transactions for safekeeping and that creates an environment where you're much more vulnerable for instance to very significant price moves that negatively affect your returns as a liquidity provider to these amms so certainly we like the capital efficiency

and we like the flexibility of these types of designs and are excited to see them proliferate in places where transaction costs are cheaper but fundamentally what we're saying in this paper is that there are trade-offs to that and those tradeoffs need to be considered in the design of these protocols interesting and that paper you're talking about it's like liveness on solana you're looking at like what is the effect of liveness where i guess if liveness goes down is that what you say if liveness disappears what happens to these i guess you're looking at the two different systems right like order book and cfm and comparing yes effectively to me it was it's kind of like an awesome case study you got to examine or could you predict it like or did you need this to happen to say like

oh this is going to happen let's put this right all the math in the paper for the most part i mean minus a couple little like technical nuances was already stuff we'd written a long time ago in fact we have this one paper that basically is like the god paper of and has like all of this stuff but i feel like no one has actually ever read it because everyone who cites it yeah i feel like every single thing i work on now just like somehow i'm referencing that paper all the time [Laughter] but uh basically like it became sort of clear that it's not like the math was new as much as it was like what's the actual story for like what this thing that happened was

using the existing framework stories are typically ones tyrone uncovers which is also descriptive of his role we jokingly call him our pi and sometimes the math will all be there but there's no story around it and bruin is our fearless leader in that regard yeah and we're like tarun please fill in you know turn autocomplete whatever the whatever the github code ai thing is that's tyrone for us and stories you just like pilot tab co-pilot yeah there we go it's the co-pilot so it's uh we have a tarun co-pilot ai where um here we message a telegram group uh and then uh you know he turned goes and fills in all of these like nice little gaps and be

like oh this beautiful story about how it all works it's like oh my god you're totally right you know i almost convinced myself that that's exactly what the paper was about at the end of the day can you share some other examples of that like what other kind of storytelling type papers have you done so so like touring said there's there's the god paper or as i like to call it the gold mine or the gift that keeps on giving and it's this weird paper that she has a hilarious title it's kind of silly but it's called uh when does the tail wag the dog okay um and it's i think i think it's called curvature and market making is the subtitle or something like that that's the that was the first the og storytelling paper it's like we realized that at a certain point

you know what became the most liquid exchange for a bunch of random tokens it wasn't coinbase it certainly wasn't ftx no it was like uniswap b2 yeah and so turin and alex had been talking about this and it was this you know at the beginning it was a little bit of a complicated story because we were like oh we have all these things that we want to show and all this like how what's the largest trade you can make in order to like incur you know or after which lps start losing money right and things like that these these kind of like things that we all knew and one or like at least had some idea that if you make you know if a trader trades like really large trades against you you kind of end up losing money and all of these things so that was the original storytelling

paper where i think the original paper uh started at what did it start at guys do you remember is like eight pages or ten pages and then i think uh if i recall correctly the last posting on archive was about 45. whoa big story yeah yeah true truly big story but it's kind of fun because it turns out um that paper predicted a bunch of weird results that we ended up writing but that was the original one where we you know tell the story about oh look you know why do you need yield far like why did you farming become so interesting and useful and it's like well it's because you know lps we're used to kind of losing money if they weren't careful about things exploding because the guild farming kind of helps you out with that and so it has this effect

you know has all of these like qualitative but mathematical you can write them down effects uh downstream for lps and for traders and so on and so forth and it was kind of a paper that outlined using very basic math exactly how these things came into play and then it told you things like okay how much do you have to incentivize a pool in order to keep lps happy and all of these things and um but that was the og story paper i think and it's it's still the gift that keeps on giving i think the meta narrative is like you know when we started it was like okay everything was on centralized exchanges but there was this kind of niche thing that was somehow growing and the initial idea was like okay well clearly the centralized exchanges are like way better for the users so like

these secondary exchanges must be for people who really need them and so most of the arbitrage must be coming from people who see the price on centralized exchange being different than the price on the default exchange and the centralized exchange is almost as if it's like an oracle it's like the perfect price but the defy exchange is this kind of like shitty price but people who have no other way of getting the asset have to go there and so we've started with this model of like oh like how do these things work assuming there exists some sort of infinitely liquid external market and then as the market progressed it just became more and more natural to say hey look wait these markets are growing faster than coinbase and ftx like coinbase and ftx their volumes are flat lining and uniswaps and sushi swaps and

stuff are mooning there must now be some other market structure thing that happened and our initial paper kind of doesn't capture that right it only captures the case when it's sort of the secondary or shittier market okay but when it's the main one then there's this kind of this this question of like oh well what happens when these defy things are actually like dominating the centralized thing and that's a very different question and you have to come up with a new way of framing it but the math is not very hard in fact guillermo usually just yells at alex and i any time we try to do any math that like a like undergrad first year like couldn't you know barely can take a derivative like type of student could use

uh which is like a sort of philosophy of his advisor of like keep it simple extremely simple stupid any of the more complicated math oh he's he's seen some things to be fair to him uh seven pages of differential equations that i sent him and i got a virtual slap in the face and uh grabbed me by the shoulders and shook me and we do for the listeners spam guillermo enough uh to to the point where he's he's become uh quite annoyed i think of these types but it is a typical approach right like we'll see something like what tyrone is describing um right which is look it looks like these cfms are

willing the centralized exchanges to obey their price as opposed to the other way around or we're seeing all these people design these private in air quotes cfms do they work or do they not and very often the first order approximation is just brute forcing an answer to the question that this type of phenomenon poses and it is typically very ugly and i think the approach that hopefully works over the long term is to simplify it as much as possible to the point where it's accessible to a wider audience and guillermo in our group is kind of tasked with doing that as annoying as it is to be fair i think that is sort of the

part of the reason that i think people from other disciplines are able to read this stuff i think one of the problems i often times had when i was an outside person and was reading like cryptography research like both zero knowledge proof stuff and proof of stake stuff is it's like almost written in a purposely like you reader you're too dumb knave like don't read don't read like and like the the way these papers are written is like obscuring oftentimes i feel like is over complicated to obscure that it's like actually a simple result totally or like extremely long like you know something that like in math would be like a one line to express someone wants to write it as an algorithm and it's like

now you you like have to do the reverse engineering as the reader to try to like understand oh they really just meant like use this concentration inequality instead of like run this algorithm with randomly on random input so you know somehow i think coming from outside of the space also for all three of us made us not necessarily like the writing style uh often found i wanna ask a little bit more about the storytelling though the mev paper that you did like are you there you're kind of going outside of some of i mean i think you're going a little bit outside of the cfm maybe maybe not actually but is that a storytelling one or is that prescriptive and maybe you can talk a

little bit about that work yeah yeah so so the meb paper essentially let's yeah so let's talk about the move paper so the fever essentially says like look miners get a bunch of of bundles which the bundles themselves are something tell you something like look i want my transaction placed before and after you know another transaction like oh some whale trade on uniswap so it's called a sandwich or i want my transaction place right before it or whatever and the question of course the miners have is okay i have this you know huge thing of this number of bundles how do i optimally accept bundles such that a they don't interfere with each other because that's kind of silly and b uh very importantly is how do i how do i do that in order to maximize my profit

right so right now people were coming up with all kinds of complicated heuristics and all this kind of stuff it turns out um there's a very very simple kind of framework to think about these problems called uh mixed integer i guess it's linear programming in this case right where essentially you have a bunch of constraints and you say okay subject to these constraints which is you can you can't have a sandwich transaction and also something coming after it that requires it be immediately after essentially you can't have these bundles interacting too much and b that like you know the the bundles are actually fit in the places that they're supposed to be placed so those are your constraints then you you want to maximize what well you want to maximize your total profit from including these bundles as a minor but this kind of you know

maybe stepping back from that that's not that's what one might call a somewhat storytelling paper in the sense of like you know all of this is a note and it's five pages long and it's really not that complicated so why are we making it that complicated but kind of stepping back from that this is uh what alex and i guess my my advisor uh steven boyd called street fighting mathematics it's this notion that at the end of the day all of these problems are actually fairly straightforward you know it look it does take a lot of thought to get them down to this like very simple looking shape and in fact we've gotten knocked for it a few times by reviewers which i continue to completely ignore and just continue with my stuff but um the point is you know there there's a set of pretty

common mathematics that doesn't require that much sophistication and you have these problems that look very complicated the surface but you and and you know they kind of are complicated if you think of all the details and everything at the same time but the point of you know math and computer science and all the stuff is abstracting a very very clean simple solution from these problems and sure it requires a little bit of math but at the end of the day it's all actually shockingly simple and so the our rabbit hole has been cfm simply because it's kind of been an interesting thing of ours but i guess maybe probably by the end of the year we'll probably be stepping back and leaving uh you know people with a set of open questions and kind of moving on to these more adjacent fields because cfm's sure are

fine they're interesting and they're great and they're like whatever you know we've done all this research about them but at the end of the day they're yet another tool in the toolbox to do things that we want to do and that's kind of the purpose of our research is not so much the you know the point of here are cfms here is all the cool things about them like yeah that's fine and that's that's a great consequence but it's not the point right the point is to do useful things and that happened to be a useful case it's now kind of i think personally reaching the end of its life in terms of like truly novel interesting solutions but there are many many more fields like it yeah i mean actually you were on the show previously to look like we were talking mostly about private amms and are they possible

and what are techniques to do that are you also looking though at these intersections so you're saying like oh there's these other problems faces mev is a very well-known one but are you ever thinking about like what do they actually do to each other i mean privacy and amms there you see it like that was part of the work but yeah are you also doing that with mev or maybe some other things one thing to say about the liveness paper and the differential privacy paper basically show that in order to get certain properties or avoid certain bad scenarios you actually do have to reason about how the consensus protocol interacts with the application so in the liveness case it really boils down to like your choice of mechanism

for liquidity provision is actually implicitly reliant on some sort of bound on the maximum time the network goes down and the maximum price deviation you might not think about that when you're designing the application you're just like i'm writing an order book how do i how do i have a fifo queue how do i do this type of order how do i do an iceberg because you're not expecting the network to go down i guess exactly right right or in the privacy case you're like oh great i'm going to hide the the balances i'm going to hide the balances so no one can figure everything out and then then you're you're not thinking of the threat model of well okay what if the person who's actually trying to de-anonymize you is also a validator also a minor right and in some sense a lot of our more recent

work i think personally the paper i'm most proud of from our thing it's definitely the differential privacy thing but that's just my this is because it kind of exemplifies this fact that like you can't really design these things without thinking about consensus at some point like they're just certain properties that will eventually be that and for uniswap it did get kind of lucky that it took so long for mev to happen right like if there was so much emmy v at the beginning when unicob started i i think it would have actually been much harder to bootstrap or like people to understand how to use the mechanism without getting blown out of the water one one thing maybe to note though the paper you just referenced the differential privacy is not the work we

talked about with guillermo so there was an evolution there right like or was the first one that you did much more like here's why people's approaches are wrong so a bit of story and here's a few ideas maybe you can talk a little bit about that journey and that like evolution of your thinking into this more i guess more specked out solution of differential privacy yeah it was kind of a a one-two punch and it was actually not intentionally a one-two punch but the point was the original paper that we talked about i think yeah um march of this year um essentially said look here's the deal you can't just willingly hide things from the user and and from you know

anyone snooping around and then expect that in fact that actually confers you privacy when you have you know when you're when you're interacting with a cfm um and and it was kind of a simple result uh it was pretty short this notion that a lot of people had intuitively but it specked out exactly how the attack would play out and then we just kind of gave a few simple paragraphs stating look here are some cases where the proof fails one is okay if you batch a bunch of transactions together then people can't tell them apart then congratulations that's one number two is uh you know if you add kind of noise to the prize so if you if you you present the user with a price that isn't actually exactly quite the price of the cfm then it's a little

harder for them to reconstruct uh up to some up to a certain degree the more noise of course the worse it could be for lps or the user but the harder it is for them to infer the the underlying prices and therefore the harder it is for them to infer what trades have happened um and so that was the first one and it kind of said look the approach is here it's not obvious right the problem isn't an obvious one it doesn't just simply add a zkp to unit swap and and be done with it yeah right and then we were left with this horrible sick and twisted edge of being like okay that this is great we've just told you that you can't do this but can you actually do this in some way that makes sense yeah right and this is this is a you know tyrone's

answer was the was a differential privacy paper yeah so around the time that we wrote this paper actually anna and i were both in austin at that time during the snowstorm yeah this was right after the snowstorm actually um and and like we put out that paper and i had basically by happenstance kind of been like a little annoyed that we didn't find some like simple trick like one like a buzzfeed article way of getting around the impossibility theorem like one easy trick that makes this wrong um but i happen to run into this paper by pure happenstance by like nika hoglatub and

roughgarden and someone else that kind of introduced me to this whole new world of things that like people were working on in terms of like privacy versus utility trade-offs so like there's this kind of this idea for any learning algorithm and so we can make the uni-swap you could think of the unisop price algorithm as a learning algorithm it's learning like in a very dumb sense it's not it's not like a very sophisticated learning algorithm but it's it is something that's sort of like learning how to update the price as a function of the the reserves and there's this kind of natural trade-off in machine learning that people in cryptography in cryptocurrency don't think of a lot which is this utility versus privacy trade-off if i add in a bunch of noise to blind your

data will i also make any function of your data worse so like if i randomize your height well maybe i preserve the mean height over everyone but i kind of mess up the variance of the height or i mess up other statistics of the height if i had like a table of people's heights and in some sense there's always this trade-off you effectively can't get out of it and going down that line of research just kind of led me through like how people in sort of machine learning think about this stuff which then i found over time over many months of agony a way to adapt it to this setting interesting just just one side question

like why in machine learning do you use these techniques like what are they for exactly so like the best example is the u.s census so the u.s census collects a lot of data right and there's tons of sociology researchers and like people who do that type of stuff historically have relied on the census for data but then people have shown these kind of attacks where people can de-anonymize people's individual census items based on the average that is produced so like let's say someone gives you the average of like the height of a certain area and it happened to be an area where someone lives that was seven feet foot tall you could effectively infer if their data was in the data set or

removed yeah and and so in some sense like i think the exceptions the unusual ones yes and those are the people who oftentimes value their privacy the most so there's kind of this weird thing where that happened then the u.s census was like okay we're going to use these privacy techniques and now sociologists are a little pissed because they didn't know how to be fair all their all they learned how to do is import our library call our library on census data but you know so i don't think they really checked carefully like the math they were doing and like how sensitive it was to noise but the point is there's a lot of times where someone can take a data set and augment it with external data a data set that's anonymized and then de-anonymize it so there's this famous

example in new york where uber had to provide new york city with all the uber trips but they were anonymized and then someone found another data set related to to like uber coupons or something and then they joined it and they were able to de-anonymize because the one was de-anonymized one was anonymized wow and so then they found all these and this this actually i i forget if it was the elliot spitzer thing or if it was like some other like sex scandal but they did find this like politician who was like constantly going this one strip club and that was like whoa i mean the other famous example which is actually really it's kind of what started kind of kicked off this whole field was uh netflix in 2008 had a very

famous kaggle challenge and a very famous challenge was can you predict what movies people are going to like you know given their previous like you know ratings or something like that and this this data set was completely public right it's a big data set and some researchers went through and essentially what they did was they just using mind you like this is ridiculous right like just using people's preferences for movies they were able to de-anonymize i can't remember the exact number but it's like either a quarter or a half or something of all people watching this is again mind you netflix movies in 08 right like think about that and that's they were able to anonymize something like half of the or a quarter of the data set just from their movie

preferences and mining with some other data set so that's crazy it's it's you know we we joke that it's an esoteric mathematical thing but at the end of the day actually it's it turns out to be hilariously simple and you can run on a laptop and and do some real damage but where the differential privacy like where was that first thought of or implemented within a machine learning context yeah if you remember like was it that census one i mean i think you've you've given great examples of because yeah were like the biggest proponent so in iphones a lot of the data you send to apple servers is encoded in a different really private manner i don't know about which things are and aren't i i haven't actually read their docs carefully enough but they

were the first like big tech company to invest a lot into it their whole like claims of being really good about privacy are about this but of course they have other things that are not so good so let's not try to make lionize anyone but yeah but the point is that basically when things started moving to the cloud in 2006 um i think apple was the first one to be like no no no we're only putting stuff on aws servers if it's differentially private because aws right it actually ironically wasn't about protecting the user it was about protecting someone else from taking the data of your user and that wasn't you and using it for their purposes wow but but the idea here is that this utility versus privacy trade-off is like say i have a machine learning algorithm that

takes your ratings right like like what guillermo said and it perfectly it can perfectly predict your ratings but it leaks information about you versus something where i add noise and now i ruin the prediction quality its prediction goes from being perfect to like 75 accurate but your privacy's gone up right so that's sort of that's the thing you should be thinking in your head like that that's why the netflix example might be the cleanest one now let's hear then how is differential privacy actually used in our context so imagine you have someone who has a machine learning algorithm where they can try to predict where the whale order is so let's say there's like a thousand uh trades of size one so it's

like one guiller coin for one alex coin and then there's the whale guillermo who's like ah i wanna ape into alex i'm gonna bank a thousand i'm gonna trade a thousand ga coins for sorry alex and so so the idea is like the the people who have this very small trade size the one coin size they all want to actually be in front of the person who is the 1000 trade size because like the once the person who trades the size a thousand trades they will push the price up a lot right and and all the people who are like the small

small fries will get kind of way worse price impact but from the whale's perspective uh you actually are like hey i just want to be fairly priced i don't care where i get put i just want it to be like fair right and so the whale is the one who's trading their privacy right in some ways because like they just stick out like a sore thumb their their trade size is way bigger than everyone else's so statistically people can try to say like oh i know this is the whales order i'm gonna front run them and this is the exception we were talking about before i guess this is the exceptional result that's very very visible yeah right and in trading that's much more common right like when there's like a a liquidation there's just like a million mev bots and

searchers trying to chase it right so and they're all have small size relative to the thing that's getting liquidated so this type of scenario is extremely common in finance even more so than in the sociological data type of scenario and so the idea is like privacy well what does privacy mean to the whale well it means that somehow they can still get the same amount of quantity the thousand orders but it's going to get interspersed so imagine if now i cut up the trade of size thousand into a hundred pieces of size 10 and then i randomly intersperse them amongst the other trades of size one do they get a better price and how and how easy is it for someone to front run every single one of their size 10 orders and right and so because i'm adding some

entropy by cutting up the orders and and splitting them that's increasing my privacy but i'm ru i'm making my price impact worse in some sense because i'm splitting up my trades and i might have had more you know it could be worse and so that's sort of the trade-off that we kind of analyze and show that you can if consensus can provide a a source of randomness for doing this kind of chopping up and permuting action then you can achieve kind of this these types of privacy guarantees when you talk about cutting them up though are you cutting like would it be cut up in equal sizes or is this also somehow like random no that's a good point that that's a very nuanced detail that guillermo asked me about five times when i yep paper because it was like

which is no no you actually do sample from an unknown distribution a way to cut up the pieces so like not only is the like the randomness not only does it randomly cut up the the pieces into different sizes but it also does it in such a way that the distribution over how it cuts them up is not known that's the key that's the very key crucial part otherwise you're kind of if someone knows the distribution they could simulate it and try to say like oh this is the most likely cutting but how would you keep that private like how would that not be known when you're talking like open source like isn't it kind of there provided you have an input source of randomness

that is that you trust like a vdf or a vrf you can sample from this distribution of how to cut things up in a differentially private manner itself right so everything the root security detail will end up being like does your vrf or vdf actually give you enough entropy i got it so that's why consensus is important to this right it's an application that takes advantage of the the randomness guarantees of consensus but this is what another situation where the application layer has to interact with the actual blockchain so i mean the example you gave was focused on the whale and this exceptional amount but are you actually imagining this running on all amounts even if they aren't exceptional

so the idea is that that would be well a there's kind of a trade-off for the user right for the whale um for most people exceptional i mean outlier yeah yeah exactly right so for most users they in the trade-off is the following it's zero because they're just gonna trade anyways right uh the trade-off is either there's kind of two there's one at the exceptional user level and there's one at the base layer level of you know actually the cfm itself is going to have some sort of price of back that's different but at the whale level it's you know you have a slider that says how much privacy do i want versus how much do i care that this thing just gets executed and doesn't cost me personally so it there is a cost in some sense the point that aruna is making is that there's a cost

to privacy right and and it's it's a cost a very literal sense of at the end of the day you're paying for that whether you want to or not right you can you can choose to not pay for it but now you've traded off your entire privacy so that could impact your actual trade exactly exactly so it's it's you either eat up the cost of the trade or you eat up the cost and privacy and take your pick right there's a slider there and that's fine but but at the end of the day there's not really an obvious way of making them together and then certainly there isn't one obvious and immediate extension because of the paper that we were previously talking about at the end of the day you you can't have your cake and eat it too this is interesting so it isn't blanket

differential privacy across the board but more of an optional differential privacy are there and i imagine you've explored this but are there any other edges or leaks that could come out of this that are not covered by the differential privacy slider so there are some uh one one case that we've made previously which is kind of interesting and also requires kind of control of the consensus layer although you could implement it later um is one thing that would actually just immediately improve privacy is just simply matching orders off chain so you can imagine that there is some sort of you know there's a bit on a contract header that if that bit is one what it does is it immediately starts some algorithm so when the block is minted part of consensus requires you

match orders prior to including them in the cfm right so the idea is like look uh alice and bob trade in opposite directions and then there's like whale i don't know willy whatever who trades in like you know some some other direction the point is you match everyone's orders off chain right and then it's kind of if no one can see the the amounts and privacy is some preserved in some sense right in the cfm itself uh and then willie just uses the remaining part of the you know whatever is left over to trade against the cfm and then of course you know here's the deal will it lose his privacy right but everyone else is pretty much safe got it assuming you obviously trust the aggregator so there's some off-chain operator that is collecting these trades

and matching they offset and there's some secure way of communicating with them which i would love to see it if somebody has it but it does assume that exactly so but at the end of the day it's all trade-offs right there's not an obvious like it's not a bomb-proof box but it does seem to be at least uh somewhat watertight maybe that's the statement so that paper came out i don't know two months ago or so have you seen anyone try to do it or play with it actually like implementing it osmosis they're they're the ones who are gonna who are actually gonna osmosis in penumbra they're they're both going to actually try to to implement it so amazing i mean just as we said earlier

these are mostly theoretical ideas and taking them to practice in particular this one is going to require some really talented people i think the osmosis and henry of penumbra and these these folks are brilliant and so if somebody's going to do it they're going to do it but it certainly is not all they are in fact very far from it um these are just high level ideas with horrible constants and so forth they give you a big idea of a set of trade-offs that one should consider but it doesn't tell you what types of parameters you should select or how you should actually implement these types of systems in production it also sounds like in that implementation i mean i'd be so curious to see if they find you know new problem spaces or maybe new solutions too through that yeah for sure i mean one of the things

henry seemed most excited about this was like he was like i don't care about the constants snarks still have the worst constants if you read the papers carefully like a lot of the original snark papers have just like horrendous like hidden exponential constants and like over time people in practice like i would say like ariel uh and zach's research it program at least for me as an outsider if i were to describe this something it's like literally just removing reducing every single constant that is in all the original snark papers to something practical you mean plonk like what they've done with plonk yeah all the entire stuff and all of them and the turbo plank and like super vlookup ff all these things are like if you read them carefully or just

like constant reduction method mechanisms which is the type of thing that you do when you're like actually doing engineering and not like armchair math in some yeah when you when you're what i like to call uh doing real people things as opposed to like fictional made up world fantasy that we do uh when writing papers like like 90 i mean 10 of it is actually useful but at the end of the day it's a you know storytelling as we like to call it well i am so excited to hear that people are now experimenting with this it's yeah it's it's awesome to hear this so i think to wrap up this episode what i would like to hear from each of you is actually a a space or topic something that you haven't yet explored where you see your research potentially

going towards so alex why don't we start with you the honest answer here is that and you know the answer uh either because we've been chatting about it is that we've been looking a lot at zka and we're so early in that exploration where the directions are less clear we're really interested in zk more generally right but some areas where we can be useful and our work can potentially be constructive out the gate or more in application areas as well as some of the things taron described in terms of acceleration of certain things and maybe some more practical examples of how these things can be used the economics of zk systems there's very little work sort of on you know there's now all these roll-ups there's now all these private layer ones right

what are the different types of economic arrangements between the agents in these networks and those are areas where we can maybe do damage out the gate but then we're certain there's much deeper and more sophisticated problems that we'll encounter as we pursue that exploration as there were in cfms and mev and staking and lending protocols that we discovered in that work but the realities were very early in that exploration very cool all right tyrone what about you um two main things to your point about there being a lot of cart for the horse i kind of think like a lot of the cryptography zk stuff is a little card before the

horse and that's not necessarily bad but you know we're gonna have all of these private networks with smart contracts soon uh tm and uh you know like the alia transitions all these types of people for sure but i i guess i think that there's not really any applications that exist to be honest other than transfers like yes we want private smart contracts we want private smart contracts we don't really have that many applications and as kind of our cfmm thing shows there's kind of this thing where you need to figure out how to design applications where you partition the private part from the public part and you're able to like cleanly abstractly separate the two of those and then then consider the set of applications

financial applications you can build conditional on there being a clean separation between public and private data and i think that is sort of the the realm of things that i want to really understand more carefully and then of course mev you know i think one thing about mev is it's like a great meme but i do think like in general the research has been quite muddled partially due to marketing partially due to like other things um you know i think like fair sequencing as a thing is very half-baked in a lot of ways and and doesn't is doesn't like kind of reveal all of its warts to the average end user at the same time i also think mev has like not

been theoretically described you know i think there's been some work like phil has a recent paper that kind of tries to get at things but everything is very brute force like it's like use formal verification or do something and there's no actual like mathematical theory that has any sort of aesthetic beauty that has shown up everything's very ugly or like extremely like you know like it doesn't really like get to the the like meat of the system which is like hey we introduced this type of behavior the economic equilibrium changes amongst the agents and the new equilibrium has all these different properties that are like very different from the fully honest non-rational user case right somehow we

don't have a story like that we we only have this thing of like hey i i can like do this kind of not very easy to explain mathematically thing and like maybe we find that you can make a profit well that's not like much of a story right that that doesn't really actually tell you like what's going on amongst all participants and so getting to something like that that's like less brute force i think and less like less computer science focused because i think like the no offense the computer science parts of mv are kind of boring in a lot of ways they like really don't illustrate like the reason that people are having these like big shooting wars you know when i say shooting words i mean like data center wars and like

co-locating next to certain minors doing x y or z right like all people don't do that stuff just because of like a computer science reason alone right there has to be some sort of like it's like finance like why do you build a microwave towers well there's like a certain equilibrium that occurs once they actually exist you change the equilibrium right and in some sense like that's what's happening and yet people want to just focus on kind of like the kind of boring details to me boring at least like there's there's no aesthetics to it like people should have some sense of aesthetics uh when they write papers and i i mean i don't know whether guillermo's laughing at me or with you yeah yeah i i think you know that to install the

the phrase aesthetics because i i i have this this weird joke that i like to say and there's and and i apologize because this might offend in fact some of my colleagues but there are there are three fields in in ranked order that have probably the worst aesthetics i've seen and the they're in exactly the following order it is um physicists and and i can say this because i'm a physicist that's what i can say without offending people and it's because um if you go read you know most physics papers absolutely illegible even if you're like in the field and often what ends up happening is you go and rederive everyone's results then write immediately after is economists uh some actually there's there's some cases very special cases where they have good aesthetics uh but almost universally completely logical and and furthermore they often depend on

parameters that like you can't even measure so they say something like oh the number of or the proportion of inform versus uninformed traders and it's like okay good luck measuring that looking at uh you know at market data and you can't even come up with models and all this crap but at the end of the day it's like whatever uh and number three and these are kind of the most impenetrable and i apologize it's computer scientists okay specifically theoretical computer scientists um some some algorithms papers are okay but for the most part almost universally illogical uh and and and the reason why is and and you know this kind of feeds into why i think our work is interesting is at the end of the day right like there is kind of one kernel of important distilled knowledge that comes from a paper and like you should write a paper

thinking very explicitly about this and it's hard it's really hard because it means you end up rewriting your paper a bunch of times and it's you know you end up reworking a bunch of the of all of these things that you know at the end of the day you're like yeah of course that's obvious it's immediate from like you know this long 50 paragraph description of the algorithm right kind of we were talking about previously but i think the the point of our research now is we like to find good and clean abstractions that explain things simply and of course you know we landed in this interesting case which was cfm's but there are there are so many more problems that have you know in some sense uh i have this heuristic that if a human can intuit the behavior of a

system in that system it's probably simple enough that you can describe it simply as well and so i think the kind of taking the meta level here as we kind of go forward the point is finding these these pockets of things that people care about and things that people find interesting and describing them using you know very simple language and at the end of the day it's almost universally very simple like if you can work out some calculation for it why the hell is it not simple you know you don't need a sat solver to describe you know x or y or z you can just write down a three paragraph definition from which like all of these results follow again we landed in cfm's as being a particularly one such abstraction but at the end of the day there's millions of

these things that people are kind of hinting at we're all slowly but surely like churning through um but no one's really sat down and it's hard work right no one's really sat down and put in the hours to be like all of these results are in fact just consequences of x right x here could be it could be many things you know it could be some framework for finding out how protocols interact with mev in a simple way that is clear it could be the same case with privacy you know what can you say what what must private protocols have in order for those things to even be you know real and all of these things so i guess that's that's kind of where where we landed and you know this is the threat of all of our work at the end of the day

but we're looking forward to you know moving past i guess maybe like what i guess this this trio is now known for which is cfm's into broader spaces that are you know hopefully at least as if not more interesting as you know this area we've been exploring for the past two years very cool all right i want to say thank you so much for walking us through all of this research kind of landing on where you're going i'm so excited to see what comes next so thanks thanks thank you thank you for having us and i want to say thank you to the podcast producer tanya the podcast editor henrik and to our listeners thanks for listening

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