 Hello and welcome to Filecoin Crypto Economics Day here at DevConnect in sunny Amsterdam. My name is Karla Kirstenow and I'm the lead of the Network Research Team at Protocol Labs, where I've had the pleasure of working closely with the crypto economics researchers at Crypto Econ Lab. We've assembled a fabulous lineup of speakers today to lead you on a whirlwind tour of the fast-paced world of crypto economics and the Filecoin ecosystem. Filecoin is a decentralized storage system designed to store humanity's most important knowledge and Filecoin's unique crypto economic system is central to its design. Since Filecoin launch, the network has grown to include over 4,000 storage providers with more than 16 expedites of storage and the network's momentum continues to grow. Along the way, we've designed a functional two-sided decentralized marketplace, created an innovative first deployment of EIP 1559 and championed scalable Layer 1 research. We're building data-driven analytics for network governance and evolution, developing Filecoin to become a data availability layer for Web3 blockchains and introducing new Web3 building blocks with new business opportunities in reputation, retrieval markets, data dows, compute over data, and other exciting areas. And so Protocol Labs Crypto Econ Lab decided to put this event together to share our research findings, opportunities, and challenges with the broader Web3 community. So we'll have about 11 speakers today, that's enough to round out either an American or European football team on a variety of topics, exciting topics related to crypto economics. Each talk will be about 20 minutes long, including time for questions. If you're participating virtually, please put your questions in the chat, and then they'll be relayed to me and I'll read them out loud to the speakers, if time permits. And so, without further ado, let's introduce our first speakers, ZX Zhang. ZX leads Crypto Econ Lab at Protocol Labs, where he studies the intersection of networks, incentives, and optimization. He'll be giving us an introduction to Filecoin, crypto economics, and its opportunities and challenges. Welcome ZX. Thank you, Karola. And well, my name is ZX, and I lead Crypto Econ Lab at Protocol Lab. I'm super, super excited to be here. Thank you for all the organizers for putting together this amazing event, and thank you to all the speakers and you for coming to our event. The goal of today really is to like kind of showcase, or my talk is to showcase the scope and this range of topics covered by Filecoin Crypto Econ, and its amazing opportunities and challenges. Here's our rough agenda. So we start with what is Filecoin to bring everybody onto the same page, and then we talk through some on a high level, some of the crypto econ challenges that we face when designing and governing the network. Then we talk about some of these latest exciting opportunities, and many of these could be either start opportunities or like just interesting research ventures. And then lastly, we just covered the agenda of a Crypto Econ day today. Just before we get started, we are hiring a Crypto Econ Lab. Our aspiration is to be this hub that goes from research to protocol, and then protocol to product. We start with work with university and research groups and all the general incentive research topics that is central to Web 3 and Crypto Econ. And the other really interesting exciting aspect working in Crypto Econ in the Protocol Lab network is with us there are always many new interesting upcoming protocol and research in cryptography consensus, which you guys will hear about some aspect of that today. And they always call for the need for new incentives. Of course, there's always protocol improvement in governance, and then we also participate very actively downstream in the network analytics and ecosystem. After all, the success of an economy really depends on the success of this ecosystem. So that we're hiring across the board from research engineering to product, if there's no road that suits you, feel free to email us and then we can always carve something out. And then we sort of also pioneer champion this Crypto Econ process, design, validation, deployment, and governance. So this is something that we kind of like apply this systematically in thinking about new problem, and how do we model them, how do we design around it, and how do we know our design is good. And then Crypto Econ is really interdisciplinary, as we can see from this diagram here, and then we are looking for collaborators from different backgrounds and experience. Without further ado, let's dive right in. So what is Filecoin? So there are many definitions of what Filecoin is, and here I'm giving one that from our take at Crypto Econ Lab, where Filecoin is besides being a storage network, we think of it as like a layer one protocol that starts with data. And then it's also a multi-sided marketplace enabled by blockchain, which also makes working in crypto economics on Filecoin so interesting, because there's suddenly tremendous scope. It's like a live economy running with different kinds of marketplaces emerging on this system. It's also an island economy. People are massing cloud resources and building new experiences. I want to highlight new experiences. As much as like storage is really cheap and competitive on Filecoin today, I think for Web3 to win, it's not about cost. It's always about the new experience, and thinking about new incentive structure, new marketplaces, and that's why it makes it such an interesting place. It's also an Airbnb for cloud services. It's a platform where anyone can become a storage provider, or a compute provider, or a connectivity provider, which we'll hear more speakers talking about later today as well. People will come together on this island, amassing different resources, and then offering this network of utility to the outside world. It's a really passionate research, engineering, and product community. I hope after today, you learn more about our unique opportunity and challenges, you will choose to work with us. Here's a stakeholder diagram of different participants on the Filecoin network, where storage providers provide reliable and useful storage and get rewarded by the blockchain work. We could incentivize other things as well in a decentralized manner, but the other things are much harder to prove. There are many teams in our ecosystem working online, improving the verifiability of these metrics, which you will also hear about later today. In some setting, let's say we can't verify completely, can we design a new incentive, a new market structure, reputation, and metrics? There are other teams that will talk about this today as well. Then our client today, they can only store data on Filecoin, but they will soon be able to do a lot more when a user defines EVM smart contract coming to Filecoin. We also hear different teams talking about that today as well, and developers actually have to build new experiences. As I mentioned earlier, it's not about cost, it's about something new. That's what all these exciting opportunities come from. Tokenholder State Filecoin, which is another big opportunity, and the ecosystem partner create a myriad of initiatives to jump stop the ecosystem. This is like a comparison in terms of Filecoin with other storage networks, as we can see within the Web3 space, that pretty much is like a dominant pair. However, from our ecosystem perspective, it's not about Web3.us versus them, but more about how can we bring even greater adoption from the traditional Web. In comparison to AWS, well, I think we're only another 10x to go to catch up to the size of AWS. Also just to call out, all this capacity and adoption was only a massive study over a year, which showed this tremendous power of crypto-economic incentives. But we are still just getting started in our quest to build greater adoption on a Web2 scale. Filecoin is a layer one. We implemented a version of EIP-1559, roughly a year before Ethereum mainnet. Back then, we had a set of requirements of our gas model and it checked the boxes. This is the network transaction fee on Filecoin since launch at its peak. As we can see, it's actually consumed more than 200,000 Filecoin in a day. And then there's an interesting dip here, which I think some of us are going to talk about this as well. And how do we really design basically the short context here in the interest of time is Filecoin introduced an upgrade that scaled the network 10 to 25x at this point in time, but then some other things happened such that the network ended up consuming less fees. But we believe this is long-term good for the network because that makes it cheaper to participate in the network. And this kind of touch and tie into something more interesting about blockchain gasses in a world where scalability is no longer a constraint. One of our researchers will talk about that. How do we think about the new gas model from a crypto-econ standpoint when blockchain scalability is no longer a constraint? So with this brief overview of Filecoin is a layer one. It's a marketplace and Airbnb for cloud services. There are many things we can provide beyond just storage. Now we want to talk a bit more about what makes working on Filecoin crypto-econ challenging. I think it really boils down to we try to do some of the network as a whole or also the protocol lab network as a whole in vision to build a network of utility where we want to make blockchain useful and then we want to make blockchain internet-scale. So instead of the blockchain merely just execute a transaction, there is something that blockchain wants to do. It's about building an economy to create something of value about empowering traditional businesses. So that's what a lot of our goals and constraints challenges come from. I want to do it in a decentralized permissionless and a trustless ways as much as possible. And this is where a lot of constraints come in. But it's a progress as a community. We have like more than 4,000 storage providers, more than 10,000 developers building our ecosystem, and then we exceed the 16 exabyte of storage capacity with more than 70 petabyte of data stored. And then you have a global community of storage providers. And also something that struck me during this DevConnect was, I think maybe like a year or two ago, the crypto-econ community was tiny. But I think during this time in Amsterdam, we actually see multiple projects working on very similar issues within the Web3 ecosystem. And we hope that throughout our talk today, we get more people excited and interested in working in Web3. So I don't talk through some of the high-level key economic policy that we picked and some of the concerns that went with Powerpoint just to give people a glimpse of some of the trade-off and considerations that have gone into it. So the biggest lever that we have is the block reward policy. Before Filecoin, most of the crypto system kind of adopts something of a flavor of an exponential decay where it came from the Bitcoin land. The early adopter gets disproportionate reward and then goes down from there. We identified a few issues and constraints with this early on. Sometimes when you give the most reward at the beginning, the network is the least mature and it's also the smallest in adoption. We want the network minting to kind of be aligned with the network's utility. Together with our collaborator at BlockScience, we innovatively came up with this mechanism called baseline minting, where the block reward is minting based on the network KPI. And then in the case of Filecoin, because storage is the only thing that we can prove, we have this exponentially increasing baseline, which is a target, the KPI, that the network needs to hit in order for the minting to be at its maximum potential. So in reality, the network will be minting somewhere in between these two bands. We are minting based on the utility that a participant provides to the network. So this is like a series of empirical data we are more than one year in. So the network started with below the baseline and it catches up to the baseline in April last year. And then we can see like the block reward was kind of the daily, the per e-part reward slow in the beginning and then slowly climbs up and then now it's following exponential decay based on the initial promise once the network is crossing over the baseline. So the area under this curve, which is this part is invisible here, this whole section, our reward, they were not minted because the network was not hitting its KPI. And this, all the reward here are being spread across to give more sustainable reward in the future. The other important policy is collateral. We want to align all the sort of providers incentive with the network. So we want to make sure the storage is reliable, which turned out to be pretty reliable. We want to make sure people are not violating the consensus security of the network. So there's both the initial storage collateral, but there's also the initial consensus pledge collateral. So this is how I love how people mind become participants of sort of providers on file point, right? They are barred to token, they kind of like commit, let's say like when you have the supply of storage, but there's no demand for it. You basically put that into a storage container. We call them committed capacity. You put this on file point as well and then you commit to the network and say, hey, we are going to prove this amount of storage on the network. And here's my collateral. And then when demand comes around, people can then say, oh, and I will take out my container, and then I can slot in the demand, I can slot in the deals. This analogy here is like empty Uber car driving on the road to demonstrate, hey, we have demand for all this, we have supply for all this capacity. And then when a client comes around, they can flag down a Uber and say, hey, I want to get into the car. So this is made even more true with snap deals. Now people can just slot in their useful data into the capacity on file point. And then one year in, like we lock I think more than 120 million file point on the network as collateral. If you compare that with like any of the DeFi protocol, you'll be easily ranked as among the top five across all web three. But this is another aspect that people don't talk so much about file point. And then this is also the percentage of the circulating supply being locked in file point. Again, there's also major opportunities here as well. But I think some others speak a little bit, they will speak more about them. Then the other interesting aspect is like slashing policy, right? This is also pretty interesting. So people before Falcon launch, people always talk about, oh, how do we guarantee some kind of quality of service on the permissionless network? And then it turns out crypto econ is to the rescue. So on the high level, the Falcon blockchain will come around every storage provider and ask them, hey, you said you're storing my data, where is it? So the provider will say, hey, here's a proof of me storing it. And then the blockchain will check, okay, good, you have it. If not, oh, no, you don't have it. And then you get slash for not, for not storing the data that you promise to both the clients and the network. So on the right side, this is the chart of like the daily thoughts of Falcon, as we can see the share of this chart highly correlated with the daily fault penalties on the Falcon network. And then later on, I think there's another talk that talks about a penalty and how how incentive is doing a really good job is making sure the file coin storage is super reliable. And then there's also gas policy, because after all, it's a layer one, right? Like we implemented, yeah, it's a different variant. And then like we had like two other variations here. One is this over estimation, we started since Genesis, basically, it has to do with how the VM execute messages without going to details. And then we also introduced a batching dynamic because file coin is a very, that's my personal take is one of the most scalable chain out there. Sometimes like from the crypto econ land, we sometimes will ask our engineer, hey, can we slow down the scalability so that we let the man catch up to the chain, right? But we introduced this like 10 x 25 x improvement in scalability earlier last year. And then we also introduced a batching dynamic to kind of like to to balance out the balance of the incentive misalignment. There's a whole separate talk there that we're not going into. But that's something interesting, some of this, some of this innovation that we did a modification that we have done to the gas policy. There will be another talk, I think, by the block science team on gas today as well. So another challenge with so we talk about all the policy side. But unfortunately, as like crypto economists, our job is not done. Once the protocol goes live, there's still like governance analytics. So we also developed this like Falcon improvement protocol and thinking about governance, how do we decide what policy go into the system? So FIP model after EIP, Ethereum improvement protocol, anyone can propose a fit. But then sometimes it's like a fit could be seemingly innocent, or it could be universally positive, but then they actually have a very profound people you call impact, right? So we also develop a governance process within crypto econ lab. And we just we start from we first take a look at the idea we perform a smell test that the smell good for the overall economy. And then we we do an impact surface assessment, right? Like if it's a very small impact surface, but it's also a very big improvement, there's no brainer. But then sometimes a big impact also comes with big impact surface. And then we need to go into a lot more considerations. And then we do analytical analysis, where sometimes we just modeling writing out the analytical equation, which you will see quite a lot from our colleague today in the various aspects of the work. And then and then once that further, sometimes we do modeling and simulation as well. The challenge with crypto econ sometime is there's no right or wrong answer. And then the outcome can also depend on many external factors as we can see from some of the hyper drive example. But of course, we also invest a lot in analytics to make sure that whatever policy we pick as a community, we have like real time feedback and how we're doing and analytics can also inform what are the most needed areas of the network and how can we improve network governance as a whole. We hear from a starboard team later to talk about how we apply analytics in network governance. There's a dashboard and then there's also both from the network level dashboard and also from the actual level dashboard. And this is also like about how people behave and stand in the network. And this is something another call to action here. In a traditional web world, people already study data to its bone, right? Like everything is going to analyze on a traditional web. And the case of web three, everything is public, right? Everything is public. But people, but we are not spending nearly the amount of effort to understand what's going on and how do we use those insights to make better informed decisions and how do we improve the welfare of everyone in the ecosystem. So there will be more talks on this topic today as well, but I highly encourage you guys to check it out too. Now that we talk about all the challenges between crypto, Ecoin and Filecoin, let's also talk about the opportunities. So as I mentioned earlier, Filecoin is one of the most scalable chains out there. Even today, Filecoin is the largest deployed snack network in the world, right? Like we hear talks from our cryptography team later today as well, talking about some new direction that they are checking out. And also, as I mentioned earlier, we did this 10 to 25 X increase in scalability earlier last year. And then there are more up in our researchers, up in our research team's sleeves. Given this kind of increase in scalability and the parallel to the Ethereum world would be like, what happened to Ethereum after all the roll-up solution comes online, right? Like, how would it impact the macro of Ethereum as a whole, right? So this presents lots of interesting questions from a crypto economic point of view that's not frequently asked. And later today, there will be a, I think right after this talk, there will be a talk on how we're thinking about new guest models in the post-scalability world. Another unique value of Filecoin, in my opinion, is really like this verifiability of data, and you have a proof that data is online every 24 hours, right? So in some sense, in my mental model, where a CID, which is a content identifier, which is a unique hash of the content of a piece of data, right? Like in my mental model, an NFT is nothing more than a unique representation of some digital content, right? You can technically create new interactions beyond just trading on NFTs, and then you bring the value back to the content itself, right? So you have this pretty unique building block that's coming onto Web3, where you have like verifiable content that's being proven every 24 hours. And what are some new interactions that we can build with this? And I think we have another talk today that dedicate towards this as well. And then this is another interesting building. This is kind of illustrated what we were talking about earlier. You have this IPFS layer of like, of a CID, which is the hashes of the content. And then you have the Filecoin layer, which bring you the proof and the internet level scalability, right? Previously, people can try to do bridges to other smart contract platform where you can do the business logic that opens the door to a whole range of business applications. But even more, even more, as an even more exciting updates, user-defined EVM smart contracts also come into Filecoin, right? So this is what we call FEM project, where now you have smart contract and also like provable storage. So kind of a challenge to the audience is Web3 evolves in waves, right? Every time there is a new building blocks, we always have, we get excited about them, but sometimes we haven't figured out what do we do with them. This is the case of ICO in 2017, and then that gave us a DeFi and CryptoKitties and FTs. The challenge here is now we have a new unique building block. What are some new experiences can we build? And then we have some other speakers sharing their kind of perspective and ideas here as well. And lastly, we have massive amounts of data being capacity, massive amount of data being stored, and then how can we, and then now people are also building different retrieval markets on top of that to really retrieve this data and then put in the context and use cases that they're using, right? So my personal take here, the retrieval is really context dependent as well. It depends on the use case, on a technological level, nothing that stops you from, that stops data from being retrieved. It's more so like a business use case, more so like a product market, that kind of question. But the good news is there are many, many teams working on this, and then we also hear another talk today by Patrick talking about retrieval markets on Falcone. And yeah, lastly, today is Falcone CryptoEcon Day. Today is the day you guys at the right spot. I'm very happy to have all of you here as a fashion earlier, where like in maybe just two years ago, it's very hard to pull together a CryptoEcon event. There aren't that many people in this space. I'm super, super excited. Now we have more and more talent, developer, researchers, mind share, interested in CryptoEcon, and I hope I have sufficiently convinced you in the context of Falcone, there are so many exciting opportunities and challenges with CryptoEcon. And with that, thank you for your attention. Great. Thanks for that, ZX. That was a great introduction and overview to the content that we'll be enjoying today. I don't think we have time for questions directly to ZX, but I'd like to point out that we have several members of the CryptoEconomics Lab here today, if you want to ask them in person during the break. And many of the things you may have questions about will be covered in the upcoming talks. So we'll move on to our next speaker, Axel Cortes-Cubaro, who is a research scientist at CryptoEcon Lab with a background in statistical physics. And he'll be speaking to us today about a gas model for a post-scarcity era. Take it away, Axel. Right, so, okay, so I, everyone here is making? Okay. Right, so I want to, my goal here is to introduce you a problem that maybe you don't know you have, and then not give you the solution to that problem. But I just want to, like, introduce the possible space of solutions for this kind of problem, which is, in short, it is like, maybe there's a lot of people focusing on how do we scale the blockchain, make it more powerful, more powerful, but basically to understand what could be the problems associated with that, or if we want to think maybe not just scale as much as we can, but scale as much as we should. That's the question. So I just, I'll talk a bit about gas in general. So, just very briefly, so gas is, what I mean by gas is just like a unit of computational effort in the blockchain. So, like, all these transactions, all the nodes need to validate that it takes effort, everything that is included in the blockchain. So, it is the amount of gas that you could have in every block, that you could use in every block is precious. It's scarce, so there's only, you cannot put all you want in there because everyone needs to validate this. So the point is, it's scarce, so it should be optimally used, utilized in a sense. So, the idea is that there should be an optimal block size that you should, so this is like the amount of gas that we can all fit in a block and we can handle it. Anything more than that, and it's too much for the whole network to process. Anything less than that, and the network is being utilized and that's not efficient, so it should be used better. So, the idea is that you should price, so it should be used optimally, and the way that it's done is basically it should be priced at a market clearing price. So, the idea is that the amount of what you should pay for a unit of gas should be the optimal value such that only the amount of transactions that fit in the block size that you're aiming for can afford this price. So, anything else would be priced out. So, the idea is that you want to select only the most valuable transactions to be included on the block. So, you move the price until you reach that price that only this exact block size makes it through the market clearing price. So, this is a normal like limited blockchain where you have scarce gas like only this amount of block size available. So, the next question is, so how do we kind of reach this market clearing price? There's different approaches to this, so there's like the original Bitcoin approach is just a first price auction which basically amounts to like everyone just makes a guess about what is the market clearing price. So, you submit your transaction and you make a guess this is what I think this is worth and everyone out there makes a guess and those with the higher guesses get through. That's how this works and that's a mechanism that has worked. Okay, but the idea is that this tends to be very volatile and people aren't great at estimating or so you might end up overpaying and the prices can fluctuate more. So, the approach we're using in Falcon and Ethereum as well is this called EIP 1559 where the idea is that as you price gas not just by the amount everyone voluntarily guesses but you have a base fee which is set by the network. So, this is posted, this is the base amount of fee that you need to pay for your gas and you can put something on top which is meant to be just like to cover the marginal costs for the miner to include your transaction. And then this base fee the idea is that this is now not paid to the to the miner but this is burned and this evolves in some like definite algorithmic way with the point being that this is adjusted based on demand on the network. So, in this case you allow the block size to be a little bit flexible but you set a target block size. So, this is the optimal block size that I want and if in a given block I spend too much gas then I raise the price so that the next block will be closer to where I want it to be. So, this adjust the base fee such that the idea is that this mechanism should equilibrate close enough to the appropriate market clear place because these are different options. Yeah, but so this is what we are using about. So, one point is that so this price doesn't come from this mechanism. So, one of these shouldn't this EIP 1559 will not make your transactions cheaper or anything where this price comes from is just from supply and demand. So, these are two mechanisms just to try to find out what the price is but they are not what is used to determine the price. So, the price is simply just supply. So, in a fixed blockchain like we are talking about with a fixed with a target blocks size supply is fixed. So, this is the amount of gas I have for block then if more people want to use this if there's more demand that will lead to a change in the price. So, this is basically the kind of a symbolic equation showing the the dynamics of so if there's a change in demand if demand increases then I should increase the market during price such that I remain at my target block size and if it's demand reduces I should reduce the price. So, one question that we think about for the script economists that maybe many engineers think less about is like do we actually want to decrease the market during price. So, this is a big complaint okay gas fees are too expensive so and so we could tell the engineers okay make it scale everything make it better make the blocks larger so we can feed more stuff and that should give us a lower market clearing price right and and that's something like CX describe is kind of what happened with this hyperdrive that suddenly we have this boom hour now we can process a 35x times the the amount of gas in the block and the question is this something we should do is this a good deal right and then we need to think about what is a good deal right and the reason why you might think that makes sense so let's make it everything more scale up we can include more stuff but but there's something you're losing by by this collaborative which is you are reducing your your market clearing price right so miners were complaining oh why did you make it so scalable now I'm making less money is the idea. So, yes I want to think a bit about so I want you to think about this question of what is a what is a good deal for the network should I always scale or what should I do so one one strictly economic way to to think about this question is just to think about the the total network revenue right so the idea is that if I increase the supply the the market clearing price will be reduced so this is bad right people are making less money but the idea is that now more transactions can get processed such that sure each one will will cost less but but there's more being processed so that's a net good right so so you can think of it so what is the there's this total network revenue it's just like what is the the current price times the amount of transactions that that you could process right so so if I scale and this gets lowered but this gets increased enough that overall this is this increases and that's what I would call a good deal economically okay so for instance this is what didn't happen right away in this hyperdrive that CX talks about so you scaled everything the base fees went down and the demand to reach that level that we needed the 25x is still working to reach that level so that's what could be considered a bad deal you might you might question like is this are we being superficial here that we're just like a good deal is just make more money but for now we'll leave it okay so this this is something to think about but that's that's one goal we could use to to work on but I think it's not that superficial in the sense that so what we want is we want to have like a healthy and very useful and active network and that involves miners being motivated right so so if the rewards if the revenue is good and rewards are good the miners will be there will be more miners or storage providers in our case in in in Filecoin joining and they need to be properly motivating to bring more miners and make it a good network so so so the general principle that I guide myself with is so the supply that we have that we provide by all these scaling solutions should be just appropriate for the level of demand of the network not like much higher than the demand record yes so just a little summary here of kind of a typical scaling solution that that we can see in the blockchain space but it's just like making a roll up or a side net on the on the so the idea is that as if you have your main net your Filecoin or your Ethereum or whatever and this is too expensive to use you can like make your own little side net where you run run your your your sub transactions in there so this is what happens like an exchange or something so okay so this exchange is going to make all these transactions and then only at the end report the the the final result of that to the main net and this allows you to do like more transactions maybe at a faster scale you can do more things without reporting each one of those without using gas on the main net for each one of those right so you only spend it here and then report report only the result after after that and so that has a result so that that so having many side nets like this could take some some demand away from the main net because these are all transactions that could have been performed on the main net but now they don't need to be performed there so that's something to to think about right at one point of this is that right so the side net that you're running your sub transactions in should have a lower market hearing price that the main net if not like user for just go back to the original net but the tradeoff is that the main net is expected to be like the with the higher security and a better reputation right so that's why you want to report back your your your final results to the main net and make them more permanent there so this is the idea so the subnets should be cheaper but less security and less reputation and so on so that brings me to this is a this is a project that we're working with is mainly consensus labs of a project that it's a solution we are working in in falcon that this could be what scalability looks like for falcon which is what they call hierarchical consensus and the idea is similar to to to this stuff about about side side nets that I talk about but just more hierarchical I guess so so here you have your main net which I guess this represents time or something but anyway so here you have falcoin let's say and then I need I have more demand that I can handle here so I spawn a subnet here so it's so you can spawn a subnet and here I process transactions fast and then I am checkpointing total results back to the main net but not everything that happens here so similar to the to the side net roll up kind of thing and so here this one has two subnets but then the idea is that when there's too much demand here also these ones can spawn their own subnets so and then these ones again could spawn their own subnets if they want and so on so so this this idea of of hierarchy of of nets that how they are connected is basically so this this one runs their own sub transactions and then periodically they post their checkpoints back to the to their parent net right so so this interaction subnets checkpoint back with their parents so the subnets remove some demand from the parent but then they also ask for something because they need to spend some gas on checkpointing with the so there are checkpointing gas fees when they checkpoint with the parents and and the rest of the idea is also that the the parent chain should provide some level of security and reputation through to the subnets by keeping track of their checkpoints right so they're saying oh this this there is some more permanence to these transactions and also there will be a collateral that can be slashed to start running this net so so you guys okay so I want to start a subnet if I want to start an official subnet with this parent I need to freeze some some amount of collateral that may be slashed by the parent if I do something bad this idea of hierarchy process sorry right so the idea is so now the the hierarchy is is limitless in principle so I can keep spawning more subnets whenever I need it there's no supply problem anymore all scalability is solved right so there's the idea of this so I grow the the hierarchy grows as needed and then right so if I'm a parent chain that have my own subnets I can kind of pull on them if I feel so I have some controls over my children nets by the checkpointing fees that I charge them and by the collaterals so I say if you want to start a subnet under me you have to pay a higher collateral so if these fees are high it could discourage the creation and survival of such subnets right so so I make it things too expensive for the subnets to to survive or if I want to spawn a lot of new subnets to take care of all this demand then I make these things cheap and they and they can join and then the idea is that while the whole hierarchy is limitless there's no there's no scarcity anymore each of the nets in the hierarchy is a finite net that each each of them should be utilized optimally and then kind of the guiding principle we want to use is that the overall total network revenue of the whole hierarchy should be maximum so growing this hierarchy should be in the name of it being better for the whole hierarchy so that's the idea uh right so so we can think of the model here what what we can do about this so this is the traditional model I I told you about about like a single chain that more demand brings higher prices and this is kind of how how this works in 1559 that this is the market clearing prices adjusted by depending if the block size was bigger than the target block size and I increased the price and so on so this is so this is this dynamic for the simple case and what we're looking at now looks more something like this so this is for a given chain for a given net in the hierarchy so these are kind of the the knobs that you have here you have your your base fee your normal gas fees that you charge for transactions you have your checkpoint in fees and you have your collateral and you should adjust this this fees based on what's happening with the hierarchy so this is the data that you read to to make your choice here how to adjust this right so this is kind of the solution space I was talking about that so right so so each of these nets must keep their they have an individual goal of like keeping the optimal block size for myself for that one given net so there can be a a IP 1559 like mechanism but then also they should have in mind like what's happening with the whole network so it's a collective goal so okay the total network revenue is increasing and if that happened because I see that more so this is the total amount of gas being spent in the network if more gas was was burned in the network and that was good for the revenue then I want to incentivize that more so I would make this fees checkpointing and collateral lower right to invite more growth and if growth was bad then I want to pull back so this is kind of the idea and so you can so this is the main picture here and and this is not the main the solution because we still I'm not showing you like an exact formula like this how to play but there's many options of what we could do that will look like this but this is should come from something like this so so this is my final answer so how large should the hierarchy grow so it should be large enough and maybe a bit larger to maybe induce a bit more demand but it shouldn't be larger than it is to be that's about all I have to say great thanks for that Axel and I think we have time for a couple of questions actually to the speaker um so we'll do this Oprah style if you raise your hand I will come at you with the mic or I will lead with my own question you mentioned that subnets inherent reputation and security features from the main net are is the reputation and security features of the sudden subnets are they strictly dependent on the main net is there any way for subnets to compete on features other than price if you start a subnet without a reason so but what I mean by this borrowing of reputation is just that you check points so you have a level of permanence that you know okay so the users of this stuff that knows there's something more permanent happening in the apparent net and also that that you that you know this subnet paid some collateral that can be slashed if they misbehave so you know they're motivated to behave it could also become the subnet could become more valuable than that by providing a valuable service so you also that subnet becomes a good subnet that is doing something good people want to use it so this is kind of a minimum value that you know that the user knows the subnet will have but the subnet can grow even above these values that is the idea fantastic thanks again Axel one more question over here I'm thinking about like the collective goal of maximizing the revenue of the network and wondering if there's any ambiguity and like how to measure that either what the elements of the network are not just miners computers but humans and like how you measure revenue is it only in file coin or are there other currencies right so this this this I guess this comes back to the question of the superficial goal or not so so the the idea here is kind of I'm assuming so right so the different subnets can actually do different tasks right so let's say we have file coin that is about storage and then let's say you you you make a subnet that is focused on retrieval market and you're incentivizing people who retrieve files and so on they're doing different things but they're kind of what join them what what we can measure them equally with is the amount of gas that they spent so so it's just like on the computational power required for for each of these things so we are assuming that the computational power of all of them can be measured and compared and yeah so this is one simple goal which is okay so this is my definition but now kind of so now in the hierarchy version this would be a sum of these guys over all the subnets in the hierarchy so what's the market clearing price of each of them and the block size of each of them yeah so this is again like a very cold economic target but it's one to to work with could be yeah it's it's also open to maybe this is not the right target thanks again for that answer I think now we'll have to move on and introduce our next speaker also from the crypto econ lab we have tom melin who comes to crypto econ lab from a background in theoretical physics and computational chemistry he spent the last couple of years modeling infectious disease and now he will be talking to us about fair prices for perpetual storage take it away tom cheers thanks very much carilla uh yeah so i'm going to talk about a fair price for perpetual utility so what does this mean so i'm just going to change my screen because we seem to be sharing the percent of you no problem give me one second okay there we are no problem okay so a fair price for perpetual utility what does this mean okay so utility first of all from the user perspective this is the expectation of access to a service now or at some time in the future from the perspective of the storage provider or what you might think of as a minor and other networks utility block rewards essentially are one in proportion to the the utility provided so utility what we're thinking about here on the on the file calling l1 it's data storage which gives a decentralized and robust and efficient foundation for humanities information but elsewhere it could be other things so it could be off-chain compute it could be networking it could be uh tension um but focus here is on data storage okay so perpetual what do i mean by this so perpetual is a pretty high target um i mean keep things in context i mean i think it was like in the 50s that when when shockly got the Nobel prize for the transistor uh you know progress has been absolutely massive so who knows where we'll be in 50 years but as an absolutely serious target we do need reliable long-term storage i mean you can you can see the kind of motivation from this in terms of this societal value i mean if you want to store important information election results humanitarian data environmental records you need to do this in a long-term way and this this kind of relies on securing a chain of evidence relies on immutability and it relies to be able to store on a scale of decades to ideally hundreds of years okay so price price we're going to talk about a little bit i'm going to skip over this slide for now but there are different things to think about there i think um an interesting aspect of this is what what we think of as the fair price so okay sure you can just say the fair price this is kind of the market equilibrium between supply and demand but um i think you know we can do a bit better than that i mean in in order to come up with a fair price you have to be able to give some assessment of what the underlying factors are that determine the price and what this is likely to be in the future and that's not simple at all so in order to come up with a fair price i think we have to be informed and we have to have sufficient models and information to do this so there's fairness in this aspect but there's also kind of a wider point as well so fairness generally has not been the domain domain um of economics as pointed out here by kahneman and taylor but i think to make a kind of wider point we have an opportunity to do something something different here um in general web two economies have been based on this kind of principle of information asymmetry you don't really know how your information is being used um take for example aws for one example i mean it's easy to get your data in there very low cost but getting your data out kind of different story and this this same story is seen across web two but i think with web three we have a chance to do something completely different everything is is it is is written in code in a transparent way and the incentives are are laid down in a completely transparent way that's much more equitable to the different parties involved so i think there is really an opportunity to to have a much fairer take on on long-term storage and other things in web three generally okay so that's kind of the background um some of the things that we can think about whenever we try to examine long-term storage are sure there's the mission this is what's motivating motivating us we want to have uh long-term storage for humanity's most important data and to an extent the principles but with which we will develop this is is motivated by fairness um and and encoded in the crypto economic incentives in the carrots and in the stick in the in the collateral and how this is slashed um and the block rewards that are earned um but some other aspects that we have to kind of drill down into if we want to price something like perpetual storage fundamentally you're relying on people having hardware having disks buying bandwidth having physical facilities so that is something we we have to consider if we're going to think about price as well as other things like redundancy and how we might might use some defi ideas or funding ideas to to structure how um the long-term storage is paid for okay so first of all the basics of the crypto economic incentives um storage providers and block rewards in order for in order from storing information but if they don't store it reliably their collateral can be slashed so you can see here that if we examine the chain you can see how how effective these incentives are so okay sure anybody can have a fault even good miners a hard disk can fail or electricity can can be cut but you can see here the the incentives are effective because for example for this minor sector that's shown I don't know if you could see it well but for this minor sector that's shown okay there's a fault but then that fault is recovered the next day and then okay there's another fault but it's it's recovered again so the the incentives are incredibly effective they're virtually always fixed but there's also sufficient slackness in the system that good miners if something breaks they don't get penalized uh straight away so this is kind of basics of crypto economic incentives and determining these parameters and how big the faults uh fee should be and what the delays are this is something we try to determine through simulation but I'm not going to go into that today I just wanted to set out the basics of the incentives okay so how do these incentives actually um affect the price so one aspect of this is hardware costs and this is kind of an interesting little problem that came up when I was in Las Vegas last week talking to some of the storage miners one of the things they were telling me about is okay we've got these hard drives and these hard drives fail sometimes and they have a warranty and the warranty is three years and they so there's different kind of options they can take they can have a strategy where okay they replace it with a warranty or they just wait as long as possible and let the disk fail um or but you know there's a kind of different outcome from each of these these strategies because if the disk fails then you know it takes some time to replace and then you're open to a losing block rewards and b getting your collateral slashed um so it's kind of like under under these kind of different trade-offs like if you buy the buy a new disk too early then you got to pay more if you wait too long of course you've got to pay less for disks but you've got more chance of having an unplanned failure and getting slashed so so under this kind of situation what's the optimal strategy so one way uh so part of what I'm doing here is I'm not going to give you a price that is $10 for uh storing something forever but I'm going to set out some of the methodologies to think about this so one way you can approach this this kind of optimal strategy problem is to treat it as a renewal reward process so as time goes on disks can fail uh after a random amount of time and they can fail it with different uh they can stop working uh by a different mode so they can fail or they can be retired and whenever this happens uh there's different ways that you can realize costs it can be through having to buy a new disk or the slashing or block rewards so to make progress in this we can use the renewal reward theorem which is given here um and okay so yeah we can use this renewal reward theorem which states uh that the expectation of this process is given in terms of the expectation of the costs divided by the expectation of the disk lifetime so good but expectation over what expectation over some distribution so we can model this distribution as the kind of classic uh failure uh distribution um from reliability modeling which is this kind of bathtub shift curve as you see here now to actually model it we can model it as a stretch dot beta distribution okay fine we can do that uh so now our optimization problem is we want to optimize this functional how can we do this um so we can't do it straight away because we don't know what the expected cost is and we don't know what the expected lifetime is but if we break it up into the different modes of in which that the expected lifetime can be realized uh then we then we can work these things out so if we if we kind of expand that expected expected cost into the different thing different ways that it can occur and we expand the expected lifetime into the different ways that that can occur as well then we've got something that we can easily evaluate and if we do this we can find this optimization problem plug in some numbers okay warranties two years three years whatever and and find an optimal replacement policy for that disk so this kind of feeds into uh knowing how long you should keep your disks for uh informing uh miners and of course it can feed into cost models so another approach we can take to think about factors that affect price over a long period of time again is hardware and so for hardware physical media we have we have price information so we have this kind of Moore's law-esque time series data I mean as you can see here over the past 20 years on this log scale it's it's effectively linear no will this continue I don't know but you you have to assume something you always have to have assumptions in models and if we if we have this assumption can we somehow use it to inform what the price of the file coin dealers might be in the future so file coin dealers I mean they've only been around for about a year so it's kind of difficult to imagine how they might go in the future but of course hardware is an underlying factor so can we use this historical data to inform what the price might look like in the in the future and yeah sure you can so we can make it like a simple generalized linear model and if we do this and we have some partial pooling of the coefficients in the model so we can say okay we're going to let the slopes of the historical data for for disk drives inform what the deal prices might look like then we can we can come up with some kind of trend for the future yeah thanks so we can we can do this and of course we can we get a distribution if we integrate these these forecasts in the future over all of the different realizations from the mc mc inference then we can work at a distribution for costs of storage in the future but of course this kind of limited cost this is not the only factor that affects deal prices there's there's so many other things you've got to think about electricity you got to think about bandwidth as well as well as just a market perception of what the demand might be okay so let's one factor mostly what i'm doing here is kind of setting out some tools and frameworks or ways that we might think about this so slight change of direction another factor is trying to consider how we might fund this so one way to fund it is in terms of a yield bearing token or some kind of bond so how does this work this works so instead of paying for the the price of the storage fully upfront is there some way that we can distribute the the payments into the future using the interest from from a bond for example so it's going to be something that's very stable that produces a yield every year and that can continuously be drawn from so if we if we invest in a bond we expect to get something something like this where the value of that grows over time and stated here now this kind of growth sort of satisfies this simple differential equation but what if we don't just let the bond grow and instead we deplete it over time by using it the interest to pay storage providers so instead we get this kind of little differential equation like at the bottom so now the kind of question becomes okay so if the client who wants to store data buys this bond and uses the interest on it just to pay the storage provider what does this look like so how much do you need to put up for the initial bond in order to to to pay for the storage so this effectively comes down to saying something like okay can you solve this equation under conditions for example like what I want is the bond to pay for the storage and for this to be completely depleted over the lifetime of the storage for example a hundred years so we can solve this we can solve it but only if we make a lot of assumptions so we still need some kind of model for what the storage is going to look like in the in the future for what the the deal prices are going to be but if we assume that the the storage price goes down very slowly then we're kind of got this scenario yeah so if we if we do make assumptions that it goes down very slowly then we're in this kind of regime where okay you've got you've got to pay a lot if you're if you have low interest rates or if you've got a very high interest rate you can pay less and you can work out what these curves are and where you sit on it but fundamentally we have a kind of tricky problem in that a we don't exactly know what the the risk-free rate of return should be and b we don't precisely know what the fundamental costs that feed into determining the price of storage in the future are going to be so these are all things that are assumed but sure we can we can come up with with a model that changes the funding and how other cost is distributed over time but there's there's more to it than that I guess that's what I'm saying yeah so I think those are the kind of key points that I wanted to make today and if you want to think about very long-term storage for sure we should absolutely do this is completely critical and very important and there's a lot of fundamental things we have to think about in terms of hardware prices and bandwidth and electricity that go into determining this and we can make a lot of progress on this and we can make a lot of progress on the funding side as well but I think there's still some way to go but yeah we're going in the right direction so I'll finish it with that thank you thanks a lot for that time for a nice reflection on the difficulties and complexities inherent in modeling this sort of perpetual storage these sorts of long-term questions speaking of long-term questions do we have any questions to the speaker thanks so much that was super super interesting and I'm curious I mean I think some of these things where you're like oh there's a ton of assumptions to consider and and it's just like very hard to do I think insurances do it right like all the time they like model very uncertain events and then there's still some price that they set to determine that you know this is what I'm willing to accept as a price and then sometimes they make wrong wets I'm curious and then our weave exists right and they have I think articulated some price where they're like you pay for the first 200 years with 30 percent of whatever you put down and then the rest goes into an endowment that I guess is similar to what you're describing that then yield some interest and assume some price decline for other things what's your take on and how they're approaching the pricing I know it's like a different model in that there's like only probabilistic guarantees for storage still being around versus in fact we're not deterministic but just curious and like right like our weave has has kind of tried to address that and has come up with some kind of mechanism to price it or curious what do you take us there yeah so it's kind of an interesting question I mean I think there's two ways to think about this in general I think there's a research aspect and looking into all of these details and you can go into definitely a lot more detail than is in the our weave paper which is kind of a relatively simple model and makes quite hardcore assumptions and trying to look into some of this sort of sub problems and sub details is what I'm doing here and that's kind of more of a research question and then there's kind of a more of a business side which is like okay we've got this idea we're going to release it there is going to be some risk associated with it it might work it might not yeah so it's kind of like two sides I don't know if that answers your question exactly but that's kind of what I feel you know you can jump into doing it but it's 100 percent not going to be guaranteed there is going to be a little bit of risk associated with it yeah right so I think you know a lot of this we're thinking having a proof of storage every day right but some of these older data they really probably don't require that necessarily so you know what what are the requirements if you want to really put it in a glacier type storage environment you know yeah so I mean I guess that's kind of an interesting question that maybe touches on some of the kind of deeper protocol level aspects that we might want to think about in the future so that you know currently we have proofs of storage that go every 24 hours maybe for very long term storage that's that's not even something we necessarily have to have you could have proofs that are only submitted once a week or something like that which might change the cost as well so I mean I think that kind of touches on quite an interesting idea for sure well thanks again Tom for a great talk and for giving us again that long-term perspective yeah we'll move on now to our next speaker who will be joining us virtually uh Edward Sheep Edward's a data scientist at starboard where he's part of the network analytics team that designs products and pipelines to support network governance in the file coin ecosystem he's passionate about geometry and number theory but today he'll be talking to us about file coin analytics for network governance take it away Edward thank you thank you for the introduction uh hello everyone this is Edward from starboard uh today I'm going to give a talk about file coin analytics for network governance all right so let me begin by my talk by making a simple observation that file coin has actually become the most vibrant data economy in the web three space with a committed storage capacity over 15 eib and active like amount of client storage deals over 70 pib now one of the key issues arising from such being such a huge complex adaptive system is that how do we properly govern governance such a huge complex adaptive system especially with a case that you know we're adopting a community driven democratized sort of governance framework here so like there is in some way there's really a strong need for an accurate comprehensive and open sourced data analytics infrastructure upon which collective decision making real-time collective decision making can be made so um in particular right um we as a team we've identified a few key challenges uh in transit not only to the file coin governance sort of framework but also to web three governance in general and we believe that you know these common challenges uh for for those data analytics is precisely the key solution to addressing those challenges so let me go go through them one by one firstly observe that each web three network is essentially an island economy that evolves decision making from individuals and organizations with essentially different preferences goals and horizons therefore what this means for the governance team is that they really need to understand how to balance the trade-offs of different decisions giving that agency in the environment basically have different press preferences goals and horizons for instance right say the governance team wants to introduce a follow-up improvement proposal uh we commonly know as fit you know they need to have a very clear understanding like whose benefits whose expenses you know how will this particular fit introduction impact the sort of entire network not only from aggregate but also from actor specific level um secondly you know despite everything is publicly available on chain public information is actually not properly diffused this is a key problem so if if i ask you a question like what is the current state of a network what are some population statistics you know what are some of the micro trends and issues existing in the network um there we need expertise or we need a middle layer to actually extract that kind of on-chain data insights from the blockchain architecture and make them available to the general public uh third key challenge that's observation is that you know governance doesn't have to be emotional and political the key really is to have an accurate and robust data analytics upon which the community can make informed decisions for their shared goals and values all right so uh what i just talked about is basically establishing that file uh network analytics is important for governance but how do we proceed right um how do we proceed go from the first principles into actually building data pipelines that should basically making the insights into product well we start from the following two initial observations uh specific to the following and we actually derive some systematic strategies from these two observations firstly notice that similar to web 2 data solutions cloud going is a data economy consisting of a globally distributed network of servers and clients this implies that like web 2 services like aws or azure we need to track operational intelligences traditional to the web 2 uh data service provisions things like storage uptime uh reliability metrics which is also tied to the fantastic talk talk tom just gave uh we need those like metrics right to to actually properly evaluate the effectiveness of of the web 2 like actually the storage provision service we also need a very clear kyc things like you know who are the top you know agencies that are making the most interactions on the network now uh on the other level right this is a second observation unlike web 2 solutions file coin is the words data center what does that mean is that essentially it's a decentralized layer one protocol right uh basically trying to democratize entire storage provision data centers or data economy services to everybody uh in this uh participants on the network this means that we also need to track intelligence or intel specific to the blockchain analytics stuff metrics like circulating supply gas block rewards right those are the concepts or matrix statistics in uh basically native to the blockchain uh ecosystem that are not traditionally available in web 2 basically services right uh so now we we've actually uh move on from the observations here's our roadmap sort of deriving derived from that uh two observations so here's what we we did and we thought is a good roadmap for making everything happening firstly we need to understand the file coins protocol structure and basically build expertise in analyzing the state synchronized data right so basically we need to uh you know call some rpcs and grab a data out build databases uh next we're going to map the blockchain data to the corresponding protocol features and design specifications right those are available usually in the source code or open source uh in the spec we're going to map the blockchain data to different parts of specific features or protocol then we're going to translate the know house into actionable uh insights charts of statistics that actually support decision making for relevant stakeholders in the ecosystem uh last point is the most important right after we did the first three steps we need to build a comprehensive data analytics platform that provides intelligence across different angles across all of different fidelity so with the principles and methods and roadmaps discussed above very probably we introduced the network house dashboard so this is a our factory product that usually you know basically try to analyze views like analyze the network's house state through different angles all of the things we just I've just talked about discuss the principles and methods it's all either so the best way we're actually trying to provide a section-based user journey where through the different sections we provide a very clear way on how we think uh the governance team should approach uh analyzing the current state of the network and so the best way to summarize the product this is a one-stop comprehensive intelligence platform that provides not only current but also historical data analytics for the ecosystem especially for the governance team so let me just briefly give a tour introduce each subsection and the information we capture so we're looking right now at the first section which is the which is the capacity and service this is the place where we track you know information related to storage provision on the network we ask questions like you know for storage state right what is the active amount of storage currently existing in the network how many sector commitment storage capacity commitment has been sort of uh committed on a daily basis we also track uh reliability indices right um the the stuff tom just talked about you know if there are thoughts happening right how quickly are those thoughts getting recovered uh what is the average fault time distribution overall on an aggregate level uh for the network we also track uh service provision economics which tells us about you know if i'm a storage provider i want to you know put a storage into this network you know what is the rate of investment versus return what are some of the current block rewards i'm going to like get if i if not now i want to like basically commit so um moving on we have the next section which is the circuit in support right this is this actually tells you about the token flow how many fill uh let's say for for example gets mined how many fill gets vested how many fill gets locked or burned on a daily basis and you also you can also do data drilling right that's the purpose of of the entire uh the process you can understand you can basically read about the component breakdowns for the aggregate statistics that you find interesting for instance you can have a uh detailed look into the different components of fill locked the amount of fill get burned all of those like detail breakdowns are there according to the protocol specification so moving on we have storage demand and deals right this is the demand side of equation here we track stuff like you know what is the deal inflow what is the deal outflow current and historical we also track stuff like you know how do we do pyc uh even in a relatively anonymous environment you know we want to identify the top adversities that are making the most deals uh out within the ecosystem uh next is exciting topic network usage and gas uh we track uh you know what what is the current uh base fee and and also tracing back to the like past 24 hours uh corresponding to the eib equivalent to the eib 1559 uh you know what is you know we we kind of see the base fee right we also want to understand you know if there is a let's say if there is a base fee spike what is the gas uh usage that actually contributes uh basically can be attributed to that particular base fee spike uh so this is where we put things like gas usage breakdown by methods telling you a volume comparison we also track stuff like aggregation right basically aggregation is a stability technology innovation uh the team has made um the PL5 team has made uh for approval storage you know want to track you know once this innovative fantastic technology is out how often are people actually using this storage provider actually using this to reduce the amount transaction cost uh so i've just talked about like a lot about the structure the theory the principles let me just give a particular use case on how the sort of product such can help with like web three governance so um this is a real real life use case so in the past few months starting january 2022 there has been various initiatives in the five point ecosystem focusing on driving five point plus deals adoption uh and in basically increasing the network utility for those of you who are not familiarized with the term five point plus deals are deals with a better deep KYC and tighter identity verification uh so let's just imagine that if i'm a business analyst and i'm asked to write a report on the initiatives uh trying to drive a five point plus adoptions but at the same time i'm asked to quantify basically quantify the impact of the various strategies uh developed to drive the adoption here's i can how i can use the dashboard next page so the first place i'm going to go to when i wake up in the morning uh is i'll go to the deal storage deal section uh in the in the dashboard and i'll observe that this chart called newly committed deals this actually tells you you know how many deal gets flow into the system on a daily basis this actually tells me as a hypothetical analyst that you know over the last three months five point plus deals has actually taken over majority of the deal growth as you can see you know by the green amount of green area presented in this chart and it's not actually five point plus deals now accounts for over 70% of all of the active source deals uh outstanding so um you know i'm a curious analyst right i don't just want to stop there i want to go to the other different parts of a protocol and basically build a multi-dimensional analysis this is where i can go check into the storage uh provision section and see that you know uh doing the past months or so we actually also see some uh spike in sector onboarding activities which tells us right like there is a event correlating with a higher deal adoption is that actually uh more storage providers are thinking about you know it's either extending or like joining the ecosystem to provide like a more service provision coming meanwhile if i look going to the gas and usage section and if i analyze closely the daily network fee breakdown i'll also realize that another thing correlating to higher deal adoption is that the network is actually getting busy again uh judging from the amount of like fees activities which is a direct indicator signal telling you how busy the network is uh next so if say our analyst right uh is not satisfied with the overall statistics or trends and wants to do a more careful KYC well i can just then track the actual highlight and basically identify the top addresses that have the highest amount of participation so here on the screen we're seeing well what are top 10 clients uh classified by verified deal bytes also for the top providers actually actively taking five point plus deals uh okay next so uh if our analyst wants to further drill right wants really to get to the bottom of this uh he or she can track basically the individual client page and identify the protocol specific behavior and patterns uh this is actually really incentive design right we were seeing here on the screen is a three-stage uh basically uh KYC funnel this is called five point plus verification protocol in which there is a note three who allocates uh who sort of approves clients initiatives to actually you know store verify storage deals in the system so uh we thought about you know how can we capture the transaction patterns in terms of graphic based method is graph based method is a great way to basically concentrate our analysis on and basically build a local level transaction graph analysis for each of the client existing in the system so if our analyst wants to dig further that's where he or she can roll all right um open source insights you know we're in web three all of the insights are supposed to be open source right remember I talked about you know not all of the public information are properly diffused but now that we have done the like the middle middle layer work in between we can just build open source these things like for everybody to participate in the ecosystem so we're currently testing we're actually released a bunch of but we have like very positive feedbacks from people's from Masari basically we're open sourcing insights in terms of data field notebooks and charts onto this platform called observable HQ so for everyone who wants to build their own analysis they can just treat us as basically a do an analytics type of style of like api and just download the data do their own analysis so exciting opportunities uh we like as I said right five point is such a fantastic ecosystem there are so many uh aspiring challenges data science challenges uh the engineering challenges like crypto cryptography challenges we can do but some of the stuff are more related to my line of work is we we have tons of visualization modeling analysis uh just as tom just presented right there's tons of problem in terms of intelligent sector reliability profiling engineering uh we have the data available and just about how we can build the best model offers there is also questions related to computational game theory incentive design evaluations and evolutionary games and also so I just gave a quick snapshot that we can do a lot of transaction graph analysis on this sort of open public chain uh as well so I'll also stay tuned for five point virtual machine which I believe I want the next speakers will get into more costs thank you all please we're hiring so you can visit us at our website and contact us and I just you know hopefully hopefully more people will join in a collective build a fantastic ecosystem thank you wonderful thanks for that Edward for that really this is very exciting opportunities for anybody who's interested in data science or data visualization definitely that last slide about exciting opportunities very true we have time for questions to the speaker so I love the dashboards that you guys set up and can you talk a little bit about how people are using it already like are are you seeing some good responses are people engaging it with it are they building on top of it I'm just curious what kind of interest you're seeing from the community in these kinds of visualizations and insights uh yes okay uh so a couple of examples right uh basically we have seen basically uh people inside ecosystem right mainly the governance team I I actually know like a few uh stakeholders on the governance side who check out dashboard on a daily basis and uh there's a lot of stories how uh they begin first observing a couple of changes uh in the in the charts they find interesting and that ends up in the ecosystem wide uh discussion on you know what is the current thing what is the top like strategies the entire system to focus so one example is we've actually throughout dashboard uh one of our a few stakeholders has actually realized you know while there's a lot of sector retirement or exploration taking in starting January and after after they saw that particular signal change on the dashboard um they sort of very quickly responded to it and just you know uh start building more initiatives on five point plus deal adoption and also sector high onboarding rate this is a I think is a perfect example illustrating this we also have external adoption of people outside ecosystems uh for instance Masari when they write the first stage five point analysis analysis report they were using our like observable sq api to build their own analysis yeah any other questions while you're thinking then I have one actually about consumption patterns of your data um you mentioned that you have both current and historical data available for the falcon ecosystem do you have a sense of how long data stays fresh are people still making a lot of requests for data from your mainnet launch is it mostly closer closer in time that's actually a fantastic question you know uh right now the main there's like a year and a half right since the mainnet launch and we've actually seen a request from stakeholders let's keep everything historical now now as the ecosystem develops right as the network develops there will be the amount of the amount of like data the data size right plus duration is going to be a challenge for us so we've actually been discussing internally how our engineers we can you know basically design a good ux experience for people you know for instance right when you guys first arrive you will see like three months or six months snapshot of the charts you'd like to see but there are also options uh on the on the on the ux side that supports you know if you want to see the full historical we can also show that yeah but so far given the short time frame everybody's like let's just do historical great thanks again Edward any other questions to the speaker before we move on seeing none then I'll thank Edward one more time for a great presentation and introduce that's right applause our next two speakers we have a tag team coming up that's right things are getting more and more exciting momentum is building Jamshid Shorish and Andrew Clark Dr. Shorish is CEO and founder of Shorish Shorish Research and a senior advisor to the Uberling Corporation a technology firm which developed Voson the virtual observatory for the study of online networks he also works with the australian national university's Voson lab where Voson is used for research research tool development teaching and training Andrew Clark is a data economist at block science with a background in financial and technology auditing with a focus on building machine learning auditing systems and together they will present an empirical study of file coin gas and some ongoing research I'll turn the mic over to Jamshid and Andrew great thank you very much for the introduction and welcome thank you for the invitation also to to zx and alex it's a pleasure to be able to present some of the extent work that block science has been cooperating with and through the crypto e-con lab my colleague Andrew should be online momentarily but in the meantime I will go ahead and just kind of give an overview of what it is that we're doing from the block science side of things and how it applies to two extent research directions that we're working on one of them having to do with the gas dynamics of the file coin network and the other one through a pass through of a simulation framework that we have into an assessment of a particular type of protocol update around the batch a batch fee and the batch balancer so we'll be looking at very quickly in our time available today is to start with a section on gas dynamics where we'll be looking at the way in which data was surfaced how that gas usage was decomposed into its pieces and then extrapolate using using that particular decomposition to understand and inform how it is that we can extrapolate forward gas usage given a particular time series of the network that in itself is extremely useful for building out a simulation framework that actually goes on a macro level step by step through the file coin network and acts as a digital twin digital twin which is built in the CAD CAD simulation framework of block science is one of the workhorses by which we can understand how for example different types of activities that may have occurred or different types of scenarios that we would like to know what might be happening can pass through the network and allow us to have actionable insights utilizing both the digital twin and the gas dynamics analysis will then go into a very quick application area which has to do specifically with the batch balancing system where we'll look at kind of what batch balancing is in a very quick snapshot and then kind of wrap up with extent research on utilizing the the framework of the digital twin and the gas dynamic systems to move forward into actually selecting among a very large class of possible batch balancing frameworks one which may meet the criteria that we would like to be most interested in for being able to assess when it is optimal to aggregate different messages and when it is optimal to keep them as separate messages so that's kind of the overview of what we've got for moving forward I'm checking to see now whether or not Andrew is available I'll do a quick check here otherwise I will jump in and take over a section ah he's coming online now very good okay Andrew I'm going to give the floor over to you I'll continue to drive just in the interest of time to make sure that we have ample time available for the rest of the talk okay that sounds good thank you um so what we started with was it looks like it's between slides oh perfect um we started with an exploratory data analysis around the gas mechanism to understand kind of how what was driving gas usage and and gas limits and other things and understand a lot of the key components to drive it um we did a lot of different methods and different aggregations um you know do our own blocks or do we do it by seconds by days um and tried to see um how the how the trends were happening um all of that kind of information to really understand what the key drivers were so the types of analysis we did if you go to the next slide please was also checking the change of actor methods we created our own data dictionary based off of what the storage minor five means for instance so we went and through the file coin code and figured out what those things were so we could see the percentage change in message counts for instance between different times like this right here is a weak percentage change between uh week of september second it was an example of this storage minor seven had a massive increase in the number of messages over this course and we did these types of analysis to really see what we're driving during in combined with macroeconomic variables we also use things such as Fourier transformation which is on the right hand side here to disaggregate the data into a frequency domain um and be able to see what the components were and see the trends we can start and then we did phase shift analyses and and things like this all to try and understand what were the key drivers so we could understand the intuition for building um predictive models off of the system so it as we're going to get into the digital twin and how that whole system works um we wanted to make sure that we could understand what the key signals were so we could drive specific models to be more predictive as we get into system identification which we'll we'll get into in a little bit for how we're forecasting states forward as the different actors are are interacting and getting very accurate gas usage from the bottom up approach one of the main techniques we use if you go to the next slide please um was something called uh grandeur causality which is a very interesting method that we used to create our vector auto regression models by what what grandeur causality does this allows us to understand if certain variables um from a var model which is uh it's just an auto regressive model with you determine the number of lags and you can have many different factors what this heat map shows here on the right is we can determine like at the top here is base fee burn does that cause um one of the actor methods to have a higher count and what grandeur causality does essentially is it's it's a way of trying to infer the causality of does this variable cause this other variable based on the on the lag from a var model so what you can see here is based off the the p values and the statistical um relationships the green are the variables if you intersect from uh the column in row you can see which variables are have that factor so what we were trying to do is based on the time period we had which is one of the key considerations we'll get into in a moment based on we were using the sentinel filecoin database that is truncated at specific periods of time um so we don't we didn't have a massive back history especially if we're doing daily data for how these different methods classes work um as we're the most likely as we're looking at it like an operational digital twin to drive business insights we kind of want daily values for these things um we had a dimensionality problem of having way too many um different actors and their different gas usage it was way too wide of data for the amount of rows we had so that's a dimensionality issue where you it's had as a very hard time training models traditionally we would use something called a var max model where it's the vector auto regression is the endogenous variables what we're trying to predict would be the gas usage of these different actors methods and then we pull in macroeconomic variables as the exogenous um in this case the macroeconomics would be you know base fee burn and some of these these higher things if we're trying to predict and all the different signals from the filecoin network minor penalty pledges that kind of thing or anything that we think is relevant that we've can derive here um to be able to predict what this gas usage would be so because we ran into this difficulty and you can actually see on the left hand side here some of our our plots of the end model we ended up with we're not going to go into all the specifics of how we have it we have a little bit more information the appendix we can definitely follow up offline we use something called a uh if you go to the next slide please um a var x model that dr shor came up with is which is essentially by using this grander causality we can understand what the key macroeconomic variables were and then we ran them as a normal var model instead of this normal var or max model and then this allowed us for based on this analysis as well we found what were the key drivers because there's so many different actor method classes we only need some of them to be doing in um you know parado principle for doing an accurate digital twin that's performant um and we can see here what the different gas usage uh per day from these different accounts um so we use this this this method that was faster that got around the computational limitations allowed us to build this integrated digital twin that allows us to get the previous state predict the next state from our operational DT as we were building this out um uh you can go to the next slide um we started running oh um yeah that works so we started running into issues as we're building into the digital twin um issues with data uh long longitudinal data for back testing and things like that nature um and be able to really fully build a model we needed a longer period of time of the data uh and we were with sentinel was truncating that data um as the operational digital twin you know we want to perform health metrics we want to inform decisions we want to semi check behavior expect behavior we need a long period of high fidelity data in order to do this um and this was the goal of all of that analysis we were building towards was being able to make this gas dynamics for extrapolation digital twin that helps make decisions for for the power point network and helps doing parameterizations and things and when we when we started really taking that initial analysis we did which we knew we had limitations based on how far we could go back in the data and then based on what the goals were here um if you go to the next slide please we created this operational digital twin infrastructure uh ourselves we're basically what we did here was we went back and got all lily data fields we got um all these different data sources and and sentinel um because of we lacked the the fidelity um and and the longitudinal data that we needed for our our analysis so there is a Filecoin did also have an internal research database but didn't quite have the right fields of things as the different aggregation layers we're using um and and all of the different uh signals we needed based on our our EDA analysis of the different macroeconomic signals for the for from our Rx analysis with that that heat map with the that we showed we had to build our own internal um infrastructure which is now up and running that that allows us to basically every night batch data from um sentinel after we back filled with lily we then can use sentinel and then aggregate into our digital twin um data fields and aggregations we need so then when we have this operational digital twin that's used by Filecoin stakeholders they're pulling directly the refined data fields from the block science RDS here that's a lot more efficient than trying to ETL and do all of these analyses each time because the code that ETL from the raw sentinel production parsed messages for instance into the the daily level or epoch level uh gas usage data that we needed is a long process and takes a long time to run so we've now ETL all of that um here so we can have build this operational digital twin with the health metrics and the ability to do what if analysis and counterfactuals and parametrizations and have that into a solid state so I'll hand over here back to Dr. George for for going forward I'll talk about how some of the gas dynamics then moved into the batch balancer right thanks very much Andrew uh so what we're going to look at now is a very quick application of this uh digital twin infrastructure with the gas dynamics extrapolation exercise built into it uh in order to understand one particular feature of the the Filecoin protocol uh that has the potential for being uh for being updated for being updated so this is an active area of research we're currently working on on actually building out the the functional form representations that we're testing in the digital twin just as a quick overview of what the batch balancer is doing what's happening in this particular environment is as many of you know that there is the opportunity for messages to be aggregated or batched in order to be able to to save gas and in particular for the largest gas usage messages of pre-commit and proof commit messages for for the proof of replication the intuition is that as the network use becomes high then batching is incentivized in order to improve efficiency and the mechanism by which this is being done which is raised initially in in 513 and then and then updated once in 524 is to have a batch fee surcharge with two different degrees of freedom one of which is a batch balancer which enables individuals to make a trade-off between what the base fee would be for a single message submission and what would be happening if you actually aggregated things together and on the other side a batch discount which is a way of incentivizing a little bit of a shaving or a little bit of a haircut on that on that surcharge depending upon how the system is performing and in these two particular degrees of freedom what we're going to see is that currently these are being set at once and then updated through governance and what is being investigated is whether or not we can actually endogenize what are both of these to respond to network conditions but just to give a very quick idea of what the file point batch balancer would be set up to implement in this particular context we would have a pool of messages that are coming in from storage onboarding as you want to have these sectors being sealed they would come into a batch decision the question of whether or not to aggregate then depends upon the state of the system at that time the base fee that is available and then what the batch balancing surcharge would be then it would be a decision on batching or not batching if you do batch then you get less gas per message but then you have the batch surcharge which is applied if you do not batch then you send messages singly you have more gas than per message but then no surcharge and they lead to different types of outcomes on the the stress let's say of the of the network if you aggregate and batch things at once then you're able to accumulate you're able to use less gas and utilize less of the network and there's a lower chance of network congestion whereas if you have individual messages that are singly being submitted if there is already a high level of network utilization then this will add to the burden to to congest and so the idea from the batch balancer is that if you have for example high network use then what would be chosen in an environment where the parameters of that batch balancer are already being specified is to say yes I would prefer to batch in that situation collect everything together sort of use less gas and then lower the network pressure so this is from the point of view of the network what would we like to see and of course we want to ensure that there's an incentive underlying this for each of the storage providers to do so by contrast if it was in the low network utilization environment we would say well it's not so interesting then to batch let's not incentivize batching as much let individuals use the network as in single messages and then perhaps raise the network usage and so we ended with kind of an equilibrating regulator system that's put into place but it's predicated upon those two degrees of freedom that I mentioned the batch balancer value in the batch discount now how those are updated presently well those are updated by governance and by introducing a fifth so as we move from 5th 13 to 5th 24 what's being introduced is a change to one or both of those parameters on the basis of what has been seen in between the two points which these have been last updated so we look over the interval and say well maybe it makes sense now that we increase the surcharge by a slight amount or perhaps we decrease the discount by a certain amount this is a way in which you can use expert knowledge to drive the update that occurs but of course it means that you cannot respond in the immediacy of a change in network conditions because you have to wait until you have an appropriate amount of time for review in the fifth to be accepted so by contrast the idea here is the active research we're looking at is to see whether or not we can put into place a functional form representation that explicitly depends upon but with state of the network and then dynamically adjusts one or both of those parameters and so if there is actually say high network utilization that this dynamic batch balancer would then take that into consideration and then adjust in total that batch surcharge and influence the incentivization of individual storage providers accordingly so by introducing a dynamic batch balancer framework you let's say not necessarily eliminate the need for having governance because you may want to change the form of that particular mechanism depending upon its performance but it does mean that you don't have to change those parameters every single time through a governance process the regulator system now has been endogenized so the challenge of course is to create such a dynamic batch balancer that responds to network behavior autonomously while continuing to incentivize batching for the storage provider when network usage is high and our current research agenda is to assess a parameterized dynamic batch balancer functional form within the digital twin the operational digital twin of the file coin network that Andrew introduced the goals in that case that are to select using the digital twin and understanding different scenarios a parameterization that is informed by simulations that we engage an employee for various storage on boarding rates or various types of demand activities for file coin services and that assists then in the recommendations for a fit to implement the dynamic batch balancer upon conclusion of those simulations these are simulations that are ongoing at the moment and finally the idea here for the batch balancing is to incorporate all of the information that we can about the existing network that is to ensure that we understand not just the laws of motion about the system as a whole but actually to understand what would the system like to have so we know for example from a storage provider's point of view they want to make a batching decision predicated on cost but from the network as a whole optimal batching is predicated upon network efficiency there's a trade-off between the network becoming too congested on the one side and not having enough of the let's say protocol revenue of the of the gas being burned on the other so the digital twin implementation allows us to model the entire network as a macro system that selects message batching in the simulated framework based upon network use and the gas usage from message traffic according to this batch balancer functional form so we dovetail in the gas dynamics on the one side into the digital twin on the other conditional upon this functional form examine the scenario simulations that occur and by using different metrics assess which one or another parameter constellations are optimal for that particular implementation of the functional form of the batch balancer that we would be suggesting so by the simulations we actually look at various storage onboarding scenarios high storage onboarding low storage onboarding high network congestion low network congestion to understand that impact of the different message traffic rates and we combine those scenarios with the gas dynamics laws of motion that Andrew Hitch uncovered in the first part of the talk to be able to run those simulations conditional upon as close to an understanding of how those gas dynamics are influencing things like the burn rates for different messages as possible in order to be able to combine those two together we then perform what is part of the engineering design work process parameter selection under uncertainty to be able to figure out a range usually of parameter values for which the dynamic batch balancer can be then recommended okay i think that's everything that we have the time available i know we went through fast fast and furious for different research opportunities but please thank you for your attention and naturally if you want to have any more information you definitely contact box lines and either of us individually so thank you very much great thanks to gem sheet and andrew for a great illustration of the power of the digital twin model for modeling and simulation and for a nice illustration of the relationship between empirical observation and mechanism design in the file coin ecosystem we have time for a couple of questions to the speakers i'll run up to you like opera don't be shy okay i'll get it started then i noticed in one slide you made reference to an intuition that you had about the system you were modeling when you were describing uh when you're designing the batch mechanism batching mechanism where um where do these intuitions come from are there any real world or digital systems that you find are fruitful sources of intuition when you're building these kinds of models and when you're doing these kinds of studies that's a great question uh so some of the kinds of environments that we look at are our control systems whether or not we're looking at open loop or closed loop feedback systems a feedback system which says that we would like to respond to a particular state of the system by taking an action that reinforces a criteria such as stability or a minimal set of volatility these become ways in which we can kind of understand the trade-offs that must exist for an individual of their own volition completely of their own choice to make decisions that affect the network as a whole in a positive fashion and so it is of course a long-standing and open question about whether or not individuals when doing their own thing will do things such that they don't end up working against the community as a whole so there is a lot of tension when you're trying to aggregate up from a micro decision to a macro decision the idea within the context of the the batching system is to say we want to make sure that people understand that they can batch when it suits them at any time but that we would see on the margin that they would decide to batch more often under conditions which benefit the network for example under conditions of high network congestion there is actually an empirical fact that we have individuals who like to batch even when there's absolutely no network congestion they batch a certain amount all the time maybe thinking it's just simply an efficiency gain even though they aren't necessarily earning any kind of savings from this because the network level is so low the base fee is so low this may call into question strategic issues of whether or not they are looking forward and saying oh maybe i'm actually going to change perhaps my my impact today changes the base fee in the future and therefore when i actually have my time tomorrow to actually engage in mess traffic i actually save money on that and part of what we're investigating in this macro model is to actually model those types of strategic decisions which are motivated from game theory but are built into a macro model of the trade-off between the current benefit and the future benefit so this is one of the ways in which we utilize that that intuition behind driving the micro level to the macro level to build such systems thank you for that we have another question what kind of changes to the system would make it way easier to measure a lot of the things that you want to measure and make it easier to either run experiments or simulations and so on i imagine a lot of this is sort of rate limited by the ability to like do experiments or design different kinds of potential systems and so on are there any kind of changes that could come into the text like itself to make it easier um good question i i'll defer also to to andrew if he has an answer to this from from my side it is the more information that you can get exposed about the distributions of things that occur in the system the better a lot of the challenge from from the data side is to be able to build let's say a model of the distribution of the things that you're not totally under your control so exogenous effects for example on the tighter bounds that you can place on that so the greater the data fidelity is to be able to run some sort of a parametric or non-parametric estimation the better off you are because then you can close the error bounds a little bit over a wide range of different simulations that you might have but let me also defer to to andrew if you want to add something to that it's a great question yes completely agree with with what you just said the other thing we we are working on to make it so it is more accessible and easier to use for the analysis on the tech stack is moving it to docker images because there's setting this type of thing up is a lot of different dependencies and things so one of the next steps that we have in the operational digital twin is like we have the data already set up in a specific way we can keep keep making it so we can do the different distributions and as many as many pre-knowledge we have but also the ability to then run this from a docker container without the full setup allows us to crowdsource a little bit more some of the analysis versus like if if we're like here here's a repo with all of this code it's going to take a long time to set that up versus like if you have docker we can package all this so then you can go in and change programs and do more experimentation so from a tech stack perspective specifically moving to the full digital twin to docker will definitely and aid the crowdsourcing of of analysis great thanks and do we have any other questions to the speakers I think in the architecture you showed like a lot of the data originated in a like sentinel database redshift or something like what would be the tradeoffs if you were sourcing that data from um uh Filecoin or IPFS directly or would yeah would that help at all or hurt and that's that's the key thing is so we also used a lily which is our s it was s3 dumps of data from actual Filecoin chain we used a lot of that as the basis of our backfilling as well for the digital twin a lot of times we're looking at a little bit more of an aggregated view then specifically from the chain and internally at block science we are looking at creating a system where we can basically etl directly from different blockchains so at that point we could actually go from the ethereum IPFS um Filecoin system but for the if we're doing operational dt for making decisions about how to structure the economics going to that level of fidelity versus relying on like an aggregation that's even epoch level or or daily or even second level that kind of data is more what we need for operational dt so at the moment it's not going to create a for the amount of additional work to hit directly from the ethereum chain is not going to outweigh the costs because of where uh it's not going to create the benefit based on on what the aggregated data we need but definitely is something we're long-term looking towards the the closer we can get to the actual data and then have an aggregation layer the better and that's a long-term research project we're working on across all block science clients great thanks again to Andrew and Jamshid for that presentation and we're right on time to move on to our next speaker Nikola Greco heads crypto net lab at protocol labs where he works to build technological empowerment through providing secure building blocks for web 3 technologies today he'll be speaking to us about his work on the Filecoin data retrievability consortium welcome Nikola hello everyone it's a pleasure to be here i am actually representing iran and it was meant to give this presentation but uh due to last minute issues uh she couldn't join and uh this is most of iran's work and i'm here just to uh present this uh right so the key topic of this uh presentation is how can we guarantee retrieval from decentralized storage networks there are several storage networks like Filecoin which do offer retrieval but not a retrieval as a guarantee and we will dive into this more and it's very important that the retrieval the retrievability of files from decentralized storage network must be web-scaled so we can't just have a small decentralized storage network with a small amount of file that can be retrieved anything on the web should be able to be retrieved and also this allows not just the unbounded retrieval of storage but unbound unbounded onboarding of new um of files in our network uh we're going to cover four topic and talk briefly about kryptonite and then about data availability and friends data retrievability the topic of this conversation and then our one of our proposals uh we if we work on data retrievability uh it's because it's we believe it's important but it doesn't mean that uh the other topics that we will look into like data availability and proof of storage are not important we're also working on this uh for context kryptonite is a uh cryptography applied research group where we do uh fundamental crypto research and vector commitments snarks and so on but also protocol design and our goal is to have our group of researchers to collaborate with several other researchers to grants or collaborations and out of these collaborations we will have new researchers dows new projects and so on uh in through time we published several papers in the academic world related to uh proof of storage uh consensus protocols and so on but also we made the substantial improvements to the file calling network uh some of the most most notable were snark by co snark deal and one of the things that we also do is uh we gather a lot of ideas and start conversations on potential new protocol design and all of our work is public all the work all the work that we do with other organizations is public and you can see our kryptonite notebook which is where we post down ideas the moment they come to us some of these ideas like the one that we're presenting uh make into products as well so let's talk in high level about the protocols of the availability can be divided in two steps one is dispersed and one is retreat dispersed protocol works more or less like this uh the node that wants to distribute the data distributes to uh several nodes majority of the nodes approve that they have seen the data and then the protocol can continue if majority of the nodes don't give approval of the data being seen then the protocol holds or wait until uh there is a disapproval why because it's very important for data availability protocols the data is distributed and the data had the chance to be distributed to enough nodes so such that when we want to retrieve there is at least one of n in some protocols or m of n in other protocols uh available nodes honest nodes they're willing to serve the data this is perfect for sorry this is perfect for roll up data or blockchain data because we this data doesn't have to be preserved for a long time it can be proven and we need to make sure that at the moment of distribution not only a few miners saw it but as many nodes as possible but there is some negative aspects of data availability as a theoretical problem one is that it requires honesty assumption the node will serve the data there is in most protocols there is no guarantee that these nodes will be will be willing to serve the data later on and the second problem is that it requires consensus on dispersal which means that um there's a limit of the throughput of new data that can be dispersed in a consensus protocol why one of the core properties of a consensus protocol is that data gets distributed well enough so that everyone can verify that something has happened the correct way and in fact most of these protocols uh aim at one to four megabytes per second which is unfortunately not web-scaled if you want to uh if you want to do a storage network or or guarantee retrieval a reminder the retrieval is not guaranteed in these settings then there is another approach which is the proof of storage approach which is the one that we mostly use in phycoin and many other protocols use this as well the idea is that the node distributes the data to as many nodes that they feel that they're willing to replicate the data to this works in phycoin and it's different for other protocols and then from this moment onwards storage providers generate a proof on a daily basis in the case of phycoin to prove that they kept on storing the file but even in this case there is not strong cryptographic guarantee that the file will be searched the way we guarantee in phycoin of course is that there's always a we believe that if you distribute your file in a with several storage providers or if you pick storage providers that do the reputation then these storage providers will do a great job and and and that's how we get that's how most protocols guarantee that people and this guarantees on board the throughput because we don't have to have an agreement or like a consensus over consensus on the fact that a file has been distributed it's the role of the client to make sure that the miners the storage providers have received the file um so the durability is trying to solve these two problems unbounded um onboarding and the guaranteeing of retrieval which doesn't rely on assumption of one of M nodes are honest also most of these protocols are not really uh designed uh for providing retrieval long term and while most of the previous uh most while the availability uh it's great for solutions are great for rollups they might not be great for say NFTs if especially if we think that NFTs may be the standard for any digital asset then one to four megabytes per second is not going to be enough plus we don't want our data to be pruned in the future we want to make sure that data can still be retrieved uh later on so can we have a proof of delivery so someone gives you a file and I have a proof that they can give me a file otherwise I would prove that they cannot give me a file well we can't really have this without adding other assumptions if we were other assumptions so for example there is a third party that checks that these exchanges been done then that would be perfect uh but without trusted party assumptions then this is not possible so let's look at some attempts on how we go around um guaranteeing retrieval one way the naive way is to use the blockchain as a weakness so if we if we assume that data in the blockchain is always going to be served uh by nodes in the network then what we can do we can do the following we ask data to the storage provider the storage provider doesn't give the data to us we force them to post the data on chain now clearly we can post a hash on chain we can't post one megabyte file so this will never work for files there are or data there is a gigabytes big so this this technology so storing data on chain doesn't work for large data the other alternative idea is proof of retrieval is a beautiful primitive that as you query the storage providers to give you proof of the retrieval they leak data but it takes a very long time to leak a full file so the idea would be to keep on querying the miners with proof of retrieval until they give you a full file this works for small data this doesn't work for large data so the alternative is to have a trusted single party but that wouldn't be a deal and can we have a trusted third party that is decentralized and this is pretty much the approach that other oracles take so for example chain link as a network of users that agree on the price that they see across exchanges for bitcoin and in a very similar way we are proposing an oracle which says whether or not a file can be retrieved or not in high level it works like this so the node asks for the retrieval to several storage providers none of them gives them the file and then the node goes to the oracle that says I didn't receive the file go check if you can receive the file the oracle goes and check and if they don't receive the file then they slash the storage provider assuming that there is some collateral that the storage provider must put in order to participate in this protocol if they receive the file well then the retrievability oracle has seen the file and the retrievability oracle can serve the file back to the node and this is in high level one of the simple solutions that we're proposing the there are many big questions we will not cover in this conversation on how can we make an oracle these are true through telling incentivize the network the MVP which we will show at the end is having the oracle as a network of referee and the clients and provider agree on our retrievability deal you can think of this almost as an insurance contract on a retrieval of a file when the client requires the request the file and it doesn't get it back then it appeals to the referee the referee or the oracle the referee committee then we have a particular protocol where instead of putting the entire committee we query a subset even a single node as a single committee node they go and check if they if they can retrieve the file if yes they serve it back if not after k attempts of different referees then the provider is penalized this is the state machine of what i just discussed soon we're going to have a very nice diagram but we're working towards a prototype that is going to show in the next few slides there are other several steps in the protocol which is to sign up the provider must sign up we have a sampling of the referee which follows a particular protocol and then the retrieval deal the goal of the retrieval deal is that it can be possible and change the beauty of this protocol is that it could be cross-chain also the appeal works very simply in the in the as i described before and there are two retrieval two retrieval steps the retrieval step number one is that the leader doesn't get the file and so if the leader doesn't get the file then they propagate a message saying that they didn't get the file and and and they go to the next step if the leader sees the file then they share the files to the rest of the to the rest of the committee and the majority of the committee signs that they've seen the file and that's how we skip posting the file on chain we just sample a smaller a smaller network to check that the file is being it is available and propagated and it can be witnessed if the oracle can receive the file this means that the file was there from the storage provider it can then be propagated to the to the client if there is enough if there are key messages where the file was not well distributed then the storage provider gets lashed and you can think of this slashing almost like an insurance contract where this the retrieval ability deal in the retrieval ability deal the clients puts a premium that the money will get if they're always honest at the end of the period and when that one deal expires and if the deal is still active and the guest and the file is not being delivered to the client then they get lashed with a prototype of this smart contract and it's we we deployed this smart contract as three days ago and it it's live on on Ethereum Testnet and you can create deals and the way you create and you can create deals you can see where are the live deals you can cancel create them withdraw funds from contracts and and there's a slashing process and right now we have a small set of retrieval nodes that are part of the referee notes that are part of the comedy and this is how it's going to look like and this UX is just for the MVP and anyone can put any FFAC ID in here they can put what is the value of the deal the premium and then what is the duration of of the deal and so for example someone wants to make sure that they can complete the deal throughout the course of a month and then you will be able to manage your deals deals will deals are stored as NFTs and so you can think of that you that you could be you will be able to see NFTs in your in your in your wallet that shows which files you wanted to ensure the goals of this project is that we don't want to be a new network by any meaning we want to be able to compose with every network we want to give the ability for anyone they want to ensure sorry we want to give the ability for anyone to ensure ipfs files and any any node not just phycon nodes should be able to provide this service of course this we want to target phycon first and although the although the smart contract is being deployed on a video as soon as we're going to have the fdm this is going to be native in phycon as well and another goal is that it must compose with other retrievability solutions and that we are ourselves protocol labs and in crypto net are in particular are also working on and and the goal is that there will be a set of smart contracts that other other future providers can pack up and offer a single optimal ability solution and we want to give flexibility on pricing so clients and storage provider can choose the premium and the slashing and then finally we want to be web-scale we want to make sure that any file on any data on phycon can be insured and also this data from other storage providers this is the qr code for the data retrievability oracle project as i said before i'm just representing the project this has been worked from luca and iran and kryptonite with the help from sebastiano at yomo digital and john are from had and you can enjoy and you can join the conversations on the news like channel that we created which is called retrievability oracle and more broadly there is a lot of work that we're working on kryptonite from snap research to vector commitments to threshold network and phycon improvement proposals and we're always looking for great engineers to join our team and in particular product managers that can take some of these ideas and turn them into products so if you're interested in that contact us if you're working on similar solutions and you want to integrate with our with the retrievability oracle but also with other projects that you're working on future challenge so thank you nikola and luca and arena for that interesting overview of your work on retrievability really exciting news about that prototype look forward to seeing it deployed on fvm and for giving us some insight into your future goals about composability and scalability in the retrieval retrieval research we have time for a few questions and we've got one over here i'm just curious if what you would consider like prior art in the third um like what have you been inspired by on the retrieval on the sort of retrieval insurance thing um i think that um so let me put it in this way uh since 2017 some of us have been thinking about these problems and in the past five years we looked into a lot of different things from um so i've been influenced personally by a lot of people that crossed through product labs even in in in person in the company but also at events like this there has been several things that we didn't think that would work in the past uh and uh and then they got wide adoption and then there are there's a there's a protocol that is changing i think they do oracle really well and what's very interesting about that is that the economics works out such that um in order to um fake the oracle so fake an oracle outcome you need to buy a large amount of tokens in the in changing and that's very expensive and and so the question is it's a it's a solved problem to do an oracle for for getting values of websites it's very difficult to do an oracle that retrieves the storage from storage providers or even from ipfs nodes and then search the data back so that's a little bit more of an oracle and most of the most of the i think changing has inspired a lot of this in a way and then also in other solutions that we had uh that we have a protocol apps that some that didn't make and some that are working in a in a different way and for example five con saturn is a great example to look into and this is just a different take on retrieval believing and this by not mean the the best way this one of the many and it provides a specific service which is insurance on retrieval thanks one more question cheers thanks very much for the really good talk Nicola I was just going to ask you to expand a little bit more if you wouldn't mind about the the insurance model and how you're thinking about the pricing and what kind of factors and how that's structured uh yeah so uh let me tell you some of our team are we're a team of researchers and not a team of economists and so what we decided was that instead of writing inside of the inside the the protocol what would be a good price for insurance we left it open or what is the tool and the needs to be used for insurance we decided to leave it open to the to the market and this gives of course a lot of flexibility in the and I'm sure in the MVP we're gonna pick some members we're working with uh I think with some people from block science and trying to understand what would be a good value or a good pricing mechanism but there's one so to answer you I don't have a good answer on what would be a good price there are too many factors to be taken into consideration and there's still analysis that needs to be built through this but there's one thing that is important is that it doesn't mean that the the the search provider is necessary using dollars or or filecoin it could even be that the search provider puts um as collateral the some reputation score there's another project that Luca is gonna present later on which is called storage metrics now and the idea is to collect metrics from the network and these metrics are basically the reputation of a storage provider and uh the every time you're gonna lose files you're gonna lose a reputation score so that's that's interesting for collateral and in in in case we want to have under collateralized uh retrievability deals um then on the premium I think uh likewise the premium if it's a file that is very important the storage the client can put uh whatever uh premium to the miner as a as a reward and thanks again for those that are interested in doing pricing models for this this is the best time uh to get into this like channel and and get in contact with us great what that called to action I think we'll thank the data retrievability consortium one more time and now it's time for everybody's favorite part of the event a break so please go down enjoy the beautiful views some coffees and pastries some juice relax and we'll reconvene at about 20 minutes past the hour for our next slate of talks hello and welcome back to the second portion of the program for the file coin crypto economics day here at dev connect in sunny amsterdam now it is my pleasure to introduce our next speaker patrick woodhead patrick is the technical program manager for the retrieval markets initiative which will of course be crucial to the further expansion of the file coin ecosystem and he's going to talk to us today about you guessed it retrieval markets take it away patrick thank you very much carola right yeah today we'll be talking about retrieval markets um and as its crypto equine day we'll have a particular focus on the crypto economics of the retrieval market as well gonna begin with some motivation so um a statistic by 2025 the global cdn market that's content delivery network market is expected to be twice as large as the cloud object storage market so cloud object storage think aws s3 cdn you're thinking cloud front cloud flare akamai now when you first think about this you might think oh that's actually remarkable um you you store files in a classic traditional cloud and then you kind of accelerate them as like an extra benefit so why would it be such a bigger market but actually when you think about it some more you realize the engineering involved and the hardware the the kind of points of presence around the world that are needed to create a great experience for everyone retrieving files to all of their browsers their games consoles etc uh and they expect an amazing experience they expect web pages to load instantly uh that's that's a big engineering challenge and because of that companies are happy to pay a large amount to create such a good experience so what i'm trying to say here really is that retrieval i forget about storage we've been talking about storage all day let's talk about retrieval for a bit because it's pretty important okay so what is a retrieval market it's the name of the talk in the name of this team um but let's it's good just to try and get a definition sorted here um so for me a retrieval market is a setting where anyone can come along and they can provide retrievals file retrievals to those who are demanding those files so i'd say that those traditional cloud cdns like cloudflare uh cloudfront akamai they're they're working in in a retrieval market but we're looking here at decentralized cdns and in particular the filecoin retrieval market so filecoin retrieval market we want to enable a decentralized cdn or dcdn to to emerge around the filecoin network so we can have performant reliable economic retrievals of filecoin data so there's lots of different topics that contribute to creating a healthy and vibrant retrieval market um i'm in the interest of time i might not go through all of these particular building blocks right now but we're going to focus instead on crypto economics okay so this diagram here uh is my attempt to draw up what's happening in what i call the primary retrieval market of filecoin uh so on the right hand side in green we've got the storage provider who's storing some content and when a retrieval client over in blue on the left wants to retrieve a file from the storage provider uh they have to do a few things it's not as simple as just a browser making an htkp request for for a file they have to set up a payment channel on the filecoin ledger uh and then they have to do what is called this optimistic fair exchange protocol uh whereby the retrieval client will send say one voucher in exchange for one bite of the file and then two vouchers in exchange for two bites because these two entities they don't trust each other and they have to build up this trusted relationship um and eventually the retrieval client will send some vouchers over and retrieve the last bites of the file the reason this is optimistic is because there's no sort of dispute mechanism if the retrieval client doesn't get some bites back for the vouchers it sent it can't go and complain to anyone it's just lost out on those vouchers and that's why we build up trust um and eventually the storage provider can redeem these vouchers that it's retrieved from the got from the retrieval client um on the on the network uh so the reason that we have to have this off chain well when you set up a payment channel then using vouchers instead of filecoin is because we have to have to have to have them really quickly uh we don't want to have to have a blockchain transaction every time we want to retrieve a few bites and that's why this whole mechanism is in place but it does mean that this retrieval client there's lots of barriers to entry to being a retrieval client you need to be able to talk to the filecoin ledger and you also need to be able to speak graph sync and do this exchange protocol um which means that the majority of clients around the world who want to retrieve files are sort of counted out from this uh for a browser to do this it's uh pretty difficult so uh yeah essentially we can't the demand for all of these files it's very difficult for that to match that to the supply in the storage providers what's more these storage providers are not really equipped to satisfy all this demand they can take you know the odd retrieval but they're not like a cdn where they can take kind of you know millions of requests a day so this was how this primary retrieval market this is how filecoin launched and what we're starting to see emerge now um is what i've tried to capture in this diagram um so we'll start down in the bottom left we have these content publishers so you can think of say an nft marketplace uh being a content publisher and they want to accelerate their files and create a great experience for someone who's looking at nft media whether videos images etc now they could go straight to the storage providers in green and create lots of deals to store that data um but what we're seeing happen is they actually speak to a deal what i've called a deal and distribution service and we could think of estuary as as such an example so they give the data they want to store to this deal and distribution service and then this deal and distribution service can create deals with storage providers to store that data on filecoin and in the case of estuary i believe it's six replicas or with six different storage providers but then what we get is on what estuary offer is ipfs retrieval so it's saying okay you can store it on filecoin and then we'll make it available through an ipfs gateway uh which is great you've now got a retrieval story which is available to browsers and other usual devices that that were used to fetch files however i think from a crypto economic perspective and also a decentralization perspective there's just a few things that are not even that we all want to kind of improve in this diagram so it'd be nice to see sort of a link between the storage side and the retrieval side um it feels like you're kind of putting something in the storage into filecoin storage and it doesn't then speak to the retrieval part of of the architecture um in terms of value flow the content publishers are will be paying to store stuff with filecoin as part of a deal the storage providers can earn block rewards for those storage but in terms of of this model here the ipfs is sort of run by whoever wants to run ipfs nodes or whoever's paying the bill for running an ipfs gateway so that takes us on into the secondary retrieval market so when i say secondary retrieval market the primary one is is retrieving from the storage providers and the secondary one is where we can cache stuff outside of the storage providers and we can start to retrieve between what we're going to call retrieval providers in blue and um the reason we've got a ring of them around the storage providers is just alluding to to saturn which is going to be introduced later on and the rings of saturn so it wasn't i just got a bit carried away with a copy and paste so yeah we've got some of the we've got some of the boxes from the previous slide we've got the content publishers again who who are going to be giving content to the deal and distribution service and they're going to be creating deals with the storage providers but also we can now connect the retrieval providers to the storage providers um and we can then enable some value flow between those two entities and also the deal and distribution service rather than just making it accessible to the ipfs gateway which they could also do they can start to contact these retrieval providers and start to create the retrieval flow through these more decentralized offerings um so yeah this suddenly means that we've got we can have retrieval providers spun up by anyone which is a requirement of this secondary retrieval market to be completely decentralized um the only i guess the only requirement that we're adding back on to our clients is that they really need to verify what files are going to get back um if it's an ipfs gateway you're sort of trusting that gateway to give you the right file but if anyone can spin up a retrieval provider then how can you trust they're going to give you back the right file i might ask for a huge video and get returned like a tiny image and that's not great so there needs to be some way that i can i can kind of prove that the file that i've retrieved it's right and and there we reach for content addressing um which is the answer for lots of things um in the file coin network and we can then verify that file but browsers don't verify content address files very easily so we have to add some extra mechanisms perhaps in a browser but whatever the client is we're going to have to add that level of verification and we can also then start to try and work on the properties which current cloud CDNs offer such as traffic spikes and uh e-dOS attacks that sort of thing and protect the storage providers uh from those internet um scale phenomenons and yeah just much lower hardware cost to contribute to the file coin network that's really important because we want it to be permissionless and decentralized but if you have to own a data center to get involved then it's not particularly decentralized so we want to allow anyone to join who's just got some some devices um or spare devices which they could contribute to the network so then we get to the question of how should a retrieval provider earn um we've got storage providers who can earn block rewards uh by doing proofs of storage and they can also earn a small amount during doing deals with people make who want to store data uh what's what I should mention here as well though is that the deals to store data can actually be negatively priced um which is a phenomenon only available in in web 3 um because the storage providers they they're so keen to get this file coin plus subsidies that they just want to take valuable verified data so they might even pay the deal in distribution service to say give me good good good data and perhaps that could then be used uh to pay for some of the retrieval providing in general though we want to find a way that these retrieval providers can earn without um well by linking it to the demand because if we just let's say one of the ideas we've looked at for how retrieval providers can earn is proving bandwidth so you just proving that you're online and you're up and you can receive some sort of block rewards for proofs of bandwidth but that doesn't again link retrieval providers to demand to the number of retrievals that they're serving um and so we might end in a situation where people are optimizing to win these block rewards again for retrieval providing but it's not linked to actually network demand and so one of the questions that we're talking about a loss at the moment is whether your rewards as a as a retrieval provider how how dependent they should be on exactly how many retrievals you're serving because that's what we want to really promote in this network is for them to serve as many retrievals as possible and if we go back again to to this diagram uh if i'm not mistaken i think the ipfs gateway run by practical labs receives something like a billion requests a week or something in that order of magnitude so there is demand for these content address files um so then if we if we're doing it on how many retrievals a provider has served um at a network level that's kind of makes sense we can try and gather a list of retrievals made and then uh remunerate each retrieval provider based on that but the issue then becomes that that's sort of centralized again so retrieval providers should they be then paid for each individual retrieval in a sort of a localized way to keep things decentralized but again then we're putting uh requirements onto the the clients who are retrieving stuff from the retrieval providers to actually be able to pay for it uh as a sort of pay as you go um approach so there's lots of trade-offs that with there's lots of trade-offs that we're facing um with these retrieval providers to try and link them up to the usual clients that people use such as browsers but also be able to uh live in a completely decentralized context um and earn rewards um appropriate to what service they're providing so yeah we're seeing a bunch of different retrieval networks emerge in this retrieval market um which i've tried to crudely put on this web2 to web3 access um and that sort of involves all these trade-offs that we've been talking about so on the one hand we have uh ipfs gateway and the recently released nft.storage gateway which they sort of they're they're aiming to really compete with those web2 offerings in terms of performance and reliability but giving up on verification or content addressable nature of the data and then moving across to the far right hand side the web3 side we have some more purists in this decentralized cdn space who are trying to reinvent uh the way that we that we retrieve data um and one i want to talk about in particular is saturn we've actually got the team building saturn here today um who do a much better job than i would at introducing it um so yeah filecoin saturn is exactly that it's the decentralized cdn for filecoin um at the moment the the way that it's structured is we have gateway nodes and station nodes forming two rings of saturn around the center of gravity of data uh in the filecoin network the gateway nodes they are uh in order for for browsers to be able to communicate with this retrieval network and as such they have to have a slightly more centralized touch where we're kind of certifying them um such that browsers can talk to them and also requiring that they have a very high bandwidth and high availability requirements but we've also got this notion of a station node and a station node is is going to be a desktop app so that anyone can download this app and start contributing to the network uh and saturn is currently testing in uh in v0 and yes just to mention now on Tuesday we have a retrieval markets workshop which is an introduction to filecoin saturn so if anyone's interested in peer-to-peer networking um some of the web uh browser protocols like web rtc that sort of thing some of the crypto economic ideas i talked about already um or and or just webs or web three cdn's in general um then are we sharing some links to this later or just come find me um and come get involved on tuesday if you're still around and i think i'm probably out of time okay well i just put this in just in case there was a bit of time and a lot a lot of time a lot of our time in retrieval markets and with the saturn team we spend discussing these trade-offs and what the real benefits are of this web 3 cdn uh compared to web 2 cdn and i think for me just as if i would just to pick one thing out of the slide i think what's really powerful and it's something actually uh which tom mentioned in his talk earlier it's about creating a more symmetric network rather than a symmetric network um it means that rather than it being client server i'm retrieving a file from from clout for etc instead i can retrieve a file from other peers in the network and then i can even earn i think that is an innovation which you don't get in the web 2 space that users can retrieve and then earn or sharing that file with others in the network and act as a much more symmetric node um in a distributed network and i will stop there um yeah thanks any questions excellent thank you patrick you started off by saying forget about storage let's talk about retrieval echoing eminem forget about dray bold words from bold men questions to the bold man in front of you does the uh hierarchical consensus mechanism that's currently being researched at protocol labs have anything to do with the Saturn project or is there any thought of like integrating the two or maybe it's too early along to save that's a great question and i'd say that has i'm gonna be honest we haven't thought about it yet i haven't been in touch with them um but it's a great idea what we have thought about is how we could use once fvm launches sort of layer two solutions to then because there's been so many retrievals so how do we how do we govern all of these retrievals so like roll up that sort of thing and to the picture so yeah lots of ideas about how we can start to manage the sheer number of retrievals we hope to serve i noticed that you had on like the spectrum of web two to web three my l or me l network are this right can you explain why that is and whether it's desirable for Saturn to be further to the right than anything else yeah sure um i mean i i don't want to speak for the my l guys but i but i think the reason i put them further along the axis was the axis is because um they they feel that it should be each transaction should be paid for in that's just be a local transaction between the client and yeah the person who's serving the retrieval um and not be governed more centrally and so if we want to transact no one else is involved we're just i'll be paying you for a retrieval and you'll be giving me a file kind of similar to the primary retrieval market but that puts a lot of constraints on what clients can can operate because it makes it very difficult for browsers in their current setup to be able to communicate like that so in order for a browser to operate as it does now you have to have a few concessions to more centralized approaches and i would say that the satin team that's exactly what's what's what we've gone for we've said okay we're just going to concede on these few things for now and hopefully we're going to tweak it and make it more centralized more web three as as we build um but we just got to use the browser as is now and try not to change it because that could be quite hard excellent thanks again to patrick i think we have time now to introduce our net no we do not we have all right well then time for a little bit of interim interim patter uh one question to get to know crypto econ lab a little bit better that i've been asking the members of the crypto econ lab is what is your favorite karaoke song we had some very interesting responses that you may not have expected um from axel who presented earlier dancing queen by abba so that was so that's a goody i'd say that's pretty classic maybe a surprising choice from alex the tpm of the lab uh hit him up by tupac i'd like to point out yes there we go pride pride pride new member vik over here he's a michael jackson fan billy gene i don't know if he can hit the high notes but i know he can moonwalk all right stay tuned maybe at the next break i'll share a little bit about zx i asked him the question he has not responded yet and will make something up about tom who keeps dancing around the issue i'm going to assign him ship building by elvis castello if he doesn't come up with something better and now let's see are we do we have our next speaker lined up yet calling for halftime ah next speaker not online so we may actually call on victor moonwalk so i guess we're all here in the room many of us are you know members of crypto econ lab are there any we could ask for general questions any questions you've you've uh thought about from presentations earlier in the day um and maybe somebody from the lab can attempt my default will be to go to tom or uh there we go another one uh yeah so um saturn has uh like uh you want to for cdn you want to prove labs working on onboarding data at scale onto the filecoin network he is focused on getting public data sets on boarded to the network via the slingshot programs and is also part of the filecoin plus governance team working on client onboarding and trust reach out to him at any time to chat about data cap dows and some excellent puns today deep will lead us through filecoin plus and data dows welcome thank you uh i hope everybody's doing well sorry i couldn't make it there in person but it looks like a fantastic event and uh really well set up um so yeah please have a blast today and celebrate a little bit extra for kicking ass and getting together and doing productive things uh in my stand as well thanks for the excellent intro i'll probably just skip this slide in the interest of time uh just covering you know leaving this up mostly for the parts of the bottom where if you need to get a hold of me find me on falcon slack is deep kippur or find me on twitter deep kippur happy to chat about any of the things that i'm going to be discussing in this presentation or anything at large with regards to uh large-scale onboarding of data onto the filecoin network so what i want to cover today with you in the short 20 minutes that we have is number one filecoin plus for those of you that aren't familiar with it we'll go over the overall sort of core concepts of the program uh how it works how the pieces sort of fit together we'll talk through slingshot and the various slingshot related programs that exist today um and at the end touch a little bit about how these things sort of are moving towards the direction of of doubt or their doubtification uh i would advise a sort of a larger scale understanding of what it actually means to do that um and then how you can get involved and what's next so without any further delay let's talk about filecoin plus so filecoin plus by definition aims to add a layer of social trust to the falcon network to incentivize useful storage what does that actually mean great question so we're going to say a bunch of things here and i don't expect you to understand all of them but then we're going to dig into the components and then hopefully it'll start to make sense so the first thing i want to tell you is that in the world of falcon plus we have these different stakeholders you can see them in colored boxes over here i don't know if this screen is big enough for you to see this in person and so i'm just going to walk you through it uh because this is the text might be a little small so you have community governance root key holders notaries clients storage providers and miners so the idea is that root key holders notaries clients storage providers interact through the allocation spending of a resource called data cap effectively notaries serve as a key stakeholder position moving into each of these by allocating data cap to clients clients spend that data cap in storing deals on the network so specifically clients who are data owners who are bringing data to the falcon network use it to make deals and this will start to make very soon but then in making those deals storage providers who receive deals that are sort of coming in with data cap receive a boost to that adjusted power on the network and so you're at a crypto account conference it's important for us to explain to you what that actually means the way falcon distributes its rewards is based on power and if you're taking deals that have data cap in them then you're getting a 10x boost to the relative power the relative footprint that you're occupying on the network with regards to that deal that you're taking on so if you're taking a 32 gigabyte deal or give you by deal in this case it looks like 320 and inflates the rewards of the network is giving you so in essence this is a network based subsidy of deal making between data owners and storage providers now let's dig into what each of these different stakeholders are define data cap and sort of help this all make sense and why it's interesting first things first what is actually data cap it's a novel generated resource within the falcon network it's programmatically generated we'll talk about how that actually happens but as I just mentioned clients and data owners use data cap to make storage deals it's a one-time use credit so it's consumed as it is used in those deals and then the storage providers receive a 10x quality adjustment to the power for serving deals that have been made with data cap so back to the sort of cost stakeholders of falcon plus and sort of defining them a little bit so you understand how they all come together first we talk about the root key holders root key holders are executors for decisions made by the community on the blockchain network so their reputations sorry their representatives the reputable organizations that are in like our ecosystem effectively falcon IPFS for three sort of spaces but ultimately what they actually are their signers on a multi-sig and that multi-sig has the power to invent data cap and issue it to notaries and notaries being other addresses on the network which have been flagged with the identity of a verifier so just recap community figures out who the notaries are and we'll talk about that in a second root key holders are the ones responsible for executing those decisions on behalf of the community this typically means assigning the value of data cap to a notary that they can allocate as well as the status of being a notary on the network notaries are arguably the most important stakeholder that keeps this engine going their role is to do kyc and do diligence on incoming data owners and clients out of the network the goal is for them to identify trustworthy clients so this goes back to the top line sort of definition of fil plus which is that it's a social trust system right and so notaries are the ones that are actually looking to establish that trust to figure out who the clients are that are worthy of receiving this data cap that will give them these extremely free or cheap storage deals and so the idea here is notary spend whatever effort they need you to ensure that they build a relationship with the client understand who they are and give them data cap commensurate with a level of trust that is warranted based on the information that is provided by the client about what they're working on who's funding it why they're doing it what their goals are what the long term plan is etc they also have a set of operational guidelines which includes how they should do these things two things that needs to be used need to keep a record bookkeeping for auditing purposes and ensure that they can actually talk through past decisions that they may not be held accountable by the community they're selected through an election process which includes putting an application out in the public which is scored on a rubric so that rubric is also open source and the idea there is the community can iterate on the rubric notaries is scored against the rubric we do a pretty open election process where a notary applies is scored on a rubric gets to sort of contest and then the community also gets to contest and then we finalize the rubric scores rank all the notaries by their rubric scores and then pick a set as defined by what the goals of the election process are so hopefully this is starting to make sense you know we elect notaries as a community group you holders give note like that particular address on chain the ability to do these special things like issue data cap to clients and so clients these are the actual data owners as the demand started the the storage marketplace they're aiming to be and come across this trustworthy so that they can get this resource to make extremely cheap deals storage providers represent the supply side as you can tell by now that loss and certainly not least is the actual governance community this includes of course all the above stakeholders but also includes just anybody interested in the community our governance processes are completely open we use github as our primary tool for making progress on the program indexing documenting and then we do open governance calls bi-weekly and so literally anybody could participate includes you and come to our calls and help with things like defining the rubric defining the roles changing adjusting the roles putting in flips to adjust the way the program works etc etc quick recap on the flow again so notaries perform due diligence or KYC on clients to verify them so you'll often hear the term verify but really it's just establish trust be a real client they're looking for real clients with valid data that would actually bolster the usefulness of productivity of the frontline network further its goals in like being the marketplace and search interaction point for all of humanity's information and ensure that the subsidy that's being created crypto economically on the network is actually utilized in the best way possible for long term whether that's for the actual clients and their ability to do things because you know the non-profit organizations scientific organizations etc or if it's for the network in terms of hey this is a really compelling use case that we want to serve and we're excited in making sure that we're able to deliver this for everybody and show them that like this can be done on Filecoin it may also include like stretching the network to understand how to handle so we've got a working group for example that just spun up they specifically think about enterprise support in Filecoin plus so how do we deal with data sets that can't be open all the way or are encrypted or have some private components like how do we still build trust with clients because traditionally sometimes you know no reason just ask clients for samples of the data but that's not necessarily possible in this world and then based on the identification of clients and their reception of our allocation of data cap they then go expend that in deals as they burn that data cap storage providers get adjusted power increases and get better rewards from the Filecoin chain as long as that deal is live in that sector designing etc etc clients have three paths to getting data cap today they can go into automatic verification a good example this is a site verify.lift.io for anybody with a good hub account greater than a year and I'm sorry the greater than six months old I think it's it's one eight days yeah you can pretty much get 32 GB every month we're adding a scaling component for this as well based on like how you're using it and how frequently you're asking for it and show that you continue getting more if needed this is extremely useful for testing purposes like you're just wanting to get your first deal or get your first training 30 deals see how it actually works like build up a system do a mini POC for yourself this is the recommended path if you're a client who has a single use case and not a massive data set and it's just looking forward to getting this done with the least amount of effort and the least amount of operational overhead you opt for the second path where you typically get between 10 and 50 GB of data cap that's through working directly with an electric notary all of this happens in public all of this happens in GitHub issues but this does have the option of oh like can I email you this stuff instead and then you maintain that in your books instead and so it has a little bit more flexibility for clients and the third and by far currently the most popular option is to go via this what we call the large data set and repath or like the filecoin plus for a large unit sets program and here the goal is you're requesting 500 terabytes plus for data cap each application can be up to five terabytes but it's really some tranches as opposed to that data cap going out on its own completely public process much larger accessible amount of data cap but because it's really some tranches there's a little bit more scrutiny every tranche that's released requires some participation from notaries etc so where are we at with the program today so on the right you can see this graph this is the last three months of active deals and heavy bytes on the network the light green portion on top is filecoin plus the blueish portion of bottom is like regular deals you can see that filecoin plus has really been on a tear since the new year the LDN process has resulted in a lot of clients getting data cap and onboarding tons of data at a much faster rate than the world before we're currently at the point where about 75 percent of all the data stored on chain is actually verified data coming into filecoin plus that means more than 55 per gigabytes of data cap being used in deals and that represents about 75 percent of the data cap that's been given to clients and so there's still about 25 percent that's sitting latent and every single day more data cap is being issued and distributed in terms of notaries we just wrapped our third election closing and so we're at about 60 active notaries soon once they're all onboarded and willing to participate last year we spent half of it at about 10 and half at about 20 and so we're three axing the amount of notaries in the program and also like significantly increasing the amount of data cap available to them to serve and this is mostly an experiment to see how the program actually scales right now if you go and work with an order it takes about 10 days to allocate data caps so it's very important to us that we work towards bringing that number down so upcoming focus areas well actually these are more like current focus areas and then upcoming development first is what I just mentioned what we call TTD that stands for time due data cap this is like our top line sort of success metric in the filecoin plus program we want to ensure the data cap is available and attainable in like the one hour to three day range at all of these levels and so that includes you know having more notaries having the notaries be more active improving the UX for clients automation and scaling for data cap applications etc you can see the little flow chart on the right which sort of walks you through how we want to move towards more automation reduce manual interaction points and only bring in the human interaction the cases where things are starting to go awry focus on risk reducing the amount of touch points more focus on sort of on-chain tracking and automation of like flags for like risky behavior or abuse or profiteering of the program like we're thinking about this as like us building out a reputation system or a credit system for data owners and search providers inside the filecoin plus world and then generally evolving the no-reader a little bit to go from what we call like the gatekeeper functionality to more like guardians of the network that changes the way in which they interact but of course comes with discussions around their incentives how to retain their attentiveness and overall have them deliver a service effectively to the network while still maintaining the best interests of the network and not themselves and participate in governance in addition to just what the community does today so how do you want to get involved we'd love to have you come join us in the filecoin plus channel in Slack we have bi-weekly governance calls you should come there we're working on sort of improving our processes we'd love to have you as you know think about notary incentives think about how we want to evolve processes and system design and generally as we move towards like structures that are similar to like a doubt we're spending our working groups experimenting with governance system design etc and generally overhauling the incentive and long-term ideation around how filecoin plus will continue serve the network into the future and we're having a filecoin plus half-day summit at Austin Texas today before Phil Austin if you haven't heard of it yet so Phil Austin will happen June 8th we're doing a filecoin plus June 7th this will be a virtual first event so even if you can't make it in person we'll be broadcasting this live streaming it of course recording it so definitely more information to follow on that keep your eyes out switching gears for the last couple of minutes here to talk about Slingshot Slingshot is a community program for data processes and developers that rewards the storage of real valuable and usable data on the filecoin network so this is completely different than filecoin plus but you'll see some similar threads and sort of enclosing hopefully I'll be able to share with you why I'm talking about both these programs and why they're compelling for you so Slingshot overall has been running since filecoin went made hat which is just about 18 months to the day through these 18 months we've had about 37-ish prebiobytes of data on boarded the network comprising of open data sets so there's like 1661 data sets all the way from like COVID data to environmental data the governmental data and like random public use things that were found that could be interesting source from scientific organizations nonprofits NGOs etc we also have a data explorer which is pretty compelling in terms of helping you identify where you can retrieve that data from its mirror on filecoin as opposed to needing to pay for an AWS retrieval service or needing to run your compute operations in different cloud provided services here you can actually get it effectively for free or extremely cheaply from the filecoin network itself recently in the last six months sort of two couple with just this onboarding thing we've added two different programs as well that are focused on the longevity in the long-term viability the data being onboarded so on the left you have evergreen the idea with evergreen is effectively as Slingshot deals reach the end of life we want to continue renewing them so right now this is a little bit more manual we're very excited for the filecoin virtual machine to show up so that we can automate some of these things but the idea here is tracking the pieces of data that are in expiring deals and then incentivizing the rebuilding of replicas for them to ensure longevity permanence long-term availability of that open data on the on the filecoin network the primary driving incentive here is actually data camp and so this goes back to the first part of the conversation about what and how are we able to do this the idea is that by adhering to an extremely high standard of quality storage and showing that we're getting to do things that push the definition of how to make data useful on filecoin how to contribute to the best practices around that both for the data owner side of the house but also for the the search provider side of the house we're able to justify to the notaries that they should sort of support the running of this program on the right we have recovery as an effort and within recovery specifically the restore program restore program specifically started out around an incident where we lost a little bit of data um well a substantive amount of data after like a freak accident effectively a fire in a data center and so we built this as like the first step towards like self-healing where we can design systems where data on sync shot if it's lost and this is specifically not in terms of end of life for deals but rather like a sector but faults or a search provider decides to walk away how do we rebuild and self-heal from that like how do we maintain a healthy number of live replicas at any given point in time how do we make sure they're available both of these programs are currently open and every unit especially if you are thinking about a search writing operation and listening to this it's a pretty interesting one to get started definitely check out check it out why i'm telling you about this quick primer on doubts i think you guys are all aware of this decentralized autonomous organizations typically refer to as a combination of community plus some sort of contract what we care about or what i care about right now is specifically doubts that care about data so the creation of value through shared data or data sets this is not just limited to the monetization of demand for data though that is one excellent use case and one way in which data does have often been described and arguably the data doubt sort of set that ideation process forward with that so what i'm trying to structure right now is like using these components together there's a very complicated approach on the right i'm not going to dig into it too much here but the idea is that you know we've got a component we've got a set of components that come together very nicely to build a system that can in a clean manner set up data for long-term preservation on the file point network so with just slingshot you've got onboarding data with evergreen you've got the aspect of guaranteeing its permanence with restore you can do the self-healing and the rebuilding and then we have this component that we've used in slingshot for a while via the deal bot where we check for retrieval success rates and so we've got retrieval success rates as a service as i like to call it where we do sampling and retrievability and have metrics on the health of the data itself so even though it might be alive and the deal might be good that doesn't actually mean that it's accessible and so by having a path to onboard maintain ensure that it's repaired and have a way in which it's like like all those things happen in a way that the clients in need access that data are able to we do have all these nice components that sort of come together and can leverage like significant automation and and development of the future to be like an interesting data structure and so we'd love to have you involved you're interested in this conversation lots of work to do ahead of us here definitely in terms of defining the incentives of governance etc leveraging the virtual machine itself of course also just the data architecture and how we want to do the analysis how we want to like build that sort of reputation aspect that i mentioned so specifically if you'd be interested in in thinking about these things further please reach out to me either on Slack or Twitter or this QR code should actually take you straight to our definition and JD for one of the roles on the team that doesn't have to be the role you want i just want to put it up there as like a hey if you want to get involved sooner definitely check that out awesome i know i'm a little bit behind schedule thank you all for bearing with me and getting on my speed train as you were hearing the the time limit for this presentation thank you for having me alex and zx and congrats once again on the event i'm i'm looking forward to being there in person next time in case there's any questions please feel free to chase me down i don't know if we have the ability to do the questions right now because i know there's a pretty packed agenda to keep you guys going i'm going to rely on the host to let me know if not really appreciate your attention thanks a lot deep for that overview of filecoin plus and slingshot and their various aspects we have time for maybe one question to the presenter uh hi yeah just really quick um so i understand storage miners who want to participate in verified deals to you know have an increased percentage of the block rewards um what is the incentive for clients to want to get you know notarized or verified to to have more data cap yeah great question so because storage riders have that incentive the amount of actual fill or like money that gets charged to make those deals is significantly lower actually the average verified deal in the network today is completely free so the incentive for ky for that kyc and due diligence process is effectively you're trading that time that effort that loss of complete privacy on what you're doing and adding a little bit more transparency for getting absurdly subsidized deal making on the network so especially as you know uh storage riders start to build long-term businesses on the network and want to offer the ability to store data at scale safely um instead of having to pay in fill you get to effectively extend that data cap and then spend it and so that's why it's extremely interesting and compelling for clients to go to that process of course we are working on making that process easier and and faster and all of that and so that is also good hopefully for clients in the long term but that is the primary incentive today i'd say there's a secondary social incentive as well where like doing this stuff in public means that everybody in the network sort of sees that you're trying to do the right thing sees the work that you're putting in behind it helps you sort of gain clout for long-term partnerships long-term relationships hopefully favorable deal making climates in the future but then you know coming into falcon plus day for example we have a couple of people on both sides the marketplace that want to be presenting share the work that they've done the tooling they've done and how they're contributing to success the network so i'd say yeah the monetary incentive of an extremely cheap deal the social incentive of building cloud in the network thanks for that deep and with that i think we'll move on to our next presentation we're pleased to welcome silvin jung silvin is product and operations lead at starboard working on various product research and development projects in the areas of network governance web three service marketplaces and creative user engagement today silvin will be charting a course through new future new frontiers in web three with filecoin evm welcome silvin hi how's everyone doing uh can you guys see my screen we can now yes uh yeah hello everyone um hope that um all of you guys are doing well and enjoying your time in amsterdam uh my name is silvin and i work at starboard uh on products and also operations i'm really honored to be here although not physically but hope you guys can feel my spirits so um today i will be talking about like while for why filecoin is an important building block of web three and then also like what new opportunities will be unlocked when you combine the provable storage of filecoin with the programmability of evm so before we get started let me just quickly introduce starboard a little bit who we are what we do and what are our goals so basically we are a main contributor to the filecoin ecosystem focusing on network analytics and also application design and the reason we're doing this is that we found the the filecoin network to be highly sophisticated right i mean i mean it's sophisticated enough for 11 speakers to talk great on various topics without any repetition and uh but this level of sophistication brings many challenges to the ecosystem and also to all its participants like storage providers clients and investors in terms of understanding filecoin and also participating in this unique web 3d economy and that's why we aim to provide analytic solutions and also product development to drive uh network understanding and adoptions and for the analytics part i think my colleague ever had had a great talk this afternoon and basically our approach is to build data analytics infrastructures and to help people understand what's happening and for the for the product part we're trying to build like intuitive products to help people use the filecoin blockchain whether it's making search deals calculating service economics or participating in a creative storage experience so we have been working in the filecoin ecosystem for quite a some time and what do we find first filecoin is enormous it has around 16 exabyte of storage capacity available to put that into perspectives if the recording of my talk of this talk is 16 exabyte it means that this talk will last about 3.8 million years but i mean it's okay i promise i won't keep you guys that long and also there are around like 72 petabytes of real data stored on filecoin from over three million storage deals and according to the stats from nft.storage there are over 52 point 58.2 million nft objects stored on filecoin and in terms of assets and this is the part that people usually didn't pay too much attention in terms of asset there are currently 130 million filecoin locked with the network which is this equivalent to around 2.6 billion in total 2.6 billion dollars in total value locked and also filecoin has one of the most vibrant developer ecosystem in web three i mean there are fabulous teams working on all kinds of use cases all kinds of infrastructures tooling and also like different programs and so far many has considered filecoin as the storage layer for web three and the current major use cases include providing storage to nfts and also web three use cases providing storage to like web three to web two data sets or like providing permanent storage and also to metaverse gaming audio and video however we think that this is just the beginning when smart contract programmability are combined with provable storage on filecoin there are a lot of value and potential to be unlocked so think of the storage and retrieval capabilities on filecoin as the layer zero and it's already a super valuable stack to web three but it's insufficient for developers who want to build more complex applications through programmable smart contracts and now let's go to the layer one of the smart contract programmability which is the key to the success of ethereum and currently filecoin does not have this kind of user defined smart contracts so in order to access programmability developers use bridges to other programmable blockchains i mean i've presented a few like for example like ethereum and also near are some of the more like commonly used blockchains for for for programmabilities on filecoin the filecoin team is working very hard to bring general programmability to filecoin and there have been tremendous progress with the development of the filecoin virtual machine or the fvm so the fvm is the wassen-based polygon of the execution environment it's designed to support both native filecoin actors between languages that compile to wassen and also foreign runtimes such as evm so it means that filecoin will bring bring together smart contracts and also provable storage on one network i'm just like so excited to talk about what opportunities that fvm will unlock so a oops so a very obvious opportunity will be in the nft space i would like to share an article i read a while ago this author makes a joke about the current nft structure where the storage of the nft metadata is not verifiable so it's pretty funny it's like really funny thought experiment but consider that billions of dollars of nfts are actually traded every day i think it maybe it's worth some attention and a great opportunity is to create entities with verifiable metadata and it's enabled by decentralized storage and also smart contracts so i mean you will never find a stranger-looking emoji in your wallet after spending significant money on an nft and another thing that fvm can enable is like content first nft so it's actually quite bizarre to me that people don't actually care about the content of the nft but we believe that there's like tremendous value in the content as well going forward so just want to briefly mention nft storage is a wonderful tool for people to store their nft with like filecoin and then we're pretty sure that with the programmability and also like capability of the fvm it will be there will be even more like use cases and more popular use cases with the the powerful proof of storage on filecoin and so earlier i mentioned that there are actually people a lot of people often ignore that there are just so much access that was committed to the filecoin network so which i think that another opportunity that lies ahead is DeFi with filecoin and as previously mentioned there are over 130 million filecoin logged with the network and if we are comparing to some of the DeFi projects a $2.6 billion tvl will actually rank filecoin eighth among all DeFi projects and what's more important is that according to our data we found that there's there are actually over one million file in demand for token collateral every week and also the annualized on-chain fuel to file to fuel to fuel return can be as high as like 90 percent which which i think i think is a great opportunity for a staking protocol that can leverage this fantastic high fuel and fuel return and then generates like a really attractive yield over those on the other blockchain networks which are normally on like like low double digits like 15 percent 20 percent right and another opportunity is data dows i i feel like many of the speakers have already talked addressed on this topic so i just want to like bring some different perspectives as well so we often hear people saying that data is the most important and also most valuable assets in 21st century so creating a dial around data assets seems we think i would say it seems like very reasonable and like from my perspective i think there are generally three kinds of value in data one is the value in evidence and record the second is in the value in information and also the third is the value in derived insights from like for example like machine learning or computation over data etc so there could be like various forms of data dows holding that data assets that generate different values for example on the evidence and records you can have like data dows and to preserve records for the internet or maybe just making sure a meme will last forever like for centuries right that would be pretty cool and for the value in information like a pay per view a paper download a business model can work well with like articles movies music right think of a dial that the host the rise to like some articles or some movies and then you have this kind of like a pay per view or pay per download mechanism all right this dial can can run itself as well right because it's generating value and also like for derived insight insights user can pay to compute over data asset on by a data dial right this can be a very common use case for machine learning and also like deep learning so i just want to bring like a bring out an example it's the ocean protocol i think it's not on FEM but it's it is a data market on on top of the leverage the the storage of falcon and basically what they do is to create they create and also use data tokens to publish and also consume data services right so this can be a very good examples for some of the data dows explorations or like data market data asset market marketplace explorations for FEM and also just to add on like what deep just introduced about falcon plus which is a great incentive program to useful storage on falcon and it gives storage providers 10x block rewards and can significantly improve their service economics so with the help of FEM and also like falcon plus we can actually create a store to earn experience to encourage users to store useful data on falcon and have storage providers speak to store them right so it actually makes a lot of sense because by adopting the falcon plus program the the storage providers will have like a higher ROI a higher storage returns and and then they are more than willing to actually take a share of that to bid for more volumes of data caps or more volumes of like useful storage and this actually also creates incentives for for clients to migrate the data or to to to actually store their data with falcon so this actually can be a very unique use case and also like a unique experience for falcon as it can never happen with like a web two storage providers and there are many more ideas and opportunities that will be enabled by FEM including perpetual storage reputation matrix storage bounties retrieval markets and so on and for the interest of time I would just provide some context but not discuss them in details so what is starboard doing or what is starboard exploring with FEM we're actually exploring a few different use cases that that can potentially leverage the the capabilities that FEM brings for example we are we're trying to explore the store to earn experience the slide I showed earlier on the on the right this this is actually a a product exploration from the starboard team so basically we're trying to build a auction style platform for people to actually put their valuable or like put their useful data on this platform for storage provider to bid to store them right so this is like a like a like a prototype of a store store to earn experience and if FEM comes online a lot of that logic can happen on chain with the programmability of smart contracts and also another thing because we we believe like we are like working a lot with data analytics and there will definitely be a lot of new metrics a lot of new data fields that we can analyze and then provide insights on them so we in one area we're also working on as FEM data analytics reputation metrics this is also very important we have a reputation dashboard for probably storage providers that provides contacts for when people want to for example make a landing deal to a minor to a storage provider make a or making a storage deal to to a storage provider right you need a lot of contacts it's just like you you are making a deal with like a Airbnb host and then you want to see their track records and also we are we're exploring like a diva lending right so that that could be a very interesting use case as well and we're all we're also hiring we're hiring smart contract engineers blockchain engineers data scientists researchers product managers so so if you're interested in writing on this amazing journey please feel free to reach out to us we can also discuss more opportunities that FEM will bring as well all right that concludes my presentation i hope you like it i hope i have provided some useful information thank you great thanks silvan for that data-based view into the possibilities inherent in the filecoin network we have time for a few questions to silvan a lot of data i know you have to take take time to come over the good well then seeing none we've got a little bit of time before our next speaker is slated to begin i'll give a thanks again to silvan and please make sure to check out those opportunities that he mentioned just to make sure that we don't get too far ahead of schedule for the people who are running who are viewing virtually i will mention that i did get a response from zx on what his go-to karaoke song is it is bohemian rhapsody that that describes the scale of his ambition there four-part harmony it's not for the faint of heart and if i get the go-ahead from technical in the back i think we can introduce our next speaker yeah we don't that's the problem this is why nobody hires mcs from germany we tend to run fast but luca is here luca nazardo i'm pleased to welcome he is a cryptography researcher in protocol labs crypto net lab where his research interests include vector commitments contingent payments and um zero knowledge proof systems and today he will be speaking to us about storage metric style welcome luca hey thanks for having me it's a pleasure to give this talk um thanks for the introduction i hope you can hear me well i'm a researcher in the crypto net lab that as you know since nicole gave a presentation a few hours ago it's a cryptography an applied cryptography working group inside the protocol labs and in particular in the last few months i have been working on on protocol opportunities which is basically a working group that is working towards improving protocols for for web three and this presentation is about one of the project that we have been working on in the last few months and it's about storage metrics um for retriever incentives so the main question that we want to answer here is how to incentivize high quality of service so we know that filecoin already takes care of file being stored in the network but if i am a client and i want to basically put my data in filecoin i also want to be sure that when i want my data back the depth is going to be back in my hands and so just to i mean a question that we could ask ourselves is who should i store my file with like how do i pick my my storage providers uh and this project tries to to answer this question and we will see where we are where we are moving towards and how can we get there so the goals that we have in this project are first having some on-chain metrics for storage providers that are realistic and objective and that are on-chain if we have this kind of metrics then we can use them to reward storage providers accordingly not only that we want that storage providers optimized toward these metrics because it's rational for them to do so so it's gonna be convenient for them to to to give high quality of service because our protocol will will make it possible um we will put in place a reward system that we aim to build as interoperable with others right everyone can build some reward system on top of ours and make it work with ours improve it like refine it and basically do whatever you want and not only that we are not focusing only on filecoin specifically but we think that our our our metrics our metrics now could be also useful in many other applications in web 3 for example there are like other application other applications that are computing over data that could profit by having some sort of metrics now on top of it in order to to make reputation systems for for service providers not only storage so what's the long-term plan we want of course this this net this metric network to be decentralized like no central authority involved open meaning that both on the auditor side and on the on the service provider storage provider side it should be incentivized in order to be sustainable on both sides and it should be resistant to auditor collisions like we do not really want auditors to kind of you know be allowed to to set up a cartel and influence these metrics in one way or another in essence what we are targeting is like having honest reporting as the best strategy for auditors and this is as we will see in what follows not so trivial to achieve how do we get there so we imagine a network of auditors that scan a network according to some metrics that we identified and that we can update on the way whenever we we think that it's the right moment there is also an incentive to report through metrics and that's you know in order to make as I was saying before honest reporting as as the rational strategy we also need to have a system to aggregate metrics on chain because it's not feasible that you know as long as the the auditor set scales we everyone post is on result on chain and of course there is also an incentive for storage providers that provide good service because if not there is no point in in all of this so we share the metrics that we identified for now we identify three basic metrics about retrieval that are time to first byte average retrieval speed and retrieval success rate but we are opening the future to introduce more metrics to be to be taken into account and of course this protocol would like to be I mean aims to be metric agnostic in a sense that you can plug in the metrics that you want and make it work so what's the overview of the protocol that we have in mind talking at high level we imagine a committee of auditor that query ideally each storage provider individually the second step is auditors taking part to a survey which basically is you can think of it as like answering some questions about storage providers performances each individual the individual servers are then aggregated final and aggregated results extracted and posted on chain and then both storage providers and auditors are are rewarded for for providing good service on the storage provider sites this will have to do with the metrics that they got in the aggregated results and with the auditors that would be according to you know the accuracy of the metrics that they provided to the to the auditor network so of course we cannot you know get everything in one shot and we have identified four milestones to to make this project happen the first milestone is the milestone we are actually in today and it's the initial consortium one so the the first thought on our side was there are a lot of people and a lot of entities that are already scanning the network for their own purposes for building reputation system base maybe not on retrieval but on other parameters and so we basically have an ongoing open call for partners that are interested in joining forces with us with the aim of building this auditing consortium of course at this stage even if you know the data that each entity has is kind of partial they do not really scan the entire network they do not test retrieval on all the storage providers at this particular stage it's fine we think that it's better to to start and to refine everything on the way and the result of these first days would be to have a non-chain reputation system for storage services based on the observation of this initial consortium we envision this initial consortium to be kind of small like five to ten entities working together as a sort of auditing league um and and and ask and querying storage providers of of pipeline for for technical reasons um the second milestone is to open uh to anyone to join um the the storage matrix uh DAO as a retrieval provider so basically we are in the second phase we are not focusing only on pipeline storage providers but ideally anyone for instance an apfs node can become a storage a retrieval provider being audited by the network um and we we can get there by for example using some project that protocol labs is developing internally like indices where people are basically publishing a list of of files of deals that they are willing to to provide on request the third milestone is actually introducing incentives for for storage providers and retrieval providers and that would be like uh where actually we are testing uh our protocol in in the in the real life uh like seeing how the the storage providers are reacting to um to to our um auditing services and the fourth milestone would be uh of course uh creating a reward program for for auditors and that would need we'll need uh like crypto economics insights and also allow anyone to become an auditor so we are basically passing through uh from a a phase where we have a few entities that kind of you know are already working on on pipeline metrics to some extent uh or on storage metrics to some extent to open it to anybody that can join as an auditor um like uh in order to make the auditor league uh going at scale so now which are the the very next question that we need to answer in order to advance the first one is how to to structure the final survey protocol and basically the main issue that we have I mean that everyone that is working on on such topics have is supporting collusion so we started this journey by by investigating some Bayesian truth serum like mechanism uh which are used usually in psychology in order to test behavior of people uh and we thought at the beginning that could be you know something that we could apply in practice because you know this this mechanism are rewarding uh unexpected results so we were thinking that maybe this could basically force people to to say unexpected truth and to find unexpected behaviors of of storage providers but unfortunately I mean either internally and also collaborating with external researchers we figured out that such tools were not designed to support collusion and so they're not kind of applicable to scenarios where you know there are entities that are communicating daily together and they can exchange uh or they can have interest in exchanging information so the main focus is making collusion irrational for the auditors uh or at least detectable and in general making honest reporting being the best strategy the second question that we have is should we anonymize auditors or making them indistinguishable from from generic clients why do I say that so the key question that we want to answer is the key point that we want to answer is we want these metrics to be reliable and in order to be reliable basically we should avoid storage providers to behave in a different way when they're talking with an auditor and when they're and when they're talking with with let's say clients that are not auditors so here we have two options the first option is more let's say protocol heavy which is uh the protocol by himself anonymizes the auditors and the second option is auditors actually are is in their interest to put in place mechanisms for which they can spot such behavior from uh from the storage providers and basically at this stage we can evaluate in the future maybe but at this stage we are leaning towards option two and the reason is that uh it's of interested of anybody that these metrics are accurate and so uh auditors can actually either cooperate themselves they cooperate with like clients they can set up multiple identities in order to make this detection more difficult but we think that you know the the system is like self governing himself at some point um the second thing that the other thing that we that we can that the one can argue is like most of the metrics that we want to measure depend or are influenced by proximity uh and again uh the we have different options on the table we can we can brainstorm about that if if anyone has ideas but one option could be like divide storage providers in clusters depending on location or like auditors having multiple nodes in different location and that's something that some organization already have for other purposes the thing that we I want to stress is like all this project works well for everybody and especially for auditors if the measures are accurate and so it's on our own auditors interest to make the measures as accurate as possible because this will maximize their reward so how can you get updates on this project we are working in the open you can go to the notion page of this project and you can find all the updates that that happen over time the team is composed by myself you find me on on Falcon's luck in on the end of Luca it's composed by Irene and Nicola and then we have also external research collaborators focused more on on game theory uh from the Oris University and we have internal engineering support by the bedrock team thank you and let me know if you have any question thanks Luca any questions to Luca storage metrics down you kind of touched on this with the voting protocol but um I guess are there any other ideas you all have for making sure the auditors don't collude so that's a really good question and that's something that we are actually you know actively working on um there are different contour measures these protocols are may are basically um divided in two steps which are like voting rules and and rewarding and payment rule where payment is like the reward that that the auditors are are getting and so one intuition that we have is like if you have for example a per round fixed reward and this is like actually your payment rule this would like not encourage you to share your observation with others why because basically if you are confident that your observation is accurate sharing your knowledge with other people would in the long term lower down your your per round uh reward uh of course this is only an intuition for now but we are kind of confident that we can put this in place and and the other thing on the voting rules are like there are uh um results that add some sort of redundancy and randomness in the voting algorithm that could help uh making collusion ineffective somehow we will post news on the website but I mean feel free to reach out if you want to know more uh this is something that we are particularly working with our external collaborators uh on on game theory so I would be much than happy to to keep talking about that thanks we have time for a few more is there any prior art that exists in other projects either other decentralized projects like you said research in other areas that would have to deal with auditors and service providers not colluding so uh kind of surprisingly uh it's the best of my knowledge it's not that common to to have people studying such such uh kind of protocols and indeed we try to you know to to get around some some limitation of the bts uh protocol and actually you know after talking with with many people which is not our primary area of expertise so we had to basically uh ask other experts it's kind of an unexplored land and we really think that in in the web tree setting it would be you know an interesting research direction to pursue uh and we are actually you know trying to to understand if like a long-term research plan on this is something that maybe we can we can get into in the future but it's not the thing is like long story short is like all the work that has been done in doing this kind of surveys did not take into account collusion because basically people were not really interested in in getting other people's opinion uh one one classic example is like you are in a in a department store and people tell you do you do you like more you know the this book or this other book and you want to basically build the survey on that for this kind of of situation there are you know bts like mechanism that work pretty well and force people to tell the truth but if you think about this for a second you are not really interested in what other people are are liking like you you are happy with what you like well instead here and when you basically put incentives on top and when you when you basically entering in systems like blockchain where people you know communicate and and exchange information all the time it's much more difficult to come up with something that is kind of effective we have time for a couple more questions any other questions to luka if not then i have one there was a mention you mentioned the auditor system um their identities they want to work on sort of a secret shopper model with the storage providers maybe so they don't they have to there might be some identity obfuscation there can you walk me through again who knows who the auditors are is there who's holding that kind of information i mean in that's exactly the point there are two options here there is the option where nobody knows who the auditor are especially when they run the survey like maybe you know that there is some organization who is an auditor who is auditing the network but you don't know like under which identity is is auditing the network but you know you can always backtrack and do this kind of thing so either you enforce this by protocol um but this you know adds some sort of uh complexity burden to the whole protocol or you basically play under the assumption that since being undetectable and and having you know a reliable matrix is uninterested of all the auditors then the auditor themselves would put in place um their own countermeasures which can mean you know making deals with like clients which would mean like having different clusters that that query different storage providers and so on and so forth and for now we are basically leaning towards the second option well thanks again luga for walking us through that very complex system and the system design i think our next presentation are we is actually occurring in person are we ready to go okay otherwise i have to moonwalk come on up excellent we'll take a five minute break and then reconvene all right welcome back everybody our next and final speaker needs no introduction but i'm paid for introduction so he gets one anyway Juan Benet is the founder of protocol labs the research development and deployment lab which developed ipfs and filecoin he's committed to building a better future using science and technology and today he's going to round out crypto economics day filecoin crypto economics day with the talk about solving large-scale problems with filecoin hey everyone great to be here with you awesome day uh today so what i want to talk about is how we can use crypto econ and filecoin to solve massive scale problems uh over the last certainly over the last 10 years it was already getting this sense but i think really the accelerate over the last three to four years um i've come to see the uh the stuff that's happening with web 3 in terms of incentive design and mechanism design as the largest levers that humanity has right now to fix massive scale problems because they enable this kind of incremental growth of some solution in a way that other macro systems have not quite enabled really awesome day to in great to dive into the depths of the pattern crypto econ this is gonna be this last talk is gonna go more broadly into thinking about how we can use this crypto econ principles to shift large-scale um other large-scale systems and how we can use the facilities of filecoin itself to um do that kind of thing uh so uh i gave a talk yesterday at shelling point called achieving paradotopia with crypto economics which is kind of a prerequisite if you are in there bummer however uh no i'll summarize it for you uh the basic idea the basic summary of the talk is is there on the right which is if regen whack me else and gmi uh the point is if we can create regenerative crypto economics we're all going to make it and we're going to advert disaster otherwise we're not this has been a this is an extremely critical century though there's been this amazing range of global improvement throughout history we are confronted with certain existential risks where we now have both the ability to wipe out ourselves and all of life um probably not all of life but most of life um and we might screw up this massive computing phase transition um that's happening uh and unfortunately right now our macro systems are inadequate though they've been uh excellent to get us through the last few centuries of global improvement um maybe debatable whether excellent is the right word but really good uh we've just had this unprecedented demand of global improvement um however uh we're headed into these kinds of problems that those government systems are not quite equipped to solve um now what we want to get to is kind of this part of the utopian outlook where we can use mechanism design to get everyone to be thinking in massive positive some um ways where you're not thinking through small resource increases but you're thinking of order of magnitude level increases and you're getting people to stop um negative competition um and engage in collaborative competition or just straight up collaboration um ideally you want to create an environment where the incentive use incentive structures to warp the actions that that various different groups might take so that you align them towards this this collaboration and you can get to uh the prior to topian outlook um prior to topia is this this conception that once you kind of realize that the future is so much so amazingly positive some if you can get there um then it sort of makes sense for everyone to sort of band together um align on that cooperation to do the science and technology um diffusion that we need to get to that that um uh fantastic future so think of crypt economics as this like tool that we can use to kind of reshape the landscape whether it's by kind of like um it boring through the whole creating a new a new a new uh hole through like this incentive field to try and get to like a better um optimum or by like moving mountains in that landscape itself um you can use mechanisms and drop them into the network uh to achieve this kind of this kind of thing just to give you a sense of um I think everybody here is already paged in into how powerful mechanism design is but um it's just every year I need to kind of like um step back and and and remind myself that web 3 is really programmable um the the tooling that web 3 gives us enables us to really reshape any kind of coordination structure and it's extremely fundamental it's it's eating um law it's eating finance eating all kinds of structures so we're going to be seeing over the next five to ten years uh and beyond um this kind of mechanism design rippling through major parts of the economy and major parts of governments and so on so it's an extremely like powerful lever um let's make sure that we use it for like really good things uh so for example in um the the bitcoin hashrate has been incredibly illustrative to me over the years in showing just how powerful of a lever this kind of mechanism design is one uh straightforward incentive structure both enabled the world to um get into the world of cryptocurrencies and actually enabled the cryptocurrency to um to grow to compete with fiat currencies but it also that same incentive structure also created the you know a massive runoff equilibrium that just wasted an enormous amount of amount of energy um and like you know there's in the second one like 12 um 12 years or so it's in 12 years we went from like you know network that didn't do anything to now one of the largest energy consuming things on the planet um wild and and that's kind of like without the massive that's also having to bust through the walls of getting people to accept your cryptocurrencies and get into that idea and forming the entire weaponry movement in its wake um now that the weaponry movement is here you could do you could redo something like this in probably two to five years um with what when we've gotten us a taste of just how powerful these incentive structures are by creating an open and permissionless network in a year and a half we've assembled 15 16 exabytes of capacity like that's an insane amount of storage um so that should give you a sense of like just how powerful these levers are uh now you have to like designing design really carefully and like it's great that we are having this day to like talk about the the nitty gritty details of these structures um but you know like the main takeaway here is like the more you can think in that kind of scale the more you can make open and permissionless networks and you get the you design the incentive structures carefully to achieve good results uh the better will be and also be careful like to take the bitcoin story as like a warning sign of like um the incentive structures you make once they like grow in to massive scales might have a bunch of unintended consequences and so um you know think through that um now I think that we can use all of this incentive structure mechanism design to accelerate the science of technology translation process itself um a lot of us here in this room and in this community are working towards these goals by um thinking through the all of these processes thinking about um how to enable funding to uh come to all kinds of efforts and groups that are doing work in science doing work in in technology building um we're creating new incentive structures to do that technology translation we're participating in the public goods ecosystem we're creating events to enable a lot of people to hear about these ideas present some other projects and so on and we're kind of trying to create a dramatically better kind of funding scale staircase for all these public goods type processes so it's a lot of work going on in here but um but I think it's been like a lot of people have been talking about this and events like shelling point and funding the commons and others are really great for for that conversation I'm going to give a different example today which is looking at the falcon green project uh I think this is a great example of using um mechanism design as this massive lever um breaking down a massive scale problem into like you know the picture of the stock is planetary scale problems right so how do we break a massive planetary scale problem into smaller bits um and the falcon green project is a great example of doing this so the I'll tell you a little bit about falcon green first and then tell you how to go from like a smaller problem to a larger scale problem uh falcon green is a project to decarbonize falcon and in so doing figure out systems by which to decarbonize the rest of crypto and the rest of the computing infrastructure and potentially other infrastructure we start small we start we we don't try to tackle the whole thing we try to tackle something much smaller let's think of just the falcon network and the energy use within the falcon network let's measure it let's figure out verifiable structures for identifying like where where the energy is going what kind of energy is it and then let's use financial instruments and structures to offset all of that energy use and ideally let's try and start applying pressure into the hardware manufacturers to produce better greener hardware so that's a stuff that maybe will come later that I don't think there's a ton of progress on this yet but be really awesome to start shifting those incentives one of the the key things here is to take a large problem break it down into something that you think will scale well that's achievable in the short term if you try to tackle something way too big that you just can't make progress on you'll you might spend many months to years tipping away the problem and you want to achieve that growth by grabbing something smaller that works good enough that isn't perfect but is good enough to show people that it can that it can scale that it can scale and it's already kind of directionally correct you kick off a movement and you kick off this broader range of improvement that can happen so the how this project works is that you know it's building a bunch of different sub projects and sub tools that are meant to kind of work together it's not like a big monolithic thing kind of a modular ecosystem oriented approach to making this kind of change it's building dashboards that track the energy use and estimate it so it doesn't have a perfect estimate it's not it doesn't matter it creates a lower bound it creates an upper bound it has some some estimate then using that estimate we can then go around and buy rex rex are renewable energy credits which enable you to to one of the really cool things about rex and why they might be better than many other carbon offset type things is that in that they enable you to show precisely where the energy is coming from in which grid from which producer at which time of day and what kind of plant it is that lets you in a in a verifiable way get a sense of precisely what type of energy is being put into the grid precisely at the time that you're pulling it out out of the grid and so that there you can have a strong claim that the energy you're pulling out of the grid is precisely the energy you're buying that when you're putting in which gets rid of a bunch of the nasty problems around carbon credits so carbon credits a great idea phenomenal idea to use the financial system to carbonize the planet however if you don't if you create a bunch of loopholes you can end up with a bunch of unintended consequences there's been a bunch of stories of that in the past so renewable energy credits kind of solve that problem solve a set of problems there of like trying to introduce way more verifiable into the picture i would expect this kind of thing to be scaled like get to the point where you have machines in the plants themselves that that are able to run some zero knowledge computation or that have like tamper proof hardware or stuff like that where you know precisely that like you know somebody's not messing messing with the way the estimates and so on but we got to start somewhere and we got to create the incentive structures for the whole system to get better towards that by creating significant rewards for this kind of work people will progressively get better we'll get better better at doing these kinds of things so the falcon project falcon green project in addition is like enabling a lot of other groups to and people to get involved because it's enabling individuals to who have other ideas or new ideas or want to pursue some of the ideas of the project to go try them out and this might be through like different kind of grant systems like a bunch of different grants that that the project sort of connected to and is building a whole set of collaborations with a bunch of other groups to kind of follow the path of these these resistance towards greater and greater impact it's also enabling a lot of people individuals that like are interested in the project or interested in the ideas to actually get involved and this is a crucial thing that I think I see tons of projects forget if you create good avenues by which individuals that are interested in helping you where they can follow the information where they can participate where they can learn about what you're doing you can turn something that might be like a small project for a small group of people into a massive movement that a lot of people are gonna gonna work on and so things like meetups end up being really important creating a sustainable blockchain summit you know that is not the sustainable falcon summit creating a sustainable blockchain summit creates the the opening to get a bunch of other crypto groups to show up and also talk about their projects to decarbonize their chains and how to use that stuff to decarbonize the planet and so by by creating kind of like all these like smaller level levers that align incentives with other groups you start kind of like tackling this larger larger problem by the way fantastic talks from that summit and can't wait for the for the next one is a bunch of like really really good stuff there and you know like you would call it like you can follow and participate directly asynchronously in in the network so that's kind of like the the the technology behind falcon green involves looking at the actual physical machines that are running the falcon network estimating the power consumption and then getting leaning into the verifiable claims stack that wreck the renewable energy credits community has built and building these two things together building a market where you can precisely buy wrecks for precisely where the sps are purchasing energy connecting a lot of things together and enabling the thing to work fully but and kicking off an incentive structure for the whole network now then the kind of the whole social incentive structure part of this is you know you're using kind of news and grants and event forming and so on to catalyze a larger scale action in in the community there's both like incentives directly in the network and the for the people that are running the network and incentives for the broader community to actually have an impact in the in the broader thing so let's scale it up so so if we get to decarbonize falcon and we can solve that as a problem we can then start scaling it up we can work with other chains that are doing the same thing we can talk to other chains about that how how to approach this problem we can give them a bunch of the technology we can encourage them or incentivize them to do this by saying hey look our chains way better it's way not only not only can we get to carbon neutral we can get to carbon negative one of the things that I want to get to is like be able to say that you know falcon is like I don't know 5x or 10x carbon negative in an area so that whenever falcon is there it is actually like pulling carbon from the atmosphere around itself for that nation or something like that that I think that would be pretty pretty awesome and if we can do that then we can incentivize a bunch of other groups in the blockchain space to do that and then if suddenly all of crypto itself is doing this and we you know get rid of this like mistake in in our past of like having produced the the big big massive energy waste that bitcoin is and we can not only offset all of the other chains but also bitcoin and we make all of crypto not only carbon neutral but but massively carbon negative then at that point the whole world is going to start paying attention paying attention and suddenly other industries entire other industries industries will be incentivized very strongly to catch up to the crypto space once you get a bunch of other industries incentivized to catch up with the crypto space you can then finally get to a point where large enormous large groups are getting to be carbon negative and we can kind of unwind this like crazy set of emissions back down so I think like all of this is start with like massive scale problem break it down into smaller groups and find ways everywhere along the way from the small scales to the medium scales to the large scales of creating incentives by which like to cost coordination you lean into competition so collaborate foundation you want to get the different crypto communities to be competing with each other of like who's the greenest blockchain because that's what's going to get crypto itself to be very green and then once crypto itself is green then we can compete with the other industries about which industry is the greenest and so you want to use the sense of like highly collaborative competitive open environments at every stack so that's kind of like an approach to solving a large-scale planetary problem now of course like there's an enormous amount of work in getting this done there's all kinds of systems that we need to improve all kinds of systems that we need to design and build and and so on all kinds of processes that that we need to kind of like refine make verifiable and so on this is an area where all kinds of tech is going to from the crypto space from now in a different angle all kinds of other really amazing improvements in the crypto space can help this kind of computation so all the zero knowledge proof stuff all the verifiable computation stuff is phenomenal for this kind of thing this is this kind of problem and many other problems in the world have a kind of crisis of incentives and trust where you want certain parties to take certain actions but ideally you want to not have to trust them you ideally want to be able to provide a verifiable variability layer to what's going on so that you can trust the entire process and the entire system I think like all of the zero knowledge proof proof type of stuff can be can come into all kinds of facets of a broader industry and broader world activity to improve how those those systems work and enable you to create financialized structures that actually work one of the big kind of points of contention that the broader mainstream world has on financializing this the solution of problems is that it's very easy to kind of warp the incentives and end up with all kinds of unintended consequences or just like like militia claims outright where like parties are lying about the the stuff that they're doing but that's where verifiability is really important be able to create verifiable structures that you know keep removing sources of potential problems the thing like all the cryptography deep cryptography work that is going on in international proofs it's going to be like a very large lever at solving a bunch of these problems because you can take those kind of verifiable claim type structures weave them into these larger industries and then kind of then at that point really financialize them and scale them into massive scales. One thing that's going to help here and I don't have a slide for this is certificates of impact are going to I think I and there's other talks about it about these I do think that once we have these verifiable claim structures certificates of impact present a way to financialize almost anything which is an extremely extremely powerful primitive so we'll see a lot of that stuff coming on coming out over time one other thing I want to I want to reflect on is the Falcon plus incentive structure is really really useful it gives us a way to incentivize the gathering collection like assembling cleaning up of and making use of usable yeah it enables us to identify a bunch of data sets that we can collect gather improve and refine and so on and make usable and so we should be using these incentive structures to go after massive scale problems so for example think of massive scale data sets that could be really impactful in certain problems that are really big and really expensive and really hard to deal with or that are really far away from being able to make verifiable or are very disconnected from it from the economy you can start doing things like think through some really important data set that you think is going to be enable some kind of financialized way to solve some major problem in the world and say okay great like what's the data that we need so that we can get a bunch of speculators to speculate on the on the prediction market about what's going to happen so for example let me go to this first no return think of like being able to make claims about the planet so there are data sets like lansat right now lansat is not really connected to the crypto economy i think i don't know if hedge funds trade on this but i'm sure they trade on all kinds of activity that is totally viewable through lansat so if you had like a real time connectivity to exactly what's happening on the planet and you had blockchains being able to act on that data right away and you had like massive scale bets going on and precisely what what what's going on and you had a verifiable way to look at it then you can you can start making making certain kinds of rates possible and that point you can you can then start warping the incentive field to achieve the outcomes that you want so this might be ways of like holding groups accountable for certain actions or this might be ways of like responding to certain problems or it might be ways of like settling important forecasts and predictions right so the crypto world has not just the crypto world but a lot of groups have pointed out that forecasting of massive scale problems could be greatly helped by prediction markets unfortunately prediction markets aren't quite allowed in a bunch of bunch of places that's a bummer in the crypto space there's going to be a lot i'm guessing there's going to be way more use of prediction markets and so hopefully we might end up with like prediction market Dow things that are able to take significant bets about cases like this so that we can understand what's going to what's going what certain actions are going to do in certain regions so you can then at that point get a lot more information about certain kinds of problems prediction markets are an extremely useful structure because they let you build a lot of trust in specific actors that get extremely good at predicting the future so that you know ahead of time how much you should trust certain kinds of possibilities and proposals and so on so all of this kind of data landscape is pretty big especially if you want to get like higher resolution imagery or you want to get not just like visual imagery but you want to get all kinds of other kind of data about the world but you know once you there are these massive scale data sets about a certain like the actual ground for example like massive mining companies have to generate like the insanely complex huge data sets about all of the telemetry data they're gathering in certain regions all of that data you could bring into Filecoin you can use the Filecoin plus incentive to just cause all these groups to gather that data and then once you have the data connected to smart contracts and then financialize it which is super super super interesting now the stuff that is going to enable a lot of that stuff is I'll say it for the 50th time today the FEM the FEM is going to enable that kind of layer to emerge because kind of like what we've been missing is the hook where we can start creating computational networks around Filecoin right so Filecoin right now doesn't have smart contracts once it does then create schedulers and then start doing data pipelines around all of this data so the missing pieces like so you can bring all that data into into Filecoin now you want to connect it to a smart contract and you need the ability to run arbitrary code in ideally a verifiable computing sort of way and so like that's that's a things that are going to that are going to happen over the next six to twelve months is building out all of these kinds of computational networks so things like and when you think about like a Filecoin storage providers they have these massive scale deployments and they already have a bunch of GPUs that's precisely what you need to build to build a massive computational network that can operate over this whole data and that can run all the all the expensive proofs for this kind of information so this kind of thing of like taking a data set like Landsat and thinking about a certain kind of action that you want to incentivize and building all these infrastructure around it needs these like two missing pieces one is like the smart contract layer and then after that like the ability to run verifiable computation over that data set and that's coming really fast that's you know going to be here in the next six to twelve months and at that point you can do you can put all these pieces together and get that kind of that kind of action so this is one example data set there's probably hundreds of these kinds of example data sets so we're getting we're building these massive levers to like move mountains and move the world there's all kinds of different kinds of use cases that that could we could lean on probably should have some confidence about this like what kind of large-scale problem is connected some like to some massive data set we're gaining the ability to financialize that would produce you know some major positive impact in the world so maybe like get to do for the future like maybe another crypto you can they could focus on what massive scale problems can we solve with the big one great so I think probably the last thing I'll mention here is that at the end of the day like the the tools that we're building will only be successful if we can get them into into the market and use by large large groups of people so that means again taking breaking down a massive scale problem into smaller and smaller and smaller pieces starting with that with that construction creating an incentive structure that enables a lot of other parties to come in and contribute you want to use collaborative competition because that's a very useful structure to cause many other groups to to go as fast as possible towards some goal you want to again be very careful about the incentive structure design because if you screw it up then you know you get the Bitcoin proof of work waste but if you can do that from smaller to larger to larger and larger and larger structures then you can really move mountains and you can fix the planet great thanks and I don't know if there's time for questions but I think the time's up so I'll stop here thanks Juan after a talk like that there's always time for questions questions to the speaker I'm gonna go back yeah I think the question I have is who designs these incentive systems and yeah that centralizes the power in in one way in their hands yeah I mean I think I think blockchain networks are vastly more more open about this than like you can propose a design change to a blockchain in a way that you can't do a financial you can submit a FIP you can submit an EIP you can't do that to the US government or to the IMF and so on right which is kind of like wild so I think I think there's all kinds of structures that can and should be improved across all these systems to get to a better spot I think you've taken in particular there's like all this complexity because you're designing incentive structures there's all kinds of groups that will benefit or lose from certain mechanisms so it very quickly gets heavily politicized and problematic but if you can kind of like remove that as much as possible get to a spot where like there's common knowledge that certain things are really useful broadly for the network and aligned with a long term mission then you can build significant agreement in those networks to get that kind of change happening a good example of this is the Ethereum community is just filled with moments like this that it had to kind of break through and it shifted its own economic structures sometimes you know like the move off of proof of work into proof of stake is like a massive one that they've been doing for years it's a huge economic shift but the community had enough interest in that to cause that kind of shift but you know these blockchain networks are dramatically easier to to change in this way there's probably like a like a from in a machine learning way a hyper parameter optimization thing that we could do here which is like how do you create um how do you create way more crypto activity happening and you kind of create a much more robust study of all these systems I think right now we know this in the blockchain community like the the level of rigor that we bring to crypto econ is way higher than many other many other groups and even when you integrate put all of that together across the the best groups and in all of them that is probably way lower than what we want we ideally want something 10 to 100 to a thousand acts better than whatever we have going on and so finding ways of doing that might be might be really good potentially might be like causing there to be 10 to 100,000 more 10 to 100x more people going into studying crypto econ we might start with a crypto econ textbook I heard that said before I think David I didn't know in who helped design a lot of the platform crypto econ I think said in the past like oh wow this is like super powerful it took me a while to learn this it'd be great if somebody wrote a crypto econ textbook so that a lot of people could like have a much faster path at understanding what you need for the mechanism design because you have to learn a lot you have to learn a bunch of game theory you have to learn a lot of distributed systems you have to learn a lot of cryptography and so on and you end up in like pulling pieces from a bunch of things that to actually get to get to crypto econ so there's like a good high lever high leverage thing for somebody somebody to do one more I feel like what right I've had a lot of conversations this week about privacy anonymity I think there's a lot of people that value maximizing that in the web through ecosystem and yet we're looking at like planetary surveillance maximization as well like do you ever wonder if or do you add what is your what are your thoughts on anonymity privacy maximization potentially being anti progressive on the context of what you're talking yeah it's a huge problem and it's very complicated um so maybe like in the most succinct way I would say that there's we're an integrated system like the idea that there are individuals and groups and so on is like useful fictions for us to like operate but at the end of the day you have like this massive integrated system you know when you go down to atoms and so on and so you end up in a situation where at various layers of the stack you want to have a different part of that solution for example at a planetary scale is very important for the whole planet planetary community to be able to understand what that what's going on with the planet you know what nuclear tests are happening what emissions are happening and so on what oil spills are happening what trash is being spilled into the ocean or picked up or whatever and maybe even at the range of a city you might want to understand what kind of like problems are happening in certain streets or whatever like you think of Europe is filled with like cctp all over the place like walking around Amsterdam you can see all the cameras I'm sure that they reduce crime I'm sure they also violate privacy I'm sure that they also create the conditions by which you can do digital digitally to tell terry and things and like that's terrifying um there's a really hard trade-off I think the where I sort of land on this is the more you can create you can agree on the policies and the preferences and what is a good mission and the more you can kind of enforce the technology to follow that and only that and limit the scope of potential misuse the better off you are so that means leaning into um verifiable computation leaning into homomorphic encryption leaning into all of those kinds of kind of privacy preserving techniques that still allow you to as a group compute on something together it doesn't you don't have to have access to the cctp camera things you can have ml algorithms figure out like oh yeah this is a problem and then at that point decrypt um so that's like a like an example kind of use case but I think this is a deeply hard question that is going to have very different answers at different layers of the stack at different points in time and in different societies and it's extremely hard to predict what's going to yield a better outcome because it's highly dynamic the world changes a lot and changes really fast and so today we have a lot of peace and and prosperity globally relative to many points in the past but that could change and if it changes then we might feel very differently about a bunch of these structures so I think we're running out of time now so if you'd like to ask any more questions you may get to um do that at the social hour after after this meeting I'd like to thank Juan one more time for allowing us to end on a very high note at planetary scale and give another shout out to all of our presenters today and for you the audience for your questions and your thoughtful attention I hope we've convinced you that Filecoin crypto crypto economics is a really fun sandbox to play in a really cool and very powerful system to explore some very deep questions and that maybe we've attracted you to come play with some of our toys and now we'd like to invite you to socialize a bit and to chat about some of the topics we discussed today below in the atrium level at the social hour thanks again to all the presenters and cheers