Zero-Knowledge AI: The Frontier of Cryptography | Zuzalu #2

Welcome to Bankless, where we explore the frontier of internet money and internet finance. In this 8-episode series, we are exploring some new frontiers. New frontiers in new technologies, all of which are poised to completely revolutionize the world and change everything about the operating system that society is currently running. This video features two conversations with big-brain builders Daniel Shorr of ZKML and Justin Drake of the Ethereum Foundation. Although cryptography quickly falls down a technical rabbit hole, it looks like we're entering a new golden age of cryptography. Keep an eye out as we roll out the rest of these boundary-pushing episodes! ----- BANKLESS SPONSOR TOOLS: 🐙KRAKEN | MOST-TRUSTED CRYPTO EXCHANGE https://k.xyz/bankless-pod-q2 🦊METAMASK LEARN | HELPFUL WEB3 RESOURCE https://bankless.cc/MetaMask ⚖️ ARBITRUM | SCALING ETHEREUM https://bankless.cc/Arbitrum 🦄UNISWAP | ON-CHAIN MARKETPLACE https://bankless.cc/uniswap 🛞MANTLE | MODULAR LAYER 2 NETWORK https://bankless.cc/Mantle ----- Timestamps: 4:00 DANIEL SHORR 6:00 Cryptography and AI 9:15 A Computing Explosion 13:10 On-Chain AI Models 15:45 Modulus Labs 19:15 Compute Integrity 24:00 A Verifiable World 26:15 Accountability for Machine Models 31:00 JUSTIN DRAKE 33:30 Nova Cryptography 39:00 Decentralizing zk-Rollups 43:30 The Future of Rollups 47:35 Improving Validity 50:45 This is a Big Deal 54:20 Justin’s Zuzalu Experience ---- Resources: Daniel Shorr https://twitter.com/realDanielShorr?s=20 Modulus Labs https://www.moduluslabs.xyz/ Justin Drake https://twitter.com/drakefjustin?s=20 ---- Not financial or tax advice. This channel is strictly educational and is not investment advice or a solicitation to buy or sell any assets or to make any financial decisions. This video is not tax advice. Talk to your accountant. Do your own research. Disclosure. From time-to-time I may add links in this newsletter to products I use. I may receive commission if you make a purchase through one of these links. Additionally, the Bankless writers hold crypto assets. See our investment disclosures here: https://www.bankless.com/disclosures

Speaker names marked ? are inferred from speaking time and the show’s known hosts — they are not voice-verified.

• 0:04David Hoffman?

  Welcome to Bankless, where we explore the frontier of internet money and internet finance. And today, on this episode of our Zuzalu series, we're exploring some new frontiers. New frontiers and new technologies, all of which are poised to completely revolutionize the world and change everything about the operating system that society is currently running. On this episode of our Zuzalu series, we're exploring the frontier of cryptography, which is maybe not as new of a frontier as some of the other ones that we've explored yet, nonetheless. The cryptography-enabled future is poised to change the landscape as all the other technologies that we've talked about. I will say that the ZK Week at Zuzalu was one of the weeks that I attended the fewest talks and workshops on, because, I mean, come on, what am I gonna do there? Which is why I pulled in a very familiar, friendly face, Justin Drake, to summarize

• 0:49David Hoffman?

  the entire ZK Week at Zuzalu in a 45-minute episode. Turns out, there's this cool new frontier of crypto called NOVA, Nova ZK Nova, which has to do with folding numbers recursively to make cryptography harder? I don't know how to explain it, but that's what Justin is for. But first, before we get to the familiar territory of Justin Drake, we're going to talk to Daniel Shore, who's working at a startup in the ZKML landscape, which has gotten a ton of hype and attention lately. And if you listen to the interview with Daniel, you'll understand why. The thesis is that there's going to be a Cambrian explosion of AI models out there, and simply verifying the model itself on-chain using Ethereum and a ZK proof can give consumers

• 1:34David Hoffman?

  and users of these models assurances of the authenticity and the outputs of that model. The fact that the input actually went through the correct model and the output is actually verified by the model that you want. Why this is important and what this unlocks, Daniel will explain in the show. Bankless Nation, this one is a doozy, but Daniel and Justin do a great job of dumbing it down for us in this episode. So let's go ahead and get right into it. But first, a moment to talk about some of these fantastic sponsors that make the show possible. Kraken Pro has easily become the best crypto trading platform in the industry. The place I use to check the charts and the crypto prices, even when I'm not looking to place a trade. On Kraken Pro, you'll have access to advanced charting tools, real-time market data, and lightning-fast trade execution, all inside their spiffy new modular interface.

• 2:19David Hoffman?

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• 3:02David Hoffman?

  available exchange with your debit card with extremely low fiat on-ramp fees, and you can seamlessly swap on mainnet, Polygon, Arbitrum, and Optimism. On the Uniswap Mobile Wallet, you can store and display your beautiful NFTs, and you can also explore Web3 with the in-app search features, market leaderboards, and price charts, or use Wallet Connect to connect to any Web3 application. So you can now go directly to DeFi with the Uniswap Mobile Wallet. Safe, simple custody from the most trusted team in DeFi. Download the Uniswap Wallet today on iOS. There is a link in the show notes. Mantle, formerly known as BitDow, is the first DAO-led Web3 ecosystem, all built on top of Mantle's first core product, the Mantle Network, a brand-new, high-performance Ethereum Layer 2 built using the OP stack, but uses EigenLayer's data availability solution instead of the expensive Ethereum Layer 1.

• 3:47David Hoffman?

  Not only does this reduce Mantle Network's gas fees by 80%, but it also reduces gas fee volatility, providing a more stable foundation for Mantle's applications. The Mantle Treasury is one of the biggest DAO-owned treasuries, which is seeding an ecosystem of projects from all around the Web3 space for Mantle. Mantle already has sub-communities from around Web3 on-boarded, like Game7 for Web3 gaming and Bybit for TVL and liquidity and on-ramps. So if you want to build on the Mantle network, Mantle is offering a grants program that provides milestone-based funding to promising projects that help expand, secure, and decentralize Mantle. If you want to get started working with the first DAO-led Layer 2 ecosystem, check out Mantle at mantle.xyz and follow them on Twitter at @zeroxmantle. Bankless Nation, it is ZK Week here at Zuzalu,

• 4:31David Hoffman?

  and I am talking to Daniel from Modulus Labs. Daniel, welcome to the show.

• 4:35Speaker 01

  Thank you for having me.

• 4:36David Hoffman?

  Okay, so there is growing hype and attention around this world of ZK ML. And so we're getting some hypey adjectives, not adjectives, hypey...

• 4:47Speaker 01

  Letters, consonants.

• 4:48David Hoffman?

  Yes, exactly, letters, yeah. Can you explain the world of ZK and ML to the best of your ability and as short as possible? And then we'll get to why these things are currently getting married in this short time.

• 5:00Speaker 01

  Yeah, I think, I mean, let's just start with the letters. ZK is the first two. Zero knowledge is often known as an accountability or integrity technology. So it basically tells you that some compute was done correctly. So that's exciting and actually has this odd property where verifying that compute is a lot less expensive than doing the compute naively. So it's often been used to compress information and then, in the case of blockchain, bring that information on-chain while retaining the security standard.

• 5:27David Hoffman?

  The metaphor we like to use a lot is that it's hard to complete a Sudoku puzzle, but once a Sudoku puzzle is completed, it is easy to verify that it was done correctly.

• 5:36Speaker 01

  Exactly.

• 5:36David Hoffman?

  And that's just like ZK in its essence.

• 5:39Speaker 01

  Yes, yes, precisely. That's kind of the most powerful property of ZK in the blockchain context. Kind of what's next then, at least for us, was, okay, if this technology can be used to scale blockchains, what is the most kind of intense or almost irresponsible kind of compute we can throw at this kind of technology? And in terms of the size of compute, it doesn't get much larger than machine learning models.

• 6:00David Hoffman?

  Irresponsible is just in like the magnitude of the compute?

• 6:04Speaker 01

  Yes, exactly.

• 6:05David Hoffman?

  So you're stress testing the whole paradigm of ZK.

• 6:08Speaker 01

  That's right.

• 6:09David Hoffman?

  And that's how you got to ML?

• 6:10Speaker 01

  That's right.

• 6:11David Hoffman?

  Why is, okay, what is ML and why is it so intensive?

• 6:13Speaker 01

  Yes. So, machine learning or just artificial intelligence in general is a process of using an algorithm to approximate human-like decision-making. So think like high semantic output space, right? So I want to look at an image and decide if it's a cat or a dog. Or I want to kind of predict what might happen to prices in the future by looking at a lot of data from before. Traditionally, this has been seen as kind of a task for human beings. But ML is this kind of wild regime where algorithms can take the place of human decision-making.

• 6:41David Hoffman?

  And specifically, these algorithms, there's a software and a hardware component, right? And the hardware is kind of the clock speed, how fast this thing can think. And you can throw a lot of hardware at this thing. You can, and that will get the result faster. But the point is, is that blockchains, Ethereum, is not, is not something that you go to to do a lot of compute quickly because that is what gas is, that is what gas fees are.

• 7:07Speaker 01

  That's right.

• 7:07David Hoffman?

  Okay, so like where does this intersection occur at? So like if you're, if you said that you want to have this irresponsible use of a ZK proof and what that means is just like throwing a lot of compute at this thing, that's fun. Why is this a real utility? Why is this actually useful?

• 7:23Speaker 01

  Well, kind of the, the magic is, I guess, the security of the blockchain, right? And the ability to bring compute of any size, but especially in this case, really large compute, up to that same security so that you can ingest AI decision-making into your smart contracts or into your DApps and on-chain services. That, we think, is a really powerful paradigm and something that's now uniquely enabled by the fact that ZK has improved so much, and especially, at least in the case of Modulus, with a focus towards machine learning compute.

• 7:52David Hoffman?

  Okay, so apps can use AI. Can you just unpack that a little bit?

• 7:55Speaker 01

  For sure.

• 7:56David Hoffman?

  It's a very general statement.

• 7:58Speaker 01

  Yeah.

• 7:58David Hoffman?

  Can you make it a little bit more just defined and illustrative?

• 8:00Speaker 01

  Sure, of course. Let's take a, some kind of DeFi service which has liquidity pools. Perhaps they want to rebalance these pools using a really advanced algorithm, maybe something even akin to an AI algorithm. Currently, all that compute needs to happen off-chain because it's just too expensive to run on-chain. But with ZK AI or ZK ML, you can imagine that off-chain compute getting that Zayha's seal of approval. It's like, okay, this compute has been pre-committed. Bless you, of course. And using zero knowledge, we can prove that that compute was done correctly, thereby upgrading it up to the security standard of the chain. And it's as though that entire process, in this case of rebalancing the pool, ran naively just on-chain.

• 8:38David Hoffman?

  Why do you need to verify? Why do you need to verify it? Why can't you just run it without actually having to verify it?

• 8:45Speaker 01

  Yeah, and I guess I'll start by saying a lot of services currently do just run compute off-chain, right? It's certainly a lot less expensive. But I think a big part of why we're in the blockchain ecosystem at all, and certainly a big part of what attracts Modulus, is the kind of security standards that's established by this decentralized decentralized network of nodes and having the social consensus and all the wonderful things we get in Ethereum and some other chains as well. And so we want to make sure that the services that write on these chains, that buy into the security ethos of the chain, gets to participate in that fully, even as they take on bigger statements of work when it comes to compute. So if I'm a D app or I'm an on-chain service and I want to use AI, maybe I don't have to give up on the security that my customer and maybe even myself, I expect out of my own service.

• 9:29David Hoffman?

  Okay, so I think the reason why this whole area of focus is getting so much attention, ML, machine learning, ChatGPT has just like elevated that into the stratosphere.

• 9:39Speaker 01

  Yes.

• 9:40David Hoffman?

  ZK, on the crypto side of things, the theme of ZK Week was how fast and compressed and capable some of these ZK circuits are becoming.

• 9:50Speaker 01

  Yes.

• 9:51David Hoffman?

  And so on one side of things, we have the growing Cambrian explosion of AI getting large in capability. And then on the other side, we have ZK decreasing. Which was what ZK does, decreasing complexity and simplifying compute.

• 10:06Speaker 01

  Yes.

• 10:07David Hoffman?

  And so like at a high level, just like broad stroke, you can see how these things kind of would work together. I'm guessing without knowing too much about this kind of naively, that the idea of putting an AI on chain implies that there's going to be tons of AI models, like generalizable, any kind of model that you can think of that an AI would be able to produce for, I don't know, DeFi liquidity comes to mind, but like literally anything.

• 10:36Speaker 01

  Sure.

• 10:36David Hoffman?

  And then the idea is that with ZKML, you get to verify the actual model, the external model that I'm assuming many, many people are going to work on, like individual models. And we want to make sure that that is the actual model that we are running on chain.

• 10:51Speaker 01

  Exactly.

• 10:51David Hoffman?

  And that's the ZK component.

• 10:52Speaker 01

  Yeah, you got it. I mean, this is, I mean, sure, it's DeFi and certain market mechanisms potentially, or maybe moving users' funds, of course, or crypto across different chains to optimize yield farming, or even NFTs, right? Let's say I want a machine algorithm to output pixel art or any type of generative art, but I want to know that that piece of art actually came from the algorithm that might be really valuable, right? So ZKML is a way of extending that kind of cryptographic promise of authenticity to all kinds of AI outputs specifically. So, you know, I guess the possibilities are substantial, so long as we can get the tech right. And obviously, all the acceleration on the ZK side really helps with that.

• 11:33David Hoffman?

  So I'm just going to like list off a bunch of models that I can think of. We got like ChatGPT is the big one, GPT-4, Midjourney. What are some other ones that are out there? Like we got Bard.

• 11:46Speaker 01

  Sure.

• 11:47David Hoffman?

  There's all of these models.

• 11:48Speaker 01

  Older models like BERT, right? Recommender models, generative models that output pixel art, GANs, generative adversarial networks, as well as maybe more subtle models, like anything from, you know, a game that uses an agent, right? An AI agent to simulate NPCs, to something more kind of outlandish, like an LLM that maybe predicts investment decisions or things like that.

• 12:11David Hoffman?

  So perhaps one model is, in the gaming world,

• 12:14Speaker 01

  Sure.

• 12:15David Hoffman?

  is something that is generatively producing a landscape.

• 12:19Speaker 01

  Sure, yeah.

• 12:20David Hoffman?

  That is a model.

• 12:21Speaker 01

  Yes.

• 12:21David Hoffman?

  And maybe this applies in the world of, for people that are familiar with Dark Forest.

• 12:25Speaker 01

  Sure, totally.

• 12:25David Hoffman?

  And we need to make sure that we're playing a game that has a specific model.

• 12:29Speaker 01

  Yes.

• 12:29David Hoffman?

  To define the landscape of the game that we are playing.

• 12:32Speaker 01

  Exactly.

• 12:32David Hoffman?

  This is an example of a model?

• 12:33Speaker 01

  Yeah, that's great. And the counterparty risk here is bigger than just swapping out the models. It's swapping out the output entirely. So let's say you have an AI responsible for the landscape, or the weather, or the in-game economy, or it's like a, almost like a god role in your game, right? If, you know, I as the operator of the game or the developer is biased to, let's say, penalize David's camp, or David's planet, right? We're gonna nuke your planet from orbit via the decree of the Galactic Government. And there might be enormous financial stakes through these in-game economies. That's like a devastating result.

• 13:04David Hoffman?

  Right.

• 13:05Speaker 01

  And so in the same way that-

• 13:06David Hoffman?

  It's a rug pull.

• 13:06Speaker 01

  It's a rug pull, yes. And so in the same way that blockchains kind of extend this veil of security, where this promise of security, rather, to everything that's on-chain, we want to make sure that keeps being the case even as we want to bring AI kind of feature sets on-chain.

• 13:20David Hoffman?

  Right, okay. And so like, just going further, like, we got the AI generative landscape, and you also have like the models of AI NPCs.

• 13:28Speaker 01

  Sure.

• 13:28David Hoffman?

  And like, I could just think of any, it's kind of fun to think of like what AI models could become. But I think that's kind of the point.

• 13:34Speaker 01

  Sure.

• 13:34David Hoffman?

  Is that it's so generalizable, right? And so, AIs, we're about to get into AI Week. Lots of AI talks happening right now. There's a talk of generalizable AI, like artificial general intelligence. But then there's also the topic of like narrow AI.

• 13:48Speaker 01

  Yes.

• 13:48David Hoffman?

  These are both models.

• 13:49Speaker 01

  Yeah.

• 13:50David Hoffman?

  Right? Like AlphaZero is a narrow AI about chess. And then we have more generalizable AIs that we think are coming, or word are coming, also a model. And so, the landscape of models, which is a, it's one of those words that you don't really understand until you see how generalized it is. The growing world of AIs, generalized models for stuff.

• 14:12Speaker 01

  Mm-hmm.

• 14:12David Hoffman?

  And then the ZK element allows it to take the soul of that model, of a particular model, and place it on-chain to become an on- Chain resource.

• 14:22Speaker 01

  That's right.

• 14:22David Hoffman?

  For the rest of the blockchain ecosystem to use.

• 14:24Speaker 01

  Exactly, and it has all those properties that we love so much about things being on chain, including it's composable. It's obviously a high security. It's in some sense really attestable and referenceable. All these things that you know are makes the chain kind of a wonderful environment. You kind of imbued that AI compute, which previously was just in a black box somewhere.

• 14:43David Hoffman?

  And so like this, I'm assuming, yeah, the composable part gets really, really cool because then you can create some sort of system that create creates a world of models. And I don't know where where is the utility coming from first like so if there's the it's exciting to see all this possibility.

• 15:00Speaker 01

  Sure.

• 15:01David Hoffman?

  What's the lowest hanging fruit? Where's it gonna get built first?

• 15:03Speaker 01

  Yeah, great question. You know, there's already kind of prototypes, including ones that we've built and others as well. Kind of exploring, it's still a very nascent category, right? Everything from you mentioned this briefly, but like chess engines. Right? So we brought a formula and parameter chess engine on chain. And now players can stake and bet against the chess engine, knowing full well that they're always playing against the same AI model. And in no way can modulus or anyone else swap out the results of this particular chess engine. By calling up our friend Magnus and it's like, hey, Magnus, what move would you play? Yes, but you know, that's just where we're starting. We're also starting a ZK GAN project, so generative art. And of course, we're kind of marching towards that LLM goal. But there's a lot of kind of space in between that, of course, right? Again, I mentioned like recommenders earlier, there's a lot of use cases for just any amount of personalization when it comes to the chain.

• 15:48Speaker 01

  But still at the kind of security standards we want, right? If you have a social media feed, but you want to know that the same algorithm that drives the equitable results you see on Daniel's feed is the same one that David sees. Then ZK ML can play a really, really key role there.

• 16:02David Hoffman?

  So, okay, at modulus labs, what's the current? What are you guys focusing on right now? What do you guys? What's the current bottleneck or constraint or problem that you guys are currently solving?

• 16:11Speaker 01

  Yeah.

• 16:11David Hoffman?

  Like, where are you? Where are you in the roadmap, I guess?

• 16:13Speaker 01

  Yeah, yeah, great question. So you kind of alluded earlier in the ZK week, right? We're seeing kind of the ZK overhead come down substantially. And that is amazing and obviously very helpful. Precisely because at the top, we mentioned that AI is like a lot of compute, it's almost irresponsible. I think the exciting here is that our job's almost easier because AI as a Class of compute is really structured, really repetitive. And those things, those like properties of that compute allows us to make the ZK stack much more efficient. Right. In some sense, almost like you've down selected to a more specialized class of problems. That gives your ZK prover a lot of space to be more efficient and kind of take advantage of that structure. And so modular is kind of what we work on for the most part is making the proving stack significantly more efficient.

• 16:55Speaker 01

  So that we can bring much larger, much more expressive models on chain at still that same security standard.

• 17:00David Hoffman?

  Okay. So this is what your guys' technology is. You guys aren't, you guys aren't bothering with the AI world. You're not here to build AI models. That's for the AI industry.

• 17:07Speaker 01

  That's right. Yes.

• 17:08David Hoffman?

  You're here just to build the bridge to be able to verify models on chain.

• 17:11Speaker 01

  Exactly.

• 17:11David Hoffman?

  So you guys are operating in the, in the ZK world.

• 17:13Speaker 01

  Precisely, precisely. You got it. Yeah.

• 17:15David Hoffman?

  Okay. Uh, sounds ambitious.

• 17:18Speaker 01

  We do our best.

• 17:18David Hoffman?

  Yeah. Uh, there, how many of there are, are there, how big is Modular Labs?

• 17:22Speaker 01

  Modular Labs is currently a very proud four people.

• 17:25David Hoffman?

  And when did you guys get started?

• 17:26Speaker 01

  Uh, about seven months ago.

• 17:28David Hoffman?

  Okay.

• 17:28Speaker 01

  Yeah.

• 17:28David Hoffman?

  Uh, where did the original inspiration come from? Was there like an aha moment? Or how, how did the team come together and what was the, the motivation?

• 17:34Speaker 01

  Yeah. I mean, this is going to sound a little ridiculous, but of course there was all this kind of excitement around stable diffusion and generative models. And around the same time, we kind of fell in love with ZK and we kind of started asking ourselves the question of, hey, kind of what would we want to build here? Right. And the background of the team is all like AI researchers from Stanford. And so it was almost a kind of an obvious thing. Uh, we kind of asked ourselves, oh, how silly would it be if we like put a very non-performing AI model on chain to, I don't know, let's say predict ETH prices? So that's what we did. We built the world's first on-chain AI project as a joke. Uh, we hacked it over, uh, or hacked it together over a week. And it started predicting prices of ETH and making trades on L1 with a uniswap contract. It's a joke, right? We, we did this purely as a proof of concept.

• 18:16David Hoffman?

  The, the point was not to make money on ETH. I don't think, I don't think the goal was to make money. That, that wasn't the goal.

• 18:22Speaker 01

  No, no, no.

• 18:23David Hoffman?

  It was just an idea.

• 18:24Speaker 01

  And this model would not be able to do that, to, to clarify. Uh, but kind of, uh, and I guess just to really nail that point home, we didn't put a call function in the smart comp. So like, once money went in, we put in like 500 bucks, right? No one could touch it, us included. But kind of something miraculous happened, which is lots of randos on the internet, Anons included, started donating money to the trading bot. And if you look at the kind of historic performance, there's like a front end, it kind of goes up into the right. It's not actually doing, that's just like buoyed up by the donations it was getting. And of course, eventually it lost all everyone's money, as we've been telling very kind of transparently. Their algorithm failed to produce more ether. It did, yes. But it did succeed at being an algorithm on-chain. Exactly.

• 19:09Speaker 01

  Which was the point. Precisely. And kind of there was nothing that Modulus team could do to, again, tamper with anything that it, it's kind of like an, imagine like an autonomous robot is executing forever.

• 19:20Speaker 01

  So kind of from that point, we were like, oh, man, what if we brought an actually performant model on-chain? Imagine how cool that would be. Yeah. Okay. So there's, there's two worlds that I see spawning here. There is the insular world of crypto, who's like, oh, we could build models to do things inside of the crypto world. Yes. And then there's external uses that need to verify models and their execution off-chain. Yes. Is that, can you talk about how these two worlds might develop independently? Yeah, exceptional kind of insight. You're totally right. And that's kind of how we see it as well. In some sense, the crypto world is a little more convenient because, you know, there's already kind of a cultural expectation of compute integrity as a really core value, right?

• 20:02Speaker 01

  We want to see that as much of the compute that's related to our D apps and on-chain services are on-chain as much as possible, right? This is kind of the excitement that's driving all the ZK rollup kind of activity. But of course, the question is what happens when you step beyond the on-chain world? Does the rest of the world care about verifiability? That this algorithm was the one that made that decision, right? And you can imagine in a future where the judicial system uses a large language models to make decisions about sentences, oh, God forbid. Or a medical system that uses a very sophisticated model that determines certain medication treatments. These kind of intersections are really sensitive where liability is a big concern. I think verifiability is going to be a big deal. And what's cool is we get to get that flywheel started, the cultural appetite for it in crypto.

• 20:48Speaker 01

  Really kind of hopefully build up a really strong kind of um almost a a a a strong example of what it's like to be able to attest and and make our algorithms accountable. Uh and then we can communicate that with the rest of the world. But modulus right now, we're very much focused in crypto uh with kind of an eye to the future.

• 21:04David Hoffman?

  Sure, sure, sure, sure. Yeah, but but knowing what the eventual TAM would be, especially as the TAM is likely going to grow as AI grows, right? So the the mental model I have is like that little like SSL certificate, like the little shield in your.

• 21:16Speaker 01

  Yes.

• 21:17David Hoffman?

  It's this, it's kind of like that.

• 21:18Speaker 01

  It is.

• 21:18David Hoffman?

  Prove this model that you're using is proved.

• 21:21Speaker 01

  Exactly.

• 21:21David Hoffman?

  And you know, thumbs up, go for it.

• 21:23Speaker 01

  That's right.

• 21:23David Hoffman?

  Rug pull resistant.

• 21:24Speaker 01

  Exactly.

• 21:25David Hoffman?

  Or a rug pull immune. Um I would imagine that like as uh all of the AI people that are over there talking about AI Doom. Uh they're they're talking about like one of the paths towards AI Doom. First before Doom, we get to AI fun times.

• 21:40Speaker 01

  Yes, that's right.

• 21:41David Hoffman?

  In the AI fun times, uh you know, an explosion of models, an explosion of usefulness, an explosion of human productivity and flourishing and wealth generation, precursoring the the inevitable blow up. But before we get there, it's a world that we like live, the humans live on models.

• 21:58Speaker 01

  Yes.

• 21:58David Hoffman?

  And our life is guided by models and determined by models. And so with that Cambrian explosion of models, I would assume that the surface area for rug pulls also grows.

• 22:09Speaker 01

  Yeah, uh it's it's actually kind of terrifying how big the attack vector can be for uh you know, generating catastrophic results in your AI models, right? There's a kind of classic example of a vision model which sees a stop sign and you go in or a picture of a stop sign, say. And you go in, you put three pieces of tape or manipulate some pixels in a totally, in a way that's totally uh indecipherable to the human eye. And it thinks the stop sign is a, you know, a go sign or god forbid is is a toaster, you know, anything, right? These AI models do have very substantial kind of adversarial environment or attack vectors. And uh it's it's a little scary for sure. Yeah.

• 22:45David Hoffman?

  Okay, so we're we're helping secure our future, which sounds pretty important. And also is like making it, it's just like trustless. There's many different aspects of AI and this is like one way to make AI applications safer. It is not, it's not the AI safety. Conversation, but it is part of it.

• 23:00Speaker 01

  Yeah, I mean, the way I like to think about it is, you know, you have all kinds of different models and your model might be more explainable, more robust to attacks, more equitable, right? It doesn't bias for specific political allegiance or anything else, right? And these are amazing attributes and very hard problems that people are actively working on. But without verifiability, right, without the ability to pin down that model at any given instance of use, you can swap out that fair model, that robust model for a different one or for no model at all. I can just be feeding it any answers I want as the operator. And so in the same way that the security center of blockchain is that it's all there, right? Like you can just go into the ledger and see exactly the transactions. We make sure that the algorithms have that same kind of quality.

• 23:44David Hoffman?

  Right, and so the one, the insular way of using this technology is that we get smart contracts that are AIs that get to do things on chain. And that's going to be pretty cool and tight. And I don't even know where to think about how to start thinking about that. But the outside world is at that point, just using the blockchain as like a timestamping tool, correct?

• 24:03Speaker 01

  Yes, yeah, yeah, as in the blockchain is kind of this amazing environment where public verifiability is like so obvious there, right? And so it could be this amazing kind of settlement arena for the world's compute.

• 24:15David Hoffman?

  Right, yeah, yeah, and, and in a world in which we are probably are going to be using AI models and we're probably not going to think about the rug pull, the surface, surface area, right? And so like humans, when, when we use these things, we're going to assume that they're the things that we want them to be. And so we're not going to be looking for the rug pull. And this actually, this technology actually allows us to be cozy as we use these models.

• 24:43Speaker 01

  Yeah, in some sense, and it's a little insidious, these models are very sticky, they're incredible, right? They're very personable, magical kind of features that we can add into every part of our compute diet as a society. And of course, while that's happening, we're very quickly expanding the surface area of potential attacks. And, you know, the goal is to make sure that before we have that catastrophic outcome, right? Before somebody gets really injured or a lot of money is lost because of the widespread use of large AI models, that we have that accountability piece in place, along with all the other AI safety technologies.

• 25:15David Hoffman?

  Daniel, I would imagine that this conversation just about ZK, ML, what we're talking about here can go on and on and on and on and on.

• 25:22Speaker 01

  It definitely can.

• 25:23David Hoffman?

  What parts of the, are there any big parts of the conversation that I haven't opened up yet?

• 25:29Speaker 01

  Good question. I mean, every part seems like it's filled with potential, right? But something that we spend a lot of time on, for example, is, and this is going to sound like the opposite of the kind of aspirational, exciting thing that's happening, is the literal cost of doing this process, of running these computes, or these are very, this very expensive class of compute in a zero-knowledge setting, and making sure that although we're excited to kind of have our heads in the sky, we're kind of marching towards real implementation, real use cases, real customers, right? So, you know, it starts with working with folks like WorldCoin on identity verification, or self-custody of biometric information, all the way to games and DeFi protocols, and of course, NFTs as well, to kind of push the envelope on accountability for machine intelligence, right?

• 26:12Speaker 01

  So, you know, we have this kind of bigger thesis, but at least for Modulus, and I think the category in general, this nascent category, we want to make sure we march to the beat of kind of real, you know, impact, right? Making sure that it's actually making a difference in the ultimate lives of these service providers.

• 26:29David Hoffman?

  The phrase "accountability" for machine models, I think, is going to be something that really resonates with a lot of people, even at just like the cursory level, right? AI has, is going to and has triggered just like a lot of people's, just like, the hairs on the back of their neck.

• 26:46Speaker 01

  Sure.

• 26:46David Hoffman?

  And so, just as a branding, it's like, "Hey, we're helping AI be safe." It's like a really good branding to lean into.

• 26:53Speaker 01

  Well, kind of what's exciting, of course, is beyond just the branding being kind of very appealing, for sure, is that this is, you know, AI researchers are not going to love kind of the way I phrase this, but I almost see these technologies with personalities, right? AI is this like very expressive, creative, like infinite potential, very powerful. But cryptography is very humble, it's very discreet. It says, "This is a statement that I can show with pure mathematics, and you know, this is kind of the kind of claim that I'm able to make and no more." And so, being able to marry these two things, which have quite a bit of tension by their nature, is something that's like deeply exciting for certainly me and I think the whole Modulus team, right? Yeah.

• 27:31David Hoffman?

  Yeah, you use that word expressive, which is like one of my favorite words.

• 27:34Speaker 01

  Sure.

• 27:34David Hoffman?

  And, and we have all of this explosion of AI models who have all of this power and personality that you're saying. And I think like maybe adding in that ZK, the ZK circuit component also adds in just like a stamp of authenticity.

• 27:50Speaker 01

  Yes, precisely. Yeah, it's like, I mean, you mentioned the little, little, little check mark or it's like a Twitter verified check mark, maybe back in the day when that was more substantial socially. But having something like that for your models, right, for your AI models or for your, your, any mechanism that is, you know, sophisticated compute, precisely.

• 28:08David Hoffman?

  Daniel, I've learned quite a lot. Where should listeners go if they want to continue going down this knowledge rabbit hole?

• 28:14Speaker 01

  Yeah, I mean, not to show our own stuff too much, but Modulus Labs, we're on Twitter. We try to put out decent content and of course.

• 28:22David Hoffman?

  I watched your guys' first video on this scroll YouTube and man, that, that broke my brain.

• 28:25Speaker 01

  Oh my goodness. Yeah, yeah, there's, there's, you know, all parts of the stack to enter, very technical to kind of more philosophical, but we want to make sure that kind of we meet people where they are because it's really cool and we want as many people kind of to be in, in the know about this stuff and kind of be part of this movement for, your words now, accountable machine intelligence.

• 28:44David Hoffman?

  Love it, love it. Daniel, thank you so much.

• 28:45Speaker 01

  Thank you, David. Really appreciate it. Yes.

• 28:47David Hoffman?

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• 29:30David Hoffman?

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• 30:13David Hoffman?

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• 30:57David Hoffman?

  and with all the taxes handled in over 100 different jurisdictions. So whether you're an early-stage company with just a team of two or you're an enterprise with 200, Toku has a solution that meets your needs. Toku is already working with the leading companies in the space: Protocol Labs, Hedera, Gitcoin, and many more. So transform your employment and token payroll operations with Toku. You can reach out to Toku at toku.com/banklist or click the link in the show notes. Bang, this nation. We are here at Zuzalu and I'm talking with our good friend, Justin Drake. What's up, Justin? How's it going?

• 31:27Speaker 02

  Yeah, all good. Thanks for having me again, David.

• 31:29David Hoffman?

  So, this is ZK week, a zero knowledge week. This is, I've hopped into two talks, trying to understand what's going on. I stepped in to the talk and it started to break my brain and I did not, nothing went in. And so, as someone who is both a cryptographer, who's on the frontier of cryptography, yet also understands how to explain these things to a more general public, I'm hoping you can kind of help me understand what the hell is going on this week.

• 31:58Speaker 02

  Right. So, I actually took a bit of a break from crypto, a one-year break more so, more or less, going into MEV. But, you know, I've been getting back into things in the last few weeks.

• 32:08David Hoffman?

  Oh, by crypto, you mean cryptography?

• 32:10Speaker 02

  By crypto, yeah, I mean cryptography, yes. And it turns out, there's been a ton of progress around this thing called NOVA. Now, NOVA is kind of this idea which started in, two years ago, in 2021, by a paper written by Trinaf Saty. And basically, he came up with this prover optimization for SNARKs. So, one of the big bottlenecks in SNARKs is the ability to prove very, very big statements with, you know, small amount of computational resources and also with low latency.

• 32:46David Hoffman?

  And just to, a SNARK is just like a compression technology. It's, it's a ZK.

• 32:52Speaker 02

  It's exactly. It's a ZK proof. And the thing that we're very, very good at is getting the ZK proofs to be extremely small and extremely easy and fast to verify. So, that's on the verifier side of things. And now, a lot of the work now is on the prover side of things. We want to generate these proofs in the first place. And, you know, we can do proof for simple statements, you know, like in the context of Zcash, I'm making a valid payment. But we want to be doing things like ZK rollups. And here we have massive statements where we want to prove that a whole Ethereum block is valid. And a lot of the complication from an engineering perspective stems from the prover side of things.

• 33:31David Hoffman?

  Okay, so just to make sure I'm with you. We have the technology to take a bunch of data and compress it into a really small packet of data.

• 33:38Speaker 02

  Yes.

• 33:39David Hoffman?

  We don't have the technology to do that fast and cheap and quickly, right?

• 33:43Speaker 02

  Right, well, maybe now we do, or at least we're we're getting there.

• 33:47David Hoffman?

  That's what we're talking about. That's what's being talked about here?

• 33:49Speaker 02

  Exactly.

• 33:49David Hoffman?

  That's the cool frontier that we're on?

• 33:51Speaker 02

  Yes, so there's a whole class of snarks that are based on so-called curves or elliptic curves. And the the the Nova techniques are on the order of 10x faster than the previous proof techniques.

• 34:04David Hoffman?

  Nova is the cool new thing that that people are discussing here.

• 34:07Speaker 02

  It's a cool new thing, yes. And the the the generic term is called folding. And the reason is, what you do is that you're gonna take structured computation. So instead of taking this huge unstructured statement, you're gonna take very, very structured statement and try and fold the various steps into each other. And this folding process is much, much cheaper than than snarking. Now, what do I mean by structure? Imagine that you have a CPU.

• 34:31David Hoffman?

  Right.

• 34:31Speaker 02

  And every cycle of the CPU, you know, CPUs run, for example, at 3 gigahertz, 3 billion instructions per per second. Every cycle is this structured thing that can be folded in into into the next step. And then ultimately-

• 34:46David Hoffman?

  By structured, do you mean like serial, like like linear, like a step by step by step by step?

• 34:51Speaker 02

  Yeah, so what I mean is that you wanna prove a very big statement that can be broken down into steps, all of which have the same format or template.

• 35:01David Hoffman?

  Okay.

• 35:01Speaker 02

  So they still all have the same length and they're all proving something same. Okay, here's an example. BLS signatures. So we all know that BLS signatures is one of the big optimizations that allows Ethereum to have half a million validators.

• 35:15David Hoffman?

  Right.

• 35:16Speaker 02

  The next-

• 35:16David Hoffman?

  It's the thing that brought the validator requirement from 1500 ether per validator down to 32. Because it was a very compressed piece of this of a previous technology.

• 35:26Speaker 02

  Exactly. And and what does BLS aggregation allow? It basically allows you to take, let's say, 10,000 BLS signatures and fold them into onto each other, such that if you verify the folded thing, you've proven that all the other unfold 10,000 unfolded signatures are also valid. And just to put numbers on this, it takes about 1 millisecond to verify a signature. So if you wanna verify all 10,000, it'll take 10 seconds.

• 35:53David Hoffman?

  Right.

• 35:53Speaker 02

  But if you wanna verify the folded one, that takes on the order of of, you know, 1 millisecond.

• 35:57David Hoffman?

  Okay, so- Some of the detail, the deep down details I don't get, but the patterns I think I'm understanding. Whereas if you tell me it takes one millisecond to verify a BLS signature, and then if you want to do 10,000 of them, well, it's a linear, it's linear up to 10,000.

• 36:11Speaker 02

  It's linear, yes.

• 36:12David Hoffman?

  And with this new folding mechanism, like, there's that meme of us, like, you can't fold anything in the universe 12 times because it's too exponential.

• 36:19Speaker 02

  Right.

• 36:20David Hoffman?

  Right? And so I'm understanding that. I understand exponential curves. And so it sounds like we have a way to do something previously linearly, that we now have something, a new way to do it, and it's exponential. And so it just gets that economies of, it gets the scale of an exponential curve. Is that, is that a way to understand it?

• 36:37Speaker 02

  So, we basically have these two tools at our disposition. You can think of like the high-duty power machine that is very expensive to use, and we have the hammer. Both of them can get the job done, but one of them is much, much cheaper to use. And the hammer turns out to be about 10 times cheaper to use. So it is a constant optimization. We do know how to, how to make snarks that are linear time, in the sense that the time it takes to do the proving grows linearly with the size of the statement. But now, we've actually reached a point in the maturity of snarks where it's a game of constants. And at least for elliptic curve-based snarks, Nova allows us to get this 10x boost relative to the status quo.

• 37:17David Hoffman?

  So what, what does that mean for crypto, cryptocurrency? Like, what does that mean? Why do we care about this? How does this really benefit? We enjoy things that are cheaper and faster, just implicitly, but how does this impact our lives?

• 37:32Speaker 02

  Right. So I guess one big thing is, it's gonna make it easier to deploy rollups en masse.

• 37:38David Hoffman?

  Okay.

• 37:39Speaker 02

  ZK rollups specifically.

• 37:40David Hoffman?

  So this is a democratizing technology for deploying ZK rollups?

• 37:44Speaker 02

  Yes. But it's also the key to getting decentralized proving. Now, what do I mean by decentralized proving? I mean, two things. I mean, first of all, the lowering the barrier to entry in terms of computational resources. So right now, if you want to be a prover for a ZK rollup, you need to hire some sort of rack in a data center, lots of compute. And it is not really friendly to doing so at home. So imagine, you know, a small box at your home, and you can be a scroll prover, or whatever the ZK saying prover. The other interesting thing about Nova is that it allows for

• 38:23Speaker 02

  Decentralized proving, which is kind of the next step after distributed proving, which is the next step of a parallel proving. So let me try and explain. So SNOWs are very parallel friendly in the sense that if you have multiple CPUs on your machine, or if you have multiple threads within each CPU, you can make use of these threads to do work in parallel.

• 38:42David Hoffman?

  Right.

• 38:43Speaker 02

  But they all sit within one machine. That's parallel proving. Then the next step is distributed proving, where you have machines that are geographically distributed all around the world, and they're separated by the networking layer. And here what you need is basically these small proofs that can be communicated fast, so that you can distribute the work.

• 39:03David Hoffman?

  And this is like distributing the sequencer, distributing the validator of a layer two, similar conversation?

• 39:09Speaker 02

  Yes. Well, here's about distributing the prover. And the key thing about distribution versus decentralization is that there's only one prover that's distributed, whereas in the decentralized model, it's an untrusted kind of coordination of provers that ultimately help build this mega proof.

• 39:31David Hoffman?

  Right. And the idea of a decentralized ZK rollup is that ultimately, we want anyone to be able to generate a proof.

• 39:38Speaker 02

  Yes.

• 39:39David Hoffman?

  But right now, that's still too costly, too expensive, because that's the state of things we're in.

• 39:45Speaker 02

  Right. So NOVAC kind of helps in two ways. One is that it's this constant optimization by 10x. But the other thing is that it makes it much, much simpler to have this best-in-class decentralized proving, where a thousand nodes, let's say, that don't trust each other can all combine work to ultimately form a final proof.

• 40:07David Hoffman?

  That's, I think, a new part of ZK rollup for me, is multiple different computers, nodes coming together to produce a proof. How does that fit into a ZK rollup? Where does that fit?

• 40:20Speaker 02

  Right, so the ZK rollups have the same kind of infrastructure as the layer ones. You have the proposers, the attesters, and you have the block builders, which are sometimes called sequences. But there's a new role, which is the prover.

• 40:36Speaker 02

  And right now this-

• 40:38David Hoffman?

  And that new role, the prover, comes because it's a ZK rollup, because it's a snark. And so the snark of that is a ZK rollup needs to be proved. That's the thing.

• 40:46Speaker 02

  Yes, so traditionally, the role of prover was assumed within the block builder. And the reason is that the block builder needs to do something, which is basically compute the state root. But now, in addition to computing the state root, there's this other thing that needs to be done, which is to compute the snark. And that is just so much more expensive than computing the state root, that it makes sense to unbundle these two roles.

• 41:09David Hoffman?

  Right. Okay. And so with a ZK rollup, what does it mean for many different nodes in a ZK rollup to produce- Like, are they, everyone is not, everyone's doing a small snippet of work that gets aggregated, but they're not doing the same work independently, correct?

• 41:26Speaker 02

  Yeah, that's correct. So just to zoom out in terms of why we care to decentralize the prover. It's all about liveness. So let's, so right now, all the ZK rollups have a centralized prover in AWS or whatever it is. If AWS goes down for one hour, the ZK rollup goes down for an hour. So we care about this strong kind of World War III grade liveness, so we need to decentralize the prover. And as you said, what we're gonna do is we're gonna take this whole big block, this rollup block, partition it into small steps, for example, transactions, but maybe even more granular, you know, at the op code by op code level. And then have each decentralized prover perform their mini proof, and then aggregate these mini proofs into a final proof.

• 42:11David Hoffman?

  So in a ZK rollup, the blocks are really big, and we need multiple nodes to process parts of this block in order to submit, create a proof that gets submitted down to the Ethereum Layer One. And the more nodes we have, the faster that that block can be processed. And the more, and also the liveness of the, of the actual rollup increases, correct?

• 42:33Speaker 02

  Yes, I mean, even without big blocks, even if you're only doing, let's say, three million gas every 12 seconds, so fifteen million gas every 12 seconds, which is what the Layer 1 does today, it's still extremely expensive. Because the EVM is not a snark friendly.

• 42:47David Hoffman?

  Right.

• 42:48Speaker 02

  So, we wanna combat this unfriendliness of the EVM. But once we've reached that, you're right, we wanna increase the gas limit. Or, another thing that we wanna do is have multiple instances of your virtual machine. And the reason is that any given instance of the EVM is gonna be bottlenecked by sequentiality. So if you take, the EVM, for example, is what's called a single-threaded virtual machine. And so it can only do so much. Uh, so let's say it can do a thousand transactions a second, or 10,000 transactions a second. And once we've reached that peak, we've maxed out on the throughput of one single instance of the EVM, it will make sense for these rollups to have multiple instances of themselves.

• 43:30David Hoffman?

  Okay, and so, when you put all these pieces together, uh, what does the, the future of ZK rollups look like before and after this new Nova technology?

• 43:41Speaker 02

  Right, um, so, I guess Nova is one of the pieces that are gonna get us from the present, which is a fully centralized prover, uh, on AWS, to this kind of utopic future, which will happen in a few years, where, um, not only do we have decentralized proving, meaning we have potentially hundreds or thousands of nodes collaborating to form the proof, but we have very high gas limits per instance of the virtual machine, and we have multiple copies of the, uh, of, of, of the rollups to consume all the data availability that, that we have on-chain. And when I say that Nova is only one of the pieces, it's like there's, there's other pieces at play. So, another very important one is hardware acceleration.

• 44:27Speaker 02

  So if you use, uh, GPUs, it turns out you can get a lot of hardware acceleration. But the step after GPUs is actually to build an ASIC, where you get another 10x in prover performance. And all these prover optimizations compound on each other, and we're gonna need all of them to, to get to where we wanna be.

• 44:44David Hoffman?

  Right, and when, I think when people hear the term ASIC, they think Bitcoin mining. But this is not what we're doing. You, only one node needs one ASIC to do the job. You don't need a, you don't need a wall or farm of ASICs, correct?

• 44:58Speaker 02

  Yeah, that's correct. So we we have what's called an honest minority assumption. We just need one prover to be online and participating and to produce the proof to have liveness. And so one of the things that we want in these proven networks is some amount of redundancy. It could be, you know, a hundred to one redundancy, it could be a thousand to one. So a hundred to one means that even if ninety nine percent of all the provers in the world just suddenly go offline, the Z K roll up still keeps on progressing forward.

• 45:29David Hoffman?

  Okay, so we are currently in, we have some ZK EVMs, Polygon, ZK Sync are live on mainnet. Scroll is coming soon. The costliness of these provers are extremely high. And there's only one because of how high they are. And that's the current state of things. With with further optimizations of each one of their own tech stack, you get some improvements. With this new Nova cryptography mechanism, you also get some improvements. With moving from GPUs to ASICs, we also get some improvements. Can you put some numbers on these things? Like, so we're going from some amount of block space in the ZK EVM world. And then there's in like, you aggregate all of these innovations together.

• 46:11David Hoffman?

  And we get a different number of how much like total block space there is. Is there any sort of like, how much magnitude more block space do we get out of as a result of all these things?

• 46:21Speaker 02

  Right, right, right. So I guess one one metric that we could be looking at is the the cost per transaction of doing the proving. And nowadays, we're on the order of one cent. So it's actually not not that high. And with all the optimizations, we'll get it down to, you know, noise. Now, to actually answer your question around the the total throughput of the system, the the really cool thing is that it's it scales horizontally. So the more people come in, the more fees are being paid. The proven network can actually grow organically.

• 46:53David Hoffman?

  It's like a natural scaling mechanism.

• 46:55Speaker 02

  It's a natural, exactly. It's a natural scaling mechanism. And I think the the main cost that we're paying right now is kind of this this somewhat subtle thing is the lack of decentralization whereby, you know, we're trusting these centralized provers for for for liveness. And one of the things that ultimately we want to do at layer one within Ethereum is snarkify the EVM itself, the layer one EVM, and and build a so-called Enshrine rollup. And in order to get to that, you know, holy grail, we need, you know, we need to do all the hard engineering work. So all the stuff that the rollups are doing, the non-entiring rollups will ultimately be useful for the layer one as well.

• 47:35David Hoffman?

  Okay, okay. So with with the cryptography conversations that are happening here at Zuzalu, this whole Nova thing, we've applied this to the ZK rollups into block space and to just reducing costs and growing growing efficiency and all that kind of stuff. Are there other verticals that this new Nova technology can apply to?

• 47:56Speaker 02

  Yes, so like generally speaking, Nova is an improvement to the ZKP world, the Snark world. And in my opinion, Snarks are just going to completely change the world. For blockchain specifically, we've talked about scalability, but it also is has a massive impact for for privacy. But even zooming out outside of the blockchain world, you know, we're trusting entities to do computation for us all the time. You know, we make a Google search, we get some sort of answer. We have no idea on the, you know, the validity of this answer.

• 48:34Speaker 02

  And so one of the possible futures is actually that the blockchain space builds so-called co-processors to the main processors. So every time you have, for example, a CPU in your phone or a CPU in the cloud, that can be accompanied by a piece of hardware, a co-processor, that does all the proving work in real time to prove that the processor's work is valid and then ultimately generates a Snark proof.

• 49:00David Hoffman?

  In this world, what does an invalid processor look like? Like, what what what's the utility here?

• 49:06Speaker 02

  Right, so one kind of very technical thing, which doesn't really happen in practice, is, okay, what if there's a bug in the CPU? That has happened, you know, sometimes, but it's extremely rare. But the the bigger threat model is basically that, you know, it's just this trusted operator problem, trusted third party. You know, maybe your banker is giving you your correct balance on your bank account, but maybe it's not. Maybe it just removed a few zeros or whatever it is. And so when you go and open your mobile banking app, you actually have a mathematical proof that this is indeed your balance as opposed to having to trust your banker.

• 49:45David Hoffman?

  So the idea, I think what you're trying to say is with this new Nova technology, we can apply it to crypto, but then also there's ways to apply it in the rest of the world just because it's more, it's cheaper to run this thing, it's cheaper to operate. And so we can start applying more trustless principles in areas outside of crypto.

• 50:03Speaker 02

  Right. And, you know, I think the end game will be that these, these coprocessors will be roughly 100 times the size and consume roughly 100 times the power of the processor. So, you know, it is, there is still a cost there, but right now we're talking more about, you know, 10,000x overhead or 100,000x overhead. And so that really limits the number of applications that SNOWs are being used for to the most high-value ones. And every time we remove an order of magnitude, we're opening up the, the, the design space. And I think just like progressively the internet has been eating the world every time you increase bandwidth by 10x, SNOWs are going to eat the world every time you reduce the prove a cost by 10x.

• 50:48David Hoffman?

  So how, how big of a deal this whole Nova thing?

• 50:51Speaker 02

  Yeah.

• 50:51David Hoffman?

  How big, in the cryptography world, how like excited, what, what are the level of it? What's the level of excitement from the cryptographers? Like, if for, because, because for the normal people like, like me who doesn't understand this thing, it's like, okay, I take this at face value, Justin's excited. But like, is how, the whole cryptography community, like, how excited are they? Like, can we, can we rate this on a scale of 1 to 10?

• 51:13Speaker 02

  So the applied cryptographers here in Zuzalu, I think, are very excited. I'm going to say, you know, 8 out of 10.

• 51:20David Hoffman?

  Okay.

• 51:20Speaker 02

  When they came here, I think it was more like a 5 or 6 out of 10.

• 51:24David Hoffman?

  Okay.

• 51:24Speaker 02

  And all the presentations that have been done, all the sharing of ideas, people were like, oh, wow, you know, we can actually combine these clever ideas. And actually, some new ideas came through at, at the various workshops.

• 51:36David Hoffman?

  So in, in the history of cryptography, like, where does this stand on like breakthroughs? Would you call this a breakthrough?

• 51:43Speaker 02

  It's definitely a breakthrough for the applied cryptographers, especially people who want to build real-world applications. Um, of course, you know, if you, if you really zoom out over the multi-decade, there's like improvements to the asymptotic asymptotics. So you might have like a quadratic prover versus a linear prover. That's like a huge improvement. But now, you know, as I said, we, we, we've reached the optimal point from asymptotics, and it's all about improving the constants.

• 52:11Speaker 02

  And in my, as I see it, Nova is optimal from a, both an asymptotic and a constant standpoint for snarks that are using curves, elliptic curves.

• 52:24David Hoffman?

  Okay.

• 52:25Speaker 02

  So this is the end of the road for the, the, the, the proof system, or we're very, very close at least.

• 52:29David Hoffman?

  Like the, the theoretical max of what we could get?

• 52:33Speaker 02

  Yeah, pretty much, yeah.

• 52:34David Hoffman?

  And so with the many different ways to scale blockchain, cryptography is one of them. You're saying that Nova is the theoretical max of the cryptography side of that equation for scaling a blockchain?

• 52:47Speaker 02

  Uh, yes, specifically for this, this proving of statements and ultimately generating a small proof that can be verified by a blockchain, yeah.

• 52:54David Hoffman?

  So now it's just like a matter of building the, the infrastructure around Nova to support it, make it better, refine it.

• 53:01Speaker 02

  Yes.

• 53:02David Hoffman?

  But Nova is the deal.

• 53:04Speaker 02

  Um, y- yes, I mean, the, I think there's gonna actually be a bifurcation of two types of snarks. There's gonna be the so-called curve-based snarks, like, like, like Nova, and there's gonna be the hash-based snarks, uh, you know, like, like Starks and, and Frye-based things. And the, the, the jury is still out because the, the, the underlying cryptographic assumptions are pretty different, and that has implications from a performance standpoint. But I think what will happen is that we will see both explored in parallel, and both are extremely promising. Um, one of the good things about the hash-based stuff is that it's post-quantum. So from an endgame perspective, that's kind of the, the most natural way to lean, uh, right now.

• 53:46Speaker 02

  But it turns out that, um, you can generalize Nova to use so-called lattice-based commitments. A little bit technical, but basically it's the equivalent of curves, but they're post-quantum.

• 53:58David Hoffman?

  Okay, so there's future-proof is the way to read that.

• 54:01Speaker 02

  There's a potential roadmap to future-proofing Nova, but right now the details haven't been fleshed out.

• 54:06David Hoffman?

  Okay, that always kind of seems to be where conversations with cryptography leave is like, there's potential road we haven't figured it out yet.

• 54:14Speaker 02

  Right, right, right. Yeah, at least for the curves, you know, we've reached something optimal from a constant standpoint, yeah.

• 54:22David Hoffman?

  So Justin, here at Zuzalu, there's just been, the idea of Zuzalu, one of the big ones is cross-pollination, like get all the brains together, get them to talk. How's it just going for you? Talk about your experience here at Zuzalu.

• 54:35Speaker 02

  Right. I mean, I've had a few kind of mind-blowing moments, just meeting people that I was not expecting to meet.

• 54:44Speaker 02

  I mean, one, within crypto, you know, there's like, for example, Lev, who's very much interested in FHE, and he's very much interested in moonmath, like witness encryption. And there's various other people like him, but outside of the cryptography world, it turns out I've learned of two different projects to build stablecoins that are backed by central banks of governments, of nation-states. And these seem to be like serious projects, one of which is Montenegro.

• 55:16Speaker 02

  And, you know, I got to meet, you know, the most likely candidate to being the prime minister of Montenegro, and, you know, people as part of his team, and they've been working on this crypto law for a very long time, and it seems to be a very serious and interesting project.

• 55:33David Hoffman?

  Yeah, I am interviewing Micky in a couple of days here.

• 55:35Speaker 02

  Oh, excellent, yes.

• 55:36David Hoffman?

  And so this will be a featured content on the Zuzalu track.

• 55:39Speaker 02

  Amazing.

• 55:39David Hoffman?

  Yeah. Who else do you think I should interview while I'm here? If you have any, if you had to pick.

• 55:44Speaker 02

  Right, so for the second fiat stablecoin backed by a central bank, I've been told to not leak the alpha, but I can tell you privately, I guess, and then you can ask them if they want to be interviewed.

• 55:58David Hoffman?

  Yeah, that seems to be the central bank stablecoin. There's only so many central banks to go around.

• 56:03Speaker 02

  Right.

• 56:05David Hoffman?

  Justin, thank you so much for guiding us through the world of ZK cryptography, and also I hope you enjoy your time here at Zuzalu.

• 56:10Speaker 02

  Yeah, thanks, David.

• 56:11David Hoffman?

  Cheers.