采访｜专注 ZK 投资的 Geometry 如何看待硬核技术公司的商业模式？
Scenius Studio 播客采访了 Geometry 的联合创始人 Tom Walton-Pocock。Tom Walton-Pocock 曾是 Aztec Network 联合创始人和 CEO。
Scenius Studio 的本期播客采访了 Geometry 的联合创始人 Tom Walton-Pocock。Geometry 是一家致力于支持密码学和零知识证明研究的公司，通过利用硬核的数学和密码学技术的协议来推动 Web3 的发展。他们的投资组合包括了与零知识技术相关的项目 Socket、Scroll、Cubist、Cartridge、RISC Zero、Herodotus、Ingonyama、Capsule、Slush，以及其他的 Web3 项目Cub3、Soap、MatchboxDAO、Magicave。
关于创立 Geometry 的动机，他认为硬核技术公司在寻求融资时，由于许多投资者对硬核技术的理解不足，无法与公司建立共鸣。为了弥补这种技术理解的差距，研究驱动的 VC 可以帮助投资者更好地了解公司的技术实力，做出更明智的投资决策。
其次，Tom 看到了加密货币和区块链领域的巨大潜力，以及这一领域的技术前沿性和复杂性，需要具备深厚的技术背景和知识才能进行投资和研究。大多数投资者缺乏这方面的技术知识，而他和 Kobi Gurkan 等人则具备专业知识和经验，能够更好地理解和评估这一领域的投资机会和技术创新。
在 Tom 看来，Geometry 的目标是确保不具有商业经验的技术背景创始人能够成功地启动商业协议。对于那些拥有深厚技术背景的创始人来说，他们可能会热衷于技术本身，而不擅长解决现实的商业问题和找到真正的用户。因此，Tom 及其团队为这些创始人提供研究合作、技术支持的同时协助他们开发商业目标，寻找第一个用户，使产品实现商业化运营。
在 Web3 生态系统中，实现技术协议的商业化是一项困难的任务。Web3 的使命是减少对中心化机构的依赖，这对商业模式的发展不利，因为商业模式通常需要建立在信任的基础上。然而，许多协议和密码学开发正在削弱对信任的需求，这使得技术协议更加难以形成商业模式。Tom 认为有一小部分与协议相关的代币可以成为宏观资产，类似于黄金这样的储值资产，例如 BTC、ETH 等。这些代币可以被视为一种货币形式，其价格可能非常高，但这需要全球范围内对其储值属性的认可。然而，对于有些协议，例如与以太坊相关的 Layer2 协议，虽然它们可以通过提高交易速度和增加以太坊的计算能力来缓解瓶颈问题，但这些网络的代币是否能够产生未来的现金流仍存在争议。因此，协议的商业模式需要根据其特定情况进行评估和制定。
当被主持人 Ben Jacobs 问及大多数创始人和加密货币投资者忽略的事物时，Tom 分两部分阐述了他的观点。他认为在加密世界中，Layer2 协议层面上的价值提取情况非常不明确，赚钱的目标与保护用户的冲动并不一致。Layer2 的安全性将来自于更规范的资产，如 ETH，这种资产比本地代币更好地提供安全性。
此外，他指出 AI 对于加密货币的投资价值有着非常重要的影响。AI 已经使计算系统高度可变，产生高度风险，加密货币作为一种更不可变、更坚固的基础设施资产，将变得非常有价值。
「如果想要在投资 AI 的领域占有优势，就需要对加密货币以同样的重视来审视。」
在加密货币之外，Tom 认为，虚拟现实（VR）是未来工作的一个重要工具，由于 COVID-19 的出现，以高度去中心化形式建立的公司使虚拟环境的房地产变得非常有价值，VR 也可能对房地产和其他资产有重大影响，目前许多人低估了它。
以上是本次采访中的重点总结，Tom 还提及一些 ZK 和 EVM 相关的基础话题，感兴趣的朋友可以收听播客或者查看以下用 AI 生成的中英文文本。
Ben：Hello, and welcome to Scenius Studio, hosted by me, Ben Jacobs of Scenius Capital. With this podcast, we aim to give listeners inside access to the best and brightest investors in the crypto asset management industry. In each episode, I will chat with a leading crypto and blockchain venture fund or hedge fund manager as we explore the complexities of operating an investment fund at the bleeding edge of innovation. In this episode, I sit down with Tom Walton-Pocock, managing founder of Geometry. Building on his experience as a co-founder and CEO of the Aztec Network, Tom launched Geometry on the premise that deeply technical founders need investors who can relate to their engineering-first approach to business building. Geometry is now a leading cryptography and mathematics research firm with a track record of successful early-stage blockchain venture deals. Let's get into it.
「Ben Jacobs is a partner at Scenius Capital Management. All views expressed by Ben and the guests of this podcast are solely their opinions and do not reflect the opinions of Scenius Capital Management. Guests and the host may maintain positions in the assets and funds discussed in this podcast. You should not treat any opinion expressed by anyone on this podcast as a specific inducement to make a particular investment or follow a particular strategy, but only as an expression of their personal opinion. This podcast is for informational purposes only. 」
Ben：Hello and welcome to Scenius Studio. I am your host, Ben Jacobs of Scenius Capital. Today's guest is calling in from the UK and we are delighted to have Tom Walpoe of Geometry. How's it going, Tom?
Tom：Thanks for having me, Ben. Very well. How are you?
Ben：I'm doing well. Where exactly are you calling in from?
Tom：So I'm calling in from a small city about an hour south of London, but London is definitely where Geometry has, I guess, sort of most people, but we're pretty sprinkled all over the world from SF, New York, right across to Singapore. So yeah, most of my work's done from here.
Ben：Great. Well, we have a lot we want to jump into today, talk about your background, geometry, and then your perspective on a number of deeply technical topics that we haven't really touched on yet at Scenius Studio. But before we get into that, I know that your background is actually in music. And so I wanted to hear a little bit about that, and then maybe how you've drawn parallels from your music background and skills to your current work in crypto.
Tom：So I really do want to overstate the music thing. This was a best of glancing blow. It was a very brief stint. And really, my background as an undergraduate was in maths, and then I ended up doing some singing, and then that became a bit more serious, and I took it seriously for about six, nine months. And the market spoke. I was one of those lucky people, I think it was an awful midway where if you are talented, but not hyper talented, you might still go after the music. And I was fortunate to be below that rung. And the market spoke pretty quickly. And so I was never put in any kind of question, am I going to be threatening anyone's jobs at Covent Garden or the Metropolitan Opera or whatever. So I feel like I had it easy. Maybe there's an element of overlap between the experience of being a singer and entrepreneur, I guess, in terms of mentality and sort of battle resilience and that sort of thing. But I think that they're probably pretty, pretty minimal.
Ben：Got it. Well, I'd love to touch on your background and experiences, both from where you went to college, grew up, but then how all of that led to your current work in the formation of geometry.
Tom：Yeah, so I grew up on a wee island called Jersey, the name that will be familiar to you, but this is old Jersey, after which New Jersey was named. It's a tiny little lump of granite floating in the English Channel. It's a lovely place. And it's kind of between the UK and France. Maybe what typified the existence there was you always slightly less well connected to the world. The internet started coming online, I guess we were probably always very slightly behind. And so maybe some information flows were sort of slightly less good there at the time. Went to university in Cambridge in the UK. I studied maths at undergraduate and then postgraduate. And there's this kind of, I still have my postgrad photo on my wall. It's kind of amazing how many people in there are now actually leading pretty major projects, including Ethereum and WorldCoin and various other really important pieces of research driven infrastructure. Maybe it's not a surprise, there's lots of mathematicians in a room basically. It's a very brief stint in music, bit of time in finance, and then co-founded a company called Aztec, which is a layer two network. So this is kind of one of these companies that is scaling Ethereum. I guess we're going to talk a little bit about scaling during the course of this podcast. And it's done a lot of the work to create the sort of the cryptographic tricks that enable you to make these systems faster without imperiling their decentralization. So this new kind of hard consensus substrate, which is going to be the basis, I think, for all future computation. You need to make sure that it remains hard and that it remains fully decentralized. And it was an encryption trick that was created. I say it was a trick, it was a really impressive piece of work, not done by me, but by my co-founder, Zach Williamson, and our chief scientist, Ariel Gabasson, that I think is now forming the basis of a lot of the scaling that Ethereum is now able to enjoy. And we can chat about that maybe later on. And as far as geometry is concerned, geometry arose really sort of not long after I left Aztec. I co-founded this with Kobi Gurkan, and we wanted geometry to be a new flavor of investor. So it would be a research-driven company first. So we do a lot of work in applied cryptography, these things, zero-knowledge proofs, which we can also touch on later on. And we also use that as a mechanism by which to invest. So it sort of acts as a way to generate inbound traffic that allows geometry to invest and justify the higher cost of operating geometry as a fund.
Ben：What was the gap in the market that you identified that you thought, given your experience at Aztec and where you thought there was opportunity, how you thought it made sense to form this research-driven investment fund?
Tom：Yeah. So I guess this could break it into two questions. One is sort of why that particular interest, and the second is sort of why a research fund at all? I think sort of, maybe answer the why a research fund. I noticed a lot of the time that when deeply technical companies were looking to raise money, they were struggling to form a rapport with the investor, who I guess suffered from a sort of a lack of deeply technical understanding of the company. It was therefore maybe a sort of lack of empathy between the investor and the company. And the investor then typically went where the most technical people were. And I think investors, they're constantly trying to work out how can we justify our position in an increasingly heated investment environment? How can we sort of justify why we should be on the cap table rather than someone else? And a lot of the time, investment funds maybe make promises, "Oh, we'll make introductions to other people in our population." I think that can work for very powerful investors, but otherwise I think you just need a USB. And then the sort of question of why particularly did we pick zero knowledge proofs and cryptography? Really this sounds like a very narrow interest, but actually it's likely to devour all computation on this future internet which we are building. The zero knowledge proof basically sits at the corner of everything that you are going to want to do on this new decentralized verified internet that we are now piling into. And so it's a very universal thing to be interested in because ultimately every time you want to interact with this internet, there will be this thing as zero knowledge proof that is created, executed, verified. And so it really allows you to sort of to see a very wide section of this future market as we felt that from a strategic perspective, it's a great place to start. And then I think also it was the commonality between me and Kobe. It was the thing that sort of, I guess, united our interests and seemed like a very natural place to start. And we still wanted to keep really closely for real, for not just investment reasons, but for reasons of intellectual interest, this is one of the most fertile areas of intellectual output. Obviously we're very parochial and the way we think about this is we breathe and eat and this stuff and we spend a lot of time in it, but I really think this has been one of the most sort of fertile periods in the advance of computation sort of through history. There have been several of these, but I think this really has that level of importance. And so we wanted to stay close to the research.
Ben：I wanted to double click on your comment about founders with deeply technical backgrounds. What do you find most investment firms aren't able to understand about these founders and how does geometry work to solve that and ensure that these deeply technical founders who may not have traditional business experience, that they're able to launch a successful business protocol, et cetera?
Tom：Yeah. So I think it basically boils down to what motivates the founders. So, you know, it's a very hackneyed thing to kind of, to comb back over history and pick out the Steve Jobs and the Bezos and all these wonderful founders. And the thing that very clearly unites them is an utter obsession over the user and the ease of use and addressing the mainstream user as much as they can. It could be said that the computation and all this computer industry grew up on a diet of hobbyists, basically financing these companies to be able to care about some mainstream user that could exist. But thereafter it was all about sort of the mainstream user and caring a lot about them. The interesting thing I think that possibly plagues deep tech founders is they come in with a thirst, a passion for the intellectual stuff. So they've come straight up possibly out of a university department. They may have worked at some deep tech startup before, but it usually will have been at the cutting edge of the research, not necessarily having to do that sort of existential problem of how do we actually find a real user? And you could say that crypto in its entirety has been plagued by the fact that it is amazing economics meets amazing mathematics, and it's really struggled to find a, I would argue, a sort of a long-term user so far. It's been built up on a diet of hobbyists. These hobbyists have actually largely been financially driven so far, and it's now really seeking its long-term end user. So the trouble is someone comes in and they say, "We've got this great thing, MPC, multi-party computation. We've got this great thing, homomorphic encryption, or this great thing, zero-knowledge proofs," or whatever it might be. And so they end up, instead of it being a light bulb moment, "Ah, this user needs this. Where's the technology?" It tends to be, "I've got this amazing thing. My old accelerators used to call it the orb, this magical piece of technology that animates them and animates all their friends." And then they spend years, and as arguably I think Aztec did for a while, and we definitely wrestled with this, you have this magic thing, but what does the magic thing do for the user? And so the one thing we always make sure that we do is try to find somewhere in the DNA of at least one of the founders, there is that obsessive impulse over a future end user, even if the end user is at the moment possibly ill-defined, possibly too general. We always try to make sure that they have the capacity to care about an end user, so that when that phase change happens in the company, research, research, research, research, product, the product is going to take over, and the product is not going to be hamstrung by the desires of the rest of the founding team to essentially run a faculty and not a business. So what does running a research-driven VC fund mean in practice for those founders that you just referenced? How are you supporting them both in terms of developing the orb, but then also bringing that orb and developing a commercial purpose around it? Yes, so we've had several ways. First of all, very often we end up doing research collaborations before we invest in a company. Often, actually, that's at least on occasion, that's how the relationship is built. We often do, we support the technical part of the founding team with some of the early breakthroughs they need to make. I think sort of a notable one lately has been Anika Monblatt's work on Sangria, which is basically a way to make these proofs even faster. We can go into more detail, but it's basically a sort of, in some ways, it's a recapitulation of things we've been doing before, but it's a kind of different layer of the cryptographic stack. And so we have that sort of very pure research-driven interaction. And I'd say probably what I bring to the team more is someone who has sort of carried the existential dread for three, four years, sort of worked out how to make the horse lead the cart. So I spend a lot more time sort of trying to work on the joinery of the business to the encryption system or the business, so this new widget, and try to draw out a definition of that first user, which very often, because the R&D looks like such hell, it's such an uphill struggle, and there's so much to do, and there's so much performance gains to make, very often that can get lost in the discussion. So I think that's where the team is quite well balanced to help on both sides.
Ben：What are, thinking through the successful projects over the last three, four years, a lot of them have been token incentive-driven and just playing financial games. What are the business models that you have conviction in that can be applied to these more technical protocols that are being developed that you guys are backing at Geometry? What are, I guess, a couple different examples of which this deeply technical work can be monetized and developed into a real business?
Tom：Yeah, okay, so the funny thing about a lot of these protocols and the development of cryptography is it takes a lot of the, what do you call it, so the residues of trust, which actually you often look for in a business to be able to justify your ability to charge for it, right? You need the whole thing about stickiness is I can charge you more if it's much harder for you to leave me or if some actor could do something bad to your information. So if I'm Oracle, I could forget all the information I've stored in my database or I could mutate it, right? So you're prepared to pay Oracle for a whole load of services, but one of the core services is please don't screw up. And that please don't screw up-ism is kind of extracted from the risks of doing stuff on Web3. Web3, actually, its whole mission has been about try to reduce the dependencies on centralized people, which is also structurally not always great for the business model. So again, that's what we look for. Where are the trust residues likely to be still left after this amazing work has been done? So maybe some good examples. So first of all, I'm probably a believer that there is some small clutch of protocol attached tokens that can have value without cash flow. And I think the reason is they are striving for candidacy as a macro asset. They want to be a store of value asset that can sit alongside gold or currencies or whatever. And so sort of, let's say Bitcoin, Ether, and maybe there's a couple of other sort of canonical layer one assets that can make this claim. So they can basically become money-like. They can essentially sit at a crazily elevated cash multiple. And that's fine because the world has agreed that they're a store of value. It still leaves an article of faith at the base of this thing, but that's fine. So then there's what we call the layer two network. So this has been, maybe we should also talk about sort of structurally how Ethereum is scaling differently to other networks. We can do that. But there's this thing called the layer two network, which is essentially it borrows off the security of Ethereum. So it kind of, in the long run, it is as secure as Ethereum. There's some stuff that it's allowed to do to kind of make transaction volumes faster and increase the amount of computation that Ethereum actually can host. And these layer two networks have also had tokens attached. Now, I think the jury is really out on whether they can, those tokens, have a future claim to cash flow. There are kind of two things to examine here. One is, is there a mechanism by which they can extract cash? And that brings in a whole load of questions like, well, from a regulatory standpoint, is this a security or does it get classed as a royalties? Somehow it kind of sits outside that framework. Does it need to generate cash? My view is probably it does. I'm not sure that the layer two has quite the canonical role in the world that it does at a layer one. You know, Ether has a very sort of central position to the entire financial machinery of Ethereum. Can the same be said for a layer two token? Not sure. But there are a lot of people sitting around the layer two network who will help it to operate where there are going to be these trust relationships and there are going to be basically input costs that cannot go away to how they operate. So a good example is, I mean, we've invested in a company called Ingeniawa. There are several of these companies that are doing acceleration of the type of computation that is going to drive all of this future internet. Basically putting together these things called zero-knowledge proofs. Like, basically it means any program that's going to go into the system, it has to go through this process of being turned into a succinct proof. And the reason it has to go through that process is that Ethereum can't check billions of transactions per second. But the succinct proofs are a way of relieving Ethereum of having to just check everything. It can just check something that allows it to know that it's all true without checking every single detail, right? So that means every piece of software, any code, anything will end up running through this zero-knowledge proof creation system. And so there are particular hardware providers that, very like the CPU, we had the GPU, which focused just on graphics. This thing called the ZPU, or ZPU, which will just focus on the construction of this very important paradigm because it's going to be everywhere. It doesn't matter what code you're running, what computer program you're interacting with, you will need a conversion to a set KPI. So that's maybe a good example of where you have to follow the line of cash flow. You have to work out, can the layer two make money? Don't know. Jury's out. The other thing about the layer two is it probably has to freeze up over time, which is kind of the opposite of what a company does. A company obviously needs to keep on innovating to drop its margin. These blockchain infrastructures have this interesting thing where the more money they host and the more people are relying on it, the less they can rebuild themselves in midair, right? Ethereum has just done this for arguably the last time. Further tweaks to Ethereum probably are now tweaks. And the layer twos might end up following that same pattern. And so that might further question, is there any impulse to extract cash? Is there any mechanism or desire to extract more than the cost of operation from the user and therefore be a good return at the protocol level? But there's loads of businesses you can build around that, like hardware, possibly even software services that help engineers to maintain their software. Bear in mind, if you're running smart contracts in the future, the threats around blockchain programs are potentially an order of magnitude higher than sort of classical programs or classical websites or whatever. And so the role of software that is constantly monitoring the threats for blockchain programs can probably extract a lot of value from engineering teams. There's lots of opportunity. It probably creates a lot of opportunity, but it also deletes all business models. Of course, we've seen this with tech lots of times, so it shouldn't necessarily surprise us.
Ben：I think this is a good time to dive into the Merriam-Webster section of today's episode. Let's start with ZK Proof, just because that's a core tenant of Geometry's fun thesis and a lot of where the innovation is going towards right now. And it seems to be a key primitive that has unlocked the next wave for blockchains. What is ZK Proof and what is the history of it? Because I find that fascinating. And why is it important?
Tom：Sure. So, ZK Proof, first of all, the first moment you encounter this thing, it seems really unlikely that it exists. It's a very odd thing. So, what are we trying to do? We're trying to allow Ethereum, let's say, or some layer one blockchain to process tens of millions, hundreds of millions, billions of transactions all at the same time. You can't do it, right? Because it only can run as fast as the slowest computer checking the system. So, what the ZK Proof enables for blockchains, and by the way, ZK Proof is a bit of a misnomer, should really be called succinct proof. And the historical reasons why we use this bad word, zero knowledge is a bit misleading. It's basically a way of taking a computation of which, let's say, 100 million transactions, that would be a big computation. And instead of asking a node on the Ethereum network, like basically everyone participating in the Ethereum network to check this thing, you can do a much bigger amount of computation. And in moral exchange for this big computation that does this big argument, produce a tiny proof that shows that those millions of transactions are all correct, which is a crazy thing to say. So, in other words, I can convince you that the result of a change in the information on Ethereum from one block to the next was authentically updated by, let's say, a million or 10 million or however many transactions, all compiled together, and you need to do no more work than it would cost you to check just a few transactions. I can really get almost limitless scaling. Now, this sort of inherits from a body of work that started in the 1980s. I actually explained this on another podcast, I'll try to find another way to explain it, but essentially, maths had a bit of an existential crisis. Mathematics, I should say, or math, I should say, on this podcast, it had an existential crisis. So, proofs in maths were getting really long. People were having to sort of basically, there was one called the four-color theorem, which basically says that you can color any map in four colors. And it was impossible for mathematicians to check it was true because it had been reduced to thousands of configurations, 500-page proofs, so no one could check it. So, this is really like a blockchain. Blockchains have too much stuff to check. And so, the maths community couldn't check these proofs, and this was a problem. And so, this sort of kicked off a body of work at MIT where they were trying to work out, is it actually possible to allow someone to verify that a computation has correctly happened and there are no mistakes, but not actually have to look at the original computation. And this is kind of where it all arose from. Blockchain really gave the sort of the economic stimulus to that going from a sort of, theoretically, this is possible, who's ever going to use it, to an actual application. It actually arose first in the context of privacy. So, one thing maybe I've not explained on podcasts in the past, I think this is really interesting, privacy and scaling are really, really closely related phenomena. And here's why. If I want to do a private transaction, let's say on Ethereum, I do a ZK proof that means I want to show you the transaction was true, but I don't want to share the information with you. And the scaling setting says I want to prove the transaction or batch of transactions is true, but I don't want to have to send you all the information because you can't possibly check it all. Now those are different motivations, but it's the same paradigm. I'm not going to send you all the information, but I still want you to be able to verify it. So, the same thing, the same kind of mathematical requirement arises out of both privacy and scaling. We actually saw it in privacy first, 2013, Zcash, 2013, 2014, something like that. But it's actually now come to the rescue of things like Ethereum as a scaling mechanism. So, yeah, these ZK proofs, they're everywhere, they're really important. And they are probably, I think without them, I don't know how we would have scaled Ethereum and it had no chance of ever catching up with the surge capacity of Visa, which can do up to 60,000, 70,000 transactions a second. We would have just not had no hope. The close correlation of privacy and scaling is an interesting one. And so I'd like to make that tangible via a number of the ZK EVM, Ethereum Virtual Machine, scaling solutions that are coming live now. We saw Polygon launch at ZK EVM, Scroll, and basically everything that's not built on optimistic rollup technology.
Ben：Can you explain what, and maybe we use Scroll as an example, what does Scroll do? Why is it important? And what is the EVM component of it?
Tom：Yeah, so the way I tend to analogize this is like the kind of microchip semiconductor industry, going back a wee bit. So what have we started with? We started with this layer one. What are the technologies it brought to us? It brought to us basically a new type of computational substance, something that's really hard and really immutable we've not had before, and is going to become incredibly important in the coming years. And I'll explain why later on in the show. It carries with it, it needs to carry with it, an agreement on how you write computer programs. So that's what a microchip does.
Ben：What's the implication of zero-knowledge proofs and the launch of Scroll for the end user, for example? What benefits does all of this technology bring to the average person walking on the street who's interested in doing something on-chain? The short term and the long term.
Tom：The short term is, it isn't going to make your transactions an order of magnitude cheaper, because you are now basically having your transactions bundled up with lots of other people's transactions, and Ethereum just has to check one consolidated proof to know that all that batch of transactions was secure. Now, there's some reasons why actually you need to make some extra assumptions to get even better scaling, but that's the short answer, is you just get cheaper transactions for the same stuff. Well, I think by far and away the more important thing is this now opens up the basket of ways that engineers can innovate, so they can now build more complicated programs, because those more complicated programs were unaffordably expensive to run on Ethereum mainnet, and now they're about to get an order of magnitude cheaper. So the more interesting thing from the investment standpoint is, what are now all of the programs that can now be built, that couldn't be built for the user because they were too complex? You have a very simple example, is something like an order book, an exchange, right? So that the amount of searching that needed to be done across all of the bids and asks in a classical exchange environment with self-custody, that was way too complex to put onto a blockchain because it's just prohibitively expensive. And so that is maybe a good example of something that now can be built on EVM or some other computing paradigm because of zero-knowledge proofs that was prohibitively expensive. By the way, very interestingly, because it was prohibitively expensive, that's why we got Uniswap, right? We got something that was computationally simpler, and it was really like this sort of creature born deep under the sea. There are certain types of creatures that can survive at the very lowest levels of ocean depths where the amount of pressure in the ocean is so high. And so you get very particular types of creatures, and they're not maybe the most sophisticated-looking types of creatures, but they're very sort of hardy creatures. But as that kind of pressure releases, as things get cheaper on blockchains, you can get more sophisticated sort of economic organisms, if you like, more sophisticated pieces of software.
Ben：Interesting comparison to Uniswap and how it was bred of a need for simplicity at a time when the tech wasn't quite there. As the leader of geometry, your job is to look into the future and stay ahead of trends and make sure that your team is conducting research on where the most bleeding edge tech is going. With your crystal ball, where are you steering the ship? And to ensure that geometry is always looking to what blockchain and crypto technology needs, looking past ZK, which has now really become a core component of blockchain infrastructure, what is the next big leap forward that we should be anticipating?
Tom： So it's something that people are starting to work on now. And I don't want to sound like one of those people that sort of suddenly has seen AI bolt from the stables and suddenly that's all I can think about. AI does usher in a couple of interesting opportunities, both from the research side and also from the investment standpoint. So first of all, I think AI has just made crypto investable. I think it's also made AI investable, but it's certainly made, in my view, crypto investable because crypto for a long time, I heard one investor describe the view of crypto as a very gothic view. You really have to believe a lot of stuff is going to go wrong for crypto to be sort of universally relevant rather than just sort of staying in lane and just handling these kind of macro assets. And I think AI might be the calling. That might be the answer, is you now have these programs, they get to decide not just how they execute, but when or whether they execute. This means that the storage on which we all rely for an account of our history, things like files, pieces of work you've done, all of that stuff is sort of increasingly potentially mutable. And so the role of crypto as a computational backend for the world, for the new internet, is increasingly important. That level of redundancy and verification that it offers is incredibly important. So that's kind of one thing to say. From a research standpoint, this is quite interesting because, and it means that by the way, the course of research that will interest geometry will remain thematically quite similar for at least the next 12 months, which is that zero-knowledge proofs now need to get a lot, lot, lot, lot faster. So if you're now going to have a world where the internet needs to be increasingly verified, that means that when people run artificial intelligence, or let's say sort of at a lower level, sort of machine learning models, and increasingly if the world is moving from this sort of compiled computation environment where you have all programs are hard-coded by an engineer, and they all go down into this instruction set, and instead increasingly the utilities that we're using online are more kind of machine learning generated, they're sort of more intuitive in that sense, intuitive compute versus maybe sort of classical compute. It's going to be very important to know that they were executed correctly when they are operating on your state. If you think blockchain state right now, yeah, it's your assets. In the long run, this could be everything that you are storing, everything you're doing on the internet, that in the long run, backed up essentially, sort of executed on this sort of decentralized network. So this means secondary proofs need to get a lot faster. These machine learning models are enormous, and so whereas before what we needed to be able to do was do a unisort, well it's pretty simple, it's x times y, it needs a really simple equation, and now you've got to verify whether an enormous model, billions or trillions of parameters, numbers flowing all over, it was actually very simple to get a lot better. So that's where these two worlds, machine learning and crypto, made very well life and produces a requirement for massive improvements in the improving system we've all been working with way in the past.
Ben：I like to think of this end user and who the two different primary groups are. There's the average consumer, and then there's corporates, institutions, businesses, et cetera.Why is ZK, verification, security, privacy, why is that so important to that second cohort, the businesses, the companies, so that they can start getting more involved on chain?
Tom：Right, well first of all I think the first beneficiary is are not on chain. So these zero knowledge proofs are massively important off chain. On chain basically just means I'm recording the order in which transactions happened, but it might be that it's really important for you to check that a computation happened but you're not on chain. Here's a great example. So you've always, as a basic input to this, you've always got to look for where is the cost of not knowing that a machine learning model was executed massively costly. Where is either a huge financial cost or a compliance cost or something, right? That's where you've got to be staring to work out where to apply this technology. So a really good example is medtech. So increasingly we're going to see, for example, oncology. So people sort of, you know, doctors, until now it's been doctors stare at a scan, try to work out is there a tumor, is there a medical problem with this person. And increasingly the move will be towards AI or sort of machine learning, right? So you actually run some model and it's able to detect all these extra features that the human eye is not able to detect from the input data. So great example of this technology would be like, can you imagine the medical, the sort of the indemnity breach that you would have if you couldn't prove a paper trace and maybe someone had just failed to run the correct model, the authorized model that is the one that is known to be the best at detecting cancers, for example, would be monumental. So great example of proving that you executed a machine learning model would be oncology. And so accompanying your scan, wherever it goes, would be this proof that says the best in class known algorithm for detecting cancer. Here's a succinct proof that you can check to make sure that the model actually ran. That's a really good example of where sort of the cost of producing the proof is not that high compared with the massive cost of not having a paper trail that proves that you did the thing that was medically the correct thing to do using early versions of AI or ML.
Ben：So I think it's kind of your job helping steer these very technical teams think about that end user. Likely the oncologist has deep knowledge of oncology, but may know nothing about crypto or zero knowledge proofs. And so that user experience, like the complexity of the crypto and the mathematics needs to be abstracted to the background. Given the very technical nature of these topics, how can these teams think about the UX and is that where geometry and other investors could step in and really try and drive an emphasis on that topic?
Tom：Indeed. I mean, the first thing to say here is that as the cryptography team, you are probably not going to be a very good person to sell directly to medical specialists. So you're going to need really to think about platformizing yourself. So what you should really be doing is providing a computation service, some kind of SDK, whatever, some sort of API service. And what it does is, and now there's a whole lot of privacy issues here, so you probably need the proof to be run sort of locally near the client so they're not broadcasting all their information. But you probably, your job is to build a great engineering experience that just allows the person building the model to sort of compile the model into a proof and create that proof. So you've got to team, team one is we're a med tech company. We're trying to create a more reliable way of detecting cancer. Who is their customer? Their customer is the medical profession, so some hospital, whatever, national health service in the UK, whatever it might be. And they are onward paying for some much more general platform whose job it is to create the certificate. So, you know, a kind of similar example would be back when we were all building these early websites. And you know that little lock in the corner of your screen when you're on your browser? That's an SSL certificate, right? You did not want website builders having to build their own SSL certificate. Also, there were standards that were required to make sure that a server and a client could interact with one another. So you needed that provided by some B2B business. It's that B2B business that is really the sort of company that Geometry would fund. In other words, they are building this ZK certification for all these applications that can't go wrong. And they make it, as you say, really easy for those engineering teams. So those engineering teams, they're probably product specialists. They probably do know their own market quite well. They know the medical profession. They know the defense profession. There's a whole load of professions that this could ultimately serve, as well as on-chain applications. And they are interfacing with those end users. And they simply have as an input, this thing that makes all the cryptography complication go away from them. And so, yeah, your bird's base is talking about an engineering tool. But it's a very powerful engineering tool, partly because the amount of R&D left to make this stuff more and more and more and more powerful. It's sort of almost limitless. And so there's a kind of great long-run business in being one of those ZKP providers.
Ben：So I wanted to save some time here to hear about some of the projects that Geometry has backed and why you're excited about them and what they offer to the world. So maybe we could do two examples here of interesting projects that you're excited to share with the audience.
Ben：What is invisible to the majority of founders and crypto investors out there that is clear as day to you in geometry?
Tom：The first is maybe a sort of a negative or at least a check against our assumptions, which is, I think the value extraction case for layer twos is extremely unclear at the protocol level. And I need to emphasize that. It doesn't mean you can't build huge businesses around it. And a lot of these layer two companies can. But the reason is that the impulse to make money does not sit particularly well with the impulse to stop bad things from happening for the user. So the first thing I think is going to happen in the long run is in layer twos, everyone is going to be staking ETH. In other words, the security for these layer twos will come probably from a more canonical asset, one that's better expressing security, that's better giving security, gives security more cheaply than a native token. So I think, therefore, there is a big question of whether the tokens themselves can value extract from one. Number two, and I think I have said it higher up the program, but I really think that AI has much or at least as much consequence for the investability of crypto as it does the investability of AI. With AI, it's very hard because either you've invested in a foundation model, which looks like it's in a very powerful position, but again, cash flow is not particularly clear. Or you invest in an application where everything comes down to the fine tuning and probably those applications can monetize. But are they actually always going to be at the behest of whoever built that foundation model? That's a big open question. Whereas AI has made computational systems highly mutable, highly at risk. Think of all your bank statements. I mean, I don't want to cause too much of a scare, but sort of every piece of data that you think is hard historical evidence that tells you where your assets are, what they are, is increasingly mutable and open to everything from cyber attack through to... Now, this is a very sort of gruesome view of the world, but I think it's an elevated risk that AI can now start to operate on computational systems in much more intuitive ways and mean that our collective record of history is much flimsier. And I think that means the crypto substance is now highly investible. It's a highly valuable asset to have your assets and all of your computational state recorded in this much more immutable, much harder substance that crypto provides. So I think if you want to invest in the virtues of AI, you need to at least be taking as hard a stare at crypto as AI itself. I think that's your spicy take within crypto, but I'm going to ask the same question. What is your spiciest take outside of crypto? What do you got? Well, I can now say it because it's been very... I just don't understand. It's been so trendy to disavow the role of virtual reality. And in particular, I feel a lot of scorn has been poured on Zuckerberg for his VR output so far. I don't know how many people, these people actually tried it. It's actually really good for the kinds of businesses that are now being created in very dispersed environments. So geometry has used this quite regularly in the past. For, we need a lecture room. We have 10 people all around the world, all in different cities. How do you do a whiteboarding session with those people? And VR was a great answer to that. They have completely eliminated the latency. Yeah, you get slight headaches. Yeah, okay. If you take a snapshot of what you see on the screen, it doesn't look immediately arresting versus the output of mid-journey or something. That's right. But the actual work that Facebook has done to get the experience so good, to get this sort of the acoustic replication so good, the user experience of sort of, you know, actually white writing on a whiteboard with your hand flailing in mid-air, amazing. I actually do think that this is the future of work and I don't think it's so very far away. And there are a lot of companies that have been built up in highly dispersed formats because of COVID. It's very natural. It's suddenly very natural. You notice this actually in the portfolio sort of two, three years ago, the idea of starting a company with a co-founder who's in a different country was unthinkable. Now it's the default in a lot of settings. And so this means that the, sort of the real estate of a virtual environment is highly, highly, highly valuable. Now what that then does to sort of all kinds of asset classes, I don't know, does it mean we all will find out going to the countryside because we have much less use for cities because we have, cities have much less of social draw. Or does it actually mean the flip side? We're much less sensitive to constrained space. And so actually we're very happy to live in highly built up environments because we don't need the outside space because we can just sort of push the wall open and fly out the window anytime we like in our VR. I have no idea. I think there's going to have some very interesting implications for classical real estate and other assets too, particularly some commercial real estate. But I think VR is going to be foundationally important to our lives when we're spending all our time. And if I just think people who are disavowing right now, there was a great hype cycle. Now people's excitement has gone. It's very classic. But I really think that that is one of the things that's going to be, going to imminently influence all of our working lives and all of our social life too. I'm in New York city right now. And just looking out at the amount of commercial real estate there is in so many of these big, you know, 10 story plus buildings, they must be vacant. With, it's just cheaper for the companies to allow people to work remotely and making use of an at home office. So I don't know what that means to the space, but there will have to be new use cases developed in order for that property not to be totally wasted. So I love the fact that Geometry's team is leveraging VR to coordinate a global organization. I think you guys are in seven or 10 countries. So the fact you're all able to congregate in a virtual room that has more depth to it than Zoom, I think is awesome. And yes, as you think of the Darwinian chances of survival of a company that has to hire only geographically versus the chances of a company that can hire entirely globally. And the only prior constraint was how do I put all these people in a room so that their communications don't get degraded by being split up and not having good communications. If actually ultimately VR fixes that, then yes, to me, I mean, it's easy to cycle this forward too fast, but I think it entirely replaces the role of the commercial office, totally replaces it. And so yes, I must admit, I would be interested to see what, I guess all that real estate gets repurposed as residential. And then the big question is, do people want that or do they want to actually move out into the country because they can get all of their sort of much of their meaningful social interaction through VR? I have no idea how that's going to play out.
Ben：It'll certainly be interesting to monitor. I think we've got some time now. Tom, this was awesome. Where can people learn more about you and geometry?
Tom：Yes, we have a website, www.geometry.xyz, I will say. I'm still training myself to say that. We have on there all kinds of things, sort of lists of our portfolio, a notebook, a health warning. It is very technical, but there are some sort of more digestible non-technical topics on there as well. That's where we put a lot of our research and otherwise, yep, we contact by email and also on Twitter as well. We have an increasingly popular Twitter account that started with quite a technical audience, but definitely broadening now.
Ben：Awesome. Well, thank you for coming on today's show. This was a fantastic episode and for teaching everyone about these deeply technical topics. I look forward to watching you and geometry succeed and going forward, staying at the cutting edge. Thank you for listening and we hope you enjoyed this episode of Scenius Studio. Please leave us a review and rating wherever you listen to podcasts if you love today's show. For more Scenius Capital content, check us out at sceniuscapital.substack.com and shoot me a follow on Twitter @bennypjacobs. We'll see you next time.
大家好，欢迎来到Scenius Studio，我是Scenius Capital的 Ben Jacobs。通过这个播客，我们旨在为听众提供加密资产管理行业中最优秀和最有才华的投资者的内部访问。在每一集中，我将与一位领先的加密货币和区块链风险基金或对冲基金经理进行交流，探索在创新的前沿运营投资基金的复杂性。在这一集中，我与Geometry的创始人Tom Walton-Pocock坐下来交谈。凭借他作为Aztec Network联合创始人和首席执行官的经验，Tom推出Geometry的前提是，资深技术创始人需要能够理解他们以工程为先的业务建设方法的投资者。Geometry现在是一家领先的密码学和数学研究公司，拥有成功的早期区块链风险基金交易记录。让我们开始吧。
「Ben Jacobs是Scenius Capital Management的合伙人。Ben和本播客的嘉宾表达的所有观点都仅代表他们个人的观点，不反映Scenius Capital Management的观点。嘉宾和主持人可能在本播客中讨论的资产和基金中保持头寸。您不应将任何在本播客中表达的观点视为具体诱因来做出特定的投资或遵循特定策略，而只应将其视为他们个人观点的表达。本播客仅供信息目的。 」
Ben：大家好，欢迎来到Scenius Studio。我是你的主持人Scenius Capital的Ben Jacobs。今天的嘉宾Tom Walpoe来自英国，我们很高兴有他参加。Tom是Geometry的创始人之一。你好，Tom，近况如何？
Tom：我在一个叫做泽西岛的小岛上长大，这个名字你可能熟悉，但这是老泽西，新泽西是以它命名的。它是一个漂浮在英吉利海峡中的小小花岗岩，是个可爱的地方。它位于英国和法国之间，也许最能代表那里的生活的是你总是稍微与世界隔绝一些。互联网开始上线，我想我们可能总是稍微落后一些。所以也许一些信息流在那里的时候稍微不那么流畅。我在英国的剑桥大学学习数学本科和研究生。我还保留着研究生时的照片挂在我的墙上。很惊人的是，现在这张照片中的很多人实际上正在领导一些重要的项目，包括以太坊和WorldCoin等各种重要的研究驱动的基础设施。也许这不是什么奇怪的事，因为房间里有很多数学家。我曾短暂涉足音乐界，花了一些时间在金融行业，然后创立了一个名为Aztec的公司，这是一个L2。这是这样一种公司，它正在扩展以太坊的规模。我想在本次播客过程中我们将会谈到一些关于规模扩展的问题。它已经完成了许多工作，创造了加密技巧，使你能够使这些系统更快，而不会危及其去中心化。因此，这种新的硬共识基础，我认为将是所有未来计算的基础。你需要确保它仍然很难，仍然完全去中心化。这是一个加密技巧，由我的联合创始人Zach Williamson和我们的首席科学家Ariel Gabasson所创造的。我认为现在它正在成为以太坊现在能够享受的许多扩展的基础。我们可以稍后讨论这个问题。至于Geometry方面，Geometry是我离开Aztec后不久出现的。我与Kobi Gurkan共同创立了这个公司，希望Geometry成为一种新的投资方式，以研究为驱动的公司。我们在应用密码学方面进行了大量的研究，包括零知识证明等。我们还利用这些技术作为投资的机制。这也成为了一种方式，可以带来投资的流量，使得Geometry可以合理地承担更高的运营成本。
Tom：我认为这基本上归结于创始人的动机。你知道，回顾历史并挑选出像Steve Jobs、Bezos 和其他伟大的创始人，这是一件非常陈词滥调的事情。很明显，将他们联系在一起的是对用户和易用性的极度痴迷，以及是尽可能地满足主流用户。可以说，计算机和整个计算机产业成长于业余爱好者的支持下，这些公司才开始关注可能存在的主流用户。但此后，一切都关注主流用户，并极度关注他们。我认为可能困扰深度技术创始人的有趣之处在于，他们带着对知识产生的渴望和热情进入这个行业。他们可能直接从大学系里走出来，可能之前在一些深度技术创业公司工作过，但通常是在研究的前沿，不一定需要解决如何实际找到真正的用户这样的重大问题。你可以说，整个加密行业被一个问题所困扰，即惊人的经济学和惊人的数学相遇，但到目前为止，它确实很难找到一个长期的用户。它是由业余爱好者的支持建立起来的。这些业余爱好者到目前为止实际上主要是出于财务驱动，现在它真的在寻求长期的最终用户。所以问题在于，有人进来说：“我们有这个伟大的东西，多方计算，同态加密，或零知识证明，或者其他任何东西。”于是他们会发现，不是灯泡瞬间，“啊，这个用户需要这个。技术在哪里？”它往往是：“我有这个神奇的东西，我的老加速器曾称之为球，这个神奇的技术驱动着他们和他们所有的朋友。”然后他们花了几年时间，就像Aztec曾经做过的那样，我们也确实曾经挣扎过，你有了这个神奇的东西，但这个神奇的东西对用户有什么用？所以我们总是确保至少有一位创始人的DNA里有对未来最终用户的执着冲动，即使此刻的最终用户可能没有明确的定义，可能过于笼统。我们总是努力确保他们有关注最终用户的能力，这样当公司发生阶段性变化时，即研究、产品，产品将接管，产品不会受到其余创始团队渴望运营大学而非企业的愿望的阻碍。那么，对于您刚刚提到的那些创始人来说，运行一个以研究驱动的VC基金意味着什么？在发展Orb的同时，您如何在商业目的周围提供支持？是的，我们有几种方式。首先，我们通常会在投资公司之前进行研究合作。实际上，通常情况下，这至少偶尔是建立关系的方式。我们经常为创始团队的技术部分提供一些早期突破所需的支持。最近一个值得注意的例子可能是Anika Monblatt在Sangria上的工作，这基本上是一种使这些证明更快的方法。我们可以深入探讨，但基本上它在某种程度上是我们以前做过的事情的重现，但它是加密栈的不同层。因此，我们有这种非常纯粹的研究驱动互动。我想我更多地为团队带来的是，我已经承受了三、四年的生存恐惧，努力让马拉着车走。因此，我花了更多时间试图处理业务与加密系统或业务与这个新小部件之间的连接，并尝试概括出第一个用户的定义，因为由于研发看起来像地狱一样，是一场艰难的攀登，有很多性能提升要做，因此通常会在讨论中丢失。因此，我认为团队在两个方面都能提供帮助，平衡得相当不错。
Ben：我觉得现在是探究今天 Merriam-Webster 部分的好时机。我们从 ZK Proof 开始，因为它是 Geometry 有趣的论文的核心，而且现在很多创新都朝着这个方向进行。它似乎是解锁区块链下一波浪潮的关键基元。ZK Proof 是什么？它的历史是什么？它为什么重要？
Tom：我通常将其类比为微芯片半导体行业，稍微回顾一下。我们从什么开始？我们从第一层开始。它给我们带来了什么技术？它为我们带来了一种全新的计算物质，一种非常坚固和不可变的东西，在未来几年中将变得非常重要。我稍后会解释为什么。它需要带着一个关于如何编写计算机程序的协议。这就是微芯片所做的事情。当然，它具有这些指令，每个你运行的程序，如 Microsoft Word，Microsoft Excel 或其他任何东西，都会编译成相同的有限指令集。通常只有几十个或几百个指令，每个计算机程序都可以由这些指令构建而成，就像字母表一样。EVM 是第一个在区块链上出现的这样的指令集，作为编码所有程序的一种方式。现在许多人不确定它是否是最佳设计，但在当时他们面临了许多约束。它仍然变得非常重要，现在你可以在其他 L 1 上到处看到 EVM。因此，作为旨在扩展以太坊的 L2 网络的自然之举是说，已经编写了许多安全程序。例如 Aave 和 MakerDAO，它们都是以这种以太坊脚本编写的，已知是安全的。这是一组以太坊的汇编集。因此，我们需要找到一种方法来扩展这些已知安全的程序，它们已经经过了严格的测试，承载着数十亿美元，因此我们需要将这个东西放入零知识证明中，以便我们现在可以在 L2 中使用相同的计算范式执行大量的这些相同的交易。这正是 Scroll 所做的，以及 ZK-Sync 所做的，它们将这个指令集放入了零知识证明中。现在事实证明，这是一件非常困难的事情。不幸的是，EVM 的设计存在一些历史选择，我不会详细解释，但这使得它很难适合于零知识证明。这是因为当设计 EVM 时，没有人意识到零知识证明是解决方案，所以在 EVM 的设计中做了一些东西，实际上导致当你需要将 EVM 放入零知识证明中以扩展它时，它需要更多的脑力。
Ben：那么，零知识证明和 Scroll 的推出对于终端用户有什么影响呢？例如，这些技术为对链上感兴趣的普通人带来了哪些好处？
Ben：太好了。感谢你今天的节目，非常棒，也让我们了解到这些深度技术话题。期待你和Geometry的成功，并继续保持在前沿。感谢大家收听本期Scenius Studio节目，如果您喜欢今天的节目，请在您收听播客的平台上给我们留下评论和评分。如果您想了解更多Scenius Capital的内容，请关注我们的Substack账号sceniuscapital.substack.com，同时欢迎在Twitter上关注我@bennypjacobs。我们下期节目再见。
Thanks for reading ChainFeeds Selection! Subscribe for free to receive new posts and support my work.