From Synapse to Crypto: Bruno Faviero's Builders Journey

Welcome to Crypto Podcast Goods, the audio home for ClubCPG. This week, Bruno Faviero, founder and CEO of Magna, shares his builder story with CPG COO Mikey Piero. The two discuss Bruno's early days as a founder in Synapse, its growth and eventual sale to Palantir, and then his recent founding of Magna and all the challenges that have come along the way. As a reminder, the hosts of CPG Pop and their guests are not registered investment advisors. All opinions are of the hosts and guests alone. Nothing discussed today should be treated as investment advice, and all content from our Genius Calls is solely for informational and entertainment purposes only. Now let's get to that Genius Call.

GM, GM, we are back with another Club CPG Genius Call podcast. We are now currently in the throes of summer, and this is the CPG Summer Camp edition of our Genius Call. We're encouraging lots of folks to get prepared for our new drop, which is coming out pretty soon, and it very much will set you up for touching lots of grass this summer. While we continue to build and formulate the next chapter of our accelerator. But before we get started, I'd like to also give a thanks to Rachel from POP who connected us with Bruno, who is our guest. Our genius call today is Bruno Faviero, and he is the founder and CEO of Magna, which provides token vesting and token holder management services to crypto companies. Magma just raised $17 million in 2022, and those investors that invested in Magma include Tiger Global, Tusk Ventures, Shima Capital, Polygon Ventures, Avalanche Ventures, and the Solana Foundation. Bruno is also a founding partner at Alloy Fund, which is an early-stage venture and focused on AI, enterprise SaaS, biotech, and crypto. But prior to Magma, Bruno was the co-founder and CEO of Synapse, which was an AI company that automated airport X-ray security screening. And after Synapse was acquired by Palantir, Bruno was the product lead for Palantir's geospatial product line and later its AI inference platform. He was also the co-founder of Kalina.io, which develops AI testing tools. I'm gonna have so much fun today. He studied computer science at MIT and started his career as an early employee at Kensho Technologies, which is a finance tool that has AI built in that was acquired by S&P for $550 million. We had a quick chat before this introduction, and it is my pleasure to introduce Bruno Faviero. Bruno, welcome and thank you so much for joining.

Hey, thanks for having me on. It's great to be on here. GM.

GM. We have so much ground to cover, and I really have so many different questions from reading through not only your bio, but Janet did a wonderful job with the questions this week. Let's start with crypto because there's a lot of other, I think, ground to cover from your career there. Where did you come across Web3, and what was your first exposure to Bitcoin or Ethereum or both?

Yeah. So My first exposure was back when I was at MIT. It was around 2012, 2013, and there was an MIT Bitcoin club at the time composed of some very nerdy, very technical individuals that were really interested in the technology. And I ran the startup club at the time, so we were always being exposed to different tech, different kind of frontier innovations. And so at the time, I didn't think too much of it. we had a guy, Jeremy, who was, he was like the crypto cheerleader at MIT. So much so that he got every single MIT student half a Bitcoin back in, I believe it was 2014. And so I kept that, I bought some more and I thought it was interesting, but at the time it was very academic. There wasn't too much going on in terms of startups and products. So fast forward, I kept a bit of a foot in that scene, mostly through the startup world, the innovation world. And so by the time Ethereum came out in 2015, I thought that was really interesting. Okay, now there's something you can do with these tokens. There's more computer science, not just an academic angle, but like a programmer angle, a hacker angle of creative applications of this tech. So I bought a bunch of Ethereum, and pretty quickly thereafter, things started to get really scammy in the space. And you had a lot of projects that said they were using blockchain just for the sake of using it, such that I kind of fell out of interest of it. And Oh gosh, come 2017 when it really picked back up, then this is like my story. I was in and out of it for so long. Then I started trading crypto in 2017, made a little bit of money, bought a ton of Ethereum. And then when it crashed, because I was only half in it, I didn't really know what was going on. Right. I wasn't truly in the community. I wasn't building a company. I was in it from a retail perspective. It was very scary when it all went from really high up in the hundreds, $1,000 for ETH to like $100. So I remember I sold all my ETH for like $120, focused on AI for the next couple of years. And then only, I'd say 2 years ago, post-acquisition, I was, hey, for the first time I get to get back into learning new things, picking a new area that I want to focus on, and just saw all that was going on with crypto from the innovation perspective, startups, you know, hackers, like things that I thought were really interesting and where I thought I could play a role. So that's when I, I fell right back into it and haven't looked back since. So yeah, been all in on crypto for just about the past 2 years.

You mentioned something that's pretty top of mind, um, in the larger world. We have AI, um, everywhere, and I'm here in the Valley, so I think it's a bit more accentuated in terms of near-term exposure to AI startups and meetups. They, they're seemingly happening every day here in San Francisco in the Bay. But you had mentioned you were an early producer and student of AI, and that brought you to Synapse. Can you tell us about where you developed and how you developed Synapse and the story of what happened? It got acquired by Palantir and how that happened.

Yeah, so I first touched AI when I was at this company called Kensho, and we were doing financial analytics tools for Wall Street banks. And the vision of Kensho— I was an early employee. I wasn't the founder. The vision of of it was to let people query the financial markets in a natural way. Right? Like, you could ask questions, hey, what happened with the stock market this week? How do hurricanes affect energy stocks on the Gulf Coast? But it was a bit ahead of its time. And so there wasn't much AI going on. It really ended up being just a lot of statistics, data science, and you couldn't take such open-ended input like you can today with LLMs and ChatGPT. So it ended up being much more structured, much more narrow. but it was still interesting. Once you had that narrowly defined problem, the AI was actually pretty good. The machine learning was pretty good at predicting, at giving you answers to specific questions. And so that was my first experience with AI, and I was there for about 4 years. And during those 4 years, which was 2013 to about 2016, I'd say computer vision was probably one of the areas that most saw advances in the machine learning world, where you went from really slow, clunky, wrong visual classifiers to about certainly by 2016, computer vision was getting pretty good in terms of being able to detect things within an image without having to look at a specific area. That was kind of the big innovation, right? It used to be if I draw a box around your head and I'm like, what is this? It'll be like, that's a head. First, can I show it a picture of a street and be like, pick me out all the people? And at MIT, it has a big machine learning group. So a lot of people studying it, but from a very academic perspective. And so I saw that advance happening. And part of what went through my mind was like, hey, what can we do with that technology out in the real world, given that I think I'm pretty close to probably being one of the first people to see what's possible, what's coming out of the research labs. So I had a conversation with a friend of mine who had just graduated and he studied neuroscience and he was studying the brains of TSA officers, a very specific research topic. And what they detected was that, you know, TSA officers were missing something like 90% of all dangerous items in bags. Like when they look at an X-ray, you know, they'll find a shampoo bottle, but they'll miss a knife a scary percent of the time. And so it really was just like hearing this problem and thinking like, oh man, that's something that's possible now that wasn't possible even 2 years ago in terms of speed, in terms of computing power, in terms of latency, in terms of accuracy. And so kind of had that problem, knew about the tech, and it truly was one of those moments where we're like, why isn't this automated? Why isn't this done? Why has no one done this before? So teamed up with a deep learning specialist, which at the time you needed to use AI. Now not so much, but at the time you needed to find a deep learning researcher, a deep learning engineer, and then, yeah, set about building Synapse. There's a lot of funny stories there. We had to buy an X-ray machine for like $150K. We put it in our backyard in a shed, which is a whole story in and of itself. We befriended the local gun store owners and convinced them to bring their guns by our backyard so we could scan them and generate training data. And then next thing you know, we were bringing literal TSA executives to our backyard in Palo Alto, which, by the way, they loved being in the literal Palo Alto garage. And then, yeah, did that for 3 years. And HP was down the road. Yeah. And so 3 years in, yeah, we were deployed in major Japanese airports. We had contracts with the TSA, with the Air Force. We were doing several million dollars a year in revenue. and we also had customers in the private sector. So airports in the US, airports in Japan. And I think the best call we ever got was about a week, actually a month after we got acquired. We had an airport call my co-founder from no number on the caller ID. And he said, hey, just wanted to let you guys know that we arrested someone today that tried to get a gun into the airport that the human missed and your system detected. So pretty proud of the fact that we built a true working AI product for its time.

That's incredible. From the acquisition, you moved into Palantir and talk to me about the transition from what you were specifically working on with Synapse to then how you broadened that because you started working then on geospatial insights and you mentioned just before the call, we were talking about a little bit of background that you had spent a lot of time on military bases in the US. So how did that transition happen and what was the impetus for it?

Yeah, so by the time we were about 2 to 3 years in, I'd say we developed a pretty specific set of expertise within our team. We had computer vision, deep learning PhDs, so we had a very technical very research team, but also a very talented product team. We've gotten good at working with the government, which both from a BD side has its own nuances, but also from a technical side, because you're working with very sensitive data. It's not quite classified, but it is secured in certain ways. And so that also translates into the models. We had to actually invent some new techniques to obfuscate the data. Because one of the biggest concerns is the government wouldn't want someone to recreate the sensors and be able to reverse engineer them to know how to trick them. So we developed a specific set of skills that when we were going out to raise our Series A, we had a lot of investor interest. And when we looked at the market we were going after, we were in a relatively narrow market, right? Security, Homeland Security specifically, that we just happen to have as investors Founders Fund and 8VC. Who were the founders of Palantir, and they facilitated the initial conversation. And mostly the thinking was, hey, Synapse, you guys are working on security. How would you like to apply all your talents to a much, much bigger market? Right. And in the case of Palantir, it's a defense market where you're not just looking at X-ray machines at airports, you're looking at really any visual input sensor that's used by the types of customers that Palantir services. So How that translated itself was I went from working on X-ray machines to working on satellite imagery was probably the biggest project that I spent my time working on. And so our team became Palantir's AI inference platform team and basically started taking the models we had built for X-rays and applying them to satellite imagery, other types of camera imagery, and even some other more exotic types of inputs that aren't even just images. But it was really about how do we go after a bigger problem? How do we go after a more interesting problem and one that has more real-time implications. And so a lot of what we used was to work on these projects, these monitoring projects, these defense projects that were active and ongoing. And so it was really exciting to be a part of kind of real-world projects, real-world deployments in such a way that, like I mentioned before the call, I was spending time on military bases during my time at Palantir, and it was really interesting to see this AI very much see the light of day in real customers that are not SaaS, they're not engineers, right? They're not SaaS companies. They're these real salt-of-the-earth soldiers on the field that are like, holy shit, this thing can do in 10 seconds what would have taken me an hour to do. And I think that was the most rewarding part of going to Palantir, was having that bigger impact.

That's heartwarming to hear as a person who has personally labeled horse blankets from geospatial imagery in Iraq, the ability to do that was tedious. And that, I think, is part of what is beautiful about AI, particularly at this moment, as it keeps getting better and better. And the time that you can then make something so much more robust and effective by having such a running start is really encouraging. I want to change the gear on Synapse a bit because this is a podcast that really focuses on feeding our accelerator crew experience and insights and kind of from the perspective of another founder. When you bought that machine, the X-ray machine, and put it in your backyard, how hard of a decision was that to go buy it? And also, like, when you were getting traction those first few years, like, how, how long did it take you to get to, like, revenue of any way, shape, or form? Because it sounds like you just mentioned the defense government. Like, it is not a fast process.

No. So I like that you keyed in on that, right? Because so I advise a lot of founders. I invest in a lot of founders. I probably invest in about 100+ companies over the last 10 years. And, you know, the best founders are always asking themselves, you know, where could I move faster? Where could I skip steps? Where can I unblock myself? And one of those biggest questions is, where am I depending on someone else, on a third party, that I could take control of that into my own hands? And so at the time, we were very much in our own heads, which happens when you're a founder, right? When you're staring at a problem for so long, sometimes you don't see any other way of solving it. It takes someone else walking in and being like, hey, what are you guys doing? So in that case, we thought we needed data from the TSA, right? We thought, hey, we need airport data. Where can we get airport data? The TSA. And so we were sitting on our hands, writing to the TSA, calling, emailing, doing meetings. Like, guys, we need training data. From their perspective, they're like, who are these guys? We're not going to give them sensitive, secure data. And so I remember we were blocked for months. I'm like, hey, we can't develop our model. And it took one of our investors walking in and they're like, guys, How much is an x-ray machine? And we're like, oh, we don't know. He's like, figure that out. We figured out, it was like $150K. He's like, okay, buy one. And we're like, can we do that? He's like, yeah, reach out. So we reached out, we put it in order and I mean the sales guides, they don't ask too many questions because they're selling these things to, they sell them to courthouses, they sell them to office buildings, they sell them to stadiums. So yeah, we just put in an order, we wired them $150,000. And the funniest part was when they showed up with a forklift and they were like, hey, where's the loading dock? And we're like, no, it's going in the driveway. But I'd say that was a big moment for us. And we're like, hey, we can actually do this ourselves. And fast forward 3 years, we actually had the largest, world's largest label data set of handguns, which was really interesting. And we ended up actually selling that data set separately as a part of the acquisition since we went on to do satellite models. And so that was part of it was like, okay, even if we're doing something we've never done before, on the one hand, how can we almost like in some ways be a little naive and be like, oh, well, what if we just get one of those? Like, okay, what if we just go and get some guns ourselves? And I think that's always what you have to do as a founder. You have to get creative. You have to solve problems in very out-of-the-box ways. And it's illegal to borrow guns. And so what we would do is we would pay the gun store owner to come and sit in our backyard reading a newspaper while we scanned the guns in front of them. And they loved it because they were like, oh, I can close up my shop and just sit there for 6 hours and you guys will pay me $100 an hour. Amazing. And so we did things in very unconventional ways. And I think relative to a lot of companies like the YC conventional advice is ship. If you're not embarrassed by the first thing you ship, you're shipping too late. Get things out there, put it in front of customers. And then Sam Altman actually came, he was on a talk last week that was all over Twitter where he said, I'd throw most of that advice away. Right. And he said, we were planning years ahead. It took us years to make our first dollar of revenue at OpenAI. And Synapse was a little bit like the second, right, where we knew that it was going to be hard. We knew that it was going to be a slog, that it was going to take a long time until we made our first dollar. But what that meant is we had to do two things. One, we had to have an extremely clear plan of how we got there. And so it took two years for us to get the first dollar from the government. But then once that spigot started turning, then we really started to get a lot of money from the government. But it took two years, and it was a very concerted plan that involved, I think the second part is we had very clear milestones of progress that would validate to us that it was worth continuing to work on. Validate to our customers that it was worth continuing to invest in, even if not dollars, they were investing time, giving us access, giving us data, and validating to the government that the technology was real, that it worked, and that we were a team worth investing in for the long term, right? That we were buttoned up, we had the tech in a good place, the product, we took our security seriously. Our team was very communicated very professionally. Your documents were very thorough. And so I think that it very much took 2 years, but all along we knew what milestones we had to hit, and those also mapped to the funding. So the first pre-seed round we raised was, okay, you guys have a good idea, let's see where this goes, right? The second round we raised was we had a working model, we had the X-ray machine in the backyard, and we had some early interest, verbal interest from the TSA and from these x-ray companies. And then the last round we raised was like, oh, okay, these guys are deployed in an airport, you can go see it live. And we had an LOI from the TSA that was their interest. And so that's kind of how we knew that we were on track to keep hitting bigger and bigger milestones.

What comes to my mind in listening to you talk, and that is an amazing story is in software development, there's often this hype about agile practices and best practices and how you can develop things very rapidly and get that, like we had said, that very first product out the door. But then when you think about NASA from the '50s and '60s, which was a complete waterfall method of knowing where they needed to get to the moon and also understanding that things could not fail. So testing it and retesting it and getting to that higher level of confidence, you know, the five nines of confidence was really critical. Like you were going to put people up into space and that was a dangerous and important mission. It's fascinating to think through the world where, like you had said, Sam Altman and AI, like you need a lot of time for these things to gestate. And then when you're a founder, it's like the clock is ticking and you got to move. And so staying the course and having a very detailed plan is something that, like, you can mix agile and waterfall together. But I think, like, having— hearing you talk about having a very clear vision of what the milestones are is really inspiring because, yeah, you couldn't have something that was like, oh, it worked like most of the time, even though you stated the TSA agents were failing constantly. Which is, yes, terrifying.

So it's interesting you say that, like the waterfall versus agile, because I think now as I'm hearing you say that, the way I think about it is it's about knowing what can you accelerate and should you accelerate versus what do you just have to wait for it to happen, right? Government dollars, you can't accelerate. You can only get so creative until you just have to like make a plan and stick to it. But then within that, there were definitely pockets where we got very creative, very agile. So our first deployment actually was going back, we had the X-ray machine in the backyard. We had a rough model that worked very much in a demo environment. We were reading the operating system, we were in the OS, and we made a really interesting—

And it comes with a pair of leather or lead-based underwear for you guys to wear around?

We had a Geiger counter actually to detect radiation. I would, but, but like, you know, this is the kind of thing that like founders don't think about. So we're like, okay, we need to find a customer to build this with. And so we just put up a really nice website that looked really legitimate and professional. And then a couple of weeks later, one of the biggest airports in Japan emailed us and they were like, hey, we want this system in our airport. And we were like, oh, okay, trying to act like we had it all together. We're like, yeah, whenever you want it, let us know. And they're like, yeah, we want it next week. And so we were like, holy shit. And so we convinced them that, hey, the AI needs to train for 3 months. And so they're like, great, we want to go live. This was like Jan 10. They're like, we want to go live March 30. And so then the clock started ticking. And in those 3 months, we built the entire UI, we trained the model end to end. We realized that when we got to Japan that we couldn't get access to the operating system. And that's when we became a hardware company on the spot, that one of our guys was like, great, I'm going to get a 3D printer, I'm going to get some equipment, and we're going to build some hardware around this. And so there are definitely pockets where we got creative, we were agile, we had to do unexpected things, but it was still part of this broader plan of that phase for us was find a commercial partner, you know, demonstrate commercial, um, you know, operations, and then show that to the TSA. Now, the TSA, when we showed them it working, they, they didn't see how the sausage was made, but it was still part of that broad— those broader milestones that we had set for ourselves.

Fantastic. Wow, 3D printing and hardware all in one. Um, I want to pivot now to to Magna and really spend a bunch of time there. As fascinating as both your experience with Synapse and how AI is really top of mind for a lot of folks these days, how did you form the idea for Magna? Where did it come from and what was the inspiration?

So we started actually first, we were a software marketplace called Maple, and the idea was to get founders discounts on software. So it's kind of like Honey for founders. We called it Maple. It's kind of like a cute tongue-in-cheek name. And as a part of that, we realized that people love saving money, but founders don't always know what they need in terms of the tools that they need, especially if you're starting a company for the first time. Whereas for us, it was like the back of our hand. And so we built this, what we called the Founder's Playbook. It was like a founder's checklist. It's like, okay, so you're starting a company. What are all the categories of tools you're going to need in the first year of operations. So we made that playbook and we shared it with people and it had categories like you need incorporation tools and we would call out what are the most popular incorporation tools, how do you choose between them? And we went through and created this playbook at about 10 categories including incorporation, cap table management, business insurance, payroll and HR, international payroll, HR, and so on and so forth. So we worked on that for a few months, got it up to a few hundred users, raised a small pre-seed round, and it's a tough business model. I think marketplaces generally, and especially software marketplaces. And so right around that time, I was personally getting interested in crypto and I bought some NFTs. I was getting really involved in the Solana community. I bought a Solana monkey and went down the rabbit hole through NFTs and meme coins. One of my close friends at the time that I lived with was a meme coin trader. And so as I got more and more into crypto, at some point me and my co-founder were like, hey, this business model is really tough. Why don't we try something in crypto? I think we were both really excited about crypto. And then as a part of the research of what do we want to solve, I knew that I like B2B. I like selling to other businesses. I think the revenue is really in some ways a very attractive company for investors and for founders. It's recurring revenue, it's high margin. If you do it right, it's very high growth. and so we knew we wanted to start something in B2B. And then, you know, we kind of went back to that list. We're like, hey, this list, you know, doesn't really exist for crypto companies. There's no tools for incorporation because crypto has to deal with offshore. The HR payroll providers don't work. Legal business insurance doesn't map. So we're like, you know, what are the opportunities on this list that, that could become companies? So basically started doing a ton of research, talking to customers, talking to investors. And, you know, as we were spitballing some ideas with one of our potential investors, from Shima, he was like, hey, that, that, you know, cap table management, that token management, I think that's something that all of our portfolio companies would need. Then we can introduce you to 10 of them that would need this tomorrow. And they did. And that month, I think we talked to 30 companies. Then we asked them, how did you launch your token? How did you distribute your token? Who on your team is managing the token? And overwhelmingly the answer was, you know, we're doing this in spreadsheets, we're doing this manually. And that can be a trap, right? I think whenever you're fighting against an efficiency gain, because it really needs to be a 10x to make it worthwhile. But where it started getting me excited was when people were saying, look, a mistake can cost us millions of dollars, right? It can mean our investors don't get their money. It can mean we talked to a company that had a typo in their contract and locked up 10% of their total supply. Those are catastrophic mistakes. And so then I was like, okay, not only is this something they would use all the time, make more efficient, but this is a really important problem that is pretty critical, not just in the beginning of your company's life, but on an ongoing basis. So we zeroed in on that, and that's when we were like, oh, shit, this is an area I know, right? It maps really well to the equity world that I'd come from, where I had a lot of familiarity with equity cap tables. No one knows how to do this well. So as a founder, I was like, oh, it's amazing opportunity to become a thought leader and become an expert in this area because there are no thought leaders, there are no experts, and I can get up to speed here pretty quickly. And I think it was kind of number 3, a product that we could get up and running and get paying customers in pretty quick order. And so we settled on that right as we started YC, and then that's what we the idea that we had going through YC that we ended up fundraising for later that quarter.

What's entering my mind right now is you went from Maple to Magna, you experimented a ton. Can you elaborate a little bit on your process of forming hypotheses for business? Because I think for a lot of folks, you had mentioned it earlier in terms of your tunnel vision, staring at the problem all the time. There's two things that really jump out. One is that you consistently look at problems and form some sort of hypothesis that has a business around it, and then you've pretty deftly pivoted out of them or modified them enough, frequently enough to not get stuck on something that just was not working. So how does that process in your mind work for you, and how does it work for your co-founder, which I think is Also an interesting area.

Yeah. You know, the first thing I'll say is like, I think a lot of that I have to credit to the amazing people that were supporting me at that time, friends, investors. And so when I trace back to those key decision points that you're saying, a lot of those were just having conversations with people and kind of sanity checking at every step along the way. right? And being like, hey, I'm about to raise, am I raising the right amount? Should I raise this amount? Right? And so even when we first started Maple, I was really gung-ho on it. I was a repeat founder. I could have raised $2, $3, $4 million. At the time, it was a much easier fundraising environment. I remember I talked to one of my investors like, hey, are you sure this is it? This is the idea. This is the thing you want to go big on, spend 5 years on. And I was like, I don't know. They're like, great, that's fine. But don't raise more than $1 million because If you raise more than that, you're locked in. It's going to be very hard to pivot without pissing people off. If you raise less than that, most people won't care. They're investing in you, not the idea. You're probably getting smaller investors. And so we did that. So we raised $750K, brought on people that I thought were generally helpful, not ultra specific to any one sector. And then fast forward, we were growing Maple, we were building Maple. And we started thinking about our roadmap. And I had another conversation with investor. I'm like, hey, I don't know, I'm trying to think about where the growth's going to come from, what this looks like 2 to 3 years from now. Right. And that's always an exercise I like to do. We had a roadmap for the next 12 months. What's the roadmap for the next 36 months? And we basically played out every possible path we could go down as a company. Right. And it was Okay, we can do this feature set. We're a software marketplace. Then eventually we're either going to have to go into software buying, and there are really strong companies there, Vendor and Tropic, that do the whole procurement process. Or we would have to go down the route of spend management, and you get into Brex and Ramp territory. And that's like a burial site of wannabe Brex and Ramp competitors. So many companies that tried and failed to do that at scale. or you end up into some sort of like HR, people management, like Rippling, amazing company, very hard to build those kind of network effects. And so we didn't really see like where it was going. And I think that business is an amazing lifestyle business. I actually have a friend of mine, Alex Cohen, who's now doing something very similar, and it's probably going to make like a million a year. But is it ever going to make $100 million a year? I don't know. And so we were talking to one of our investors and we're like, hey, what do you think? And they were the ones that were like, you know, you're great, but I don't know if this idea, if this concept is the one that you should be focusing on for the next 5 years. I think you could be tackling something so much bigger. And so after that conversation was when we made the tough decision of being like, hey, if we know that we want to do something else potentially, we're not only going to dedicate 5, 10 hours a week to exploring new ideas. We're going to dedicate 40 hours a week to exploring new ideas. And so we very much shifted into, okay, this isn't working, let's put it on life support so people can continue using it. It's actually still up and running to this day that people are still using it. The codebase works, but let's go out there and be very explicit about what mindset we're in. And so I went from the mindset of grow Maple, focus on Maple, to the mindset of I'm going to maximize how many problems and opportunities I get exposed to. Because I need to make sure that I find kind of the best idea possible. And so, yeah, it's about having those people to sanity check you, just like the X-ray story, and saying, hey, are you sure you're working on the right thing? Are you sure there's nothing more you could be doing? It sounds like you had a slow month. What can you do to take that into your own hands? And part of that also is just from an internal restlessness. I think also the best founders are the ones that don't want to waste their time. And so I knew I wanted to build a large company, and so it wasn't enough for me to just have a company that was doing okay or making a little bit of revenue or just getting by. So the moment I saw, hey, we're not on that rocket ship trajectory, we had to find the idea or the company that would have the potential to have that trajectory. And the one thing I want to add too is I think I learned a lot from Synapse. Where we are like, we made things so much harder for ourselves by building hardware, by selling to the government, working with classified information. That the other criteria I had was like, I want to work in pure software, no hardware, no government, selling to enterprises. Because there's a lot of companies that are SaaS-like, right? They sell software. but what SaaS truly is, like what Salesforce invented, was high gross margins. It's zero cost of onboarding a new customer, supporting a new customer. It's usually a positive net dollar retention, right? Your customers are growing and every year they're paying you more, paying you more seats, paying you more volume. And that's what makes SaaS get at the time 100x multiple. Now it's still the highest multiple, but not that high. And so Synapse was SaaS-like, but there are a lot of things about it that I'm like, okay, if I'm now getting to choose from scratch the world I work in, how can I choose the variables that are most going to make my life easier as a founder, but also going to be most conducive to that upward trajectory?

The idea that your early Synapse experience really shapes your mindset I think would resonate with a ton of founders as they go forward because you really, like you just said, learn not so much what you want to do, but a lot of what you don't want to do. And it's a very powerful mechanism to just take pieces off the table. Hardware is hard. It's the hardest. And so some people absolutely love it and they really enjoy that journey. And they have that patience for it and that discipline for it. But if it's not you, it's not you. And pure software is a way where you can do a lot of things very broadly and quickly. I keep thinking about how can we talk more about Magma and the journey that you're going on now. So I don't know that we ever got a very succinct, what does Magma actually do for users of it? And, you know, and also like, how is it going right now?

Yeah. So, so Magna, what we do is, you know, our core value proposition to customers right now is we help you automate your distributions of tokens to your stakeholders. Usually it's investors and employees and put those unlock schedules on autopilot, right? So what that means is when you're a protocol, you launch your token, you have your TGE. By that point, you've usually figured out your tokenomics and what the distribution schedule is going to look like for your investors, for your employees. And so we had a customer, for example, the other day, they came to us, they said, hey, our token's going live on Monday. We have about 100 investors. They're on about 4 different unlock schedules depending on when they invested. Some of them need to get their tokens daily, some of them need to get their tokens monthly, and some of them are locked up for a year. And so without Magna, you're tracking that in a spreadsheet, you're going in manually transferring the tokens, you know, every month, every quarter, or you're writing up your own smart contract that either you're paying a lot, you know, 30, 40 grand to get audited, or you're not getting audited at all. That you then have to continuously update and get audited. And so, you know, for teams, a lot of the pain points around distributing manually is it's a time sink, it's complicated, it's hard to track, or it's expensive. And even some teams that do it themselves scale out of it. They say, hey, we had this contract, this process, it worked, but now we want to do it differently and we're not set up for that. And so what we do is we're building kind of an all-in-one suite for usually it's, you know, the operations, the HR, the legal team, and they can go into Magna, they can track investor allocations, the employee allocations, they can automate the unlocks if they want to, or they can use Magna to track it and airdrop the tokens within Magna. And then we're also building a suite of tools that, you know, go a step beyond just the process improvement around things like automated tax withholding, compliance reporting, integrations with payroll tools and things like that. And really our goal is to make it as easy as possible to manage your outgoing tokens to all of your stakeholders and also manage all of the obligations that you have as a company in terms of tax compliance and kind of any other considerations that you might have based on what jurisdiction you're in. So yeah, all-in-one token management, token holder tracking, and token distribution. And we do that— we started on Solana, where we're still live, and we're also on EVM chains. So Ethereum, Polygon, Binance Chain, Arbitrum, Avalanche, you know, the usual suspects.

Fantastic. I do want to apologize. I have probably said the word magma, and there's a quick and reasonable story for this. And I have— I'll start with saying, my sister's name is Marissa. I had a coworker whose name was Marisa, one S as opposed to two. I was the only person that Marisa would allow to make the mistake on. And I hope you'll grant me this because at Meta, I also worked on Magma, which is an open source project. That works actually, it's with Helium using their open— it's a core networking piece of software. So I've said the word Magna and I've said the word Magma and I've interchanged them and I apologize in advance, but no worries. For Magna, it sounds like a lot of the wonderful things that you're working on also compete with some other kind of cap table management software. Out there, or SaaS as a, as a general rule. I think my real big question is, what do you think the future holds in terms of companies that are going to need to do both, both manage tokens and manage regular equity? And what's your vision for how you're, you're working on that currently?

So when we think about that, we think about how are each of those areas evolving and then where do they intersect? And then when we think about Magna's role in that, we think about where do we want to go deep versus where do we want to maybe rely more on integrations or partnerships, right? And so I think equity, there are great tools out there that do equity management, and I think we thought about doing that up to a certain level, right? I think equity management up to priced round or up until like your first round is relatively straightforward from a finite set of ways that companies do things and structured grants. Once you get into your growth, your Series B, C, D, it gets incredibly complex. And so it's hard to say that we want to go deep in equity management. Now, when we think about the token management, I think there's a lot of ways that we're going deep, I think, on the tech side. Right. And so when you think about evolving technical capabilities, we're seeing more people doing cross-chain or multi-chain tokens, more teams that want more flexible ways to distribute ownership, not just based on time, based on milestones, based on on-chain activity. We've worked with some games that want to distribute tokens based on in-game activity. So we really want to focus on being best in class at working with tokens and all of the technical and workflow challenges around blockchain problems. So that's one class of problems where we want to go deep on, and we think that traditional equity management, we know most of them, they're having a hard time going deep in that. The second class I would say is all of the real-world things that affect blockchain. And by real-world things, I mean taxes, compliance, regulation, everything that's not an on-chain technical problem. It's, you know, a regulatory problem. There's some legal consideration, HR, finance, operations. And so that's another area where we're also going deep, right? Because we want to make sure that we support different types of token grants that can be given to people and administer everything that makes equity complicated. There are similarly complicated things like it in the token world, right? So there's outright token grants. You can have restricted token grants that vest. You can have restricted token units that are like RSUs where they're effectively settled. So there's all these complex mechanisms that we're working on supporting that then also have tax implications. And so we have this internal tax blog post that I share with most of our customers that most of them will say is the most detailed write-up anywhere on the internet that exists about token incentive plan taxation. I'm just too much of a perfectionist to publish it, but maybe I'll do that soon. And so that's an area we're also going deep, right? And we're building expertise and we're getting people on our side, legal partners, token valuation partners that we can collaborate on. And so by being best in class in that, we then say, okay, well, where does that intersect with what's going on in equity management? And so I think there's a lot of opportunities for us to partner, integrate with equity management companies and make sure that to the extent that we need to mirror what's going on on the equity side and put that on chain, that we have the tools to do that. And so one example of that is when founders are fundraising, sometimes they will base their investors' token ownership. It'll be based on a multiple of the equity valuation. It's pretty common for the token valuation to be a multiple of the equity valuation. Similarly, they might own a multiple of their equity ownership in the token ownership, right? So you might see a fund that'll put in, you know, $1 million at $10 million valuation. They own 10% of the equity. but they might say, you know, they want 20% of the total supply or vice versa. And so we're starting to focus on those areas where we say, how can we make sure that information that's on-chain is, you know, kind of accurate and really connected to the information that's off-chain? And so another example of that is we're starting to integrate with payroll providers and say, hey, when even when you onboard someone or you terminate someone in the payroll, you know, what severance are you giving them? And can we reflect that into their on-chain agreement as well. So all these areas, again, things that we want to focus on so founders don't have to worry about it. So this tax stuff we're doing so we can handle it for founders and so they don't have to worry about, am I doing it wrong? They just have to know that Magna's taking care of it for them.

You mentioned gaming, and I've personally heard not only is there a member of our accelerator cohort who are doing carbon credits through tokenized or tokenization. What are the other sectors that you're really intersecting with now that you would expect to continue to grow in, in the years to come?

We've been seeing a lot more gaming, which we're pretty excited about, and gaming studios as well that have multiple tokens. I think that DeFi still continues to grow despite the macro. We're still seeing DeFi teams come out with new innovative ways to use tokens. And I think for me, it's more about less of a specific vertical, and I think more about new technologies that are going to enable capabilities in ways that we haven't thought about using them yet. And so I'm trying to dig more into ZK and the capabilities of ZK proofs and what that's going to enable in terms of privacy, in terms of when you think about the world we live in, in equity analogs, right? Transactions, markets, loans, things of that nature. Very private in the Web2 world, very public in the Web3 world. And so I think if we're truly going to take the Nasdaq or the NYSE and put it on chain, if we're going to take employee secondaries and put it on chain, if we're going to truly do massive institutional private equity deals, eventually we're going to need a whole privacy layer on it. So I've been digging a lot more into zero knowledge. I've been digging a lot into cross-chain. I think that a lot of the work that like LayerZero is doing is really interesting. And we're, you know, every team always struggles, you know, hey, we want mainnet Ethereum because, you know, the volume, but it's so expensive. And so they also go to Polygon, they go to Arbitrum. And so I think we're seeing more teams start to get creative about launching on multiple networks and maybe even across multiple networks. And then I think, you know, like all of us, right, we're intently watching the regulatory developments and to the degree to which Magna is very US-based today, thinking about do we eventually develop more of an international presence? Do we build more of our international team out? The biggest conferences I'm going between now and the end of the year, ETHCC in Paris, Solana Breakpoint in Amsterdam, Token2049 in Singapore. I think it's inevitable that there's just going to be a significant amount of innovation internationally, and we'd be dumb not to be participating in that. And so I think the international ecosystem is one that I'm also trying to get more connected with.

I have one interesting— all of Janet's questions are interesting, but I have one that's kind of jumped out at me. We're almost at time, and before we round out time, want to thank you for spending so much time with us. You had a recent tweet where you said, just met an AI founder that did $3 million in revenue in 6 months, bootstrapped, $2.8 million of profit. That sounds crazy town to me, but would love to understand as much as you can share about that tweet and or what you're thinking about in terms of AI for Magna, if it has some sort of intersection in your roadmap as well.

Yeah, I'll tackle those two separately. The first part, it is a true story. Sometimes people think I make things up for Twitter. I was catching up with a friend of mine I hadn't seen in many years, and he told me this had blown up on the side. And the first thought I had is something a lot of founders don't think about. There's a whole class of companies like that that you can get to a million in revenue, multiple millions in revenue, maybe not over 6 months, but over 1 year, over 2 years. But he's probably not going to raise money for it because I don't know that that's a venture-backable company, right? In that case, it was an AI tool, and there's a lot of those types of tools that long-term, like we were talking about with Maple, when you play out what does this look like 2, 3 years down the road, it's like, okay, well, there's not really much of a moat. Maybe it was kind of lightning in a bottle. There's not really much of a direction there. It was a thing. It worked. It made some money. Great lifestyle business. But you do see a lot of founders sometimes over-key on this kind of early traction and say, okay, this is the thing I want to spend the next 5 years of my life on. And so in his case, he's like, hey, it was more of a fun story of like, hey, we did this thing, it worked, it's awesome. Now I'm trying to figure out what I'm going to do next and think about it from the other perspective was I think sometimes just like don't overthink things, right? I can't say too much about what it is. It was a little tool. And I think he charged a nominal amount for it and I think just got like 20,000, 30,000 people to sign up for it on the internet. And so I think that a lot of times if you happen to catch things at the right time, in this case, people are excited about AI, it's easy to ship, everyone's down to try AI tools, everyone's telling their friends to try AI tools. It truly was like good timing with the founder that wasn't even the most technical of products. I think anyone that had any little bit of engineering ability could have shipped that. And so I think it, and then to your second question, how will AI impact Magna? I think something that I've, I used to think that I'd answer those types of questions by sitting, locking myself in a room for a weekend and emerging with the right answer. And now more so I'm like, we just need to go back to the roots. Let's just try shit out. So we're doing an AI hackathon internally. We're like, hey, everyone's going to spend a week, pick an idea. Could be for a feature within Magna, could be something totally outside of Magna for the crypto space. And let's just go ship some stuff and see what's possible, see what works, see how easy it is. And I think use that to get the creative juices flowing. And it might be something that we do there. It might be something that we do months later that is inspired from there. But I think that when I see something like AI, I think anyone would be stupid not to say, okay, I need to go spend some time playing around with it, seeing how it works, and then letting that simmer for weeks and months after until some inspiration, I'm sure, I'm sure will strike or we'll think about something and say, okay, that's it, that's what we have to ship. So yeah, Magna AI hackathon coming this summer. So maybe we'll have some AI features by the end of the summer.

Fantastic. Summer of AI indeed.

Yeah. And AI is a tool. It's a technology, right? I think, you know, it's kind of like mobile. The best take I ever saw on AI was that, you know, very few truly mobile companies were generational. In reality, every good company had a mobile strategy, had a mobile component. Similarly, I think every good company is going to have an AI component to it.

That makes a ton of sense. All right, so we are very much at time. I really want to thank you again. Before we close out, where can people find you and where can people find more on Magna? Where's the newsletter where you can hopefully hit go on your very well-polished tax advice? Where can everybody find all that information?

You can find us on our website at magna.so, and you can also find me on Twitter. @bfaviero, my first initial, last name. Newsletter coming soon. But yeah, if you know, even if you, if you have launched a token, we can help. If you're about to launch token, we can help. But even if you're just early in the days, you can still use Magna to track all your allocations and your tokenomics and share that with all of your stakeholders. And if there's any way I can help outside of that as well, you know, just DM me on Twitter. Give me a shout.

And that's where we have to end it for today. If you want to check out the full Q&A and learn more about joining Club CPG, visit cryptopackagegoods.com. Thanks to all our CPG and POP members for making these kinds of events possible. Crypto Podcast Goods is produced by Genius Media, a division of Crypto Package Goods.