Are you an entrepreneur or startup founder in the AI and crypto industries who wants to discover the untapped potential at the intersection of AI and crypto for innovative applications? Look no further! Our guest, Bruno Faviero, will share the solution to help you unlock the endless possibilities and achieve groundbreaking outcomes by harnessing the power of AI and crypto convergence. Recorded on June 21, 2023 for Crypto Packaged Goods Genius Call series.
Does the idea of combining AI and crypto to create innovative applications sound familiar? Have you been told that you need to follow a traditional approach and rely solely on one of these technologies? If you're feeling frustrated by the lack of results and the missed opportunities, it's time to explore the intersection of AI and crypto for groundbreaking solutions that will revolutionize your business.
In this episode, you will be able to:
"The best founders are always asking themselves, where could I move faster? Where could I skip steps? Where can I unblock myself?" - Bruno Faviero"
Follow Bruno at https://twitter.com/Bfaviero
Follow Club CPG at https://twitter.com/CPGCLUB
To learn more about Crypto Packages Goods, visit https://www.cryptopackagedgoods.com/
00:00:08
Welcome to Crypto Podcast Goods, the audio home for Club CPG. This week, Bruno Faviero, founder and CEO of Magna shares his builder story with CPG COO Mikey Piro. The two discuss Bruno's early day as a founder in Synapse, its growth and eventual sell 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.
00:00:46
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 GMGM. We are back with another club. CPG. Genius call Podcast.
00:01:06
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.
00:01:35
Who is our guest. Our Genius Call today is Bruno Faviero. And he is the founder and CEO of Magna, which provides token investing and token holder management services to crypto companies. Magna just raised $17 million in 2022. And those investors that invested in Magna include Tiger Global, Tusk Ventures, Shima Capital, Polygon Ventures, avalanche Ventures and the Solana Foundation.
00:02:02
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 Magna, 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.
00:02:30
He was also the co founder of Kalina IO, which develops AI testing tools. I'm going to 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 and P for $550,000,000. We had a quick chat before this. Introduction, and it is my pleasure to introduce Bruno Faviero.
00:03:00
Bruno, welcome and thank you so much for joining. Hey, thanks for having me on. It's great to be on here. GM. GM.
00:03:11
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 web three and what was your first exposure to bitcoin or Ethereum or both?
00:03:36
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 you know, we were always being exposed to different tech, different kind of frontier innovations. And so the time, I didn't think too much of it, but we had a guy, Jeremy, who was he was like the crypto cheerleader at MIT.
00:04:09
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.
00:04:36
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.
00:04:59
And he 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 my story, I was like, 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 real retail perspective. It was very scary when it all went from really high up in the hundreds, $1,000 for ETH to like, $100.
00:05:40
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 two 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 hackers like things that I thought were really interesting and where I thought I could play a role. So that's when I fell right back into it and haven't looked back since. So, yeah, been all in on crypto for just about the past two years. You mentioned something that's pretty top of mind in the larger world.
00:06:22
We have AI 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'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.
00:06:47
Can you tell us about where you. Developed and how you developed Synapse and. The story of what happened? 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.
00:07:04
And the vision of Kensho I was an early employee. I wasn't the founder. The vision of it was to let people query the financial markets in a natural way. You could ask questions, hey, what happened with the stock market this week? How do hurricanes affect energy stocks on the Gulf Coast?
00:07:21
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 Chat GPT. 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.
00:07:45
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 four years. And during those four 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 visual is 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?
00:08:27
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, right?
00:08:42
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 TSA officers were missing something like 90% of all dangerous items in bags. Like, when they look at an X ray, they'll find a shampoo bottle, but they'll miss a knife a scary percent of the time.
00:09:28
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 two 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?
00:09:55
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.
00:10:18
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 the 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 three years.
00:10:43
HP was down the road, and so three years, you know, 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.
00:11:00
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 color ID. And he said, hey, just want 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.
00:11:32
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 and that you had spent a lot of time on military bases in the US. So how did that transition happen and.
00:12:04
What was the impetus for it? Yeah, so by the time we were about two to three 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'd 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.
00:12:32
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?
00:13:05
Security, homeland security, specifically, that we just happened to have as investors, founders, fund and APC, 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?
00:13:29
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 know, input sensor that's used by the types of customers at 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, know, other types of camera imagery, and even some know, more exotic types of inputs that aren't even just images. But it was really about like, 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 were used was to work on these projects, these monitoring projects, these defense projects that were active and ongoing.
00:14:25
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.
00:14:59
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.
00:15:17
And that, I think, is part of what is beautiful about AI, particularly at this moment, is 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?
00:15:58
And also, when you were getting traction. Those first few years, how long did it take you to get to revenue of any way, shape or form? Because it sounds like you just mentioned the defense government. It is not a fast process. No.
00:16:14
So I like that you keyed in on that, right? Because I advise a lot of founders. I invest in a lot of founders. I probably invest in about 100 plus companies over the last ten years. And the best founders are always asking themselves, where could I move faster?
00:16:31
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 know, 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, that it takes someone else walking in and being like, hey, what are you guys doing?
00:16:57
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 know, writing to the TSA, calling emailing, doing meetings.
00:17:12
Guys like, 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.
00:17:22
And it's like 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 I was like, 150K. He's like, okay, buy one.
00:17:32
And we're like, can we do that? He's like, yeah, reach out. So he reached out. We put it in order. And the sales guides, they don't ask too many questions because they're selling these things.
00:17:43
They sell them to courthouses, they sell them 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?
00:17:58
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 three years. We actually had the world's largest label data set of handguns, which was really interesting.
00:18:13
And we ended up actually selling that data set separately as a part of the acquisition since we went on to do satellite models. 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, in some ways, be a little naive and be like, oh, what if we just get one of those? 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.
00:18:40
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, like 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.
00:19:00
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, you know, came he was on a talk last week that was all over Twitter, where he know I'd throw most of that advice away, right?
00:19:28
And he know 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.
00:19:52
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.
00:20:32
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 two years.
00:20:49
But all along, we knew what milestones we had to hit, and those also mapped to the funding. So the first precede 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.
00:21:04
We had the X ray machine in the backyard, and we had some early, know, 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 know, 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.
00:21:30
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, like, NASA from the, which was a complete waterfall method of knowing where they needed to get to the moon, and also understanding that things could not fail.
00:22:06
So testing it and retesting it and getting to that higher level of confidence, 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 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.
00:22:32
And so staying the course and having. A very detailed plan is something that you can mix agile and waterfall together. But I think 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 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.
00:23:18
You can't accelerate. You can only get so creative until you just have to 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.
00:23:40
We had a rough model that worked very much in a demo environment. Like, we're reading the operating system. We were like, in the OS, and we made a really did it come. With a pair of lead based underwear for you guys to wear around? We had a Geiger counter, actually, to detect radiation.
00:23:59
I would, but this is the kind of thing that 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 okay.
00:24:21
You know, 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 three months.
00:24:34
And so they're like, great, we want to go live. This was like Jan ten. They're like, we want to go live. March 30. And so then the clock started ticking, and in those three months, we built the entire UI.
00:24:45
We trained a 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 is 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.
00:25:05
We were agile. We had to do unexpected things. But it was still part of this broader plan of know that phase for us was find a commercial partner, demonstrate commercial operations, and then show that to the TSA. Now, the TSA, when we showed them it working, they didn't see how the sausage was made, but it was still part of those broader milestones that we had set for ourselves. Fantastic.
00:25:36
Wow. 3D printing and hardware all in one. I want to pivot now 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?
00:26:02
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.
00:26:18
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 founders playbook. It was like a founder's checklist. It's like, okay, so you're starting a company.
00:26:38
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 ten categories, including incorporation, cap table management, business insurance, payroll and HR, international payroll, HR, and so on and so forth.
00:27:07
So we worked on that for a few months, got it up to a few hundred users, raised a small precede. You know, 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.
00:27:34
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're both really excited about crypto. And then as a part of the research of like, what do we want to solve?
00:27:55
I knew that I like b to B. 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.
00:28:10
And so we knew we wanted to start something in B to B. And then we kind of went back to that list. We're like, hey, this list doesn't really exist for crypto companies. There's no tools for incorporation because crypto is to deal with offshore. The HR payroll providers don't work legal business insurance doesn't map.
00:28:28
So we're know what are the opportunities on this list that could become companies. So basically started doing a ton of research, talking to customers, talking to investors, and as we were spitballing some ideas with one of our potential investors from Shima, he was like, hey, that cap table management, that token management. I think that's something. That all of our portfolio companies would need. We can introduce you to ten of them that would need this tomorrow.
00:28:53
And they did. In 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, we're doing this in spreadsheets. We're doing this manually.
00:29:08
And that can be a trap. Right. I think whenever you're fighting against an efficiency gain, because it really needs to be a ten X 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.
00:29:26
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?
00:29:55
It maps really well to the equity world that I had 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 three, a product that we could get up and running and get paying customers in pretty quick order. And so we settled on that right.
00:30:24
As we started, YC, and then that's what 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 kind of process of forming hypotheses for business? Because I think for a lot of folks, I had mentioned it earlier.
00:30:53
In terms of your tunnel vision, you're 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.
00:31:14
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, the first thing I'll say is, I think a lot of that I have to credit to, you know, the amazing people that were supporting me at that time. Friends, investors.
00:31:40
And so when I trace back to, you know, 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 and being like, hey, I'm about to raise, am I raising the right amount? Should I raise this amount right? Even when we first started Maple, I was really gung ho on it. I was a repeat founder. I could have raised two, three, 4 million at the time.
00:32:05
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 five years on. And I was like, I don't know. They're like, great, that's fine, but don't raise more than a million dollars, because if you raise more than that, you're locked in.
00:32:22
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.
00:32:33
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 is going to come from, what this looks like two to three years from now, right? And that's always an exercise I like to do.
00:33:02
We had a roadmap for the next twelve 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 or a software marketplace, then eventually we're either going to have to go into software buying, and there are really strong companies there, vendor entropic 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 want to be 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.
00:33:49
And so we didn't really see 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?
00:34:06
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 five 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 510 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.
00:34:41
Let's put it on life support so people can continue using it's actually still up and running to this day that people are still using it. The code base 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?
00:35:15
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.
00:35:29
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.
00:36:15
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.
00:36:40
And that's what makes SaaS get at the time 100 x multiple. Now, it's still the highest multiple, but not that high. 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.
00:37:16
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.
00:37:31
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.
00:37:47
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 like, what does. Magma actually do for users of it?
00:38:11
And also, how is it going right now? Yeah, magna, what we do is 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 unlocked schedules on autopilot. Right. So what that means is when you're a protocol, you launch your token, you have your Tge.
00:38:40
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 is going live on Monday. We have about 100 investors. They're on about four different unlocked schedules depending on when they invested. Some of them need to get their tokens daily.
00:39:04
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 every month, every quarter, or you're writing up your own smart contract that either you're paying a lot, 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 for teams, a lot of the pain points around distributing manually is it's a time sync, it's complicated, it's hard to track, or it's expensive. And even some teams that do it themselves scale out of it.
00:39:45
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 the operations, the HR, the legal team, and they can go into Magna. They can track the 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 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.
00:40:51
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, Arbitram, Avalanche, the usual suspects. Fantastic. I do want to apologize.
00:41:14
I have probably said the word magma. And there's a quick and reasonable story for this. And 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.
00:41:29
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 uses. It's a core networking piece of software.
00:41:50
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 general rule. I think my real big question is.
00:42:16
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 working on that currently? So when we think about that, we think about kind of how are each of those areas evolving and then where do they intersect?
00:42:39
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.
00:43:00
I think equity management up to priced round or up until your first round is relatively straightforward from a finite set of ways that companies do things and structure 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 we think about evolving technical capabilities, we're seeing more people doing cross chain or MultiChain Tokens, more teams that want more flexible ways to distribute ownership, not just based on time, based on milestones, based on, on chain activity.
00:43:47
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.
00:44:20
And by real world things, I mean taxes, compliance, regulation, everything. That's not an onchain technical problem, it's 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 everything that makes equity complicated, there are similarly complicated things like it in the Token world, right?
00:44:48
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.
00:45:23
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.
00:46:03
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 will put in a million dollars, a $10 million valuation. They own 10% of the equity, but they might say they want 20% of the total supply or vice versa.
00:46:28
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 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, even when you onboard someone or you terminate someone in the payroll, 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 wanted to focus on so founders don't have to worry about, you know, this tax know 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 Magnus taking care of it for them.
00:47:11
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 the years to come? We've been seeing a lot more gaming, which we're pretty excited about in gaming studios as well, that have multiple Tokens.
00:47:39
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?
00:48:16
Transactions, markets, loans, things of that nature, very private in the Web two world, very public in the web three world. And so I think if we're truly going to take the Nasdaq or the Nice 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 layer zero is doing is really interesting.
00:48:50
And every team always struggles, hey, we want mainnet ethereum because of 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, like all of us, 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.
00:49:31
ECC in Paris, salana breakpoint in Amsterdam, token 2049 in Singapore. I think it's inevitable that there is 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 me. We're almost at time.
00:50:00
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 six 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.
00:50:38
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 six months, but over one year, over two years.
00:51:06
But he's probably not going to raise money for it because I don't know. 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? Two, three years down the road, you're like, okay, well, there's not really much of a moat.
00:51:28
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 of sometime over key on this kind of early traction and say, okay, this is the thing I want to spend the next five years of my life on. And so in his case, he's like, hey.
00:51:48
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 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, 30,000 people to sign up for it on the Internet.
00:52:12
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.
00:52:39
And then to your second question, how will AI impact Magna? I think something that 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.
00:53:03
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.
00:53:21
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 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.
00:53:55
Summer of AI. Indeed. Yeah. And AI is a tool. It's a technology, right?
00:54:01
I think it's kind of like mobile. The best take I ever saw in AI was that 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.
00:54:21
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.
00:54:41
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 at bfaviro. My first initial last name newsletter coming soon. But yeah, even if you have launched the token, we can help. If you're about to launch token, we can help.
00:55:05
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 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.
00:55:36
Crypto Podcast Goods is produced by Genius Media, a division of Crypto Packaged Goods.