#1082 Samir Vasavada The 22 Year Old Genius Who A Built $1 Billion Company

Samir Vasavada is the Founder of Vise In this conversation, we discuss being "crazy" as a founder and if that's a necessary trait for success. We also talk about building A. I. systems, how Samir started his company at 16, changing the financial advisor market, the future of Crypto and advice he has for young founders. ======================= Don't miss Mainnet, the most anticipated crypto event of the year, September 21-23 in New York City. Join 4000+ crypto builders and thought leaders for 3-days of can't-be-missed keynotes, fireside chats, demos, networking, and more. Get $300 off of your pass today by visiting https://mainnet.events and entering promo code "POMP" at check out. See you this fall at Mainnet 2022! ======================= If you’re trying to grow and preserve your crypto-wealth, optimizing your taxes is just as lucrative as trying to find the next hidden gem. Alto IRA can help you invest in crypto in tax-advantaged ways to help you preserve your hard earned money. So, ready to take your investments to the next level? Diversify like the pros and trade without tax headaches. Open an Alto CryptoIRA to invest in crypto tax-free. Just go to https://altoira.com/pomp ======================= Crypto wallets and browser extensions are outdated, limited in features, and don’t meet the needs of today’s Web3 users. Core, the free, non-custodial browser extension built by Ava Labs, is more than just a wallet. Core is packed with features that give Avalanche users a more seamless, and secure, Web3 experience. With Core, any crypto user can easily swap assets, display NFTs in a beautiful interface, and store your assets in a Ledger-enabled wallet. Plus you can put real dollars in your Core wallet in just a few clicks. Go to www.core.app to access the full power of Web3 on Avalanche! ======================= The number one name in NFT domains and the world’s most powerful wallet are teaming up to bring something new to the crypto and Web3 world: That’s right, Unstoppable Domains and Blockchain.com partnered to create NFT domain names ending in . Blockchain. It’s the perfect ending to show that you’re a believer in a decentralized future. The Blockchain.com community can get one, for free by signing up for the waitlist here. Free NFT domains provide all the benefits of premium Unstoppable Domains, including fee-free, lifelong ownership. Don’t have a Blockchain.com wallet? No worries, these new domains are available to everyone for as low as $5. Either sign up for a free blockchain.wallet or visit Unstoppabledomains.com to buy your domain today. ================== Bullish is a powerful new exchange for digital assets that offers deep liquidity, automated market making, and industry-leading security. Combining the innovations of DeFi with the regulated environment of traditional finance, Bullish empowers users to trade with certainty and earn passively at scale across variable market conditions, in an environment backed by multibillion-dollar liquidity contributions from the Bullish Treasury. Visit bullish.com/pomp today to learn more. Note: Bullish is licensed by the Gibraltar Financial Services Commission. Virtual assets and related products are high risk. Consult your investment advisor and trade responsibly. Bullish is available in select locations only and not to U. S persons. Visit bullish.com/legal for important information and risk warnings. =======================

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

• 0:02Anthony Pompliano?

  What's up everyone? This is Anthony Pompliano. Most of you know me as Pomp. You're listening to the Pomp Podcast, simply the best podcast out there. Now let's kick this thing off. Samir Vasavada is the co-founder and CEO of Viz. In this conversation, we talk about artificial intelligence, Viz, direct indexing, personalized portfolios, OKRs, how to run certain meetings, how to actually operate with a board of directors, why Kanye West may be the most brilliant philosopher of our generation, and what exactly the wealth 3.0 movement is really about. I really enjoyed this conversation with Samir, and I hope that you guys enjoy it as well. Once you get done actually listening to this episode, let me know on Twitter or in the comments what you think. All right, let's get into the episode. I hope you guys enjoy it.

• 0:44Speaker 01

  Anthony Pompliano runs Pomp Investments. All views of him and the guests on his podcast are solely their opinions and do not reflect the opinions of Pomp Investments. You should not treat any opinion expressed by Pomp or his guests as a specific inducement to make a particular investment or follow a particular strategy, but only as an expression of his personal opinion. This podcast is for informational purposes only.

• 1:07Anthony Pompliano?

  All right, guys. Bang, bang. I've got Samir here with me. I'm very excited to talk to you. You started a company when you were very young. You've scaled it at an incredible pace, but I thought a great place to start would actually be with a tweet that I dug up from the past, which was Kanye West is the most brilliant philosopher of our lifetime. I tend to agree, but I'd love to hear your thought process as to why Kanye is so philosophically superior to others maybe that are living right now.

• 1:35Speaker 02

  Yeah, I mean, I have been tweeting a lot lately, so maybe that one stands out for sure. You know, I think I was listening to a Kanye song and I was just super inspired. I think there was this quote of his of like, you know, not having money is everything, but having money isn't everything. And I was like, hmm, pretty insightful.

• 1:53Anthony Pompliano?

  Yeah. Well, that's part of, I think, most good songs is like, my brothers and I always joke like bars, right? If a good tweet is essentially just like a one liner, whether that's a stand-up joke, whether that is some kind of lyric from a music song, like ultimately the ability to Communicate complex ideas very simply and in short form is a pretty powerful concept today.

• 2:16Speaker 02

  Yeah, I think he's incredible at doing that. And I think like some of the greatest philosophers of our time are also crazy people. And I think Kanye is a crazy person and crazy people have different views on the world that, you know, can sometimes be reflected in.

• 2:27Anthony Pompliano?

  How do you know if he's crazy or if he's right?

• 2:31Speaker 02

  You know, I don't know, but I think I've heard some stories from some friends that know him that seem absolutely crazy. That, you know, even the most right person would not do. So I don't know.

• 2:43Anthony Pompliano?

  Well, it begs the question, like, if you start especially thinking of like startup founders, like, can you be crazy and right? Right? Like, to some degree, I always think about like, it's crazy until it works, right? So when people sit down and they're like, I'm gonna do X, everyone's like, yeah, okay, crazy person. And then all of a sudden you accomplish it, then it's like, oh, you were right.

• 3:01Speaker 02

  Yeah.

• 3:02Anthony Pompliano?

  But at the same time, what's his line? I think like, show me a genius that's not crazy. And so it begs the question of like, how do you label when somebody's doing something different? Ultimately, it's just, are they right or not?

• 3:15Speaker 02

  Look, if you're gonna be a startup founder, you have to be crazy. And I think the startup founders that fail, right, sometimes fail in really meaningful ways. Like, if you look at like Elizabeth Holmes, and yes, like she committed fraud. But like, if somehow Theranos worked, everyone would make her look out like a genius, right? Even if there was the fraud behind it, right? Or Adam Newman, or any of these other people. You know, they're all crazy. Every founder is crazy. It's just the ones that are successful are the ones that write the history books, not the ones that fail.

• 3:42Anthony Pompliano?

  Yeah. And when you think about that, where do you see that showing up the most? Like, when you say crazy, is it the speed at which they want to build the company? Is it their vision of doing something so disruptive to a market? Like, how do you measure whether a founder, if you were to sit down and talk to them, is crazy? Or, you know, maybe they don't have that it component to be a founder?

• 4:01Speaker 02

  So I think it always starts with the vision, right? If you are a founder and you believe you can disrupt an industry that like has probably done the same thing, the same way for the last, I don't know how many decades, you are objectively crazy to think that you can take that on because all of the odds, the whole deck is stacked against you. So you have to be crazy in that sense. I think the other side that it shows, where you can look at any of these founders like Steve Jobs or others that like they are just crazy in how they operate, right? They have extremely high bar like for talent, for expectations from others, and they expect everyone to operate at a certain level that, you know, people just generally don't tend to operate if you're not a non-founder. So they have those expectations for the people they work with, like they can be difficult people.

• 4:45Anthony Pompliano?

  Yeah. How much does age play into it, right? Like you're a young person who I think you started Vise when you were 16. You're how old now?

• 4:53Speaker 02

  I'm 22.

• 4:54Anthony Pompliano?

  22 now, so for six years, right? You've been running this business. 22 is still very young compared to most people in the workforce and kind of the quote-unquote "traditional market." Do people lose the craziness as they get older or do the special people just keep it no matter the age?

• 5:10Speaker 02

  So what happens is you get a little bit more jaded as you get older. So when you're young, you have this like certain naivety about you that like you believe because you don't know how hard something is going to be, that you can take it on. So like when we first started, so my co-founder and I met at the age of 12 at summer camp.

• 5:27Anthony Pompliano?

  With summer camp?

• 5:28Speaker 02

  We were, our parents are like, "Oh, you cannot do normal summer camp. You need to do college courses." So we did, we did college courses at Northwestern.

• 5:37Anthony Pompliano?

  Okay.

• 5:38Speaker 02

  And his dorm was across from mine and the two of us-

• 5:39Anthony Pompliano?

  At 12?

• 5:40Speaker 02

  At 12.

• 5:41Anthony Pompliano?

  Okay.

• 5:42Speaker 02

  And the two of us, you know, we started in app development firms. We started building apps for small, medium-sized businesses, but you know, when we came out of that, like our idea was like, "How can we use AI to build apps?" Like what if you could just, what if anyone could just type in an app idea and our system will build you an app using artificial intelligence? That is a really, really, really hard problem. And, you know, we didn't know that we couldn't solve it at the time and we couldn't end up solving it. But when you're really young, you believe you can do things that you didn't know were possible. And sometimes you can make the impossible happen and just because you didn't know that that could happen is why it happens.

• 6:16Anthony Pompliano?

  And when you think of kind of this idea of I just want to dictate to a machine to create something, if we were talking 10 years ago, like that sounds insane, right? Now, we're starting to see with GPT-3, maybe with DALL-E, like a couple of these services, you can't maybe go create the full application that's super complex that would serve all the needs of a user. I've seen some people do like super simple things like, you know, it'll auto click something or whatever. But also, we're getting, hey, write me an essay, or hey, create this piece of art out of just a couple of words that I dictate to the machine. And so, does it feel like the path we're on, eventually, we will be able to solve that problem, where somebody could just describe an application and build it?

• 7:00Speaker 02

  Yeah, I mean, I think the limiting regent has always been like, it's a data problem.

• 7:03Anthony Pompliano?

  Okay.

• 7:03Speaker 02

  Right? And I think the challenge was, like, chips weren't designed previously to be able to, you know, process nearly as much data, like the training data sets you need to build, let's say, an app or write an essay or something like that. Just previously, were not possible, right? And for now, like, especially as you look at the evolution with Moore's Law, right? The data we're able to process, or the data GPT-3, for example, is able to process, is truly incredible. And the, the things that that thing will be able to create over the next five to 10 years are going to be pretty, pretty endless.

• 7:35Anthony Pompliano?

  Talk, for those that don't know how this works, right? Like, so you get a data set. Where are they getting the data sets from? And then how are they actually training these models on those data sets so that when I go and I use it and I just type in, you know, hey, write me a paper on something, it just kicks it out, and I think it's magic? But like, what is the work that went into actually creating the quote-unquote "artificial intelligence" that I'm using?

• 7:55Speaker 02

  So, for like a, like a Grammarly or something like that, you are collecting lots and lots of data from, like, you know, pre-existing, like, content, right? Like, either like, broken down sentences, or like, what it looks like to be correct. The challenge is like data labeling. Right? So, my buddy at Scale AI created this really great company to kind of help solve this problem. But it's like, how do you train the model on what's actually right and what it should pattern match? Like, if you think about any of these AI models, they're just functionally pattern matching. So, like, how do you? Pattern match to what is right versus what is wrong that the algorithm can pattern match to. So you need to, the challenge in this problem is how do you effectively label these data sets?

• 8:36Speaker 02

  And the way people label the data sets is they have, you know, teams of people in like India or, you know, really low cost places where they're manually labeling these data sets. And then those data sets labeled are going to be fed to these algorithms. And when that problem is solved in some kind of automated way, which I think we're still kind of far from, but scale has definitely made a lot of progress towards doing that. You know, you're going to see a lot more progress on broad AI.

• 9:02Anthony Pompliano?

  So a couple of things that jumped to mind, Mechanical Turk, right? Obviously, Amazon has this service where you basically can put in some data. It will get labeled essentially for you and then you can get it back and you can do quality control or whatever. But for the most part, that is one thing that you could do. Another is a lot of the captures now will show you, you know, six or seven photos. And it's like, hey, click for, click the three that have the sidewalk, right? Or the chimney or a school bus or a bicycle or whatever. And in some way, you're getting people on the internet to actually train the data for you, right?

• 9:35Speaker 02

  Exactly. Or like in, you know, in the instance of Tesla, I think like what's brilliant about their self-driving car is like they, you know, you've got all these self-driving car companies that have to send cars out on the road and like, you know, have engineers go out and sit with them and, you know, drive them and collect data. Whereas Tesla has the data of every Tesla out there that's going out and collecting self-driving car data.

• 9:57Anthony Pompliano?

  And is it the idea like Tesla, as an example, the compounding nature of every single day, they're basically collecting almost as much data probably as Google's collected, you know, in its lifetime. And I don't know if that's exactly correct, but definitely directionally. And so if you do that for a decade, Tesla just has such a mountain of data and so many more insights than any of the other companies. And therefore that advantage is almost insurmountable.

• 10:22Speaker 02

  Exactly. Right? You can use your scale. Like that's what the genius of some of these big platforms are, is they're able to use their scale, leverage that to collect data, and then that data can kind of improve their products and kind of unlock things that they couldn't do before.

• 10:36Anthony Pompliano?

  You mentioned Scale AI. What are they doing?

• 10:39Speaker 02

  So Scale AI is like a data labeling company, right?

• 10:41Anthony Pompliano?

  Okay.

• 10:41Speaker 02

  So they're building, you know, technology basically augmented by artificial intelligence to help like label data sets.

• 10:48Anthony Pompliano?

  Got it. And the data sets are just anything somebody can feed them or do they use specific types of data sets?

• 10:54Speaker 02

  So I think it started with self-driving cars. Now they're like working with the military. It's different types of data sets. So you could feed them a data set and theoretically they could label it.

• 11:01Anthony Pompliano?

  Got it. And so when we think about this labeling of data and then the ability to extract insights and then apply it for whatever the use cases. One thing I always hear my nerdiest friends say is like, "Artificial intelligence doesn't exist. It's machine learning." When you hear that, like, talk through a little bit of the nuance between like machine learning and artificial intelligence.

• 11:19Speaker 02

  Artificial intelligence is an umbrella term, right? It always has been. Like, maybe you can conflate like AGI, artificial general intelligence, with like the broad term of AI. But like, artificial intelligence is a general term that can kind of encapsulate lots of different things, machine learning being one of them. And like, where we've seen the most progress, and I'm a little rusty on this because we've kind of strayed away from the AI focus, which I'll talk about in a little bit, is like machine learning, right? So narrow intelligence. So you've got AI applied to one problem set, one kind of narrow solution that you are applying kind of some kind of machine learning algorithm to.

• 11:54Anthony Pompliano?

  So once you have a data set and it's labeled, talk to me about the machine learning itself. Like, what's happening there in terms of the insights or the applications people are able to use with this machine learning on a labeled data set?

• 12:04Speaker 02

  So you have a recurrent neural net, or like some kind of neural network. And the neural network has nodes and it's able to draw conclusions and basically draw a pattern. So given the set of data, this is what we expect to happen, not necessarily a an algorithm where it's just a set of instructions, basically. So it is a black box in some ways because we're trying to interpret a pattern, given this broad set of data. And the better data you have, the better pattern recognition you're fundamentally going to get.

• 12:35Anthony Pompliano?

  So in the most basic sense, like you could think of, hey, I've got a set of data. I've already kind of massaged it and helped synthesize it to some degree via the labeling and all the stuff. I throw it to a machine learning model, which has these neural nets in it. And all I'm really saying is like, yo, find the patterns.

• 12:51Speaker 02

  Basically.

• 12:51Anthony Pompliano?

  And then the machine's like, cool, here's the patterns. And then I can go do whatever I want with those.

• 12:55Speaker 02

  Yeah. And your model is only as good as the data you feed into it, right? And the challenge is oftentimes explainability, right? So when you think about financial models, right? Like in our instance, we sell to wealth managers, right? Wealth managers want to understand the why behind their trades. Why was this decision made? And when we started, we built a pure AI model. And I think the challenge was, you know, we couldn't, you know, the model performed very, very well, right? But you couldn't explain, you know, here's how this algorithm jumped to the conclusion of why we made this set of trades. We were looking at a set of data. We were trying to find the patterns of that data. And then we, you know, had an output and we didn't know how the model basically came to that output.

• 13:34Anthony Pompliano?

  And was the problem in that scenario, like people, they're like, I don't trust the box. Like, I don't trust, I don't trust the computer. I need to understand what the computer's doing in order to put my trust in the conclusion. Or what was the, the kind of limiting factor there?

• 13:48Speaker 02

  So generally speaking, I think people just trust the box. Like in most problems with a finance problem, people, when they're giving you all your money, all their money, they want to understand how did the algorithm make decisions? But it was like split, right? Some people are like, look, this is the future. Like, we're going to trust this algorithm. And others were saying, you know, I want to understand how the decisions are made. So I think the reality is, is you have to get somewhere in the middle.

• 14:13Anthony Pompliano?

  Yeah, I always think of like Google Maps, right? Which again, is just looking at streets and trying to figure out how do you move from point A to point B? Would people follow the directions if they only showed you the next step? Like if there wasn't actually the line on the road that showed you, okay, you're here. This is how you're going to get there. If they just said, go right, and you didn't know what was coming after that? Like, there is an element of humans being like, ah, should I go right? Or what, you know, where are you taking me? By being able to see the full route before you kind of hit, you know, start. I think people are like, okay, cool. Like, yes, that does get to the, the end.

• 14:45Speaker 02

  Well, like, is this the best path to getting there? Like, what if there's a better path to getting there, right? How does Google Maps know?

• 14:50Anthony Pompliano?

  And do you think that consumer behavior has to change, like consumer psychology? Obviously, Google search results, right? There's some machine learning that's going on there and kind of learning what your preferences are. What's the best information out there, helping to kind of surface the best search results? If I think of music recommendations and if I'm using Spotify or iTunes or something like that, and they go ahead and they tell me what's the next song that they think I'll like. We've talked a little bit about investing and the ability for analysis of a data set and then kick out kind of a conclusion. Like, there's plenty of people who are trusting this stuff today. What's holding back the rest? Is it just time and the changing of their consumer psychology?

• 15:29Speaker 02

  So I think about it a couple of ways. You need, like, for mainstream crypto adoption, you need consumer behavior to change, right? They're doing something one way, and now they have to do something a completely different way. So that's going to take a long time, and it requires like a major shift in psychology. However, for AI or for machine learning, or whatever you want to call it, it's happening in the background, and it's progressively getting better and better every day. And it's just happening as we kind of run through our day to day lives. So there's either people that are using technology, or they're not using technology. And if you are using technology, it is going to start to have an incremental advantage of your life every single day.

• 16:03Anthony Pompliano?

  How important are optimizing for specified outcomes? So in the example that you gave earlier, where you're like, oh, we thought we could type it something in, and eventually we would just like create an application. Obviously, it's pretty important if you want to build something that looks more like Instagram versus maybe an enterprise software tool, right? You'd have to tell the machine, I want X, and then it would figure out how to go do that. When it comes to machine learning, do you need to know what insight you're looking for? Or is there the ability to just serve at the data set and say like, hey, you just come back to me with whatever patterns you find. I actually don't know what I'm looking for yet.

• 16:36Speaker 02

  It's an interesting question. It depends. So I think the way we think about it, is that problem, for example, is a very broad problem. So the algorithm wouldn't necessarily be able to get you like the perfect app, right? The algorithm would not be able to tell you like, you know, based on what you're thinking in your head, this is what the app you want to look like. That is why it was a really hard problem to solve. But For specific instances, right? So for example, I'm trying to like, I'm trying to do stock price performance and I'm looking at all of the historic price to earnings on a particular stock, right? And I want to understand, like, given all of the previous historicals of the stock, what do I think is a likely kind of next outcome? It's very precise. What do I think the like forward looking price to earnings might be? That

• 17:18Speaker 02

  is a clear scoped problem that you can task the algorithm on. But like, having something that's very creative, very broad is a very difficult problem to solve.

• 17:27Anthony Pompliano?

  And when you think about it in the investing framework, is it rather than like, hey, pick a stock, instead you can say like, which is the one that is likely to go up the most by the end of the year, or pick the thing that is likely to go down the least in, you know, a certain type of environment? Like, how do you think about this machine learning, artificial intelligence approach to investing?

• 17:48Speaker 02

  I think it's less broad even than that.

• 17:51Anthony Pompliano?

  Okay.

• 17:51Speaker 02

  Like, rather like on the stock level, it's more on the specific indicators. Right? So like sentiment is a great example of this. And we don't necessarily know if sentiment is a leading or a lagging indicator. So like, is a stock with good sentiment going to go up? Is a stock with bad sentiment going to go down? But the way we think about it as part of our models is, you know, stocks that have, you know, call it higher or low sentiment, could just be a good indicator not to buy. So you take an example like a Valiant Pharmaceuticals, right? This company took a huge hit because, you know, fraud, all kinds of horrible stuff going on. And, you know, they're, they still did meaningful revenues, right? And any kind of value stocks, Greener would say, okay, Valiant Pharmaceuticals makes sense as a purchase. But any investor, any institutional investor that knew the stock would say, I wouldn't

• 18:34Speaker 02

  touch this company with a 10-foot pole. So what this can tell us is like, look, you know, the fundamentals of the company might be good, but the sentiment of that company is pretty bad. We should just stay away from that company, rather than using as an indicator to buy or sell. The way we think about it, though, is that sentiment is like, sentiment is a clearly scoped problem, where you can use artificial intelligence or machine learning to understand what is the stock sentiment, right? So we can look at tweets, we can look at news articles, we can look at analyst commentary, we can aggregate those various data sets, we can scrub that data with relative ease, and then we can, you know, create a sentiment score.

• 19:08Anthony Pompliano?

  And when you do that, how accurate are these types of models? So let's just stick on sentiment, for example. If I gave you two stocks, could you come, you know, pretty close? Is it something that like, oh, we're always improving, but like, we still don't have a ton of confidence in actual sentiment like measurement? How do you think about accuracy?

• 19:28Speaker 02

  I would say it's very accurate when you think about is something high or low sentiment. Right? So a company that is getting, you know, bashed in the press, for example, our model is going to say that is a low sentiment stock. A company that is getting lots of great positive commentary is probably going to be a high sentiment stock. The challenge is, what do you do with that data? Right? How do you know whether or not there's something that you can trade on that stock or not? There's a lot of people that have tried, they've created hedge funds around this. How do we try trading on sentiment? How do we trade on stock tweets? But it's a really hard problem because there's no real answer as to does sentiment move a stock up or down?

• 20:08Anthony Pompliano?

  So sentiment's a thing where if I go and I read the news, I probably can come to the same conclusion as the computer. Right? If I'm on Twitter, if I'm in forums or kind of whatever your means of content ends up being like, a human likely will come to the same conclusion on sentiment. Are there things where humans have a really hard time coming to the same conclusion as the computer that you guys look at?

• 20:29Speaker 02

  The challenge, when you think about holistic portfolio construction, is you're looking at so many different types of assets. Right? So a human is going to be able to do a similar to just as good of a job, looking at one particular stock. But we might be able to look at way more data points and do it faster. But if you're thinking about building a holistic portfolio, you need to be looking at tons of data points for tons of different stocks. You know, hundreds of stocks potentially. And a human just, you know, can't do that nearly as well as a computer can with the speed and the precision that a computer can do it.

• 21:01Anthony Pompliano?

  Makes sense. And also, the computer doesn't sleep, the computer doesn't get sick, right? All these things that I think kind of older folks usually joke about the computer, but like, they are true to a degree. And so when you think about kind of the market that you're going after today, how much of the finance world is already using some of these technologies and kind of up to speed on what I think you and I would look at as like no brainers in the future, like these technologies will be used versus it's still being done the same way it was, you know, 10, 20, 30 years ago?

• 21:33Speaker 02

  Very far behind.

• 21:34Anthony Pompliano?

  Okay.

• 21:35Speaker 02

  And everyone knows it needs to change, but there's a couple of problems. First is switching costs. So when you think about some of these large institutions that have built software, there is so much tech debt that's continuously stacked up on software that is like 20-plus years old, you know, trading systems, portfolio management systems, software that it will cost these institutions billions of dollars, like hundreds of millions, billions of dollars to move away from onto something new. And a lot of those institutions just don't want to, you know, go through that pain. They don't want to go through that hassle. But it's part of the reason why IBM is such a big business, right? Because someone has to maintain all of this. But that's going to slowly start to erode over time.

• 22:17Speaker 02

  Because what's going to happen is technology right now, you know, might not, you know, hold one of these institutions back, but in five years, in seven years, in 10 years, as people start to do, you know, do more things on their own, you know, through technology, these institutions are going to have to start to change. But the time it's going to take them to change, the time it's going to take them to evolve is going to be too far gone.

• 22:39Anthony Pompliano?

  We interrupt this podcast to tell you about the most anticipated crypto event of the year. Messari's Mainnet Conference 2022 is happening this year, September 21st to the 23rd in New York City. It's three days of can't be missed keynotes, fireside chats, demos, networking, and more. They'll have folks like Ryan Selkis of Messari interviewing Ripple's Brad Garlandhouse, and that one is going to be absolute fireworks. They also have keynotes from Balaji Srinivasan and OpenSea's Devin Finzer. You can get $300 off if you go to mainnet/events. Again, just type in www-mainnet/events, use promo code POMP, and you'll get $300 off. I'll see you this fall at Mainnet 2022. This episode is brought to you by Alto IRA.

• 23:22Anthony Pompliano?

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• 24:07Anthony Pompliano?

  They're the safe, regulated way to buy and sell Bitcoin and other digital assets. Trade crypto with up to 85% lower fees than the top competitors. There are no fixed minimums, no ACH transaction fees, and no withdrawal fees. Download the FTX app today and use referral code POMP to earn free crypto on every trade over $10. The more you trade, the more you earn. Go download the FTX app today and use referral code POMP. Taking this as kind of like a viewpoint that you have today, six years after starting the company. Take me back to when you started the company. So you meet your co-founder when you're 12 at, I'm gonna call it intelligent camp. Because unfortunately, my parents were not forward thinking enough to send me to a camp like that. Between 12 and 16, you guys were still working on things together or just like staying in touch?

• 24:51Anthony Pompliano?

  Or what was your relationship between when you met and then you actually started the company?

• 24:55Speaker 02

  Yeah, so like the specific stories, it's pretty funny. So I'm from Cleveland, Ohio. My co-founder is from Detroit, Michigan.

• 25:00Anthony Pompliano?

  Okay.

• 25:00Speaker 02

  And we have very traditional families, very traditional backgrounds. And when we got back from camp, we're like, well, we stayed friends. We're like, we want to make some money. Like, school's easy, like, we should make some money. So we were like-

• 25:12Anthony Pompliano?

  That's why we're different, by the way, because you said school was easy.

• 25:16Speaker 02

  We should make some money. And the way we think we should make money is by building apps. You know, it started as an iPhone game we built. Then we started building apps for small businesses. So I created a little webpage and posted a press release, actually, to get my first couple of apps. And small business owners reached out. We had like the Canadian Craigslist, there's one that reached out. We had like a gas station. We had a couple of conferences, conferences like saps. And we were able to start building apps for all these people. We made thousands of dollars.

• 25:45Anthony Pompliano?

  How much were you charging?

• 25:46Speaker 02

  We were charging different prices. It was like, honestly, I wish I had some kind of pricing formula, but we didn't. We just, whatever we- Thought we could get the person to pay is what we would charge them. So anywhere between kind of $5,000 an app, all the way up to like 20, $30,000 an app.

• 26:02Anthony Pompliano?

  That is usually how most young people start with pricing is like, do I think they'll say yes to a thousand, right? Like, oh, they said yes, damn, I should ask for 2,000, right? You just kind of work your way up until somebody starts telling you no.

• 26:12Speaker 02

  And you know what's funny is because like, you know, the amount of value we were able to provide for the little cost compared to like, you know, some of the bills I see advise for specific things. I'm like, wow, these people got a lot of value out of us for very little cost.

• 26:25Anthony Pompliano?

  Yeah.

• 26:25Speaker 02

  But we started building apps and, you know, Runic, my co-founder is this math prodigy. And he was one of the brightest math minds in the country. And a professor reaches out from a very notable institution, you know, asking, hey, you know, I run the artificial intelligence lab here. Can you kind of join, you know, you're a math researcher and math research translates very well over to AI research. Can you join and can you start, you know, helping me solve some of these problems? And he's working on restricted Boltzmann machines and it's a type of artificial intelligence. And we were wondering like, you know, all these small businesses want apps. It's 2013, 2014, they all want apps. This is becoming the hottest thing, like every, every business was expected to have an app.

• 27:05Speaker 02

  Like, what if we built the software, we call it Siscat, that you could type in your app idea and our system would build you the app. And we spent all of the money we made on building apps, you know, bootstrapping it to build this, you know, company. We were like, we're not going to be able to raise any venture financing. We're going to find some people on the internet and they're going to be our first engineers. And we just went on all kinds of different web forums to try and meet people. We ended up meeting this engineer who actually unfortunately had to hack into Sony Media as part of the hacks. And, and I went to jail, so that was unfortunate. But a number of different engineers and they helped us bootstrap the first initial version of the product. But we were running out of money, like we didn't think we could solve the technical problem.

• 27:49Speaker 02

  It was just too complicated of a, of a tool to solve. At this point in time, my, you know, grades in school aren't, aren't that great because I'm spending all of my time building, building the startup. But we had met this guy who was a former investment banker and he's like, look, you know, even though this company might not be worth. Working out, you guys know so much about AI and machine learning. There are all these consulting opportunities through these expert networks and working with these financial institutions. Specifically, you can consult on how AI works and teach, you know, financial institutions how AI works. So like, that sounds great. So we started doing that and we're charging 700 bucks an hour to charge, to, to, to meet with financial institutions and educate them on how artificial intelligence works. And these are like big bulge bracket banks. So, you know, MassMut, RBC Royal, Deutsche Asset Management, big firms, and we would get on the phone with managing directors, analysts, whomever,

• 28:34Speaker 02

  and talk through artificial intelligence, talk through the latest and greatest technologies. But what started to happen is that we were talking to different people in these organizations and the wealth management divisions kept on coming up. The sense that we have these massive wealth management groups, 50% of Morgan Stanley's revenues, 30% of Goldman Sachs's revenues, like massive scale to these wealth management orgs. But our wealth managers, you know, we believed at the time were great money managers, but in reality, they were good relationship managers. And it was why they weren't going away. It was why robo-advisors weren't going to replace them, because they had this relationship they could keep with the client. But all these institutions were trying to understand, how can we leverage AI, machine learning, to be able to make our wealth managers smarter, to be able to help them make better investment decisions,

• 29:18Speaker 02

  make better portfolio decisions? So we start to think about this problem and we do a couple of projects with, you know, large banks and we realize, what if we just build this on our own? Like, what if we build our own software, portfolio optimization software using machine learning? And we sell it to large institutions.

• 29:36Anthony Pompliano?

  And what were they trying to get at? They were trying to get at getting a better return for their clients at like tax, tax loss harvesting, something else. What were they optimizing for?

• 29:47Speaker 02

  So at the time, what they were optimizing for and what we were optimizing for was slightly different.

• 29:52Anthony Pompliano?

  Okay.

• 29:53Speaker 02

  They were optimizing for, how can we help tell the story to our wealth managers? How can we help, you know, how do we have one kind of platform that can deliver insights on clients' portfolios? It can help them recommend investments, all powered by AI.

• 30:08Speaker 02

  What we were thinking about is like, all of these institutions have all these portfolio managers. They don't need to have portfolio managers. An algorithm can do their job just as well. And the advisor themselves can work directly with the algorithm, can work directly with the platform, and you can cut the portfolio manager out.

• 30:22Anthony Pompliano?

  So basically, there's a bank, let's call it, right? You know, ABC Bank. That bank has the financial advisor. Now, the advisor, in some capacity, is really doing customer relationship, right? It's managing the relationship. People do not want to talk to a computer. They want to talk to a person. They're able to sell, but they're also able to do client retention and this type of services. But a lot of times, the financial advisor is not the one actually making the investments. They're simply saying, okay, you know, client Joe, why don't you take 10% of your assets and put it into our public market, you know, whatever, vertical-focused fund? Oh, take another 10% of your assets and put it in this other fund or whatever. And then there's portfolio managers for those funds that are actually managing the capital.

• 31:07Speaker 02

  Exactly.

• 31:08Anthony Pompliano?

  And that's where you guys thought that you could basically rip those folks out and replace them with technology?

• 31:13Speaker 02

  Exactly. Sorry if there's any portfolio managers that listen to the podcast.

• 31:16Anthony Pompliano?

  There's a lot, don't worry.

• 31:17Speaker 02

  But the idea was like, you know, there's all of this, you know, all of these people in these institutions that are delivering relatively generic investment solutions. And we can replace it with one kind of piece of software that can deliver highly personalized investment solutions to each individual client. But at the time, we were thinking more at the fund level. So we were trying to think like, okay, given a strategy that one of these places might have, they might sell through their advisors, how can we just automate that strategy? We called that tool FSAI, Financial Services Artificial Intelligence. The two of us built it ourselves.

• 31:50Anthony Pompliano?

  How long did it take?

• 31:52Speaker 02

  It took us six months. And the funny thing was, there was a state stock market challenge going on, and I used the algorithm to allocate my portfolio in the state stock market challenge.

• 32:03Anthony Pompliano?

  Did you win?

• 32:04Speaker 02

  And I came in second place, so.

• 32:05Anthony Pompliano?

  Interesting. Who was the winner?

• 32:07Speaker 02

  I got a thousand bucks. Some kid from some other school.

• 32:10Anthony Pompliano?

  And he was just licking his finger and sticking it in the air and picking stocks?

• 32:12Speaker 02

  Must have been. I don't know.

• 32:14Anthony Pompliano?

  That or inside trading. Right, who knows what he was doing?

• 32:18Speaker 02

  But either way, the idea back then was returns, it was performance. But there was a school trip to Detroit Startup Week and Jamie Dimon had this big initiative in Detroit. And he was speaking on stage and he gets offstage, we rush him onstage. And you're like, Jamie, this is what we're working on, FSAI. Do you think that JP Morgan would buy this? And he's like, look, JP Morgan has a lot of tools. I don't think JP Morgan is going to buy this. You know, it was a whole, it was a whole discussion and

• 32:47Anthony Pompliano?

  Long discussion?

• 32:47Speaker 02

  No, for

• 32:48Anthony Pompliano?

  Like five minutes?

• 32:49Speaker 02

  Five minutes, yeah. But we were like, oh shit, like, you know, JP Morgan is not going to use this. And, you know, we were young and they're not going to use this. Like, what are we, what do we do? So we ended up meeting this financial advisor in Philadelphia. And he had this $300 million independent RIA practice. And he said to us, advisors are leaving big institutions at such a rapid rate. They're all going independent. Advisors are leaving the big, you know, bulge bracket institutions and they are taking their books of business with them. They're starting up their own practices and they have very little technology that can help them build and manage portfolios. They don't really have an operating system around their investments. So they would, they would eat this up, they would dig it. So we were like, okay, we're going to build the software for independent advisors.

• 33:33Speaker 02

  We're almost going to build the Shopify-like operating system for independent RIAs to help them build and manage portfolios. So every single day after school, we would talk to like five to eight financial advisors. We would post these job ads and just have lots and lots of advisors apply to the job ads. And it was our growth hack way of meeting advisors and doing customer research. And the way we thought about it was, how do we understand the psychology of these advisors? Most of these people in the space are just building investment products. So not actually trying to enable advisors to scale their business and like make them better at their jobs. How do we understand the psychology of the advisor and then sell into that psychology? So we spent all of this time talking to advisors. We had hundreds of conversations with advisors and we realized there was something there. And one of those people we hired

• 34:18Speaker 02

  Was a former advisor and he loved the product. He loved the vision that we were going after. He's like, okay, I'll be your first employee and I will build this company with you guys. And he'd sold his RIA practice. He could afford to work on equity. We were using the money from consulting to kind of bootstrap the business early days. And we met these two PhD machine learning AI quants. And one worked at Morgan Stanley, the other one worked ex-Google. And they were the early team that basically helped us build.

• 34:45Anthony Pompliano?

  Where do you meet those people?

• 34:45Speaker 02

  The first version of eyes, Angel List, actually.

• 34:48Anthony Pompliano?

  Interesting.

• 34:48Speaker 02

  So some, Runic actually worked with in the past. Others, we were, there were resources that were available that just weren't utilized very, very well by startups. Back in, you know, 2015, like Angel List and other job sites that now are probably inundated. Where there was a lot of really high quality talent that wanted to just work on startups with startup problems. That was a great place to recruit from.

• 35:09Anthony Pompliano?

  And was the team all together when you first started or was it remote?

• 35:12Speaker 02

  Everyone, it was actually, we all started remote. I was running, you know, it out of my parents' house in Cleveland. And so was my co-founder in Detroit. We were in high school at the time. And then the team was actually all, for the most part, based on the West Coast in San Francisco. Which is an interesting part of the story because what happens is I realized that this is becoming such a core part of my life. And this is what, like, you know, some people have a passion, others have a calling. Like this was my calling. This is the thing I wanted to spend the rest of my life on. And I decided, you know what? Screw school, like I'm going to drop out and I'm going to go move to San Francisco. So I moved to San Francisco. I live in the Tenderloin, which was not too fun of an experience, but it was all I could afford at the time. And we had a WeWork and we couldn't even afford the WeWork membership.

• 35:54Speaker 02

  So we just snuck, we had one membership and we snuck all people in every day. And no one seemed to notice. And my co-founder at the time was still in high school. And his parents said, you know, you have to go to college. And, you know, at this point, like my parents don't like me because I'd left school.

• 36:10Anthony Pompliano?

  So your parents were pissed that you left?

• 36:11Speaker 02

  They were pissed. Okay. They ended up coming around to it, but they were upset. And

• 36:17Anthony Pompliano?

  Your, uh, your parents are Indian?

• 36:18Speaker 02

  Yes, they are Indian.

• 36:19Anthony Pompliano?

  And are they, uh, traditional Indian parents who wanted you to be a, a doctor or a lawyer?

• 36:24Speaker 02

  Yes, doctor, lawyer, engineer.

• 36:25Anthony Pompliano?

  Yeah, I didn't know this whole thing and I've got a couple of friends who are Indian and like the joke is always like, oh, uh, Indian parents, whenever something happens to the news, they like text their kids and like, why can't you be like, you know, whatever they see in the news?

• 36:36Speaker 02

  Oh, my gosh, there's

• 36:37Anthony Pompliano?

  Is it real?

• 36:37Speaker 02

  So many people that we were compared to, you know, my co-founder is this huge chip on his shoulder because his cousin is like this, you know, brilliant kid who like went to Harvard and got a job at Palantir and his parents are always like, oh, you know, you know, Hirsch did this big thing very well, you know, live up to that. Every Indian kid has this chip on their shoulder. And like, I definitely had one, I think like a lot, like they're very, very comparative.

• 37:01Anthony Pompliano?

  Yeah. How, how old were you when you moved to San Francisco?

• 37:04Speaker 02

  I was 16.

• 37:05Anthony Pompliano?

  16?

• 37:06Speaker 02

  So just about turned 17.

• 37:07Anthony Pompliano?

  And, uh, did you know anyone in San Francisco other than the people who were working at the company?

• 37:11Speaker 02

  I knew the people that were working at the company and I cold emailed and cold LinkedIn messaged a couple of people to like, meet my first group

• 37:18Anthony Pompliano?

  Yep.

• 37:19Speaker 02

  of people and

• 37:20Anthony Pompliano?

  Did they think you were crazy?

• 37:21Speaker 02

  Oh, everyone thought I was crazy.

• 37:23Anthony Pompliano?

  Okay.

• 37:23Speaker 02

  But I'm living in the Tender Line and my co-founder gets an invite to this barbecue at this place called the Crypto Castle. I'm not sure if you've heard of it.

• 37:34Anthony Pompliano?

  Yeah, Jeremy Gardner.

• 37:34Speaker 02

  Jeremy Gardner. So, you know, Jeremy Gardner says, hey, look, I've got this bunk bedroom. You can live here. So I lived in the Crypto Castle, um, for about two years and I saw the Bitcoin run up, which was a pretty cool thing. And it was a, it was kind of my initial launch pad in San Francisco and I was around a lot of young

• 37:51Anthony Pompliano?

  Lots of people coming in and out of there, right?

• 37:53Speaker 02

  Exactly.

• 37:54Anthony Pompliano?

  A lot of people building companies, uh, all of that. When you actually quit high school, you didn't graduate?

• 38:03Speaker 02

  I didn't graduate.

• 38:04Anthony Pompliano?

  Okay.

• 38:04Speaker 02

  But I did some community college credits.

• 38:06Anthony Pompliano?

  Okay.

• 38:07Speaker 02

  And I did these summer college classes. So I was able to get enough credits at the end of the day to get a diploma like a couple of years later.

• 38:13Anthony Pompliano?

  Got it. And so when you do that, is this like a burn the boats moment? Like, if you're leaving, there's no like coming back home, be like, mom and dad, you were right. Like I should have gone to college and like, I'm, I apologize. Or is this like, no, like I gotta make this work because I'm definitely not going back?

• 38:29Speaker 02

  Oh, I had no choice. Like, I think that entrepreneurs, when they have a set of like, when there's desperation, right? When they have like no other choice, when you have no other optionality, you are forced to make it work. And that's the situation I was in. I was forced to make it work. There was no going back. And it was like more like it was my passion. It was the thing I wanted to spend my life on. But like, I had no alternatives, right? Like, there was no, like, there was nothing waiting for me on the other side. And I had to do it with very little money.

• 38:56Anthony Pompliano?

  So at 16, you, you start to run the company, right? What are some of the things that, looking back now, like, what are some of the mistakes that you made where you're like, ah, I was 16, like, I just didn't know?

• 39:08Speaker 02

  I mean, I would say every mistake is probably, probably what, what ended up happening? I mean, I would say I probably have better learnings, given that, given the period after we were funded and after we started to have scale about what I would have done differently.

• 39:23Anthony Pompliano?

  Okay.

• 39:23Speaker 02

  But when I was 16, I was trying to build a company. I was doing whatever I could. And I was trying to be as scrappy as possible. So I think the lesson, you know, going forward that I think I did well, was I stuck it out, right? I had like a semblance of grit and like I didn't give up. And I think in order to be an entrepreneur, and like you can see it in this market environment, you can see it just broadly speaking, if you quit, right? Because something is hard, because there's not something proven, whatever it might be, because it's the easy way out, like, you're never going to find like a meaningful amount of success. And I think that if you continue to work at something and you continue to iterate, success is going to start to compound over time. It's going to continuously get easier. And you're going to learn more and that learning is going to continue to compound over time.

• 40:04Anthony Pompliano?

  What was fundraising like initially?

• 40:06Speaker 02

  So what happened with fundraising was, so my co-founder decides to go to college. And I'm like, shit, my co-founder is going to college. What am I going to do? So I decided to move on to his dorm room floor. Okay. He has this little mattress topper. It's two inches thick.

• 40:21Anthony Pompliano?

  Okay.

• 40:21Speaker 02

  And we put it on the floor. He's single. And like, I just sleep on that. And I'm like, Runic, if you drop out of school, like what, like what, what it needs to be true? And he's like, look, if we raise a million bucks, I can convince my parents to drop out of school. So I'm like, sure, like, let's go out and raise some money. And originally, we actually tried to raise money and I had just made cold calls and cold emails. I cold, cold emailed a hundred people. And I would just continuously email them and no one would ever reply. So I was like, okay, well, the only way that I can get fundraising to work is if we have some kind of social proof, if someone like introduces us to someone.

• 40:54Anthony Pompliano?

  Yep.

• 40:55Speaker 02

  But like, at this point, we still don't really know anyone. So we're at UPenn. We start using the UPenn resources. There are some investors, there's this guy named Josh Koppelman, who went to UPenn, who's an incredible VC from First Round Capital. And he had introduced us to these two guys, Nat Turner and Zach Weinberg, who founded Flatter and Health. And they said, look, we don't know if this whole RIA, wealth management, technology thing is going to work, but we like you guys, so we'll give you your first hundred grand. And it came in perfect timing, because the employees, they were working for, you know, basically no salary for two years. And they were finally starting to get pressure from their spouses to like, you know, take some cash.

• 41:35Anthony Pompliano?

  Why are you working for a 16 year old for no money?

• 41:37Speaker 02

  You know, it's funny, because we didn't even tell them our age at the time.

• 41:39Anthony Pompliano?

  Yep.

• 41:41Speaker 02

  But they asked for cash and, you know, we were like about to call it quits. We were like, look, if this doesn't work by the end of the year, if we aren't able to raise capital, and it was probably February, it was the lowest point it had ever been. I was very depressed. It's like, if we can't make this work, if we can't raise capital, like, we'll call it quits, like, we'll, we'll, we'll stop the company. And we ended up getting that first check. It was the most excited I'd ever been. And I could, I knew I could like finally pay to, you know, cover operations and pay for salaries. And Nat and Zach helped us raise the next couple hundred grand and then...

• 42:15Anthony Pompliano?

  They introduced you to like angel investors, to VCs?

• 42:17Speaker 02

  Investors, VCs. And Keith Raboy and Ben Ling co-led our seed round. So I think we were Keith's first investment at Founders Fund, first or second investor.

• 42:25Anthony Pompliano?

  Okay.

• 42:26Speaker 02

  And then Ben Ling, with his new fund, they had co-led our seed round, and we raised two million bucks. So then Runic dropped out of school, moved back to the Bay Area. We were back in the crypto castle, because we still didn't pay ourselves any salary. We realized that wasn't sustainable, so we paid ourselves something a little, lived in an apartment, and started scaling the business. We started, we got registered with the SEC. We were actually the youngest ever people to be registered with the Securities Exchange Commission, which is very fun for us. And we realized that we needed to be a money manager. We needed to actually manage the money. With the general premise advised being, we're going to sell the software to independent RIAs, we're going to help them build their clients' highly personalized investment portfolios, we're going to automate the management of those portfolios, we're going to provide the technology to explain those portfolios.

• 43:08Speaker 02

  So advisors, instead of building portfolios, could now be focused on their client relationships, could now be focused on growing their practice, everything else would be handled by our system. And that was like, kind of what we were working towards at that point in time. We, I'd seen the show Silicon Valley, and always like, wanted to do this like, startup battlefield thing, I thought it would be interesting, and I would recommend any entrepreneur do this, and the reason why is because there's a lot of great recruiting benefits to having that kind of pitch or video on, on the TechCrunch website. But TechCrunch was having this, like, happy hour with Sequoia. Okay. And we went to this happy hour, we didn't really feel like we needed Sequoia, or any kind of investors, we had Founders Fund at the, at the time, we had some money, like, we don't need to raise any more capital, and we're sitting at the pretzel station, because

• 43:53Speaker 02

  we just love free food, we didn't want to pay for food, and chowing down on pretzels, and this guy comes up to us, his name was Sean, and is like, "What are you, what are you idiots working on?" That's basically how he tweets, so yeah. We love Sean, Sean, Sean's brilliant, but, you know, he's like, "What are you guys working on?" And we're like, "Oh, we're building this software for financial advisors, to like, help them build portfolios, the whole vision is to kind of automate asset management." And he's like, "That's really interesting." Like, I was at this retreat with John and Patrick Collison, and someone mentioned that, you know, financial advisors are the one part of the market that, for some reason, technology hasn't replaced them, and they continue to grow year after year after year, but no one's built any software for them.

• 44:35Speaker 02

  And, you know, shortly after that, Sequoia preempted us on a seed round, and then Sean actually helped individually recruit lots of talent for us, which was helpful. And then we decided we needed to move the company from New York, from San Francisco to New York, because New York was a better hub for financial talent, because all of the financial DNA was actually in New York, wasn't in San Francisco. Sequoia then preempted us on a Series A round, and the pandemic hits, and then shortly thereafter, Ravi at Sequoia preempted us on a Series B round, and we've now raised like, 50 million bucks, and we start to scale assets on the platform. We're still only managing, you know, a couple million bucks in assets across a handful of financial advisory firms, but it's proving that the product works, and it's working pretty well at this point in time.

• 45:19Speaker 02

  And then we start to build out a small sales team. I'm starting to spend a lot of time on sales and we scale basically in the period of six months from zero, call it four or five million dollars in assets, basically nothing, to close to half a billion dollars in assets.

• 45:34Anthony Pompliano?

  Got it. And why was Sequoia preempting the rounds?

• 45:37Speaker 02

  I think they liked us. They saw the market opportunity. They saw the sizes of the market and they saw that, you know, standard VC, like if, they didn't preempt us, someone else would. And I think that was pretty clear because lots and lots of VCs at the time were kind of swarming around us and VCs loved to pattern match. So I think when they see young founder, big market, like, oh, this is the next Stripe and we're going to invest. I think the challenge is history, you know, seems like, you know, some of these companies were built much quicker with much more ease than they actually were. And when you think about tackling a space like the asset management space where Viis' vision is almost to build the next BlackRock, right?

• 46:19Speaker 02

  We want to build one platform that all investors, regardless of age or net worth, can get a personalized portfolio in an automated way across all asset classes, which is a very challenging thing to do, given the size of the market and how, you know, taken over the market is by kind of legacy incumbents. Although it is the direction the market's heading in, it's going to be the direction the market heads in over a, you know, 10, 20 year period, maybe 30 years. It's not going to be something where, you know, you see immediate venture scale success in the period of, you know, six to eight months, whereas like you can apply, you know, some capital, you have a standard kind of SaaS operating model and you're able to continuously start to prove revenue. It's very much a space where you can compound growth more and more over time.

• 47:01Anthony Pompliano?

  So there's a lot of people who are investors who will listen to this conversation. They hear you say personalized portfolio and they're asking themselves, what the hell does that mean? What is a personalized portfolio compared to a normal portfolio?

• 47:14Speaker 02

  That's a great question. So if you think about what financial advisors deliver to clients today, they deliver ETFs, mutual funds, managed investment strategies, very generic investments. They don't take into account your values, your goals, your risk tolerance. They're just kind of generic investment strategies. And there's a lot of drawbacks to this, right? They're expensive, they don't, you know, optimize for your taxes, they aren't very explainable and they're sometimes like pretty hard to manage. So what Viis does Is working with a financial advisor, we can take in all these different inputs about a client, how much money they have to invest, their goals, their net worth, needs they might have, their environmental preferences, like do they want to invest in environmentally

• 47:57Speaker 02

  friendly companies, do they not, strategies they might want to take, maybe they care about value investing or growth investing, maybe they've got embedded positions because they work at a company or they have stock that they inherited at a low cost basis. And then career risks or risks they might have, right, maybe they own real estate, they have angel investments, they work in a highly regulated industry, whatever it might be. Fives can take all of those inputs in one simple questionnaire, 30 seconds later, build a highly personalized portfolio of individual stocks, so stocks that match up, a portfolio that matches up to the client's individual needs, so we will give them a risk adjusted portfolio over a certain time horizon that is adjusted and continuously readjusts to the client's various goals, minus all of their preferences. So let's say the client works at Facebook, they won't have exposure to technology, or

• 48:42Speaker 02

  nearly as much exposure to technology, they will have no exposure to Facebook, they will be de-risked from their Facebook holding, because Facebook makes such a large percentage of the S and P 500, even if you're just investing in index funds, you're overexposed to Facebook. Or, you know, those environmental restrictions, you want companies that are environmentally friendly in your portfolio, you'll take out the companies that are not environmentally friendly.

• 49:01Anthony Pompliano?

  Is there like a core philosophy that you all follow at like the highest level, like modern portfolio theory, 60/40 stocks and bonds, and then you kind of operate off of that or move off of that based on the preferences? Or is it you start from scratch, look at the preferences and then build a portfolio, and if somebody's preferences lead to 90% fixed income and, you know, 10% REITs, sure that violates everything in a 60/40, you know, portfolio philosophy, but like, starting from scratch gets you there and like, that's the goal? Like, how do you think about which direction?

• 49:33Speaker 02

  We do both.

• 49:34Anthony Pompliano?

  Okay.

• 49:35Speaker 02

  So we have a philosophy, like, kind of grounded in empirical evidence, like a value-based investing strategy, kind of like dimensional fund advisors.

• 49:43Speaker 02

  That advisors can customize based on various clients' needs. And then we offer all kinds of other different types of strategies as well. But the idea is the advisor has probably their own strategies. And we want to be able to operationalize their strategy. So the way our optimizer is built, it's very modular. It's very open-ended. So given a number of different kind of restrictions and inputs, client inputs or inputs from the advisor, and strategies the advisor might have, we can build this personalized portfolio for them. But let's say something doesn't make sense. Let's say they have a goal that's unrealistic. Let's say they have a risk tolerance that's too high, given the client's specific needs. Our idea is that we will tell the advisor and the investor through the whole process,

• 50:25Speaker 02

  the risk and return trade-offs and the implications of the decisions they're making on their portfolio. So the advisor will understand, okay, given these set of inputs, this is what my portfolio is expected to return over this period of time, kind of like a forward-looking Monte Carlo. And then the back-tested returns, you know, fitted for that particular portfolio, given a set of individual stocks. But I think what's also interesting about Viz is because the customization is basically endless, it can serve us any different type of client and it can do it in a highly efficient way. So you think, okay, what are the other drawbacks as to using Viz over index funds? Like what about tax loss harvesting or something like that? And the reality is, so most advisors today don't actually do tax loss harvesting. Most investors don't do tax loss harvesting.

• 51:09Speaker 02

  But Viz, not only do we do tax loss harvesting, but let's say you're using a robo-advisor or you're using kind of a generic model portfolio of ETFs, you're tax loss harvesting on the fund level. So if the fund goes down, you can tax loss harvest the incremental, you know, down. But if you're investing in the individual stocks, you can tax loss harvest the individual stocks that go down.

• 51:29Anthony Pompliano?

  So if inside of a portfolio or a fund, one stock goes down 20%, but the overall fund's only down 2%, most people are tax loss harvesting the 2%, not the 20% on that specific position?

• 51:41Speaker 02

  Exactly. So whereas Viz, you're investing in the individual stocks. And this is important because you can understand what you're investing in. You can understand why you're investing in it. But you can tax loss harvest the individual positions.

• 51:52Anthony Pompliano?

  Who gets screwed if you guys are successful?

• 51:56Speaker 02

  I think the traditional mutual fund companies, the traditional asset managers, like I have this general belief and you can already see it, mutual funds are seeing record outflows and it's going to continue to compound over time. I think there's always going to be a place for ETFs, but I think mutual funds are going to see record outflows. I think the challenge is the retirement business is so big and it's built on top of the mutual fund business, which is why it's not happening faster.

• 52:20Anthony Pompliano?

  What's drawing the withdrawals?

• 52:25Speaker 02

  ETFs were the big initial driver and then step two is what you can call direct indexing or custom indexing, which is part of, a large part of what Viz does. And there's only a handful of companies that are doing it, but I think like four years ago, there were less than $3 billion in direct indexing assets. Now there are hundreds of billions and we're on track for trillions of dollars in assets over the next five years.

• 52:48Anthony Pompliano?

  Do you think that Viz or similar types of direct indexing products will eat ETFs and mutual funds completely? Or do you think that it's something where, no, they may just be products within the platform. So if somebody comes in and gives you your preference, they may go and buy individual stocks, but like, could you still allocate to an ETF or to a mutual fund?

• 53:07Speaker 02

  For sure. For ETFs and mutual funds for, you know, other assets. So, but for core equities, I think over some period of time, assuming the on-ramps are gotten right. So the way people start to initially invest in making it easy, low account minimums, things like that, it will be very, you know, very much direct indexing or custom indexing for those core assets.

• 53:30Anthony Pompliano?

  So a financial advisor goes and adopts the technology. Is this something where they're going to their clients and they're like, ha, we've got, you know, we've got the black box. Let's fill out this questionnaire and then in 30 seconds, it's going to tell us, you know, exactly what you're going to be invested in? Or is this something where they're still having the same conversation and kind of relationship management with their client, but then they're going back to the office and they're using this tech?

• 53:51Speaker 02

  It's both.

• 53:52Anthony Pompliano?

  And what I'm trying to get at is like, how much awareness does the client have that it's actually this software product that's determining what the portfolio is versus they think that it's still the RAA or something like that?

• 54:03Speaker 02

  It's both. I mean, it's disclosed. So the client has to fill out paperwork, but sometimes advisors kind of take credit for it. They use it as a sales tool. Advisors are like, I've spent this time building you this personalized portfolio of individual stocks that perfectly matches your needs compared to I've invested you into, you know, this generic model strategy or set of mutual funds. And advisors, when stacked up head to head, you know, the strategy of personalized stocks, we'll just always do better, ideally.

• 54:33Anthony Pompliano?

  How do you guys make money?

• 54:34Speaker 02

  We charge an asset management fee.

• 54:36Anthony Pompliano?

  Okay.

• 54:36Speaker 02

  So we charge a fee.

• 54:37Anthony Pompliano?

  Got it. And when you go to the RIAs, what is the biggest objection that they have?

• 54:41Speaker 02

  There is probably three or four different objections, one being technology. So advisors haven't adopted technology historically until relatively recently. So technology adoption in the space has been slow and it's been really slow around investment management. So as it starts to kind of increase, it's going to, it's going to continuously go up. I think that's one key one. I think two, which you talked a little bit about earlier is that some advisors just see themselves as stock pickers. I think it's 10%, like 80 to 90% of advisors now outsource some or all of their investment management. So it's becoming a broad part of what they do. So advisors realize they need to outsource. Someone gave one of our advisors a really good piece of advice that, you know, the advisor told me, which kind of resonates,

• 55:25Speaker 02

  is that in this business, you can do one of two things. You can either manage the money or you can manage the people. And managing the people is a hell of a lot easier than managing the money. So most advisors realize that their job is to be their client's sherpa. It's to be their client's therapist, their marriage counselor, their closest advisor, their financial planner, the relationship manager, so to speak, between them and their money. And they're staying away from the money management. So they see themselves as the manager of managers. And it's advisor's job to enable those advisors, not the advisors that are saying, okay, I'm a hedge fund manager. I'm going to give you my 10 stocks and we're going to outperform the market.

• 56:00Anthony Pompliano?

  Yeah. How much impact are the RIAs seeing right now through things like the rise of Robin Hood and SoFi's of the world, meme stocks, kind of this like day trader mentality that seems to have taken part in some part of the market? Like, is that affecting inflows to RIAs from younger people or are they not seeing that?

• 56:19Speaker 02

  This is really interesting and I spent a lot of time diving deep on this. So the average account size on like a Robinhood is like, you know, 5, 6,000. The average account size on a Betterment or Wealthfront or traditional robo-advisors, probably in the low twenties, thousands. And what starts to happen is it is a great on-ramp for investors at the earliest stages to start investing and to initially get into the markets. But what people realize is when there's any meaningful amount of wealth, like anything, you know, 50,000 plus, they are so far away from understanding how to manage that, even with technology in the loop, that they need to go talk to an advisor. Someone recommends them talking to an advisor. Someone recommends them to talk to a human and that human is a critical part of that process.

• 57:04Speaker 02

  So I would say that those traditional fintechs do a great job at getting, you know, people to invest earlier, early on. I think that like people meme stock trading is just kind of replacing the gambling budget. I don't think that's actually like investing. I think that's gambling and I think a lot of those people know that's gambling and the money that people use to gamble is very different than the money they use to invest. You're not day trading, at least you're really not a smart investor if you're day trading your life savings for your retirement.

• 57:32Anthony Pompliano?

  Somebody out there is for sure doing it, but we should not.

• 57:35Speaker 02

  The vast majority of people probably not.

• 57:36Anthony Pompliano?

  We should not encourage those people, but 100% somebody out there doing it.

• 57:40Speaker 02

  But advisors, the thing people always wonder about advisors is that advisors for younger clients, right, like these older, you know, baby boomer advisors, aren't going to be the same types of advisors that are going to be working with younger clients. And people wonder like, well, my, you know, like I'm a young client, like why would I want to work with a financial advisor that doesn't understand me, that doesn't understand who I am? And the answer to that is pretty simple, which is more people that look like you, that are young, are becoming financial advisors. And there is a new generation of financial advisors, independent financial advisors, that understand all kinds of different types of clients. So we've got clients that specialize in musicians. We've got clients that specialize with tech people.

• 58:22Speaker 02

  We've got clients that specialize with doctors, with lawyers, right? Like there's even LGBTQ specific advisory firm that just works with and understands that client base. So advisors are starting to change, become, you know, take different forms than the traditional kind of golf and country club, you know, advisor in their mid 50s that, you know, your parents probably use.

• 58:45Anthony Pompliano?

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• 59:28Anthony Pompliano?

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• 1:00:12Anthony Pompliano?

  Bullish's total trading volumes have exceeded $25 billion just in the seven months since it launched. And their industry-leading order depth means you can trade confidently when you want, at scale, with better pricing and lower risk, all within a regulated market environment. Good reason to be bullish. Learn more at bullish.com/pomp and follow at bullish on Twitter today. What about Bitcoin cryptocurrencies? Like, it seems on the internet to be this really dominant asset class and everyone's super excited about it, or they really, really hate it and you kind of get the extreme reactions. But $2 trillion-ish dollars for the entire industry is like pretty small compared to equities or any of the other asset classes. Like, what are you seeing there?

• 1:00:53Speaker 02

  See, there's $85 trillion in Assets sitting with financial advisors and it's growing at this staggering rate. And I think what's interesting is like, Anthony, you and I live in a bubble, right? We live in a bubble in the tech world where it seems like crypto is the most important thing, or one of the most important things. And the reality is for most financial advisors, if you're a financial advisor in Iowa, or your client, a person, a teacher in Iowa, right? You probably have heard of Bitcoin a couple of times. You don't really know what it is or how to invest in it. And if you want to see mainstream adoption of crypto in people's long-term investment accounts, you need both institutional adoption on like retirement accounts and kind of on a mainstream scale to get to investors that aren't sophisticated and have it as a default portfolio allocation.

• 1:01:36Speaker 02

  Or you need to educate the advisor really well. And advisors are starting to seek out getting educated. And through a platform like Avise or like a tool that services advisors, having crypto on that platform is part of our vision, right? We want to have crypto as an asset class. We're probably going to roll it out relatively soon to having crypto as an asset class that any investor can invest in as part of a standard portfolio.

• 1:01:59Anthony Pompliano?

  And what about like regulators? How do they look at some of these technologies, right? The, it's very clear, like, how do you regulate a portfolio manager who's making decisions? They either fulfill their fiduciary duty or they don't. And there are certain rules that they have to follow. And for the most part, 99.9% of them do. When you start to introduce machines and algorithms and artificial intelligence and machine learning, like, do any of the regulatory environment change? Or do they look at this differently or is it the exact same thing?

• 1:02:25Speaker 02

  Yeah, I mean, the regulatory environment has gotten very difficult for fintechs.

• 1:02:29Anthony Pompliano?

  Okay.

• 1:02:30Speaker 02

  The SEC is going after fintechs really hard, but more about, are these fintechs doing what's in the best interest of their client? Or are they doing what's in the best interest of themselves? And are they treating the client the way they should be kind of treated? The way that they think about these algorithms, it's interesting because we've gone through some of these processes with regulators, is they want to understand how they work. They want to understand how does the algorithm work? How does it make decisions in the same way that an advisor would want to understand it?

• 1:02:57Anthony Pompliano?

  And the same way the consumer, as we talked about earlier, they don't want to just know like, hey, just kick out this random output. It sounds like the regulators want to know as well, well, if you gave it this data set, it came to this conclusion, why?

• 1:03:07Speaker 02

  Exactly.

• 1:03:08Anthony Pompliano?

  And are they enforcing rules there? Or is this more of like, we're on a fact finding mission? We want to learn and understand how this stuff works. And then we'll decide if we need new rules or not.

• 1:03:19Speaker 02

  I think it's fact finding right now.

• 1:03:21Anthony Pompliano?

  Okay.

• 1:03:22Speaker 02

  There is going to be, it's still very new, right? And the SEC is still, most of the regulatory agencies are still, you know, they're very smart, which is, I think, contrary to what a lot of people believe, is that these regulators are very, very smart, but they're very slow, right? It takes them a long time to enact new rules and to understand real problems. And I think that we're still in kind of fact finding, data gathering, until we can, you know, create new policy around some of these software. However, regulators are taking note, they are starting to act in a meaningful way. You can see it in a very public way against companies that are fintech, that are not doing what's, that's clearly not doing what's in their clients' best interest.

• 1:04:04Anthony Pompliano?

  Yeah.

• 1:04:04Speaker 02

  And they're being deceptive or otherwise. And there's almost like a zero tolerance policy for it by the SEC, which I think has been pretty interesting.

• 1:04:11Anthony Pompliano?

  Yeah. You're a young person who's now run a company for six years, or this company for six years. You obviously had a company before. And what I find fascinating is I think that you've learned a lot. You've changed your mind about a lot of ways of actually operating the business itself. So I thought that what we could do is I'll throw out a couple of topics about just running a business and you mind dump on me in terms of how you do it and things that you've learned over the last six years. The first one, which I know you have changed your mind on is OKRs. What is your current thought process and why did you change your mind on OKRs?

• 1:04:41Speaker 02

  All right. So I thought OKRs were the most brilliant thing when someone introduced them to me. I think it was a ex-Google executive.

• 1:04:49Anthony Pompliano?

  Of course.

• 1:04:50Speaker 02

  Really big Google executive who's, who's friends with us. And I thought they were brilliant. Our whole exec team was like, hey, we should do OKRs. We'll do it on a quarterly cycle. We'll have our main objective. We'll have all of our different key results and our feeding inputs. And then we'll have this big deck. I created this big deck presentation that outlines each OKR, each input and the owner of the input. And here's the problem, maybe for a big company they work, for startups, they're terrible. Because they're There's usually one thing in the startup that is holding you back. There's one limiting region problem over all of the rest, but the entire company needs to be focused on. One metric, one objective, one issue. And the problem with OKRs is you're distracting yourself from the key focus.

• 1:05:34Speaker 02

  If you've got five different objectives or three different objectives and like eight different key results or nine different key results, like you are looking at so much stuff. And to keep a small team focused on a broad set of priorities and objectives, you are naturally going to create yourself into a situation where you kind of like half ass each of them. You kind of make some progress on each of them. Maybe you make like a lot of progress on one of them, but it's not the right one. Whereas to work in a, to be successful in a startup, you need to have one thing that is holding you back between now and hyperscale. That maybe it's one objective. So in our case, it was like assets under management at the time. And one kind of leading input into that assets under management,

• 1:06:17Speaker 02

  which is like onboarded AUM from net new customers that everyone focuses on. The whole organization obsesses over. The whole organization spends all of their time and energy thinking about that. Everything else, you can just let run and let it operate. But if you're thinking about, oh, here's how I kind of slightly improve this and slightly improve that and slightly improve this, you're not really going to be able to make step function order change in a like major way across all of them. And you need to see success in one, in a meaningful way.

• 1:06:47Anthony Pompliano?

  Let's walk through that decision. So when you decide what the one thing is, how did you guys go through that process to understand it was the AUM?

• 1:06:55Speaker 02

  I mean, what is the difference between more customer adoption and less customer adoption? Like what is, usually everyone knows, right? Like

• 1:07:03Anthony Pompliano?

  It's something that's so obvious.

• 1:07:04Speaker 02

  It's so obvious, right? Like if you're having a problem with retention, right? You have all these customers that are joining your new app. And, you know, the initial growth is incredible, but retention is staying really low. The whole organization is going to know that like retention is bad. When you ask everyone in the company, what is the biggest problem you think the company has? Everyone's going to have an answer and hopefully like 50 to 70%, if not more, are going to have the same answer. That is usually your answer of what is the key objective.

• 1:07:32Anthony Pompliano?

  And then how do you communicate that to the whole team? So like, once you and the executive team decide this is the thing, all hands meeting, is it like an email, a Slack message? How do you actually communicate it?

• 1:07:42Speaker 02

  See, this is the mind of a CEO that, you know, to you, it might be the most important thing. To everyone else, it might also not also be the most important thing. So when you communicate, this is the other problem with OPRs, because there's so many of them. When you communicate one of them on an all hands or send an email about it, like, it might be important that day. People might not show up. People might not remember. Like, if you've got one key objective, if you've got one key focus of your business, one metric that you're tracking, it needs to be plastered fricking everywhere. Right? You need to talk about this in every conversation. You need to be asking every single person, every single second of the day, what is going on with this one particular thing? And then when you nail that, you can focus on everything else.

• 1:08:26Speaker 02

  But until you nail the main thing, until you nail the main focus of the business, nothing else matters. And I think the challenge is, as founders, we all think, okay, there's so much stuff on our mind, there's so many balls we're trying to juggle. And like, we're going to talk about it once in an all hands. We're going to send a one company-wide email and everyone's going to get it. And that's just not how people work. You need to talk about it everywhere.

• 1:08:47Anthony Pompliano?

  In one of our companies, every single meeting starts off with a reminder of how many days since the company was started, and then what the mission of the business is.

• 1:08:56Speaker 02

  Yeah.

• 1:08:56Anthony Pompliano?

  Just over and over and over again. To the point where sometimes we call on people and say, hey, what is this? And if somebody doesn't know, I don't care if you're brand new or if you've been around for a while, you better know the answer to that question.

• 1:09:08Speaker 02

  I agree.

• 1:09:09Anthony Pompliano?

  And I think that your point about the repetition ends up being very, very important until it's literally ingrained in people's heads.

• 1:09:15Speaker 02

  It's all repetition, and it's got to be simple. Right? It's got to be a very simple thing that everyone can think of. So we had a new product, like, initiative that we wanted everyone focused on. The whole company had to be focused on it. Like, we just had a very simple name for it. When everyone thought about that product initiative, are we working on PCE2? And everyone focused on working on PCE2. It was a simple, easy thing. When you have these long, drawn-out objectives, it's really hard to kind of keep that in your mind. But if you have a simple acronym or simple target... That everyone can think about, then everyone's gonna resonate to it.

• 1:09:46Anthony Pompliano?

  How do you do recruiting today? Like, what are some of the things you've learned over the last six years? And how to be good at recruiting high quality talent?

• 1:09:51Speaker 02

  So I was really good at this early on. I made a lot of mistakes later on.

• 1:09:55Anthony Pompliano?

  Okay.

• 1:09:56Speaker 02

  There's a couple interesting things. So the first is having a Sequoia. Love Sequoia, but having a Sequoia is a blessing and a curse. And the reason why it's a curse is because having such a high quality venture fund helps you. And like big backers and all this funding and all this press and all this excitement helps you recruit people that are actually the wrong people to recruit at your organization. Because they are there because you're a rocket ship that they think they're gonna make lots of money on. But the reality is, you need people that are obsessed with your mission, that are willing to work their asses off. And they're only there because they love what they're building. They're willing to take a pay cut. They're willing to work extra hard, harder than they would have before. And they're willing to just take shit that they wouldn't have taken before.

• 1:10:39Speaker 02

  And I think the like pattern, like if people look at, you know, the success and the draw that you have behind your company, like they're gonna join for that reason. Whereas I think early on, when we didn't really have all the backing, we only had people that joined because they loved the mission and they wanted to join an early stage startup. And they wanted to work really hard and they were willing to take a cut and they were willing to be extra loyal. Versus what happens after a certain scale is you start to get people that are there because you are this big flashy company. You need to avoid that like the plague. Like, I used to think, like, part of the reason why we were able to raise so much capital and we wanted to raise so much capital at, you know, high valuations and, you know,

• 1:11:21Speaker 02

  with big investors is because we thought it was going to be a magnet for talent. And it is a magnet for talent. It just oftentimes is the magnet for the wrong talent.

• 1:11:30Anthony Pompliano?

  Did you run into problems with raising a lot of money or with high valuations?

• 1:11:36Speaker 02

  Depends.

• 1:11:37Anthony Pompliano?

  Okay.

• 1:11:37Speaker 02

  So there are problems in the sense that there's this general expectation with high valuations. And last year, I think everyone had a high valuation, you needed a high valuation. Because what would happen is engineers would come and they'd say, hey, this company I'm interviewing at has a high valuation, why don't you have a high valuation too?

• 1:11:52Anthony Pompliano?

  Which is actually the opposite of what they should be asking, right? Because that it means that there's less growth.

• 1:11:57Speaker 02

  That's not how people think, right? Because the way that these companies say is like, we're giving you $2 million of equity. And we might be giving them, you know, 300K of equity, but it's at a way lower valuation that could be, you know, $10 million of equity in the near future. Whereas that $3 million of equity at that other company, you know, could be

• 1:12:15Anthony Pompliano?

  A million.

• 1:12:16Speaker 02

  A million dollars of equity, right?

• 1:12:19Speaker 02

  So, but that's how people are trained to think, and that's where they were trained to think.

• 1:12:22Anthony Pompliano?

  They assign value of the company as a proxy for quality and likelihood of success.

• 1:12:27Speaker 02

  Exactly. And we, like, you have high expectations, like, you have to grow really fast. And I think the reality is like, it was a good learning lesson for us, but the reality is like, this is a business that is going to take a long time to build. And you do need a lot of capital, so it was a good idea to raise a lot of capital. Do you need a lot of people? I'm not sure. I don't believe so. I think that the narrative in Silicon Valley was always like, you have to hire a lot of people, you have to hire executives, you have to scale your team in a really big, meaningful way, and you're going to get more progress and you're going to grow faster as a result of it. I don't actually think that's true. I think if you are crafty, you build a really great product, you have a team that is incredibly loyal and mission driven, even though they might be a much smaller team, but they're, you know, obsessed with what you're building and they're in it with you every single day

• 1:13:11Speaker 02

  in the trenches, then you're probably going to have a higher likelihood outcome of success than the company that has 150 employees, big executives, you know, a lot of pedigree that, you know, probably moving a lot slower. They're probably moving like a big tech company, not like a nimble startup.

• 1:13:28Anthony Pompliano?

  Yeah. When you think about working with the board of directors, what are some things you've learned there?

• 1:13:35Speaker 02

  A lot.

• 1:13:36Anthony Pompliano?

  Okay.

• 1:13:39Speaker 02

  This challenge with boards, generally even speaking outside of eyes, is you've got lots of different perspectives. So everyone has lots of different perspectives and they're operating with very little information. So they're only operating with the information from what you've told them or from what they've seen historically in financials or from other metrics or memos that you've given them. I think the challenge is they're giving you advice based on what little information they have and they typically expect you to take that advice. And sometimes you need to know when to listen to them and when not to listen to them. And it's a hard line to draw. You don't know when the board might be right and their experience might be great. You don't know when you actually are right on this particular decision.

• 1:14:22Speaker 02

  And you should take that decision the way that you want to have it solved. And I think that the other issue is that incentives are very misaligned for early-stage businesses where it's like you have a financial investor and the financial investor has their LPs, they have their partners, they have their reputation. Investors care a lot about their reputation. And quite frankly, that doesn't really matter for your business. What matters for your business is what is right for your business, what is right for the growth of your business, what is right for the customers, your employees. And you're oftentimes the best person to make that decision, not a board member. And I think it gets to this even broader point, which is a problem that not a lot of founders talk about,

• 1:15:06Speaker 02

  but it's actually a really big problem, is advice overload. There are too many people giving you advice, too much of the time. There's this founder that you know, there's your employees, there's your customers, there's your board members, there's other investors, and you don't know which direction to go. And I think that it's important just to kind of listen to yourself and know, take advice from a small amount of people that are consistent in their advice and that can deliver one way or another.

• 1:15:33Anthony Pompliano?

  How have you balanced doing the work yourself versus delegating? Are you good at that or not good at that?

• 1:15:40Speaker 02

  I'm not great at this.

• 1:15:42Anthony Pompliano?

  Okay.

• 1:15:43Speaker 02

  And the reason why I'm not great at this is because when you start to scale an organization, you're kind of playing this game of telephone. So you'll give work to a leader and then that work will give their, will give that work to their team and they'll give you some version of what you want back. But oftentimes it is very far from what you actually wanted originally. And I think that the larger the organization gets, the harder it is to communicate, the more you're playing that kind of game of telephone. And I think when you think about remote work, it becomes a catastrophe. Like, I just don't believe that early-stage startups can be done in a remote way. Maybe if you've been working with a team for a long time and like you have a lot of

• 1:16:27Speaker 02

  embedded trust and you spend a lot of time with them in person somehow, it can get done. But I don't believe remote, remote companies work because of this communication mis-overlap.

• 1:16:39Anthony Pompliano?

  And when you see that you're not good at something, right? Or you think, hey, I could get better at this. What are the steps you go through to improve on something like that? Are there specific, like, do you go seek out an advisor? Do you go and try to find someone who is good at it? That's like another founder? What have you done to try to get better at something like delegation or whatever?

• 1:16:57Speaker 02

  So historically, I will call the person I think is best. And the nice thing is we've had lots of resources, the best founders, the best executives, the best investors that we can call and understand, how do you delegate? The challenge and why I'm not good at this is everyone has a different perspective. Everyone has different advice and you don't know what advice is always right for you. You don't know the right direction. You can take some people's advice on some things, other people's advice on other things, and they're not always going to fit in sync with each other when, when those two things are related. So. That's why I think I'm not that great at it because like I don't always know what direction, like what consistent path to take from an advice standpoint.

• 1:17:42Anthony Pompliano?

  Talk to me about the touchpoints with your executive team, right? So as you're running the business, do you meet with them once a day, once a week, once a month? Like, what does that kind of cadence look like? And I think this is more a style, right? Is each business leader thinks about it differently. Some people wanna, hey, I want to talk to you every single day. Other people say, look, we've got one big meeting on Wednesday for three hours. All the executives come together and we kind of review the business. How do you work?

• 1:18:07Speaker 02

  So we've gone through multiple different iterations of this. Like, we've had executives, we've lost executives, we've worked without executives. I don't actually believe that, again, like, early-stage companies, like, slightly post-product-market fit, work with, like, seasoned executive teams. So if you're pulling seasoned leaders out of, you know, big companies and expecting them to work well in startups, you know, it's a lost cause, it's not gonna work. And the structure that those people typically type to, like, to type, try and work in, don't work too well for startups. So what we did was, we did weekly leadership meetings on Wednesday. It was two hours. We would have everyone put together a memo the night before. We'd have a general memo, you know, and what happens is everyone gives their opinion on every different thing. And it's not clear, is this

• 1:18:52Speaker 02

  a decision-making meeting? Are there, how many different decision-makers are there? Everyone wants to feel like their input is heard on a particular decision. Everyone wants to feel like an owner of the decision. And you, as the founder, the person that's running the meeting, is like going crazy, because everyone wants to have their input on the thing. And certain people have different levels of impact on a particular decision. But if they're all in the meeting, their voices are all relatively equal, right? So if you're thinking about a product decision, and your chief people officer is weighing in a ton on the product decision, and she's the loudest voice in the room, and your head of product isn't really talking too much, which is something that commonly happens in a leadership meeting, you know, you're diluting that meeting, right? So you kind of need to either tell people, like, hey, like, you know, stop talking, and like, you know, this person's loud.

• 1:19:37Anthony Pompliano?

  How do you handle it? Is it, you literally just right in the middle of the meeting, say, hey, you know, be quiet?

• 1:19:41Speaker 02

  So, I started to do that. In the beginning, I was very polite.

• 1:19:47Speaker 02

  What I actually think works in the right structure doing this is leadership teams don't work as big teams. Like you have your team of, you have your group of functional leaders. So this could be five to seven people. And then you have your S team. And your S team, maybe it's your like CTO, maybe it's your finance leader, maybe it's your co-founder. It can't be any more than like three or four people. It's gonna be a small group and you have to have a lot of like respect and trust in that group. And you were talking to this team every single day. Like they are helping you run the whole business and they are filled in, they are keyed in on everything. This team will help you make the decisions. This team is the decision-making body. Then you have the information body. And the information body is all the different functional executive leaders.

• 1:20:32Speaker 02

  They might run the people function, they might run the legal function, whatever it might be. And they will help, you keep them informed, you do your two-hour weekly leadership meeting and you will keep them informed as to what is going on. You will get their perspective, but the decisions are not made in that meeting. The decisions are not made in that meeting.

• 1:20:49Anthony Pompliano?

  Do you make the decisions before or after the meeting?

• 1:20:53Speaker 02

  That, it's kind of, you're making them all the time. But you're clearly logging those decisions. You're logging who's the DRI, who's responsible for this particular decision. You have to be very clear, you have a leadership operating system. So in our case, we had a leadership operating system. This is how we make decisions. This is who, how we appoint DRIs. This is what a DRI is responsible for.

• 1:21:11Anthony Pompliano?

  What's a DRI?

• 1:21:11Speaker 02

  A directly responsible individual. We have AORs, which was really helpful. So AORs are areas of responsibility. We basically had a big spreadsheet. In early-stage companies, people don't know who's responsible for what. You literally write out to the specific thing, what is the area of responsibility? Who is the owner of the AOR? What are they specifically responsible for? What is the backup? So who will, in the case they're not there, who's the person you go to? And then what is the process write-up? How do you go through this actual process? So the way it works is when someone quits, when someone leaves, when someone's out, it's really easy for people to swap in and take over transition AORs.

• 1:21:50Anthony Pompliano?

  And when you do all of this, how much are you having to train employees on it when they first show up, so a brand new employee, and you're saying, hey, here's how we run meetings, here's how we do information sessions, here's who the leadership team is that's going to actually make the decision, solve that, versus it's more of, hey, you are an engineer, you are a marketing person, you are on the customer service team, here's what you're going to need to know on a day-to-day basis, and then they'll kind of, over time, through osmosis essentially, just pick up, okay, this is kind of how the organization works.

• 1:22:21Speaker 02

  So we did the latter, but moving forward, I think having a formal, like, week-long training program, even if it only works 1% of the time, that 1% will continue to compound, and hopefully it works much more than that, to how, here's how the company operates, here's our culture, here's how we view our values, here's how we make decisions, all of those different things, and deeply embedding it, like, almost having an onboarding class, and making sure people get it right, is really critical. And the other part to culture is, like, how do you hold people accountable when they don't do things like that?

• 1:22:57Anthony Pompliano?

  How do you do it?

• 1:22:59Speaker 02

  We're still figuring out the specific, the right way to doing it, but it's usually rewarding people when they do, so we have channel, we have Slack channels where we call people out for positively doing it, and then I always give people feedback of, like, hey, I would have liked you to see this differently in a one-on-one setting, like, never criticize people in public.

• 1:23:17Anthony Pompliano?

  And how often do you have those feedback sessions or those one-on-ones? Are you doing that multiple times a day, all week long, with various people in the company? Are you doing it just with the leadership team?

• 1:23:28Speaker 02

  So previously, we did it just with the leadership team, and we did two things as far as feedback. So we did feedback at the end of every meeting, so people would write down their feedback. They would say, okay, here's how we thought the meeting went. We'd carve out 10 to 15 minutes at the end of the meeting, here's how the meeting went, here's some of the learnings, here's what I would do differently next time. And everyone would write it down, and we would review it as a team, and then we would put a next action of, like, next week, we're going to do this better. And then we'd always make sure to remember that, as a team, after every meeting. The second thing we did was, on one-on-ones, I would do basic Check-ins. So I would ask people, I could ask people for specific like questions about how they would rate like working with us. So how do you feel our working relationship is this week?

• 1:24:11Speaker 02

  Is it a two? Is it a five? Is it an eight? Is it a 10? If it's less than eight, what would make it an eight or higher? And you would score it and you would do this like once a month or once every two weeks. And you would see the scores progressively change and get better over time.

• 1:24:25Anthony Pompliano?

  And when you saw the score low, is that your problem or their problem?

• 1:24:31Speaker 02

  I mean, it's sometimes both. You have to understand like, what is the component part like, why? Sometimes it is your problem. Sometimes it is their problem. And then you work out a plan like, what is the next action? What are we going to do differently?

• 1:24:45Anthony Pompliano?

  The last question I have for you is, after running the business for six years, if you could go back and there's plenty of young founders who are starting companies today. What's like the one or two things that you wish that you knew and you would really hammer home?

• 1:25:02Speaker 02

  So there's a lot of distractions that is really important to avoid. And I think there's more distractions now than there was in 2016, 2015, like whenever I was starting. And I think avoiding those distractions is really important. There's only one thing that really matters and like it's your customers and your product as a result. So maybe two things. Other than that, like product and customers and like maybe like you can kind of start to think about like, okay, so once you nail product and customers and you've got culture and you know, some of these other things to running a company, nothing, none of that other stuff matters, right? Founders get too obsessed, especially young founders with status, right?

• 1:25:44Speaker 02

  And I fell into this trap. I personally like, it just seemed like what everyone else was talking about. And it seemed like the important thing. If you can't nail customers and product, you're going to fail at everything else. Like the types of VCs you raise money from, the pedigree of the employees you recruit, the time that you spend on going to founder networking events, all of it, it's a waste of time.

• 1:26:07Anthony Pompliano?

  That I 100% agree. It doesn't feel like that in the beginning, right? But over time, I think that you realize that.

• 1:26:12Speaker 02

  I'm sure you had a ton of impact on making this together, but like, and I'm a huge fan of Keith or boy, but like the Miami Tech Week thing, like half of our employees were like, oh, like we want to go to this Miami Tech Week thing. And I'm like, look, like, The VCs that are like retired and like want to hang out and like they can go to Miami Tech Week.

• 1:26:32Anthony Pompliano?

  Keith's gonna kill you for saying retired.

• 1:26:37Speaker 02

  They can, maybe not Keith, but Keith has a broader mission, which is trying to get people to Miami. But Keith aside, like you need to stay focused on the core objective, which is our customers and our product. And anything else that isn't that Miami Tech Week is a distraction. If you're going for vacation, go on vacation. But like all of this other stuff, if it's not vacation, it is a distraction.

• 1:26:59Anthony Pompliano?

  I think, look, it's just singular focus on what you feel is the most important thing, right? I think it's a pretty fair way to look at a business. And I actually think it's a very good way to look at a business. And for those that end up not liking that, like they can go work at a different business.

• 1:27:15Speaker 02

  Yeah, exactly.

• 1:27:16Anthony Pompliano?

  Right. Where can we send people to find you on the internet or find out more about Vise?

• 1:27:20Speaker 02

  Vise.com and

• 1:27:23Anthony Pompliano?

  V I

• 1:27:23Speaker 02

  V I S E dot com. And I guess I'm on Twitter. I haven't been tweeting much, but maybe I'll start again.

• 1:27:28Anthony Pompliano?

  Well, if I can find the Kanye tweet, that means you definitely got to start tweeting more.

• 1:27:31Speaker 02

  Exactly.

• 1:27:33Anthony Pompliano?

  Awesome. Well, listen, I really appreciate this. I think people will learn a ton, not only about artificial intelligence, machine learning, but also what you guys are building and then how you actually run the business. We'll definitely have to do it again in the future.

• 1:27:42Speaker 02

  Cool. Thank you so much for having me on.

• 1:27:44Anthony Pompliano?

  Thanks so much for listening to today's episode. I really hope you enjoyed this one. Make sure you're subscribed on Apple, Spotify, or your favorite podcast player. And if you're looking to transition into a brand new job in the Bitcoin or crypto industry, we've got you covered. Head over to thecryptoacademy.io. My team and I have been working with the top HR teams in the industry to develop an intensive three-week training program with over 50 live events. We teach you exactly what you need to know to break into the industry, including live interview prep and resume review. Our students have been hired at over 75 of the world's best Bitcoin and crypto companies. Go to thecryptoacademy.io to learn more. Again, that's thecryptoacademy.io. If you enjoyed today's episode, make sure you share it with your friends and I'll see you all for the next episode.