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

Episode Date: August 29, 2022

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. =======================

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 Vize. In this conversation, we talk about artificial intelligence, Vize, 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. 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. All right, guys, bang, bang.

I've got somebody 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. Yeah. I mean, I haven't 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. Yeah. 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 standup 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. 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 can sometimes be reflected.

How do you know if he's crazy or if he's right? I don't know, but I think I've heard some stories from some friends that know him that seem absolutely crazy that even the most right person would not do. So I don't know. Well, it begs the question, if you start especially thinking of startup founders, can you be crazy and right?

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 going to do X, everyone's like, yeah, yeah, yeah, okay, crazy person. And then all of a sudden you accomplish it, then it's like, oh, you were right. 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? Look, if you're going to be a startup founder, you have to be crazy. And I think the startup founders that fail, right? And 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, um, 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. 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? How do you measure whether a founder, if you were to sit down and talk to them,

is crazy or maybe they don't have that it component to be a founder? I think it always starts with a vision. If you are a founder and you believe you can disrupt an industry that 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, you can look at any of these founders like Steve Jobs or others that they are just crazy in how they operate. They have extremely high bar for talent, for expectations from others, and they expect everyone to operate at a certain level that 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. They can be difficult people. How much does age play into it? right? Like you're a young person who I think you started a vice when you were 16. You're how old now? I'm 22, 22 now. So for six years, right, you've been running this business. Uh, 22 is still very young compared to most people in the workforce, uh, and kind of the quote unquote traditional market. Um, do people lose the craziness as they get older or do the special people just keep it at no, no matter the age? 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 and 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 a Northwestern and his dorm was across from mine. And the two of And the two of us, we started an app development firm, so we started building apps for small and medium-sized businesses. But when we came out of that, our idea was,

how can we use AI to build apps? 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 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. 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 DALI, 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 super simple things like 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? Yeah, I think the limiting regent has always been it's a data problem. and I think the challenge was chips weren't designed previously

to be able to 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 and for now especially as you look at the evolution Moore's Law the data we're able to process

the data GPT-3 for example is able to process is truly incredible and the things that that thing will be able to create over the next five to 10 years are going to be pretty, pretty endless. 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? So for like a, like a Grammarly or something like that, you are collecting lots and lots of data from pre-existing content. Either broken down sentences or what it looks like to be correct. The challenge is data labeling.

My buddy at Scale AI created this really great company to 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? If you think about it, any of these AI models are just functionally pattern matching. So how do you pattern match to what is right versus what is wrong that the algorithm can pattern match to.

So the challenge in this problem is how do you effectively label these data sets? And the way people label the data sets is they have teams of people in India or 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're going to see a lot more progress on broad AI. 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 CAPTCHAs now will show you six or seven photos and it's like, hey, 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? Exactly. Or like 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.

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 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? Exactly, right? You can use your scale. 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. You mentioned Scale AI. What are they doing? So Scale AI is like a data labeling company, right? So they're building technology, basically, augmented by artificial intelligence to help label data sets. Got it.

And the data sets are just anything somebody can feed them or do they use specific types of data sets? 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. 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 case is, 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 machine learning and artificial intelligence? Artificial intelligence is an umbrella term.

It always has been. Maybe you can conflate AGI, artificial general intelligence, with the broad term of AI, but artificial intelligence is a general term that can encapsulate lots of different things, machine learning being one of them. And where we've seen the most progress, and I'm a little rusty on this,

because we've strayed away from the AI focus, which I'll talk about in a little bit, is machine learning, so narrow intelligence. So you've got AI applied to one problem set, one kind of narrow solution that you are applying some kind of machine learning algorithm to. So once you have a data set and it's labeled, talk to me about the machine learning itself.

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? So you have a recurrent neural net, or 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 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. 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 this stuff.

I throw it to a machine learning model which has these neural nets in it and all I'm really saying is, yo, find the patterns. And then the machine's like, cool, here's the patterns and then I can go do whatever I want with those patterns. And your model's only as good as the data you feed into it. And the challenge is oftentimes explainability. So when you think about financial models,

in our instance, we sell to wealth managers. 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 the model performed very very well but you couldn't explain 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 had an output and we didn't know how the model basically came to that output and was the problem in that scenario people, they're like I don't trust the box 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 kind of limiting factor there? 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 you have to get somewhere in the middle 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 uh 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 uh if they just said go right and you didn't know what was coming after that like there is an element of humans be like ah should i go right or what you know, where are you taking me by being able to see the full route, uh, before you kind of hit, you know, start, I think people are like, okay, cool. Like, yes, that does get to the, uh, the end location. 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? And do you think that consumer behavior has to

change like consumer psychology? Uh, 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 uh 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 uh we've talked a little bit uh about investing and the ability for uh 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 so i think about it a couple ways

for mainstream crypto adoption you need consumer behavior to change. They're doing something one way and now they have to do something a completely different way. That's going to take a long time and it requires 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. It's just happening as we run through our day-to-day lives. 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.

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 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 it's an interesting question um 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 look like that this 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 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 is a clear scoped problem that you can task the algorithm on. But having something that's very creative, very broad is a very difficult problem to solve. And when you think about it in the investing framework, is it rather than like, hey, pick a stock, instead you can say 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 a certain type of environment? How do you think about this machine learning, artificial intelligence approach to investing? I think it's less broad even than that. 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 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 stocks that have, call it high 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 still did meaningful revenues, right? And any kind of value stock screener 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 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 a clearly scoped problem where you can use artificial intelligence or machine learning to understand what is the stock sentiment. 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 create a sentiment score. 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 pretty close? Is it something that like, oh, we're always improving,

but we still don't have a ton of confidence in actual sentiment measurement? How do you think about accuracy? I would say it's very accurate when you think about is something high or low sentiment, right? So a company that is getting 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? 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. 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 if I'm on Twitter or if I'm in forums or whatever your means of content ends up being 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? The challenge when you think about holistic portfolio construction is you're looking at so many different types of assets so a human is going to be able to do So a similar to just as good of a job looking at one particular stock, 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, hundreds of stocks potentially. And a human just can't do that nearly as well as a computer can with the speed and the precision that a computer can do it.

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. Very far behind. Okay. And everyone knows it needs to change, but there's a couple problems. First is switching costs. So when you think about some of these large institutions that have built software, there's 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 go through that pain. They don't want to go through that hassle. It's part of the reason why IBM is such a big business, because someone has to maintain all of this. But that's going to slowly start to erode over time. Because what's going to happen is technology right now

might not hold one of these institutions back, but in five years, in seven years, in ten years, as people start to do more things on their own 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. notes 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.

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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 going to 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 or what was your relationship between when you met and then you actually started the company yeah so like the specific story is it's pretty funny so i'm from cleveland ohio my co-founders from detroit michigan okay and you know 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 right like school's easy like we should make some money so we were like that's why we're different by the way because you said school was easy like we should make some money and the way we think you know 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 web page 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 because one that reached out we had like a gas station we had a couple conferences conferences like zaps and we were able to start building apps for all these people we made thousands of dollars

how much were you charging 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 uh is is what we would charge them so anywhere between kind of five thousand dollars an app all the way up to like twenty thirty thousand dollars an app 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 two thousand right you just kind of work your way up until somebody starts telling you no and you know what's funny it's 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. 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 at 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 business was expected to have an app. Like what if we built the software? We call it Syscat, 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, bootstrapping it to build this 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. We just went on all kinds of different web forums to try and meet people. We ended up meeting this engineer who actually unfortunately hacked into Sony Media as part of the hacks and 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. We didn't think we could solve the technical problem. It was just too complicated of a tool to solve. At this point in time, my grades in school aren't that great because I'm spending all of my time building the startup. But we had met this guy who was a former investment banker and he's like, look, even though this company

might not be 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 financial institutions how AI works. So we're like, that sounds great. So we started doing that, and we're charging $700 an hour

to meet with financial institutions and educate them on how artificial intelligence works. And these are like big, bulge-backed banks. So MassMutual, RBC Royal, Deutsche Asset Management, big firms, and we would get on the phone with managing directors, analysts, whomever, 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 had these massive wealth management groups, 50% of Morgan Stanley's revenues, 30% of Goldman Sachs' revenues, massive scale to these wealth management orgs. But our wealth managers, we believed at the time,

were great money managers, but in reality they were great relationship managers. 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 a 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, make better portfolio decisions. So we start to think about this problem and we do a couple projects with large banks and we realize, what if we just build this on our own? What if we build our own software, portfolio optimization software using machine learning and we sell it to large institutions?

And what were they trying to get at? They were trying to get at getting a better return for their clients at tax loss, harvesting, something else? What were they optimizing for? So at the time, what they were optimizing for and what we were optimizing for was slightly different. They were optimizing for how can we help tell the story to our wealth managers?

How do we have one kind of platform that can deliver insights on clients' portfolios, that can help them recommend investments, all powered by AI? What we were thinking about is 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. 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 those types 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 uh client uh uh joe why don't you take uh ten percent of your assets and put it into our uh public market you know whatever vertical focused fund oh take another ten percent 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. And that's where you guys thought that you could basically rip those folks out and replace them with technology. Exactly. Sorry if there's any portfolio managers that listen to the podcast. There's a lot, don't worry. 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. And-

How long did it take? 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 and I came in second place.

Interesting. Who was the winner? I got a thousand bucks. Some kid from some other school. And he was just licking his finger and sticking it in the air and picking stocks? Must have been. I don't know.

That or inside trading. Who knows what he was doing. 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 off stage we rush him on stage and we're like Jamie

this is what we're working on, FSAI do you think that J.P. Morgan would buy this and he's like oh J.P. Morgan has a lot of tools I don't think J.P. Morgan's going to buy this it was a whole discussion but we were like well shit if JP Morgan's not going to use this and we were young

and they're not going to use this, 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 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 eat this up, they would dig it. So we were like, okay, we're going to build the software for independent advisors. 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 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-hacked 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. They're not actually trying to enable advisors to scale their business and 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 was a former advisor and he loved the product, he loved the vision that we were going after. He was like, okay, I'll be your first employee and I will build this company with you guys. And he had 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 at XGoogle and they were the early team that basically helped us build. Where do you meet those people? The first version of Eyes, AngelList actually. So some Runic actually worked with in the past, others, there were resources that were available

that just weren't utilized very, very well by startups back in 2015 like AngelList 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. And was the team all together when you first started or was it remote?

Everyone, it was actually, we all started remote. um, I was running, you know, it out of my parents or like house in Cleveland. And so as 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. Like 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. 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 left school. So your parents were pissed that you left. They were pissed. They ended up coming around to it, but they were upset. And your, uh, your parents are Indian. Yes, they are Indian.

And are they a traditional Indian parents who wanted you to be a doctor or a lawyer? Yes. 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 in the news, they like text their kids and like, why can't you be like, you know, whatever they see in the news. Oh my gosh there's 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 harsh did this big thing very well you know live up to that so every indian kid has this chip on their shoulder

um and like i definitely had one i think like a lot like they're very very comparative yeah How old were you when you moved to San Francisco? I was 16. 16, okay. So I just turned 17. And did you know anyone in San Francisco other than the people who were working at the company? 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 of people. And did they think you were crazy? Oh, everyone thought I was crazy. Okay. But I'm living in the Tenderloin and my co-founder gets an invite to this barbecue you at this place called the crypto castle. I'm not sure if you've heard of it. Jeremy Gardner.

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. Um, and it was a, it was kind of my initial launch pad in San Francisco. And I was around a lot of young, lots of people coming in and out of there. and a lot of people building companies all of that when you actually quit high school you didn't graduate

I didn't graduate but I did some community college credits 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 a couple years later got it and so when you do that is this like a burn the boats moment

if you're leaving there's no coming back home 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 got to make this work. Cause I'm definitely not going back. 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 so at 16 you you start to run the company right uh what are some of the things that looking back now like what are some of the mistakes that you made where you're like i was 16 like i just didn't know i mean i would say every mistake is probably probably 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. 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

what was fundraising like initially 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 onto his dorm room floor. Okay. It's this little mattress topper. It's two inches thick and we put it on the floor. He's a 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 and cold, cold emailed a hundred

people. Um, 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, 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 Flatiron 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 $100,000. And it came in perfect timing, because the employees, they were working for basically no salary for two years, and they were finally starting to get pressure from their spouses to like, you know, take some cash. Why are you working for a 16 year old for no money?

You know, it's funny because we didn't even tell them our age at the time, 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 was the lowest point it had ever been.

I was very depressed. It was like, if we can't make this work, if we can't raise capital, like we'll call it quits. Like we'll stop the company. And we ended up getting that first check. it was the most excited I'd ever been and I knew I could finally pay to cover operations

and pay for salaries and Nat and Zach helped us raise the next couple hundred grand and then they introduced you to angel investors 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 and then Ben Ling with his new fund they'd 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 little lived in an apartment and started scaling the business we got registered with the SEC we were actually the youngest ever people to be registered with the Securities and 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 of VICE 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. 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 kind of what we were working towards at that point in time. I had seen the show Silicon Valley

and always wanted to do this 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 the TechCrunch website. But TechCrunch was having this happy hour with Sequoia. 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 time. We had some money. We're like, we don't need to raise any more capital. And we're sitting at the pretzel station because 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 is Sean. He's like, what are you idiots working on?

It's basically how he tweets. We love Sean. Sean's brilliant. He's like, what are you guys working on? We're like, oh, we're building this software for financial advisors to help them build portfolios. The whole vision is to automate asset management. He's like, that's really interesting.

I was at this retreat with John and Patrick Collison and someone mentioned that 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. Shortly after that, Sequoia preempts us on a seed round and then Sean actually helped individually recruit

lots of talent for us, which was helpful. Then we decided we needed to move the company 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 preempts us on a Series A round and the pandemic hits.

And shortly thereafter, Ravi at Sequoia preempts us on a Series B round. We've now raised like 50 million bucks and we start to scale assets on the platform. We're still only managing 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.

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 $4 or $5 million in assets, basically nothing, to close to half a billion dollars in assets. Got it. And why was Sequoia preempting the rounds? I think they liked us.

They saw the market opportunity. They saw the sizes of the market. And they saw that standard VC, 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 love 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 seems like 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 VICE's vision is almost to build the next BlackRock 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 taken over the market is

by 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 10, 20-year period, maybe 30 years. It's not going to be something where you see immediate venture-scale success in the period of six to eight months, whereas you can apply 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. 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? 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 optimize for your taxes, they aren't very explainable, and they're sometimes pretty hard to manage. So what Vize 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 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. Maybe they own real estate, they have angel investments, they work in a highly regulated industry, whatever it might be. FIIs 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 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&P 500. even if you're just investing in index funds you're overexposed to Facebook or those environmental restrictions you want companies that are environmentally friendly in your portfolio

you'll take out the companies that are not environmentally friendly Is there like a core philosophy that you all follow at 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 10% REITs. Sure, that violates everything in a 60-40 portfolio philosophy, but starting from scratch gets you there and that's the goal. How do you think about which direction? We do both. We have a philosophy, grounded in empirical evidence,

like a value-based investing strategy, kind of like dimensional fund advisors, that advisors can customize based on various clients' needs. 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 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 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 fitted for that particular portfolio given a set of individual stocks. But I think what's also interesting about Vize is because the customization is basically endless, it can service 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 Vize over index funds? What about tax loss harvesting or something like that? And the reality is, most advisors today don't actually do tax loss harvesting. Most investors don't do tax loss harvesting. But Vize, 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 down. But if you're investing in the individual stocks, you can tax-loss harvest the individual stocks that go down. 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. Exactly. So whereas Vi is 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. Who gets screwed if you guys are successful? I think the traditional mutual fund companies, the traditional asset managers. 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. What's drawing the withdrawals?

ETFs were the big initial driver and then step two is what you can call direct indexing or custom indexing which is a large part of what VICE 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. Do you think that Vize 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 could you still allocate to an ETF or to a mutual fund? For sure. For ETFs and mutual funds for other assets. 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 and making it easy

low account minimums, things like that it will be very much direct indexing or custom indexing for those core assets So a financial advisor adopts the technology is this something where they're going to their clients and they're like, ha, we've got the black box

let's fill out this questionnaire and then in 30 seconds it's going to tell us exactly what you're going to be invested in or is this something where they're still having the same conversation and relationship management with their client but then they're going back to the office and they're using this tech and what I'm trying to get at is

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 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 the 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 this generic model strategy or set of mutual funds. And advisors, when stacked up head-to-head, the strategy of personalized stocks will just always do better, ideally. How do you guys make money?

We charge an asset management fee. Got it. And when you go to the RAs, what is the biggest objection that they have? There's probably three or four different objections. One being technology. Advisors haven't adopted technology historically until relatively recently. Technology adoption in the space has been slow

and it's been really slow around investment management. As it starts to increase, 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 the advisor told me, which kind of resonates, 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 Vice'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. Yeah.

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

I spent a lot of time diving deep on this. The average account size on a Robinhood is like 5,000, 6,000. The average account size on a Betterment or Wealthfront or traditional robo-advisors is probably in the low 20s. 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 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. so I would say that those traditional fintechs do a great job at getting people to invest earlier or early on I think that people meme stock trading is just kind of replacing the gambling budget I don't think that's actually investing I think that's gambling

and I think a lot of those people know that it'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 Somebody out there is for sure doing it,

but we should not encourage those people, but 100% somebody's out there doing it. But advisors, the thing people always wonder about advisors is that advisors for younger clients, like these older baby boomer advisors, aren't going to be the same types of advisors that are going to be working with younger clients. People wonder, I'm a young client,

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. We've got clients that specialize with doctors, with lawyers. There's even an LGBTQ-specific advisory firm that just works with and understands that client base. So advisors are starting to change, take different forms than the traditional kind of golf and country club advisor

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internet to be this really dominant uh asset class and everyone's super excited about it or they really really hate it and you kind of get the extreme reactions but two 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? 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, you know, 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 you're a 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 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, or you need to educate the advisor really well. And advisors are starting to seek out getting educated. And through a platform like Avize or like a tool that services advisors, having crypto on that platform and it's 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 and what about like regulators how do they look at some of these technologies right the uh it's very clear like how do you regulate a portfolio manager who's making decisions either fulfill their fiduciary duty or they don't and there's 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? Yeah. I mean, the regulatory environment has gotten very difficult for fintechs.

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, and 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. And the same way that consumers, as we talked about earlier, they don't want to just know, like, hey, just kicked 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? Exactly. 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? I think it's fact-finding right now. It's still very new. The SEC is still, most of the regulatory agencies are still, 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. 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 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's clearly not doing what's in their client's best interest, 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. 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 so I thought OKRs were the most brilliant thing when someone introduced them to me I think it was an ex-Google executive

a really big Google executive 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 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 there's usually one thing in the startup that is holding you back.

There's one limiting region problem over all of the rest that 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. If you've got five different objectives or three different objectives and eight different key results

or nine different key results, 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 half-ass each of them. You kind of make some progress on each of them. Maybe you make a lot of progress on one of them,

but it's not the right one. And 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, 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. You can let it operate. But if you're thinking about, oh, here's how I 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 major way across all of them. And you need to see success in one, in a meaningful way. 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? I mean, what is the difference between more customer adoption and less customer adoption?

usually everyone knows it's something that's so obvious if you're having a problem with retention you have all these customers that are joining your new app and the initial growth is incredible but retention is staying really low the whole organization is going to know that 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 50-70% if not more are going to have the same answer That is usually your answer of what is the key objective. And then how do you communicate that to the whole team? So 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? See, this is the mind of a CEO that, to you it might be the most important thing. To everyone else, it might not also be the most important thing. So when you communicate, this is the other problem with OKRs because there's so many of them. when you communicate one of them on all hands or send an email about it

it might be important that day people might not show up people might not remember 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 freaking everywhere 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. 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 we're going to talk about it once and in 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. And 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. It's 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. I agree. And I think that your point about the repetition ends up being very, very important until it's literally ingrained in people's heads. 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 initiative that we wanted everyone focused on, the whole company had to be focused on it. 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 keep that in your mind. But if you have a simple acronym or simple target that everyone can think about, then everyone's going to resonate to it. How do you do recruiting today? What are some of the things you've learned

over the last six years on how to be good at recruiting high-quality talent? So I was really good at this early on. I made a lot of mistakes later on. There's a couple interesting things. So the first is having a Sequoia, and 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 going to 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. And I think if people look at the success and the draw that you have behind your company, they're going to 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 and you need to avoid that like the plague

I used to think part of the reason why we were able to raise so much capital and we wanted to raise so much capital at high valuations and 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.

Did you run into problems with raising a lot of money or with high valuations? It depends. So there are problems in the sense that there's this general expectation of high valuations. And last year I think everyone had a high valuation and 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? Which is actually the opposite of what they should be asking because it means that there's less growth. That's not how people think because the way these companies say is we're giving you $2 million of equity and we might be giving them $300K of equity but it's at a way lower valuation

that could be $10 million of equity in the near future whereas that $3 million of equity at that other company could be a million dollars of equity, right? But that's how people are trained to think and that's how they were trained to think. They assigned value of the company as a proxy for quality and likelihood of success. Exactly.

And you have high expectations. You have to grow really fast. And I think the reality is, it was a good learning lesson for us, but the reality is 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 obsessed with what you're building and they're in it with you every single day

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, a lot of pedigree that's probably moving a lot slower. They're probably moving like a big tech company not like a nimble startup. When you think about working with the board

of directors, what are some things you've learned there? A lot. The 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 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 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 that financial investor has their LPs, they have their partners, they have their reputation. Investors care a lot about their reputation. 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. You're oftentimes the best person to make that decision, not a board member. I think it gets to this even broader point, which is a problem that not a lot of founders talk about, 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 take advice from a small amount of people that are consistent in their advice and that can deliver one way or another. How have you balanced doing the work yourself versus delegating? Are you good at that or not good at that? I'm not great at this. 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 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 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 you have a lot of embedded trust and spend a lot of time with them in person somehow

it can get done but I don't believe remote companies work because of this communication mis-overlap. And when you see that you're not good at something 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 things?

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? 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 those two things are related. So that's why I think I'm not that great at it because I don't always know what direction, what consistent path to take from an advice standpoint. Talk to me about the touch points with your executive team. So as you're running the business,

do you meet with them once a day, once a week, once a month? What does that kind of cadence look like? And I think this is more a style. is each business leader thinks about it differently. Some people want to, 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?

So we've gone through multiple different iterations of this. We've had executives, we've lost executives, we've worked without executives. I don't actually believe that, again, early stage companies, like slightly post-product market fit, work with seasoned executive teams. So if you're pulling seasoned leaders out of big companies and expecting them to work well in startups,

it's a lost cause, it's not going to work. And the structure that those people typically like to 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. And what happens is everyone gives their opinion on every different thing.

And it's not clear, is this a decision-making meeting? 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 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. 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're diluting that meeting. So you kind of need to either tell people, hey, stop talking and this person's loud. How do you handle it? You literally just right in the middle of the meeting say, hey, be quiet? So I started to do that. In the beginning I was very polite.

What I actually think works in the right structure doing this is leadership teams don't work as big teams. 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 CTO, maybe it's your finance leader, maybe it's your co-founder.

It can't be any more than three or four people. It's got to be a small group, and you have to have a lot of respect and trust in that group. And you are talking to this team every single day. They are helping you run the whole business, and they are filled in, and 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. The information body is all the different functional executive leaders. They might run the people function, they might run the legal function, whatever it might be. 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. Do you make the decisions before or after the meeting? 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 how we appoint DRIs. This is what a DRI is responsible for. What's a DRI? 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. 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 decisions, all 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. So we did the latter, but moving forward I think having a formal 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 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 How do you do it? 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 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. 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?

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 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 basically check-ins

so I would ask people for specific questions about how they would rate working with us so how do you feel our working relationship is this week is it a 2, is it a 5, is it an 8, is it a 10 if it's less than 8 what would make it an 8 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 and when you saw the score low is that your problem or their problem I mean sometimes both you have to understand what is the component part like why sometimes it is your problem sometimes it is their problem and then you work out a plan.

What is the next action? What are we going to do differently? 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 the one or two things that you wish that you knew and you would really hammer home?

There's a lot of distractions that is really important to avoid. I think there's more distractions now than there was in 2016, 2015, whenever I was starting. And I think avoiding those distractions is really important. There's only one thing that really matters

and it's your customers and your product as a result, so maybe two things. Other than that, product and customers and maybe you can start to think about once you nail product and customers and you've got culture and some of these other things

to running a company, none of that other stuff matters founders get too obsessed especially young founders with status I fell into this trap 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 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. I 100% agree. It doesn't feel like that in the beginning, but over time I think that you realize that.

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

maybe not Keith but Keith has a broader mission which is trying to get people to Miami but Keith aside 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 all of this other stuff if it's not vacation it is a distraction I think it's just singular focus on what you feel is the most important thing

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 they can go work at a different business where can we send people to find you on the internet

or find out more about VICE VICE.com V-I-S-E and I guess I'm on Twitter I haven't been tweeting much but maybe I'll start again well if I can find the Kanye tweet that means you definitely gotta start tweeting more awesome well listen

And 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. And we'll definitely have to do it again in the future. Cool.

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