The below is a full (unedited), machine-generated transcript of a Youtube session / podcasting episode I recorded with Ali Tamaseb in Q2 2021. You can view the video/listen to the podcast on Youtube, Apple Podcast, Stitcher or wherever you get your podcasts.
Erasmus Elsner 0:06
All right, welcome everybody to another episode of Sand Hill Road. And I’m super excited to have my guests with me here today Ali Tamaseb, who is a founder-turned-venture capitalists at Data Collective. And we’re here to talk about his recent book “Super Founder”. It’s been a long time in the making. And I’ve started following the process in December of 2018, when he published the first medium post on the subject matter, and it is now turned into a wonderful book. Ali, you’re ready to take it from the top?
Ali Tamaseb 0:34
Of course, let’s do it.
Erasmus Elsner 0:34
So where does this podcast find you today?
Ali Tamaseb 0:37
I am at home in the San Francisco Bay Area.
Erasmus Elsner 0:41
Wonderful home, and we can see the book already in the background. To kick things off, talk a little bit about you, you’re not an academic, you’re not a writer, you’re actually a founder of turned venture capitalists at data collective. As part of this work. You’ve sort of stumbled into this rabbit hole of collecting all of this data and engaged on this book project. But maybe before we jump into the book, maybe talk a little bit about about yourself.
Ali Tamaseb 1:04
For sure. So yeah, I mean, I was a founder before I’ve started companies before various level of you know, success and failure. The last company I was working on, it was an industrial hardware company producing variable technologies. I came to Silicon Valley about five years ago. And you know, the way I got into venture was actually to writing as well writing and Twitter. And I was publishing some of my thoughts about you know, what would be good thesis areas for investing in healthcare and AI and blockchain infrastructure. Now, I was writing about those back in 15, and 16, and 17. That’s how I found myself, you know, networking with a lot of, you know, investors and venture capitalists. And all of a sudden, you know, that there was an opening there was there was a firm that we wanted to work together. So I joined data collective DCVC. For those who may not know, DC VC is a your Silicon Valley based venture capital firm, started about 10 years ago from a very small fund, but now it’s one of the larger funds outside in Silicon Valley, we manage over $2 billion. Our latest fund is a $725 million fund all focused on deep tech. So we typically don’t invest in consumer apps or SAS software. What we love to invest is companies that are solving something very complex in terms of engineering or science. So that includes a lot of, you know, therapeutics, manufacturing, construction, I love old industries, I’ve invested in mining technologies, in material and food, and obviously healthcare, blockchain infrastructure, data, kind of more mainstream type of industries. And we typically lead students who is a tax. So I’ve been fortunate in my time, I’ve you know, being an investor to have you know, made about 15 investments have led a number of companies, some of them have become very successful carbon health, that’s, you know, they now employ about 2000 people, it’s a unicorn Starker, that’s another good one. That’s a blockchain infrastructure company that’s taking a lot of strikes, and a number of others.
Erasmus Elsner 2:58
And so let’s start off talking a little bit about the motivation for this book, you were collecting this data, and you continue to hear all these stereotypes. And there’s this saying around Silicon Valley being really a pattern matching ecosystem, VCs look for certain traits and founders for certain traits and companies for certain founder team combinations. And you set out to sort of debunk a lot of these stereotypes, or you at least you wanted to know, is there really some truth to it, maybe talk a little bit about this initial spark of motivation.
Ali Tamaseb 3:29
So I think that the initial part of that came from an internal motivation to become a better venture investor, you know, a lot of investors when they want to become better investors, they ask, they ask other people, what are your patterns? What do you look for, and just from my experience, you know, being a founder, and then later investor seeing some of these companies that succeeded versus the ones that did not, I felt like a lot of these patterns do not match with reality over a lot of these stereotypes are not matching the reality. So I said, you know, there should be something in the data, if somebody were to collect data on every company that succeeded, and every company that failed, something should come out of that. And I searched very hard, very long for that. And there’s nothing out there. Nobody, nobody has done this work before. Because it’s a very tedious manual task. And obviously, there’s been different researchers have been different parts of this, mostly around funding, or mostly around, you know, a little bit of background, the founders, but not, for example, about competition, not about, you know, market dynamics, not about the career path of the founders, or the way these people found race or where the ideas came from. So there’s a lot of different elements. So I wrote 65 different elements that I thought, you know, may somehow matter in what what a startup would end up becoming. And I collected these 65 data points on all the billion dollar companies that are created in the past 14 years. And also I randomly sampled a group of Companies that, you know, raise the minimum of $3 million in venture capital funding, but did not become unicorns that I can compare and contrast on each of these data elements. And while I was collecting this data, I realised, you know, the data is telling me a bunch of these things don’t matter. Like when I finished it analysis, it turns out a lot of factors that people still typically think are related or are correlated with success does not matter. And then a bunch of new kind of signals or new information on Earth itself. So it’s a very, I’ll say, it’s a very valuable data set that shows us a bunch of new patterns, and also dispels more importantly dispels a lot of myths, and stereotypes in Silicon Valley in tech and investor entrepreneurship.
Erasmus Elsner 5:46
Yeah, I think what’s interesting about this, and what a lot of people don’t realise is, they would maybe tell you, well, why don’t you just go on Crunchbase. But the kind of dimensions that you collect, this is not readily available data. And so you have to go through a lot of internet archives, podcasts, news outlets, you have to be very creative. And this primary data collection exercise, it really started in 2017. And took you I think, couple of 100 hours to just collect the basic dimensions for your data set. Maybe talk a little bit about, you know, why are you such a venture nerd? You know, why, why, why did you do this? Where did you take the motivation for, for doing this on the side?
Ali Tamaseb 6:29
Yeah, I mean, this, you’re right, this data doesn’t exist anywhere, you know, crunchbase, pitchbook, a bunch of these sources, they have data on what the name of these companies are, and maybe, who are the investors. And also, even that data is not accurate. On the dimensions that I wanted to see these companies, I had to, you know, go through LinkedIn profiles to interns, archives, listen to previous interviews, listen to podcasts, in some cases, even cold emailed some of these founders, you know, ask them, Hey, is this true? Or is it’s not? Can you confirm this not I’m doing your study. And a lot of these people, you know, responded back. And that’s how we know I collected this 30,000 data points, like one by one by one. And that’s why it took four years. I think the motivation came from, you know, I, I thought this is something that that obviously should have been done. And it was weird to me that nobody had done it before. But I guess the reason it’s hard, it’s, it’s takes a long time, and you can’t automate it, you can’t outsource it, somebody with the judgement or the intuition that they actually should have done this research manually. So I’m glad I put the time and you know, it revealed a lot of insights to me. And you know, I hope it helps me become a better investor. And also, you know, help push the industry forward a little bit, maybe take some biases out, and help help investors be better helped more people start companies help more failed founders come in and go at it again. No, there’s there’s a lot of inspiration you can find in the data, and also in the interviews, that I hope would help the venture industry forward and help more founders take a pass to become a founder of a successful company.
Erasmus Elsner 8:04
Absolutely, I think it’s, it’s super fascinating what you put together there. And if I can double click a little bit on the data set, it’s a data set that’s collected for the timeframe between 2005 and 2018. And it includes data from the well known unicorns that the Ubers, the Airbnb is of this world, but also some lesser known companies company like Nevro, a maker of a medical device, let’s talk a little bit about finding those lesser known companies and finding information on those that are not always seen on the headlines of TechCrunch.
Ali Tamaseb 8:38
Yeah, the good thing about data collection is or like doing any, any type of study like this is you have to be comprehensive, you can’t you can create a bias and say, but this is not or this is, this is a unicorn, and this is not, there’s certain metrics, if you’re valued over a billion dollars, and if you’re, you know, within certain categories, this startup, this is a unicorn. And it’s fascinating that, you know, there’s a little news on the majority of these, you know, 200 plus companies. And there’s a lot of news on the top 20. And basically, that creates a narrative bias, you know, we are more likely to think and create the patterns with only those, you know, 10 or 15 that get the attention, it’s the stripes and the Ubers or the Amazons of the world. But you know, there’s 200 something, companies that have succeeded, and there’s 1000s of companies that have failed, that we can draw patterns and understand from them. Now, obviously, the data collection that the lesser known to company was that data collection was harder, obviously, it was much harder for the failed companies, or the companies that you know, didn’t become a massive success. But again, most of these data you can find, you know, it’s in that you can find and read in between the lines and LinkedIn profiles of these founders or executives and the Crunchbase profiles, like old interviews of these companies that maybe didn’t go anywhere, and you have to go very far back into, you know, the first year that the company was created to get to the source of truth about these companies.
Erasmus Elsner 10:12
I would say let’s now dig really into the findings. Let’s start with the age. And there’s obviously there’s this huge myth in Silicon Valley of, you know, creating a company out of the college dorm room, we have the sucker Berg, the Bill Gates dropped out from Harvard, and there’s really this ageism in Silicon Valley. And so one really interesting finding you have is that the median age of founders in Silicon Valley is actually 34. Talk a little bit about ageism, in Silicon Valley, and this first major finding that you don’t have to be 20 to start a company.
Ali Tamaseb 10:46
Yeah, so I guess this, this bias kind of goes in different cycles. in the, in the 90s. And like, like East Coast way of thinking and venture investment, they always wanted the grey hairs, they always wanted, you know, to bring like invest in, in the old people or like people who have had a longer time with more experience nowadays, I would say it’s the reverse, there’s ageism against people were older and more experienced. And there’s a bias for you know, if, if you’re a dropout, if you’ve done this by by the age of 19, you should be successful, right? At or at least that exists in the media, like every every news that you read talks about how this 23 year old, you know, founder did this, nobody talks about, you know, the company, or nobody talks about how this 47 year old founder did this is that not that’s not exciting news. And that, again, creates a narrative bias. So the data showed, you know, comparing these billion dollar companies with companies that got venture funding, but didn’t get anywhere, there was no correlation between age and success, it’s, it’s a lot more deep, it’s deep rooted into the characteristics of these founders. And you might be 47. And you know, don’t have those characteristics, you might be 18 and have those characteristics. So that’s that’s basically how you know the age by itself is not a correlation factor. The median age was 34 years old. And it’s not just Silicon Valley, it’s every billion dollar company created the US and every, you know, $3 million venture funded company created in the US, that’s also 3034. And then in healthcare, and biotech, it skews older, there’s like 42 is the median, which doesn’t mean you, you have to be 42, or older or younger, it’s just saying that’s the median. And there’s no, there’s no right or wrong in terms of how old you are, you might be 18, you might have done a bunch of companies before, you might be 50 to one, you may have never had any entrepreneurial in your background, nothing before. So that’s that’s the finding from the book, and interview, Enrique from breaks that kind of first chapter talking about, you know, how being young helped him?
Erasmus Elsner 12:58
Yeah, I think he’s a great example of a recent super young founder. But let’s move on to a related and really interesting second finding, which is about the number of optimal number of co founders at Y Combinator, they almost tell you that you shouldn’t apply as a solo founder. And there’s this saying around the optimal number of co founders is to having one technical guy and one commercial guy. And that’s another myth that you debunk in your data centre. Talk a little bit about that.
Ali Tamaseb 13:27
Yeah, I guess you know, this, this goes back again, to to the narrative bias that we have, we have seen so many examples of the successful dual co founders from from going back, all the way from HP to Google founders, Larry Page and Sergey Brin to you know, Steve Jobs and Wozniak, the story about you know, Wozniak and jobs. That’s how you have, you know, a technical genius, and you have a business, business savvy, visionary, or product savvy, visionary. And that’s seems to create this stereotype or narrative that that’s the perfect founding team. Again, when I compared the two groups, it turns out the number of co founders is not correlated success, you you’re not less or more likely because to to succeed, because of the number of co founders you have. There’s probably things about, you know, the dynamics of how these co founder relationships are what each person brings to the table that matters. But on its own, you know, these are not correlated. Solo founder, you know, 1/5 20% of unicorns was with solo founder. And then, yes, two co founders is the most common, but it doesn’t mean that you’re more likely if you have two co founders. There’s a lot of successful companies with three co founders and four co founders. What I saw in that data was it seems like one of these co founders is always dominant, not always, but in many of these cases, is the dominant founder. Typically the CEO, they own more shares, they set the vision. And you know, a lot of these companies obviously have a single CEO So that may matter. Another thing that I found was in 45% of these unicorns, these founders either went to the same school or worked at the same company before. So sometimes there’s this history between them history of trust, and that are very counterintuitive thing that I saw in the data was, if you are a non technical founding CEO of a billion dollar company, it’s more possible that your second person in the company, your co founder, is also non technical, which is very weird, you would assume if you’re non technical, your co founders technical and the other way around, but if you’re, if you’re a technical CEO, it’s more likely your second person is also technical. If you’re a non technical CEO, it’s more like your second person is non technical to the point is none of these necessarily make or break your company. But the aim of the book is to break some of these stereotypes. And proxy rules are not to say you should be to non technical people, or you should be to technical people, like basically it’s saying none of these rules necessarily matter, like do what’s best for your company. This regarding what’s normal, or what’s typical.
Erasmus Elsner 16:09
Yeah, no, absolutely agree. And then let’s move on to the next topic, which is education. And there’s another common false narrative, I would say, and that you debunk in your in your book, which is, you know, this myth around the college dropout, maybe talk a little bit about that. I don’t want to take away too much from the thunder there of your findings.
Ali Tamaseb 16:31
Sure, I think, again, because it makes for an interesting story. We think that a lot of these successful founders were college dropouts, it turns out, it’s again, it’s mostly an exception, only 4% of billion dollar company founders are, you know, these Ivy League college dropouts the other night, you know, looked more like the other people, they had bachelor’s degrees. 21% had MBAs. Some had medical degrees, some at law degrees, some are professors, some are high school dropouts. And again, when I compare the two groups shows that education level by itself is not correlated. So if you’ve raised you know, a little bit of money than it, then what was your level of education doesn’t matter. And yet, there are more PhDs than dropout. So I think a lot of people have a bias against PhDs, you know, PhDs can’t start companies. There’s more PhDs.
Erasmus Elsner 17:24
I know all about this, I know all about bias against PhDs.
Ali Tamaseb 17:28
Yeah. So you know, you’ve probably heard about all these different biases, there’s sometimes biases for or against MBAs. And again, data shows these are non correlated.
Erasmus Elsner 17:39
Again, let’s talk a little bit about the the kind of institutions that the founders go to, to a large degree, these are signal institutions, the Harvard’s and MITs of this world, that many founder go. Maybe talk a little bit about that aspect.
Ali Tamaseb 17:53
Yeah, so when I looked at the rankings of the universities that the founders of, you know, billion dollar companies had gone to, and compared with the random group, it is true that a larger proportion of founders of these unicorns did go to Stanford did go to Harvard did go to MIT, and these kind of top schools. However, there’s two other things that they found, number one, is there as many founders of billion dollar company is who had gone to schools that were top 100, as those who have gone to the schools that are in the top 10. So if you look at the distribution, yes, the was it meant to top 10? Or more likely, and you can find more of them in the unicorns group. But still, there’s as many founders went to school, not even at the top 100 as those that went to top 10 schools. That’s one. The other thing is it not directly correlated with the ranking location. And the entrepreneurship culture has a high effect to so for example, you don’t see Caltech or Princeton, or University of Chicago, in the list of, you know, large producers of unicorns, but you do see University of Southern California, you do see, the University of Michigan, you do see San Jose State, you do see, you know, so it seems like location, the entrepreneurial culture also have a big impact, you know, if the alumni have a bursting, created a bunch of companies, and maybe some of them are successful, that brings back some of that entrepreneurial culture, and some of those connections to these universities.
Erasmus Elsner 19:28
I think that’s super interesting. I think another interesting aspect that you that you highlight is that this pattern of top tier universities or top tier institutions, perpetuates once the founders enter the workforce, and you make it an interesting classification of top tier employers or top tier companies, the likes of McKinsey, I think, or Goldman Sachs, or, or Microsoft or Amazon, you list there and you find that a number of founders actually have a background in pre founding employment at one of these top tier companies.
Ali Tamaseb 20:00
Yes, so I think one, one stereotype that may exist is thinking that the founders of these billion dollar companies, they had worked for a year or two somewhere and started these companies. Number one, it turns out on average, they had worked for 11 years on other stuff before building their billion dollar company. That’s number one. And when you look at those 11 years, that’s either they had mostly worked for themselves. So they had created companies before maybe they failed, maybe they were small successes of those that were that had not worked for themselves. The rest, they were more likely to have worked for these kind of tier one companies, as as you said, like 60% of those have worked in companies like Google and McKinsey and you know, Amazon. But it’s also very interesting that the other, you know, 30%, had worked for themselves all that time, kind of creating companies grinding through maybe failing a bunch of times, and then starting the kind of billion dollar company.
Erasmus Elsner 20:59
Yeah, and now let’s get to the really juicy bit, which is the original of the term super founder. And it emerges out of this one section of your book, where you talk about the serial entrepreneurs. And you mentioned there in the section that many successful founders not only had prior experience as employees before, but also as founders, as a venture capitalist, you pay a premium on serial founders, you create the term super founders is someone who has founded at least one company that either exited for at least $10 million, or head over $10 million in revenues. So you’re not excluding the bootstrap company founders here, which I found really great. Maybe talk a little bit about this, this class of super founders. Sure.
Ali Tamaseb 21:43
So that data kind of goes into the previous entrepreneurial endeavours of these successful founders. Number one, it’s great to see 40% of these billion dollar founders are first time entrepreneurs. So I don’t know, number one, I don’t think there should be any bias against that kind of first time entrepreneurs. However, when I compare the billion dollar group and the random group, you clearly see that those that are under second or third or fourth try, they were more likely to succeed, even if they had failed before, even if their previous companies were like, very small outcomes. And the point, the main thing that I saw in the data, it’s not about you know, I think when people talk about serial entrepreneur, they expect you, if you have sold your previous company for $250 million, now, you know, I’m gonna pay a premium price for you. The goal of this chapter of the book is actually, you know, that $10 million bar is an arbitrary number I talk about No, why does an arbitrary number, the aim is, you know, if you’ve started a company, and it was a small, small success, maybe it was an aqua hire, maybe you know, somebody bought your source code, maybe you, you know, you, you wrote and sold something online, and created some real value out of that first startup, then in your next company, you’ve already accumulated so much resources and network and you know, you know what type of people to hire, that then you become more likely. So there’s a bunch of kind of good examples. One is Alex to you, founder of calm is a $2 billion meditation app. And when you go look at this past, when he was you know, a 22 year old student that nothing kill Nottingham University, he created this website called the million dollar homepage, it’s quite fascinating. It’s like a 1000 by 1000 pixel grid. And advertisers would pay you know, $1 per pixel by this like before display ads or something, he made a million dollars like that. This is the thing that, you know, the founders were disbarred for creating something from like making some money online and had this hustle they’re much more likely to start billion dollar companies then somebody who has, you know, steadily moved above the ranking Google that’s that’s the point I’m trying to make. The founders of stripe, super young. And that’s that’s why we talked when you’re the age by itself doesn’t matter. But before stripe, they had started this auction management company that was acquired for four and a half million dollars. If you look back into, you know, the founder of Spotify, he had sold this kind of little project for a million dollars, like an advertising company. So in different giant, and this also holds for a lot of international companies. Like if you’ve looked at the founder of Kareem, the Uber of Middle East, if you go and look at a bunch of founders in, you know, in Asia that become successful, you see this pattern that, you know, they always had this bug for creating a bunch of side hustles and you’re creating companies and some of them failed. Some of them are like small outcomes. And that’s that’s kind of what this chapter is talking about that, you know, if your first first startup was a small outcome, go at it again, do it again, use the learnings and this time you’re more likely to build a billion dollar company.
Erasmus Elsner 24:56
This now love this aspect. And this is really the aspect of the book I just had to pick up the phone and call a friend of mine and told him like this This section is it’s it’s pure gold where you said like, the most important thing is that you always have to, if you want to be a founder, you always have to be creating something, you have to be that type of person, whether it’s like a hobby project, a side hustle is school club, just the pure fact that nonprofit, creating something gets you out of this passive mode and many people are in today, let’s move on to another great section where I think it really showed that you did not collect just you know that the headline items, or that you went one level deeper, and it’s about the product and you collected this data, or you classified the data on whether the products created by the billion dollar startups were painkillers or vitamins, maybe talk a little bit about this.
Ali Tamaseb 25:49
So the definition is basically you know, if your painkiller there is existing pain, the customer knows about it, and you take that away, it’s about taking away a pain. When you’re vitamin pill, you’re making something better, you’re making something more entertaining, you’re kind of adding something. And so that’s kind of I’ve tried to make the definition a little bit more tangible. So when I collected data, it makes sense. So it turns out you know, this, this is not a stereotype, this is correct, painkillers are more likely to to win and succeed than vitamin pills. However, when again, you look at the data turns out, you know, 1/3 of all the billion dollar companies, they mirror vitamin pills. So if you’re creating a vitamin pill, that creates a habit that creates you know, sticky product that creates a community that creates a brand, then it’s possible that companies can succeed. And it’s possible that this company can become a multi billion dollar outcome. But again, it’s it’s it’s better, and you’re more likely to succeed if it’s in a well defined pain, and you’re a painkiller. However, these vitamin pills work, too,
Erasmus Elsner 26:57
I really love this, because a lot of startup ideas, I think, are dismissed prematurely by just trying to be too novel and too disruptive, because a lot of people think, Okay, I have to have this great idea for something absolutely novel and disrupting. And I think it fits really well into the next point about competition, and about the competitive landscape that some of these startups are facing. Maybe talk a little bit about what you found there.
Ali Tamaseb 27:21
Yeah, one main thing that a lot of startups try to say is that we don’t have competition. And I think a lot of people try to take that as a point of pride that, you know, this is a completely new space, nobody else is doing this. Nobody else knows anything about this. And it turns out 85% of billion dollar companies did have competition from day one, only 15% did not have any competition. Although it’s important to see what type of competition it was. So the most common case among these billion dollar companies was when you were competing with an incumbent company, an old giant, sleepy company, that cannot catch your speed and would not, you know, does not have the talent to come after you. So that’s one category. And if you do that, you’re more likely to succeed. The second most common category is when you go into a market that is fragmented. There’s a lot of players, but everyone is a small player, everybody has single digit market share. And the example I talked about there is flexport in the freight shipping industry, and you may think that you know, FedEx and companies like that hasn’t had the lion’s share, but they don’t like it specifically in that kind of, you know, shipping massive things from Taiwan to to the US, they’re not the biggest player, or they’re not the only player. There’s 2030 companies, each of them with two 3% market share that have existed since you know, at least 40 years ago, each of them. And a company like flexport can go in and create a new position for themselves and bring talent and you know, startup mentality and speed to a very fragmented market. The least common case that I saw in the data was when you were competing with another startup when you were competing with another highly funded startup. So if you’re copying if you’re a copycat, and it turns out a lot of VCs and investors funded those types of companies. But if you’re copying what exactly what another startup is doing, and you’re just behind them, then these companies are less likely to succeed.
Erasmus Elsner 29:30
I think Reed Hoffman calls this the “black hole of hyper competition”, if you go into a very obvious and capital-intensive space and sort of everybody’s is over-capitalized. And it gets really hard to break out as a company. And this brings me to the next point on the typical VC memo, which is around defensibility. And you talk about this and collected some data on this, and where sort of the billion dollar startups got their defensibility from and interestingly enough “network effects” is not the leader when it comes to defensibility, but it’s actually “engineering”. So maybe talk a little bit about defensibility, and the different categories you identified there.
Ali Tamaseb 30:11
Sure, so I think defensibility can come from a number of things. One of it is engineering, just how hard this thing is for anyone else to replicate. And none of these sorts of defensibility factors are like super long term, like you can break it network effect, you can break engineering defensibility, you can even break ip defensibility these are all about creating these barriers that you know, you will always stay ahead, you know, six months from the from the competition, and you can always create the next product and the next generation of the product. The most common case that I found was these companies were defensible to engineering. However, the network effects does matter. So it’s not the most common, but when you compare it with the non unicorn group, and then you see its value, the companies that had network effects were a lot more likely to become the successful billion dollar companies. So it definitely certainly matters. And obviously, you have the auto source, it seems like generally what matters is defensibility dasco correlate that success, like companies that have something that’s that is defensible, even if it’s just a brand, then they are more likely to become billion dollar companies compared to companies that like don’t have anything that makes them defensible or unique. Or like highly differentiated in the marketplace, it could be engineering, it could be IP could be network effects. It could be brand, it could be scale, but it’s something rather than nothing.
Erasmus Elsner 31:38
Maybe let’s move on to the next aspect, which I personally quite loved. It’s this, this old question of VC funding versus bootstrapping. And actually, in the last episode, I had Justin Jackson from transistor FM, on who bootstraps to more than a million dollars arr in less than three years, and he really swears by bootstrapping. But obviously from the VC side, there’s always this this old saying that this is going to be a lifestyle business. And there’s no way to create a billion dollar company by bootstrapping. And then there are these recent examples like Qualtrics, which bootstrapped for years. And what I liked about your data set is that you you made room for, for the bootstrapped companies in the first place. By making this definition of having companies that didn’t raise venture funding in the first four years, you classify them as bootstrap companies. And I think it’s a very fair definition, maybe talk a little bit about what you found with respect to bootstrapping.
Ali Tamaseb 32:37
Yeah, so I think in that chapter, I talk a lot about the history of venture capital. And you know, how recent this thing is and how small venture capital as an asset class is, compared to the financial system, it’s it’s ridiculously small, but it has an outsized impact. And that’s why we get to talk about, you know, a lot of these venture backed companies. So the data shows, you know, over 90% of these companies that ended up becoming billion dollar companies were venture backed, and you know, something less than, you know, about 8% of them were bootstrapped. And, like, a little bit less than 1%, or self finance, which is, the founder had the means to invest in a company and that for the first four years of the of the company, so, yeah, this this doesn’t say anything good or bad about the bootstrap companies, you know, obviously, there’s, there’s a smaller number of companies that that are bootstrap. And also it talks about, you know, the kind of goals and your VCs want to take risky bets. And I talked about the math behind capital funds, and how, you know, even for a small venture capital fund, even for a $300 million venture capital fund, they need a $2 billion exit for, for it to move the needle on work for them. So it’s partly rooted in the intentions, you know, it’s amazing, if you can create a $30 million a year business and you own it, and you don’t have venture capital backing, you take less risks, and you own all that money. That’s amazing. I think the people who should go to the venture capital route is the people who want to take these risks of either losing the company or becoming an two or $5 billion company. So it’s a very different route. And Julian, you’re reserved for those type of risky bets.
Erasmus Elsner 34:19
And what I love about the statement that you make here is that it’s not just an empty statement, because you already have the next data set available to sort of prove the point that you just made that there’s a place for venture capital, and it’s the capital intensive business models. And you talk about this and collected some data on this that most of these billion dollar startups they are actually I think it’s 58% of them are in what you classify as very capital intensive business models. Maybe talk a little bit about that.
Ali Tamaseb 34:53
Yeah, so you know, I think a lot of a lot of times we think that most businesses are like super capital light. But it turns out a lot of these companies require a lot of capital to get to the, to the minimum scale that’s needed for them to kind of work out and deploy the network effects to to work out. And they require like a minimum of funding to get to that scale, that things work out that the unit economics work out that the venture scale works out. So a larger than expected, your percentage of these billion dollar companies had business models that was not necessarily capital light. And and also I talked about, you can be capital efficient, even if your company, even if your business model is not capital, white.
Erasmus Elsner 35:37
Now let’s talk about the capital. An interesting finding you have is that the first funding rounds of the Billion Dollar Startup cohort compared to the random cohort is significantly larger, maybe talk a little bit about the fundraising trajectory.
Ali Tamaseb 35:51
Yeah, so what I signed the data for the companies that ended up becoming billion dollar outcomes, on average, had raised much larger seed rounds, and much larger eight rounds, and at a faster cadence compared to the random group, which tells you something, it doesn’t tell you, if you raise more, you’re more likely to succeed, it tells you that these founders, even from day one, maybe had the network maybe had, you know, the previous track record, or like an interesting idea that they could capture the imagination of investors from day one. So a lot of these kind of companies, they weren’t obvious deals from day one, but they were interesting deals, or they were strong deals, even from the series seed and series A rounds. And also in another chart, by talk about, you know, even in the first and second round of them, 60% of these, you know, unit companies that ended up becoming unicorns raised their first round of funding from these brand name VCs. Again, it doesn’t mean because of brand name VC invested in them, they became a billion dollar company, it means they were a strong team and a strong company. And even from the first round, they were able to raise capital from great investors.
Erasmus Elsner 37:01
It’s quite interesting to think about this signal investors in the first round. And you mentioned there that 60% of these billion dollar companies raised from top tier VC firms. And then maybe as a last point, let’s talk about the years it takes to hatch for these companies to pop. There’s this old saying “it’s an overnight success 10 years in the making”. There are a couple of companies that really popped after one, one or two years. And I think the cycles are getting shorter and shorter, most recently with this dynamics in the growth equity space. But maybe talk a little bit about the time it takes to hatch.
Ali Tamaseb 37:37
Right? So you know, a lot of these kind of Yes, that’s right. They’re not overnight successes. But a lot of it goes back to what these founders had done before actually starting this company, before coming up with the idea, their previous, you know, startups or things that they’ve done. You’re right, that the cycles getting faster and faster. And it might be signs of, you know, getting into frothy market, but I think on average, you know, a good number of these companies became unicorns, some in four years, some in six years, you have companies that you know, just became a unicorn, like within one or two years, we have like clubhouse now that that thing happened to it. But you know, there’s been companies that that it took more than 12 years also to become a unicorn. So it’s a very wide range from your one years to 12 years or even more.
Erasmus Elsner 38:25
All right, so Ali, as we were running against the clock, I have one last question to ask you. What’s the next step in your research journey? Are you going to turn this into a PhD? Or what’s the next plan?
Ali Tamaseb 38:36
Yeah, I mean, I don’t know yet, though. It’s very valuable data set. So I’m trying to see, you know, what are the possibilities from it? How can it help me become a better investor, have less bias when I’m deciding on investments, and also use it as a framework for making better investment decisions and for sourcing companies better?
Erasmus Elsner 38:55
Absolutely. Love it. And so, Ali, where can people find out more about you and what you’re up to?
Ali Tamaseb 39:02
Yeah, so the book is is now available on Amazon, Kindle, Audible, you know, wherever they sell books, so you can you can go and order it there. And the website of the book is called Super founders book calm, and if you want you can follow me on Twitter or LinkedIn. Ali tomasa.
Erasmus Elsner 39:19
Thank you all for being with us here today.
Ali Tamaseb 39:21
Awesome. Glad to be here.