Tech Brew Ride Home - (Bonus) Land of the "Super Founders" With Ali Tamaseb @alitamaseb

Episode Date: January 20, 2019

Ali's Medium Post: Land of the Super Founders On this bonus episode, we’re going to revisit a past weekend longread suggestion and talk to the author of that longread to go further in-depth. Do you remember I recommended Land of the Super Founders a medium piece by Ali Tamaseb who spent 300 hours gathering data on unicorn startups to answer the simple question what did billion-dollar startups look like when they were getting started? What common traits did they share?  This episode has a full transcript. Sponsor: Squarespace.com/listen Learn more about your ad choices. Visit megaphone.fm/adchoices

On April 4th, 2023, around 2 in the morning, a man was found stabbed multiple times on a sidewalk in downtown San Francisco. Hey, who did this to you? What happened next turned the story into a political firestorm. Reports have identified the victim as Bob Lee, the founder of Cash App. From Bloomberg Podcasts, this is Foundering, the Killing of Bob Lee, beginning April 16. Welcome to a bonus episode of the Tech Meme Ride Home. I'm Brian McCullough. On this bonus episode, we're going to revisit a past weekend long read suggestion from last month

and talk to the author of that long read to go further in depth. Do you remember I recommended Land of the Super Founders, a medium piece by Ali Tamaseb, who spent 300 hours gathering data on unicorn startups to answer the simple question, what did billion-dollar startups look like when they were just getting started? What common traits did they share? If you read the piece, there's a link in the show notes, it was chock full of data, and so Ali was kind enough to sit down with me

for a deeper dive into some of the learnings therein. But first, this bonus episode is being brought to you by Squarespace. From websites and online stores to marketing tools and analytics, Squarespace is the all-in-one platform, to build a beautiful online presence and run your business. You can use Squarespace to turn your cool idea into a business, showcase your work, blog, or even sell products. But, heck, you can use Squarespace too quickly and easily set up an online presence

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All right, let's talk the common features of you. Unicorn Startups with Ali Tomaseb. All right, so you're at Data Collective VC. What sort of investments does DCVC make? Are you guys focused on specific companies, spaces, industries, that sort of thing? So Data Collective VC, we're a Silicon Valley-based entry capital firm. Start about 10 years ago, you're an early stage investor, the investment seed in Series A, And we are primarily a deep tech investor.

And what we mean by deep tech is highly technical companies, highly technical founders, oftentimes are PhDs or academics. They're working on something that's hard. It's not an app. It's not a consumer brand. It's not a business model innovation. It's a technically challenging product. And that and only that team can actually make it or they're far ahead from the other team in doing that. So we like defensible companies swim back and we like very large industry.

So it's a high problem, very hard problem. Nobody has solved it. It's a massive market. If you make it happen, if you sort out the technology, you open up a big market. That's what we are interested in. Boy, that's interesting to learn that having read the medium piece because now I can see, you know, some of the way that you framed your research. And you actually, you have an academic research background yourself, right?

I do, yes. So I come from an academic background in neuroscience. I've done a lot of research on brain computer interfaces, on generally biomedical engineering, biomedical signal processing, as a lot of human computer interaction. So we're here to talk about your medium piece, which got a lot of attention, land of the super founders. What prompted you to do this project?

I think you said that you did this in your free time? Yes. And it's taken a lot of time. I've at least spent 300 hours, maybe 400 hours. It's about one and a half years. All my weekends gone on gathering this data. But I was fascinating. And when I started, I didn't actually have the mindset of writing something about it.

It was genuinely a question for me of what is separating these very large companies. There's a lot of things people say. But there is no data. You go and Google and there's a bunch of answers on Kora here and there. There's no definitive answer on a lot of these questions. So I said, okay, somebody should do it. Somebody should put it online. So I started gathering the data.

The problem is the data is hard to find. It's not on crunch base. It's on LinkedIn. It's in interviews. You should email the founder. So it was a lot of manual work. It's not something you can basically automate it. So I had to go manually and get all this data.

So essentially the question that you're asking is unicorns, billion dollar startups. What did they look like? What did they have in common when they were just getting started? Are you, is this an exercise in like pattern matching? Were you looking for commonalities? Like what in your mind would be the usefulness of this sort of data? Yeah. So, I mean, I guess both of the kind of venture capitalists, the way I was looking at it is,

how we can better pattern match of basically understanding these companies and also have a better mindset of understanding is this a unique situation or has this happened before if this company didn't go to an accelerator program if they were rejected if they're a solo pound there's there's a lot of things about solo pounds right why Fominator doesn't normally fund them or whatever and like is it true or not that's that was the question should I negatively bias myself just because people are saying this, have they done the work to actually do the pattern matching or not? And one kind of main thing here about this article is it's not a correlation and causation study. So I'm not suggesting any of these metrics are suggesting what we should invest in. This is basically a review

of what the billion dollar companies today looks like at that time. Now in the future, they may change in the kind of time period before that they may have been different. But this is what they look like now, they don't necessarily correlate, but some of them show a lot of interesting stuff, especially the things that we don't expect to see. So it's a lot of things that we didn't expect to see and we're seeing it in the billion dollar company, not the, not the other way around. Well, yeah, let's let's dive right into that then. So you already mentioned it, you know, the idea that the, we're kind of all used to this, but like the most common number of co-founders, you usually have a two or a three-person team and only like 20% of, you

Unicorns, according to your data, had solo founders. Again, I feel like we all are used to that, but is there a particular reason why you think that that's true that solo founders are not the norm? Well, part of it is just the bias of starting. The point I was trying to show here is solo founders work, companies with 10 co-founders work as well. And part of it is just how many companies, a lot of companies started to do. or three co-founders. It's natural that we see a lot more companies, two or three co-founders

in the unicorns. But what we oftentimes here is solo founders don't work or six founders don't work. And we're just seeing there is a number of unicorns with five co-founders or during 40 companies that's just one founder. And I mean, I guess it's, you know, obviously it's useful to have a team. Like it's very rare that you've got that one loan genius out there. But also like looking at the data, like you said, there are big founding teams, but again, those are comparatively rare. Like the fat part of the graph is the two or three co-founders, and then it's comparatively rare to have like an eight-member founding team, and it's comparatively rare to have a one-person founding team.

So like, do you have any sort of thoughts on like why that two or three number? Like maybe it's almost like there could be too many cooks in the kitchen, and then it's also rare that there's a lone wolf genius out there. So like two or three, a nice, tight team maybe makes the most sense. Yeah, I mean, there's been a lot of research on why just, just just in the human psychology, just two or three people work back together, at least in the very early stages of the company. And it's just a different kind of naming convention. You may call six different people, the kind of co-founders. But at the end of the day, two or three people who are driving the company forward or who are the main core team that in the early

days before anything they were working on the whiteboarder to just thinking together well and then you know i i found this interesting but um like 60 percent um of of the unicorn founders were repeat entrepreneurs i'm uh and then like more than half were founding CEOs were over 35 so i'm wondering that probably correlates to being having the experience like maybe actually having failed before having this be their second or third go around and learning lessons from from other startups and things like that like does that correlate that like it's also exceedingly rare even though that we get a lot of headlines for this but it's exceedingly rare to be a 24 year old starting a startup in your dorm room and having it become a unicorn absolutely

So one thing that I think that was the kind of biggest outcome, and that's the number one conclusion that I'm taking here, is there's this proportionally high number of repeat entrepreneurs and repeat successful entrepreneurs that are building these billion-dollar companies. And you're seeing a lot less of those 20-year-old college dropouts, basically, entrepreneurs. But that doesn't mean that 20-year-old should not start a company, because the kind of pattern that I saw is they actually do start.

and then they're 20-something years old. The first company, it may fail, it may be small success. Then it's their second company or third company. This is a journey for them. So they start with the first one. They go on the second one. They go on the third one. And what matters is they're trying and you're trying to do different stuff.

And when it comes to their kind of big success, which is the unicorn company, then they're already at their second or third company, if it's one previous success or one previous failure. Well, and I found this fascinating also. So according to your data, there are almost exactly as many non-technical CEOs as technical CEOs. And you wrote, while this seems to go against intuition, when the first person in rank at a unicorn was non-technical, the second person in rank also had a higher chance of being non-technical too.

Do you have any idea of why that proved out? Well, I just imagine, you know, who you're friends with or where you're working or what type of people you're working with. If you're non-coder or if you're non-technical, and because this is a broad study, it includes energy companies, material companies, biotech companies, it doesn't necessarily mean coding. It means being good with chemistry. It means being very technical on what you're doing. There's a higher chance that your closest friends or your closest network, they are non-technical too. Or if you're technical, there's a good chance they're technical too because there are the kind of people you're working with.

You end up starting a company with someone you're closed with. You have a lot of experience working with them. So that may be one reason. I think one thing that I wanted to show with this graph is a lot of people were thinking, okay, if this person is non-technical, you definitely need a technical co-founder, a second person. Or if the first person is technical, you need a business second person. That's not true. It can be any of these situations work.

Yeah, we're used to that sort of paradigm of Steve Jobs and Steve Wozniak. There's the guy with the vision and the guy with the tech chops. Yeah. So that's super fascinating that necessarily doesn't have to hold up. But I also found this. So your data shows that directly relevant industry experience, previous experience in whatever industry it is that the unicorn is starting in does not seem to matter. You know, my instinct would be that maybe is the phenomenon of coming to something with fresh eyes. Like, it's almost, you're better off if you don't know what isn't possible, what you shouldn't be able to do.

Do you think that that's part of that phenomenon? Maybe that is. Maybe it's just that at different times, different kind of spaces or industries are picking up. So, for example, right now I'm seeing a lot of successful. tech entrepreneurs are starting companies in their health care or spaces near bio and health. And that's what's probably working now or what's the future. So at different times, these successful people who have experienced or worked on good companies before, they are taking their experience to a new space.

So what I've kind of seen in this data is people, what matters is experience running a company, experience running a big team, experience, raising fun, experience, seen the VT. It doesn't matter if you don't know what this exact space is because that's something you learn. What transfers is the knowledge of how to run a team in a company, not the specific knowledge about this kind of tech or this kind of space. That you can learn, you can hire, you can, and because you have this open eye to go and learn, maybe you come up with better ideas. That's fascinating. Also in the data, Google, Oracle, and IBM, people that had that were veterans of those three companies were the among the biggest contributors

to billion-dollar founder startups. Google I get every, you know, Google has this huge reputation for seeding a bunch of other companies and then buying them back, Acu hiring them. Yeah. And even Oracle for, you know, that whole SaaS diaspora, but IBM surprised me. And then even more surprising was number four on your list was Yahoo ahead of even Facebook. The couple of things that's basically biases. One is just the number of employees. Google had 50, 67,000 people working there. One is timing. So Facebook, the timing of this period of basically $50 billion companies that I studied was from 2005 to end of 2018.

for some part of that, basically Yahoo was around for a longer time than basically the Facebook team or the people who came out of Facebook, as we can say. So one part of it is that. But the other part is generally the type of people that these kind of companies attract, for example, Google, or Oracle, you see a lot of these data and database-related unicorn companies that are founded in 2012, 2013, a lot of them are coming from Oracle. similarly from IBM. And I would say a lot of the kind of social or productivity type of companies are coming

out of Yahoo. A couple other details before I ask you some like wrap up summary questions. But you said that over 50% of the companies you studied were competing with multiple incumbents at the time of the founding. So like you suggest that it almost seems like competing with multiple large incumbents is a good thing because it's a sign that the market opportunity is large. Like you said, that that's what your firm likes to invest in. And that maybe large incumbents have already seeded and educated the markets, but most of, you said, over 65% of the cases you saw, the aim of the startup was to get market share

from the others. So again, we often think of like unicorns as like, oh my God, they're the first to plant the flag in a new market. They prove the market. They create the market. But you You're saying that in 65% of the cases you studied, that wasn't the case. It was almost the follow-up, the disruptors coming up from behind. And not true. So, yes, absolutely a big percentage of these companies were competing from day one with a large number of a couple of these kind of incumbents that have been in that market and have been operating in that market. And the thing is it shows there is a market, there's massive markets, the market is educated. and just the inefficiencies in these companies,

the couple percentage of inefficiencies that you can win, that's enough for a company to become a billion dollar company or to show the initial traction to raise that amount of funding at that valuation. But that has been one of the kind of main reasons that we see this. One thing to not kind of mix up here

is the difference between kind of the market and demand and technology. So oftentimes a market exists, a demand exists, but you are building a new technology to solve the same problem and it's better. You're still going after getting the market share from those people, from those incumbents, but with a different technology, with a different type of product. So oftentimes the product, and that's one of the other charts, the product had 70% of these companies, the product was very differentiated from these incumbents,

but they were still competing with these incumbents, if that makes sense. What surprises you the most about the data that you came up with, like either from like a VC perspective or from a founder perspective. Was there one sort of like data point that like really shocked you? Well, I guess just one of them is this market thing that it seems intuitive that big market, big company. But oftentimes VCs like to see super unique things that are going after, you know, not very well the time markets or needs and that's that's there I need to specify again a demand exists and the demand is large right now and the company is going there and getting there

because we can never gain the timing so the company should even the timing is correct or those are the kind of companies that are working out so I think that's that's one of the main things that I thought the other thing is just the super founders so the main thing in the article that amazed me and that's the title of the article the Land of Superfunders. are the people who work, who start different companies, maybe their first company is a failure, their second company is a $50 million exit, and then it's their third company that becomes a unicorn. It's oftentimes not just your first experience of running a company or starting a company. They're trying different things.

They're succeeding. So you see this path. And now when I see people, when I see LinkedIn, I see this looks like a superfounder. I have that pattern matching. wonders now that you see he's on a path and maybe his next company is not a unicorn but his second next company is a unicorn and be better back this person because in the next 10 years we have an opportunity with him or her so that's the big lesson that vCs should take away from your research but well if if there are founders listening or if there are people that want to be

founders what do you think would be one big key lesson that founders could take away from your research. Big markets, massive markets. Do not be afraid of competition with big guys, not small guys. If there's a highly funded startup that's doing that and you're just copying them, that doesn't work. But if there's a 20-year-old big company with a massive market, don't be afraid of that competition. Don't be afraid of losing it to them because they have a lot of inefficiency. Don't be afraid of competing with Google over the world. That's what the same. And look for that big market and have a very differentiated product. It seems like a lot of these companies have a very high differentiation.

And when I actually filter out, companies over $5 billion, rather than $1 billion, I actually saw there's even more differentiation. So the more differentiation in the core product offering, that more 10x, the more 100x, better product experience, the better the company is. I'm going to end almost with not really a question, but you made this point, and I actually think it's crucial because this is what we've been talking about on this show a lot recently.

You noted that the most common themes in your research in terms of the types of companies, it was like cloud, data, mobile, marketplace, that sort of thing. But you made the point, the themes of the next 14 years will not necessarily be those of the past 14 years, the past 14 years in your research. I'm wondering. Absolutely. Maybe, right. So maybe the question would be, do you think that this is a pivot point where, like,

Like now, like, we're in this nexus point where we're still trying to find what these new themes are and they're not obvious yet. But at the very least, you're probably not going to, your best chance of success is to not copy the playbook of the last 14 years. Exactly, exactly. Or those types of companies. So I'm big on construction and manufacturing and mining, healthcare insurance. These are big trillion dollar markets that are still run by incumbents. And that's the best opportunity for startups to go after. So don't go after another tech for tech companies, go and find these trillion-dollar market opportunities and disrupt them.