289. Data on the Key Traits of Billion Dollar Startups with the Author of Super Founders: What Data Reveals About Billion-Dollar Startups (Ali Tamaseb)

289. Data on the Key Traits of Billion Dollar Startups with the Author of Super Founders What Data Reveals About Billion-Dollar Startups (Ali Tamaseb)


Ali Tamaseb of DCVC joins Nick to discuss Data on the Key Traits of Billion Dollar Startups with the Author of Super Founders: What Data Reveals About Billion-Dollar Startups. In this episode we cover:

  • Can you walk us through your background and your path to DCVC?
  • What is the thesis at DCVC?
  • What was the original motivation behind writing the book and conducting the research?
  • Was the data in the book purely objective, or did some of the subjectivity have to be screened and filtered as you were collecting?
  • What was the biggest surprise after collecting all this data?
  • What were some of the common traits or characteristics that these “super founders” had in common?
  • Did you find that the non-technical CEOs more often had a technical co-founder?
  • Did you get any data on the MVP being built by someone other than in-house and the level of success? 
  • What were the insights around pivots in sort of that early flexibility of startups and the original concept versus the ultimate winning form?
  • On this show we have heard the classic trope many a time, “you should invest in painkillers and not vitamin pills.” I’m curious if this is an area you looked at, and if you agree or disagree.
  • Do you have some examples of what some of those standout vitamin pill companies were?
  • Did you get any insights on geography and any trends over time?
  • As a VC firm, New Stack tries to rank the founding teams on a variety of characteristics, things like resourcefulness, speed and tenacity, capital efficiency, and there are a few others? Did you attempt to assess some of these characteristics as well? 
  • Any good takeaways on recruiting and hiring at the early stages?
  • Did you find a correlation between the number of dollars raised and either the likelihood of an outcome or the scale of the outcome?
  • Was there any advantage to being a first-mover vs. companies that were not first movers? Did that play a role in success in the competitive landscape that a new startup was entering?
  • How did you think about timing, both from a launch standpoint and then finding the right market for the right time and technology?
  • Talk to us about your thoughts or predictions about some future trends, and what will make the super founders of the next decade when you write the follow-up book.
  • If we could feature anyone here on the program investor or founder, who should it be and why?
  • What do you know, you need to get better at?
  • What’s the best way for listeners to connect with you and follow along with DCVC?

Guest Links:

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Transcribed with AI:

0:00
Ali Tamaseb joins us today from San Mateo. He is a partner at DCVC and author of the new book “Super Founders: What Data Reveals About Billion Dollar Startups.” The scientist turned entrepreneur turned investor has six publications, holds a number of patents, and his work has been featured in the BBC Guardian, Forbes, the Telegraph and many others. He is an investor in Carbon Health, Starkwear, Kettle and PlotLogic. Ali, welcome to the show.

0:27
I’m glad to be here, Nick.

0:28
Yeah. Can you walk us through our quick background and your path to DCVC?

0:32
Yeah, so my, my, my past is I was a researcher, I was studying brain computer interfaces way before Elon Musk made made that area cool. So that was kind of my life publishing papers, going to conferences and stuff. And then I started started two companies before one in the industrial variable segment and one into financial technology sector. And that’s how I ended up joining DCVC, almost four years ago, and have been investing on behalf of DCVC. In a range of companies, we typically do deep tech. And I’ve invested in the companies you mentioned, like Carbon Health and Starkwear and I think by 15 companies in total so far,

1:12
Very good, and remind us of the thesis at DCVC.

1:15
So DCVC is all about deep tech. So companies that are solving complex engineering, or scientific challenges, these are things that require a lot of capital and are hard to do and hard to replicate. But are solving trillion dollar problems, market opportunities anywhere in food, agriculture, space, computing, chips, hardware, and anything else.

1:37
Good. But let’s talk a bit about the book here, Ali. You know, it’s been often discussed amongst many of the VCs out there and emerging managers that I interact with, what was the original motivation? And you know, what’s the story behind writing the book and conducting the research?

1:54
Yeah, so this started about four years ago, and that the key question for me was, for me to do my job better as a venture capitalist, you can either, you know, talk to mentors, and talk to people who have been around before you. And that’s, that’s one great way. And the other thing is, you know, is there any data out there that tells you if the successful companies were any different from the non successful companies and something to help me better sort through the opportunities that I see. And, you know, this data doesn’t exist out there. You know, maybe some people have done it and never published it. But it’s, we don’t know that much about what is different between the successful companies aren’t the others, and maybe there isn’t anything, so maybe it’s all about luck. So I set out to understand, you know, if there’s anything different and it took me about, you know, three, three and a half years to collect this data, it’s about, you know, 30,000 data points. So I’ve collected, you know, a lot of data on all the unicorns that are founded in the past 15 years and know a bunch of non unicorns, you know, companies that raise money and did not become successful. And, you know, I compare that the data statistically between these startups on 65 different elements. So that’s a data driven part of the book. And then I also interviewed a number of people anywhere from Peter Thiel to Eric Yuan of Zoom to Tony Fadell of Nest to Tom Preston, founder of GitHub, Keith Rabois, Alfred Lin, you know, Michele Zaplan, Rachel Carlson. So I interviewed a number of investors and founders, and also this kind of data, help navigate these interviews. So the book is a mix of these interviews, and the data was it just came out one months ago,

3:28
did the data was a purely objective, or did some of the subjectivity have to be screened, and kind of filtered as you’re collecting?

3:36
So I tried to be the least biased, so I did not analyze the data until I finished collecting the data. So I don’t bias myself. But you know, I said, I wrote down 65 elements that I thought would matter, and then I collected data. But you know, it’s it’s not an easy task, you can just automate this, there’s a lot of judgment that goes through this. So for example, I had to look through, you know, how many competitors did these startups have, when they were founded, that took a lot of time, it’s a very manual task. And I had to, you know, go through the internet archives, and email these people, and just try to understand what the market dynamics looked like back in 2011, for the specific space that this, you know, started was was working on, and, you know, trying to understand the pivots that they went through. So there was a lot of judgmental work and a lot of, you know, listening to old podcasts and reading old interviews, and you know, a lot of times just calling these founders and asking them questions about you know, year one, in your startup, you know, what happened to who you were competing with?

4:32
What was the biggest surprise after collecting all this data and, and looking at it, what jumped out and what was shocking or surprising?

4:40
Yeah, so there’s a bunch I mean, interesting, the what the data shows us is a lot of things that we may have thought matter and or, you know, is statistically correlated with success or more success or not. These are just made up stories and narrative bias that we have created because, you know, media turns You know, sometimes over shows or over, expresses some features among special companies, and it doesn’t, among others. And you know, it’s just there’s a lot of narrative bias that’s created from the press. And that that kind of perpetuates itself through, you know, people in the industry and investors and advisors and, you know, incubators and everything else, you know, normal example of that, that comes to my mind is, you know, you have to have co founders, you know, the data shows, if you’re a solo founder, you’re not less likely to succeed, just statistically. And you know, a lot of people say, solo founders are less likely to succeed, and you know, data that just goes against that, or, you know, we care a lot about, you know, your technical CEO or your non technical CEO. Turns out these having a very, very small statistical correlation bit with the success factor of the startup.

5:50
Got it? Yeah, I want to go into some of those specifics. And some of these, these biases that maybe you just proved, before we do that, what were some of the common threads, or some of the common traits, characteristics that these super founders so to speak had in common?

6:05
Yeah. So you know, I think that the main goal of the book is, let’s have less bias. And then let’s dig deeper. So a lot of times, we look at these first layer elements about what makes a company successful. And the data basically shows if you’re looking at the first layer, if you’re looking at proxy metrics, like if this person has an MBA or doesn’t have an MBA is this market, you know, competitive or not, if you’re looking at the first layer of proxy metrics, they are not necessarily correlated to success, you have to go one layer deeper, you need to dig in one layer deeper. And one thing that I found among the founders of these billion dollar companies, something that’s shirred is a trait of just building and hacking. And you know, the Y Combinator this ask this great question of, you know, tell us about something a non computer system that you have hacked before. And that’s a trait that you see among a lot of successful founders that maybe they have started a company before, or maybe it was just a nonprofit, maybe it was, you know, small project that they had started before. All of these successful founders, they’ve hacked something, before they’ve made something worthy of some money. And maybe it was a venture backed, you know, successful exits, a lot of times, you know, investors are looking for, you know, if you’ve sold a company for $300 million, then you know, of course, they’ll back you. But it’s not just that there’s a lot of smaller things as well. But you know, if, if an entrepreneur has showed that, that’s, that’s a great sign of success, even people who we think are first time founders, like Zuckerberg, or Bill Gates, they’re not they’ve built projects. Before, you know, Zuckerberg, Microsoft wanted to acquire Zuckerberg, music apps and apps, you know, $4 million before Bill Gates built a traffic hardware device company, before Microsoft. And that’s a trait that you see among you know, a lot of these founders, you know, fount of call, he built this website called the million dollar homepage, you know, he made $1 million from creating that website. And then he started Calm, you know, a $2 million meditation app. And that’s, that’s, you know, story that we see among a lot of these super founders. And one thing that I specifically studied in the book is, if a founder has sold a company for, you know, about $10 million, before $10 million or more, they are like three, four times more likely to build a unicorn next time around. So once they have had a small chain of trust, and they have built a little bit of reputation, maybe they got lucky to get to get there. But the next time around that it comes, they know people to recruit, they know some investors, they know how to deal with investors, they’ve learned some stuff, and they have better and bigger ambitions, and they’re not going to sell the company fast. And they’ll just be more likely they’re like 350% more likely to start unicorn next time around.

8:41
Gotta find those those hackers, tinkerers and starters. Huh.

8:45
yeah.

8:46
Interesting.

8:46
And oftentimes from different industries. I mean, a lot of times we, we think if somebody is jumping from one industry to another, that’s a bad thing. But the data shows it’s not I mean, the founders of Clubhouse, the massive, massive company, no matter valuation, now who knows where, where it will go. But like nine months before starting a company, he was working on a crypto idea. And for nine years before that, they were trying a bunch of companies. You know, they saw that very small exits after their seed rounds, one to Opendoor and one to Pinterest. So they tried, you know, different ideas, even in different industries. And even as I say, like, six, nine months before starting Clubhouse, he was Paul was doing Yeah, you know, crypto company. Now people jump around and eventually find the right idea. And that becomes their billion dollar success.

9:30
Yeah, that was one of the other data points. I noticed in the book. The majority of folks starting businesses had no industry expertise or domain expertise in their background in that specific industry. Is that right?

9:42
Correct. Yes, they did not have they had work experience. On average, they had 11-12 years of work experience.

9:49
Yeah

9:49
They may have started their own companies or they may have worked at other companies. They may have worked at Google. But they did not necessarily have domain expertise, like they did not come from an insurance industry. If they were disrupting insurance, they did not come from you know, that that specific part of the SaaS industry if they were disrupting that, so it’s it’s kind of it’s the softer skills and their connections and reputation that stays and they can, you know, learn about a specific industry and go super deep and learn more than anybody else.

10:18
Although, you know, 60 to 70%, not having some industry expertise or domain expertise would still imply that 30 to 40% did. Yeah?

10:26
Yeah. Yeah, I mean, and the data shows, you know, it’s not, it’s not like, if you don’t have domain expertise, you’re more likely to succeed. It’s just saying, like, bring it when I can person, I have two groups, right? I have the unicorns and I have a control group, which is non unicorns, and then compare these two on all these 65 elements. And it basically shows you know, domain expertise is specifically is not correlated with with more luck, or success in other unicorns

10:50
Got it. Interesting. I imagine, you probably looked at this data many different ways with regressions, and clustering and segmentation. You had mentioned earlier that you found that solo founders were not less likely to build billion dollar companies. I’m curious the relationship between this and some other factors. You know, you also found that half of the founding CEOs were non technical, right? Was there any relationship between the two? Did you find that, you know, the non technical CEOs more often had a technical co founder?

11:22
Let me first answer your first question. So, for example, in the solo founders, the solo founders were successful, they were more likely to have been a second time founder. So it was more likely that if you have already had, you know, a smaller exit in your first startup, in your second startup, you’ll go solo, and you’ll still be successful. So and I had to look at the data, you know, it’s a combination of choose two out of 65. So there’s a lot of ways to look at this data. But for example, that was one, the second one that you mentioned, you know, if you do so basically, half, exactly half 49.5%, of founding CEOs of unicorns are technical, and 50.5%, of founding CEOs of unicorns are non technical, so basically half and half. And it is also another misconception that you always should have these dual pairs of technical and non technical. So actually turns out, non technical CEOs are more likely to pick a non technical second co founder, among unicorns,

12:23
Wow, interesting.

12:24
And technical founding CEOs, they were more likely to have a second co founder, who was technical as well. And it you know, it kind of makes sense, because, you know, if you’re in business school, you start doing no company with two friends. And maybe they’re both non technical. Or if you’re doing no sales or business, then you’re more likely your friends are more likely to look like yourself. And if you’re CSS students, you know, at Stanford and starting company, you’re more likely to start with a friend. And you’re both technical. So it makes sense from that aspect. But we have always kind of, you know, fantasize this idea of you need a technical, you know…

12:55
The builder and the seller

12:56
and non-technical founder. Yeah. And I see, no, I read the stuff on Twitter, and you know, people talk about a clubhouse and, you know, I want to pull out my hair and say, like, just don’t talk without data backing it just like, you know, there’s a lot of data showing just, if you just make up stuff that they’re not necessarily correct.

13:15
Did you get any data on sort of the MVP, and who that was built by? I know, famously, the first version of Uber was outsourced dev agency, right. And that was often a flag that angels and early stage investors would use, like, I will not invest. If it wasn’t built by somebody, you know, in house, did you discover any data on that?

13:35
Very interesting question. No, I did not. I did not even ask this question from the data or didn’t collect this, it will probably be an impossible data to collect, unless you kind of ask every every one of those founders, right? Did you outsource or not? But you know, given half of these founders were non technical, it is possible, they may have outsourced it. And you know, again, what I’m trying to say with this with the book is, you know, let’s forget these proxy rules that, you know, if if you’re doing this in debt, that’s necessarily a bad sign. It’s not, you have to go kind of one layer deeper into, you know, what is the character of this founder? Like? Do they know enough people? Do they have enough resources? Can they go, you know, through this wall, if they see a wall, can they you know, break the wall? And those those are characteristics that that we need to look at, rather than, you know, proxy? Like layer rules? Like, did you did you outsource this? Or, you know, do you have an MBA or not?

14:27
Right, right. You know, it reminds me of an interesting debate that was kind of on Twitter about Slack, and who designed the first iteration of Slack that ultimately was successful. But one of the themes that was in your book, you actually cited Slack, and I think YouTube, you know, people often forget that YouTube started as a dating website, right, Slack started as an online multiplayer game. You know, what were the insights around pivots in sort of that early flexibility of startups and the original concept versus, you know, the ultimate winning for

15:01
Yeah, so there’s one misconception that I’m talking in the book that is about, you know, we sometimes think that these these ideas that become winners, the founders are always visionary about them, it was their life mission, they had thought about this for, you know, 20 years, and they face that that problem in their childhood, and, you know, somehow they, they ended up solving their own personal problem. And, you know, again, it’s one of those things that make sense, you know, if you’re solving your own personal problem, you may be likely to succeed. But the data shows, you know, not necessarily a lot of founders are opportunistic, they were looking for a trend, they wanted to start a company, they found a good trend and spotted a good opportunity and started, they had the resources they hired, they raise money, and they weren’t, and they were happy with it. And that’s basically no kind of strong opinion, loosely held, or, you know, they were just smart people who were looking to start a great company, and they were not afraid of changing the idea. And a lot of, you know, these companies that you see, they change their idea. So, in the book, I have an interview with the founders of Flatiron Health. And, you know, they’re the typical example of a super founder, you know, started a company to get it started a bunch of companies together. And the first one was a pizza delivery company in college. And then the second one was ad tech company that Google acquired. And then the third one was Flatiron Health. And the way it started, they started from, you know, health insurance, and then they changed it to second second opinion, medical diagnosis. And then the third one was, you know, how can we build software for cancer centers. And, you know, three years after the company started, that the whole concept of real world evidence that if you have a lot of data about what treatment works on what type of cancer and what that patient, that is valuable data, set two years into the company, they they understood that and realize there’s value in that. So you know, they were just flexible about finding out what is working, or DoorDash. You know, there’s a lot of interviews that that kind of tried to portray that, you know, because the founder of DoorDash, Tony, you know, her mother had a restaurant and these things are linked, that he eventually started, they started DoorDash. And that’s obviously a massive success. But then you you read, you look at the first Y Combinator video interview that they’ve recorded. Basically, what they say is, you know, we didn’t we were just like, for Business School students, we wanted to do a company. We thought SMEs are good, we just went to downtown Palo Alto, we talked to a bunch of SMBs. And you know, ask them, Hey, what’s your problem? What did you do today? And then, you know, they created a survey app, you know, one of these hardware tablet things that you put in front of restaurants and asked, you know, what’s good or bad. And then that kind of changed into they realize, you know, a macaroon shop, couldn’t deliver their macaroons, and that sort of clicked. So it’s not like there was a direct line he was working at, you know, her mother had a restaurant, you always knew there’s a problem with, you know, delivery of restaurant, and he ended up creating this missionary company, they wanted to start a company, there were four business school friends, they went out, they asked, you know, a bunch of people, you know, what, what’s your today problem, and they solved it. So, you know, oftentimes we try to make things look like, you know, people always knew about it, and they revisionary about it. And you know, a lot of founders when they’re talking to press, try to portray it that way. But when you dig deep, it’s like, you know, there were smart people who wanted to create a big company, and they try to hunt and find a good problem solve.

18:18
It’s interesting, there is this consistent theme about these serial creators and in builders, not necessarily serial tech founders, but people that have been either, you know, grinding in different industries or in tech, prior to their big success. You know, I’m curious, while we’re talking about ideas, we hear on this show have heard the classic trope many a time, you should invest in painkillers and not vitamin pills. I’m curious if this is an area you looked at, and you know, if you agree or disagree,

18:48
I did. I did look at that. And so the data shows, I think 70% of unicorns are painkillers and 30% are vitamin pills. And the data also shows you’re more likely to become a unicorn if you’re a painkiller versus a vitamin pill. So it is a truth it holds. However, you know, still 30% of unicorns are vitamin pills. And these are the companies and the ones that succeed are the ones that created the sticky habit, created the community paid a lot of attention to brand and basing your created created habits. And you know, so creating vitamin pool companies are totally fine. You just need to know what your game is, and tried to create a strong brand and habit and community around that vitamin pill type of company.

19:35
Do you have some examples of what some of those standout vitamin pill companies were?

19:40
I would say you know, BuzzFeed, Glossier obviously Tick Tock, Snapchat. I wouldn’t classify them as painkillers, I would classify them as vitamin pills. And but you know, each and every one of them had a great brand and had a great community and they formed a very sticky habits.

19:58
Mm hmm. Yeah, there’s a lot out there, especially in the consumer sphere that I would say, maybe doesn’t even fall into that way of thinking, you know, they’re more like emotional decisions, they’re more things that appeal to people’s desire for self actualization or interaction or community. And so it kind of extends beyond just the the pain or vitamin. But it’s an interesting, interesting way to think about it. How about geography, we’ve had folks on the program, like Ed from boldstart, or Ben Sun from Primary and 10 years ago, the greater LP community was extremely bearish on New York City. This is like only 10 years ago. And of course, now it’s commonly accepted, as you know, great center for exporting unicorns. Did you get any insights on geography and any trends over time?

20:46
Yes, so you know, the data is historical. So it’s not going to predict what’s going to happen in the future. And I think there was a tectonic shift that happened, you know, in the past couple years, especially with with the pandemic, towards, you know, distributed remote work. So I think it’s going to be irrelevant. But you know, historically, when I look at, look back at the data, half of unicorns were founded in Silicon Valley, and companies that very started in Silicon Valley, they were more likely to become unicorns. So there’s no doubt in that statistic. But there’s a lot of other metros as mentioned, you Southern California, New York, and a bunch of other, you know, metros, and Boston, you know, each had almost 10% 8, 7, 8, 9% of the share of unicorns. Looking back. Now, I think looking looking into the future, a lot of these things may change, I think, we may no longer be able to actually say, you know, a company is headquartered where the CEO might be somewhere and their employees might be in other locations. So who knows what’s going to happen?

21:44
Part of our analysis here, we’re investors as well at pre seed and seed, and we tend to invest in non-coastal founding companies. And we have something called the founder meter, we look at the business side as well. And some other factors, of course, but we try and rank the founding teams on a variety of characteristics, things like resourcefulness, things like speed and tenacity, things like capital efficiency, and there’s a few others, right? Did you attempt to assess some of these characteristics as well? When you know, you’re doing your your data collection and analysis on what makes super founders?

22:19
Yeah, so you know, the ones that you can quantify, right? So what you can quantify is, how many how many years they worked, where they worked? What was their title, you know, obviously, quantify things about how much money they raised, they have raised or you know, how fast they raised money or how frequent they were? It was their fundraising. So you can quantify things that are quantifiable and are less kind of relying on Judgment.

22:44
Yeah,

22:44
But I couldn’t just say no this founder was disconnected or not. But you know, but just looking at the stories, and then I did a bunch of interviews as well. Intuitively, you can, you can definitely see those things that you know, it wasn’t random, these people didn’t just become lucky. Yeah, maybe they make they got lucky summer in their life. Maybe their first company, maybe, you know, they could drop out, maybe they could, you know, not care about student debt. They were privileged and lucky summer 100%. But then they use that lock or whatever kind of reputability that they did gathered to go and create a lot of resources and connections and people at the end of day, I feel like, you know, a lot of it came back to you know, how resourceful these founders were, who they knew how they could hire, how they could talk to the investors, if they could raise money. And if they could recruit great people, they ended up, you know, changing the idea a bunch of times and finding something that worked.

23:36
Any good takeaways on recruiting and hiring at the early stages.

23:40
Yeah, so, you know, again, this, this is mostly anecdotal, not from the data, but from the interviews. And, you know, we hear that from everybody, like, you know, hire the best people, but you know, these people just made it late. It’s, it’s very hard if you don’t have the next engineer, and your product timelines are, you know, getting delayed by three months, but they were, they were happy to make that sacrifice, and delay things and wait for the right person to add to the team, especially in the early days. That’s one thing. And the other thing is the CEOs, like they were involved with recruiting and it was their number one priority for a very, very long time until they were you know, hundreds of people even in some cases 1000s of people, they are still, you know, involved in recruiting and even, you know, having having to say hello to a new employee or interviewee,

24:30
Very good. You know, before we move on, just want to close the loop on on the capital efficiency. part of the discussion, there was this great chapter on it. And you talked about Katrina Lake and Stitch Fix. And it lends well to the discussion about VCs impact on outcomes, both the volume and the likelihood, did you find correlation between amount of dollars raised and either the likelihood of outcome or the scale of the outcome

24:59
so When I compared companies that later on ended up becoming unicorns versus the ones that so my control group is companies that raised $3 million in venture capital and did not become a unicorn, most of them failed over smaller exits. So when I compare these to the unicorns, even from the seed round, they were able to raise larger rounds, and they were able to raise more frequently. And at higher valuations. So a lot of times, I think, especially in this macro, it’s very hard to say that’s, you know, it’s fine to pay up for a good company. Because every company is now super, super expensive. every deal is super expensive. But you know, historically, when I look into data, that that has been the fact for the past, you know, 15 years where the data goes that companies that ended up becoming unicorns, they were known quantities, even from the seed round, they they raised larger seed rounds, they raised on higher valuations, they raised faster, and on the series a round, the difference was very stark. So the series, a round of the companies that ended up becoming unicorns, I think was an average two point something times 2.3 times larger than the non unicorns. And they raised money 40% faster than the non unicorns, even their seed round was one point something times 1.78 times larger than the typical seed that you see, do you know that a lot of these companies, people recognize the value of those founders, and it was a great idea or a great team or market, and even the seed round was competitive in terms of capital efficiency, what the data found was, you know, we typically think about, you know, most capital efficient companies or SaaS companies, that’s kind of what comes to your mind, right. But not necessarily, among the most capital efficient companies, you see, even former companies, you see hardware companies use, you see companies in a wide range of companies. So SaaS companies could be among the least capital efficient companies, and could have been among the most capital efficient companies. So if you go very high, on that capital efficiency table, you see a bunch of SaaS companies, but you also see a bunch of, you know, other types of companies. Same thing, if you go at the end of the table, what are the worst companies in terms of capital efficiency, you still see a bunch of SaaS companies and a bunch of other companies. So, and companies that actually their business models were required a bunch of capital, so they were kind of I labeled them as medium or high capital requirement, they were more likely to become unicorns. Now there’s, there’s a little bit of a, you know, discussion there of what that means. And it’s not it’s not that direct, what it means is, you know, a bunch of companies that are, you know, maybe consumer, or maybe SaaS, there’s a ton of companies that are created with, you know, very low cost type of companies like low tech companies, so the denominator is high, there’s a lot of companies that get started. And that’s, that’s why, you know, the chances of those companies that becoming unicorn are lower, it’s bigger, because the denominators is larger. But then in companies that are deep tech, or require a lot of money, there aren’t that many founders that can raise, you know, massive rounds on like, very high, high capital type of projects, like building rockets, or building satellites and these kind of stuff. So that requires a special type of founders with certain pedigrees. And then, you know, there’s less companies there, and you know, they’re more likely to succeed and become billion dollar companies.

28:23
Interesting. Yeah, I want to talk about differentiation in competition a bit. Before we do that, though, you made this point about sort of the cycle we’re in now, and expensive valuations. You know, it’s funny, because on one hand, we see data in pitchbook, and others that say, you know, the least number of emerging funds in seed focus funds are being founded right now at the moment. But on the other hand, it just seems like, there’s more money that’s being deployed into these deals and more players, you know, how do you reconcile that? And what do you attribute sort of these escalating valuations to?

29:01
Yeah, I mean, at the end of the day, it all goes back to the macro, the macro is doing doing well, interest rates are low, inflation is high. There’s a bunch of macro level things that are impacting venture capital, and it shouldn’t necessarily be this direct, but it is, and you know, the venue, when you can have billion dollar acquisitions and you know, 10s of billions of dollars of IPO outcomes there, then, you know, paying paying 40 million versus 20 million post money valuation for a company may not seem different. It definitely does make a difference in the portfolio level. But, you know, people make exceptions. And I think to reconcile what what you were saying that the point you made, it seems to me there’s, there’s more money deployed in maybe less companies, so it’s being more concentrated. So we see a lot of balloons, you know, because there’s a lot of micro seed or small seed funds that that want to participate. You see a lot of you know, seed rounds like 5, 6, 7, 8, 9, $10 million seed round. That, you know, is made of a bunch of 500 paychecks or 250 paychecks. And I’m not, that’s not necessarily a bad thing. But that’s that’s the reality of what you’re seeing, you know, when there’s a quality founder quality company founded, a lot of people with small checks want to get involved and you know, they can probably help as well in the future. So that you’re, you’re seeing a change of Dino seed rather than having one lead and a bunch of very small checks. Nowadays, you’re seeing a bunch of small checks a bunch of 200k checks, creating ballooned up seed round.

30:31
Interesting, but there still is a lead involved that’s pricing those things at levels that are quite a bit higher than they were two three years ago.

30:39
Yeah, yeah.

30:41
Ali, so let’s talk a bit more about competition. You know, I hear almost daily from investors that don’t I don’t like this space, because there’s too many players, right. A lot of existing players. Was there any advantage? I guess, to being a first mover vs. technologies are companies that that were not first movers, did that play a role in success, you know, the competitive landscape that a new startup was entering?

31:05
So I talked about this concept in two different places that book in terms of being being a first mover, no. So in fact, only 30%, less than 30% of unicorns are for first movers, or for the first company to enter that market, or do that thing. 40% were fifth or later, and another 30% are second, third, fourth, or fifth? When you look at competition, you know, a lot of founders tried to say we don’t have any competition or feel like that’s a good thing. Turns out only 15%. One. Five. 15 percent of unicorns did not have competition, when they started 85% of unicorns did have competition when they started. Now, this is where when I talked about, you know, you need to go above and beyond the proxy rule one go one layer deeper.

31:51
Yeah.

31:51
Right. Having competition is not a bad thing, you need to look at what type of competition. So the most common case among unicorns was a vendor competing with a large incumbent. So you know, your new startup, and you’re using the inefficiencies in the market by the incumbents, companies. And those type of companies, not only they were the most common among the unicorns, but also they were more likely to become unicorns compared with the control group with the with the random sample that I had. Same thing for fragmented markets, if it’s a fragmented markets, there’s a bunch of you know, very small players, there’s a very long tail of maybe old, maybe new, but you know, nobody has major market share. That was also a case that it was it was fine to have competition, it was even, you know, Major, more likely to become a unicorn in that sector. The one, you know, the thing that was less common among unicorns was the cases where there’s already a couple of highly funded startups in that space. And then you also enter the same space. And I think like, you know, a lot of a lot, a lot of investors kind of fear that this is a very competitive market. Know that that kind of makes sense. If there’s a bunch of startups, not old companies, not Google, not Salesforce, when there’s a bunch of startups that already have raised a ton of money and doing the exact same thing, then that’s that was kind of the rare case. Among the unicorns, the most common case was, you know, you’re competing against JP Morgan, you’re competing against American Express, you’re competing against, you know, the old housing companies, or even in this day and age, you can say you’re competing against Google or Oracle or that type of companies.

33:32
Guess, this is also related to creating new new markets versus entering existing. Right.

33:38
Yeah, it’s it’s kind of directly tied to that. And there is also another misconception there, that, you know, if you’re creating a new market, you will create a larger company.

33:50
Yeah.

33:50
And that is a misconception.

33:51
Really?

33:52
Yeah. So on average…

33:54
not to interrupt. But many years ago, we had I mean, I think it was episode five or six. Jerry Newman was on talking about Eileen Lee’s report, that we’re dating ourselves here, there’s probably younger listeners that have no idea what we’re talking about. But there was a very small number of unicorns at the time. And it was

34:11
78

34:12

  1. And the belief was that the vast, vast majority are creating new markets, and those that are entering existing markets or crappy companies. It sounds like you You found that was not the case.

34:25
Yeah, and I guess part of it would be like, you know, how you are labeling and what your definitions are. So maybe these two things look contradicting now, but when you go deeper and see, you know, how she labeled what’s, you know, what you what you define as entering new markets, or creating a new market versus expanding and how I labeled them and to give examples in the book. So maybe, maybe you’re talking about the same thing. Maybe the markets have changed, maybe dynamics have changed. But yeah, I mean, on average to companies that competed for share in an existing large market, they created larger companies, then companies that created a new market. So for example, Zoom massive company that wasn’t first to market it was competing for share and you know, started with, you know, no market share. And you know, six years later was the first, you know, the largest market roller.

35:10
Yeah.

35:11
So you see a lot of those type of companies that come in and compete with, you know, old incumbents and take the majority of the market share, and they’re, you know, massive 100 billion dollar market cap companies.

35:20
Well, even what Google was the 17th, search engine and Dropbox was the 15th file storage solution and Airbnb…

35:26
Right, we just, we just tend to forget, we just tend to forget the historical context,

35:30
right? You know, this actually, this dovetails? Well, I wasn’t gonna ask this. But, you know, how do you think about timing? You know, we have so many people come on the show, talk about post mortems. And they say, the timing was wrong, right? Either the timing for the opportunity, or the startup just launched at a bad time, because the market was down or whatever else. And it bothers me every time I hear that, because best founders, I think, should find the timing and should talk to customers and figure out how to pair technology solutions. But, you know, how did you think about timing, both from a launch standpoint, and then also sort of, you know, finding the right market for the right time and technology?

36:10
Yeah. So that that I think that that is and will remain the hardest code to crack, I don’t think I cracked that code. I don’t think the data showed anything useful there. So the data showed, you know, less than 30%, were first time first company to try that idea, another 30%, or second, fifth, and more than 40% for 5th or later. And from the anecdotal examples and interviews, when you look at them, a lot of these companies became successful because something happened in the market. And even they became lucky. So for example, Oscar Health company sells health insurance. And it became successful because the Supreme Court reenacted, reaffirmed the Affordable Care Act, and it opened up the individual health insurance market. And that was why this company became successful. And then they started a company, this was not reaffirmed, but like two months later, this was reaffirmed, and that’s how the whole company got built.

37:08
Sure

37:08
You can say the same thing about you know, why Snapchat kind of worked, because it was the time that you know, the front facing cameras, were getting better policy, you can say the same thing for Uber, you know, a lot, Gil in the book talks about, you know, that the European mobile carriers, they used to charge, you know, $1 per API per call for GPS location. And it was right at the tip at that time that, you know, GPS was becoming abundant. And, you know, it was just cheaper free for carriers to get that information to, to an app developer for free. And it was after the iPhone in the app store, and that, you know, enabled all these things. So a lot of times it feels like, you know, you can you can link the success of a company even, you know, epic and Cerner, you know, massive companies in healthcare IT, they you can link all of their success to a regulation change that, you know, it mandated electronic record records. Even in a FinTech industry, there was the thing that enabled a lot of financial technology companies was a Dodd Frank Wall Street production act, that basically forced banks to give electronic access to customers data to a third party, it was part of that act. And it just opened up the whole space and created a bunch of companies. So you can you can kind of link every successful company to some sort of regulation change or technology enablement or something about the market. But on the other hand, it might all be just luck. And you know, the good the great founders just work on an idea at the right time, because you know, they see a shift in the market, or they change the idea so much that they can catch a tailwind. So I don’t think we can necessarily say if it was the founder, great founders finding something or when people do not succeed, they just say it was bad luck and wrong timing. I don’t think I’ve cracked that code. And it’s gonna be a hard one.

38:49
But you did, you did have some conclusions around when companies are started, right. Like if there started in a in a down market versus an up market. I seem to recall in the book there, there was some takeaways there about sort of the size and scale of outcomes.

39:05
So it was just an just an observation on on the 2008, 2009. area, which, you know, weirdly the companies that were started during the 2008 and 2009 cohorts, they ended up creating the largest outcomes among all the unicorns that were created in the past 15 years. But, you know, part of that might be just luck. You know, there, there was one Uber and one Slack, you know, that just changes the average and everything else. It might be just because I feel like you’re seeing it now. You know, riding the pandemic has voted with less job security, a lot of people ended up leaving their jobs and starting new companies, starting companies. So it might be the case that more companies were started. I see no. Now in the past year that a lot of people are starting companies. I feel like there’s a lot more founders, a lot of people left their jobs and are starting companies. It might just all be just luck. We don’t have enough historical data, there’s a lot of companies that were created in recessions, there are a lot of companies that were created in, you know, bull markets. And, you know, just just there’s one observation that the companies that were created in 2008, 2009 ended up becoming larger companies, but it might just be the whole thing might be luck. So I don’t think we can, we can conclude anything specific from the data.

40:21
So Ali, the data isn’t predictive. But I’m sure you’ve looked at a lot of data at this point, you’ve seen probably some trends. And you probably have some ideas about which of these factors may change over time over the future in the next decade. And I imagine it informs your investment approach a bit. Talk to us about maybe your thoughts or predictions about maybe some future trends. And what will make sort of the super founders of the next decade, you know, when you write the follow up book?

40:56
Yeah, yeah, I mean, 100%, I think a bunch of these things will change, like geography, I don’t think that will stay around, I don’t think 50% of the unicorns that are that are going to be found within the next 10 years are going to be started in Silicon Valley. I feel like the the whole, the general idea of the book is predictive, that, you know, just forget about these proxy rules, it doesn’t matter if you have an MBA or not, it doesn’t matter if you’re technical or not, it doesn’t matter if you’re a solo founder or not. And what matters is, you know, if the founder has just kept on building and I basically the way it has changed the way I look at the companies, his ideas can change, markets can change. Let me forget the kind of these preconceived notions and judgments that I have that because you, you were working on a different idea, then, and now you’re working on this idea, you’re a bad founder, or because this market, there’s two companies that have failed, it’s a bad market, and look at the grit of the founder than you how many times they failed? What if they built how they made a million dollars? Have they created a company before? I don’t care if it was in a $500 million exit? But you know, did you create something? Did you have an actual actual higher cut kind of situation? Did you have a small exit? So that’s that’s kind of how it informs the way I look at the data. And, you know, I definitely saw it when when we invested in Carbon health. So you know, I was kind of one or two years ago, which is fresh from from analyzing this data. And that kind of informed my conviction for the investment in Carbon Health. You know, that’s, that’s a massive company now with with 1000s of employees. And that’s the story of that founder, you know, I think Carbon is his fourth or fifth company, annual couple of couple failures and one Udemy. And then we invested Udemy was in kind of as big. But, you know, you see that pass and pattern among, among Aaron Bolli and you know, a lot of other founders that that I’ve tried to back

42:48
Ali if we could feature anyone here on the program investor or founder, who should it be and why.

42:53
Eren Bali the co founder of Carbon Health, I don’t know if you’ve had him on board or not. And then I have a founder Andrew Job of PlotLogic that says another company that’s that’s basically building the Rio Tinto 2.0. That’s basically the future of mining technologies, how the mind of the future in 2050 would look like. And that’s a very exciting company. And, you know, very interesting industry that I’m excited about, you know, how can we change the mining industry?

43:21
Ali, what do you know, you need to get better at

43:24
a lot of things. I think, maintaining maintaining Long, long relationships and building your reputation over the long run. I feel like that’s, that’s the most important thing for any investor. And it just takes a lot of practice to get better at it at you know, building your reputation and your brand is things that people talk about you when you’re not in the room. So I hope I’m going to the right direction of you know, building brand that people people talk good things about moving, I’m not indifferent.

43:54
Well, if the book is any indication, it seems like you’ve had a lot of good thought partners and people helping out. So kudos to you on that. And then finally here, Ali, what’s the best way for listeners to connect with you and follow along with dcvc?

44:07
Yeah, so my email is Ali at DCVC.com. And you know, you can find more about the book at SuperFoundersbooks.com, it’s available on Amazon, audible Kindle, and, you know, basically every country and then you know, if you have a pitch or a company, that’s deep tech, my email is Ali at DCVC.com.

44:26
Well, Ali, thanks for going through the three year journey to research it and write it. We pride ourselves here on demystifying VC and dispelling conventional wisdom. And you’ve done that in many ways, which I think will really encourage a lot of founders out there that don’t have the shiny resume, so to speak, but have all the right characteristics to build world changing tech companies. So thanks. Thanks for that.

44:50
Thank you for having me. And great questions, Nick.

44:52
All right. Take care, sir.

Transcribed by https://otter.ai