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Hi. This is James Currier, and today on the NFX podcast, we've got Ali Thomasp with us. He's a partner at DCVC. And originally, he came on Morgan and moved to London, started startups, and then came to Silicon Valley in 2015, and he's written a book called Super founders. It just came out yesterday.
And that book was the result of his study of 100 of startup founders who had created companies that were worth over a $1,000,000,000 And there's a history of people in the venture industry studying the overview about what makes for these unicorn companies, and guys like Nabeel Hyatt over Spark Capital or people like Chris Farmer over at Signifire or ourselves here at NFX where we've studied the unicorns and to see what produce The returns and what,
of course, we found is that it's largely correlated to having network effects in your business. And that's why we're called NFX, but unlike the rest of us, he's really published his results in this which is very wide ranging. And so today, we've got him on the Pete to talk about what he learned, where the data came from, and what he thinks the best advice for founders would be.
And so today, we've got Ali on the NFX podcast, and we're gonna talk with him about what the best learnings are for founders. So, Ali, so glad to have you here on the NFX podcast today. I'm super excited to be here with you, James. Yeah. So a few years back, you published a blog post about super founders. And now you've turned into a book.
You've gone Currier. And I wanna get into that with you today and just talk broadly more about how we think about analyzing what makes for great companies, what makes for great startups. So talk through us about your background, and I'd love to just have everyone here sort of where you came from because it's a great story. My own history. Sure. I'm originally from Morgan, and I actually lived there and grew up there and started a little company back there.
And then went to studied electrical engineering and then went to London, did biomedical engineering, did a lot of research on brain computer interfaces. This was a decade before Elon Musk made, made that space sexy. You know, published papers there and then started a consumer industrial hardware company, grew it to, you know, a larger team and 1,000,000 of dollars in revenues.
That was before starting my Currier adventure and getting into the dark side or the other side of the table as people say it. And it's been a very rewarding journey. I think it's And how did you come to the US? I came to the US through the screen card program called the extraordinary ability program, EB 1a, did you go to the GSP? I did a program there. I didn't do a full MBA. I just needed something to get my foot in here. Got it. And what year did you come to the valley in 2015. Got it.
And then what happened? How did you end up at DCVC? I had my startup, and I was growing a team in Sunnyvale and then you know, in that transition and then I went to GSP and I was thinking about what's next, you know, maybe it's a different company or going on a venture side, or I didn't specifically know what to do. So I started writing. I was thinking about, you know, what was next.
I started analyzing a bunch of spaces, and I, you know, wrote about different ideas that I had in Venture and, you know, in Healthcare AI, and this was the early days of blockchain infrastructure. So I wrote about them. You know, some of those early media articles piqued the interest of some Pete, and I got, you know, Matt aqua through Twitter direct messages. And I had a job, I think, a couple of weeks later. That's fantastic, and that's great. This guy's doing a great job over there.
And so then you got it to DCBC. And while you were there, is that when you decided to do this study on what super founders are? Right. At that point, I mean, the idea I had it was even before that, and I think some part of the work I'd even started before kind of starting my job at DCDC. The question definitely was there and a little bit of the work was started, but yes, I continued to work along the way. Got it.
So talk us through the methodology you used to gather the data to, write this article and then the book. How'd that work? Sure. So, you know, I think as founders, a lot of our inspiration Morgan lot of our role models, from what the media tells us and comes from what we typically Pete. And what we typically see is the very, very famous stories. It's the story of Zuckerberg. It's the story of Bill James.
And, you know, they made their way into movies and every popular media about startup entrepreneurship. And, you know, it was a very curious question to me that does Beller successful company look like them, or do they not? Because I was getting a sense true some of these companies that are becoming successful or were becoming successful at a time that a lot of them look different.
And I think I had an inherent question that, you know, can I become a better bench Capitalists by knowing, you know, by data driven, if, hey, if some companies work better than other companies? And I guess, you know, a lot of people have probably thought about this question, but it's a hard thing to collect the data on.
Not that many people have collected a similar data set Of course, there's data on funding on platforms like pitchbook and Crunchbase about the history of fundraising, and we know the names of the founders of companies. But there's not that much data about, you know, for example, how many competitors did a specific company have when they started? What was the market size? What was the carrier pass?
You know, we don't have quantified data on the titles of these people, the companies they had worked, the companies they'd started their, you know, success level or whatnot. You know, there's a little bit of study about academic background of some success and founders, but, you know, a lot of these come from the academic side, which oftentimes I feel like they don't define a startup similar way to how, you know, people in the venture or startup community define what a startup is.
For example, there's this, you know, Harvard article about the age of these successful founders, you know, that shows they are 45 and older and those are more successful. But when you at it, you know, only 20 percent of those companies they had analyzed are C corps. And I guess, you know, a 100% of the companies we analyze are C Corp. So that kind of shows, you know, we're looking at different types of universes from the academics who are looking at startups.
So, you know, I thought Pete me look at every company that has reached a $1,000,000,000 or more valuation through, you know, private funding, public IPOs acquisition. And I defined 65 elements from founder and team, market dynamics, ideal origination, defensibility, all the way to fundraising, you know, and the elements around money and efficiency with money. And I collect this data and publish some of the early results that media article back in 2018.
And, you know, a lot of people talked about it. Probably one of the most viral articles of that time, you know, close to a million people read that article. 2017. Was it 17? Oh, no. May 18. Maybe 18. So, you know, a lot of people started talking about that article. You know, I I got a lot of very interesting bosses, a lot of very, very famous people reached out talking about this research.
And I guess one of the key feedback that I got is, you know, this doesn't mean anything without comparison to a baseline. You know, if you just look at unicorns, what does that mean? And that was fear of feedback. But collecting data on a control group is, you know, very, very hard task. So for the next 2 years, I set on to collect the same 65 data elements on non unicorns, companies that raised a minimum of $3,000,000 in venture capital.
And, you know, probably 1 or 2% of them became unicorns, but the majority of 98% of them did not become unicorns failed, you know, over Omri. So I collected the same data set. It took me 2 years. I know a lot of people again think this data piece is you can automate that. You can put NLP on that. It's all judgment. Like, literally, I collected this piece by piece of one data point by one data point, you know, taking me 10 minutes each data Flint. Was a very, very manual task.
You can't even outsource that because I think there's a lot of judgment that goes through trying to get to the truth. I used the way back machine and went to the historical interviews. Sometimes I emailed these founders and asked them about information about the early days of their company. So it was a very hard data collection task. And, you know, after 4 years of data collection, I finally got to analysis and, you know, compare them. And the results for talking.
I think a lot of these things that we think matter in terms of what success is, they don't, and it was shocking to me. And, you know, there was a bunch signals that I also observed about what does matter and what makes people more successful. And I got to, you know, interview. I think 16 people, founders of these $1,000,000,000 companies, anywhere from Eric Yuan of Zoom and Tony Fidel of Nest and Tom Preston of GitHub to investors from Alfred Flint and a lot Gil and Peter Teal.
So that was a good kind of process for me to learn from them and write this data and put everything together into book. The pandemic came at the right time for me, so I could write the book. Yeah. No. It makes sense. And so of these people that you interviewed, who give you the most surprising interview? Rachel Carlson, founder of Guild Education. It's one of the most fascinating interviews I did.
I learned so much from her, and it's a very honest interview that teaches a lot of things and goes into a lot of interesting details. And it's all in the book. It's mostly in the book. Yeah. The other one is Max Mullen of Instacart. I think I had an interesting conversation with him as well and kind of learning about his history and only life hacks he does. That was interesting. Got it. And so with the guilt, what was the thing that stood out?
What were some of the things that she said that stood out? Let's see. So one thing is about you know, not solving your personal problem. And this is also another, you know, a topic that I discussed in the book that, you know, a lot of times we talk about founders are more successful when they are their own customers. And they're solving a problem for themselves. And that's not really true.
I feel like a lot of people are trying to create that narrative to get better press and stories and you end up hearing that story, but when you dig deeper, you see everybody went through an ideation process. I actually have a coach from you in the book that, you know, you talk about. All of these successful founders, they go through this liberate ideation process, and we don't necessarily hear about a couple of years after the startup was founded.
So same case for her, I mean, she's a Stanford GSB student, very successful work at the White House. She's not a college student, but her product works for the customer is college students. So she was never in their shoes, but she created a product that works perfectly for them and understands them and, you know, got to work for So that's one about, you know, how she got to learn one side of the marketplace that she was creating even though she was never in their shoes.
And then the other part was about the location geography that, you know, the company was founded and funded in Silicon Valley, and they deliberately moved the company to Denver, which, you know, isn't a big tech hub necessarily or not Pete. There was a sharp decision to do that and how they convinced They basically ran a blind recruiting process and showed the board the candidates, and the board picked the candidates that were in Denver. And they like them more than the candidates.
They could recruit in the San Francisco Bay area in terms of quality. That's how they convince investors. Got it. That's interesting. So the book is shooting some sacred cows in the startup community, right? When you set out to write this, did you start with that intention? No. I did not.
I mean, I feel like I wanted to be very honest in the book, and I hope that comes away that, you know, at every Flint, I talk about, you know, This is what you're seeing the data, but here is what opposes the data, and this is representative. This is not. These are, you know, try to be very honest. And in the conclusion, I say, you know, take all of these data points and analysis with a grain of salt and, you know, like any business book Pete with a little bit of a skepticism.
So, no, I'm not that type of person who tries to, you know, create controversy and get media around it was just what I found and I wanted to share it with the world. And, you know, some of them are against what we thought are true. Some of them, you know, confirmed truisms. Yeah. Got it. There has been this wonderful history of people trying to do you've gone much further than anyone else, I think, in collecting the data and then publishing it. You know, I know that Nabeel Hyatt did this.
He's over at Spark. He's a great investor over there. He looked at this. He'd never published. I know that farmer over at Signal Fire has done this. I know we've done this, in particular, you know, around looking at all the unicorns and how the network in the business models have affected their outcomes. And of course, you know, the state of Massachusetts back in the eighties nineties did similar studies in an attempt to figure out why they were losing to Silicon Valley in the race for growing.
You know, and so there's a long history of people generally looking at this. And now I'm wondering Have you looked at other broader issues than the ones you wrote about the book and you might follow-up with it?
You know, have we looked at, you know, in and out networks you know, people who are in network versus out network or something like that because we did a article called your life on network effects that is one of our more popular ones where analyzes the network decisions that we as people make.
They either put us in network to learn a certain amount of information so that we can learn enough to, you know, start a company like Gil, or we've made choices that take us into networks where we don't learn those things and we end up, a different life path. Do you ever look at in network or out network. Because you, yourself, you know, have spent time at the GSP, you yourself are working at DCVC.
You've moved to Silicon Valley, you know, from to London to Silicon Valley is moving up the chain toward the, you know, Rome, if you will, for this industry. You know, have you looked at in network or out of network yet? Think a lot of what I learned from looking at data is basically this first layer of data, a lot of times their proxy rules, and you can't make many decisions from the first layer. You need to go one layer deeper at least on everything.
So for example, you know, the data says age by itself is an correlating factor. And now you can go deeper into the, you know, types of years of experience or what you've done during those years that you've worked. And that might be a predictive factor. Or, you know, a lot of people talk about, you know, you should be technical. If you're nontechnical, you know, we don't invest. And, you know, again, you can go deeper and say, okay.
You can be nontechnical and, you know, in fact, half a $1,000,000,000 companies are started by nontechnical CEOs. And you can go in and say, okay, if you're nontechnical, but you've done this and that, then you're Flint. And you're even probably more likely to start a $1,000,000,000 company. And you could be technical and you may have not done these things and these other things, and you may be less likely.
So I feel like in a lot of these factors that I analyzed, you have to go one layer deeper. And I think one of the great feedbacks that I got when I was writing the book was, you know, the first draft of the book was a lot of these first James. And the feedback that I got was, you know, Flint these secondary James. And I think that helped the book, you know, improve a lot by me kind of trying to match one signal to another and kind of go deeper in a lot of these factors.
In specifically, I think one thing that I found is a lack of socioeconomic diversity. So, you know, obviously, the problems gender diversity and racial diversity. And there are things happening there. You see proof that things are improving. You're more female founders starting companies getting funded and starting unicorns. We see that trend still a very small part, but at least it's growing. What I don't see in the data is socioeconomic diversity.
And it's basically, you know, if you did an MBA and you could start a company right after or if you went to college and you or the company right after. They didn't have to worry about paying debt or you could fail forward. Now you see a lot of these founders came from families that they could fail. They could take the risk. They could you know, not take a job and go and start a company or do a lot of these things.
And I think that is one risk that we are running into that you know, at the end of the day, we are creating a filter with socioeconomic diversity, and I hope we can do more there. And I will be donating all the proceeds from the book towards kind of offer social mobility, and it wouldn't do anything. But, you know, I think that's a larger trend that we should look for. Yeah. It's interesting.
You know, we talk a bunch of in our analyses about networks with preferred attachment where those who are ahead get further ahead. And, you know, it's such a pernicious that it was mentioned five times in the Beller. Mhmm. So it's been around human societies forever. And the quote in book of Matthew says, to he who hath shall be given. And what people don't normally quote is the semicolon, and then it says, and to he who hath not, everything shall be taken away. It's like, woah.
You know, it just feels like there are these boundary layers of sort of mathematical activity around the network where people who are socioeconomically advantaged have the opportunity to not only learn what they need to learn to build a $1,000,000,000 company, but they have the sort of emotional space and the safety to try and fail. And it allows the winds of time and compounding math to sort of keep pushing them forward. And so you're seeing some of that in the data, I guess.
Let's dig into what you found. You've got a lot of things you've looked at in the book. What were the sort of top 3 things that you think were surprising to you or things that Beller you in your daily activity as an investor at TCBC. Of course. Let's start with a couple in different categories. The first one is solo founders. I think there has historically been this negative connotation around, you know, if you're solo, you're not going to be successful, go find a co fund.
The first step to start a company is to go and find a co founder. And, you know, the data shows 1 out of every 5 unicorn that was started in the past 15 years was solo founded. And when I compare the two groups, they are not less likely or they're not more likely. In fact, I found, again, similar to age, number of co founders is not a correlating factor with success. So you can have 5 co founders be successful.
That's rare, but doesn't mean if you have 5 co founders, you're less likely to succeed. And you can Beller, you know, be a solo founder and succeed. And I think that there, where it relates to action for founders is, you know, if you think you have everything, you don't have to force yourself to find a co founder.
On the other hand, I think the bigger problem is a lot of people think that exactly needs to be 2 co founders in a company, and they wouldn't give a co founder title to the 3rd person or the next two people, the next three Pete, because they want to make sure the company stays at a dual co founder situation.
And, you know, as long as you have a CEO and you have a process, it helps you attract the best team, you can call everybody of all the 5 initial team members, a co founder, and attract best talent So that's one of the interesting points that I saw. Yeah. Interesting. The other one, which is a little bit counterintuitive because, you know, everybody talks about pairing a nontechnical founder with a technical founder or vice versa.
In fact, when you look at the unicorns, the nontechnical CEOs they were more likely to have picked a nontechnical second co founder. And the technical CEOs they were more likely to have picked a second technical co founder. So it's more likely that you're technical, technical, or nontechnical, non tech than what we typically think as, you know, the Wozniak and James of the world. Got it.
And what about sort of where people have worked You know, because one of the things that people say is, oh, I saw this varsity blues thing saying, oh, parents are paying 1,000,000 of dollars to get their kids into the best schools so that they can get into the network to learn, you know, and then be more successful in life. Is there any correlation there in schools? There is. Basically, the unicorn founders, they were more likely to attend Pete 10 schools.
However, this is the important part, and I think that's the honesty part of the book that when you look at the full distribution, there is as many and even a little bit more founders of these $1,000,000,000 companies that had gone to schools not even in the top 100. So that's a long tail. The long tail is even Morgan. Than the top 10. So obviously, they are more likely because from 10 schools, you have a third of the unicorn founders, but the long term is long.
And, you know, there is a lot of founders that didn't even go to school that I had heard of the name and they found that, you know, massive companies. Got it. So the school thing is correlated, but, shouldn't make everyone feel as if they have no chance given that they weren't in, on the top 10th. Of course. Yeah. So basically 64% of Unicorn founders did not go to a top 10 school. That's a good thing. What about where they worked?
Because a lot of people say, oh, you know, if you get a job at Facebook, then you're trained in all the ways of, you know, viral growth and you're trained in data, and then you have an advantage when you go and start your company. Have you found that to be true? It's a very similar distribution to the university think.
So, yes, they were more likely to have worked at brand name schools and some companies come in that, you know, for example, McKinsey and Goldman as well come into that it's not just Facebook. And even when you go older, you see companies like Oracle and in a more recent cohort, companies like Square come on top. In both of these cases, I don't think there's necessarily a causation effect here. There might be a lot of reverse correlation in that, you know, these very, you know, smart Pete.
They ended up working at Facebook. They ended up starting a company. They ended up, you know, raising money. It wasn't because they had worked at Facebook. Beller learned anything specific at Facebook that they ended up becoming successful. They already had those characteristics, those resources, even if they had worked at another company that didn't have or create those toolsets for them, they would have, you know, gone on to continue in Duval.
Probably the same thing for universities, but, you know, eventually you are the of things you've done and the connections that you collect. So they do end up mattering, but not necessarily directly. Interesting. Because one of the things I saw in the history of the unicorn founders, a lot of them, according to the data, had come from Google. And yet, when I look out at the unicorns I've seen and when I talk to Google alumni.
And I asked them to name, you know, the great unicorn companies that have come from X Googlers. There's a lot of silence in the room, and I haven't done a full study of it, but I do have so I'm just wondering. That was really surprising to me to see that Google was scoring at the top of where the founders were coming from. It is. Yeah. Google was still number 1. Hey, a part of it is, you know, Google hires 100,000 very smart people.
There's a little bit of a number game there that, you know, you can pay 100,000 super smart people that are there to work for a couple of years and go on and start a company, you will end up, you know, having hired a bunch of them. You're listening to the NFX podcast. If you're enjoying this episode, feel free to rate and review our channel and share this conversation with someone you think would benefit from these insights. Follow us on social at nfx and visit nfx.com for more content.
And now back to the show. I I love this quote that, you know, the venture capital game or the unicorn game isn't just a game of home runs. It's a game of grand slams, right? Mhmm. And, you know, these are outliers. These companies themselves are outliers. And so, of course, there's a lot of noise in the patterns, right, because of the attempts, millions of attempts at startups, we're ending up looking at a data set that's just a few 100, right? 100 Morgan 1000 somewhere there?
Yeah. Yeah. Yeah. There is. There is a lot of noise, and I think I tried to denoise this a little bit with some statistical work and try to understand what is actually a difference in the distribution. And now let me tell you about one of the signals that I did observe and that goes back to more of a character thing. That a lot of these founders of these $1,000,000,000 companies, regardless of, you know, where they had worked or what school they had gone or where they were technical or not.
They had for building. When you look at their history and carrier, they had built projects. They had built stuff. They had sold not necessarily companies. They had sold the stuff online. They had sold projects, and you see that. And those kind of people, like even if you don't have the perfect resume. If you didn't go to Stanford and worked at Google, it was like a PM at Google and start a company.
But if you had never started something, if you've never sold something, if you've never created something, the Otter founder who has done those stuff, but never had a chance to work at Google or stafford. I would bet on the other person. I would bet on the person with, you know, who doesn't necessarily have the resume, but has that hustle, has the itch for building, and you can see that.
You can see that in the history of them, you know, failing, winning, Morgan, and it feels like, you know, from media again and from recency bias, that a lot of times we just see the last thing that these exceptional founders have done. And the last company, we feel like it's their only company that they've done. And the only thing that they've created. And so if you're a founder, maybe think, am I a natural builder? Have I always been building since I was or 10 or 8 or 12. Right?
Exactly. Is in my DNA to build stuff? Yeah. Building and selling. Building and selling. So, yeah, that's the character stick that's more important than, you know. Does that come out in the data, or is that sort of anecdotal having with you going through all this, or did you get into measuring That does come from data. Yeah. It did go into measuring that.
And, you know, specifically in the world of venture capital, you know, if a company gets acquired, or, you know, it's a small tech acquisition, it's normally considered a small failure, right, in the world of, you know, venture capital when we're looking for $1,000,000,000 outcomes or, you know, a couple $100,000,000 exits depending on the fund size. You may be even, you know, an exit of less than $3,000,000,000 might not move the needle or be big enough to return to fund.
So a lot of these smaller exits may not mean that much, but they do. And that's one of the key things that the data showed that, you know, like everything practice makes perfect even in entrepreneurship. And founders who had, you know, this is a common trait that you see, you know, founder has been building a bunch of stuff, creates a company, maybe that James, the 2nd company is, you know, a small aqua hire, and it's the 3rd company that becomes the $10,000,000,000 company.
And in each step, they have accumulated these resources, this network they can call on, that first 5 employees they can hire. They know how to deal with investors and, you know, the raise that seed round or series a round. So these things add up and, you know, the founders who've learned the process once and maybe even gone through a small acquisition and seen the full cycle ones, they tend to be a much more likely to build $1,000,000,000 companies next. Right.
So this 10000 hour concept, if you're in the field, and learning every day and having a 100 paper cuts every day, you build up a lot of knowledge and cultural acumen sort of cultural capital as well. As skills as well as knowledge about how to move or probabilities about what's gonna happen next. You get a much more detailed probability tree to move through the idea space to so you can be more successful at every decision Flint. And that's iterative. That's cumulative. That's incremental.
And so that you're seeing has been much more predictive than age or gender or things like or technical or even university and where you worked. Got it. And so as you, you know, think about what a founder should get out of this book, I mean, as helping people to remove some mental roadblocks for people? Yes. I would summarize the book or what I want people to take away from this.
That, you know, there's a lot of unknown bias that we have mostly coming from narrative bias and, you know, very famous stories get a lot more attention and that they tend to create what we think, but there's $300,000,000,000 companies that have gotten created and nobody has seen it at all. Like, even the most successful investors have seen 20 like directly. So nobody has seen it all unless you holistically look at the data.
I mean, if you do look at the data, it seems like a bunch of factors that we thought do matter. They don't. They're just proxy metrics. So, you know, your age, your university, where you worked, being technical or not, a lot of things about competition, you know, is this a competitive space, the dynamics of the Morgan? You know, there's a lot of things on that in the book that, you know, you can mostly forget about.
Them. What does matter is just keep on building and keep on learning at each step. Nobody had that one overnight success And if you start a company and you expect, you know, to start the $10,000,000,000 company in that first try, you know, that may happen, but it's more likely to happen in your second try 5th dry or 10th dry, it's not just talking about, you know, being a serial entrepreneur. It's not about that.
It's about, you know, having an itch about creating a stuff finishing them and, you know, it's these small failures and small successes that prepares founders for what's to come for them. So I hope it removes some stereotypes and biases on the venture capitalist side.
And for founders, it's inspiration that, you know, if you keep doing what you're doing and learn from every step and you know, hopefully you are able to fail forward and start again, you will get there as long as you learn from each step and learn faster and better than anybody else. Yeah. No. I mean, I definitely think we should be encouraging people to get there. I think it doesn't diminish the sort of pain of being a founder.
I remember coming out of HBS and selling my first company a few weeks before the 5th reunion and going back and saying, look guys, it was great to be here for a year and a half you know, working at HPS, but you guys did not do a good job of ever warning us how much pain and suffering it takes to build one of these things.
You know, you have your entrepreneurial curriculum, but you never have a case that just says you know, you are in pain for many of the months weeks years to try to figure this out. And I think with you writing this book and me seeing it and you sending it to me, and thank sending me a copy. That's a beautiful book.
And I felt like, you know, since I started doing startups in the 90s and investing in them, This is sort of a culmination of something that's been going on for 25 years, which is that starting a company, doing a startup, if you will, has become a thing. It's become a path. It's become a package, a lifestyle.
And in the eighties, investment banking, this happened to investment banking where it wasn't a thing before the It was just, you know, your dad did it or whatever, and then you just start doing it. And there was only a few people doing it, but then became a thing. And people would go to high school, or they'd go to college, and the college recruiters would say, hey. You might consider working in investment banking and you say, what's investment banking? And they say, well, we've got a track.
We've got a path. You apply for the job, and then we move you up analyst associate vote. And you investment banker and you make all this money and you're like, woah, I had no idea. And so all these people went into it, even if they weren't natural investment bankers because it was sort of the next rung in the ladder. And the same thing's been happening to startups, hasn't it? I mean, with the transparency, I mean, in the nineties, it wasn't really a thing.
The only people who were entrepreneurs in the nineties were people who were compelled be by their genetics. They were selling worms to fishermen when they were 6 near the lake in their house. And then they were selling seeds from the comic books, or then they were, you know, starting little school stores and selling pencils. They were building. They were selling just like you said. And they couldn't do anything Beller, and they were compelled to be on for nurse. They did it.
There was 80 active venture capitalists in the whole country. You know, that everybody had copied, you know, Greylock and Sequoia and Draper from the sixties and the fifties. And but there's a still only 80. And then it started to become a thing. And people are growing up reading about spoken watching the movies and seeing this. And now everyone's like, well, how do I wanna be that guy? You know, just like they looked at Hollywood said, I wanna be on the screen or they looked Wall Street.
So I wanna make a ton of money doing that. And then it became a thing. And so now just in the last 20 years, because of the transparency of the blogosphere, because it's so publicized how much money is being made and start up it's become a thing. And your book starts to say, okay. Now that it's a thing, let's study it. Yeah. And sort of package it for people to say, well, what are the rules? What are the paths?
For you to follow this path to pursue this lifestyle, which has been held out now in the press as a desired outcome. And so it's drawing in people who might not have always been builders, sellers, doers, but they want the outcome, which is the back end. Right? Yeah. And it's not gonna happen. It's not going to be overnight and everything going up to the right. Right? Yeah. It's not gonna be like they're reading about just like going to Hollywood ended up in tears for so many people.
You know, they get on a less than the seventies and they'd head to Hollywood and they'd get sexually assaulted or whatever. I mean, it's, like, a mess. And I think that's what's happening now. There's this siren call for people to come and do this. And no one's really telling them how hard it is and how deep you need to dig. And your book is helping people understand the context and the probabilities in a way that no one's really shown it to them, made it even more transparent.
I think it's a kind of a watershed moment that somebody's actually published an analysis of this path and what it takes. So good for you for doing that. I'm glad to hear that. And I hope it's more inspiration than being a downer that, you know, yes, it's hard. And, yes, it takes many years. And, yes, it may take many attempts, but a lot of people have gone through that same path.
And I think for a lot of, you know, first time founders or founders would just failed or not doing super great or it was, you know, they couldn't raise the next funding, so they had to sell the company for cents on the dollar. You know, it's a lot of inspiration for them that, you know, you learned a ton from that attempt go at it again. This is the pass. Like, everybody else who was successful took the same pass. So, you know, you can do it. Right.
And so that's the minimal you want people to take on as grit. Yep. Yeah. And not to let some assumptions about yourself or about the conditions under which you're starting this company be what stops you from doing And hopefully for the investment community as well to go deeper into characteristic levels than proxy metrics, like do you have an MBA or not, or You know, is this Morgan? There's 5 competitors, so not, or is Google here or not?
So we should probably look at these things deeper than take the proxy metrics because Again, data kind of shows a lot of these proxy rules on that first layer, they are non correlated. So one thing that you want people to do is to change their sort of mental model about the amount of grit it's gonna take And then also the fact that the blockers you see as being blockers may not be the blockers, and the VC should realize that too.
What are maybe 2 other things that you would want startup founders to take away and do now that they've read your book? Maybe I think we can refer to some of the parts. You know, one truism is the fact about the pain Beller versus vitamin pills. So, you know, the data clearly shows the pain Beller have a higher chance of becoming $1,000,000,000 companies. However, you know, if you define the vitamin pills of VEL and you know you're creating a vitamin pill.
And if you do certain things, you know, vitamin pills can be good companies too. And that community and brand and, you know, sticky habits. If you add these things to your vitamin company and product, you know, you can actually build a decent company even though if if it's a vitamin pill. That's one. The other one is competition. And, you know, the data showed in those cases that startups were competing with large incumbent companies in, you know, these old industries.
Doesn't necessarily need to be even in an old industry, but in an industry that has only these old sleeping incumbents, they are more likely to succeed rather than, you know, copying an auto company, which is, you know, super highly funded at this point, the point that you are starting a company. So, you know, when you see that there is a space and there's like 20 companies getting super highly funded on the very same exact idea on premise even couple years after.
That makes me feel like, you know, there's a ton of other opportunities left you know, and also I feel like given that we are deep tech investors and I typically want to go into old industries, you know, tech is almost a, you know, $800,000,000,000 1,000,000,000 Morgan, there is many, many industries that are not tech and they are a couple times Morgan than the whole tech industry together. Altogether.
So there's a lot of other opportunities, and I think a little bit part of it comes from the fact that we have fed each other that you need to be solving a personal problem. Climate change isn't anybody's personal problem, food security, water security isn't anybody's personal problem, higher level, macro level, you know, government civic education problems isn't anybody's personal problem.
Each and every one of these problems, they're much bigger than the whole tech industry or SAS everything altogether. So, you know, go and try to solve these bigger, harder problems, and the rewards are going to be bigger. Got it. That's really good. That's really good. And day to day, when you're investing in DCBC, do you use some of these in talking with your founders to help them understand what they can and can't do or, you know, to encourage them? 100%.
I use it for sourcing and I use it for decision making. And I think number 1, I use it to make sure I don't let my biases and preconceived notions about any of these elements come into the way of me backing an exceptional entrepreneur. So there's a lot of James. You can create problems and you can, you know, take yourself out of investing in a good company by diligence and get kind of too much, right?
And I think the data kinda Beller me to understand that, you know, there's always all these cases. And if you think this is specific situation that the market is a problem, then I go and look at the data and say, okay, there's this many companies that's Pete it that way.
And if you think this type of competition is a hard thing, or if you think this kind of what this pivot happened, there's a lot of mental models that kind of be create and say, okay, if this founder did this and then this did that, that's not a good sign. And a lot of cases, you know, we take ourselves out of him thing and backing an exceptional entrepreneur. So number 1, I hope, and I actively actually see that it helps me reduce those kind of bias. Signal.
If a team is good and the entrepreneur is good, and I believe in this, you know, it's great on one axis, and then it can go against the odds in this 2 utter to access. And then the other one is decision making that, you know, if I see 2 different deals and I have to pick 1, it actually does help me to put it down on paper and kind of get a sense of everybody have to go against the data and how many different, you know, risk layers they're taking. So doing it a little bit more methodically.
And the questions that I ask founders, I try to go kind of deeper than these, more than proxy metrics and proxy rules to kind of your past and the characteristics rather than kind of higher level, just surface level questions. Yeah. Interesting. Interesting. It might be that once people read the book, they're gonna start making up all sorts of stories about how entrepreneurial they were when they were 7 and 10. That they can give the VCs what they wanna just get the damn money and move on.
It's kinda interesting. There's always a The good thing is in this digital ish world, like, at this point, there is kind of proof. If you dig deeper, you can find or not find a proof Morgan anything people claim, and I'm pretty sure you do a lot of this, but you will be amazed. A lot of things founders say, and then I do a check, and I see, okay, that does not check out, especially about founding companies when, you know, people who are early team members Yeah. That's a tough one.
That's a tough one. Well, everyone's trying to stretch to reach the heights, and it's a constant battle. You know, there's a goldilocks zone between exaggerating and lying. Yep. There's a goldilocks zone you pointed out, which I love, which is just, you know, as an entrepreneur, you want to be knowledgeable enough about your market and your technology, but not so knowledgeable that it destroys all of your naivete because without that naivete, you're probably not gonna take the jump.
You're not gonna wander into a market and take them on if you know too much. So there's a Goldilocks zone there. And the point you made is that the VCs should take the same Goldilocks approach. You need to do those. Is it? But if you over analyze, you can kill any deal. You gotta have some faith. And there is so much randomness in our market. I don't think people like to admit that. Yeah. You can figure out a lot of things. Like great entrepreneurs would end up changing the idea and problems.
And the problems that the company would face after 3 years after you invest has changed so much that, you know, your initial concerns about the company. I don't think I've ever seen any case that in your initial investment memo that, you know, problems that you necessarily think are true. Like, they come and the great entrepreneurs find a way to solve that problem, but they have the next set of problems to go after. Yeah. So, Ali, what's next now that you've done the book?
I mean, I just published yesterday, but what's next for you? I don't know yet. It took a lot of time getting it to line. I'm happy it's out there. I am going to, you know, go spend more time with the entrepreneurs back for now. And let's see what the future brings. Awesome. Well, it's great to talk to you, my friend. Thank you for spending time and congrats on the book, and, we'll see you out there. I was glad to be here. Thank you, James.
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