unSILOed with Greg LaBlanc Super Founders: Analyzing and Understanding the Data Behind Billion-Dollar Startups feat. Ali Tamaseb
Most billion-dollar startups are founded by non-technical individuals— one of the many insights that Ali Tamaseb notes in his recent book. Ali spent countless hours manually collecting what may be the biggest data set ever on startups — comparing billion-dollar startups with those that failed to succeed.
In his book Super Founders, Ali brings together 30,000 data points on nearly every factor that influences a new business: number of competitors, market size, the founder's age, academic performance, quality of investors, timelines of fundraising, and others.
In this episode, Ali shares stories from the early days of billion-dollar startups, and talks about the founders and investors of Dropbox, Coinbase, and Facebook. Tune in as Ali busts some myths on entrepreneurship and startups by sharing key findings from his book.
Episode Quotes:
Should founders look outside their domains for problems to solve, or should they focus on solving the problems that they themselves have encountered?
I think that's another thing that gets talked about. Solve your own problem, be your own customer. And that seems like perfect advice. But that's why we have so many valet parking and grocery delivery app companies. It's nobody's problem — climate change is nobody's specific problem. Agriculture, food, security, water scarcity— these are not anyone's specific problems. But these are hundreds of times larger problems by economics, by the scope, than grocery delivery, or valet parking, or any other consumer thing that you may be a customer of. So, when I looked at these billion-dollar companies, in a lot of these cases, they were not solving their own problems. They deliberately went on to find the right idea.
Thoughts on why repeat founders to succeed more than first-time founders
So, practice does make perfect. It's the experience of generating value, building something, selling it, and generating some revenue. Whatever that is. It doesn't matter how much it is. It's that experience that you learn.
Thoughts on maximizing the likelihood of success for the early stages of fundraising
But, I think the thing about founders is that things are a little bit more obvious to VCs. Or that hot deals are hot deals for a reason. These are some of the hot deals that become obvious to VCs. And, it's the VCs who are fighting for those deals. It tends to be that these companies are more likely to become the billion-dollar companies at the end of the day as well. But it's important that as a new fund, you can get yourself into the right 40%. And as a new founder, which is not a super founder or doesn't have a big degree, you can become part of that 40% that gets to the billion-dollar outcomes.
Do you think that we'll ever be able to use machine learning for at least some parts of the venture process?
My goal with the book is the reverse of that, how we can use data to decrease biases. To put aside, things that don't matter. Because right now, a lot of VCs, a lot of people create these scoring things, like a lot of these judged competitions and stuff. Like, people have the scores. Okay, I'm going to give five scores to this element and five scores to this element. And you see, the scoring system is wrong. The book that I'm trying to do is, 'Okay, just throw away that scoring system because half of it is the wrong elements you're looking at. You're looking at the domain expertise of the founder, five scores. It turns out that's not correlated with success. What are you scoring? So, a big part of this book is putting aside things that don't matter. Age, gender, race. Things that don't matter should be set aside. And then, how can we use data to better source founders and source companies? I think something that's becoming more and more useful.
Time Code Guide:
00:00:53 How Ali gathered data for his book
00:02:04 Motivation for writing the book
00:04:38 The different kinds of VCs
00:06:02 The extent of work the author had to go through to collect the 30,000 dataset
00:07:39 The extent of work the author had to go through to collect the 30,000 data sets
00:11:02 Archetypes, stereotypes and how each dataset relates to one another
00:13:58 Patterns on the data about founders
00:16:26 Importance of having technical and non-technical founders onboard
00:18:27 Data on VCs funding family members and relationships between founders
00:25:48 Were you able to find data indicating any biases from VCS when it comes to funding startups
00:30:11 Were you able to find data indicating any biases from VCS when it comes to funding startups
00:35:18 The relationship between defensibility and scale
00:44:03 Should startups immediately to get into an accelerator program and start grabbing seed money as soon as possible?
Show Links:
Guest Profile
His Work
- Super Founders Official Website
- Follow Ali Tamaseb on Medium
- Ali Tamaseb on Inc.
- Super Founders: What Data Reveals About Billion-Dollar Startups
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