BRAVE: Why Startups Fail: Power Laws, Failure Patterns & Being Too Early - E664
episode
BRAVE Southeast Asia Tech: Singapore, Indonesia, Vietnam, Philippines, Thailand & Malaysia Startups, Founders & Venture Capital VC (English)
31 min
1 speaker
8 chapters
transcribed 16 days ago
Transcript
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Transcript generated automatically by AI and may contain errors.
Why do only about 1 % of startups become unicorns?
Out of a thousand plus C companies at the top, only half of them effectively raised. the next round, which is a series A, right? And then if you look at the next stage, is that out of the five hundred plus, sixty percent of the companies managed to make it to the next rounds. And so out of this total subset of the unicorns, you know, only one percent of these companies became a unicorn. Welcome to Brave. Learn from Southeast Asia's best tech leaders. Build the future, learn from our past, and stay human in between. No BS on Success. I'm Jeremy O, Venture Capitalist, Sarah founder, Harvard MBA, science fiction nerd, and dad of two daughters. Every week, we debate startup news, interview change makers, answer listener questions, and share personal insights.
Join our movement of over 40,000 members and get transcripts, resources, and community at www.bravesea.com. Stay well and stay brave. We will be talking about failure patterns and value addition of this component. We're talking about deals, which is very much that starting process of getting them into the portfolio company. You can imagine that when a VC brings on a company, one of the first things they have already identified as part of the due diligence is they already identified that this company, for example, may need more salespeople.
Are failed startups really failures or just too‑early pioneers?
They may need a new marketing leader. And so they may need more connections to more businesses. And so very much as part of that process, they say, look, this is a germ in the rough. This is a company that's gonna become a unicorn in the next 10 years. And as part of our due diligence, we have identified that these are the things that we need to improve. And so, very much how do we help them improve this? Because if these companies do not improve, what we have talked about is that the default scenario is that these companies will eventually fail and die. So now the question that we have is then if we talk about this, is that they have to generate value. And so they identify how they have to help these companies.
is because if they don't help these companies, then the most likely scenarios that these companies will die. And so we look at this failure rate. And yes, you know, in this scenario you can see here is that out of a thousand plus C companies at the top, only half of them effectively raise next round, which is a series A, right? And then if you look at the next stage, is that out of the 500 plus, only about 300 plus, so about 60% of the companies managed to make it to the next round. So there's about 40% death rate.
What can the Jibo social‑robot story teach about timing and hardware costs?
And then out of the 235, only 172. So again, about 40% of those companies died at the next stage. And then out of 172, only Again, ninety six, which is about forty percent death rate. And then out of 96 out of them, only 96 made it to blah blah blah. And so out of this total subset of the unicorns, you know, only 1% of these companies became a unicorn, right? Which was 12 companies, which in this subset includes obviously Stripe. But also Docker, which is a very famous kind of like infrastructure for coding a company. And of course, there's a certain set of number of companies that exit over 50 mil, 100 mil, 200 mil, 500 mil, and over $1 billion plus, right? And so I think the crux of it is that even when you make that investment, there's a very high.
death rate that's still happening over and over again. And so you really have to be quite thoughtful about this because we talked about how these investments, most of them don't generate capital, but some of them do have a power law and achieve that. And so when we think about failure, there's always this debate about failure. Are we trying to destigmatize failure? We need to make our kids be okay with failure.
Which six repeatable failure patterns do most startups fall into?
Oh, he's a failure. The startup's a failure. So I think for us, we need to think about startups quite interestingly and quite carefully, right? Which was that, you know, are they failures or pioneers? So for example, Jaibo was the world's first social robot. They raised $73 million starting from 2013, and then he died in 2018.
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Chapters
8 chapters
1
Why do only about 1 % of startups become unicorns?
0:00–1:24
2
Are failed startups really failures or just too‑early pioneers?
1:24–2:33
3
What can the Jibo social‑robot story teach about timing and hardware costs?
2:33–3:42
4
Which six repeatable failure patterns do most startups fall into?
3:42–4:36
5
How do funding winters and macro‑economic shocks kill otherwise healthy startups?
4:36–5:23
6
What is a “speed trap” and why does rapid expansion often backfire?
5:23–6:20
7
How do “cascading miracles” illustrate the extreme risk of visionary bets?
6:20–7:34
8
What are the three founder‑post‑failure paths: move on, rebound, revenge, or rebirth?
7:34–31:08
Speakers
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