S3E1 – Why Startups Fail

Show Notes

In this first episode of Season 3, Mon-Chaio and Andy welcome listeners back and dive into the theme of ‘The Turnaround’. They analyze reasons why startups fail, discussing both proximate and distal causes. They explore insights from multiple research reports, studies, and personal experiences to paint a comprehensive picture of startup failure scenarios. Topics include common failure reasons like running out of cash, flawed business models, wrong people, false starts, and external factors. Let them know your feedback about which topics you’re are most interested in for future episodes!

References

• Demography of Startup Software Companies: An Empirical Investigation on the Success and Failure
• Success criteria in high-tech new ventures
• The Top 12 Reasons Startups Fail
• 483 startup failure post-mortems
• Why Start-ups Fail
• A Study on High-tech Startup Failure
• Failure of Tech Startups: A Systematic Literature Review
• Systematic literature review of critical success factors of Information Technology startups
• Conventional Wisdom Says 90% of Startups Fail. Data Says Otherwise

Transcript

Mon-Chaio: Happy 2025, faithful and new listeners. We’re glad to welcome everyone back to season three of The TTL Podcast. Season three, it’s been two years, Andy. Well, a year and a half.

Andy: Year and a half, year and a half, but it feels like a decade. No …

Mon-Chaio: So we had some time off. Andy, did you spend that time wisely? You read a lot of papers, I’m assuming?

Andy: I read a few papers. I almost finished a chair.

Mon-Chaio: Nice! Okay!

Andy: It’s coming along nicely. How about you?

Mon-Chaio: I spent it obviously reading some papers , we had to get ready for this season after all. But my sister-in-law had her second kid. So …

Andy: Ooh, congratulations!

Mon-Chaio: So we traveled down there for the holidays and then have been back since. But yeah, it’s been a pretty interesting start of the year.

Andy: Nice!

Mon-Chaio: But, getting back to TTL, season three. We started thinking about season three themes and we said, well, where do we want to focus? And I think Andy, where we landed was we wanted to focus on transformation.

Andy: I think we wrote it down as The Turnaround.

Mon-Chaio: Ah, I like that a lot better. The Turnaround. So things aren’t going right. Why aren’t they going right? What does the research say about why they’re not going right? And what can we do about it?

Andy: This would include things like “how do you diagnose the problem?” In fact, our very first episode was actually about diagnosis, and it’s still our most listened episode.

Mon-Chaio: It really is.

Andy: So to me, I think diagnosis, I used to call it learning to see, but figuring out how you actually see what’s going on, how you diagnose that and work your way through it. And I think in the context of thinking about an organization, a team, a company that’s somehow struggling and how can you start applying these things to turn it around, to go from struggles to everything is just working.

And that’s not to say that you won’t have struggles once everything’s working, but I would say that the turnaround is from, you see the writing on the wall, and you see the cliff edge, to, all right, the path is clear, and the road is long.

Mon-Chaio: But to get there, Andy, we thought that’s great, but there’s a number of different things that we can think about, about why your company might be failing and need to be turned around. What we thought we’d do this episode to kick off season three is to look and see if there’s research around why tech startups fail. What are the common failure scenarios for tech startups? And, by kicking off here, that helps define our vocabulary as we think about future episodes for season three and what we’re going to diagnose against and how we might steer this ship that might be sinking. Yes?

Andy: Yeah. And I think a core concept that we’re probably going to talk about as we go through these is, is this a proximate cause or is this a distal cause? So the proximate cause is the one that you can easily see. The distal cause is the one that’s very likely a little hidden, but probably has much more impact because it caused so many other things that eventually led to your proximate cause.

And so a really simple proximate cause for why did a company shut down is because someone fired all of the employees and then filed the paperwork to shut down the company.

Mon-Chaio: They couldn’t shut down the paperwork filed.

Andy: Yeah, the company can’t have shut down without the paperwork getting filed. Okay. Yes. So that is the proximate cause of the company shutting down.

Mon-Chaio: Yeah, that makes sense. And I think that, like we do, we do really want to dig into the deeper details, the distal causes, the root causes, the things that are interesting to talk about. It’s certainly not interesting to talk about a company shut down because paperwork was filed, so just don’t file the paperwork. On to episode two, right?

Andy: Yeah, but I think that there’s others that would also be classified as proximate causes that I think quite often get cited as The Cause.

Mon-Chaio: Mm hmm.

Andy: And the one that I think both of us in our research in this, the one that kept coming up again and again, that kept getting cited was, oh, the startup failed because they ran out of cash.

Mon-Chaio: Yep.

Andy: And it’s kind of like, well, duh. I mean, sometimes you shut down before you run out of cash because you can see that it’s happening or because you can see that there’s nowhere to go. And you try to return money to investors or pay out everyone who is involved and whatever. But most of the time, you ran out of cash probably isn’t the actual cause of why you shut down.

It’s the thing that forced it. It’s the thing that forced your hand. But, like, there’s all sorts of things that would have led to that.

Mon-Chaio: I agree, Andy. I … you know thinking about it now as we’re talking about it through the episode, I think there is a real reason why they talk about that. One of the research paper was saying that there’s a way that you allocate the money that you have and a lot of people have missed tactics on that. So I think those are interesting conversations and maybe if we didn’t have this running out of cash thing, we wouldn’t have had this insight around how do you spend the cash that you have. So, okay, okay, I can see that. I don’t know that that’s really what we want to get into or what this podcast is about.

Maybe it is though.

Andy: I can see how the we ran out of cash is there, but I think the much more interesting thing is to do kind of like the five whys on it. On its own, it’s a statement of yes, the company failed because they had no cash.

Mon-Chaio: Right, right.

Andy: So, should we just start listing some of these things that we found? Go through this piece by piece and kind of see where it takes us?

Mon-Chaio: See where it takes us, yeah. Do you want to start?

Andy: Yeah, so I’ll go off of the one that a company called CB Insights published, and they claim to have done it from an analysis of 111 startup postmortems since 2018. And what they did is they either interviewed people or they found write ups of postmortems online, and then they did a little bit of a qualitative analysis. They’re a research company. I don’t think they’re like full researchers. And so they did a little kind of like, what are the themes here? And so their top one was they ran out of or failed to raise new capital. And they said that that was in 38%.

The next one, very close behind, was no market need. So this is, they failed because they couldn’t find a product for a market. Got out competed is the next one. Someone else got there first and then they couldn’t keep up. Flawed business model. I think this is the one that engineers have a hard time understanding the most probably, which is how exactly you charge people for your product, like, are you charging per use? Are you charging per seat? How do you charge for it? Who are you targeting? How do you structure the company? What markets are you going to? All of that’s kind of your business model. And so one of the reasons is it’s just a flawed business model. I think flawed is a judgment word though. And this one on its own, like as a proximate cause, isn’t that interesting because the interesting thing is to get into the why of the flawed or the what of the flawed.

Mon-Chaio: Mm hmm. And flaw is, like you were saying, is a judgment word, right? When we started in, you know, late 90s, early 2000s into the startup world, there were many business models, which were get users, question mark, and then make money. And I thought that was flawed, but obviously it wasn’t because a lot of people had success with that business model.

Andy: That was the time of be the loss leader. Uh, you’ll make it up in scale.

Mon-Chaio: Yeah, exactly.

Andy: But the thing is, is that it was a business model that no one fully understood until people started succeeding with it. And I have to admit, it’s kind of a monopolistic business model.

Mon-Chaio: Oh, absolutely!

Andy: It’s a winner take all. It’s one I’m not very comfortable with, but I admit it exists.

Uh, so then we get into regulatory or legal challenges. Sometimes the laws just change, and the thing you want it to do? Nope. Not possible or not easy.

Mon-Chaio: Or the laws are exactly the same, and you hoped you could change them but couldn’t.

Andy: Yeah. Uh, pricing or cost issues. This to me seems like it connects really well to the business model. You couldn’t price it at a level that would work. Not the right team. I think that’s one that we’ve talked about a fair amount. Who is the leaders, who are the engineers, how do you get that working? Product mistimed. Now that’s an interesting one, because for that to be true, you have to have very clear information that it was either a good product prior or a good product after you failed.

Mon-Chaio: Mm hmm.

Andy: Usually it’ll probably be something like a good product prior, otherwise you don’t know that it was mistimed or you just are making it up.

Mon-Chaio: Yeah, I mean, I think , at least in my mind, the most cited example of this was the Apple Newton, which was stated as a product before its time. And we can say, well, Palm Pilot followed onto that and then tablet, so maybe, maybe not. But it’s difficult to really make a clear and accurate judgment call if there is any sense of accuracy with these judgments.

Andy: Yeah.

Mon-Chaio: How can you compare a product from the 90s with a product of, you know, the 2010s? Or I just don’t think you can really compare it that way.

Andy: Yeah. Like for instance, here’s an interesting one. Puppet, the company I worked for, eventually got acquired by Perforce, I think it was, and now they’re kind of, well, they’re doing stuff, but most people’s minds, Puppet disappeared. Was it a product mistimed? Because it kind of showed up at the tail end of configuration management of servers being the primary thing that people did for operations.

Was that a mistiming? Or was that a flawed business model? I don’t know. It seems like it’s just a natural progression of a market.

Uh, and I should say, we’re already down into kind of like the long tail. This one, product mistimed was 10%. 10% of the stuff.

Mon-Chaio: And product mistime versus didn’t pivot. I don’t know. Those seem kind of related in my mind as well.

Andy: Oh yeah, well, and that’s a little bit further down. That’s pivot gone bad at 6%.

Mon-Chaio: Mmm hmm.

Andy: Then we have poor product at 8%. And this is just like poor quality. People didn’t like it. They didn’t want it. It was terrible. Disharmony among team or investors was 7%. Pivot gone bad, 6%. And burned out or lacking passion, 5%.

And that one there’s primarily talking about the founding team.

Mon-Chaio: Mm hmm.

Andy: And so there we have it. Once you get past those kind of, like, few big ones that we were saying, ran out of cash or no market need, that we’re up past 30 percent of people reciting that, and we’re saying that those are really just the proximate cause, it’s really the underlying causes that are much more interesting, it starts spreading out a lot about what could be happening here.

Mon-Chaio: Mmm hmm. And this is what struck me when I was going through the research on this. Most of this is taking data, or retrospectives that were written by founders or VCs after their companies had gone bad.

Andy: Mmm hmm.

Mon-Chaio: Some of this , very few, but there were a few sort of academic case studies around specific companies that really dove into the details. But I would say I felt like compared to other research, the source data didn’t seem very compelling to me. But I don’t know that there’s really any better way to do it.

Andy: Yeah. Yeah. So there was another one that I found, which was that they surveyed venture capitalists and they asked them a set of questions specifically about what did the venture capitalists attribute the success of their most successful company to, or their most successful investment, and what did they attribute the failure of their worst investment to.

Mon-Chaio: Mm hmm.

Andy: In terms of what happened to that company?

43 venture capitalists, that is how many they contacted, 27 participated. And they eventually got down to a little bit of fancy statistics , some cluster analysis and came up with what were the highest risks for failure , and which of these explain the most of the failure happening?

So the first one was explaining 37% or 38% if I round of the variance, was incapability risk, which was basically you just aren’t capable of doing this. You don’t have the leadership, you don’t have the management capability, you don’t have the technical capability, you don’t know how to market. Do you actually have the capabilities it takes to do this?

Mon-Chaio: Whew. That’s at one point very interesting, and another point completely uninteresting.

Andy: Yeah.

Mon-Chaio: ‘Cause one could say any failure, was it failed because somewhere along the line, the needs …

Andy: You weren’t able to do it.

Mon-Chaio: That’s right. Exactly.

Andy: But, I think it does give an interesting way of thinking through it. Because, just like when we were talking about in the Peter Principle article, you kind of have to be aware of when you’re hitting your level of incompetence. The incapability risk is a thing to say, if you’re looking at your company or looking around, you have to start asking yourself, who here is maybe not capable? Especially if it seems like the incapability problem is the one that explains 38 percent of these failed businesses.

The next big one in this one was 12 percent, was inexperience. And that was not inexperience in leadership or management. That would be the capability. It’s inexperience of what it is you’re trying to do. So you’re trying to build a product for a market. So one part of this is, do you have any familiarity with that market? So it’s like, all right, I’m an American living in England. I’m going to go build some app for Sub-Saharan Africa. I have no experience in Sub-Saharan Africa. I would have nothing to say about the target market, or a track record that was relevant to the venture. So maybe I was going to say, I’m going to go do startup for Sub-Saharan Africa fertilizer for agriculture. No software involved, market I’m not at all familiar with. That’s the kind of risk that they’re talking about there.

And then they get into stuff that explains some, but not a huge amount. Get down to 9 percent explanation with product risk. Is it a unique product relative to your competitors? Is it something you can actually protect? Is there a moat around it? Was there actually an untapped market potential?

I thought that was interesting. That seems like a kind of thing where it’s like, you might think, oh yeah, well, if someone’s done this before, yeah, there’s a high risk to this. They’re saying, mmm, probably not a huge risk.

Mon-Chaio: Interesting. Yeah, when it starts to get down into those low percentages, I wonder what the error bars end up being on those numbers.,

Andy: True. Yeah, yeah.

Mon-Chaio: But it’s really interesting that even at the high end, if you can say 30 percent is high, it really ends up being around capability, right?

Andy: Mm hmm.

Mon-Chaio: And that differs a lot with what a lot of VCs or other people or founders in the field might think. It’s funny, I was reading this paper and they cited Paul Graham, who’s a venture capitalist. In 2007, he gave two primary reasons for startup collapses, and they were running out of money or founders giving up. Those were his two. Which …

Andy: So his philosophy is as long as you have the money, you just keep going.

Mon-Chaio: Yeah!

Andy: And if you want to keep going, just find more money.

Mon-Chaio: Sure. Yeah. And have passion, right? The CB Insights report mentioned passion is very, very low down, but for Paul Graham, at least at that time in 2007, it was right up there, you know, lack the passion or for whatever reason they gave up, didn’t want to work hard, whatever the case may be. That’s why startups failed.

And this is one of the reasons why this type of research is difficult to wrap your head around, because you get 20 Paul Grahams and you have a certain piece of insight that may or may not translate well to, well, what can you actually do about it .

Andy: Yeah. I can understand his way of thinking that would lead to that result, that outcome, which is if you think, okay, I’m backing this individual because they are capable, they are driven, and they’re going to find something, because Paul Graham Y Combinator’s approach is basically like spread your money out all over the place because these people are likely to find something.

And you combine that with the philosophy of Lean Startup, which is hypothesis, test, and pivot and keep trying and find something. Then, in the end, yeah, basically you can say those are the only two possible ways that a startup could fail. Either you’ve run out of money, so you can’t keep doing it, or you just are tired and you don’t want to keep going.

Mon-Chaio: I agree. Those are the two ways, proximately, that a startup right? And what I find interesting about this is when you dig into the next level of detail. So, there was a professor who wrote an article on Harvard Business Review. He actually wrote a book. I don’t have the book in front of me right now. I think it’s actually called Why Startups Fail. And his research was around these postmortems, he’s talking to founders, doing case studies. He teaches a class on this. He also participates himself as an angel investor, as many of these folks do. And in his book, he has six reasons that startups fail. But in the article, he highlighted two of them because he said that these two were the most avoidable ones. That the other ones were maybe more product of circumstance or other types of things.

Now, I’m not sure I necessarily agree with that. I’ll give you an example. One, he called Help Wanted and he said, it was not getting needed VCs or delays in hiring, or hiring the wrong senior executives, which are needed to scale. So his big thing was as a startup grows, you need the skills of senior executives. And so you need hire them. But oftentimes startups delay in hiring them because they don’t recognize the need or they don’t know how to hire them. And they hire the wrong people. Now, why is that not an avoidable one? I don’t understand. Okay, but he said those were one of the four that were not as avoidable as the top two.

The top two, though, were fairly interesting in my mind. So the first one was Right Idea, Wrong People. And here he’s not just talking about the founders. And in fact, he’s talking less about the founders than about the collaborators and the partners. So the example he gives on a case study is this startup that’s producing clothes. Their founders didn’t have any experience in textiles or in that sort of manufacturing chain. So they went and partnered with factories that did have that experience. That makes a lot of sense. But his point is, when you partner with the wrong factories – these factories perhaps can’t make stuff to the scale that you need, or the speed at which you need, because they haven’t ever participated at internet speeds before, as an example – that those are big things that can tank a startup.

Getting the wrong VCs is a big one. For example, software VCs are very famous around this fail fast, do a lot of hypotheses type of thing, which we all agree with, right? You and I sit on this podcast a lot and talk about this a lot. But there are certain verticals where that may not be the right case where, you know, the inventory costs are really high and you have to maintain large inventory. And so when you get VCs that say, here’s five million dollars but like, I want to see returns in a year, or else I won’t give you any repeat money. He says that can be kind of a death spiral, right? Because other VCs then see the first VC not putting in a second round of funding, which deters them in putting in a round of funding.

And also, you’ve kind of spent this money building towards what the VC wants for growth. The VC’s like, hey, I want you to have four factories, not just one. I gave you five million dollars, you have the runway. So I want to see scale. I want to see rapid growth. Versus if you had a more patient VC that gave you a million dollars and said, look, prove out the smaller business. And then the next you gave you a million more or 2 million more or something like that, that can build trust in your company that lets other VCs come in and say, hey, they gave a second round, I can see steady growth , I’m going to come in. So that’s all what he talks about. Like the idea is there, the market is there, everything is there, but the wrong people are involved in this collaboration.

Andy: How does he distinguish that – Right Idea, Wrong People from Help Wanted? Since he’s saying one is avoidable and the other one’s not.

Mon-Chaio: I don’t think he says the other one is not avoidable. I think he’s saying this one, the Right Idea, Wrong People, is more avoidable.

Andy: Okay.

Mon-Chaio: I did have the same question, right? Which is why I brought up the Help Wanted thing first. Because I was like, I don’t, I think that’s pretty avoidable too. So, I’m not sure why you put it at the bottom with stuff like Cascading Miracles, which yeah. So Cascading Miracles he defines as you have a company whose success is built on the need for a chain of medium-hypothesis probability events. So, I need five of these 50-percent hypothesis events to all succeed or else my company fails. And that’s like 3%, right? So, I can see how that’s a less avoidable thing. Yeah.

Andy: I think actually I just realized when you talked about that, we should probably talk a little bit about what is failure. We’ve so far kind of alluded to and spoken directly about the complete shutdown of the company.

Mon-Chaio: Mm hmm.

Andy: That’s definitely failure. But there’s many other things that are failure and which might cause the company to have to shut down, which might cause people to say that they failed.

Mon-Chaio: Mmm hmm.

Andy: And one of those is, like, if you’re talking about VCs or investors of some sort, they just didn’t get back more than they put in.

Mon-Chaio: Mm hmm.

Andy: That’s a failure. The company could keep going. The company could have grown its business. But they just didn’t get back out as much as they had put in.

Mon-Chaio: Or even they got back a multiple of much less than they were anticipating based on their investment money.

Andy: Yeah. Based on their thesis and the needs of their fund and all of that, that that could have been a failure of the investment. And so it doesn’t take an extreme, like the company just shuts down, to necessarily, in a lot of these things, be called a failure.

Mon-Chaio: Mmm hmm.

Andy: In fact, that was the definition on one of these articles that I read, which was that it’s anything where the investors got back 1x or less of their investment.

Mon-Chaio: Interesting. Okay. Which paints a lot broader picture of failure in that case. Yeah.

Andy: And the reason I wanted to bring that up is because that Cascading Miracles thing, that is a big risk when you have a very high price, long lead time sales product.

Mon-Chaio: Oh, for sure. And I also think VCs build failure into their models, right? In some ways, I think VCs like investing in companies that can have the possibility of Cascading Failures, because those are also the ones that price in such a way that the returns are higher. So, as a VC, you’re always looking for that unicorn. And so you’re not going to get that by say, investing in 20 companies who, you know, their average return of your investment is going to be two-and-a-half-X at like a 60%, probability, right? You want a hundred companies whose returns are going to be a hundred or a thousand X at 10 percent probability. Like that’s what you’re investing for. That’s a simplified way to think about investment. The reason portfolios exist is you need a bunch of investment different types, right? So you also need the two-and-a-half-X returns, that sort of a thing.

But especially with Silicon Valley VCs, I feel like a lot of them try to invest in these moonshots and so then course because you’re investing in them that gets people on their radar and then they count as a failure. Where in another world, another situation, those companies probably never get investment in the first place and it never becomes quote-unquote a company that can get started to eventually fail.

Andy: But also a lot of them start without a big investment. And then at some point they decide to get the investment to try to grow more.

Mon-Chaio: Yeah that does happen a lot, but I think a lot of it also is the investment that gets them started. I think about like SpaceX’s of the world, right? Like you don’t build rockets without investment.

Andy: I was kind of discounting the capital intensive businesses and thinking about almost like pure software because pure software really changes the model of starting a business because you need so little capital upfront.

Mon-Chaio: Absolutely. So I mentioned Right Idea, wrong people, but I do want to get into a second one because I think it is one that we will probably talk a lot about in various different ways throughout season three. And that’s what he calls False Starts. And what he says is, there’s actually too much focus on the fail fast and iterate. That’s really, really important. But what people have forgotten is the upfront research that’s needed so that you get yourself pointed in a direction where fail fast and iterate can lead to success.

And so then because of that, you end up with a lot of false starts. You do this experiment, which if you did your research, you might understand only has a 30 percent chance of success. And so you probably wouldn’t do it. Or you would do another experiment instead. But, especially with people with funding, you just splatter 30 percent experiments against the wall. And you’re like, oh, that failed. Oh, that failed. Oh, I gotta pivot here. Oh, that failed. Oh, wait, no, that actually succeeded. Oh, pivot here. And that’s what he calls False Starts. and it’s really interesting that he mentions that as one of the top two avoidable ways that startups fail.

Andy: Yeah. I think that would be an interesting one for us to dig into some more because the immediate thing that comes to mind for me about False Starts is a thing that I learned, which was, if your product team is constantly complaining the development team is just too slow, it’s like, you need more developers, almost certainly – especially if you’re still trying to figure out what is our product and how are we going to sell it and all that – almost certainly, you are not spending enough time actually validating your product ideas before spending all of the cash, speaking of running out of cash, to try to build it.

And so in lean terms, you’re putting in a whole bunch of waste into your next step in the process, your development team. And they’re producing waste. So you need to be figuring out how to stop doing all sorts of just stupid ideas and validate that it’s a really good bet before you start trying to build it.

Mon-Chaio: And we talked about that in our scaling series at the end of last season, season two. And I will say that out of all of the ideas that we give there, one of the biggest ones I consistently hear feedback about is that market research at the beginning, right? Where we talk about, hey, the first thing you don’t do is you don’t build an app. You certainly don’t build two apps. You’re doing things like surveys, you’re taking taxi rides, you are going to concerts and just doing a little ride thing for a concert to see whether people actually want to get around different venues. That is the biggest thing I get pushback about.

Andy: Yeah.

Mon-Chaio: So I think it would be an interesting thing to talk more about, especially in line with him saying that this is one of, at least in his experience, the two top avoidable reasons for a startup failure.

Um, I do want to bring one more thing into our discussion. There was a literature review of startup failures, for information technology startups. So specifically, software startups, right? And what they did instead is they clustered them into different stages of startups. So what they said is that the experience of previous startups of the founding team and government support factors, were the critical pieces to enable a seed-stage startup. That those were the most important things for the success of a seed-stage startup. For an early stage startup, it became venture capital was the factor that was the most important. We talked about that a little bit, right? You’re at Series A, you’re looking to scale, right? You need money to scale, scale, scale. So, venture capital. For growth-stage startups, they said the clustering – and by clustering, they mean partnerships or ways that different companies competing in the same space can enable each other to make a bigger market and to drive each other’s success – they said clustering, technological business capabilities of the founding team, and venture capital affected the growth stage. And then at the expansion stage, they said the clustering effect was biggest. So the partnership thing again.

Um, I don’t know that I have a ton to say about that. But I do think that that’s another interesting lens that I at least hadn’t thought about, which is what are the common failures of startups at different stages in their evolution?

Andy: Yeah.

Mon-Chaio: Because once you’re at the expansion stage, it’s probably not product not fit for market need, right? I mean, you’ve gone through …

Andy: Yeah! Cause some of them just drop out based on just by definition to be really in that stage. You’ve got it.

Mon-Chaio: So, that might be another interesting vector for us to explore, in trying to dissect different stages of startups and failure scenarios for each startup. One thing that we talked about with the hiring executives, right? Obviously at the seed-stage startup, you’re not talking about how are you interviewing and hiring executives, right? But you better be doing that. long before the expansion stage, I would say.

Andy: Yeah.

Mon-Chaio: Anything else here to mention at least in this episode?

Andy: I think the last thing that we can go over Mon-Chaio is just really fast, just to say, like, how often do companies actually fail? And, I guess this isn’t how often, it’s more of how many. And there was a person who said that the conventional wisdom, published in Fortune, is 90 percent of startups fail.

Mon-Chaio: Mmm hmm.

Andy: 90 percent of startups fail. I think we hear the same thing about restaurants. 90 percent of restaurants fail. And he had been parotting it, and then he one time found himself saying it, and then thinking, is that true?

And he says that – this is all hearsay, which is why I’m kind of saying it that way – he says that Cambridge Associates, a firm in Boston, tracked the performance of venture investments in 27,259 startups between 1990 and 2010. And it had not risen above 60 percent since 2001. So remember, this is up to 2010. So this includes the 2008 crash. And even among the dot-com bust of 2000, the failure rate topped out at 79%. So, 90%? Not really true.

Mon-Chaio: When it gets to your point of how do you define failure? And also a lot of these papers will talk about how do you define startup?

Andy: Yes, yes. a company I worked at, TIM Group, often called itself a startup when it was profitable and 10 years old. It’s like, no, it’s just a small company.

Mon-Chaio: And maybe that’s, you know, I don’t know, a late expansion stage startup or, you know, Paul Graham defined it as a company that’s designed to grow fast, which is a very simple definition. So that introduces a lot of – I wouldn’t call them error – uncertainty, I think, into the research.

But I think that’s okay, right? When you have a lot of uncertainty, but you read a lot of different points of view and they kind of align on a certain set of things, you can’t say for sure – I mean, this isn’t airtight research, so you can’t say for sure – absolutely, these are the things that cause startup failure in these percentages. But you can say that there is enough signal here to make it interesting to talk about. And, to what we started out with, to use it as part of our diagnostic model as we look at companies that are treading water or sinking and thinking about how do we affect this turnaround.

Andy: Yeah. And I think that is a good place to stop this episode.

Mon-Chaio: Fantastic, I like it! So we’ve explored a lot of different ideas around why startups fail. All again, in the vein of trying to figure out, well, if we’re thinking about talking around turnaround, we probably want to have some mental map into common failure scenarios. Now, I don’t think that we’re going to be able to talk about all of these in season three, even in 51 episodes or something. I think it’s going to be real tricky to be able to touch in depth on all of these. Some topics we might touch on multiple times from various different angles.

But what we really want to know is, listeners that are listening to this episode, what topics really stuck with you? Are there any that you heard that you’re like, man, I hope that within season three, they really touch on this. If so, that would really help us. So let us know, write us. You can reach us at hosts@thettlpodcast.com. That’s hosts with an ‘s’. Because what we would really like to do is talk more about the things that listeners like y’all are interested in.

So, this is the kickoff to season three. Andy and I are super excited to get back into this in 2025, and we will have something interesting around turnarounds for you all next week. But until then, be kind and stay curious.