Here I was, supposedly, a, expert on entrepreneurship and educator and I couldn't explain the most important phenomenon in my field. In today's episode, we'd listen in as NFX partner James Currier talks with Professor Tom Eisen about why more than 75% of startups fail. Tom has been teaching entrepreneurship at Harvard Business School for over a Gade and has now uncovered patterns of startup failure that we want every founder everywhere to hear. This is the NFX podcast.
Professor Tom Eisenman, it's fantastic to have you here. You are a professor at HPS, and you were there when I was there. You've been there now a total of 22 years. You've focused on technology startups. And over the years, a lot of people have turned to you for studies on furnorship and on network effects. This includes me and Scott Cook. Your name came up in a recent NFX podcast with him. A lot of people, including you, have tried to capture why startups succeed.
And now you're finishing up a book uncovering the patterns of why startups fail, which I think is a fascinating topic. And let's, let's talk about both of those today, both my startups, fail, and network effects things. So thanks again for coming on. And, and, yeah, thank you for having me. What sparked you to focus on this topic of startup failure? I spent the first half of my time on the faculty at Harvard Business School studying platforms and network effects.
And, I came up for tenure about halfway through, they got a lot of leverage over you when when you're, facing that promotion. So they pointed out that I had never taught the core 1st year course on entrepreneurship. I said it'd be much easier to promote you if we could make the case that you were indispensable in in several ways, you know, not just some knowledge of network effects what could I say? But okay. And I was, wrapping up, having taught many years, of course, on network effects.
So this is the first time I'd really taught the core of how you start a startup. And I just fell in love with it. It was a fantastic change, and pretty much that's what I've been doing for the for the last 11 years, is focusing on startup management But what I found when I taught the 1st year course, so like most everything we do at Harvard Business School, that course is a required course on entrepreneurship, and it's case based. And, we get feedback from the students at the end.
Year after year, the students would say, hey, you told us along the way that something like three quarters of startups fail, but we just did 30 cases on these spectacular success stories. And, and, these founders march in and sort of spread their feathers like peacocks and and, you know, everything sounds fantastic. And, of course, that's inspiring, but what about the other three quarters of the time? Mo most of the HPS cases are I was brilliant and then I won.
Yep. And, and it's fun to come back to school and and tell the story of how that happened. We do that a lot, and and students can learn a lot from that, of course. So but I took that feedback seriously, and I wrote a couple of James, about failures. They they happen to be, we're allowed to invest in student startups after students graduate, and I had done that. And and, at least 3 quarters of my angel investments had failed. So I have Flint, plenty to choose from.
So I wrote a couple of cases on on, failed startups that had been led by former students and taught them. I would say, classes were pretty wobbly. It was very hard for the students to figure out what happened and why. Some students were, as as often the case for MBAs, really good at analyzing by looking in the rearview mirror? Well, isn't it obvious that they didn't do this or that?
And other students, a little more thoughtful, would say, yeah, but some, some really smart investors put money behind this idea. And if it's terrible idea, that doesn't happen. So there were a lot, there were obviously a lot of contributing factors, it was hard to understand which, if any, were decisive, the students were really debating. We couldn't tell if it was death by a 1000 cuts. There are a lot of things going on or were some of the factors more central than others.
So here I was, supposedly, a expert on entrepreneurship and educator, and I couldn't explain the most important phenomenon in my field. So that was a little sobering. The most important phenomenon being the fact that 75% of startups Yeah. This is this is it. This this is this is central. And, and the other thing that that was going on then, I had, recently discovered the lean startup ideas. I've met Steve Blank and Eric Reese. This is 2009 or 10 before the before they got a lot of momentum.
And and absorbed all that and and thought it was really powerful and was figuring out ways to bring it into the classroom at Harvard Business School But when I matched those ideas and and and methods to what I was seeing in these cases, at least one of the cases that I wrote, they were textbook pitch perfect application of, of the early part of lean startup and minimum viable product test and so forth. And they still failed. Yeah. So that they, they found a great opportunity.
They validated demand. They've done everything they're supposed to do, and yet they still failed. So so that made me wonder if the playbook that I was starting to teach was incomplete. And so, so next step was, okay. What other people have to say about this? So I tracked down everything that practitioners had had ever written, as, as as you'll attest, a lot of venture capitalists are happy to explain why some startups fail and, and, and others succeed. And there was some academic work.
It was not much and pretty thin. And a lot of what I read, both practitioner and academic, was, I would describe as oversimplified. Read a lot about horse and jockey. And, I know, I know you're a fan of psychology.
So, you know, it turns out that humans have a penchant to oversimplify we take complicated things and, you know, the team failed in the, in in the August, September homestretch because the star pitcher to our hamstring or the presidential campaign failed because, the candidate ignored a swing state.
Usually, there's much, much more going on than than just those single, simple causes and and the other, psychological phenomenon I saw all over the place, psychologists have a name for something they call the fundamental attribution Beller. Is basically when something goes wrong, if it happened to somebody else, we're always inclined to blame dispositional factors. That the, that the individual wasn't very talented or they didn't work very hard. And if it happens to us, we blame the situation.
The structural problems. Yeah. The jerk cut me off in his BMW. He's a self centered. I cut somebody off, and there's a blind spot that, you know, I've been trying to figure out what to do about it. As as you've dug into this topic, the research has been a little bit thwarted by doing interviews with people who will give you fundamental attribution errors both in the simplification and in the attribution area. So that that James it hard for you to sort of pick through why things have failed.
Yeah. And that's that's where I realized a lot of people had done the academic work in Beller. They were just simple surveys. They would ask people like, what are the top 5 reasons why startups fail? And, you know, guess what if you're talking to VCs say, well, weak team. If you're talking to founder, they say, you know, the market moved away from us in unexpected ways.
And what I learned and realized was the case method, the the approach we actually use at Harvard Business School, because when a case is well done, you've come at the problem from a lot of different directions and talk to a lot of different people. It actually was pretty good at triangulating what was going on in a in a failure situation. So talk to the founder, of course, but other team members and investors look at other similar companies in the space and so forth.
And when you surround the problem that way, you'll you'll get some attribution errors course, but but then you can make up your own mind about what's really going on. So the cases in the book I'm writing, the research I'm doing, the case studies have ended up actually being super important. Right. Having having run 4 of these startups and invested another 100 myself, I I've I find there's this balance between playing to win and then playing not to lose.
And a lot of, companies need to what they're doing at any one time because if if you don't pay attention to these failure modes, then you're going to end up, failing. And, you know, it's also the case with these starters, but seems as if 20 things often have to go right in order you know, you have to flip heads twenty times in a row in order for your startup to succeed in the end.
And so as you as you go back and look at the at the failure modes, there's so many triggers that could trigger failure, whereas everything has to add up to go right. Yep. And, Paul Graham in one of his essays, he worked this this very theme, and basically said, I'm gonna tell you reasons why startups failed.
So it's a great list as a matter of fact, said, because it's easier for you to remember the things not to do, then figure out all the things you have to do you'll you'll remember what not to do more easily than figuring out all the things you have to do. And and what you have to do is different in every age and every company and every market. It it might be really Mhmm. Very interesting. So do you actually have now? You've been doing this now for, what, 2, 3 years.
You've been looking at this in a systematic way. Do you actually have a handle on why the vast majority of 75% of startups fail, particularly for early stage companies. You know, we're most of our listeners are early stage, but so I'd love to start there. So, I Pete to be an academic. We, we like to define things. So one of the first things you have to do if you're studying entrepreneurial failure, you, you have to define who's an entrepreneur.
And I won't go there, but it won't surprise you to hear that people actually disagree about what is entrepreneurship. Some people think it's anybody who runs a small business as an entrepreneur Morgan anybody who owns a company. And, of course, you have to also have to define failure. So, That came home.
I taught a course on entrepreneurial failure this past fall at Harvard Business School, and we did a case on Jibo, the social robot out of the MIT MediaLab sort of a robot that could actually strike up a conversation with you, and move and dance and so forth. It was really remarkable product. It, failed. They've lost $61,000,000, but the question is from whose perspective.
So, you know, failure, is it from the founder's perspective, if the founder's goal was to build an amazing product and and that some people would love, which they did with Gevo, and sort of prove, in this case, the, the founder was Cynthia Brazil, who's the pioneer of social robotics. She wanted to prove that people wanted a social robot in their home, and she did.
From society's perspective, that venture didn't work, but but the next generation of social robots are out there working with autistic kids and the elderly and so Morgan. Investor's perspective, $61,000,000 gone. And then, when you look at failure, you also ask questions about what outcomes. So does a company literally have to go out of business? Not every company, fails, of course.
It's not a, a, an endearing term, but, as a, a lot of investors will talk about zombies sort of companies in their portfolio that they know will never yield a return to the investor, but they're making enough money to keep going. So is that a failure, does something have to go bankrupt? I settled for the project on on a definition of failure, which I think will ring true to most venture capitalists, which is early investors did not and never will make money.
So that that was the definition of failure. So what we're talking about is is how we define failure, and you defined it narrowly as if the early investors haven't and will never make money. And that's a a convenient way to describe it that gives us a hard line in the sand to then define the problem and then talk about it. Makes sense. Yeah. And then, to your question of of do I have a framework that leads you in the direction of okay, why?
And, there's a easy answer, approximate cause of death you know, like if you're a forensic investigator, loss of blood, right? Company ran out of money trying to find a good opportunity and couldn't raise more. So loss of blood. Why? Well, gunshot wound. Okay. Why? What's going on? Was it self inflicted? Was it a jealous spouse? So you really it's it's like Toyota production system 5 wise when there's a problem in the factory. You just have to keep asking why?
And that doesn't lend itself to a single simple explanation for why startups fail. So so you know, some some startups, do not find the great opportunities, some don't have a great team, bad market, bad founder, There's a temptation, I think, for everybody, for academics in particular to look for one theory that stretches across lots of situations. I think the my my colleague, Lake Clay Christensen, was was that kind of scholar.
He everywhere he looked, he saw disruptive technology and sort of had the hammer and he could sort of bash down on any problem, looked like a nail. I the the the the failure, the startup failure question really doesn't lend itself to that way of thinking. So, no single cause, but there are patterns. And, and I saw some, patterns repeated with early early stage failures. So I don't I wouldn't say I have the framework Morgan least not a newly invented one.
When we teach MBAs at Harvard Business School how to diagnose, the prospects for an early stage startup, We use a framework we call diamond and square. Now, there's a diamond that represents the opportunity in in the Four corners of the diamond, if you will, are, it's it's it's a monomic mnemonic device to help people remember. So the corners customer value proposition, the go to market strategy, technology, and operations. So how are you gonna build the thing and and and deliver it?
And then the cash flow formula. And then surrounding the diamond is a square, which represents the different folks who have to contribute resources, the founders themselves, the rest of the team, outside investors and strategic partners. And and so we teach the students to ask how does how does the opportunity look? How do the resources look? And are they are they in dynamic alignment? Do you have the right resources in the right quantities to to actually pursue this opportunity.
And that's, that that's where the exploration of early stage failure gets really interesting because what I found were, some teams that had mobilized a great set of resources, strong founders, great team, supportive investors, but that team never managed to find an opportunity, a good opportunity, and And then at the opposite end, I found teams that really had, often through lean startup techniques identified a great opportunity and validated
demand for a solution they weren't able to mobilize the resources to capture it. So so those were, those are 2 of the failure patterns that I saw, called 1 a false start. And the other bad bedfellows and talk a little bit more about those. And then there's a third pattern, which is false positive.
You get you get off to a a a a start with early adopters, and then it turns out that mainstream demand, that doesn't share the same needs and and you've you've actually mobilized the wrong resources to pursue the mainstream market. Got it. And so these these three sort of patterns are the the big bulky patterns that stand out and within them, there must be lots of different flavors. Yeah. Exactly. The false start is a is a is a difficult and tricky one.
So I mean, what I found in in the you may know Sunil Nagaraj. She's Pete Alum, started before he became a venture capitalist, and now he has his own seed fund, ubiquity ventures, and worked at Bessemer before that for many years, but, straight out of business school, they launched an online dating site called Traangulate. And, Sunil was an engineer, like a lot of engineers, is great at building things, love to build things.
And so he dove headfirst into into launching triangulate, without really studying the market without getting a good sense for customer needs without running what we would think of today as as good minimum viable product tests. And the first version of the product was was off Morgan. And he spent several months building it and launching it before he figured that out. He had a fantastic team. They were really agile and could iterate and build new things fast.
So He he went through a couple of pivots, but the point is is he'd only raised $750,000 and he he only had time for a few pivots. Eric Reese, actually, in in his book, defines, runway as not the number of months of cash you've got left before you exhaust your balance. But rather, how many pivots can you complete before you talk to cash balance? Sure. That makes a ton of sense. And, and the false start is essentially you you get going too fast. And, and, you you waste a pivot essentially.
And and so you have less less capital available, and you can try a fewer things. That's That's what happened to Sunil and triangulate. It's very understandable. Right? Entrepreneurs have a bias for action. They love to build that. We're told to to launch early and Morgan. There's actually a lot of the rhetoric of of lean startup. I would say, pushes entrepreneurs in that direction. I would agree.
And another another nuance to that that I noticed in working with with myself and with other people in this space is that, you know, how much how much belief do you need in order to pursue an idea at a 100%. And if someone needs to have a lot of belief before they'll really go at something, that gets them so that they often waste a pivot or 2 because it takes them a long time to get revved up and a long time to get revved off of the idea they get too attached to it.
There's a lot of the more facile founders, the people with the personalities that really allow for the lean methodology to work better are people who can wake up every day, go a 100% at their goal of that day. And then immediately when they get the data that it's not working, they can move on to the next idea and then the next day Pete up with a 100%. But a lot of people I find need to to have a lot more, belief before they can get rubbed up, and that that causes them to go slowly.
Yeah. No. That that point, echoes an important theme in the book, about founders and storytelling in reality distortion fields So that's that's a term, originally from the 1960s start start, track episodes but co opted to describe Steve Jobs and his ability to mesmerize people and get them to sort of see his dream and work 90 hour weeks for months months on end and and and and help him put a dent in the universe. And and when a founder, invest that kind of ego in in selling the concept.
It does make it hard to be flexible in the way you just described. So so I think I think they're 2 extremes. You described, somebody who was ambivalent and needed a lot of validation, and I think that's very much true. But the other extreme is somebody who's too headstrong and stubborn and just sort of sees a future and is is less willing to to depart from that. So so both both extremes can be dangerous.
Unless unless the stubborn person just happens to be right with their first which is where we get a lot of the, you know, the the decacorns and other companies that Exactly. Yeah. Federal express, 40 or 50 years ago was in in the 19 seventies was, exactly that. Fred Smith was sure that the world needed a hub and spoke away to move packages around. And it then was the biggest venture capital launch in history, and lots of people thought he was crazy. And, he was as headstrong as they come.
Yep. He just happened to be right. Got it. So these are some of the these are some of the frameworks for these early stage companies, or this is a a way of looking at some of the, I think, You've also mentioned the, the failure of false positives. Right? So seduced by strong results with early adopters. And then it doesn't work with the mainstream companies. What happens there?
If you if you create a product that's perfectly suited to the early adopters, it often won't be the right product for the mainstream. So, the, example of that in the book fab.comecommercecompanystarted off with a really, highly curated set of of funky distinctive products that were, sold to flash James. So they put forward, a chandelier made out of champagne glasses you know, at Rhinestone encrusted motorcycle helmet, things like that. And, the early adopters were crazy about this stuff.
This sort of very distinctive vision of design that that the fab founders had. And, and bought a lot and referred their friends, so it took off on social media. And the first cohorts that fab, recruited Pete just fantastic. Some of the best that e commerce has ever seen. And So the repeat purchase rate was high and the sales price was high, and they would take this deck to the venture capitalist, and they would say I've never seen anything that's great.
It was there in Philadelphia, and they raised $50,000,000 or something. Yep. Exactly. And, DCs pumped a lot of money in and, and the expectation was that they could go go go Beller, the the next generation of of customers, the next cohorts weren't nearly as excited. They didn't repurchase The first cohorts, came through social media referrals. So the cost of customer acquisition was 0 for them. You had to buy the next cohorts.
So had this LTV lifetime value of a customer CAC customer acquisition called squeeze where the customers became less valuable, because they weren't repurchasing. And they became much more costly to acquire. And, and so they burn through a ton of capital, and mostly because the mainstream market didn't share the taste of the early adopters. Talk to me about the aggregating resources and the catch 22 around that.
So so, you folks are all about network effects, so you're very familiar with a catch 22. For the listeners, in in case they should read the book. It's an amazing book. But a cash 22 is a logical impasse that takes the form. Can't have a without b and can't have b without a. So you can't get a job without experience and you can't get experience without a job. Network effects on in two sided markets have that flavor. Right? Side a won't come on board unless side b is there and vice versa.
And so the cash 22 with an early stage startup is you can't get resource providers to commit and by that I mean, the rest of the team, strategic partners and, crucially outside investors until you've resolved some risk. That they're taking a risk by lending their time, their money to you. And you can't reduce risk until you've actually mobilized some resources. I mean, you can talk about it.
But but, people will be looking for more validation and more proof the catch 22, you can't get resources without reducing risk. You can't reduce risk until you get started. And to get started, you need resources. Right. The found the founder kinda has to fake it until she makes it. That's part of it.
And and that I would say that's 1 of 4 tactics So, when we talk about this, at school, we talk about storytelling, and and I think fake it till you make it is is that But in in general, we're looking for ways to either mitigate the risk, reduce it in some way or reduce the resource requirements. If we can do one of those 2 things, we've we've helped with a cash 22. So the 4 tactics resolve risk defer it, shift it to another party or get people to ignore it.
Storytelling fake until you make it is, is basically a way to get people to ignore the risk because you've, you faked them out essentially. You've, or you've dazzled in the case of reality distortion fields style storytelling, you've dazzled them. Partnering is another thing entrepreneurs always do, right, that they don't have resources. So let's go to somebody who's resource rich and borrow their resources and will shift the risk to them.
We have to persuade them that it's worth their while staging, of course, you know, when new ventures get funded inside big companies. They don't come with seed series a, series b. It's just next year's money. You know, here's a big lump of capital. But VCs will stage, which essentially defers the risk, right, until you've met some milestone.
Now if you haven't met the milestone, we may not give you Series B And then, lastly, lean testing is a a way to resolve the risk if if cheaply, and and and still, in a rigorous way before you have to commit too much in the way of resources. So those are the four ways you get around it. Each of them has a dark side. And, and, and you see some of that in these failure stories.
So, the dark side of lean testing is people think they're doing it, but they skip the Flint phase of of essentially customer discovery. They go right to the building. You know, let's let's build and put it in the put a product in customer's hands and then iterate as fast as you can, but they've skipped that, 1st few weeks or maybe even a couple of months of of really interviewing customers and understanding their need and doing ethnography and so forth.
Staging can go bad when you pick the wrong investors. Partnering goes bad a lot, basically because big players are hard to negotiate with it. It's hard to align your interest with theirs, if you can get their attention at all.
And then the the risk with storytelling reality distortion field and so forth is that the, the field, the reality distortion field folds back on itself and the, the the founder, is persuaded of the of the truth of what they're doing and doesn't hear the universe saying this idea is really isn't working. Yeah. It's interesting. You know, you mentioned the dark side, and and I I don't think people are very comfortable talking about that.
I think that you look at the percentage of of traffic that was coming from the sort of a hookup area on Craigslist or, there there's a lot of sort of darkness of of that's driving their early stage of some of the companies we know and love that gets cleaned up later. Mhmm. And those those, you know, I I know that, YouTube had to pay a big copyright infringement, payoffs after they were acquired by Google because they had been infringing on copyright. In order to get going.
And and, I it reminds me a little bit of Jean Bao Jean from Lemiz, right, who who does something wrong. I mean, he he steals the silver candlesticks. And if if the Victor decides to rat on him, he's going to jail and will die in prison in the next year. And he gets he gets a pass, and that lets him jump start his startup. It jumps start, but there was a dark side to Jean Bell Jean beginning. And then we, as as the audience, to decide do we forgive them for that or not?
I think the social network, that movie shows the dark side at the beginning of Facebook, and many of these companies have these these dark sides to them as they they do something unusual.
They do something out of the norm, you know, whether it's Airbnb or Uber or Lyft or lime or or bird, you know, sort of looking at the gray area of of what local states and and cities have as their ordinances and and pushing those boundaries and getting cease and desists and you know, it's, there's a there's a tussle on the boundary that if you put a bright light on it, it really doesn't look good.
Yeah. Beller to ask for forgiveness and permission, when it comes to all of these ventures that are have got ambiguous legal standing. And then just a general problem that you point out of of overstating maybe in the extreme misrepresenting the progress you've made, when when when you're selling the concept to Exactly. Exactly. And there's this there's this game between the Pete people and the founders, and I was a founder for 20 years before I became a venture guy two and a half years ago.
But, you know, now that I'm sitting on the other side, I can see this game where we're trying to penetrate the exaggerations of the founders, and the founders need to exaggerate in order to get the confidence ball rolling. And in order to attract resources into their company.
And so they there's this there's this, culturally permitted boundary that if you're not in the network and in the system, understanding where that boundary is, it's often hard, easy for founders to cross the line into flat out misrepresentation. Yeah. Versus what, you know, the community of of investors and whatnot has come to understand as general exaggeration.
I remember working at Beller Ventures as an associate in the nineties, and we would make an investment, and it was my job to do a lot of the due diligence on the company. And we would make always. No matter how many people, no matter how many calls we made, no matter how many spreadsheets we looked at, things were always exaggerated, things were always hidden. And so there's this cat and mouse game between the investors and the founders now.
And and, the this is the dark side of of being on the cutting edge and trying to aggregate resources. This is the dark side of the founders trying to solve this catch 22. It it's, it's it's out there. I I wish I could say that we at Harvard Business School have figured out how to teach this, to MBAs. We were acutely aware of the need to do that.
There is, essentially a business ethics course that all the MBAs take, but we've been in in in my unit wrestling with the question of whether entrepreneurship is somehow different. Right? Are these just business ethics problems that, you would approach the same way, as Johnson and Johnson sort of figuring out what to do with all the tainted tylenol bottles, you know, or or is this, is this different?
And so a lot of smart folks, my counterpart at Stanford Engineering Tom Byers has, put a big push on to get entrepreneurship educators to think about ways to, to, to teach the next generation of founders how to approach these questions responsibly. We're we're getting there. And when the system when the system is, you know, rewarding the people who are the most aggressive, like at the Uber versus Lyft situation. It's hard to guide founders in a in a really clean way.
One one thing I've heard is and I used myself with the founders I work with is just don't do anything you don't want on the cover of the Wall Street Journal. Yeah. That comes up in class discussions. You know, people will draw a line between outright lying and, you know, that where it gets gray and tricky is if they don't ask you, do you have an obligation to tell them? Right. That's right.
We're we're about to lose a big customer and, if you asked me, I know you would wanna know the answer to this, but you haven't asked me yet. So can I just sit here and and let it go? What's my ethical responsibility? Yeah. Yeah. It's it's it's tough. And then, you know, the founders will say, well, you know, they're sophisticated investors. They should know to ask the right questions. You know, I'm reminded of a but this is true in all of life, not just the start ups.
I know that my neighbor down in Dutsbury, you know, we had some friends try to do a a a building project, and they were they were declined. And then another guy bought the pieces of land, and then he got the building project done. And And the first guy goes and asks that little town, why did he get to do it? And he said, well, you didn't know what you didn't know what questions to ask.
So, yeah, I mean, spending time in the startup environment and learning the the, the exact lines of these things, I think, benefits everybody. It's why why network effects are even powerful within the network of people building startups because you have to know all these little things. So is there anything that stands out as the most surprising thing you learn about startup failure at the early stage. Is there is there one thing that you're like, gee, I hadn't expected that.
Yeah. So the big surprise was the early adopter challenge. Knight, I knew, most of us have, been in this business for a long time have read Jeffrey Morgan crossing the CASM book, and that book puts a spotlight on the difference between early adopters and and mainstream customers. So and I think I I knew about it conceptually, but to see how hard it was for for early stage founders, to deal with this question of do you build a product for the early adopters?
Do you build a product for the mainstream? And, how do you bridge from one to the other? And the mistakes you could make if you get that wrong and and how it can tank a company. So that was eye opening. That's interesting. That was very interesting. And what's the difference between the early stage and the late stage startup failure?
Oh, well, I mean, the big difference is when a late stage startup James, it leaves a giant steaming crater in the landscape, right, 100 of 1,000,000 of dollars and 100 of employees. And, and we read about it. Like, early stage things fail, and you know about it if you've invested in it. But, they don't quite have the impact. So, the late stage failures are, they they could put your hair on end some of them.
Now you've made the point maybe that the failure rate of late stage is almost equal to the failure rate of early stage, or is that I might Yeah. That's fair. If if you use the definition we did before, which is investors didn't make money, then what happens with a lot of late stage startups is they've got momentum. The Series C investor buys in at a high share price and the thing goes sideways.
It doesn't necessarily fail, but, there's a down round or or even an up round, but a lot more capital comes in higher in the liquidations, you know, at a different place in the liquidation stack. And if the thing is sold, Series C, you know, might make some money, but it isn't gonna be the 5 or 10 times they were hoping for. So and and and often, they may lose money. So if so if that's the metrics, sort of, not getting all your money back, then then 75% is indeed the number.
That's incredible, Tom. That that's super surprising to me, but I guess it makes sense because what we're seeing is that the capital markets are kind of efficient. Meaning, if there was one class of or one investing stage that was far outperforming the others, then everyone would be doing I like that explanation. Never thought of it that way, but I agree completely. Okay. And so what what are some of the differences with late stage. One is that it's a much bigger crater.
Many more people are affected. We hear about it in the press. You you got this great phrase. It's it's a cascading miracles. Can you walk me through that? Yeah. So that's just one of, 3 failure patterns that I'm looking at late stage. That one, I I love the expression. It actually came. You'll remember who John Malone is. Yeah. The, entrepreneur who built, TCI the communications ain't the biggest US cable company.
He got it from a mentor of his, the phrase, and, it just basically it's you you were making this point early in the podcast that it's often true that many things have to go right And if any one of them doesn't, the venture James. Right? It's it's a math equation where you multiply a bunch of of of, outcomes. And if any of them goes to 0, the whole expression goes to 0. And, you use the you you use use 20 coin flips.
If you just take 5 coin Flint, So if there are five things that have to go right Yeah. And this fiftyfifty odds, the chance that you're gonna come up heads five times in a row is 3%. It's like spinning a roulette wheel. And open you land on 31 or whatever. And and so that's the problem. And where you see this in particular, I I reserve the the expression for audatiously bold startups where there's this, really ambitious innovation plan.
And, often a a founder, who can sell that because of the scope of innovation, It's gonna take a long, long time to develop the product. Because of the nature of these businesses, you often have network effects in the background or high switching cost attributes that are going to cause you to want to go fast once you actually do launch. And so there's a scaling imperative. There's a long development cycle they're often partnerships.
There's often government approvals because, again, ambiguous legal standing. And, and a whole bunch of things conspire, you you've got a moving target because you're not actually gonna launch this thing for 5 years, 7 years. Of course, the markets keep moving. Technologies keep moving. You have to figure out whether you wanna incorporate the new technology. And so examples of this, gpo was a good example. It was a cascading miracles.
Iridium, if you remember, or satellite phones, $6,000,000,000 loss, satellite phone service anywhere on the planet. Segway was, that that kind of business Silicon Valley veterans will remember Gocorp, which was Pete and tablet computing back in the eighties, early nineties. And the case we use SpaceX and Tesla are are probably examples of this working, you know, Flint flipped heads many times in a row, at least so far.
But the the case I use in the book is Beller Place, Project Better Place, which was, $900,000,000 spent and lost on a, on an effort to, create a network of charging stations for electric vehicles, launched in 2007 went out of business in 2013. And so, so the there's a tendency to overestimate demand if you saw the original projections for Iridium or segue, what they thought they were gonna sell.
And, you know, because of the delays, you eventually cut some corners with the product, the costs because of the delays and partners not, carrying their end, costs escalate. And, and so ultimately, when you launch, disappointing as it was for Gevo, as it was for Segway, as it was for Iridium, over and over again. And, these things when they go down, it's 100 of 1,000,000 of dollars lost. And for them to have worked, they would have had to have a series of cascading miracles from the work.
And when people were investing in them, there was either a reality distortion field or there was some sort of metrics early on that allowed the investors to pour another. Let's say, a 1000000 into a better place, thinking that a lot of the risk had been taken out, thinking that, that the miracles had already happened in the past, and now we just need it to scale. Yep. Yeah. And, reality distortion field, Shay Gossey was the entrepreneur behind Better Place.
Positively James in in his ability to to, dazzle an audience. And with Segway Dean came in. Same thing, you know, really a riveting, inspiring presenter. The Segway was backed by, John Morgan of Flint Perkins. Steve Jobs himself wanted to put $50,000,000 in and and, Dean James in, wasn't comfortable with the amount of control that jobs might want. Bezos wanted to invest. So, yeah, some of these ventures get going in in boom James, sort of the, the height of a bubble, but not all of them.
Now and and it's interesting because the the positive side of someone with a reality distortion field is that every company has financing risk. And if this person through their personality is really good at fine at fundraising, then use an investor think, they'll attract more capital, and we'll get more and more at bats at this. Yeah. Exactly. And and the financing risk is particularly acute because the the development cycle is so long.
You know, if if, if you're doing a Beller place essentially was a clean tech startup, right? Started in 2007 and, launched in Israel in in, in 2012. So so you gotta live with 5 years of financing risk, and we went from BoomTown for CleanTech Investing to basically people, deserting the sector. Sure. Sure. And this was one of 3 failure modes you're seeing. In, in later stage, just cascading miracles. What are the other 2?
The one of the others that I think is a we've been talking about financing risk. I I I call the all of the failure patterns have James. So the name for this one is a help wanted, and it's basically a late stage venture that still has product market Flint. The customers love the product, the basic formula in terms of LTV CAC is is on track. But something on the resource front goes awry.
And, it might be a mistake made, or it might just be, by the way, that's one of what failure in general, you've gotta decide, are you only gonna label a thing of failure if mistakes Pete made by the management team Sometimes it's Morgan, right? We're in the middle of a pandemic and a lot of startups will fail, not do it any fault, any, any bad decisions by the entrepreneurs.
So, dotting Bow was the example in this chapter of of it's a a online retailer of of fernet home furnishings and they really, the the formula was working. The demand side was very strong. But two things went wrong. One, they got hit by you'll remember this Beller, James, sort of circa 2013, 2014, big downdraft and spending on e commerce. You know, probably a 50% decline across the board. And even in a downdraft like that financing risk like that, even healthy companies can't raise more money.
And so just as, that notice When you mean the downdraft, you're talking about the down draft from investors. Yeah. Exactly. Not from buyers. Yeah. Exactly. Consumers. But from the investors, the the the just became ecommerce fell out of favor. It did. And, and stayed out of favor for long enough that if you had just stepped on the accelerator, as as you were heading into that period of financing risk, you were in big trouble.
And, it turned out that in Bo's demand model relied heavily on morality and and social media, and and, it can be harder to turn that off than than the kind of customer acquisition that relies on paid marketing.
So, so that was one Morgan, really, I suppose you can always ask questions about whether management should plan for something like that, whether they could see it coming, but the other problem they had was shipping couches across the country is logistically and operationally intensive like Nothing you've ever seen except maybe like a barrel manufacturing. Right. And, it took them the longest time to figure out how to do that and how to do efficiently and effectively.
And essentially, they went through 3 vice presidents of operations before they found somebody that could get the the logistics and operations and and shipping under control.
And in the meantime, so they had strong demand, but they had poor margins because they were expediting things because Pete were canceling orders because there were all sorts of queries to customer service and Anthony Sahoo, the entrepreneur, I I think would say made a mistake in these in these hiring moves first person he hired, he he wanted because he knew he had these, operational intensive demands.
He wanted a generalist, a chief operating officer type, and hired somebody who was good at that, but had never really had, e commerce experience of shipping heavy things. Second person had more of that but had the kind of big company background we were talking about a little while ago, and, and didn't work out. Finally nailed it the third time, but by that point, he burned through a lot of capital. And then he hit the financing risk. Got it. Got it. Yeah. It's an incomplete team. Exactly.
Missing missing manager. 1 single in a really decisive senior role, having the wrong person in there. So help wanted. Yeah. So so often as I sit there with the founders, I I finally can call it. I lean back and I say, well, we have a recruiting problem, don't we? Yeah. Right? We're missing someone. And, and I think that it takes 3 months, 6 months to fill a position like that, you know, and then you bring somebody on, and it's 3 months or 6 months before you figure out whether it's working.
So Yeah. It's easy to lose here. 12 months of burn. Yeah. Exactly. Because you're missing one person. Right. And if you've gotten everybody else in place, then that that just increases your burn every day. Yeah. Exactly. And and that's what happened. And then the Pete pattern, I I call it speed trap. I talked a little bit about fab.com a minute ago. That's the case there. We can blame this one on the venture capitalists. Although, entrepreneurs can be complicit.
So, early momentum, VC buy into a company that's growing fast at a high share price and expect more of the James, entrepreneur loves that, who doesn't want to grow the thing, and, it's, so you you step on the gas, the customers that arrive later just aren't nearly as attractive as the ones who came in the beginning, If you're at all operationally intensive, if it's just a pure software business, it's a little more forgiving, but if
you've got to, operate warehouses and, call centers and so forth. You're now hiring legions of employees that you've got to train. You've got a layer in middle managers. You've got create processes and so forth, and so you're going, Beller as you can, you've got, often chaos operationally. You've got problems culturally because you have old guard, new guard conflict. The the the new guard is jealous of the of the option gains that the that the old guard is sitting on.
The old guard looks these new people who don't understand the mission who just sort of see this as a job. The new guard are specialists, and they think their skills aren't appreciated. The old guard are generalists. And, their jobs increasingly are like, what what do we do with Fred? You know, he's good at he's a he's pretty good at a lot of things, but he can't run performance marketing. He can't run the warehouse. He can't run the call center.
And so so you have all sorts of problems And, and, you know, and then, of course, the right answer is to slow down and fix things, but you got a lot of pressure to keep going. And, and that's where the Pete trap comes. So I wanna switch gears and talk about start up success and network effects because then, obviously, it end effects stands for network effects.
We believe that network effects are the strongest durable competitive advantage, and we see a strong correlation between network effects and success. And, you know, over over the last few years, we've done this research project where we've noted that 70% of the market cap in the tech industry comes from the 30% of companies with network effects at their core. So that underlies our thesis about how we invest.
And you have been such a masterful contributor to the overall thinking about network effects over the last decade or more. And so it's always great to to sit down and talk with you about this. And are you still teaching network effects to your HBS students? Do you mean me or my colleagues? You and your colleagues. I don't do it much anymore. 2 years ago, I teamed up. I know you've met my colleague Scott commoners.
Absolutely brilliant economist who's an expert on market design, market place design, protege of Nobel Prize winner, and, helped him launch a course on marketplace design. So that was really the first time I've been back at it, and of course, in any kind of marketplace, the network effects, loom large. So that's the first time I've done it in years. And I'm rusty. So Oh, that's that's But but it's all over our curriculum at Harvard Business School.
I would say half of the first required 1st year course is the burnership course, the technology and operations course marketing strategy. All of these courses, teach some aspect of it and and plenty of the 2nd year electives. Interesting. Interesting. You know, for mo most people in the world don't get to go to HPS, and and I was super lucky to get in and and enjoy my time there.
And And so for those people who are listening who who have who don't get to go, just knowing that you guys spend a lot of time studying it, I think, is helpful to know that if if they were going there and spending 2 years and spending their 140 or whatever it is, $1000 to That's the number. To get the to get the education that they would be studying a lot about network effects. And so, that's that's certainly, certainly having the syllabus is sort of half half the battle. So that's good.
Well, you guys have done a great your website is the syllabus now. So, thank you. Thank you, Tom. I appreciate that. I you know, so what do you think people are frequently getting wrong about network effects? Less than in the past, but still a lot. The, just the definition even still, I know you're sensitive to this. There's a lot of in the a a lot of people will, point to morality and, assume there's a network effect at work.
Sometimes there is, but if it's just a word-of-mouth referral, that's not a network effect. That's word-of-mouth referral. And, and the other place where it gets confusing, and I think people, even experts on network effects, can have a a reasonable debate about this is a situation where density of the network to share traffic scale of some sort makes the product more valuable.
And I I tend to reserve network effect for when the users of the network are actually interacting with each other usually through a platform. And, if there's no interaction, I don't consider it a network effect.
It is a scale economy you know, so, you get into businesses like, dockless bike sharing, you know, where that the company buys a whole bunch of bikes and the more bikes there are, a more comfortable you are as a user going to be that there'll be a bike in the right place at the right time. It's not really a network effect. And Google in some ways is the same way, right?
That they, with a huge amount of search traffic, they can draw better inferences about what the best, listings are to serve up to you. So the product gets more valuable, the more people use it, but they're not really interacting with each other through through the product. So those definitions are an issue. There's there's I think it's easy for founders to wave their hands in the say they're gonna harness network effects without really understanding how strong they are.
And and we Pete, I still see a lot of mistakes sort of assuming that network effects are stronger than they are. Well, Tom, it is a real pleasure to hear your voice and to talk with you about these things. And when is the book coming out? The book, I'm writing the conclusion right now. As you might guess, it takes a long time to go from there to a printed book on the bookshelves. So I think the target date is March or April, essentially 10 months from now. Got it.
And the name of the book will be. Why startups fail? I can't wait to read the whole thing. Tom, thank you so much. Yeah. Thanks, James.