Ann Miura-Ko on FLOODGATE's Thunder Lizard Theory and Achieving Product Market Fit | Blitzscaling 04

So yeah, my talk is called Hunting Thunder Lizards. And Thunder Lizards is actually a term that my co-founding partner has talked a lot about. His name is Mike Maples. We've been working together for a number of years since 2008. So just to dive in, give you a little bit of background about me. Palo Alto native, so I grew up here, went to Paly, snuck into some Stanford parties as a high school senior, went to Yale, electrical engineering, went to McKinsey, and then got into investing kind of by chance. A partner at Charles River Ventures by the name of Ted Dershowitz gave me a shot. And we actually had this incredible conversation about books and music that we loved, and at the end of it he basically offered me a job. I just ended up starting working for him a day before 9/11. So my second day of work was 9/11, and I got to see what happens in venture investing when everything comes to a screeching halt. I eventually ended up at Stanford PhD. I got my PhD in the operations research group, doing math modeling of computer security. Had the opportunity to interact a lot with Stanford students and really fell in love with the act of teaching. In fact, I'm going to give a plug for my class. In the spring quarter, I'm going to be teaching MS&E 275, and this is sort of a test run for some of the elements of the course that I'm going to be teaching. And then I ended up at Floodgate. Floodgate, we started in 2008. I was thinking of starting my own company at the time, and I was told by one of my mentors I should go and find an angel investor who has a lot of experience looking at very early stage things and see what's going on in the real world. I started doing that with Mike. Once a week we'd look at companies together. In the midst of that, he turned to me and said, 'I think I've accidentally raised a fund, and I think in that case you should join me. This isn't a venture-backed startup like you're thinking of doing, but it's a backed venture startup, so let's go.' And so in 2008, I got started. These are some of the companies I've been involved with. So I had a really great string of beginner's luck. So in 2008, as you can imagine, was a really hard year economically, but turns out it was a great year to become a venture investor because not many people were actually writing checks. And so between 2009 and '10, I actually invested in three companies. My first was TaskRabbit, my second one was ModCloth, and the third one was Zimride, which turned into Lyft. And all three of those companies are, I really kind of wax nostalgic about them because it was a period where I really had no background in investing. I had nothing to turn to to say this is my track record. And so each one of those founders, whether it was Leah Busque or John Zimmer and Logan Green or Eric and Susan Kogut, they as much as I took a bet on them, they took a bet on me. And so since then, you know, invested in Refinery29, which has really taken off. Ayasdi actually was born in this very building in the math department. And so I'll talk a little bit more about why I think companies like that are significant.

And so, you know, what are Thunder Lizards? This is sort of a central theme for Floodgate. We talk a lot about these animals. They are obviously inspired by, not by dinosaurs, but they are inspired by Godzilla. And they represent what we think of when we think of entrepreneurs, like the tremendous scale entrepreneurs that have gone through blitzscaling. They are born from what we call radioactive atomic eggs. And so there is something, you know, there's a genetic mutation from the day zero with these entrepreneurs. They then swim across the Pacific Ocean and have this incredible long journey through lots of dangerous things. They stop off in Hawaii often and then end up in what, depending on whether Mike tells the story, he says they end up in Tokyo. I like the version where it ends up in San Francisco. And he emerges in San Francisco Bay with an attitude and starts to wreak havoc. And we like the older versions of this as well. They shoot laser beams out of their mouths. The best ones, you know, they eat trains, some of them will eat ships. But what does that mean? You know, why does that imagery evoke entrepreneurship for me, for Mike, for people? We talk about Thunder Lizards to, well, we think about it in sort of four different areas. One is, and probably this is the most powerful notion, is that they've somehow converted their advantage to truly disruptive power. And when I say disruptive power, that to me evokes a lot of what we see in Godzilla. You don't see this in Godzilla, but these companies will minimize technical and organizational risk, or organizational debt. These are the things that happen in a company as you're acting quickly where you accrue some cost. And great companies will minimize that debt. You will achieve product-market fit. So all of these companies will have an incredible feel for the product, but will also have a pull for the product from the market. It's almost uncontrolled pull of their product. And then lastly, this is something that Peter Thiel has also talked about, they avoid competition. And so these are the four things that we believe result from the fact that you are a Thunder Lizard.

But Thunder Lizards are really rare. And this is actually a slide from McKinsey, and I thought this was really fascinating because if you took all of the IPOs that happened between 1980 and 2012, you know, there's about 3,000 of them. But the number that was most striking to me was how many of these have more than $4 billion in revenues. Well, turns out it's only 17 companies. And if you can imagine like what it takes to get to be one of those 17, those are the truly legendary businesses. And you know, even getting to an IPO is already exceedingly rare. There are roughly only about 25 companies in any given year that exit at greater than $500 million. And so if you think about that, that is the actual, you know, when you have your exit event, there's only about 25 of those in any given year. And it really doesn't matter what year it is. You might have a slight blip one year, but this year looks like it's going to be right around 20 to 25 again. And so when we think about how do you describe what that looks like, right? So we know that they're exceedingly rare. But Floodgate, we invest either really early or way too early. So we are the first institutional financing that happens for a startup company. We're cutting checks between $500,000 and $3 million into a company. And so that means that we need to spot it way earlier than most other larger venture capital firms. And so Mike and I spent a lot of time thinking about not only what do we try to spot, but also as we work with these companies, what's the groundwork that needs to be laid so that when they go on to their next round of financing or future rounds of financing, they don't accrue the kind of debt that could really hamper the company's growth. And so we think about it in terms of what I call the value stack. And this is really sausage being made. I describe this as sort of stuff that I'm still working on. This is the first time actually teaching this in a course format. I've whiteboarded it previously. If any of you have questions on this, I'd love to get feedback as we go along. If I start at the bottom, this, you know, I call it proprietary power. I used to call it technology. And so this is the place where you get something very unique. It's an insight that you have that then allows you to avoid competition. You go to the next layer and it's product power. That's where you achieve this magical product-market fit. And then you get to a point where you have that product-market fit, but now you need to make sure that you have a real sustainable company. And then ultimately from that company, you don't want to just have a great sustainable company, you want to have one that dominates. And that's what we call category kings. And so if you can translate the initial energy that you have within your company as a startup into all of these different levels of power, then we believe you have the capacity to become one of these Thunder Lizards. And we're looking for early signs of that. So I'm going to go into each of these little levels of power.

Yeah, so I'll repeat the question. Do I actually have sort of a scorecard essentially is the question, along the lines of all these different dimensions. We don't actually have a scorecard, but these are different levels that we will talk through. And we will pretty explicitly talk about what the company has and what the company doesn't have. And oftentimes when we're turning down a company, it's because there's something in there that we feel is lacking on one of these dimensions. So if we start with proprietary power, this is probably the part that I spend the most time because this is probably the most evident at the early stage of a company. And this is, you know, it could be a technical insight. So oftentimes it's going to turn up as proprietary IP. So Ayasdi's story is that it started out with Gunnar Carlson, who was the chairman of the math department here at Stanford. He had 25 years of research in topological data analysis, and his graduate student actually turned it into a real product. And from there they published four papers. I countered, Git at that time, and then he turned it into a business plan, and ultimately we funded it at that moment. But it was 25 years of research. So when you look at Ayasdi, one of the things that's interesting about it is they have technology that is in many ways unassailable. Another way to look at it is do you have access to scarce supply, right? And I provide De Beers as a canonical example. If you have access to, you know, diamonds, then you're probably in a pretty good spot. And so scarce supply can actually be a great advantage. The creation of high switching costs, that's Keurig, right? So if I buy a Keurig machine, then I'm probably going to keep buying those little Keurig cups because I already bought the Keurig machine and I want my coffee. And so that's a really good locking-in mechanism. Network effects, well, you're being taught, you know, by Reid Hoffman, who is probably the king of network effects, so I won't say much about that. But obviously that also creates incredible power and a lock-in for both the consumer, but then from the network, then you derive even more power and then more people come. And then the authentic team. When I thought about authentic team, the immediate story that came to mind for me was Lyft. And the reason why was when Zimride first came to pitch for me, they had this incredible story of how the two founders met. And so I was looking through the notes from the last lecture where Michael Deering was talking about transportation, right? And I love that part of his lecture because it was actually very reminiscent of the pitch that John and Logan gave me when John talked about why he joined Zimride. So Logan was first to get it started, and he had this interesting story of how he had joined the transportation board of Santa Barbara because he was convinced there was going to be something there. And then we turned to John and said, 'Well, how did you get interested?' He told this incredible story of how when he was at Cornell, he had this professor who did essentially Michael Deering's talk about transportation. And there was this one slide that he put up where he said, 'Look, whenever there's been a huge discontinuous shift in the economy, it's always been led by something that's changed in transportation.' He talked about the canals, he talked about trains, then he talked about the highway system. And John said, 'I know that there's something next and I want to be part of that.' And that's what Zimride's about. And what was interesting to me is as Zimride then switched into Lyft, that story, I've shown that pitch, the pitch for Zimride to other people, and you wouldn't actually know until you got to the product description that the pitch was not for Lyft. And so we really look at the authenticity of the team. So when I invested in Zimride in 2010, it took two years for them to get to the product pivot which got them to Lyft. And so that was two years of quite a bit of work, quite a bit of heartache to get to that point. And you can imagine what kind of that has. And so we have a strong viewpoint that, you know, all of this proprietary power can be unlocked pretty quickly in the early stages of a startup, and something that's probably easiest for us to assess. Any questions on that?

So building product is easier, actually developing these insights is not. And so, and I think that's what's important to me is there's something that you know that no one else knows. And you know, one of the things that was told to me as I was doing research for developing the stack was the founder of Veeva Systems, whose company IPO'd a couple years ago, they had only raised like $7 million total, and I think four of it was touched. But he said every great startup has one fundamental assumption that has a less than 50% chance of being correct, but if true, will give you a 20x, 100x advantage in the market. And I love that kind of notion of what is non-consensus but right for these magical startups. For Veeva Systems, it was actually building their entire platform on top of Force.com. A lot of people thought that was just a crazy, kind of stupid idea. Why would you build, how would you possibly build a billion-dollar company on top of Salesforce? He proved that he could, right? And so regardless of how easy it is to build a company or build a product, we think that having some sort of insight that says, well, I have access to very scarce supply, or I have proprietary IP, or I know something about the market shift that's happening that no one else does, that's what enables you to unlock the market power before anyone else does. Any other questions?

All right, then we go to product power. And what's interesting is, you know, with product, it's being able to achieve that product-market fit. But before you get here, so if you have, you know, proprietary IP, there's a good chance that you never actually get to product power. And we see examples of that probably all over Stanford, which is you find this really interesting piece of research and you're trying to figure out how to turn it into a startup company, and then someone at some point says to you, 'That's technology in search of a problem,' right? And so you might have incredible proprietary power, but it can be very difficult to turn into product power. Product power is when you achieve product-market fit. And a lot of people throw out product-market fit as if it's a product that hits any kind of market, someone in the world likes it, then you have product-market fit. That's not product-market fit. Product-market fit implies market power, right? It's the market that needs to be large and growing. Oftentimes when you hit product-market fit, it feels uncontrolled. In fact, with Instagram, there's a great example of that. Mike talks about at the very first week when they launched Instagram, they had this moment where they realized that, and they knew they had the sense that on the weekends the traffic would suddenly increase. And he knew it was like on a Monday or Tuesday that if they didn't get the Instagram service off of servers and into the cloud, that they were completely screwed for the weekend. And so they worked day and night to get that to happen, and they were finally able to get it to be a scalable service by Friday. And he knew that at that point, he said within, you know, 12 hours of having launched Instagram, they actually had a really good sense, that Kevin Systrom had a very good sense that this was going to be a huge hit. And I think Mike was a little bit more skeptical for a while, but he always felt like it was a train whose wheels were coming off. And so the question I often get from founders is, 'Do I have product-market fit?' And the answer to that question is, if you're asking, you don't. And then, you know, a lot of people will also ask, 'Well, isn't it true that you can actually create a market if you have a transformative product?' And that's true too, but it rarely happens. I think the example that I could come up with was VMware. VMware was so transformative that it created an entire market where there really wasn't one previously. Any questions there?

I felt like there was actually even with Airbnb, there was this moment where I think it was a Democratic National Convention where Obama first presented, that was a real moment for them. Like there is actually a moment where you start to see that grow. I think the place where it can be harder is marketplaces. And marketplaces can actually take a really, really, really long time because it's this very difficult balance of the supply and the demand. And oftentimes you'll see companies kind of getting that balance wrong in the early days and it takes a little while for them to really mesh. But I think of this as really a binary concept and there can be early signs. It really depends on what kind of company you are, what those metrics are. Any other questions?

Yeah, I mean, so customer discovery, I've taught a lot with Steve Blank. There's the whole sort of process and Four Steps to the Epiphany and what Eric Ries talks about. I think there's good ways of, you know, just going out to customers and testing out your hypotheses. Oftentimes, actually, I've seen entrepreneurs track all of their hypotheses, even in an Excel spreadsheet, and start to say, 'This is my hypothesis, this is how I'm going to test it, and this is what I expect to see.' And it's very much a scientific method. I think the thing where that can go wrong is that it's as much an art as it is a science. You have to know when your experiments were wrong, so that the experiment itself could be flawed. It could also be that the way in which it was presented wasn't quite right. And so you have to be fairly honest with yourself about what went wrong in those tests. But I do think that there are ways in which you can actually access the customer, get in front of them, and see if there is an aha moment. Again, with Instagram, it was interesting. They said when they were with Burbn, they noticed actually that you could post pictures, but it was like a five-step process, right? You had to like go into settings, you had to find like this bespoke email address, and then you would have to email your pictures to this bespoke email address, and then it would be somehow posted into Burbn. But people were actually doing that, right? And they noticed within that stream that that was something they were interested in, and that's why they doubled down on it. And so oftentimes it's not what you want to see and what you're looking for, but the things that are sort of outside of that realm that help you notice, 'Oh, that's a problem that I might want to solve.' And so oftentimes when we say, you know, when you go in to do customer discovery, you don't want to say, 'Take a look at my product and tell me what you think.' You want to ask them about, well, you know, what's your life like? What are some of the problems that you face? And it's in those conversations that you might actually find inspiration for the real product that you want to build. Anything else?

Okay, and then from there we go to company power. And this is actually where when you're scaling your company, you're talking about discovering a scalable business model, you're trying to figure out what the cultural values are for your company and what your identity is as a company. It's how you develop your own company narrative, the personality of the place. It's how you develop talent, reward talent, provide career paths for individuals, and it's how you foster communication within that culture. And companies that I think have done this really, really well, Facebook, but also Netflix is really well known for some of their HR documentation. But it goes way more, way beyond what I would say is just HR. And I would emphasize, do you have that scalable business model? Does it make sense today? I would argue that there's lots of unicorns today that are still searching for that scalable business model. And that means that there's a lot of debt that can actually be incurred. If you don't have the right processes in place, you will actually incur a lot of technical debt. You will also incur a lot of company organizational debt. I see this in my own companies where, you know, you have like eight C-level executives and you're thinking to yourself, 'How did we get here?' Well, it's just organizational debt, right? And then the compensation schemes are really messed up, that's organizational debt. People feel like they don't have a career path, that's organizational debt. And so these are also in some ways, you know, when you look at the founder and you talk to them, you can also start to spot are these people who are thinking about these things, who will be open to thinking about these things, who want to think about these things. And how open are they to that kind of conversation? And those are things that we actually do look for in the early stages as well, because again, if we believe that the founder is going to be the CEO for the long term, these are things that they're going to have to grow into, and we want to at least be aware of whether or not they care about these things.

Yeah, I think one of the things is actually early hiring. So how good are they at hiring and what are they willing to give up to get the best people? You know, we have one company actually that has been able to attract like really ridiculous talent from, you know, big companies, Facebook, Square, Twitter. And you talk to him about how he was able to do that. He spends a lot of time thinking about not only who he's hiring and what kind of networks they'll bring to the table, but also how they do interviewing, what do they put together for a compensation plan. They think about it in a really rigorous manner, almost just as much as they think about the product. And to me, those are early signs. When I invested in Ayasdi, part of the thing that convinced me was that Git brought on board this incredible product person. And he was very open about the fact that there were things that he did not know and that he wanted to know, but he also wanted to surround himself with people who would support him in that. And so all of these types of signals are around what is the type of company you want to build, what's your fundamental values. You know, a lot of the founders that are successful are fairly generous with equity. They want people to feel like an owner and not just an employee. So those are the types of things that we're looking for. It's more philosophy rather than explicit things that you'll actually see as signs. It's not metrics-driven. And then lastly, category power. This, from my friends at Play Bigger, which is a great organization, two guys who are CMOs of companies, but they talk about how do you derive category kings. And to me, what is it? These are the guys that convert all of these advantages that they have and then create this incredible disruptive power. And in a lot of senses, they will actually define a whole new category because they don't want to compete. They want to be the big sort of Godzilla on the block, right? And so I think a great example of that is Netflix, actually. When they came in, they weren't saying, 'I'm going to compete with Blockbuster.' They didn't compete. They created a whole new category and then destroyed Blockbuster. They destroyed the category and rebuilt it in a way that suited them. And I think, you know, Starbucks is another great example. Who would have thought that people would want to drink $3 coffee or $5 coffee when you could get it for 50 cents? They completely redefined the buying behavior and enabled a new category to emerge. Amazon's done the same thing. Apple's done the same thing. And when we look at companies, one of the things that we think is really powerful is how does the founder think about the language that defines the area that they're going into and how do they position their company within that market. If they feel like they're not in control of that market and they're allowing the market to define who they are, that's something that worries us a little bit more. We would prefer to see companies be more proactive about it. And so those are the four elements of the value stack that we look at. And obviously, you know, this is actually probably the hardest to view at the stage that we invest in, but in a lot of ways it can actually be the most important. If you can become a category king, that's how you capture value the most.

No, I think it can. I mean, like you can start defining a lot of that. It's separate in some ways, right? Category power in a lot of ways is external, whereas company power is internal. It's how you look internally at your business. It's how you build that sustainable business model. Some people will try to get to category power by buying their way in. So there are companies that will spend a ton of money on customer acquisition and just buy a ton of customers and try to claim category power when in reality it makes no sense from a unit economic standpoint, right? And so my argument is at that stage, those are both very important. You need to actually make sure you have a good sustainable business model, and there must be another way that you can actually get to category power.

Yeah, I think it's more what do you focus on first and foremost. How do you prioritize? For me, the priority is at the bottom of that stack, is like what is that unique advantage that you're building today, that proprietary power. So if it's IP that you have, you know, how strong is that? How do you build that into an increasing moat? If you have a supply chain advantage, how do you really make that into a product? I think the second piece you can't ignore is the product-market fit. How do you get there? Those are probably the two things that I focus on alongside the team. Like how do you develop the team internally? So zero to 10, like you don't really need to think about, you know, a career path at that point. You don't need to think about titles so much. So the organizational debt probably comes a little bit later. You could actually start to incur technical debt, but a lot of times you don't even know. It's more important to be aware of what the technical debt is rather than seeking to really fix everything before you move on, because you don't, at the stage that we're at, you don't know what's going to stay and what's going to go, right? And so I would say the one thing that I would say is the most important for us is the speed at which you're making these decisions. So, you know, when a founder is frozen or just needs more data, that's the first sign of distress that we see.

Well, I really like seeing some sort of tie. It doesn't need to be like 10 years in the making, right? But I do like to see a tie between the idea and why you, like, why is it that you have that unique insight that no one else has. And I feel like for almost every single investment that we've made, there is some sort of tie between that individual and why they're doing that startup. But authenticity can come in a lot of different forms, right? It could be that you are actually the customer, and so you have an unusual empathy for the customer because you happen to be the person you're building it for. And then the proof point is how big is that market. But the other could be like, I just see something, like I've worked in this industry for some time, and there's one team member who really understands that market. And, you know, I always ask that question in every single pitch, and so I know that that's an important piece of what we invest in. The question you ask is, 'Why you?' Yeah, why you, like why is it? And the reason why it's important is, you know, you know this, there's a moment in time with every single startup, and it's maybe even two or three, where you march through the valley of death. And in that moment, if this was one of the 10 startups you had whiteboarded on the board like in your office, and you knew of like nine other pretty good ideas, but this was just the one that caught your eye, in that moment you are more likely to flee the scene in that first step through the valley of death. And so we would rather have entrepreneurs who it wasn't, you know, the 10th idea that they came about, but this was the one, and it's pretty much the only one that they want to do.

Yeah, that's what Michael Deering says. It kind of has to come out. It's like literally you see it sort of physically leaping out of their body. And so it's...

No, I don't think so. But at the proprietary level, we like to see some sort of insight there.

No, it's like I would say it's some sort of proprietary insight, whether it's like supply chain or network effects, or it could be some fixed cost that you're able to get capitalized in some way. I think there's lots of different things that can kind of lock you in, that's the customer, in a proprietary way. I think that piece is important. I would agree most things are not necessarily technology-led. When we originally drew out the stack, I had technology at the bottom and then product. One of the things I would say was some people just come in at the product level and you can ignore the technology stack. And in fact, most of the accelerator companies, right, those are ones that are coming in at that product level. And so all you're focused on is getting product-market fit at that stage.

Yeah, and I think the tricky piece, and this is... I invest in a sector, one part of my investments is what I call radical science investing. And that's, you know, PhDs and some crazy piece of technology they have. And the massive risk that we take when we make those investments is that it's a technology in search of a problem. And I think the reason why Ayasdi actually resonated for me was in my PhD I actually had extraordinarily complex data sets. They weren't big data in the traditional sense, the files weren't that big, but it was complex. And so it was hard to see the actual important parts of that data set. And so I had some empathy for why being able to take very complex data sets and figure out what was inside of it would be important. I think so that actually came more from a feeling, it was more of a gut instinct of I think there's lots of data sets out there where you have this problem of it's just a big hairy thing and I still don't know how to query it. We also, at that same time period, we were creating from an investment standpoint a thesis on big data. And one of the things that I had decided pretty early on was when data becomes that large or that complex, I was less interested in where you were going to store it or how you were going to access it. I thought the big problem would be what do you do with it. And I had gone out and spoken to financial institutions, some pharma companies to see what they were doing. And so from that perspective, had some insight of okay, these are the types of data sets they're going to have and probably you might be able to use that in that setting. And so in some ways that was led by an investment thesis. We were looking for analytical companies. There's another company in our portfolio which is a company called Inscopix. It is literally a microscope you can put on the brain of a mouse and you can see the way it's thinking. And it was a PhD student in electrical engineering and for probably five years or four years now, we were sort of searching for what's the right business model. And it was one of these things we didn't really know how you turn it into this huge business.

Direct channel to the mice, yeah. You literally plug it into your USB port.

Yeah, you can sell the mouse like, 'This is what you're thinking.' But what was really interesting was the guy was really capital efficient. Right, so he took $1.5 million from me and he never touched it. And he sold something like $18.5 million worth of this device. And he kept on iterating on like, well, I think the business model might look like this, I'm not so sure. And his advisors would come back and say, well, let's massage it this way and that. Finally, he's come back and said, here's what I think is a billion-dollar idea. And everyone said, of course, totally makes sense. And so he's off to the races building a company around more like an Illumina for neuroscience is what he believes. And so it's a big idea and he had the time to come up with that because he was so capital efficient. So I think some of these will take very different paths. Ideally, they don't spend a lot of money in that process. And I think the places where we as VCs have struggled is is it big R, little D, or is it little r, big D? And your hope is that you're skewed more towards development than research.

Yeah, that's interesting. I think the other thing is that one of the lessons here is to assume, not think about investors as firms, but about investors as people. Mike and I are both very, very different, just like Reid and I are very different. We have different backgrounds. And if you do a little bit of research, you can find proclivities and interests, and it's not too hard. So you just learned a little bit about Ann's patience around science. Now we'll talk about marketplaces in just a second, which also requires a certain amount of patience. But then, and a funny thing is like one thing you should be careful to assume too much, but you should check interests. So like everybody in the world assumes because of my background in Mozilla that I love investing in web things, and mostly they make me terrified right now because of the rise of mobile. And actually a lot of operators who, if you have operating experience, you mostly are terrified of investing in that because you know where all the bad parts are. A lot of people, I mean like I actually am interested in security. It was an area of research for me for my PhD. I haven't made many investments in the space because it does still scare me. I feel like I know a little bit too much. But that's an area I would still invest in. Another example is a lot of people say, oh, she did optimization so she's clearly going to love ad tech. I don't.