Anu Hariharan from YCombinator's Continuity fund is one of Silicon Valley's top experts on network effects and how they can be applied to all types of different businesses. In this interview, Mike Maples of Floodgate talks to Anu about how a raw startup can design network effects into its business as a core strategy from the very beginning.
Anu Hariharan:
If you, as a founder, pay attention to whether your product has the potential to have network effects, it can really give you a market leading position relative to all the competitors.
Mike Maples:
That's the voice of Anu Hariharan, network effects guru and partner at Y Combinator Continuity Fund. To this day, Anu has created some of the very best content I've seen on how to design networks. And today, I'm excited that founders throughout the world will get to learn from her wisdom and apply it to their own startups as they seek greatness. This is Mike Maples Jr. of Floodgate, and it's go time with Anu Hariharan.
Network effects. It's perhaps the most important value proposition and competitive advantage in business today. Understanding network effects not only helps you build better products, it helps a company build moats that protect its franchise often over the course of many decades. But what is a network effect? And do they happen by accident or can networks be designed proactively? The answer is yes, they can be designed. And one of the foremost experts in how to do this is Anu Hariharan.
If you've ever wondered how you can create a network that becomes a winner-take-all scenario, Anu will help you learn the state-of-the-art approaches to help you connect the dots. Let's catch up with her.
Anu, welcome to the podcast.
Anu Hariharan:
Thank you. Great to be here.
Mike Maples:
I'm excited to talk to you about network effects. What is a network effect anyway? How do you define a network effect?
Anu Hariharan:
The simplest way to define a network effect is as more users join a network, the service becomes valuable to all the users, including the existing users. So think of Airbnb, it's a two-sided marketplace. You have hosts on one side, you have guests on the other side. As more guests join the platform, more hosts are able to get their rooms booked and make money. And therefore, more hosts joined the platform. As there is more availability, more guests joined the platform. So it's literally as more users joined the platform it's useful to everyone.
Mike Maples:
Why is that a big deal? So what? What makes network effects so special?
Anu Hariharan:
It has the ability to tip the markets to a winner-take-all. Okay. And if you are able to get to winner-take-all, it translates also into software margins and pricing power in the market. Therefore, if you, as a founder, pay attention to whether your product has the potential to have network effects, it can really give you a market-leading position relative to all the competitors.
Mike Maples:
A lot of these network effects companies, it seems like they were a hobby that turned into a business. eBay, I think Pierre Omidyar was selling Pez dispensers. What can entrepreneurs do to develop a network in a premeditated fashion? And what kind of decisions or trade-offs can they make that would help?
Anu Hariharan:
Great question. I've run into both types of founders. Actually, when we did the deck at Andreessen Horowitz, we spoke to a lot of founders to understand whether in the zero to one phase, did they realize that they have potential to network effects? And I'd say the answer was in the middle.
So if you look at Facebook, I think the founders of Facebook knew they were building a network from day one. And you can see that by the way they launched the product. The first question you have to ask yourself is, does your product have a utility? In the case of Facebook, the utility was it's a class directory. And if you ask some of the Facebook users from 2004, which were predominantly university students, they would say, especially in freshman year, it was a great way to get to know the rest of the class, because it could be quite intimidating to know who are the other folks in your class. And Facebook just made it really easy.
So the product utility was it's a online class directory that you could use to get to know the rest of your class. But Facebook knew very early on in 2004 and '05, that they were not just going to be at Harvard. And they needed to go to Columbia and Stanford and all the other universities.
They also paid attention to, do I need to be a cluster in each university? Or should there be a cross-collaboration. The reason they asked that question was not because they thought about network effect, simply because there were other competitors in other universities. And so, they asked themselves, my product has a utility, but how do I get it to scale? How do I get students from other universities who have a competitor product to use us?
And they were the first to figure out cross university collaborations, because your high school grads went to different universities. And so, Facebook was the first to introduce cross friendship connections, and they paid attention to that. And I think they realized that they were building a network of users who could connect across universities.
Mike Maples:
I gotcha. And so, were there any particular decisions they made about measurement or metrics that you think were important back in those days? Maybe Zuck wasn't running around saying, "I'm going to design a network effects company," but intuitively perhaps he just grasped it.
Anu Hariharan:
Yes. I think that a metric that is a measure of network effect is usage, not growth. So this is the number one mistake most of them make, which is, they assume if I'm growing really fast and I'm growing organically, I have a network effect. You don't because that's just wild growth. Think of Angry Birds. You grow really fast doesn't mean if the 10th player joined and started playing Angry Birds, it doesn't make it more useful or effective for the first user that joined.
But in Facebook's case, the number one thing that Zuck paid attention to was retention. So they tracked what percentage of the users in a university logged in daily. But I think Facebook was one of the early platforms to pioneer something called a smile curve. Have you heard of the smile curve?
Mike Maples:
Yeah, but I think people would find this interesting.
Anu Hariharan:
So they literally plotted from day one through day 30 in a month. And they would say, what percentage of the users logged one day of the month, two day of the month, three day of the month? The goal that Facebook was trying to get to was how can I get a smile curve that is majority of the users were logging in 30 days of the month? So it was a product that would be used effectively every day.
Mike Maples:
And if I remember correctly, the reason for the smile curve is that in some cases, user engagement might go down, but it might return because their friends are using it or they give it another shot or they just, it occurs to them to use it again. And then they kind of pass this threshold of becoming more active users. So the initial engagement is about the novelty of it. And then the later engagement, which trends back up is about the utility value of engagement.
Anu Hariharan:
Yes. Also, it was not accidental that the utility happened. One of the, I think, big changes that Facebook did in the early days was the introduction of the relationship button, which showed your relationship status. All of a sudden, you didn't have to ask someone, "Hey, are you single? Or would you be interested in a date?" People used the app to check the relationship status before they approached the person.
I think the focus on the smile curve was not just to boost the numbers, but it helped them think through what product features do I need to build so that I can increase the utility of the platform to a daily use case. That is a very important distinction, and I think the Facebook founders really paid attention to that. They may not have thought of network effects, but the focus was on retention.
And so, every university that they went to, the playbook was what percentage of the university users have we captured? And what does the retention look like? Facebook is one of those unusual platforms whereas they saw user growth, retention improved. That's rare, and that doesn't happen by accident. And I think the founders, they really paid attention to how to improve retention.
Mike Maples:
We've touched on some of these things a little bit, but I think it might be useful to talk about how you have network effects, you have virality, and you have economies of scale. They're all three different things. Why don't we talk about what each one of them really are and how they're different and how people confuse them?
Anu Hariharan:
Yes. So network effects, the definition is very simple, which is as more users join, the service is more valuable to everyone. And I think that everyone is important. People often forget that. It also has to be useful to also the existing users.
Mike Maples:
Yeah. And so, for just a second, because you're going to get to economies of scale, but for better or worse, I'm a little bit of a history buff. Economies of scale didn't really exist as a notion until the transcontinental railroad and the steam engine and mass production. And so, economies of scale used to be, I can make this widget at massive volume and then use the railroads to distribute it out to people.
So I have mass production, mass distribution, and I would get a moat because I could build a huge factory. And my variable costs would go down. And I could amortize it across a massive fixed cost of that huge barriers to entry. But I guess network effects are really, you have that supply-side demand or economies of scale, but it seems like network effects are really for the first time demand-side economies of scale.
Anu Hariharan:
The other definition for network effects, a lot of research scientists refer to it and economists refer to it as demand-side economies of scale. So if you look at any of the papers on network effects, they don't call it network effects. They call it as demand-side economies of scale, whereas economies of scale that you described is usually supply-side economies of scale.
Mike Maples:
What I find interesting about that is when you're on the supply side economies of scale, it's intuitively obvious to understand why if somebody had a massive factory, they could have economies of scale, because they can make a whole lot more widgets for less money. And it's hard for the next company to go build a factory that big. So when Intel back in the day would make the next generation Pentium chip or whatever, they spent $3 billion building the factory to go do this.
But I think this is one of the defenses of some of the network effects companies that take a while to monetize, because on some level, the time you take and the infrastructure that you build to have a winner-take-all outcome, intellectually, it's the same notion, but just apply it on the demand side rather than the supply side.
Anu Hariharan:
Absolutely. In fact, this is why we often tell founders that you can get to monopolistic share with economies of scale as well, because if you think that the path to producing your product, which is the cost per unit at scale, is going to be the cheapest, then you can have monopolistic share, which is literally Amazon first party business. That's why they're able to price the shipping or price the product that effectively. And no one else can match it.
Mike Maples:
So back to the network effects then, I guess Airbnb has network effects. Amazon's original business has economies of scale. And the reason, if I understand this correctly, is that as Amazon gets more booksellers to adopt their platform, they can offer books for cheaper. They can get better terms, negotiate better terms for people. It's almost like the same advantages of having a massive factory or massive distribution. And then you've got virality.
Anu Hariharan:
Actually, people more often than not confuse vitality with network effects. Virality means there's massive adoption, rapid growth without spending anything for acquisition of users. Say when Angry Bird launched, they grew exponentially with literally no marketing dollars. And I would say the same thing was true for Pokemon GO recently for at least the first few months, a lot of users were attracted to the product.
So you get word of mouth, that's virality, which a lot of people are referring because it's just cool or it's an excellent product to use or it's catchy, whatever it is. But even though more users are using it, it doesn't mean it's valuable to the existing users. It's just more fun.
And so, that's one way you get virality. Second is referrals without any incentives. So people are just referring the product to each other as a result of which, and you're not paying for the referrals.
Mike Maples:
So exponential, organic word of mouth?
Anu Hariharan:
Yes.
Mike Maples:
Or however you get it.
Anu Hariharan:
Yeah, or Hotmail, for example. When Hotmail was launched, you sent a mail to someone and it would say below, "Sign up for Hotmail." That was a great acquisition channel. And people signed up for Hotmail.
Mike Maples:
One of the things that I've seen you put together in the past is there's all these different sort of facets to a network. Is it heterogeneous or homogeneous? What kinds of connections, what kind of clustering? What's the direction of the connections? Is it unidirectional or bidirectional? And then you've got complimentary networks like Microsoft Office, Microsoft Windows, and things like that.
So if you're a founder in these early days, and you're trying to build something unique that people are desperate for, how do you incorporate some of those factors in in your thought process?
Anu Hariharan:
I think that there are like four to six basic questions any founder should ask when they're building something, even from day one. One is, what is the product's utility? And does it have the opportunity to connect different groups of users to form a network? So in the case of Facebook, it was clear the utility itself was the social connection. In the case of Airbnb, it was guests connecting to hosts, which is a traditional two-sided marketplace. In the case of Skype, it was placing phone call to each other, so they were just homogeneous users. In the case of Open Table, it was restaurants connecting to diners.
More recent examples, Zapier. Wade and his team knew from day one they had potential to build a network. The idea literally was workflow automation to help integrate apps. And so, they knew very well very early on that if they became the destination to connect apps to various app developers to help them integrate workflows, that they had the potential to become a platform.
So you can know based on what you're building, whether you're connecting nodes and is the utility primarily connecting nodes. And so, if you pay attention to do I have a network, then you have three other sets of questions you have to ask, which is one, once you know you have a network, what is my growth tactic? Because you can't just build it and expect the users to come. You need to have a growth tactic. And once you have a growth tactic, you need to figure get to critical mass, because there's no point having potential for network effect and not getting the critical mass.
So in the case of Zapier, it was literally what is the growth tactic? Well, what viral loop mechanisms can I put in place via the product to scale the number of users as well as apps that integrate with Zapier? What they did was amazingly brilliant. They had a landing page for pretty much everything. So if you were searching online for an app to integrate with, there would be a page about that. There would be a separate SEO page about what the integration is. And then there would be a separate page about the use case of that integration.
So almost if you search on Google for any sort of workflow integration, Zapier was one way to find out. And when you land on Zapier, you may have looked for, for example, a Gmail integration, but then you see all these other apps and you figure out that I can do integrations with all these other apps as well, which may help improve my workflow. That was their hack to get users to sign up without paid acquisition. That's where the critical mass comes into play.
So once you have enough data, depending on how users are using Zapier and what is making Zapier more sticky, you can then look historically and see what have been the aha moments for what drives retention. They call these interconnections Zaps, and the sooner you get a user to sign up for X number of Zaps, the more sticky they are and the more convinced they are that Zapier is the platform for all workflow integration.
And so, when they discovered that through their historical data, and this can usually take few months or years even, like one or two years. But then the whole mechanism was every new customer that was onboarded, how do we get them to that X number of Zaps within a few months? And so, today they've been around for more than five years, but they have more than 1,500 apps that integrate with Zapier.
Also, in the world of where we have build so many integrations, Zapier is the number one priority in most apps. So they want to make an integration available because they know all the companies are using Zapier for integrating their workflows. So that is a two-sided network effect for them, because they have the customer, which is the company and the apps that are onboarding, which in some way is an overlap, which gives them huge benefit.
Mike Maples:
I've also heard you talk about the three different laws of network effects. I think it's Sarnoff's, Metcalfe's and Reed's. So why don't we unpack those one at a time? So Sarnoff's Law, what's that all about?
Anu Hariharan:
The simplest way to think about Sarnoff's Law is it's a broadcast. The value of the network increases proportionally to the number of viewers. Think of Yahoo, because there is no bidirectional connection. At Facebook, you will send me a friend request and I need to accept it. I'm acknowledging that two of us are going to be friends. Whereas this broadcast is more like I'm broadcasting information for anyone who wants to see our listen. And so, the value of the network does exist, but it's limited in terms of, it's just proportional to the number of users.
The way to think of Metcalfe's is it's a true network effect. And so, the growth you see is non-linear, because there, the value of the network actually increases as the [inaudible 00:19:15] of the number of users. As a result of which the value of the platform increases non-linearly as more users join the platform, which is Facebook. Facebook is literally one-on-one friend connection, and you can see that the value of the network increased as more users join because you made more friends.
I would say Facebook also was built in an era where there were not that many big internet platforms that were competing for attention and time from the users. I think the era has changed a little bit now. And so, Facebook also had that advantage because of which they were able to scale and be the default social network.
Mike Maples:
And then you've got Reed's Law.
Anu Hariharan:
Yes. Reed's Law is, think of it as WhatsApp groups or Slack. This is a group forming network. The users, especially in the case of...
Anu Hariharan:
This is a group forming network, right? The users, especially in the case of Slack, it's not useful to a single user. They launched in teams. WhatsApp in the early days, scaled in groups. The value of the group forming network is like an exponential curve. It increases within both the number and the ease with which the groups are formed. WhatsApp is a great example of this because the utility was, it's a replacement to expensive SMS. It actually started in the European countries where SMS was expensive and interoperability, between Nokia, Blackberry was really hard. WhatsApp was this app that you could download anywhere, no login required, address book was hacked, and you suddenly launched the cluster of close connections in groups. That's why when you look at WhatsApp's growth curve, actually, if you look at the year one to year four versus Facebook, WhatsApp grew three times faster than Facebook.
Mike Maples:
In the case of Reed, then I guess it's values proportional.
Anu Hariharan:
Much more, faster than a non-linear code that you get from Metcalf's law.
Mike Maples:
Okay. Then, we talked a little bit about Facebook and some of the critical decisions that they made. What about Airbnb and Brian Chesky?
Anu Hariharan:
Airbnb is a great example of a two-sided network effect, right? This is a good example for also why net, virality is not always required for network effects. The first three years of Airbnb was really hard, right? You went and told people, "Hey, come stay at a stranger's home." It was extremely difficult value proposition to scale, but what was their product utility? When they did a few experiments, it came down to two things. When there is an event, if all the hotels are expensive or sold out, we have new inventory that's not available anywhere, and it's cheaper. That was literally the product utility when they launched. Today, of course, people look at it as unique supply and there are beautiful homes and I can go stay there. When they launched, the utility was, if hotels are sold out or they're expensive because of a conference, we have inventory no one else has, and it's cheaper.
That was the product utility or the hack that they used. If that was the hack, they decided the growth hack for them ... The second question which is, "Okay, I have the utility. Now, how do I grow it?" The hack was, "How do I advertise around events?" That's how they targeted the Democratic National Convention, the Design Conference NSF. They targeted events that they knew were attractive in cities that brought lots of people, and they would advertise only during those periods, and try to find guests for the hosts that had listed on the platform. Then critical mass happened after three years as a measure of liquidity.
Mike Maples:
Yeah.
Anu Hariharan:
I think the two things that really helped with critical mass was photographic services and social connections. They actually included a connection info. It said if another friend had stayed in that home, which gave the trust element. One distinction there is, Airbnb could have easily been just a US focused Airbnb, if they had followed the expansion plan differently.
When a company has potential for network effects, the decision they're going to make on scale is going to be super-important. They may not know it at the moment, but in hindsight, they will know it. Airbnb, I think two or three years after they had launched, once they had sufficient liquidity and they were growing really well in some of the markets in the US, had to make a call. The question was, "Should I expand to all the NFL cities, or should I go to Paris, Berlin, London, the tourism cities?"
Their decision to go to London and Berlin that early was extremely important and probably helped establish a global network effect. If you think about it, the user travels anywhere. The user is not restricted to NFL, and I think they paid so much attention to customer journey that they had the insight to follow what the customer needed. We give this advice to startups in general. If your domestic market is so big, don't lose focus by going international, but in cases where your platform has potential for network effects, you really have to pay attention. If you stay too long domestic, someone else might build a local platform.
Mike Maples:
Then back to the network effects, businesses in the digital domain, network effects have been around for awhile, right?
Anu Hariharan:
Yeah.
Mike Maples:
You had the telephone company, the railroad. What is new about network effects in recent time? Haven't there been network effects businesses forever, pretty much?
Anu Hariharan:
Yeah. There have been. In fact, it started with the telephone. Now there's this whole literature around, there are varying degrees of network effects. I'm not sure I fully agree with that, but there is some relevance to it. Let me explain what it means, the varying degrees of network effects. There's something called local network effects, which Open Table had, right? You sign up all the restaurants. All the diners in that city will use Open Table, but cross city network effect was really hard, unless you were traveling, but majority of your business was local. In the era when Open Table was built, Open Table could scale nationally and be pretty much the leader.
I don't think that's possible today because of multiple reasons. One, it's easier to start a startup. The cost it takes to start a startup is lower. The funding available to launch a startup is also more accessible. The reach given the Apple and the Android platforms, the reach of the users it's costly, but it's doable. You have this tension where if you have a local network effect, you have people approaching from different regions.
Mike Maples:
Yeah.
Anu Hariharan:
I don't think it's slam dunk like Facebook days to be able to build a national network effect, unless your product itself allows that like Airbnb. That's one, there is a varying degree in network effect. Second, even if you look at Slack, which is a group forming, reads lock network effect, the product utility is such that it's contained within a company. It's not cross company. Now, they may decide to open it at some point, but for the right reasons, it has to be company contained because a lot of the confidential information that's discussed within a team resides on Slack. I think that there are these varying degrees of network effects you see as a result of product utility and the fact that access to users and the ability to launch a startup are easier, that it's no longer slam dunk to be able to build a global network.
Mike Maples:
You've outlined some of the premeditated steps. I remember you had five strategies. One was, what's your entry strategy? It's actually reminiscent of what Jeffrey Moore and Crossing the Chasm would say.
Anu Hariharan:
Absolutely.
Mike Maples:
Yeah, so how does that work?
Anu Hariharan:
If you think you're building a network or you think you have the potential for a network, you have to find a beachhead. If you try to go all in across the US, you won't really be able to find that network. Here's why. Think of network as nodes and clusters. If you think of clusters, clusters are usually your closest friends, which more often than not tend to be geographically concentrated. Right? University Dance, great clusters. Same city, great clusters. Same segment, nursing. It's a cluster. If you think you're building a network and you just say, "I'm just going to do paid advertising on Facebook and try to get my first 1,000 users," you're doing a disservice to yourself, because you don't even know whether those 1,000 users have connections.
That's why it's very important to figure out where your cluster is. That's the entry strategy. How do you figure out what my cluster is? For Facebook, it was university. For Airbnb, it was events, literally around events. If you're a founder in year one or year two, you're job is to first get 100 users that love you, 1,000 users that love you. By year two, or by the time you have say three or five universities or 11 universities, you can figure out what drives retention. Right?
That's the critical mass question. In the case of Facebook, they had figured out that if you get to 10 friends in 14 days, this is the one that's widely reported, but if you get 10 friends in 14 days, that that user is going to be very sticky on the platform because they have 10 strong connections that they want to interact with, because of which, they come back to the platform.
It's not perfect science, because you're looking at the data you have and the early adopters, but it is a pretty good indicator more often than not in driving that critical mass engagement. Having that, the question to ask is, what is the driver of critical mass, and then using that as an algorithm to build scale.
Mike Maples:
Okay. I guess it combines the critical mass inflection point, but also the engagement triggers. It's the threshold where when they pass that threshold, you've got them.
Anu Hariharan:
Yes. Yes. I'd say you have to define what the retention metric is for your platform, for your product. Right? People often get confused here because they think, "Oh, but I have 20% of the users that came back this week, that month." I often tell founders, the retention metric is not something that an investor sets. The retention metric is for what you have designed and developed, how often do you think your user should use it?
Mike Maples:
Yeah.
Anu Hariharan:
The question to ask is, am I driving that? As more users that join the platform, that number cannot degrade. That number has to improve, and only if it improves, you have had true network effect.
Mike Maples:
Okay, and then
Anu Hariharan:
If it improves, you have had true network effect.
Mike Maples:
Okay. Then you had a tactical step that people could take, which was leveraging irregular network topologies. What's up with that?
Anu Hariharan:
Yeah. Irregular network topologies, WhatsApp is a great example. People often think of clusters as, as I said, geography, right? Geography, industry verticals, very intuitive. Our office's Slack, it was everybody in the office. WhatsApp was literally about sending an SMS message, and it was a replacement for expensive SMS message. What are some of the most expensive SMS messages? Sending international messages. I remember when I moved from India in 2007, I had to pay $14 a month to place a call to my parents. I would have X number of calls as a limit.
Today, I don't pay anything. Right. We all use WhatsApp. It's the irregular network topology, which WhatsApp exploited, which is the users are 8,000 miles away, but they used the demographic where it was the Russians in the Bay Area would communicate to the Russians in Russia via WhatsApp. The Europeans, because each country is different and the telecom networks in each of the countries were different, the interoperability was really bad. Some networks would charge for incoming SMS messages, some didn't. If you use WhatsApp, it was fast, easy, and nothing to pay.
Another example I can give as a more recent YC company called Flock Safety. I don't know if you've heard of them. They launched out of Georgia, and what they are building is essentially their whole mission is to reduce crime in the United States. They work with the police and the city council to launch sensors and they help read the driver's license plate. Everyone submits that information. The videos are sent back to the same platform, which the city councils use, and the police chiefs and adjacent counties have access to that. Now, the irregular network topology there is, there have been crimes where someone did something in Austin, but might be caught in Oklahoma.
If you think of just Austin as your network, you're going to miss it, because someone takes the car and they run across state lines and they go to a different state. In that particular case, the irregular network topology is literally going across state lines, which require working with federal as well as state governments. Rather, you can start hyper-local in Zipcar because they have to go city by city, council by council. The value definitely increases as 10 councils sign up, the adjacent councils sign up, then the 11th city council wants to sign up with them.
Mike Maples:
Somewhere out there, there's a founder who's thinking about instrumenting network effects into their business, or maybe it's even just core to what they want to do. What advice would you give them about starting a great network effects company today?
Anu Hariharan:
I think that asking the five questions that we went through from very early on will be extremely important. Also, I wouldn't rush it. People often think that when they see these stories about WhatsApp or Facebook, which probably was more true for Facebook, they think it happened very quickly and accidental. A lot of network effect businesses take a long time. The more cash you have, it's a detriment to building a network effect.
Mike Maples:
Oh really? Why is that?
Anu Hariharan:
You'll end up spending on paid marketing because you think that's a way to build the network, and you're not using or improving. Yes. You're not improving the product features to improve retention. Also, I think more people distract you from building the network. If you hire very quickly a 10 or 15 people team, you might be doing a disservice, and I think it's super important.
If you are extremely lean, maybe you're just three to five people, but you're really tweaking the product and paying attention by what is it this network of users really need? How do I replicate this cluster? The better the quality of conversations and the better the rollout of the features and better the experimentation.
Mike Maples:
Right.
Anu Hariharan:
Right. The retention is super important. I think a lot of founders forget that. In a network effect, the other question I would ask founders to really pay attention to is, barrier to exit for the user. We often think about barrier to entry. Nobody talks about barrier to exit for the user. This is something I learned from Marc Andreessen. If you look at Facebook, the barrier to exit for the users really high. That's why when Google plus launched, nobody switched. It was a pretty good product, but if I had all my connections, my inventory of photographs, my history, it was really hard for me to switch, right?
As a guest on Airbnb, the barrier is medium, not high, right? If I can find the same quality of supply on VRVO or on booking.com, I would probably go ahead and book, but it's not easy for those platforms to have the same supply quality because of the investment Airbnb did in reviews, ratings, as well as building the whole experience on the platform. It's not as defensible as Facebook. Look at Uber and Lyft. The barrier to exit for the user is literally very low because I would launch one app. If I can't find a car in less than five minutes, or if I don't like the price, I launch the second app.
Mike Maples:
All right, Anu. Well, thanks for coming.
Anu Hariharan:
Thank you. Thank you for having me.
Mike Maples:
Thanks for listening to the Starting Greatness Podcast. If you've enjoyed this episode or you're new to the show, I hope you listen to our past interviews with legendary founders like Reed Hoffman, Marc Andreessen, the Instagram founders, and Keith Rabois. I'd love to have you subscribe wherever you get your podcasts so you don't miss an episode. If you liked the show, I'd be grateful if you leave us a review on Apple Podcasts. You can also follow me on Twitter @M2JR, and subscribe to our newsletter for exclusive content and events at greatness.substack.com. Until we catch up again, I hope you'll never let go of your inner power to do great things and whatever matters to you. Thank you for listening.