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Doing nothing is. You can uncover opportunity in uncertainty. Crow offers top-flight services, an audit, tax, advisory and consulting to help you take on your biggest challenges. Visit embrace volatility.com to discover how Crow can help you embrace volatility. Once again, that's embrace volatility.com. Hello everyone, you know, over the many years of doing this show, we've come to learn that there are certain episodes that just resonate with a huge number of people.
And this one happens to be one of them. We first ran this back in 2020 and be sure to listen through to the very end to hear a quick update. And now, here it is. I was feeling good. I thought, okay, well, we can continue raising money for a while. And at some point, we raised money from Google, Capital G, is one of their investment arms. And the partner from that firm sat me down and she said, nobody else will invest in this company if you don't figure out how to make money.
You know, you're not going to find a bigger fool. Where the biggest fool. And that's when we started kind of freaking out and try to figure out how we're going to make money. But we had no idea how we were going to make money. Welcome to How I Built This, a show about innovators, entrepreneurs, idealists and the stories behind the movements they built.
I'm Guy Ross and on the show today have a perfect storm of skill and luck drove Luis van And to create capture, then recapture, and then Duolingo, a foreign language app valued at $1.5 billion. Think about the small moments or decisions in your life that actually had a huge impact on how your life turned out. Maybe it was a conversation you struck up with the person next to you on a airplane.
Maybe it was a party you reluctantly went to only to meet the person you'd eventually marry. Or maybe it was a decision to stay on vacation an extra day that sparked a new idea. For Kevin's system, it was a random remark from his girlfriend that made him decide to use filters on Instagram. For Blake Mikoski, it was a chance meeting with a group of young Argentinians who took him to the countryside where he saw kids with no shoes. That one day inspired him to create Tom's.
And for Luis van And it was a free lecture at Carnegie Mellon University in 2000. That single lecture would lead him to invent two ingenious new tools. The first was capture. Yes, capture. Those annoying twisted and blurred letters you have to type into a website to prove your human. And the second one was Duolingo. Now the biggest language learning app in the world. Both capture and Duolingo work designed to harness the power of crowdsourcing to solve problems.
And I'm going to blow your mind here. If you have ever typed in a capture or used Duolingo, there's a good chance you've taken part in a massive online collaboration that you probably weren't even aware of. And it's amazing how Luis came up with all this, but let's start at the beginning. Luis was born in Guatemala in the late 1970s. Both his parents were doctors. And though he was surrounded by poverty and violence in Guatemala City,
Luis grew up in comparative privilege. And as a kid, he spent a lot of time hanging out at the family business. My mother's family actually had a candy factory. Everybody is always amazed at the fact that I grew up with a candy factory. They think that it was like Willy Wonka or something. I was not all that much into the candy itself. I was into the machines because basically the candies made by these gigantic machines that pump out, I don't know how many thousands of pieces of candy per hour.
And basically all my weekends I spent playing at the candy factory and I would take the machines apart and put them back together. There would be some extra pieces after I put them back together usually and that would be a problem. What kind of student were you? Were you schooled pretty easy for you? Yeah, I was pretty nerdy. Basically I was really good at math. Math was just easy to me.
What I would do during the summers is basically get either next year's or a couple years later math books and basically do all the exercises. Wow. It kind of came easy but the way I really got good at it is by doing hundreds and hundreds of exercises. That's what you would do in the summertime? Yeah, I kind of was bored. I mean I was an only child. I didn't have that much to do. This is a remember. This is also pre-internet, pre-everything. So what was I going to do?
Man. That's what I did. I was putting playing cards in the spokes of my bicycle and buying Jolly Ranchers at 7-11. Woo! Should've done math books. So you were, did you just love math? I mean was it, it sounds like kids don't think about their future. They're not like I'm going to study math so I can be in tech one day. Like you must have really enjoyed it. I did. I enjoyed it. It was like a puzzle for me. By the way this is not the only thing I did. I also played a lot of video games.
Pired video games in my Commodore 64. Like floppy disks? I'm floppy disks. Floppy disks? Yeah, that's right. I wanted an Nintendo. When I was eight my mother would not get me Nintendo. She instead got me a computer or Commodore 64. I couldn't figure out how to use it. But eventually I kind of read like the manual and stuff and I figured out how to use it more. Then I figured out I could pirate other people's video games.
And so I became a little hub in my little neighborhood. But these were not other kids. These were adults. Or kind of basically young adults who had a computer and they would come to my house and I would take their games and give them my games in exchange. So I then I collected a pretty large number of video games. But she mentioned right that I mean because your childhood sounds pretty nice but like as a kid I guess or even as a teenager there was a civil war on Guatemala.
I mean we know that today there's a lot of violence there and obviously there's violence in the US and in other countries too. But Guatemala has been particularly hard hit. I mean did it feel dangerous when you were a kid? Yes it did. There was a civil war pretty much since I was born in 1979 to 1996. There was a civil war going on the whole time. It always felt dangerous. When I was 15 or so my aunt was kidnapped for ransom. I mean she was gone for seven or eight days.
People's cars would be stolen. I don't know. Every couple of months somebody's car would be stolen in my family. Going out past 730pm was rare. You needed to go out in a large group if you were going to go out past 730pm. And you know I did have my house had walls and barbed wire and yeah it felt dangerous. I mean this is one of the reasons I came to the US actually. I mean you know when I was after my aunt was kidnapped I thought to myself I don't want to live here.
Yeah and I guess you did end up leaving Guatemala for college because you went to Duke in North Carolina. And you described yourself as a like a math nerd in school and is that what you intended to do like to do something in math? That's what I wanted to become an academic math professor. I was pretty certain I wanted to become a math professor. You know at the time I thought the best thing that I can do is really learn a lot of math and I really love it.
And I thought it was futile to learn how to deal with other people. And it is interesting because my job these days is 100% just dealing with other people and people's problems. I'm just trying to understand this. So by becoming a math professor you thought hey I wouldn't have to deal with people. I would just deal with like facts and data and numbers.
Yes, yes. And you know I'll do math research all day long and every now and then I'll teach a class but whatever that's like a tax. That's what I thought. So alright so you get your degree and you're following this path to go into academia and you go into a PhD program at Carnegie Mellon. Correct. And I guess you go into computer science right?
Yes, I change from math to computer science because I visited a math grad school and what people were saying, the professor was saying, oh I'm working on this open problem that nobody's been able to solve for the last 300 years. And I thought hmm I don't think I'm smart enough. I mean if you haven't done it and nobody's done it in 300 years that's kind of not for me. Whereas when you visit in computer science I mean this is crazy thing people are like oh yeah I saw the open problem yesterday.
Wow. It's a much younger field. Yeah. And so that I thought that was much more exciting for me at least. So you are, you started zero 2000, you start your PhD program at Carnegie Mellon. But I guess like really soon after you start you go to some talk by someone from Yahoo who comes to campus to talk about Yahoo, Yahoo is a big deal in 2000. Who is that? What's the story?
Yeah that was serendipity again. Most of the things that have happened in my life are serendipitous. I was a first year PhD student I had been at Carnegie Mellon for maybe two months. I had, you know the first thing you got to do when you become a PhD student is find an advisor. I had found myself an advisor and we went to a talk together. This guy from Yahoo who was the chief scientist of Yahoo at the time. And like you said Yahoo at the time was the biggest tech company out there.
And he came to give a talk at Carnegie Mellon and it was the talk was basically 10 problems that they didn't know how to solve a Yahoo. And I was an enterprising PhD student and I thought I'm going to try to solve these problems. And what was the problem that he said they had? Yeah so the problem for this particular one was there were people who were writing programs to obtain millions of free email accounts.
So Yahoo at the time gave out email accounts for free and some people thought it would be good to send spam from Yahoo accounts. The problem is each Yahoo account only allowed you to send like 500 messages a day. So if you want to send 10 million spam messages per day, you just have to get a bunch of Yahoo accounts. And from each one of them you send 500 messages.
And this is the era, I remember this, we all remember this where you would get hundreds of messages, spam messages about certain bodily messages. Certain bodily enhancements you would get messages about the hormone growth things. I mean baldness, it was an idiot farm as an idiot by Yahoo. Just spam, it was a huge problem. And he was like we can't figure out how to stop these computers or programmers from creating all these email accounts.
Yeah, and so that was that was the problem. And just to be clear, these spammers were not physically setting up each individual email account. No, they had written programs that would just set up millions of email accounts a day or however many per day. So I thought about it for weeks and months and I talked about it with my PhD advisor Manuel.
And together we came up with a solution which was the thinking here is look computer programs can get a million email accounts per day because well computer program can do things very fast. Yeah, and it also doesn't get bored right humans. Human can't get very many email accounts. So how about this? How about if we make sure that whatever is getting an email account is actually a human and not a computer program.
Then we started thinking okay, well how can we distinguish between a human and a computer? Yeah. And that's where this idea came about. It's this idea of a capture where it's basically these distorted characters that you have to type whenever you're buying tickets on ticket master or getting an email account or stuff like that. The idea is that a computer can generate one of these.
You know, basically take some letters, put them in an image, distort them. And then it can give them out and it turns out that humans can read these what the time. And then this could read these very well they still can but computers could not. By the way, I can't read them. I'm the human that can't read them. I can't read. Well, I don't know how much of a human you are. Now they're like point to every stop sign point every bridge.
Yeah, yes, it's changed. But basically at the time the idea was humans can read these distorted characters much better than computers. So let's give basically every time that somebody's trying to get an email account, let's give them a test to see if they're human or not. So you just start doing this on your own like did you tell Yahoo, did you tell him or we just like this is fun. I want to sort of figure this out like just working independently.
Yeah, I mean with my PhD advisor it was it became our research became our research subject. And yeah, we did not tell Yahoo because it sounds like here's the kid getting the mathematics workbooks in the summer. It's like you just seem like a fun thing to solve. Yeah, it was fun. But that's like that's the core of the PhD program, I think. You know, you're trying to find problems that others have installed.
I got you. And you're this seems like a problem that other people hadn't solved. So we were working on it. And you called it capture. Did you name it capture? And what does that mean? It's an acronym. It's a capture. It's like to capture. Yeah, it's not what it is. It's an acronym that sounds like capture, which you know, the idea was to capture the you know, the bot, the computer program.
It stands for completely automated public touring test to tell computers and humans apart. So you know, it was just an acronym. But but now I think most people have heard of the word capture will do not know what it stands for. And you come up with this thing capture. And what do you do? Do you just like email this guy? Yeah, who say, hey, I've got your solution for you. You basically, I mean, we email them and we said, hey, you know, we think we have a solution. Here's some code.
You know, we explain how it works. And what was amazing. And now that I know how large companies operate on how how slow things move. What was amazing is within about a week of when we sent them this, it was already function. Wow. And you know, live, it was live. So I guess the problem that they had was so big that when they saw this, they were like, okay, let's do it.
So they saw this and they're like, my God, this is great. And so you must have just, I mean, you were like what 25, 26, 21, 21, 21. You see, you look right, all of a sudden you're an overnight millionaire, multi millionaire. No, no, no, no, no, no, no money was no money exchange hands here. Wait, you gave capture to them. You just said, hey, here you go. Yep. I mean, listen. You gave it to a multi billion dollar company. Just gave them capture.
I didn't know how things worked. And you know, the truth of the matter is this is probably a good thing in retrospect. I mean, right. Who knows what would have happened. But you know, I don't have regrets. But yes, that was, that's what started happening. And then basically every other website started, you know, they saw that it wasn't in the front page.
So every other website started copying or making their own version. And within a couple of years, pretty much everywhere on the web, you know, when you had to get an email account, when you had to enter a comment on a blog, when you had to buy tickets on ticket master, you had to enter one of these. You were the guy. You were the guy who made it really hard for dopes like me to get it right. I had to constantly. It was annoying. Yeah.
It was annoying to people and every time that I would be at a party or something, and you know, they would ask me what I did. And then they would get me to explain my research. I would explain that I had done that. And I would say, what the hell? They would kick you out of the party. It would screw you. But some of the research you did, I mean, you did actually write some software that you sold for, I think for some money this time.
I mean, just like, you could just briefly explain how that happened and what you were working on. Yeah. I mean, this is, it's funny. I mean, at the time, it was not called crowdsourcing, but my PhD research was basically on crowdsourcing. I had worked on capture where the idea was that humans can do some things. Computers cannot. So my PhD research was basically that finding things that computers could not yet do and then finding ways to get people to do them.
And in particular, the main thing that I did in that time was a game and people loved to play it. But as they were playing it, they were actually helping computers figure out what's inside an image. How did the game work? The way the game work was this. You went to a website and you got randomly paired with somebody else who had shown up to the website. And you were both shown the same image and image or random image like an elephant image from the internet. Yes.
And you were told type whatever the other person's typing just typing words. And if you are one of your words matches one of their words, you both get points. Got it. What was interesting about this game is that the words that people typed, the only thing they had in common was the image.
So the words that people typed were basically related to the image because if you're told to do this, what are you going to type if it's a picture of an elephant, you're going to type elephant because you think the other person is also typing elephant. So those words were really good labels for the image. We're really good tags for the image basically. So I put it online, a program that millions of people played it and this was a game that was helping label all images on the web.
And at some point actually Google in fact bought it. They just bought the technology and then it got implemented. They changed the name from the ESP game, which I call it the ESP game because it's like ES extra sensory perception. The idea was like try to think what the other person was thinking to the very amazing name of the Google image labeler, which is much less interesting. And so they bought this from you. And was that like life changing money? I mean, were you all of a sudden super rich?
No, it was not super rich, but it was good. Got it. Okay. So you went on to earn your PhD and you get a job at Carnegie Mellon. Correct. You're a young professor, you're on tenure track. And by the way, early on in your academic career, I should have mentioned this. You were granted a very like one of the most prestigious awards.
The MacArthur grant, and there's a genius grant. Basically they pick a few people a year and give them like half a million dollars of unrestricted money to work on whatever they want. I mean, it's been mind blowing. Yeah, that was, you know, I've gotten a number of awards in my life, but I mean, that was a particular, that was probably the most impactful one in terms of just how I felt.
Also, they do this crazy thing where they don't, you have no idea that you're being considered or anything. And they call you random. Yeah. They figure out your phone number one, they you pick up and then they just tell you, you know, have you ever heard of the MacArthur Fellowship? And, you know, you say yes. And then they just tell you and you're like, wow, what the hell. First, you think it's a prank? I thought it was a prank.
Yeah. And I guess like around this time too, I don't know if this is a powerful or not, but I ran then. Bill Gates personally called you to try to convince you to leave your job at Carnegie Mellon and to go work for Microsoft. Is that true? That is true. Like what do you pick up your cell phone? It's like, please hold for Mr. Gates or was it like he won a line?
It was pretty much like that. It was pretty much like that. I mean, it was somebody, you know, who basically said that, please hold for Mr. Gates. And you were like, okay, and he gets on the phone and he's like, Louise, it's Bill Gates here. That's exactly right. I had been an intern. I had been like a year and a half before that. I had been an intern at Microsoft. And so I had good ties with Microsoft and they really wanted to hire me for Microsoft Research.
And so that is, you know, Bill Gates spent, I don't know, 45 minutes to an hour trying to convince me to go here. I was very flattered, but you know, at the time I just wanted to do my own thing. You have to be a professor. How did you say no to Bill Gates? You were like, oh wow, thank Mr. Gates. It's really pretty so nice. I'm so honored, but I just, you know, no. I didn't actually say no. I said I would think about it. And then I said no to like a recruiter.
But yeah, it was not easy. I mean, he's a major hero of mine. I mean, he's just an amazing human being. But I just wanted to be a professor at the time. I thought I want to do my own thing. I want to be a professor. And that's what I wanted to do. Was money important to you? I mean, you know, was it important to you to... It mattered. And interestingly, I guess it mattered more than it does now. It was nice to know that I could buy a nicer car or get a nicer apartment or something.
So it was nice, but it's never been my driving force, I would say. Okay, so you decide I'm not Bill Gates. Thank you. No thanks. I'm not going to Microsoft. I'm going to stay doing this work. So you are now, um, were you lecturing or are you teaching classes? I was teaching. I was teaching a huge class. It was called great theoretical ideas and computer science. It's just a fancy name. It's basically a discreet math class.
The people who don't do well there usually change major. And you're like not like John Nash, but I'm thinking about that movie. You're in front of a, like a big blackboard or a whiteboard and doing equations and problems in front of 250 kids. Yeah, slides had already been around. So I use, I use PowerPoint. I use PowerPoint. I guess not that old. Right. I got you. Yeah. You're right. Okay. So you're teaching and you had done capture and lots of people are saying, hey, it's annoying.
And you're like, yep, I hear you. And people kept saying that to you, like, kept hearing that. Yep. Yep. And that's, that's when I started thinking each time you do it, you waste about 10 seconds of your time. So this thought just came to me. Wow. How much time am I wasting of humanity? And I did a little back of the envelope calculation, realized about 200 million times a day, somebody types a capture. And that's when you get 200 million times a day, times 10 seconds.
So humanity as a whole is wasting about $500,000 a day, typing these captures. And that's when I started thinking, huh, related to all my research that I have done. Is there something we can get humans to do while they're typing a capture that is also useful? And we got them to do something useful. And it occurred to me that, yeah, we could get them to help digitize books. How did, sorry, pause. How did you come to that idea?
So like when you type in a random random letters from a capture, you were thinking, wait, how do we have a secondary use for this? And maybe it's books digitizing books. How did you even, how did that even occur to you? I was thinking for a long time, can we get people to do something, but at the same time kind of extract valuable computational effort from it? Until it finally occurred to me, oh, there's all these projects out there trying to digitize all the world's books.
Google had announced really at the time it's kind of insane and ambitious project that we're going to take all of the world's books. Everything that's ever been written is 100 million books ever written. And we're going to put them online. This is what we're going to do. And now in this process, the way the process works is you take a book, a physical book, then you scan every page, and then the computer needs to be able to decipher all of the words in those photographs.
Now the problem is that computers cannot record, or at the time could not recognize many of the words, about 30% of the words computers could not recognize. And the reason for that, by the way, which is this amazing thing, is the same reason why computers could not recaptures. Basically, this looked kind of like distorted letters. And distorted letters are just inherently difficult for computers to recognize. Like, that's the point of capture.
That's right. And so it occurred to me, oh, wow, what if we just take the words out of a book that the computer could not read? And then we send them to people while they're typing captures on the internet. So basically, next time you type a capture, the words that you see are actually coming from a book and whatever you enter is in fact used to help digitize a book. Do you remember when you came upon that idea, was it like in a flash, or was it just like, did it occur to you?
I do remember, I was driving from Washington, DC back to Pittsburgh, because I had been to some National Science Foundation panel or something like that. Then I spent the next few weeks just kind of going over it and going over it, and I thought, okay, this will work. And I started building the system, but then I recruited a guy who was his name is Ben Moore. He was maybe a freshman in computer science at Carnegie Mellon.
He was one of my students. He was in my class, in my large class. He was the best student. And I thought, you, you can help me build this. And so together we built a system that would take a scan of a book and extract all the words that the computer could not recognize and then send them as a capture. Now, we decided we're going to go to big websites and we're going to tell them, hey, look, we have this capture services free.
It's better than yours. Yours is relatively crappy. This one's pretty good. And we'll give it to you for free. But all we have to be able to do is see what your users can type our typing during this so that we get to digitize the books. And did websites start like start to use this tool? You know, some small websites started using it. The first largest, it wasn't even large. The largest website for a while that was using us was called online booty call. Nice.
And they were to get an account in online booty call. You had to type a capture. And it was our capture. And every time every person that was getting an account in online booty call was starting to help digitize them. God bless them. God bless them indeed. And at some point, a website that was relatively new at the time decided they needed to put a capture on their on their registration flow. And they called us up. And it was a website called Facebook dot com.
And they were relatively new and they were like, hey, listen, you know, we saw that we came across it. It seems like you have a good capture. Can we, you know, can we put it up in 2007? Probably 2006, 2006. Yeah, you know, we said, okay, cool. And they did it. And that website was growing very fast. And so pretty soon, we just had a ton of people using our thing. And then at some point I was giving a talk somewhere in Dallas about this system and how we could digitize books, et cetera.
And the guy who was the CTO of the New York Times was in the talk and he said to me, hey, you know what, we have this archive of 130 years of content from the New York Times that we have not been able to digitize particularly because computers cannot recognize the words. How about you digitize it for us? And I'm just just out of curiosity. Eventually I'm assuming you thought this could make money.
We thought, we thought, but we really didn't, we kind of thought we could make money, but we didn't know how and what happened is this guy, you know, he then sent me an email. He said, no, I was serious. Let's do it. How much would you charge me to do this? Charge them to do what? To digitize? To help digitize their content. The New York Old New York Times.
The entirety of the New York Times archive. And so, you know, we kind of scrambling. We had no idea how much to charge them. And then we just thought to ourselves, okay, how much would this person have to pay people to digitize? Because the only other solution is people. So we came up with something and we said, okay, divide that by four. And that's what we'll charge on it. The number we came up with was for every year of content, we will charge you $42,000.
For every year of content in the times, okay? Yes, so for every, if you take one year, all editions of that year, we'll charge you $42,000 to digitize that whole thing. That's the number we came up with. And then they said yes. For the years of that's like 120, 30 years of. Yes, this is several million dollars of a contract there. But they said, okay, we'll pay you per year. Let's do one year, then another year, then let's see what happens.
And so they sent us the first year we did it. And it turns out because Facebook was already using our capture, we actually were digitizing relatively fast. Like how quickly would it take to digitize a year? A year was being done in about a week. What? A week of people typing in captures could digitize a whole year of the New York Times? Yes, so that's what was going on. I'm just, here's a question, right?
Which is, say I was getting on Facebook in 2008, I think when the Times approaches you, right? Yep. So I'm on Facebook in 2008 and I sign up for it. And I've got to write a capture and the word that I've got to decipher is anxiety. One of my favorite words. And I typed that in. But how do you know, how does capture know that I did that accurately? That's a very good question. So what happens is for you, we actually give you two words.
We give you one is the word anxiety. It's a new word that we just got out of whatever the New York Times. And then the other word is a word that we already have digitize. We already know what the answer is for the word. And then we don't tell you which ones which we just tell you, please type both. Yes. And if you type the correct word for the one for which we already know the answer, we assume you're a human. And then we also get some confidence that you typed the other word correct at it.
And if we give this new word to like 10 different people and they all type the same, you know, the same word they all type anxiety. Then we know that you know, so your accuracy was pretty high. Very high. Wow. So within a week or so, you get this first year of the New York Times digitized. And then presumably they're like, okay, let's keep going. That's right. And so what started happening is they started sending us checks for $42,000 coming in pretty quickly.
Nice. And by the way, we had no company. We had nothing on that. At some point Carnegie Mellon got wind of this. And they're like, wait a second. Yeah. You're kind of running a company here, but not really because there's not even a company. You got to get out of here. Yeah. They had a standard deal that says, okay, if you're going to start a company based on something that you came up in your research, we'll take 5% of this company. And so that's what happened.
So we just said, okay, how about you keep 5% will start a company. And so we did we start a company. I got a lawyer. We formed a company and pretty soon we were making $42,000 every few days. And did you call it recapture? It was recapture. And did you leave Carnegie Mellon or did you stay on as a member? No, I stayed as a professor. This was really a side hustle. Everybody. I stayed as a professor then who was also working on it. Stayed as an undergrad.
He was like, by then he was like a sophomore. He's a just state as an undergrad. I stayed as a professor. It was a side hustle. But it was making quite a bit of money for us not doing all that much in there. It's incredible. I mean, you basically have a side hustle where all of the work is being done for you by people who don't even know and wouldn't really care because it takes them two seconds. And they're getting a free Facebook account. Meantime, they're digitizing the times.
And you're getting $42,000 checks every couple of days from the times. Yep. That was the, that was the, that was the, that was the, what's going on. It's like one of the most genius businesses ever. Like, can we, can we talk after this interview is over to figure out a thing like this? Because this is amazing. You don't, you don't need any employees. It's this incredible. Yeah, it was, it was pretty good.
But then, you know, that went on for, for some time and then actually Google in fact bought it for their own book digitization process. Wait, hold on. They approached you. They saw this and they saw that this was really, and they, and they said to you, hey, we can use this for our books digitization. Yes. And, and what they like threw a number on the table. Yes, I mean, multiple numbers were thrown in the, in the end, we, we decided this was a good home for it.
So, we, you know, sold it to Google. And this time I did go to Google for two years. So I took a leave of being a professor. Okay. So you were running this company, the small business. Google buys it from you, which is just incredible. You're still at this point really young. This is really life changing. Yeah, especially since we didn't have, we didn't have investors or anything. It's all the money you could go into us. And, and your startup costs were relatively low.
Yeah. Wow. Alright, so you get acquired by Google. You are still a professor at Carnegie Mellon in Pittsburgh, but, but you went to California to Silicon Valley to, to go work at Google? Google happens to have an office here in Pittsburgh. And so I was spending about half time here in Pittsburgh and half time in California. And what were you doing at Google? Were you still just basically running recapture? Well, the first order of business was to integrate recaption to the Google infrastructure.
That took about a year. But I was still a professor. And even though I was on leave, I still had PhD students. And towards the end of that first year, I started getting very, very interested in the project which happened to turn into dual-ingual. And presumably, Luis, I mean, given that you'd already turned down Microsoft like four years earlier, you were not going to become a Googler. You really, I mean, this is part of the deal. No, I wasn't. I worked with Google, make this happen.
But it sounds to me like you're so restless, you were not going to just work in a big organization for the rest of your career. I wasn't going to do that. Yeah, it's not my thing. I kind of have to do my own thing. What do you think the reason for that is? I don't know. I mean, I probably because I'm an only child. I don't know. Are you just restless? I'm restless and I'm also obsessive. I obsess on one thing. And this is what happened with Google in the end.
I mean, I was starting to get obsessed on this new project to Alingo. And I just had to leave. And by the way, leaving cost me quite a bit of money because some of the recapture payment required that I stay there for three years. Yeah, and I did not stay for three years. So you had to give up some of the money. But in the meantime, you're still at Carnegie Mellon teaching. And I guess you've got a graduate student there who would eventually become your co-founder at Duolingo.
And I think his name is Severin Hacker. Is that right? Severin Hacker. Last name is Hacker. I mean, it's like a movie name. And by the way, the way I met him, I was literally sitting in my office one day, reminding my own business. And then this Lanky guy shows up. Super tall and super skinny. And he says, hi, I'm Severin Hacker. And I just said, what? They didn't even parse it. And then he just did hand gestures. Severin, he did a gesture that is like basically cutting his arm.
Yeah. And so he was severing it. And then Hacker, he just did like as if he was like moving his fingers like, typing with the computer. And then I'm like, oh, Severin Hacker. Wow. And he said, yeah, I'm here. And I would like to do research with you, kind of work with you. And I said it to him. Sure. Your name's so amazing. I'll do it.
When we come back in just a moment, Hallouis and yes, Severin Hacker take the recap, share business model and try to use it again to teach people foreign languages for free. And how they discover that particular model is just not going to work. Stay with us. I'm Guy Raaz, and you're listening to How I Built This. I've talked to hundreds of founders on How I Built This. And I've heard time and time again how important it is to have a strong web presence in order to really grow a business.
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One more thing before we get back to the show. Every time I run into a high-built this fan, the first thing they want to do is tell me about their favorite episode, which is so awesome. So now I want to share your favorite episode with the millions of people who listen to this show. Episodes they might not have heard or might want to hear again. So here's what I want you to do. Grab your smartphone and record a short memo short like less than 30 seconds and tell us your name.
Where you live and which episode is your favorite and why you loved it. So for example, I might say, hey, it's Guy Ross here in San Francisco and my favorite episode of the show is the one about Hamdi, Ulecaya and Chabani because I learned so much about how to just push through when nothing seems to be working out. And it gave me a whole new perspective on being resilient. So that's it. Something like that. You know, and by the way, that's not my favorite episode. I love them all equally.
Anyway, once you're done with the recording, email or message it to us at hibt.id.wondery.com and we'll share your favorites right here in the ad breaks in future episodes. Thanks so much. You guys are the best. Hey, welcome back to How I Built This. I'm Guy Ross. So it's around 2012 and Louise has been working at Google for two years and he's got one more year to go to get fully paid out. But he's itching to get going on his next project. So he walks away from Google early.
Interestingly, what motivates me started changing quite a bit after the recapture acquisition because I really thought, okay, I'm in a really fortunate position in my life. I've now, you know, I don't particularly need to worry about money. Can I do something that helps people? And so I really started thinking about education. And severance, fortunately, was also very much into education. So I started thinking, can we do something with education that gives it away for free?
And the thinking really was, there's all these people that I grew up within Guatemala, very poor. And a lot of people talk about education as something that brings equality to different social classes. But I always thought as the opposite, something that brings inequality because what happened at least, you know, in my case, those who have money can buy themselves the best education in the world. And those who don't barely learn how to read and write.
But you're thinking along the lines of like, what is something that can be free, but that would kind of be self generating. So for example, many business models offer a free service with advertising. That's Facebook's model. That's Google's model. They capture your data, which you get this service in return. But you were thinking, how can I create something that doesn't cost people anything, but that can be sustainable?
That's what I was thinking. I was thinking, can we make it sustainable and can we make it free? And languages in particular changed. I mean, dual English is a way to learn languages. We ended up going to languages, particularly also because in both of our cases, we're not native English speakers. Severin was not either. No, he's from Switzerland. He's a Swiss German native speaker. And so in both of our cases, learning English completely changed our lives.
And I just knew when I was growing up, everybody in Guatemala wants to learn English. Nobody could afford it. And turns out that in most countries in the world, if you know English, you can double your income potential. So we thought, okay, can we figure out a way to teach people language in particular, teach you English in a way that's free, but also self-sustaining. How can we make it so that it pays for itself?
Okay, so you've got this language, you think language is where it's going to be at, because that's how we can actually help people increase their income, for example, especially in other countries. And then the next question is, okay, what does that thing look like? Yep, that's what we were thinking. And then eventually came up with this idea, which is pretty similar to recapture. Turns out today, dual English does not work this way. It is a good idea, but it is a relatively impractical idea.
What was it? So the idea was this. We're going to give the service entirely for free, but instead of having people pay us, we're going to get them to help us translate stuff. So look, there are all these companies that want to translate stuff. For example, CNN would like to translate all their news that they publish from English to Spanish. They're currently paying people to do that translation.
We thought, okay, what if we get these people who are learning English on our platform? What if as a part of their way to practice, you know, after they learn some stuff, we tell them, hey, here's the CNN article. Can you translate it? It's in English, you're learning English. Can you translate it into Spanish?
And then if we get multiple people to translate the same thing and they collaborate with each other to make a translation, at the end we'll get a translation and we thought, okay, we could sell that translation back to somebody like CNN and make money that way. And you would use the same technique as you use with recapture, like if 10 people got the same exact sentence, you knew it was right?
It would be a little smarter than that. It turns out if you give a sentence to 10 people, they'll all translate it differently. But generally, yes, I mean, we would use, you know, kind of they had to be pretty close to each other. Then we also had another step where the exercise to the person wasn't translate this, but tell us whether the translations are right or wrong.
So some people would check the translations. But in the end, we built the system actually, but we're pretty well that basically you could give it a text in English. And then the people who were learning English or could be learning English, and then they would be, they would help translate it. And then the system would pop out of translation that was made by people and the translation was very good. It's instead of CNN paying some translation service, they could just pay you.
Yes. And like the New York Times said, you 42,000 contracts, you would just have your students learning a language, but they would also be translating these articles into their native language. That's exactly what the idea was. So, okay, so you have this really great idea. By the way, and I'm assuming you're funding the whole project, right?
Yeah, it was a Carnegie Mellon project and it was being funded by a grant from the National Science Foundation and also from my MacArthur grant at Carnegie Mellon. And funding at the time, what it meant is basically paying for Severin's salary. Yeah. And so, at what point did you say to Severin, all right, let's spin this out and start business and figure out who knows. This was also an interesting thing.
Okay, we thought, okay, let's do this. This is a good idea. We're convinced it's a good idea. Let's do this. But the first thing we do is hire more people because the two of us just can't do it. So, we started hiring people, but all inside Carnegie Mellon, we realized actually hiring people was pretty expensive inside our university. And then we thought, okay, well, what I need to do is apply for a bigger grant from a National Science Foundation.
And I started working on that and these grant applications, this is like 30 pages long and you have to wait like a year to hear whether you get it or not. And it's like a couple million dollars maybe. And so, I was working on that and then suddenly I got connected to a venture capital firm, Union Square Ventures. And, you know, I talked to them about this and very quickly they get back to me and they're like, hey, we could fund this.
I didn't have to write 30 pages. It was much easier to get that money. So, we thought, okay, well. They wanted to just, they wanted it in on this because, well, first of all, you got a track record. It was mainly the track record in retrospect. And, you know, I know I'm going to have a relatively good relationship with the Union Square folks. I mean, they've now told me, look, we'd never thought this would work, but you had a really good track record. So, why not?
They want to invest in you. So, when they approach you, they said, we want to fund this, we want to invest in this. And at this point, you would not dealt with an outside investors in your career. We had not and also this, this was still not a company. So, at that time, we just thought, okay, well, let's make it a company and let's take some funding. Yeah. And how did you do that? Did you, was there, I mean, seven was your student. So, was there an uncomfortable conversation?
Was it pretty clear? You're like, listen, that was my idea. I'm the boss. So, I'm going to have X percentage. You know, seven is just, I think it's partly because he's Swiss or because he's like a computer. He's like Mr. Spock. He had that conversation with me. I didn't have it. He sat down and he said, I want us to write a contract. But it's not a lawyer contract. I just want to write basically a word document that we write a few lines long that just basically says what our understanding is.
And we're both going to sign it. And we wrote down a contract that was just really simple. It's like we will go in it half and half. And all decisions related to hiring, we're going to do together. All decisions related to, I don't even remember what else. But there's like three or four bullet points. And then it just said his name and my name and we signed it and that was it. So, all right. So you create a company and you've got some interesting investors.
And I think you raised like three plus million dollars from a bunch of different investors. It's 3.3 million, which at the time this was a normal series A. So it's three million dollars from Union Square Ventures and 300,000 from a combination of Tim Ferris and Ashton Kutchen. Ashton Kutchen, he was like way ahead of the rest of Hollywood. Not everybody wants to be Ashton Kutchen. Ashton Kutchen was a very smart guy in that respect. He was way ahead of the game.
Everybody now wants to be in Ashton Kutchen. They're like, right? There's LeBron James and there's Serena Williams. And there's, anyway. All right. So Ashton Kutchen, with that money, did you get an office? Did you get a staff? How many, were you able to hire a bunch of people? Yeah, we hired a few people. We did get an office very close to the Carnegie Mellon campus. And we started working on it. And at the time, pretty much everybody told us,
you're not going to be able to beat Rosetta Stone. Like, why would anybody use this? We just kept on saying, well, because it's free. This is going to be free. That was our main selling point. We didn't know if it was going to work. But we worked on it and we launched. We launched duelinga.com in 2012. And it was very fortunate that I had given a TED Talk about this. As soon as that went online, we got a ton of people come to duelinga.com to try it out to try it out.
And it started growing pretty fast because it was a free way to learn a language. And at the end of your lessons, we would say, hey, here's this article. You want to help us translate it. And we landed a contract with CNN and a contract with Buzzfeed that we were going to translate all their news from English to Spanish. By the way, how did you convince? I mean, it was it was a relatively cheaper.
They didn't cost us any money to get the translations. Our users were doing it. We just said, oh, how much do you pay for at the time? It was like they paid like 10 cents a word. We said, I will do for five cents a word. I got you. And it worked. You know, the translations worked and everything. There was one problem. The amount of money that we were going to make was actually relatively small. And it's because translations are a pretty shitty business translation.
It's five cents a word, but then pretty soon suddenly, you know, the people from Buzzfeed found somebody else that we do for four cents a word because we're sure. And then we're like fine. We'll do for three. It's a race to the bottom. It's a race to the bottom. And then computers are getting better at it. And by the way, you I'm assuming you didn't want to found a translation service. You were founding an education company.
That's right. And when one thing we realized this was we were executing on these contracts. We realized that's where the money's coming from. So we're spending more and more of our effort making sure the translations are accurate as opposed to teaching well. And it was there was no ads. It was just a website initially. And but you had a lot of people who signed up, right? Like a couple of million.
Yeah, exactly. We were growing. It was good. But then a couple of things happen. The first thing that happened is we realized this was not going to be a great business. The other thing that happened is we hired a one engineer and a summer intern. They started on the same day on a summer. And we told them, Hey, the two of you, you don't know each other. We've heard these apps are the thing now instead of websites or they're starting to become the thing.
Can you to make a companion app to do a link to do a link.com? Let's try that. That's going to be your, you know, kind of summer project. Let's make a companion app to do a link.com. They came back at some point. They say, you know what? It doesn't have to be a companion app. We can actually do all the functionality of dual.
We can fully learn a lot of aspects of the language from this app. So we developed it. We launched it and we were at exactly the right time because there were other apps to learn a language in the app store. But they were pretty crappy and also cost money. And so very quickly, this became the most downloaded education app of all apps. Wow. And you guys were there like right as apps were like totally starting to explode. Yeah. That's right.
And how are you just curious? How are I mean, you did not have an, you're an educator, you're a professor, but how are you building a curriculum in different languages? This is great. I mean, we are, we didn't, we had no idea. I mean, we went and read some books about how to teach languages.
And first of all, most everybody does not agree on what the best way is to teach a language. And then so we thought, okay, this is pretty hard. Let's just take what we can from the books and put it out there. So I made the first Spanish course myself because I'm a native Spanish speaker. Severin made the first German course himself. And this is going to be just like kind of the gamification model where it was just level up, level up, level up, level up, level up.
That's the other thing that we realized in the process. When we started making the first versions of the courses, we had people try it out and people would say, oh my God, this is really boring. The hardest thing about learning, learning something by yourself is staying motivated. So let's make it fun. So fortunately early on, we made something that was both fun and also taught you a language.
Got it. All right. So you, so you launched this thing. Is it pretty clear to you, you know, by the end of that year, by 20, 2013, I mean, I think you got 15 million users by the end of that year. Yep. Was it pretty clear to you that it was not going to make money? It was not going to be sustainable just by doing contracts with news organizations. It was. It was. And particularly because we couldn't figure out how to make the translation stuff work on our app.
Just people couldn't type that much in the app, et cetera. So we just could not figure that out. And the majority of our users were in the app. So I made the difficult decision to cancel those contracts and forget about trying to make money and just raise more venture capital. And sure enough, we, we, we had some very good investors who decided to invest. And that's how we were sustaining ourselves.
So you're basically sustaining yourself of investment cash. But did you feel like you had to, you had to change your business model? I mean, that, you know, this by the way, has been one of these biggest struggles with dual, you know, our mission from the beginning has been to give free language education. This is why we started the company. That is the thing. There's an easy way to make money here, which is just charge for it.
And so when we talk to investors, the first thing I would tell them is, look, the one thing that will not change is I'm not going to charge for people to learn a language here. And so, you know, people would invest. And I think a lot of times they would invest and they would think, okay, eventually they're going to charge. But I told them, you know, I'm not going to charge. And so we, we raised funding and we raised, at some point we had raised close to about $100 million.
Wow. Wait, hold on a stoppuss. 100 million, I mean, you're not manufacturing a product in a factory. Why? I'm just thinking it's ones and zeros in a computer. You got 10 months of expenses. Here's the thing. You want to hire really good engineers and really good designers and really good product managers. They are not cheap. I got to pay them. And that's the expense. Right. These are the most expensive software engineers, not the most expensive part of.
And but you're based in Pittsburgh. You're not based in California. Correct. And you would think that because we're based in Pittsburgh. You cheaper. Yeah, it's not true. There's a reason from the beginning we decided we're going to hire only the very best people and the very best people have offers from companies in California. And they say, I have this offer that is for, you know, I'm just graduated from college. I have this offer that is for $130,000. Are you going to match it or not?
Yeah. And so what do you do? But we had no idea how we were going to make money. I mean, the first way we tried, which was these translations, we had given up on. And at some point, we raised money from Google, capital G is one of their investment arms. We raised money from them. I believe it's $45 million bucks that we raised from them. Wow.
You know, I was feeling good. I thought, okay, well, we can continue raising money for a while. Like whatever. I don't know. I don't need to figure out how to make money. We can just continue raising money for a while.
And the partner from that firm sat me down and she said, you know, you're not going to find a bigger fool. We're the biggest fool. And nobody else will invest in this company if you don't figure out how to make money. If you have zero plans to make money, nobody else is going to invest in this company. And that's when we started kind of freaking out and try to figure out how we're going to make money.
When we come back in just a moment, while Luis says no to one of the most obvious ways to make money and why he ultimately decides to say yes, stay with us. I'm Guy Riz and you're listening to how I built this.
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And one thing he doesn't want to do is sell ads. We're very precious about this. We cared a lot about the user experience in Duolingo. And we thought, first of all, we're never going to put ads in here. Secondly, we're never going to charge people. And basically, Layla from Google was like, well, I don't know how you're going to make money because these are the standard ways of making money, but you've got to figure it out.
This is an important point because I think a lot of businesses start out this way with really strong principles that seem right. But when you say that, you actually kind of put your box yourself in because sometimes there are reasons to do some of those things in order to accomplish the bigger goal of, for example, of delivering a free product.
You are completely right. So you had to basically do a 180. You had to have hard conversations with people and say, hey, you know, we, we kind of start thinking about maybe advertising on our side. We did. At the time we had, I don't know how many employees 60. But it's funny because the people we had hired early on, they had offers from companies that had much bigger names than us, Facebook, et cetera.
And the reason they had come work for us is because they really believed in our mission of kind of giving free language education. So we ended up with a basically 60 to 70 zealots,
who all just wanted to get a mission education. Yeah, the mission. Like we're here for the mission to this day. It is still like that. So just great. But we had a year of turmoil where basically we're like, okay, well, we need to figure out how we're going to make this sustainable because we're not going to be able to raise much more funding, you know, if we never make money. So sorry, but this got to be a way to make money. And if you don't have money, you don't, you don't have a business.
We don't have a business and we're not going to be able to reach the number of people we want to reach. At some point, enough people got convinced that, okay, we'll put an ad at the end of every lesson. I remember we showed it to our head designer who he, he almost vomited. I get it. I mean, did people say to you, or did you get the feeling that like, I don't know, people thought that you were selling out?
Yeah, I think so. And now I don't think it's too many people because I think I myself went through a transformation that took me, it took me several months to go through this transformation. So I, and you know, we put the ad at the end of every lesson. And fortunately, that actually gave us quite a bit of money. I mean, as soon as we put that ad, our run rate went from zero to like 10 million bucks a year. One tiny ad didn't even cover the whole screen.
What was the company? Oh, we, you know, we use Google ads. So they came from everywhere. Everyone's random, random places, not late night booty call.com. I hope. No. And soon after that, we got a bunch of people who would tell us, hey, I don't like ads. Can I pay you to turn off the ads? And we thought, okay, how about we launch the subscription service where the main thing is you can pay us to turn off the ads.
It turned out, and this is just, we really just didn't realize this. Subscriptions made us a ton more money than the ads. And what, what, what, what was the, what would it cost to subscribe? 999 per month. Okay, so a little bit more than Spotify, but, and, but through that, you could do any language you wanted, any course with no ads. And if you didn't want to pay, you just had to watch, you know, see the ads at the end of a lesson.
And so all of a sudden, people were willing to pay some money. But still, I mean, we, we, of course, I know we know the advertising model and podcasting and then it's still a small percentage of people. When you get a freemium version, right, even Spotify, most people are still getting it for free and dealing with the ads. Yes, so, well, at first it was very few. By now, I mean, it's been a couple of years since we added our subscription by now, 3% of our users pay for the subscription.
Which is a lot, because how many totally users do you have? We are a lot, I mean, this is about a hundred million. Yeah, I mean, active users right now, we have about 40 million monthly active users and about 3% of them are paying subscribers. All right, now here comes the tough part, which is, Duelingo of course is free. So, you know, it's a free offering.
But of course, even with free offerings, there's, there's going to be criticism. And, you know, there've been professors who specialize in this have said, you know, you can't really get that proficient with Duelingo. You know, I may be I can learn some words or phrases, but it's not, doesn't really actually teach me a language.
And before we dive into this, just in general, when you hear this criticism, does it make you defensive or does it make you think like, maybe we got to make this a little better. The truth is, there's a lot of marketing out there that tells you that you can learn a language in nine days. This is not true. Actually learning a language takes years. This is the first thing to understand.
Yeah, secondly, there's there's various levels, of course. Duelingo teaches you pretty well from zero. So, starting from the beginning to about intermediate, depending on the language that you're learning. Yeah. If you want to use Duelingo and start from zero and get to the point where you're the poet laureate of that country that is unlikely to happen with Duelingo by itself. That's just not going to happen.
But the way we see it is a, we keep getting better and better. If you look at any single method, I mean, take the, for example, the single method of US high school education. Yeah. It kind of doesn't work very well. How proficient are people who take Spanish throughout high school at the end of high school? On average, not that proficient. Right.
So, we're going to do a goal with Duelingo and we're not there yet. But our goal is to get people from zero to a level of the language is a level called B2, which is where you can get a knowledge job in that language. Google, for example, employees, software engineers here in the United States whose English level is B2. Okay. That is our goal. We're not there yet. So, we interview James Park who created Fitbit and Fitbit, the genius of Fitbit was it was like a gamified exercise, right?
Like people are like, I did 10,000 steps today. Or, you know, I made it. It's hard to get humans to change habits. So, how do you get, let's say, a significant percentage of people to finish a language program?
Yeah. This is what we spend a lot of our time on. And, you know, this is one of the things that when we get criticisms, one of the things that they don't take into account is a lot of the research about how to teach a language is done with people who are in some sense held hostage in the classroom.
Yeah. The hardest thing about learning a language is staying motivated. So, we spend a lot of effort trying to make Duelingo more motivating and more fun. And the way we do that is just by adding game elements. I mean, using Dueling, a lot of people refer to it as playing Duelingo is just, it feels a lot like playing a game. You get rewards, you get like level up kind of thing. That's exactly right. And, and, and other things we do, for example, here, this is another interesting thing we do.
We want you to use Duelingo every day. I mean, the truth is, if you actually want to learn a language, you got to turn it into a habit. One thing that is really powerful for us is send, we send you notifications. So, we have a pretty sophisticated artificial intelligence system. It's one of the most sophisticated things we have that figures out when to send you a notification and what to say in them.
And, for example, one of the most powerful things we, you know, we've done is after about five days, if you haven't come back, we send you a notification that says, these notifications don't seem to be working. We're going to stop sending them for now. That turns out to be an extremely powerful way to get people to come back. Huh. Because they feel guilty that we're stopping to send the notifications so that gets them to come back. Yeah. It's like, hold, you got a whole people accountable. Yes.
Which is very hard. This is why people have personal trainers because otherwise you won't go to the gym. Yeah. But why would somebody pick Duelingo over any other language app? I mentioned Rosetta Stone, for example, right? And I'm sure you've looked at it. And I mean, obviously you're a competitor, but they probably do a decent job teaching languages. So why wouldn't you just like do that?
Yeah. I mean, before we launched, Rosetta Stone was the biggest thing in the world and everybody told us, you're not going to be able to beat them. Yeah. By now, I would say we probably have 50 times more uses than they do. Our revenue is probably 5x, their revenue. Oh my God. So that effect, there's a big reason it's the fact that we're free. And this is one of the things that our mission, which is really giving free language education, I think it's actually good for business.
Yeah. We don't spend very much in marketing. We have all these people. 97% of our users don't pay us. Yeah. So it's just, I think we just have this huge user base of people who are basically our marketing engine. And a lot of companies in education eventually turn into marketing companies. Yeah. For the following reason, you realize man, making improvements in how well you teach is hard. And it really is very hard. It takes years.
So for every dollar you have, you quickly realize it's actually better to spend your dollar marketing your product rather than making your product better. I've been, you know, hardliner on this. We spend all our effort just making the product better. And the main reason is I think we're really in a kind of for the long term. And I think in the long term, the better product will win. And this is why we don't spend so much money on marketing.
Luis, in December of 2019, your company was valued at 1.5 billion dollars. So what's the exit? I mean, you got investors. Obviously they convinced you were convinced that you had to keep it sustainable and to grow it and now value to 1.5 billion dollars. And sounds like you're, you know, keeping the lights on pretty well. What's the end game at some point? You know, it's sell or go public.
The end game here is to go public. That's what we've been working towards. We have the revenue now to be able to go public. We are cash flow positive. And one of the things that I think is really important is the way I see it, we have so much more still to go in terms of how much better we can teach. And also teaching all the stuff we recently released an app to teach reading to young kids. It's called Dualingo ABC. I think there's a huge amount of impact that we can do with that.
So I mean, to me, it's just keep going with this. That's the goal. Luis, given all of the things that have happened to you, I mean, your incredible success. How much of that do you attribute to your intelligence and how hard you work? And how much do you think it's because you were lucky? I was definitely lucky. I mean, launching an app at the time we launched the app was exactly the right time. And launching an app in late 2012 was a really good time to launch an app.
So, you know, I've been incredibly lucky. I think that and I think just hard work on the same thing. I get obsessed with the same thing. I mean, I've been working on Dualingo for now eight years. And I'm pretty obsessive about it. So I think it's both. That's Luis Bonan, co-founder of Dualingo. We first ran this interview back in May of 2020. And a little over a year later, the company did in fact go public at a valuation of nearly five billion dollars.
And today, it's valued at nearly nine billion dollars. Hey, thanks so much for listening to the show this week. Please make sure to click the follow button on your podcast apps. You never miss a new episode of the show. And as always, it's totally free. This episode was produced by Casey Herman with music composed by Routine Ara-Blui. It was edited by Niva Grant.
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