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This episode is brought to you in partnership with Airbnb. This past summer, I took my family to Athens, and it was truly an incredible trip. We ate amazing food, we saw the Parthenon and the Agora, and all the incredible things that you can see in one of the most amazing cities in the world. And one of the things that made it special was the home we booked on Airbnb.
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Your home might be worth more than you think. Find out how much at Airbnb.ca.host. I was feeling good. I thought, okay, well, we can continue raising money for a while.
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.
We're the biggest fool. And that's when we started kind of freaking out and tried to figure out how we're going to make money. But we had no idea how we were going to make money. From NPR, it's how I built this show about innovators, entrepreneurs, and dealers.
It's got the stories behind the movements. They built. I'm Guy Rosin on the show today. I have a perfect storm of skill and luck.
Drove Luis Vana on to create CAPTCHA, then reCAPTCHA, 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 an 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 Systrom, it was a random remark from his girlfriend that made him decide to use filters on Instagram. For Blake Mysoski, 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 Vanaan, it was a free lecture at Carnegie Mellon University in 2000. We'll get deeper into the story in a few minutes, but that single lecture would lead him to invent two ingenious new tools. The first was CAPTCHA. Yes, CAPTCHA.
Those annoying, twisted, blurred letters you have to type into a website to prove you're human. And the second one was Duolingo, now the biggest language learning app in the world, which is now getting even more popular because people are looking for new things to do now that they're stuck at home. Both CAPTCHA and Duolingo were 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 CAPTCHA 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, and I think that it was really wonky or something. I was not all that much into the candy itself.
I was into the machines because basically the candy is 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 school 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 years 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 were doing this summertime?
Yeah, I was born. I was an only child. I didn't have that much to do. This is, remember, this is also pre-internet, pre-everything.
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-Eleven. Woo!
Should have done math books. Did you just love math? I mean, 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.
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. I'm tired of video games in my Commodore 64. Like floppy disks?
I'm floppy disks. floppy disks? Yeah, that's right. I wanted a Nintendo and I was eight.
My mother would not get me Nintendo. I got me a computer at Commodore 64. I couldn't figure out how to use it. But eventually I read 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. I became a little hub in my little neighborhood. But these were not other kids. These were adults.
Basically young adults who had a computer and they would come to my house. I would take their games and give them my games in exchange. So I collected a pretty large number of video games. But I should mention, right, that because your childhood sounds pretty nice, but like as a kid, I guess, or even as a teenager, there's a civil war in Guatemala.
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. 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 a ransom.
I mean, she was gone for seven or eight days. People's cars would be stolen. Every couple of months somebody's car would be stolen in my family. Going out past seven, thirty p.m.
was rare. You needed to go out in a large group if you were going to go out past seven, thirty p.m. I did him my house, had walls, and barbed wire and it felt dangerous. This is one of the reasons I came to the US actually.
When I was after my aunt was kidnapped, I thought to myself, I don't want to live here. I guess you did end up leaving Guatemala for college because you went to Duke in North Carolina. You described yourself as a math nerd in school. Is that what you intended to do?
Do something in math? That's what I wanted to become. I'm a math professor. I was pretty certain I wanted to become a math professor.
At the time, I thought the best thing that I can do is really learn a lot of math. I really loved it. I thought it was futile to learn how to deal with other people. It is interesting because my job these days is one hundred percent just dealing with other people and people's problems.
I'm just trying to understand this. By becoming a math professor, you thought, hey, I wouldn't have to deal with people. I would just deal with facts and data and numbers. Yes.
I'll do math research all day long. Every now and then I'll teach a class, but whatever. That's what I thought. 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. I guess you go into computer science. Yes. I changed from math to computer science because I visited a math grad school.
What people were saying, the professor was saying, I'm working on this open problem that nobody's been able to solve for the last 300 years. I thought, I don't think I'm smart enough. If you haven't done it and nobody's done it in 300 years, that's not for me. Whereas when you're visiting computer science, this is crazy thing.
People are like, oh, I solved the open problem yesterday. It's a much younger field. I thought that was much more exciting for me at least. You start your PhD program at Carnegie Mellon.
I guess really soon after you start, you go to someone from Yahoo! comes to campus to talk about Yahoo! It was a big deal in 2000. 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.
The first thing you got to do when you become a PhD student is find an advisor. I found myself an advisor. We went to a talk together. This guy from Yahoo!
was the chief scientist of Yahoo! at the time. Like you said, Yahoo! at the time was the biggest tech company out there.
He came to give a talk at Carnegie Mellon and it was to talk was basically 10 problems that they didn't know how to solve at Yahoo! I was an enterprise PhD student and I thought, I'm going to try to solve these problems. What was the problem that he said they had? The problem for this particular one was there were people who were writing programs to obtain millions of free email accounts.
Yahoo! at the time gave out email accounts for free. Some people thought it would be good to send spam from Yahoo! accounts.
The problem is each Yahoo! account only allowed you to send 500 messages a day. 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. This is the era. I remember this. We all remember this.
You would get hundreds of messages, spam messages about certain bodily enhancements. You would get messages about hormone growth things. I mean it was bad. It was bad.
It was bad. It was a huge problem. We can't figure out how to stop these computers or programmers or creating all these email accounts. 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 programs can do things very fast.
And it also doesn't get bored. 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? And that's where this idea came about. It's this idea of a capture where it's basically this 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. Well, at that time humans 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 catches. I don't know how much of a human you are. Now there's like point to every stop sign.
Point to every bridge. 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 that's given a test to see if they're a human or not.
And you just start doing this on your own. Like, did you tell Yahoo? Did you tell him? Or were we just like, this is fun.
I want to figure this out. Like just working independently. Yeah, I mean, with my PhD advice, it became my research. It became my 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 the core of the PhD program, I think. You know, you're trying to find problems that others have installed. And this seems like a problem that other people had themselves. So we were working on it.
And you called it kaptcha. Did you name it kaptcha? And what does that mean? It's not like kaptcha.
It's not like kaptcha. Yeah, it's not like kaptcha. It's a acronym that sounds like kaptcha, which the idea was to capture the bot or the computer. It stands for completely automated public during test to tell computers and human support.
So it's just a acronym. But now I think most people have heard of the word kaptcha but do not know what it stands for. And you come up with this thing kaptcha. And what do you do?
Do you know this guy? Hey, I've got your solution for you. Basically, I mean, we met Lemon. We said, hey, we think we have a solution.
Here's some code. We explain how it works. And what's amazing and now that I know how large companies operate and how slow things move. The only thing is within about a week of when we sent them this, it was already functional.
Wow. 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, oh my God, this is great.
And so you must have just, I mean, you were like what, 25, 26? 21, 21, 21. So you, right, all of a sudden you're an overnight millionaire, multi-millionaire. No, no, no, no, no money was exchanged hands.
Wait, you gave kaptcha to them? You just said, hey, here you go. Yep, I mean, listen. You gave it to a multi-billion dollar company that gave them kaptcha?
I don't know how things work. And you know, the truth of the matter is probably a good thing in retrospect. I mean, who knows what would have happened, but you know, I don't have regrets. But yes, that's what started happening.
And then basically every other website started, you know, they saw that it was 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 right to constantly.
It was annoying to people. And every time that I would be at a party or something, 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 a party. They 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, it's like, just briefly explain how that happened and what you were working on. Yeah, I mean, this is funny. I mean, at the time, it was not called crowdsourcing. But my PhD research was basically on crowdsourcing.
I had worked on kaptcha 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 worked 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.
An image, a random image, like an elephant. An image from the internet, yes. And you were told, type whatever the other person's typing. Just that typing words.
And if you're one of your words matches one of their words, you both get points. 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's 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.
I programmed it. 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 they got implemented. They changed the name from the ESP game, which I call it the ESP game because it's an extra sensory perception. The idea was 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 life changing money? Were you all of a sudden super rich? No, it was not super rich.
But it was good. Got it. 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 a tenure track. And by the way, early on in your academic career, I should mention this. You were granted a very, like, one of the most prestigious awards, the MacArthur 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 must have been mind blowing. Yeah, that was, you know, I've gotten a number of awards in my life. But I mean, that was probably the most impactful one in terms of just how I felt.
And also, they do this crazy thing where they have no idea that you're being considered or anything. And they call you randomly. They figure out your phone number one that you pick up. And then they just tell you, you know, have you ever heard of the MacArthur Fellowship?
And 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 it's a pucker or not, but I read that 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, when you pick up your cell phones, like, please hold for Mr. Gates or was he like, he was pretty much like that. I mean, it was somebody, you know, 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 there. Wow.
It was very flattered. But, you know, at the time I just wanted to do my own thing. How do you say no to Bill Gates? You were like, oh, wow, I think Mr.
Gates is so nice and so honored, but I just, you know, no. I didn't actually say no. I said no to think about it. And then I said no to, like, a recruiter.
But, yeah, he 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. I guess it mattered more than it does now. It was nice to know that I could buy a nicer car or a nicer apartment or something.
It was never been my driving force, I would say. Okay. So you decide, Bill Gates, thank you. No, thanks.
I'm not going to Microsoft. I'm going to stay doing this work. So you are now, where are you lecturing? Are you teaching classes?
I was teaching a huge class. It was called Great Theoretical Ideas in Computer Science. It's just a fancy name. It's basically a discrete math class.
People who don't do well there usually change majors. And you're like, not like John Nash, but I'm thinking about that movie. You're in front of a big blackboard or whiteboard and doing equations and problems in front of 250. Slides had already been around.
I used PowerPoint. I used PowerPoint. I used PowerPoint. Not that old.
You're right. So you're teaching and you've done CAPTCHA. And lots of people are saying, hey, it's annoying. And you're like, yeah, I hear you.
And people kept saying that to you? Like, kept hearing that? Yep. And that's when I started thinking, each time you do it, you waste about 10 seconds of your time.
And I started, 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 and realized about 200 million times a day, somebody types of CAPTCHA.
And that's when you get 200 million times a day, times 10 seconds. So humanity gets a whole listing of about 500,000 dollars a day typing these CAPTCHA. 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 CAPTCHA that is also useful? And we get them to do something useful.
And it occurred to me that, yeah, we could get them to help digitize books. Sorry, pause. How did you come to that idea? So like, when you type in a random letter from a CAPTCHA, you were thinking, wait, how do we have a secondary use for this?
And maybe it's books, digitizing books. How did you even occur to you? I was thinking for a long time, can we get people to do something but at the same time 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 that really, at the time, it seemed kind of insane and ambitious project that we're going to take all of the world's books. Everything that's ever 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 recognize, many of the words, about 30% of the words computers cannot recognize. And the reason for that, by the way, which is the same reason why computers could not read CAPTCHA's. Basically, these look kind of like distorted letters. And distorted letters are just inherently difficult for computers to recognize.
Like, yeah, that's the point of CAPTCHA. 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 cannot read, and then we send them to people while they're typing CAPTCHA's on the internet?
So basically, the next time you type a CAPTCHA, the words that you see are actually coming from a book, and whatever you enter is in fact used to help digitize a book. And then remember when you came upon that idea, was it like in a flash, or was it just like, did it occur to you? I 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 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 the computer could not recognize and then send them as a CAPTCHA. Now, we decided we're going to go to big websites and we're going to tell them, hey, look, we have this CAPTCHA service.
It's free. It's better than yours. It's relatively crappy. This one's pretty good.
And we'll give it to you for free. But all we have 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 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 CAPTCHA. And it was our CAPTCHA. And every time every person that was getting an account in Online Booty Call was starting to help digitize. 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 CAPTCHA on their registration flow. And they called us up and it was a website called Facebook.com. And they were relatively new.
And they were like, hey, listen, we came across it. It seems like you have a good CAPTCHA. Can we put it up? This is 2007.
Probably 2006. Yeah. 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 digitized books, etc. 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 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. We didn't know how. And what happened is this guy, you know, he 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 their content.
The New York Times. The entirety of the New York Times archive. And so we kind of scrambled and we had no idea how much 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. Yeah. So we came up with something and we said, okay, divide that by four. And that's what we'll charge them.
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 every every one year, all editions of that year will charge you $42,000 digitize that whole thing.
That's the number we came up with. And then they said, yes. And how many years of that's like a hundred and twenty, thirty 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 it one year, then another year, then let's see what happens. And so they set us the first year.
And it turns out because Facebook was already using our capture, we actually would digitize it relatively fast. Like how quickly would it take to digitize a year of 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 saying I was getting on Facebook in 2000.
This is like 2008, I think, when the Times approaches you, right? So I'm on Facebook in 2008 and I sign up for it. And I 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 the word that we already have digitized. We already know what the answer is for the other word. And then we don't tell you which one. So we just tell you please type both.
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 type the other word correctly. 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 it's right.
So your accuracy was pretty high. Very high. Yeah. 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. At some point Carnegie Mellon got a lot of money.
And they're like, wait a second. 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. They had a standard deal.
That's 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% we'll start a company.
And so we did. We started a company. I got a lawyer. We formed a company and pretty soon we were making, you know, $42,000 in a few days.
And did you call it ReKatcha? It was ReKatcha Inc. Yes. And did you leave Carnegie Mellon or did you stay on?
No, I stayed as a professor. This was a really aside. I stayed as a professor who was also working on it. I stayed as an undergrad.
He was like, by then he was like, sophomore. He's like, you just stayed as an undergrad. I stayed as a professor. It was a aside 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 2 seconds. And they're getting a free Facebook account.
In the meantime, they're digitizing the times and you're getting $42,000 checks every couple of days from the times. It's like one of the most genius businesses ever. Can we talk after this interview is over to figure out a thing like this? This is amazing.
You don't have any employees. This is incredible. Yeah, it was pretty good. But then that went on 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 good. And they said to you, hey, we can use this for our books digitization.
And what they threw a number on the table? Yes. Multiple numbers were thrown in and out. In the end, we decided this was a good home for it.
So we 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 investors or anything, it's all the money that you went to us. And your startup costs were relatively low. Yeah. All right.
So you get acquired by Google. You are still a professor at Carnegie Mellon in Pittsburgh. But you went to California to Silicon Valley 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 Rekapture? Well, the first order of business was to integrate Rekapture to 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 Duolingo.
And presumably, at least, given that you'd already turned on Microsoft like four years earlier, you were not going to become a Googler. This is part of the deal. You had to go work 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. I wasn't going to do that. Yeah. It's not my thing.
I mean, 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. I just 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 be obsessed on this new project, Duolingo. And I just had to leave. And by the way, leaving cost me quite a bit of money because some of the Rekapture payment required that I stay there 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. I think his name is Severin Hacker.
Is that right? Severin Hacker. Last name is Hacker. I mean, it's like a movie name.
His name is Severin Hacker. 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 basically cutting his arm. 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 I'm like, Oh, Severin Hacker. Wow. And he said, yeah, I'm here. And I would like to do research with you.
And I said, sure, your name is so amazing. I'll do it. When we come back in just a moment, Eloise and yes, Severin Hacker take the Rekapture 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 Raz. And you're listening to How I Built This from NPR. Hey, welcome back to How I Built This from NPR.
I'm Guy Raz. So it's around 2012. And Luis 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 Rekapture 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 Severin fortunately was also very much into education. So we 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 with in 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 as 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. Can we make it sustainable?
And can we make it free? And languages in particular changed. I mean, dual language 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 it 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 a language in particular, teach you English in a way that's free, and what else is self-sustaining?
Like, how can we make it so that it can pace 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 was pretty similar to recaption. Turns out today, Duolingo does not work this way.
It is a good idea, but it is a relatively impractical idea. 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, 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 then 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 would recapture. Like if 10 people got the same exact sentence, you knew it was right.
I had to be a little smarter than that. It turns out if you give a sentence to 10 people, they'll 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 this translation is right or wrong.
So some people would check the translations. But in the end, we built the system, actually. But worked pretty well. That basically you could give it a text in English.
There were people who were learning English or who could be learning English, and then they would be, they would help translate it. And then the system would pop out a 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. And you, again, like the New York Times is sending you 42,000 objects, 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 a whole project, right? Yeah.
It was a Carnegie Mellon project that was being funded by a grant from the National Science Foundation, and also from my MacArthur grant at Carnegie Mellon. Got it. And funding at the time, what it meant is basically paying for severance salary. Yeah.
And so at what point did you say to severance? All right, let's spin this out and start a business and figure out who, you know... This was also an interesting thing. We, okay, we thought, okay, let's do this.
This is a good idea. We're convinced this is a good idea. Let's do this. But the first thing we need to 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 it used to be this grant application. It's 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, first of all, you got a track record. It was mainly the track record.
In retrospect, 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 never thought this would work, but you had a really good track record, so why not? They want to invest in you, right? So when they approached you, they said, we want to fund this, we want to invest in this.
And at this point, you would not deal with it outside investors. And you're... We had not, and also this, this, it 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, I mean, seven was your student, so was there an uncomfortable conversation? It was pretty clear.
You're like, listen, that was my idea on the boss, so I'm going to have X percentage. You know, seven is just, I think it's partly because he swissed, or because he's 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 along 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'll go in 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's just 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 interest investors. And I think you raised like three plus million dollars from a bunch of different investors, right?
It was 3.3 million, which at the time, this was a normal series A. So it's $3 million from Unisquare Ventures, and $300,000 from a combination of Tim Ferriss and Ashton Kutcher. Ashton Kutcher. He was like way ahead of the rest of Hollywood.