We view language learning right now as an unsolved problem.If you want to learn how to speak English.It's kind of impossible unless you moveto the US for like 20 years.Language studying, especially when it comes to speaking,has been a privilege for peoplewho can afford to pay for another person's time.And it was very clear to us that machine learning would change everythingWe built an entire speech recognition systemthat could not only understandwhat people were saying, but understandthat the accents upon which they were speaking with.that is actually better than if you hired a human tutor.We got it out to the AppStoreand I think like three people paid.I think we made $18 the first dayand we celebrated.I'm Connor and I am the co-founder and CEO of Speak.Speak is a mobile app that helpspeople practice speaking English.We use AI to replace a human,but build an experience that feels like you'retalking to a real live human tutor.[AI] Hey, I was just about to call you. What's up?[Human] Not much. How's it going?[AI] Pretty good. I was thinking of going out this Friday night.- Want to come?Yeah. What are you thinking?[AI] I was thinking of getting some dinnerat the new Italian place in town.We actually grew so fast.We became the number one education app in Korea.And we have now grown to over 30 people.We have an office in San Francisco,and we have an office in Seoul.And then we have an office in Europewhere we do all of our engineering.I started my first company in high school.It was called Flashcards+.I've always been really interested in computers.I got my first iPhone and I realized the iPhonewas kind of the same size as the index cardsthat I was studying and memorizing for tests.And so I built a mobile app to solve my own problem,and that became super popular.I went to school at Harvard one year before droppingout for the Thiel Fellowship, which eventually led meto sell that first company and have an exit when I was around 21.Yeah, we lived in a dorm together, you know,three bedroom, five guys, and Conor was one of the guys.I mean, a very, very first impression.He was a very tall guy.He's about six three. So that was my first first impression.Connor was like a different type of a studentbecause he already had this productand this business out before he even came to school.He wasn't always busy, but there were timeswhere we would be hanging out with like talk about random things,and then he would just give us a heads up.He'd be like, Okay, by the way,I have a business call I have to make in 5 minutes,so I'm going to go to my room.There were times where he wouldjust fly out to San Francisco.Yeah, he seemed very ahead of the curvein terms of like knowing how things worked in societyand like how the whole Silicon Valley scene worked.Connor would be telling us, like,what happened is,like last meeting, what kind of like deals are happening in Silicon Valley right now?And then, like, you know, we would all kindof like listen in because although we're allcollege students, we haven't had the experienceto look at things in that perspective.And Connor was like the window to that world.Yeah, I think the experience of selling Flashcards+, my first company at a young age,and I think the biggest impact that it had for me wasthat it allowed me to not have to worry or optimizearound making money, but allowed me the freedom, I guess,to really just like focus on thinking about what Iwanted to pursue from a passion perspective.And that's ultimately what led me to take a yearoff of doing anything and just pursue A.I. researchwith my venture co-founder.16 year old young man is racking up college degrees fasterthan most kids his age can collect video games.Andrew Shue isn't even old enough to vote yet,but he already has three degreesfrom the University of Washington and he's workingon a doctor from Stanford right now.I had actually a very unusual educational path.I was in public school for fourth grade.I was basically just racing through the curriculumand I finished everything early and then startedannoying other kids and causing problems in the class.My parents found out about this one day.They decided to homeschool me because I clearlywasn't a good fit for the public school system.It kind of unlocked very rapid growthand I ended up going through middle schooland high school curriculum extremely fast.When I was 12, I actually finished everything.So the next step was college.So I ended up actually goingto the University of Washington when I was 12.It was obviously a very unusual situation.I spent four years in college, studied biochemistryand neurobiology, and ended up going afterwardsto do a PhD in neuroscience at Stanford.I did three and a half years of my PhDand then decided to drop out to pursue startups.So I met my co founder actually through the fellowshipand actually as a roommate for many yearsbefore we even started a company together.We didn't really know what we wanted to do with it.We were just interested in AI. And the first step we realized was to actually take a yearand do an extremely deep dive into machine learning.We both were reading everything and it wasvery clear to us that machine learning wouldchange everything and actually spending a whole year learning,doing research, taking classes,really getting deep into machine learning.We built a ton of different algorithms to solve various problems,and one area that we became super,super excited about was actually speech recognition.We built an entire speech recognition systemthat could not only understandwhat people were saying, but understandthat the accents upon which they were speaking with.We did this and we created a state of the art result,and it kind of blew us away because wewere just using like random data on YouTube.It wasn't even that well labeled.And we created these kind of crazy, accurate results.Essentially, you can boil down the story of speechas a series of these hypotheses that we de-risked.Really, probably the first one that we focusedon was can we actually build a languagelearning experience that people will use at all?Can we collect enough data from that in orderto feed our algorithms and create a flywheel of datato to better modeling, to better product experience,to getting more data and getting that going?And that was reallywhat we raised our seed round off of.It was mostly just like a technical proof of concept.We knew nothing about language running when we started.We didn't know anything about how peoplewanted to learn languages.Essentially we just started trying to build conceptsand learn as much as possible,get them in front of as many users as possible,test it and inevitably wouldn't work well enough.We would learn a bunch and we would go backto the drawing board and we would dothat over and over and over again.So the first few years of speak were definitelya struggle to find product market fit.We were trying a lot of different productexperiences launching new things.It kind of felt like nothing was working.We launch worldwide in every market.It would have short conversationsthat you could speak with when you first open it up waskind of like a category selectorwhere you could choose what you wanted to speak about.You could choose anything,and then you could have a short conversation.There were a bunch of times where we released somethingand people said they liked it, but no one loved it.Everyone would churn within the first 30 daysand they wouldn't use it long enough.And this was very, very exhausting processand it was very hard to stay motivated.And this is kind of like the the period, I think,where you need to be the most resilientas before you actually have something people love.But we stayed super obsessed on that.There were probably two or three timeswhere we felt like we might have had something.People kind of were using it.We talked to investors and they were like,You just scale this.And we had to kind of push against that mentalityand just say, No, this isn't good enough.This is not an experience that we canbuild on top of to build a category defining product.We were trying to launch globally.And we realized very quickly that if we wantedto build something people love,we would need to pick a single market and start there.We actually just flew to a bunch of different countries.We flew to Korea, we flew to Japan, we looked at Europe,we just talked to a bunch of users in those markets.I was born and raised in Koreauntil I was in fifth grade.Even after Connor left, we kept in touch.During my second year working in New York,he called me out of the blue.He asked me, S.J., I have a crazy idea.I have to go to all these countriesto do user testing for Korea.I would like you to come with me for a week.I would like you to be the translatorslash like planner for this trip.At the time, I had a lot of vacation days left and I was like,Wow, this seems like a free trip to Korea.So I said, yes.And then I immediately kind of got involvedin helping him recruit user testers.So everybody was in a small room.We were sitting just all around the central tableso the user would come in and then we wouldgive them a test phone that they would use.And then we had this sort of awkward setupwith like a phone on the side on a tripod where we would tryto record the whole session so that wecould see the screen and how they were using it.And the thing is that a lot of our usersin Korea are able to understand me speakingEnglish to them at least 50, 60, 70%.They just have a lot more difficulty speaking back.In Korea, people had so many opinionsand they had tried so many options.Even just driving around Seoul,you would see these giant skyscrapersthat were dedicated to English classrooms.One statistic that is pretty crazy isthat at one point in time, South Korea wasspending 1% of their GDP on learning English.The amount of money that the average Korean personspends is probably 2 to 3 times more than mostother comparable markets and was a super dynamic market.And if you could make something work in South Korea,then you could make it work anywhere.In the early days of a product,the answers are very clear.What you just always told us was, I want to speak more.There isn't enough speaking. We develop this product.We've got to really optimizeit for the speaking experience.We got it out to the AppStoreand I think like three people paid.I think we made $18 the first dayand we celebrated January of 2018.Yeah.I think there were many reasons why people don't like a productwhen you're building a consumer product way morethan like a B2B product, consumers are super finicky.It's not so much that you're building like a bad experience,but it's more the fact that peoplehave a very limited attention span and they haveso many options of how to spend their free time.And so you're competing against Instagramand YouTube or taking a walk and going to the gym.There's all these other thingsthat people could be doing, building an experience.It isn't about building a good enough experience,more so than it's building an experiencethat is sufficiently good to outcompete all the other choicesthat someone has in their life at that time.And that's really hard to do.And I think that's why you see mostconsumer companies not really go anywhere.When we realized that a big reason why peopleweren't using our product was because our productrequired people to speak into their phone,and the time of day that people wantedto use our product was on a subway or on a bus commuting.People in Korea spend a lot of time on busesand trains every single day, and that's actually oneof the key times of day where you can build a habit.That's when people use their phones.The most counterintuitive realization we had wasthat by building a way to use our appin that context would actually allow them to build a habitso that they would continue to use the productin other circumstances where they could then actuallyuse it the way we intend it, which was speaking.And that was something that we didn't reallyhave any insight into until we went to Korea.And we kept asking people, why?Why aren't you using the product?What happened? And literally just observing people in Korea.As soon as we did that, we saw usage spike.Incredibly, conversion rate went up,retention rate went up, every metric went up significantly.I think about product market fitas kind of a thing that is not just like a single point,but it's more of a spectrum.The more you improve the product market fit,the faster you typically grow.Our first moment of product market fit is where wefirst started to see people really use, speak and retain,and that was when we started to really start growing.That was a few years ago and I think since thenwe've essentially continued to ship tons of content,tons of new product features as quickly as possible.When speak first achieved some level of product market fit,I wouldn't say it was complete product market fit.I don't think we even have that today.And honestly, that's kind of somethingthat you're always trying to improve.But I think we did feel like, Hey,we have something real now that people are paying for that we can actually charge more for.It felt awesome.It felt really great that we finally landed on somesort of formula that was working in the market.And I think at the same time it was alsovery motivational that, hey, now that we have like the very beginning of something that is working now,let's work really hard and make it even better.I think there are three main componentsto making a really valuable service.First of all, there's the machine learning capabilitiesthat are super hard to do,but they power the entire experience and we're constantly trainingand building new models to build new features.For the first several years of speakers lifetime,we're not able to put a lot of bandwidthand energy into doing machine learning.We were more focused on finding product market fit,building the app and tryingnew things to get that product market fit.When we first started speak, we had no data.There's this classic chicken and egg problem.To make a model, you need data, but to get data,either it costs a ton of money and you doit all manually or you are able to create a productwhere you can collect that sort of data.But that only works if your model is good enough.So the thing that actually allowed us to solvethis was the fact that off the shelf speech recognition in 2015,in some certain cases was just good enoughto produce an acceptable product experience.So that allowed us to actually launch a firstversion of Speak without training and custom model.That worked well enough that moreand more people started using it.And then as they spoke into the app, we could usethat training data to fine tune the machine learning model,the speech recognition model,and improve its performance and get that whole cycle started.It's really only been in the past year.We now have a machine learning team internallythat is working on all sorts of really exciting thingsthat will start to power featuresin the product over the next year or so,including this computational feature we're thinking about.Fundamentally, how do cutting edge a MLmodels unlock capabilities on the product side?What are our magical new experiences?Foreign language learning.The second is continuing to ship as many new product features as possible, as quickly as possible,to build the most appealing product, the most useful product.And the third part that is super,super important is building content that people love.Obviously, I think we're probably more of a technologycompany than a lot of the English players out there,so it's obvious that we probably have a better product.But I think the content is the other part where it'svery common for English companies to invest one timein building a single content library and then from therejust spend all their time and money on marketing.But we believe that we can constantly improvethe quality of the content and make it better.And we take a very,very product oriented mindset to our content.So we A, B test it and we'reconstantly iterating it and fixing it.I think the second major component though,is the marketing.Obviously, you need to have a great productin order to be able to market effectively.It's really hard to escape the noise of all the marketingthat's happening in Korea and get peopleto become aware of speak and actually try it,because we have a local marketing team in Korea that's really,I think, truly world class that have been supercreative and original and trying lots of different options.They've been able to create a unique brand voice for usthat resonates with a lot of people in Korea.Early marketing wise.We tried a few different copies and a few different formsof medium to attract users to download the app.The thing that we really liked initially before knowingmuch about what would happen was like the AI angle.You know, we were like, Oh, the AI tutor.It's like learn, speak using AI.But we quickly realized that the audience hada very different concept of what an AI is.They were expecting something different.So basically for us we were using AI technologyfor speech recognition purposes,but people were thinking of like kind of like robotsand like freeform conversations and other things around it.So because like the expectation didn't like meetwhat people were like while we were providing,I don't think that worked very well.What worked really well the first timearound was the idea that we'd speak.You get to speak,really focusing on the idea of like speaking.It's like, you know, learn English through speaking.The fact that, oh,we'll make you speak more in the first 20 minutes.Then you've probably spoken English your entire life.So, you know, if you go through like our firstfew exercises, it's like within 20 minuteswe would have our usersspeak anywhere between 80 to 120 sentences.We were like, Okay, let's put some metrics around this.So, you know, the copy being speak 100 sentenceswithin your first 20 minutes of experience.And then I think that really caught up with people.One metric that I really like to look at and we'revery proud of is the factthat over 50% of our subscribers are active on the 30th dayafter they start a subscription.It's actually a little crazy that we've beenso focused on Korea up until now,but I think it was necessary to stay super focusedon one market and its key to our success.But now at this point, we feel like we're finallyready to truly kind of expand to other places.We feel like we've proven the modeland now we need to expand.So we're going to be launching in Japanin a few months and we're also goingto be launching in the US in a few months.We are taking the exact same approach that we took with Korea,where prioritizing buildinga local team immediately that can kind of figureout how to customize the product for Japan.I'm personally going to be going thereand talking with users.We're already testing with a bunch of users there.We can apply a lot of the lessons of how to launchin a new market that we learned originallyin Korea to Japan and every market from there.Our ultimate mission isto solve the problem of language learning.We view language learning right now as an unsolved problemif you want to learn how to speak English.It's kind of impossible unless you moveto the US for like 20 years.Language studying, especially when it comes to speaking,has been a privilege for peoplewho can afford to pay for another person's time, you know,to like, you know, book a time for with another person,who can afford to pay for another person's time, you know,to like, you know, book a time for with another person,like a native speaker,to have that speaking experience, to have that conversation.So it was like very limiting.The ability to speak English just openedso many doors for so many people.For example, if you're like a kick ass developer in Brazil,there's a big difference as to whether youcan speak English to a professional capacity or not.You could be working locally or if you can speak English,you can virtually work at any company in the world.And it really opens the doors for you.For us here at SPEAK, we really want to equalizethe playing field, the language learning,especially when it came to speaking what has always been privilegefor the people who have the money to afford it.But we want to make it much more accessible for peoplearound the world, whether it's English or not,and just open doors of opportunities for many, many people.We think that there is a future that we are buildingwhere anybody that wants to learn English will beable to use software that is powered by various typesof speech recognition and voice models, language models,natural language models that will allow you to speak out loudand get feedback on your speaking in a waythat is actually better than if you hired a human tutor.At SPEAK, We ask the question What happenswhen you can get instead of ten or 20 million peopleactively studying the language, but 100 million,200 million, 300 million of those people.And so we think the market is actually much,much larger than even what we currently see today.Long term mission at Speak is and how it hasalways been trying to help the maximum numberof people achieve their language learning goals,because we believe that the more peoplethat speak common languages, the better the world is.So our goal is to become like default waythat people learn languages,learning any language anywhere in the world from total beginner,even to you've moved to a country.Maybe you have a small foreign accentthat you want to get rid of and we're listeningto it and diagnosing that and coaching you.I think the ultimate vision isbeyond just language learning.The technology that we're buildinghere can be applied to almost anything.The machine learning challenges that we're solving hereto build a virtual tutor can be appliednot only to different subjects,but also to any other use case that you can imaginewhere people are trying to communicate with machines.Fundamentally, that is the problemthat we're building and solving.