How I built $2B AI Translation Startup | DeepL, Jarek Kutylowski

Intro

00:00We've been the first company to the market with an AI based neural network translation solution, which just blew away everybody else. And we gathered the early adopters that they started spreading the word of mouth and it was all about speed.

00:14If we have come up like half a year later, a year later, I don't know if that would have worked so well. You have to move fast. You have to figure out what is the next challenge, approach it, solve it, and then move on. Hi, I'm Jared Kotlowski, founder and CEO of DeepL, a company that builds AI that helps breaking down language barriers by making translations available to everyone.

00:34Deepl has grown out of this huge free service that everybody out there in the world can use, and this service is being used by hundreds of millions of people every month. And out of that, we've built a base of over 100,000 companies that are working people on top of that.

00:49A few months ago, we just completed a fundraise which was valued at $2 billion.

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Chapter 1. An immigrant who couldn't speak German

01:51I grew up in Poland, which was at this point in time kind of a country in a switch between the communist system towards a post-communist one. I got access to technology a little bit later than I think people in the rest of the world, and I was just amazed by how much you can achieve with tech, especially with software.

02:09I moved to Germany with my family, have been thrown into school very, very quickly. I didn't speak basically a word in German, which made me struggle quite a bit. I still remember the first day in in class when I walked in and I couldn't even really spell my name.

02:27I had to learn how to survive in an environment maybe a little bit more complicated. I was lucky to learn German very, very quickly, so that made me feel welcome and belonging in that community quickly. I learned language and communication is super important in this world.

02:43If you want to belong to a community, you need to be able to understand each other. Basically, from the very early days when I was fascinated by technology, this was really the path also that I have chosen in school and later in in studies, I majored in computer science.

02:59I went on to do a PhD in like really theoretical computer science. I really enjoyed a very solid theoretical foundation. I think doing a PhD really also builds up a lot of in that process, because usually you're really thrown into a field of research that is not yet discovered.

03:18You have to really uncover something. You have to go through that process pretty much on your own, at least for myself, but also for all of the peers that I've seen built up a lot of resilience for for the future of their of their life. After my PhD, after my really academic years, I spent a little bit of time working for a larger corporation that didn't really fully suit me.

03:40And then at some point in time, I really, I really realized I want to build something, I want to be part of something bigger and like really starting a company, funding a company, kind of embarking on DeepL journey. That was something that that really fascinated me.

Chapter 2. Bridging Language Barriers with AI

03:56I think in 2016 to 2017. There was this great moment when it has become pretty clear, at least in the in the academic environment, there is a lot that can be done with neural networks in AI. So that was an excellent point in time in which we started at the beginning, really to play around with the technology and see what we can do with that, how that can be applied for some problems that we might be thinking of.

04:21And I had this background in language. I've been living in two countries. I knew what it means to speak different languages. I knew how big of a problem that is from a European perspective. Each and every time you want to travel to another country, but most importantly do business with another country, there is going to be a language barrier.

04:38If we look at Germany, the companies in the country are selling to French customers, they're selling to Italian customers, they're going to be selling to Polish customers. And you can try doing that just by speaking English all of the time.

04:53But at the end, every person wants to be addressed in some ways in their local language, they will understand much better what you're offering them. I think for companies, it's really hard to establish those new markets. What you have to do is you have to hire people really in the specific region, or you have to find people who are qualified to speak in a particular language in your country.

05:15And that can mean even like doubling your sales headcount, or that may mean like adding a lot of customer service jobs, for example, for within within your company translation language industry is being told to be like 60 billion. If that is more efficient, if that is more productive, they're going to build better products and be more successful in that market.

05:35I think the very early days were pretty specific for DPL because, like, we knew that this problem of translation that this is that this is a big one. I think what we didn't know is whether the technology that we're going to be able to build is going to be enough to solve those problems and be better than our competition.

05:52I think that was the unknown, but it was pretty clear that there's this big problem that can be solved. I think what we didn't know really particularly well also was how to embed maybe that technological solution into real life applications, how we can go fully to the market.

06:07And for that, we've just kind of tried to go the path of least resistance. We built the technology, we put out a free service that was that was super basic, that just gave a very bare bones access to the to the technology itself. But at the at the same time was also simple to start using as few barriers as possible, like no login, nothing like that, just go there and start using the product.

06:32And for us, that was a great way to validate whether this technology and this early product idea actually make sense. And if there's market opportunity for that. And out of that, we've seen like a very clear signals that this is actually what people want.

06:46And this is actually what, what users what users need through just purely looking at the usage numbers. That was super simple. I think the next step and the challenge there was to look whether this is something that people are going to also be willing to pay for, whether there is a monetization pattern for that.

07:03And this is something that we then started doing in 2018, introducing new functionality into the product, potentially putting those behind paywalls and seeing whether we can convert customers. We can convert free users into into being customers.

07:19A lot of that at the very beginning was really based on a gut feeling. And I think at the very beginning you have to have those hypotheses which come out of the founding team. But I think specifically when you're working in such a slightly more consumer ish market at the beginning, you have to rely a lot on quantitative data rather than on qualitative findings.

07:38The more customer focused that came slightly later when we started shifting the product towards a B2B and enterprise persona. As a buyer, I think the biggest challenge as a first time CEO is really making sure that you're making your decisions and that you're kind of pushing the company at the speed that you could because you don't know what the next step potentially is.

08:00You have to rely on a lot of advice on how the company is going to look in the future. You have to find out things on your own, like you kind of understand the point which you are in and extrapolate to the next point. Doing that is incredibly slow.

08:12Maybe sometimes. If I were now to found another company and do this the second time, I think I could be just much, much faster in that. Other than that, I do not think that we've as a company made too many like big really mistakes. I think speeding it all up would just make such a big difference I guess.

Chapter 3. Speed Matters

08:37If you're in a startup, and especially if you're in a competitive field like ours, like with all of the big tech also having their solutions, you always have to grow and you have to think about growing fast. That is essential for a company for for like even the company's motivational health in a way like it always needs to very fast grow all of the time.

08:57And that made us obviously also scale the business in terms of employees, in terms of the number of customers that we have, in terms of the amount of products that we are offering. And all of that was really tailored to that. In 2018, the business was operating profitably already.

09:12That is just a function of how cost effective a PLG growth motion is, where you don't have to hire a lot of salespeople. Your customers pretty much come to yourself because they're convinced of the product, but at the same time being financially responsible.

09:26We've been lucky in that way as a company, really, and I think the biggest lesson is, once again, it's all about the speed. We've been the first company to the market with, with like an AI based neural network translation solution, which just blew away everybody else and, and made sure that we that we gathered our first user base that that we gathered the early adopters, that they started spreading the word of mouth in the world.

09:51And that was all about speed. If we have come up like half a year later, a year later, I don't know if that would have worked so well. So the biggest lesson is really it's all about speed. You have to move fast. You have to figure out what is the next challenge, approach it, solve it, and then move on.

10:07If you decided to grow your company very fast, you need to be aware of the change that is happening there. You have to make sure that you understand what is happening, and that you help all of the people in the company be on the change journey.

10:20I think the biggest problem with moving fast in a high growth company is really the fact that everybody in that company has to go through a lot of change, because nothing is going to be the same this year as it was the last year. Change is hard for us.

10:34This is something our brains, they do not really like going through, but also trying to make sure that everybody knows why we have to go through these change processes, why is it important for the company and everybody is on board with that?

10:48And that is incredibly important if you if you're building an organization. But also if you want to go through the change on your own. And and I think context and understanding of the why helps in addition to that like really a lot.

Chapter 4. Choosing the Right AI: General vs. Specialized

11:05So there's pretty much two types of AI models that are on the markets right now. The very general generative AI models that can do pretty much anything, and the specialized models which really focus on creating one particular solution at the best quality possible.

11:20I think the very big generalized models, or those models that you can actually use for pretty much anything. They have surprised us with their ability to do a wide variety of of tasks really I think what needs to be understood that some of those models actually do not perform that well on particular instances, on particular use cases of problems that we might have, especially in business.

11:42In the case of translation, for for stable quality and accuracy, that is not only on one email, on two emails, but like really across the board on a wide range of inputs, and I think this is where specialized models can really shine. They are usually quality tested for a very particular reason, for a very particular use case, and can deliver that quality very, very consistently, while at the same time being potentially also more cost effective and quicker early to run, which which matters very much in some of the use cases.

12:13So so I think it's, it's for, for businesses. It's always good to take a look at what business problem are we trying to solve here. What are the general solutions for that versus what are the specialized solutions and what kinds of advantages they bring.

12:28And especially I think the world hasn't changed too much in a way that technology itself doesn't yet fully solve the problem. You also have to have the product and the integration and the user experience and the UI solve for having that problem really meaningfully impact your workforce's productivity, efficiency.

12:47That case, those specialized solutions will usually come with a full suite that helps solve that holistically. I think if you're thinking about bringing AI into your business, you really have to start with the basics like what kind of problem do you want to solve?

13:02Like what is maybe at the core of the performance of your company? What is important for your company and what is potentially working slower where you have a problem? And starting out with that business problem, you can try to to kind of find out what are the potential AI solutions for that.

13:18I'm not an advocate of trying to to find applications for AI just as a technology. I'm an advocate of starting with the problem and then looking for the AI solution that can potentially solve that problem in a very good way, because through this, you will be optimizing what is really worth optimizing in your business rather than trying to apply AI to pretty much everything.