How to Build $1.5B AI Startup in Just 3 Years | fal's co-founders

1. 0:00 – 2:18

   From Zero to $1.5B: fal's Milestones

   1. BGBurkay Gur0:00

      Language models at the time was like all the hype, crazy optimism about language models. We felt similarly about image models. Yes, this is a niche place. Where could this be going?

   2. GYGorkem Yurtseven0:10

      Finding a niche market that is fast-growing is the key to startup success. After these big models were released, is that the number of users 10X, 100X, maybe over a million X. We, we saw this, this change early on. We have to build systems that are ready for this change.

   3. BGBurkay Gur0:29

      We're fast at everything we do. We put the models, usually we have day zero releases. Space is moving so fast, we have to be like very ahead of getting these models in front of people, making it easy to, uh, use. There's a lot of startups out there, you know, they will be stuck on an idea for like months and years, right, with no traction. You have to really take that to the extreme. Like, I don't think people stress that enough. Think of moving fast, take that and multiply with like 100, and move that fast.

   4. GYGorkem Yurtseven0:56

      If we are wrong, we can always revisit our decision. Focusing on image and video is gonna be an important differentiator.

   5. BGBurkay Gur1:05

      We just raised our Series C round $125M, which values us at a $1.5B valuation. We're very prepared. You know, we're prepared to scale. ChatGPT moment for video is that I don't think we've hit it yet. Right now we, we employed, you know, language models at massive scale. We're gonna have to do that for AI video, AI image, AI audio, even AI games. And we wanna be the place where like all the builders that are building with this technology, we want them to do that through fal. So my name is Burkay Gur, and I'm co-founder and CEO at fal. fal is a generative media platform for developers. We host models that can generate images, videos, 3D audio. Typically, these models are very hard to host, so the problem we solve is like hosting these models as APIs, which makes it very easy to consume for developers. We also have a inference engine that we built in-house that is specifically optimized to run diffusion models to run two, three times better. I think latency kills creativity. Latency kills productivity. We work with customers like Adobe, Canva, Shopify, Perplexity. We are at a $90M annual run rate revenue.

2. 2:18 – 5:14

   How Two Immigrants Without Connections Built a $1.5B Startup

   1. BGBurkay Gur2:20

      My co-founder and I have been long-term friends. We've actually-- We're both from Turkey. I grew up in Turkey. I moved to the States for college. There was definitely like culture shock. I think like even a decade makes a big difference here, right? Like I moved to the States 2007. I think Facebook had just come out. It definitely felt like I wasn't as tapped in to like the culture, right? So there's like a big gap between how I, how I grew up in Turkey and like what people like to do there versus like how they're in the US.

   2. GYGorkem Yurtseven2:48

      I would say like schoolwork felt a little bit easier than, than I thought because we have a pretty good education system in high school in Turkey, especially with maths and sciences. The, the, the biggest challenge actually was like the understanding the job market, how people do internships, because immediately people start school and prepare for their summer internship and then the next summer and like they have a whole plan on how their career is gonna happen. I didn't know I should be doing that, so couple years I wasn't really planning my internships towards my career, so that was a big, big shock.

   3. BGBurkay Gur3:26

      I actually did an internship at Oracle like during college. I was working on some like fairly boring things in the beginning, to be honest, and I had started my green card process. This is like a very typical thing for immigrants in the US. You can kind of like be stuck in jobs if you start your green card process. Around 2015 was a very interesting time. Deep learning was just like kind of starting to become popular. I started getting really into it. Around that time, I had a few other friends at Coinbase, and Coinbase was a very small company back then. It's like maybe forty, fifty people. One of my friends told me like, "Hey, we're building a machine learning team." And I was like, "Okay, this sounds very interesting." Like, I can go like do some deep learning in this new company and there's a lot of things, things I can learn there. But I was mainly excited about like starting my own thing. I had actually talked to a lot of my founder friends, seeing their experience. I had a lot of encouragement from friends to actually go and start my own thing.

   4. GYGorkem Yurtseven4:20

      In the beginning of COVID, Burkay and I rent a house in Palm Springs for a while. We were talking about potentially starting a company, but we didn't have particular angle or idea to go after. So we knew that we wanna do this, we would have to go through a period of exploration where we find something that we are both passionate about. Burkay quit maybe four months before me, and then I joined him. It is liberating, uh, because all my life I also had to deal with immigration, a work visa, and then green card. That's one of the reasons actually I stayed at working at a big company. I wouldn't say that's the only reason, but that's definitely a factor. And at that time, all my immigration process had ended as well. That was also liberating in the sense that I didn't have to work for a big tech company to stay in the country. I could do whatever I want, and I took the opportunity then

3. 5:14 – 8:32

   Why We Bet on Gen AI Video Instead

   1. BGBurkay Gur5:17

      Starting with the post ChatGPT era, it was brand new to everybody. It was such a new environment that, like, nobody knew where things are going. We started running image workloads, and we saw tremendous growth in the companies that are working with us. That made us really excited about the space. We also sat down and thought, like, where could this be going? Two and a half years ago, people saw LLMs and, and ChatGPT and, and they sort of, like, drew out where could this technology go and, you know, they immediately said, "Okay, you know, we're going to AGI." We felt similarly about image models. We thought, like, as the models get better, there's gonna be more capabilities. Quality is gonna increase, and the resolutions are gonna increase, and the controllability is gonna increase.

   2. GYGorkem Yurtseven5:58

      I think finding a niche market that is fast-growing is the key to startup success. There are a lot of niche markets that stay niche and never grow, but we were lucky that market we operated in was very niche and small, but also was growing incredibly fast. What changed after these big models were released is that you didn't have to train it anymore. You can just pick it off the shelf and start building something useful, and that meant the number of users maybe 10X'd, 100X'd, maybe over a million X. We saw this, this change early on, and we decided, "Okay, this changes everything. Now that these models are gonna be used by millions of people, we have to build systems that are ready for this change," and that's why we decided to build an inference platform. Early on, another decision we had to make, when the revenue was constant for a couple of months, one tempting thing we could have done, run inference for LLM models as well. Focusing on image and video is gonna be an important differentiator. We already have a technical advantage because we've been working on, on this type of models for a while. If we are wrong, we can always revisit our decision, but it's gonna be harder for us to go from general to specific, so we tried to stay specific. I, I think if you focus on a specific market, you get to work with your users in a closer manner. You understand their problems better. For us, this was image models and fine-tuning image models in the beginning. All of our customers were doing very, very similar things, so we were able to focus on it, get really good at it, and differentiate ourselves from others.

   3. BGBurkay Gur7:36

      So our ultimate vision is basically we want to be the infrastructure layer for this new technology. ChatGPT moment for video is that I don't think we've hit it yet. I think there's a lot of signs, like we're getting very close to it. Like, if you've seen Veo 3, it's close to the ChatGPT moment. It's a very capable model, but I think, I think we're still not there yet. But interestingly, like, you know, now if you go to your Instagram, TikTok feed, like third or half the m- the videos are AI-generated, right? It is already happening in a way. It's just happening in, like, a little bit of a slow motion. There may be a point this year is that we see, like, even better models that can actually, like, be edited, uh, real time, and you can interact with the characters that are in the video and, and, you know, generate very, very interesting content. And we wanna be the place where, like, all of this infrastructure is being hosted and all the builders that are building with this technology, we want them to do that through Fal. [gentle music]

4. 8:32 – 10:37

   Kill Your AI Product If It Doesn't Sell on Day1

   1. GYGorkem Yurtseven8:34

      I think there are two things happening with AI. People are willing to pay, but there are questions about the quality of that revenue or how durable that revenue is going to be. I think AI markets are, are incredible markets. Generative media is, is one of those things where that it can be monetized right away. In the previous versions of internet businesses, people waited years and years to monetize their, their business. First built a user base, and then maybe tried to monetize with subscription or ads. But with AI, people are willing to pay for it right away. The MVP you are building should be good enough for people to start paying, and it is really easy to get signs the revenue numbers are increasing or not. Now, monetization should be something a priority from day zero, and it's actually easier for the founder to see if this is a good idea or if this is a good product by the revenue they are making from, from the first day.

   2. BGBurkay Gur9:34

      Um, we are very particular about what models we want to put because there's a lot of models out there, there's a lot of projects out there, research projects, even things that, like, big funded companies that put out there that are cherry-picked. Basically, like, you take the results and you look at the good ones, and you just use those for your demo or, like, for your launch. So that's called cherry-picking. So there's a lot of cherry-picking happening in, in models. When you- when we look at the model, first thing we do is we take the model, we run the model, and we run bunch of queries to understand, like, is it actually doing the thing that is advertised? And then we, we will go and optimize it and make sure, like, it can run faster and faster, especially if there's a lot of demand. Developers, like, they spend so much time optimizing their, like, iterative loop, right? Like, making sure that, like, once they do something, they can see the result, they can see the tests, and go iterate. So no one wants to, like, sit and wait around five minutes for a video to generate. In the future, this is gonna be seconds. It's gonna be real time, and we're, we're preparing ourselves from infrastructure standpoint for that future.

5. 10:37 – 12:40

   Stay Small to Grow Big

   1. BGBurkay Gur10:40

      Scaling the company has been one of the, like, most exciting things about this job, to be honest. We were, like, very small team, like six people for the first two years, uh, almost. I think small teams is very important. Before product market fit, you actually do wanna have, like, the smallest team that you can, right? And, and, and experiment and, like, have a small group making decisions and move really, really fast. I think this, like, alignment with the company's mission is very important. This is something people talk about. This is another thing, like, I really learned from Coinbase. Like, Coinbase early days, like, everyone was a crypto-head. You would not find anybody that is not, you know, just insanely excited about crypto, and, and that created the foundation for the company that, that is just so, it's just so specific and so, like, you know, just by default, people are just excited about what they're working on. You know, but one of my criteria was that, like, I had to love, I had to love it. You know, that was, like, super important to me. Like, the intersection of creativity and AI, I mean, there's, like, unlimited fun there, at least for me. I wake up every day, I'm very excited about, like, the next models that are released, what this tech- where this technology is going, like, what amazing things are people building. Uh, it, it is, it is literally, like, the most fun thing I could be, I could be doing. If I wasn't doing this, I would probably go play with these models myself. I, I love this technology, and, and that's the thing that's, like, that, that, you know, gives me a lot of drive. [gentle music]

Episode duration: 12:40