00:00:02 · Andrew Reed
[00:00:02] S0: So I love, line charts and bar graphs as much as the next guy, probably more. The story of ElevenLabs is also interesting from a human perspective, because you started a company with a childhood friend. So maybe take us back to 2022 or earlier and just tell the human side of the ElevenLabs story to start.
00:00:22 · Mati Staniszewski
[00:00:22] S1: I most have luck in the story of ElevenLabs because well, it started in 2022. It felt... Feels like it started seventeen years ago when I met my my cofounder, Piotr. All the names in Polish are complicated, luckily, for for for us. But we we met in high school, became best friends, took all the same classes together, and then through the years, did everything together. So we traveled together, studied together, worked together, and time is on our side. We are still best friends. It's working it's working out. And and part of what started ElevenLabs is is is inspiration from where we are both from. We are both from Poland, suburbs of Warsaw. And there's a very peculiar thing in Poland. If you if you watch any foreign movie in Polish language, all the voices, whether that's a male voice or a female voice, get narrated with one single character. So as you can imagine, pretty terrible experience. You have literally one voice narrating everything. It usually also on purpose is kept in monotone, so you are meant to interpret your own emotions for that content. And while we grew up with this, this is still happening today for majority of content. And that kind of opened our eyes into one of the clear things across the domain, across audio domain, across the future will be this ability for everybody to speak any language with the same emotion, the same intonation. And we started diving deeper into that problem and realized the problem of audio exists in so many other domains too, whether that's narrating the content around us, whether that's the books not being available in audio form, whether that's the news articles that we could read, whether that's that language barrier, in the future, as we heard in the previous conversations, the future where humanoids, the robots are around us, the voice will be the primary interface to a lot of that technology and something we would love to fix and solve.
00:02:09 · Andrew Reed
[00:02:09] S0: Excellent. And ElevenLabs builds frontier models for audio. I think there's a paradigm now where to build a frontier model, you have to start with hundreds of millions or billions of dollars and then figure out the rest later. ElevenLabs did not take that path. Can you talk a little bit about your approach towards building this company, why this hasn't been replicated, is that even possible in 2026, etcetera?
00:02:36 · Mati Staniszewski
[00:02:36] S1: Yeah. That goes... I think that continues that great lack in timing because we started in 2022. For those of you working in the domain at the time, that that was the year of crypto and metaverse. Nobody was still working on the AI side. Even further, people were starting to work, of course, on the text models, on the visual models, but audio as a domain was still considered a big niche. There's so few researchers in the space working on on that work. So for us, that was a good part of picking that domain where, a, we were excited about where that future is called. We felt the people around just didn't realize the value of that domain. But three, the requirements of what you needed to solve were very different. The audio models were smaller, so you don't need as much compute as you need for some of the other sister domains. The data needs are big, but while there's a lot of audio data, we knew that the thing to actually get that audio working, you will need to figure out how to transcribe a lot of that data and annotate all of that data, which we knew we can do. And then ultimately, it all boiled down to architectural side of can we can we solve that part in a good way. And here, my co founder is one of the smartest people I know and a great researcher and has been able to assemble some of the best people in audio to help us. And we took a slightly untraditional approach at the time. We started in London. We had a lot of people between London and Warsaw and started a company in remote... Completely remote way. So we wanted to hire the best researchers wherever they were. We were going for the classic GitHub scraping and and and trying to reach people based on their work instead of based on their presence. And based on that work, we would reach out to those people. We would always share our samples and try to get them to join the team. And that's how we assembled the first the first set of of of people who we think are some of the best researchers in that audio domain. And through the years, they still help us crank a lot of those models in... Into production. Then we launched the product. I think the slightly different approach we took was monetizing very quickly. So trying to get some of the revenue stream back so we can fund a lot of the work in the models.
00:04:47 · Mati Staniszewski, Andrew Reed
We try to healthy on the margins so we can continue investing with the assumption that it's better for us to figure out that stream and be able to be independent in that development. But then as the ambitions grew, we knew that we needed to train models. So we, of course, brought a lot of money externally as well. And I think, like, projecting to today, one thing that's clear for us is there's still so many of those niches that people don't tackle that you can start with and then step by step start opening them up. [00:05:17] S0: I think a lot of customers see ElevenLabs through their narrow needs, right? Maybe take a zoomed out view, like what is the suite of models that ElevenLabs works on? How do you prioritize them? How do you organize R and D, etcetera?
00:05:32 · Mati Staniszewski
[00:05:32] S1: So we started we started with the first text to speech model, so the model that could finally understand the context of what's being written. And based on that context understanding, get the right emotion, the right intonation from text. So it was a happy sentence, you get that happiness out. If it's a it's a dialogue, it can pronounce the dialogue out. And then continuously started adding that. So it started with the problem of of breaking down language barriers. The things you need to solve dubbing is transcription, so understanding, then the the translation, and then text to speech. So you first solve text to speech. Then we knew we needed to add a data component, is speech to text and being able to transcribe content in a in a great way. Then how we combine those models together. So that's kind of what the first three models in the first couple of years. And then, of course, the other things started happening across the space, which is that a lot of the reasoning models started becoming quick enough and smart enough at the same time where you could imagine those interactive experiences being possible. And that's where we started launching our more of the real time streaming models across audio and then combining those into conversational experiences. So added effectively all the stack, all the turn taking and orchestration to create a voice engine for a voice for a voice agent. And then on the other side, as we realized that the emotionality is something we can solve, we added some of the hardest modality in in in audio, which is music and being able to produce music. So today, we span entirety of the research of audio, whether it's text to speech, speech to text, combining those models together in both localization with dubbing, with orchestration with voice agent... Voice engine, and then being able to do that across music as well.