We ran into each other at Michael Dell's event a few months ago and you're like, 'Oh, I'm very curious how building a company today is different from building one in the past.' And Michael has 40 plus years of experience. He's been dominating for decades. And when I saw him, we had like a 30-minute conversation about this. You read all these biographies of history entrepreneurs. And you just see this over and over again. It's like, 'Oh, this time is different.' Turns out, 'No, this time is not different.' And Michael's like, 'No, no, I actually think this time is different.' So I want to start with like how you think about building your company AI native from today.
Yeah, Michael is a legend. He must have so many interesting perspectives, especially like he's still running the company. So it will be interesting whether he applies some of that difference to the current running of the show. But for us, you know, in some ways, given we are first-time founders, me and Piotr, my co-founder, my best friend of 15 years, ElevenLabs is the first venture we started.
That's insane. You started in 2022. You launched before the first version of ChatGPT, right?
We did. It was still a year when the topics of the day were crypto and metaverse. So 2022 was still a year where like everybody was obsessed about those two. So it was a perfect time because we could actually focus and build a lot on the AI side.
What were you doing before you founded this company?
I was at Palantir. So I was helping build optimization models and bring optimization models to the customers. So NHS during the COVID response on how you distribute vaccines across the UK, or working with oil and gas industry and figuring out how to optimize the energy work. So a lot of optimization models and then working with customers on like actually figuring out how you bring that into production. And my co-founder was at Google and he did a lot of the text models for knowledge graph before that, did research at university around image, visual models. So incredible brain, the smartest person I know for developing a lot of the research work. And I was happily on that intersection of building the product and bringing it out to the customers. So that was before Palantir was BlackRock, building risk models, bringing it out to customers, and by background study mathematics. So good intersection of the things I liked. And now at ElevenLabs that's also what I do, and that's also from the company lens when we started, that was the whole goal of like, can we combine research and product deployment under one hood. On the research side, built all the audio models, starting with model to produce speech, text-to-speech model, and that was that 2022, the first model that could finally cross that human-like quality. And then over time now it's entirety of audio models, transcription models, localization models, orchestration for voice conversations with agents. And then on the product side, can we unify that as one platform that helps people, businesses transform how they would communicate with their audience.
Wait, so the first idea you started with audio?
The actual trigger point comes from where you're from, from Poland. Very peculiar thing, if you watch a movie in Polish, all the voices, whether it's a male voice or whether it's a female voice, get narrated with one single character. So you have one voice narrating the whole movie, all the emotional intonation disappears. And it's still so we grew up with this, everybody in Poland grows up with it, it's like, you know, one person dubbing. And then in 2021 we retested or re-experienced this still happening in their content. And then second thing happened which was...
It was still happening in movies in 2021 that you'd watch in Poland.
Same thing that was happening when you were a kid.
And it's crazy because it's, you know, of course it's cheaper, easier to do, quicker timeline, but the quality is poor. Like as you can imagine, it's a pretty terrible experience. And that was like a trigger point. In the future, that experience will be completely different. You will have original voice, original emotions, original intonation, and actually be able to experience that in that incredible way, which is very timely because just a week ago we released finally a model that is able to do that extremely well and finally bring the content from one language to another. But maybe more broadly, that was also our eye-opening of how we interact with technology. How you can interact through technology will change, and that will apply the language barrier that will break, but also just the general conversations with devices, with digital world will happen differently across different modalities, across different channels. And we wanted to do...
This is what you thought back in 2022 or what you think today?
In 2022 we knew that you will need to unlock the stories, the content across the modalities and channels. We didn't yet know how quickly the kind of the shift from static to interactive will happen. We hoped this will happen, but we didn't know how quickly. So the first idea was...
Dub static content that I was watching. I'm watching a movie in Poland. I want this to actually feel like I'm watching in my native language.
That had to appear like a tiny business though, right? At the time, like if that's your initial idea, you didn't go into it thinking this is going to be a giant business. It's like you're one of the fastest growing startups today.
You know, we actually thought there was a huge business at the time too. So we thought it's like if we think about all the content, all the stories out there, how incredible if they were available in audio. And whether it's some of the biggest streaming companies, whether that's TV, whether that's the conversations, all of those could be actually delivered in the local language. So we thought it's actually huge and I still think it's huge. And then if you shift this to the conversation we're having now, could this be in the future this version where I speak Polish and you understand me in English, or I speak English and you understand me in any language that you want. In Hitchhiker's Guide to the Galaxy, there's this idea of a Babel fish that you put next to the ear and you can understand everything around you regardless of the language that you speak. So we knew that we will get there, but initially it was like dubbing. So I'll give you like a full way of how kind of it progressed. Initially it was dubbing, and then as we started diving into what do we need to do to solve dubbing, we realized there are three steps in the dubbing process. There is a transcription step, then it's a translation step to another language, and then you need to regenerate that in another language. But the research that existed at the time for each of those steps wasn't very good.
Which companies were doing the research?
There was some good NVIDIA models, open source models.
But wait, is that just independent research or is it not any of the big companies?
No, I mean everything was poor. Everything was robotic. Everything was pretty robotic. It was still, you know, it was still like you could immediately tell it was like a robotic voice. And nothing really crossed that uncanny valley yet. There was one good open source paper, an open source repo that I'm trying to recall that was pretty good but very unstable and still took so much time to generate, that was like the best. And NVIDIA had some good research on some of those components, especially on the speech-to-text side. Translation was okayish, I mean at the time DeepL was doing incredibly well. And then you had the kind of Google Translate version of that, but all of the components to do dubbing were not good enough. So that's part one, the research wasn't there. And part two, when we started testing the dubbing idea with a lot of initially creators, the message we are getting back from the creators is like, great, I would love to get dubbing one day, but today I have different problems. My problems are I want to post-produce and change the line that was recorded in the wrong way. Or I want before I record my video, I want to be able to narrate the script and see how it sounds. Or instead of me speaking over a video, could I just have AI speak over that? So before I even think about dubbing, can you give me that? And that for us was like, okay, before we can solve dubbing, let's solve the research component to generate speech and make it sound great. And on the product side, let's actually deliver for people to be able to just narrate content. So like let's pause and ignore for a second the language shift. Let's just help bring content alive with audio, with high quality.
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Let me make sure I'm understanding this correctly. You're like, we're going to dub. We're going to reach out to creators first. So, I'm, you know, reaching a couple million people in English. Let me see if I can get this guy to say, hey, can we dub this for Polish or Spanish and all these other things? And then the feedback you're getting is, yeah, that's kind of nice, but I have many more immediate concerns, and can you help me with XYZ?
Exactly. And it kind of was two things at the same time were great because that was also true on the research side where you have these like three steps you need to solve in dubbing. Nothing in the space is good. We need to solve one of those steps ourselves. That's where Piotr comes in and is able to create and assemble the best research team to solve it, and he himself is an incredible researcher to actually bring that to life.
Yeah. Cause I'm friends with MrBeast and I remember talking to him about this a few years ago where, you know, he was the largest creator in the world and he used to run separate YouTube channels and he told me he's just like, yeah, for my Japanese, like the Japanese versions of my videos, it's like he doesn't just dub them, he'll hire like a voice actor that's like the voice is famous in that country.
Yeah, one day you should, whether with us or any other company, you think you should dub your podcast. It's so much knowledge that could be brought into so many different entrepreneurs worldwide, like in Japan, if those conversations were...
It's funny you said Japan because I was thinking, I knew we were going to talk today and I've been thinking about you the last few days and I'm working on this episode for the founder of Honda for my other podcast Founders. And what's surprising about that is, you know, there it's not Honda the cars at this point when this guy's alive. He created the most successful mass-produced motor vehicle in history, which is the Honda Super Cub. And the way they describe their company, he's like, we're a research lab for engines. Literally, that's what he's like, 'All I do is think about engines all day.' He actually separated out and made the Honda R&D a separate company because he thought it was so important. And then they were just funded by a percentage of sales, but then they turned over all the research to the manufacturing division. And he's just like, if you just have a research division and you only tell them to experiment and you staff with engineers and researchers, he's like, they're going to constantly invent new things. And then if you're a manufacturer, you can figure out how to apply those to products. Like I would never think, you would think, oh, they're a car manufacturer. No, this is a research team.
To be honest, you know, and you started with this question. This is not too dissimilar from how I think about how we run ElevenLabs today.
I know, that's why I'm telling you this.
It's very much, look, there's a focused research lab, there's research engineering on bringing that to the product work, and then we have a lot of small teams running after specific product problems that we can solve, and then of course the wider go-to-market and deployment of how you bring that to the customers worldwide. But the general philosophy is like a lot of small teams, usually less than 10 people, having flexibility, autonomy to just run ahead and apply their best judgment in what we can solve for the customer.
Okay. You need to say more about this though because this is where I still, I'm a little confused by this. Somebody doesn't know who you are, they just met you for the first time. They don't know what ElevenLabs is. Like describe to that person how you view your own company. It's like a research lab. Is that the word you're going to use? Like how would you describe this?
I would introduce consistently the company as a combination of research and product deployment. Research is frontier audio models, text-to-speech, speech-to-text, orchestration. Product is one platform that helps companies transform how they communicate with the world around them. And that can be marketing with helping them tell a story like Ramp creating their Super Bowl ad. It can be support working with Deutsche Telekom on creating voice agents for their call center, or it can be sales helping on inbound sales qualification to make sure that that streams through, or even wider operations and working with the government of Poland to help create an agent that can take a healthcare appointment, follow up with a patient to remind them about that appointment, ask how they are feeling, and that kind of one combination between those is building the research, building the frontier of all voice, of all audio, and on the product doing the part of applying that audio, combining that with knowledge, combining that with the creative work to then allow companies, people to change how they communicate.
And you are solely focused on audio?
On the research side, solely focused on audio. Product, we combine the best of audio with integrations, knowledge, LLMs to help deliver for...
Explain why you think it's so important to be solely focused on audio on the research side.
We think we have incredible talent to be able to go after that. The focus is so important in effectively building the best architecture for those audio models. And this, like we think audio is a, there's a good combination of not only science but also the arts that you need to solve. It's a little bit subjective. The voices that you produce will be subjective. So you really need to get it right. But ultimately as you think about AI models, there's data, compute, architecture that you need on audio. We think a lot of the problems that still exist are on the architecture side. We want to be solely focused on solving those architecture problems. So you can actually get the best quality out there. A lot of the team, a lot of the people that work on our side are the best audio researchers in the world. They're also excited by the premise of being able to continue deploying the best frontier audio models.
And do you feel you have technology on the other side that no one else in the world has?
There's a weird analogy. I'm so glad I'm reading this book at the same time we're talking because like for Honda, they kept trying to get him to diversify. He's got other products. Obviously, he builds a bunch of things with engines. But he's like, 'Does that product have an engine?' And they're like, 'No.' He's like, 'Then I'm not building that product.' He's like, 'I just focus on engines and sometimes it's on two wheels and four wheels and everything else, but it's like just engines.' It's very similar to what you're saying about audio. And he also said that he felt he had the best engine technology in the world and no one else could replicate what they did.
There is a, you know, common question as we think like internally of like, it's 100% true of, well, how you described it now too. And like when we think about new product, the big question is, do we think we have a unique advantage by applying our audio models in that product experience? If the product experience doesn't have a big bottleneck in that audio and the voice communication side, then it's not our forte.
Explain a situation where you realized that you shouldn't go after that opportunity. Could you give an example of what you... So you're describing...
The example I would give, two slightly different spaces. One is for a long time, and I think it's still true, the ideal version in the audio space is if you create a marketing campaign, is how that combines with a lot of the image and video work to deliver better content. Two years ago, we would have first tried to see whether we can help people create effectively lip dubbing or avatars for their content. So let's say you switch from one language to another, you need to move the lips, or let's say you want to narrate something, you create an avatar. That was roughly two years ago. A lot of the models that existed on that side just weren't good enough. And deploying a product in the space would be such a defocus and such a shift for the company that it didn't have the audio as a superpower, it still had a bottleneck of the quality of avatars, quality of a lot of...
Because the problem to solve there is video, not audio.
Exactly. So like this was like still poor. So even if you had the best audio applied into that work, the experience still wouldn't be very good. So we decided to effectively pause any of that effort. For today, it's of course shifting now. I think there's a good set of open source models now that exist in that space where the combination of audio, image, and video can actually deliver that result. So we are revisiting that today. And the recent London ads engine of bringing an ad and bringing that internationally and shifting things in the video, perfect use case of that. But still a conscious decision we are taking is not creating the model from scratch. So like true text-to-video or like, you know, V3 type models that exist. We want to be at that intersection where we know the audio can give you the unique advantage, which is usually you already have an existing asset. You need to modify that asset, add that audio component, and bring it home.
Do you feel this like intense focus or relentless focus on audio is kind of like defense against like the larger labs?
You talk about this in the company.
We do. The big part is like our focus is audio on the research side and that's where we want to win. I think that we also talk about this like in the long term, that advantage that we have today will hopefully still be there, but it might not be as big on just the pure research. That's why the other components, that's why the product is so important, and that's why the ecosystem is so important that we build around that product. And maybe just to explain what I mean by the ecosystem, of course there's the brand and trust that you build, but there are also other parts that you can build. And in our case this was investing into effectively a marketplace model where people can create an asset, we authenticate it, and then you can share it and earn compensation as a result. So like trying to create a completely different model for how that works.
Give me details about that because I don't...
Like an example is voices. So we did it with voices where people can create your voice. We authenticate it, then you can share your voice, your AI voice, and passively as your voice is being used, you earn compensation. We have 20,000 voices today on the marketplace. And new people that come in now have a selection of different accents, different styles, different languages, of course, different age, gender that you can pick. Every time you use it, that person can do.
You as a company make these voices, any of these voices, or is it just third parties?
We of course created a system for people to do that. And some of them, when we see pockets that are missing, we'll ourselves try to go after and find people to fill those pockets that are missing and create those voices for the wider ecosystem to benefit.
No one knows about ElevenReader. You have to do a better job. I was listening to your episode with John Carlson who I love and I don't think he even knew. He kept saying, he's like, why doesn't this exist? And you're like, there's an app. And then the follow-up, he told like, no dude, I use that app. What you're asking for, John, he already has it. You need to like show it to them on your phone when you were recording the podcast with them. So I use George. That's the voice. In fact, we do all these like research reports for the people that come on.
Being a user, that's amazing.
No, of course, it's a fantastic product and it turns every document into essentially a podcast that I can, you know, listen to when my eyes are busy. But the funny thing is we do these research dossiers about everybody that comes on and so I put the one about you into ElevenReader. So I'm listening to you about you, on your own product. But like in George's case, is that coming from you guys? Is somebody else getting paid when I'm listening to George?
Somebody else is getting paid. That's a great voice actor that worked with us for now a long time. And every time you listen, he gets paid.
Do you understand? When we sat down, I was like, you're one I keep running into everywhere. And so that was like, dude, we got to just do a podcast together. But like you're so confusing to me and in a great way. This is not like a negative thing because like I was like, I don't even know how to describe your company. You're doing all this research, but then you have all these other different products. This is very unusual, especially for somebody that's so super focused.
But you know, it is very fair. And I think we are seeing that everywhere across AI companies of like, you know, the model becomes the platform becomes application. But for us, the unifying theme across all of them is that angle of communication. Like we think how you communicate as a company, how you think about content, communicate, all of that is changing and we want to be at the intersection of that. We build the best models to help you do that and then build a platform that effectively delivers that content, delivers the knowledge, delivers the conversation a completely new way. And that's of course, there's just so many different applications. There's ElevenReader that lets you enjoy content a completely new way.
So research, platform, applications on that platform.
And you know, like we explicitly are not planning to touch any of the intelligence or knowledge work or coding, not our strength, not our domain, but we are...
There's a vicious battle too.
Very vicious battle, lots of great companies in there. But what we would love to be is if you think about the next three years or however many years, there will be probably three platforms that you said all the interactions on those platforms, we want to be the leading platform for those interactions, for that communication way.
The whole way, and that kind of builds on that theme. How you engage with content, how you engage with that company will change. We want to help people, companies have one place, one platform where you can set it up, you can set up your integrations, you can bring your knowledge, you can bring your brand, you can bring your assets from the company and then deploy that for the entirety of customer journey. Whether that's in the marketing example where you tell that story through content, capture that knowledge back about the user. Whether that's in sales when you're trying to engage and bring your product to the customers, whether you are supporting that across the customer journey, or maybe in a simpler way as you're trying to understand the customer journey, every on-brand interaction that that journey has, we would love to be able to capture and help you make that better for you.
Okay, so let's make this concrete because I still want to be able to wrap my head around this. Let's take your most deeply integrated customer.
Right, and say, you know, it's Goldman Sachs, I'm making this up, and this is how they use this product, all the different products they use. Like who is the company or one of the companies that is most deeply integrated with your company and explain how all the different suite of products that they're using that are powered by your technology.
Deutsche Telekom is a great example. They will use a lot of the audio work to create their podcast and their app and the Magenta. They will do that for the ads so they can create ads and distribute that to their audience. They will use a lot of our Eleven Creative work to be able to do that. Then they will use our Eleven Agents work for in call center where you call in, you want help, you get that help through the voice agent integrated with the knowledge from Docom so you can make sure that if somebody's calling in to learn about the product, they get information very quickly. If they are calling to get support on how to get a refund or the recent billing request, they can get that help. And then more recently they even deployed agent inside of the network. So if you are a T-Mobile subscriber, if you call, you can ask agent to join the call and help you out, schedule a booking, or real-time translate your conversation to the other person. And they'll use our effectively combination of agents work and bringing that real-time dubbing to be able to communicate in another language. And that across all of that, all of that information is captured back. So you understand how customers engage with, in this case, the full spectrum of the journey, marketing, the support, and then the wider operations, even sales, and we help them do all that.
Okay. So in a situation like that, because I think you mentioned like obviously a huge increase in your revenue that's growing really fast is like you're targeting bigger companies, right? What was the first product Deutsche Telekom used from you?
Uh, sorry. Yeah, the first was marketing. So it was two years ago. Two years ago still most of the agents weren't really very good. They weren't very reliable. They weren't very quick. So marketing was the most obvious one. So they started with that. They deployed that. The quality of the content was great. So people could engage with the podcast effectively. The use case that you mentioned of like, you know, bringing your notes and then being able to read them out loud. They were just creating that daily for all the customers so they could read about what's happening in the world through the podcast with Eleven voices. So that was the first use case. Then of course support is a combination of voice but also the integrations.
Let's talk about how you expanded them from one product to the next. Like how do you actually do that?
In that case? The main... I ask mostly like how do we partner with them to help them bring that product to life or...
They start with one but you have 10 different products you could sell them. So how do you go from one to two and then two to five and so on and so forth?
Yeah, I think the main thing is one, you of course deploy the first one, you make sure that there is value behind that. So across any customer engagement we try to make sure that we don't only prove concepts, we prove the impact, prove the value, and only after that we try to get the companies working with us at a broader scale. So we proved the impact on the marketing side to get the support going. What you actually need to do is not only the audio, you need to build the integrations. So you need to connect it with all the CRM systems that Deutsche Telekom... how you make sure that that connection exists. So you would spend a lot of time on integrations, making sure that their logic is respected on how when you do pick up the phone call, the agent behaves the way you want it to behave. So here our deployed engineers would partner with the team, spend time in Bonn in this case in Germany, working through side by side together on the integration, on just being able to make sure that the agent follows the flow that you want, has the knowledge that it should have. And then in that step, the hardest thing in any voice agent work is actual deployment of how you actually test that it works. So then you need to trial that with initially simulated that you verify the calls, then you do it at smaller scale.
And you're doing this with FDEs.
Yes, we do all that with FDEs, then we deploy, and then of course we scale over time.
When did you realize that a huge path to greatly increasing your revenue was doing FDEs?
And before I answer this, and of course the last part is, you know, like as you deploy, the job isn't done. You still want to continue evaluate, monitor, and refine the behavior over time. And that applies across all of the use cases. And FDEs for that are great too. So I used to be at Palantir, so it always felt like a big thing of how we want to work with customers. It's like almost, I don't like the word customers because like in many ways I feel like all of them are like partners where you are working together on the same problem and trying to solve that. So there was a lot of the philosophy of the FDE was there from the start to make it specific working with the partners side by side on their problem.
Hold on, this is actually really interesting. So tell me what you observed that was working at Palantir where like if I start my company I want to do that too and why?
I think the most incredible thing was that like you were meant to be on the same side of customer, obsessed with their problem and try to understand them deeply from the beginning. So I used to work at BlackRock before Palantir. And BlackRock, when I joined, given the compliance, security, the first month or two, when you send an email it gets verified by your team, gets trained before you send it externally. So it's like a pretty detailed flow of making sure that it's good. In Palantir, when I joined after the onboarding, in the first month I'm like, okay, you now need to work and understand a customer. You're flying to Aberdeen to North Sea to work side by side with them, understand what's happening and then bring it back. And that was complete shock for me and like the kind of the approach and culture of like I have never almost interacted with a customer on the BlackRock side and now here I am in the first weeks and like meant to go there and like be next to them, which was crazy. I thought it was great. I thought it was that mentality and like every time kind of you are going to be there on the, I'll call it front line loosely, on the front line to work with them and bring that knowledge back, understand what is actually the problem, what's fixable problem, and then actually fix it. And that mentality is so true now at ElevenLabs too, where all of our FDEs, our go-to-market team too, is going to obsess of like how can I actually be there with the customer, understand the problem and work with that. There was other companies that I think were great. The small teams, they had usually small deployment teams that worked on a lot of that, that the best idea wins concept was very true where in that small team you very quickly assembled the best knowledge of what you think should be done for that customer and you had power to then actually enact on it.
About five people was considered big, and in ElevenLabs it's going to be also relatively big. I'll tell you something more though on the FDE side because of course now you probably see every company doing FDEs. And in our case, FDEs are part of the product team, they are not part of the go-to-market team, they are part of the product team, they are deeply embedded on understanding what's the roadmap of the product. But there's a second reason, which is one, you want all the FDEs to actually solve the customer problem and stretch your product in that direction, but second thing you want them to do is bring any of that knowledge back to the product. So the product becomes better for the next generation of companies building on top of it. And I feel like the second part is always that frequently missed. Was like first, okay, FDEs are, you know, doing effectively a lot of the hard integration work to solve the problem. But if you don't do the second step of how you actually learn the expertise and bring it back to the product, then it's effectively just services or just...
Wait, say more about that second part. So you're gleaning information through trial and error working closely with them. Then you can take that back and spread that knowledge across the other tens of thousands of customers that you have. Is that what you're talking about?
Exactly. And it's a product knowledge of like how you operate with, you know, there are simple things. Let's say you build an integration. How do I now have that integration available to everyone? That's a simpler version of that. But let's say you are working in healthcare space. In healthcare space, if you deploy the work, then you want to optimize the product experience for the right... if you are now starting as a second customer, you want to make sure that you have...
So wait a minute, you're using your customer base as almost like R&D, another form of R&D.
You are definitely accelerating your R&D through understanding the domain that you're working with. And it's a... it's, you know...
Their problems are not, very rarely a company's problems is unique to that company.
That's right. And the beauty of that is like kind of everybody benefits because you work with one customer, you learn, you bring that into the product experience. You learn with another one, you learn, you bring that back to the product experience. Both of those customers benefited from having the product optimized for the better work. The domain expertise they have is still theirs. You can win based on the domain expertise, but the product experience of how you build based on that domain expertise can be abstracted, can be reusable.
What else did you learn working at Palantir?
They also were very much a no-title organization, which we carry over at ElevenLabs, which does help in that best idea wins approach where people do feel... I feel like I'm mimicking you on that shirt.
I thought the same thing. I was like, you don't have to if you don't want to. It's... I know it's a complete coincidence.
We're leaving that part in.
No titles, relatively flat organization, very few layers. So it's like all you know between me and the C-suite, I worked there before the IPO, it was four, five steps or less. So you always felt like a proximity between...
Yeah, I think it was like four or three actually.
So like there was a good proximity of being able to work together. At ElevenLabs we have a couple of, five today like maximum depth of how many layers there should be, and hopefully over time actually there will be fewer, no more layers, if AI helps you run the organization the way we would like to run.
Well, say more about that. What do you, can you not do today in the way you run your organization that you hope AI can change in the future?
I think there's like always you want the information from the person working as closely to the problem. So let's say you're developing a product, you want to work, you want to speak with the engineer that actually develops that product rather than their manager. If it's a client conversation, similar, you want to understand what is that client saying from the person on the ground rather than a manager summarizing that information and giving that to you. And I think that that information flow will change with AI where you will have access to everything that's happening in a better granularity than you could ever have before because it summarizes that back and forth. Second, I think that in general roughly at ElevenLabs, most people will have close to 10 direct reports. So pretty wide span, which helps us build that small team approach that we have.
It only works if you can amplify a lot of what's happening across all the teams, all the people that you work with, and to summarize information of what's happening in their teams, how they're performing, what are some of the gaps. That definitely helps and helps shift it from reactive to proactive. I think too, by what I mean by this is frequently in the past, I think you would rely on specific individuals surfacing the information to you. Now, as we think about ElevenLabs and we run it, is that you can summarize all what's happening, all the data of what's working, what's not, and you get that signal, I get that signal, and I can proactively engage on where I think it's not working, which helps. It helps, of course, everybody across the company because you have that same ability wherever you are. I think that the maybe last part, which is very related to those two, why it's even possible today, we took a choice to be extremely transparent with a lot of the data that we have, a lot of the docs that we write, so everybody has almost access to all the docs that are there in the company, which ultimately helps you create that system where you can actually tap into that knowledge.
Okay. So, I want you to say more about this because I think this is one of the things I want to talk to you about, because you're relatively young. This is your first company. Your first company you built, you got into AI right away, and then you were perfectly positioned for this huge explosion. Obviously, people have been talking about AI for 80 years or whatever, but like you're at the right place, right time, with I think the right set of skills based on what the company you built so far. So, like, how different do you think in the future the way you run your organization can be than what it is today?
I think the small teams approach will definitely be there across many of the companies that I believe will be created in the future. It also helps with a very different piece, which is like if you just think about adopting AI technology, in our case, it doesn't have to be a top-down mandate of like you need to use this technology to get better, and then the teams enable others. Given it's relatively small and independent, people bottoms-up adopt what they think is best and are able to run with it straight away. So that helped a lot in that sense. I'll give it two others which we believe strongly on. One, in a lot of the small teams that we have, and of teams that we bring even non-technical teams, we bring engineering talent in those teams. So our talent team, our ops team, our legal team, all of that will have engineers that help both automate some of the work but also elevate everybody else in how they are using AI. I think that will be a bigger pattern in the future across the companies too, where I think increasingly some of the biggest and smallest companies will try to infuse all teams with engineering resources so they can get smarter.
Is there any function that is like centralized at ElevenLabs that then all these small individual teams can then access? Like, I spent some time with Luca Ferrari of Bending Spoons, and he had this wild idea, wild idea to me, where he's just like, well, HR is actually really important. He thinks like the fact that tech people are like dismissed of it is like ridiculous. And he's like, "But my HR is like 50 people and they're all engineers." And then it's like one centralized HR that every single other, because he owns I don't know how many companies, they all utilize it, but it's just like 50, I think 50 engineers in HR, and then all the different structures, teams and companies he has like tap into that for that resource.
