00:07:03 · Mati Staniszewski
[00:07:03] S3: Because in the short term, they are the biggest advantage and the biggest step change you can have. Today where the voices still don't sound good, we need to solve that problem. Now when the interactions don't sound good, we need to solve that problem. The only way to solve it is for building the models yourself. And then over long term, there'll be other players that will solve that too. Hopefully, are first time and time again, but the call is to solve those. But to your question, the pattern we are seeing, if you want reliable, scalable use case, you will likely still use different models for different use cases. So best of breed across. So like in the agent space, you want the reliability and scalability across that, the kind of as the key component, you want explainability of the model. So then you are good with using speech to text, LLM text to speech, so you know each of the steps, you can link it up to the tooling in the right way. If it's a slightly different space, what we do expect in the next year or two is kind of what you mentioned, where increasing amount of models will move into a multi model or fused approaches. So you will create audio and video at the same time, or audio and LMs at the same time in a conversational setting. Google with Veo 3 incredible model that they released, is a good example of what you can achieve if you combine those together. And we are planning to do experiments in partnership with other companies and and independently to see whether we can combine our audio expertise with some of the expertise of other models to make that easier for our customers. Because ultimately, in the long long term, yes, it will be needed.
00:08:28 · Taya, Mati Staniszewski
[00:08:28] S2: Other companies training AI video models you're thinking about partnering with? [00:08:32] S3: Either AI, other AI companies or Open Source. [00:08:35] S2: Yeah. What are some of those companies? [00:08:36] S3: They are still figuring this one out. We have close conversations with a few of our close close friends. [00:08:42] S2: That is a trend that we see a lot where these, you know, Sora and v o three, you generate both at the same time. And I imagine creatives are increasingly looking to do that. I mean, there's other challenge that you face of you're competing against these players and they're far more capitalized than you are. So are you thinking more about partnerships or potentially being acquired by someone in the future? Like, do you compete with them?
00:09:05 · Mati Staniszewski
[00:09:05] S3: We are building a generational company, and we we want to stay independent and build something that will create that value in the long term. So the whole thesis of how we think about combining the model and other work is that you cannot just rely on the model. You need to build the models, but then you'd also need that application side. That's why for us, it's so important to treat models as the head start, invest, be the best in the audio space, while at the same time, build a creative platform offering and build agents' platform offering. And that's the combination of building AI and building product. That's that's the magic. The same way like software and hardware was the magic for Apple. We think the product and AI will be the magic in the next generation of the best use cases. Now, what I think is tricky, and that's where where we expect in the next couple of years is essential, is on the audio side itself. So far, we've been able to create a text to speech model that was beating out of some benchmarks recently on speech to text, now on music. Then there's a second step of how you combine that model with, let's say you take an open source video model. That fusing step is actually challenging and tricky. So here too, we hope to be one of the first to figure that out, to do it in both high quality way and reliable way. We don't today plan to train some of the compute or data intensive models that competitors do. And of course, that's just a segment. That's why it's so important to do the product side.
00:10:23 · Taya, Mati Staniszewski
[00:10:23] S2: Well, whatever you're doing, I mean, you talk about the application layer, and that has really been a success for you guys. I know a lot of companies that are really using ElevenLabs. And I'm curious, when I look at OpenAI and Google and their suite of products, I don't see a one to one competitor to a lot of the things that you build. And I'm curious why you think some of your larger competitors haven't encroached on these products, Or have they? [00:10:48] S3: I don't know. I don't know. They, you know, of course they do set of use cases across. All of those companies will have some version of a creator tooling and some version of distribution. But I think the the truth is in all those cases, it's it's a matter of focus. And I think in our case, were straight focused on solving this human computer interaction problem. Initial is voice, now it's linking it to other systems. And I think many of the companies have just so many other parts that they rightly are trying to fix and tackle. But at the same time, think, you know, like as you think about the competition, in many ways we are not looking at them in that way. Of course, you know, we are seeing what's the newest things that are happening, but kind of our vision is independent of what they do.
00:10:48 · Mati Staniszewski, Taya
[00:10:48] S3: I don't know. I don't know. They, you know, of course they do set of use cases across. All of those companies will have some version of a creator tooling and some version of distribution. But I think the the truth is in all those cases, it's it's a matter of focus. And I think in our case, were straight focused on solving this human computer interaction problem. Initial is voice, now it's linking it to other systems. And I think many of the companies have just so many other parts that they rightly are trying to fix and tackle. But at the same time, think, you know, like as you think about the competition, in many ways we are not looking at them in that way. Of course, you know, we are seeing what's the newest things that are happening, but kind of our vision is independent of what they do. [00:11:28] S2: I think there's one argument to be made where critics would say that what you've created is something of a risky platform where people have been able to use and to create deep fakes and maybe OpenAI and Google who are already facing a lot of criticism for their AI tools don't want to subject themselves to that. And do you think that's a fair argument?
00:11:44 · Mati Staniszewski
[00:11:44] S3: You could also argue that with recent to release, they are taking much more risk than we would ever So as we think about ourselves as like a leading company in the voice space, so important that you combine a lot of releases with the safeguards. So for us, though, what this means is from the moment we release, you can always trace back the content back to who generated that content. You can trace and understand whether it's AI or not AI, which I think will be an important part in the space, that you understand AI versus not AI content. But then second, we do invest lot in the moderation of different voices and different content itself, so you can catch that before it's created or flag it for further review. And then the last thing is we, especially over last year, we collaborated a lot in how we can use a lot of those things that we find ourselves with other players to hopefully bring that as a standard to the space. So one of those examples is you need to be able to detect AI content, but not only for ElevenLabs, but for all audio companies. And now the biggest wave that we see is a lot of open source companies, a lot of other commercial models from China that are coming as a competition, and you cannot detect that. So we are collaborating with University of Oxford, Berkeley, Reality Defender to create effectively classifier models to detect it. But maybe last thing, which was a good example, recently, a few months ago, we worked with a charity in UK where there was a use effectively, the voice agents, AI voice agents to, as the call was coming in, based on IP, if it was likely a scammer, the voice agent would take over with the purpose of wasting the scammer's time, and it worked beautifully. And it was a nice way of using AI voices exactly against the scam and fraud that was happening.