00:09:08 · Mati Staniszewski
[00:09:08] S1: Cool. So in, like, the nutshell I'll describe, ElevenLabs is a research and product deployment company. We built foundational audio and voice models and then build a platform for businesses to transform how they communicate with their customers, with their employees, and that will apply through Yep. AI agents, from customer support, sales, hiring training, all the way through to marketing and storytelling through our creative tools. And in that set, we've created all types of foundational audio models, so text to speech models for producing speech, speech to text models that work over a 100 languages and happily beat others on benchmarks, all the way through to conversational models of how you loop them together Mhmm. To music, to other domains of audio. And then, of course, beyond the models, when you actually bring them to production, that's where the second level of the platform comes in, where that meets their businesses on the specific use case. So on the agent specific example, it would be how you now connect those models to the knowledge base, to telephony, to to the integrations that you need to perform the actions, how you evaluate and monitor the agent that behaves in the right way, how you build the right save rates. On the on the creative side, on the marketing side, it's how do you create a good ad so you can create a good video voice over for for one of the campaigns, how you create an article that's narrated with a specific voice that represents the brand in a good way. So that's where we combine kind of the models and understanding of the customers we work with into into one policy platform.
00:10:44 · John Collison, Mati Staniszewski
[00:10:44] S0: Every platform company has this question about how far they go into applications. So how do you think about where you go horizontal and power the whole ecosystem versus where you develop application? Because, like, you can imagine there being a whole ecosystem of closed captioning tools that grow up that, again, are built on the ElevenLabs tech. Like, it's not necessarily a space that you would have to go after yourself. [00:11:06] S1: I think the big difference between... And your kind of question, today, we we we we see ourselves as a platform where if you're building a horizontal use case in your business, a great place to come. If you have a lot of domain specificity, that's where I see a lot of kind of the application companies forming over time, where they will, where where that's the specifically not the spaces we will go into. [00:11:28] S0: And I think it also is interesting when the tech is moving as quickly as it is here. It's one thing, you know, with SaaS where you get these, like, vertical specific providers, but I would imagine one of the biggest risks for you guys in being intermediated is if there is, you know, like in this example, a closed captioning service that is on a two versions... Old version of ElevenLabs and hasn't upgraded, that's a problem because you want people to be using the latest and greatest model that you've developed, and you'll be kind of deploying new capabilities every week. And I presume that's part of your thinking, is that just when it's moving that quickly, you need to go direct in a of cases.
00:11:28 · John Collison, Mati Staniszewski
[00:11:28] S0: And I think it also is interesting when the tech is moving as quickly as it is here. It's one thing, you know, with SaaS where you get these, like, vertical specific providers, but I would imagine one of the biggest risks for you guys in being intermediated is if there is, you know, like in this example, a closed captioning service that is on a two versions... Old version of ElevenLabs and hasn't upgraded, that's a problem because you want people to be using the latest and greatest model that you've developed, and you'll be kind of deploying new capabilities every week. And I presume that's part of your thinking, is that just when it's moving that quickly, you need to go direct in a of cases. [00:12:04] S1: That's right. In closed captioning, like, you know, here, already now, we know that our services is going to be able to tackle, like, 99.9% of the cases that customers have. And then there's, like, added benefit of we work with health care customers where we will create custom models for those customers where it will get that transcription perfectly. [00:12:24] S0: The context is the tricky thing in closed captions where, like, we talk a lot about a lot of technical stuff on on this. [00:12:30] S1: Yeah. For sure. And that's where you need, like, effectively, like, a dictionary of words that do the tech before beforehand, which which, as we work with the businesses, we know we need to we need to embed in that creation process.
00:12:30 · Mati Staniszewski, John Collison
[00:12:30] S1: Yeah. For sure. And that's where you need, like, effectively, like, a dictionary of words that do the tech before beforehand, which which, as we work with the businesses, we know we need to we need to embed in that creation process. [00:12:42] S0: We're talking a bit about kind of products here. And one thing I note is that LMs are amazing, and, you know, you have the usage stats of ChatGPT and Gemini and all the popular LMs where they're working and people use them a ton. It feels like there's a big product overhang when it comes to voice, where the leading edge voice models are incredibly capable. And yet, I was driving home the other day, and I needed to read a PDF. But I was driving, and so I said, okay. I'll just have my phone read the PDF to me. You can kinda try and hack it with, like, iOS screen reader, but it doesn't really work with the scrolling. And then in theory, you can upload a Gemini, but you're trying to get it to not summarize it and actually just hung when I tried to press the, like, read this to me button. And so there was no way I could get my phone to read me something, which seemed like a fairly basic, you know, feature. And, you know, all cars advertise voice control, and, yes, it sucks in separately. You know, if you want to, you know, input something to the navigation, just no car has a good version of that yet. Maybe Tesla does. And and and and so why does it seem like with LLMs and Claude Code and everything, we are using all the capabilities of the intelligence, whereas with voice, like we're living ten years ago somehow.
00:12:42 · John Collison, Mati Staniszewski
[00:12:42] S0: We're talking a bit about kind of products here. And one thing I note is that LMs are amazing, and, you know, you have the usage stats of ChatGPT and Gemini and all the popular LMs where they're working and people use them a ton. It feels like there's a big product overhang when it comes to voice, where the leading edge voice models are incredibly capable. And yet, I was driving home the other day, and I needed to read a PDF. But I was driving, and so I said, okay. I'll just have my phone read the PDF to me. You can kinda try and hack it with, like, iOS screen reader, but it doesn't really work with the scrolling. And then in theory, you can upload a Gemini, but you're trying to get it to not summarize it and actually just hung when I tried to press the, like, read this to me button. And so there was no way I could get my phone to read me something, which seemed like a fairly basic, you know, feature. And, you know, all cars advertise voice control, and, yes, it sucks in separately. You know, if you want to, you know, input something to the navigation, just no car has a good version of that yet. Maybe Tesla does. And and and and so why does it seem like with LLMs and Claude Code and everything, we are using all the capabilities of the intelligence, whereas with voice, like we're living ten years ago somehow. [00:13:57] S1: Well, I'm thinking what I agree of the premise that we are ten years behind.
00:13:57 · Mati Staniszewski, John Collison
[00:13:57] S1: Well, I'm thinking what I agree of the premise that we are ten years behind. [00:14:01] S0: In the lived experience of people day to like, they're using series transcription, which has gotten better. Yeah. And it's still way behind the leading edge. [00:14:08] S1: Yeah. Like, there there is definitely a piece of, like, the the... Like, we... I think the technology in many of those cases is ready. There's a deployment gap to what you are saying. It's, like, in automotive or some of the big companies are not adopting that quickly enough or bringing that into the production. But, yeah, like, plenty of different problems that you need to fix along the way. I mean, the the the quality of voice models for them to actually sound good, like, this this is only, like, last three three years thing. [00:14:34] S0: Yeah. That's three years. [00:14:35] S1: It's it's a three years [00:14:36] S0: Paris have over the years software updates now. [00:14:38] S1: So so that's three years for the first voice model that can narrate Yes. Text async. Two years ago, you can start seeing the real time version of that, and not really. Like, it's I think the real break was like a year ago [00:14:49] S0: Okay.