ElevenLabs stays ahead of Anthropic and OpenAI in voice not through model scale but through specialized architecture, proprietary labeled audio data from over a thousand contractors, and deep vertical product work that generalist labs won't do.
Facing competition from Anthropic and OpenAI, ElevenLabs' CEO says their edge comes from research architecture and a large in-house data-labeling operation, not from scale, plus vertical product depth those labs won't replicate. ✦ AI generated
Mati Staniszewski · All-In Podcast · 2026-07-14 · original ↗
starts at this moment · 27:04
“How do you think about your partnerships with those type of frontier models and the fact that they want to kill your company?”
It's the architecture that matters, not the scale. You really need to change how the model operates. Two, you need very specific data that there's of course a wide set of data out there, but it's unlabelled data and where we spend a lot of time. So we build a internal team of over thousand contractors that label all those audio assets to make them good.
verbatim transcript · starts at 27:04
27:04the reason is it's on the research side. It's the architecture that matters, not the scale. You really need to change how the model operates. Two, you need very specific data that there's of course a wide set of data out there, but it's unlabelled data and where we spend a lot of time. So we build a internal team of over thousand contractors that label all those audio assets to make them to make
27:24them good. So that's on the research side. And then as you think about the rest of product stack, we want to create a fully verticalized solution for that communication angle. The product understanding the right workflow in financial services is very different to healthcare, very different to telos. We spend all of our product team to figure out how that works and those companies don't. And then ultimately last piece is
27:44the ecosystem. Can you build the wider set of integrations voices that you use templates for the agent authentication that you can benefit from instead of starting from scratch? And so far we've been we've been able to create a new model for that. >> Certainly though you must be concerned about hey the reinforcement learning the data leakage. They say they're not using your data, but they're kind of using
28:06your data. And so, do you have an open-source project internally as the like in case of Glass, we got to break this? And when do you think >> you'll be able to discontin working with them if you had to? >> We we we we know that some companies are continuously trying to figure out how to distill and use the data. So that is uh that is a existing problem and we have
28:34few mechanism to to to stop it. Um or slow it down not stop it. Um but um but on the open source question or like creating our own um um uh versions we are we are looking a little bit closer on like how we could use our expertise of how does like you know we won't focus on knowledge work. we won't focus on coding but any interaction and how you can
28:58combine all those pieces together and make sure this is this is great. Yeah, we want to own. So we are spending more time there. Uh but it's also just great to be in the arena and compete with those guys and uh and and every so often show that we can do it and do it better. Yeah, it's pretty clear in my estimation that that's where you you will wind up