The a16z Show
00:13:32 · Mati Staniszewski
[00:13:32] S0: So I think the first piece is actually spending time with the industry and trying to understand what are their priorities, their incentives. Of course, it's sometimes tricky. Sometimes you then end up being starstrucked. We had an honor and pleasure to work with Jarrett on some of his incredible work and and learn from him on, like, what is important and, like, which parts of the production process you can actually use AI, which ones you wanna keep, where is it actually helpful. And so so I think that's the super important thesis across all the partnerships in the space. In our case, we we try to figure out how to do that on the on the voice space, which is, of course, with that technology, a, how will the voice acting space look like in the future, and then, two, of course, to be able to cater to all those different use cases, you need such a big array of different voices, different languages, different accents, different styles. So we launched Voice Marketplace where you could create your voice and then share it, and when the voice is shared, you earn money in the return. Today, have almost 10,000 voices. We paid $10,000,000 back to the people in the community. There are some crazy stories from the voices. One of our first voices will say a deep Spanish voice, and the magic of the technology is that the same voice now is available on all different languages in the same way. So it's 30 different languages at the time now it's 70, but 30 languages at the time, and we had the Spanish voice join us, and it wasn't picking up on the Spain. Nobody really liked it as much, and then it picked up in an English speaking country, that same voice, because of that deepness, and now it's our top free voice for all the use cases. Hidden messages, you can all register to our Voice Marketplace and maybe earn some money too. The... So that's the the... I think the second important thing is, like, figuring out how we can be part how we can bring the industry together to disrupt together rather than just to disrupt, and with labels, I think I'm still learning how to interact. So we worked with labels, Merlin and Kobalt, so fourth majors, to bring their music into the music model so we can do it in a licensed way, so you can generate that and give commercial rights so you're fully protected. That was a hard process.
00:15:52 · Mati Staniszewski, Jennifer Li
It took us eighteen months to figure out the agreement that works, and in the end, I think the main thing was adding a set of forcing functions or forcing timings to find effectively a trigger of like, okay, this is when we do it, and we either do it together or we do it separately, and those forcing functions really help add urgency. Then we needed to move that forcing function a few times, it still worked to a large extent to go after that, and then two is, of course, know, finding the compromise wasn't wasn't wasn't easy. But then in our case, working with the with the with the labels there was kind of protecting what they are caring about, and they, of course, also care about how how they continue doing well by their members, by their artists that they work with. So we would spend a lot of time working with their members, speaking about how are we thinking about technology, what's going to happen in the next couple of years, and that really helped. So just just speaking through exactly the technology, showing the examples, and kind of avoiding this initial knee jerk reaction that AI is bad has has been has been tremendous. [00:17:01] S2: And maybe tying back to the earlier question, as you are navigating, like, this landscape, how do you think about, like, bringing the right talent that can head and lead some of these functions? And these are mostly unknown territories of how to navigate it. Like, where have you been seeing success in bringing the right people?
00:17:01 · Jennifer Li, Mati Staniszewski
[00:17:01] S2: And maybe tying back to the earlier question, as you are navigating, like, this landscape, how do you think about, like, bringing the right talent that can head and lead some of these functions? And these are mostly unknown territories of how to navigate it. Like, where have you been seeing success in bringing the right people? [00:17:22] S0: So here, for the spaces that are kind of completely new to us, so this and, like, Legalis and I get another example, we would always kind of bring at least one or two people that were in that space that kind of have interacted with the same parties full time in the past, but then would actually adjust that with a lot of consulting people that would help us in a specific conversation. So in this case, in music, we had music lawyers that worked very closely with us that consult across a few of them, and the good thing is that they know all the players and they effectively were this bridging gap between both of us so we could speak the same language. And then that was really helpful. [00:18:06] S2: Yeah. And they have had a very specific taste for people that are risk tolerant enough and also understand the commercial business opportunities to, you know, help guide the right chain of actions in each of those domains. I found that very fascinating.
00:18:28 · Mati Staniszewski
[00:18:28] S0: 100%. I mean, legal, I don't know how many of you are trying to find the first legal counsel or have a number of those. For us, this was, I think, one of the trickiest roles to hire for because you are hiring into the space you don't know know very little about, and then we had the first couple of legal people that were clearly not fit, so we separated us. Then we hired a third person, and that person came from like a number of Fortune 500 companies, and they never worked in start up space, never worked in venture, and what resulted is, like, everything... Every conversation was pointing out the risks that we see. So, like, anything we wanted to do was, like, the number of risks that this could carry. And it was really tricky to work because we... It's like you kind of get risks, but you've gone that risk advice of, like, okay, and this is where we should draw the line, but everything was back to decision. And now we hired a person working previously in a number of companies as a council, and don't poach them. They are interesting, and they understand the risk equation a lot better, where they are not only like a counterpart to figuring out what the risks are, but also like, okay, this is what other companies do. This is what we should potentially do. And then they are like a true thought partner and a tremendous change.
00:19:50 · Jennifer Li
[00:19:50] S2: For sure. ElevenLabs started as more of a creator brand everywhere from the individual creators to the creators that are building businesses. But now you have been having a lot of success moving into enterprise, not just started from the AI agent platform, but even with the text to speech speech to text models. How have you been navigating that transition? Because that's one of the very commonplace where, you know, a lot of really great consumer creator brands fall down, but you have had so far a pretty smooth transition.
00:20:26 · Mati Staniszewski
[00:20:26] S0: So when we launched, we had a lot of early inbound when we started kind of the classic PLG, a lot of inbound from enterprise. And I remember speaking with a16z team when they joined us, where our initial take was, of course, we want to be an engineering company, we don't want salespeople, we would like to reinvent that and have, like, engineers do the sales. We did hire one traditional salesperson and one non traditional salesperson, like an engineer, and we told them, like, do sales now, and that really, as you can imagine, didn't work out in this specific case. But we learned our lesson, and we now do invest in a combination of that. It's 80% sales, 20% engineering, so still a little bit of that. But this was, like, super important lever of understanding who are the customers, what they care about, and working deeply with them to bring it back. And then that kind of working with them was kind of opening of what we need to actually do on the product and research side. Munjal from Hippocratic is is is here. He was one of the the earliest incredible use cases in the health care space where they would create effectively agents that would take inbound calls that are calling the hospitals to take and schedule appointments, and beyond that they would do all the other parts of outbounding to the patients to remind them about taking medicine or reminding them about the appointment that's happening, and and to be able to do that, that suddenly shifts from using a one foundational model into combining the speech to text, the LLM, the text to speech to orchestrate them together, then the integrations you need to build, then you actually need to deploy, and they were one of the areas that was 2023, but then we've seen this repeated pattern across across a number of other customers and customer experience base and and many others, and and we decided to invest more into helping with the entire orchestration. So instead of just doing text to speech, we can help combining our research to make this whole whole combination of that more fluid. But then if you are thinking about enterprise, you do need to build the combination of knowledge base inside a system. You need to help deploy that with telephony providers, whether it's Twilio, the SIP trunking. Like, how do you do that in a templatized and easier easier way?