The a16z Show
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?
00:22:39 · Mati Staniszewski
And then, of course, the the biggest gap that's the most common, it's easy to do a demo, but how do you actually build it to production? How do you test? How do you version control? How do you evaluate, monitor over time, fine tune over time based on the results? And and all of that is... Has been a big big part. And underlying all of all of that, and we spoke a little bit with with Mat before coming here, the the foundation needs to be there, is the security, the compliance, serving serving the the customers across that will rely on that infrastructure. That's something that we want to shine through at ElevenLabs where if you are using the software, it's going to always be reliable and always the four nines or five nines, hopefully, one day will be will be there, which is tricky in AI space. But the the... That's the that's the goal. Of course, the the difference between the the one obvious difference between PLG and sales is the the cycle to work through and identify the right customers is much longer, and and I think that's where eagerness from our internal team was was interesting to observe, where you had a lot of people that didn't work in an enterprise setting, and then you had other side of the company that did, and the side that didn't was very skeptic about going enterprise and, like, kind of waiting the six months or twelve months to results, and in the early days we needed to shield them from that information and, like, trust us, we'll do this, and it will work, but they were very sceptic, and then, of course, after twelve months it worked out, but that was probably the hardest culture really of how you kind of still keep everyone jumping on same train.