ElevenLabs CEO: Why Voice is the Next AI Interface

1. 0:00 – 2:20

   Intro

   1. MSMati Staniszewski0:00

      -els. We don't want to become same as previous generation of, of the editing suites. So instead, let's solve it on the research level, where it will know based on the voice exactly how it should speak with the speed. To be able to cater to all those different use cases, you need such a big array of different voices, different languages, different accents, um, different styles. Um, so we launched Voice Marketplace, where you, you could create your voice and then, uh, share it. And when the voice is shared, you earn money in the return. Today, we have almost ten thousand voices. We paid ten million dollars back to the people in the community. There are some crazy stories from the voices. 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.

   2. JLJennifer Li0:43

      [soft music] Um, I'm excited to welcome our first speaker, Mati, cofounder and CEO of ElevenLabs. [audience cheering] [upbeat music] Great. All right. So good to have you here, Mati.

   3. MSMati Staniszewski1:06

      Thanks so much for having me here. Great, great to see everyone, and good morning.

   4. JLJennifer Li1:10

      And that- Good morning.

   5. MSMati Staniszewski1:12

      [laughs]

   6. JLJennifer Li1:13

      [laughs]

   7. MSMati Staniszewski1:13

      Thank you.

   8. JLJennifer Li1:13

      That was, uh, the walk- welcome music generated by ElevenLabs, was it?

   9. MSMati Staniszewski1:17

      It was. We- we- we expand continuously across the audio space. So we started with voices, then created orchestration of how to build voice agents, and now also create a fully licensed music model. So can produce amazing music to go alongside of it.

   10. JLJennifer Li1:32

       Awesome. We'll talk about, all, all about that. I've, um, had the opportunity and also, uh, the luck to get to know from the very early days when ElevenLabs got started and got to partner over the last three years to just see your execution everywhere from product launches to shipping new lines and models like you just mentioned, everything from, uh, text to speech models, speech to text, and then we, uh, started, uh, doing music, sound effects, and now the AI agent platform. Um, I'm very curious. First, I'm still in awe of the, the shipping speed, uh, after all the three years. Um, but I wanna ask, how do you actually maintain both the speed and quality when you have such expansive product roadmap?

   11. MSMati Staniszewski2:18

       So fir-first of all, we, we, so we partnered

2. 2:20 – 2:50

   Lucky Number Eleven

   1. MSMati Staniszewski2:20

      almost three years ago, and, um, so it's, it's, uh, great to hear all the kind notes. But also, what they didn't realize when we partnered, the infrastructure team was three people. And of course, now I'm ElevenLabs founder. We love number eleven, and the company infra team is eleven people. So we've seen the growth of the other side as well. Um, and, and I hear-

   2. JLJennifer Li2:41

      That's right

   3. MSMati Staniszewski2:41

      ... that the companies here raised sixty-six billion dollars in total fundraising, so the number eleven is everywhere here.

   4. JLJennifer Li2:47

      [laughs]

   5. MSMati Staniszewski2:48

      Uh, but the-- I think the,

3. 2:50 – 3:35

   Early Research and Product Work with Piotr

   1. MSMati Staniszewski2:50

      to start off, I think first piece, I have a-- I think the smartest person I, I, I got to know as my cofounder, Piotr, who, who has been the research brain for creating a lot of the, the models and then being able to assemble what we think are the most incredible researchers in the voice space to really create the first text-to-speech model that could understand the context in a better way and turn that into the emotion, intonation, then find a way to, um, capture the characteristics of the voice, so you have the voice sound, uh, uh, with the right style, with the right age, with the right gender, dialect, everything in, in one. Um, and then the researchers across, of course, now expanded that to speech-to-text, music, and other work. So that's our foundation. And then the way we structure it to be able to ship quickly, especially with so many things,

4. 3:35 – 4:40

   Shipping quickly with small, high ownership independent teams

   1. MSMati Staniszewski3:35

      uh, happening in the AI space, is a lot of small teams. So today we have roughly twenty product teams, each of five to ten people size, which with full independence can go ahead and ship products. Of course, that carries some of the, um, uh, um, uh, sometimes issues of duplicative work or sometimes, uh, uh, people going, uh, um, at different speeds. But at the positive end, the ownership of each of the teams is extremely high, so people know that this is down to them to really deliver and ship. Um, and it allows us to move extremely quickly. And we bucket our work into creative space, so creative platform where we help with narrations, voiceovers, dubs for, for creatives and creatives in the, in the media entertainment space. Um, and, um, and then on the agent sp- side, where we help people recreate voice agent experience, conversational agent experience across customer experience all the way through to immersive, immersive media.

   2. JLJennifer Li4:30

      Great. ElevenLabs has the labs name in the-- labs in the name, uh, very similar to many of the other big labs, which means you're doing your first-party R&D and model development,

5. 4:40 – 6:50

   Balancing research and product launches

   1. JLJennifer Li4:40

      but also building all these twenty products. How do you think about balancing both? Like, keep progressing on the model research, but at the same time not delaying sort of the product launches?

   2. MSMati Staniszewski4:51

      Yeah, it's very tricky. I'm sure many of you have the same thing. Like, do you, do you build the product while, uh, whe-when you don't know if the research innovation will displace the product you just built? We had this in the early days too. So one of the simple examples was we, um, we, we had a model at work, and one of the most common requests was could we do at different speeds for voices. So could you have additional slider to modify the speed of how audio gets generated and how quickly it speaks. And we are very against this idea of like, no, we don't want to do any sliders, any toggles. We don't want to become same as previous generation of, of the editing suites. So instead, let's solve it on the research level, where it will know based on the voice exactly how it should speak with the speed. And, um, and we resisted this for, I think, good amount of nine months, and we couldn't solve it on the research side. And then the product was super simple solve that got, got all the, all the users across. Um, and now the approach we take and, like, look-looking at this is if we think the research work will take more than three months, then the product is, um, uh, can do any, any, anything they want to, to start, um, adding other models, adding some of the extensions. Of course, sometimes the timeline is, is tricky to predict, but roughly the guidance we have from our internal research team, what are the initiatives we hop-hope to ship this quarter? What are long-term initiatives? And then for anything long term, you can use any, any other work to close that gap and make it, and make it better.

   3. JLJennifer Li6:18

      I guess first, uh, you kind of have to figure out if the research commitment is going to meet the, the timeline first and then go on to align with the, the product teams, um, that, that make a lot of sense. Um, as everyone is moving to San Francisco and building in person and locked in, uh, like, in the same space, Eleven has always been building globally and having people more distributed, but you now have centers, I guess, in different locations from London, Warsaw, San Francisco

6. 6:50 – 10:01

   A Remote-first approach: Meeting talent where they are

   1. JLJennifer Li6:50

      to New York and, and other places. Um, how do you think about building this global expansion and finding talent, um, globally versus, I guess, the trade-offs of building at, in the same place?

   2. MSMati Staniszewski7:03

      Yeah. And we-- so we started-- Uh, so me and my co-founder are Polish. We started between Warsaw and London at the time. Um, and we, we, we... I think ElevenLabs wouldn't have existed if we weren't starting from Europe. It's a, it's a very peculiar thing, but in Poland, if you watch a movie in Polish language, like a foreign movie in Polish language, all the voices, whether that's a male voice or a female voice, get narrated with one single character. No emotions, no intonation. As you can imagine, it's pretty terrible, and it's still happening today for most of the content out there. And-

   3. JLJennifer Li7:34

      I've had a similar experience growing up in China, that we have a lot of Western movies dubbed in Chinese monotone. [laughs]

   4. MSMati Staniszewski7:40

      It's so bad. So bad. And it's like in, in Poland, of course, post-communist country, it's a cheaper way to do it, so you don't have to hire as many people. You have one monotone, um, audiobook reading of a, of a movie. And, um, and that was kind of where the company started, and we started initially in, in Europe. And we realized that if we wanted the best people to solve what was a research problem at the time, we need to hire wherever they are. And, um, we couldn't lock ourselves to just San Francisco or, uh, or, or look at the West Coast. We, we knew that we need to find them across Europe, across Asia, and bring them into the company. So we started fully remote and, um, and started looking at those, those, those people. And then on engineering, we also were very against this, uh, traditional hiring method of looking at LinkedIn, looking at traditional, uh, traditional background and trying to figure out could we go and, and, and figure out a different method to, to hire people. That led to some very interesting hires. So we hired a person that, uh, had a incredible open source text-to-speech model and was working in a call center at the same time as a recipient of the calls to make money.

   5. JLJennifer Li8:48

      Wow.

   6. MSMati Staniszewski8:48

      And, uh, and he's now in the team, one of the most brilliant researchers we have, uh, doing all the data processing. But, um, the, the, the, the same pattern kind of followed. And of course, the early team was very distributed. And then as we started scaling, so beyond thirty people, we realized that the new people joining, there's benefit of them having a space to, um, to be next to others, to get deeper into the culture, understand how-- w-what are all the products that are happening in the company. So we started the hubs where you can go into London and Warsaw and San Francisco, where you can work with others in person. And that's why we try to, like, marry those two. If you are early in your career, you can-- you, you-- we, we try to hire you in the hub so you can immerse yourself in the company. If you are used to remote work, completely fine. Uh, but then if you want, you can always come and join us in the, in the hub. And that worked really well. Currently, we continue, like, hiring very untraditional backgrounds in some of the place of the company and then fusing that with very traditional backgrounds, which can teach the others. And, um, in sales, for example, we, we, we, we we've done some of those experiments too, uh, where that combination worked really well.

   7. JLJennifer Li9:54

      The lesson is you can really find talent everywhere, uh, it just how hard and how you, you look for

7. 10:01 – 10:40

   US vs Europe work cultures

   1. JLJennifer Li10:01

      them.

   2. MSMati Staniszewski10:01

      And the thing in Europe also, people like-- th-this was a interesting one. In, in US, people are very keen and excited to, to, to work, and if you, if you go for any social event, it's, it's like you, you want to talk about work. And in Europe, I didn't have this feeling where it's like most people don't want to do that. It's like the cultural piece is different. But then you do have the pockets of people that actually strive it too. They just don't have the companies where they could do that in. So I feel like our, our team from Europe is, is the most motivated and passionate set of people that, that, that, that, uh, that we are lucky to have.

   3. JLJennifer Li10:34

      Yeah. I can attest to that, given I've met some of them. Uh, very hardcore, very good work ethic for sure.

8. 10:40 – 13:35

   Removing titles and flat leadership layers

   1. JLJennifer Li10:40

      And you have also maintained a pretty flat org structure, um, and have people own quite laterally a lot of, um, responsibilities. Can you talk about the rationale behind that? And I guess there was also a no title policy.

   2. MSMati Staniszewski10:53

      Yeah. So we removed titles a year ago, and then, um-- and it's, it's going well. It still works. And, and I do think we, you know, we-- I-- we, we, we, we said we did it, but I thought a lot of AI companies kind of do it too already with member of technical staff being like, uh, the usual piece you have for engineering and then in a lot of the go-to-market, you are just go-to-market, not VP of sales or other roles. So I think it's, it's actually a, a pretty common pattern. But in our case, we, we, we had a small team approach where you have extremely small amount of people, usually the five to ten. And, um, and we wanted to make it very clear that every team, we, we, we create those teams, you have six months to prove it. If it's proven, the team will stay and continue working. But it really is that the moment you join, you can have any impact on the company. So you can have any role in that team. The tenure will not define your position in the hierarchy. If you are smart and quick and passionate, you can, you can, you can elevate yourself very quickly, which this, this really, this really helped. And also, it's a common, um, common layer to the external world where everybody looking at ElevenLabs knows that we are-- the go-to-market team is go-to-market team. There's no, like, uh, positioning, uh, to, to, to the, to the same extent. What this allows us to do is I think when we speak with a lot of our partners, with a lot of our customers, um, they also know that, um, that they are getting the, the best people, um, and always. And, and we can also send people to, to different conferences, different events, regardless of that, that positioning. Um, I think the tricky thing in the flat structure, there's not only positives. In the way we currently have, it's, it's a, a set of leads effectively for the subdivisions. So the research, creative work, agents work, go to market, self-serve and sales-led. Um-And of course ops. And o- only that's the layer of leads, and then under that, there's pretty flat small team approach across, across the world. Um, but then you really want the leads to be able to carry the complexity around the team, so suggest things between one team to another if they see that there's something valuable between them happening. Um, so I think picking those, those people that can context switch between is super important, and then letting the team fully focus on, on that. Um, and then having-- which is, uh, which was interesting learning, where if you, if you put a person into all the Slack channels and give them transparency, they actually get frequently distracted because then they read all the messages. You can still choose not to read them, but they still, they still do. So you kind of need to cut the access to a lot of those pieces to force the attention, and that kind of works. All those small things works, work really well.

   3. JLJennifer Li13:28

      Maybe we can borrow some of that lesson too. [laughing] Um, let's switch in gear a little bit. Uh, you're, you're on the front

9. 13:35 – 15:10

   The creative industry’s adoption of AI

   1. JLJennifer Li13:35

      line seeing a lot of the creative work, whether it's from, uh, art, music, or advertising that are starting to adopt AI tools. And in the beginning, that was not the case. There was a lot of resistance, and now we're just seeing the, the adaptation and the, the welcoming of using more of the generative AI tools, including, you know, AI audio. Um, and you have done some really smart things from the marketplace payouts to, like, working with these creative industries since day one, actually. Um, I remember how much you stressed, like, we have to find a way to work with them and sort of, um, observing sort of market shift over time. So the question is, um, h-how do you actually adapt to these changes and find the ways, um, to, to work with the industry in, in the infancy, in the beginning, and how did you navigate some of the challenges in that?

   2. MSMati Staniszewski14:25

      The, um-- So I think the first piece is, is, uh, is actually spending time with the industry and trying to understand what are their priorities, their incentives. Um, of course, it's sometimes tricky. Sometimes you, you then end up being starstrucked. We had a, a honor and pleasure to work with Jared on, on some of his incredible work and, uh, and learn from him on h- like what is important and, uh, like which parts of the production process you can actually use AI, which ones you wanna keep, um, where is it actually helpful. Um, uh, uh, and so, so I think that's the super important thesis across all the partnerships in the space. In our case, we, we tried to figure out how to do that on the, on the voice space, which is, of course, with that technology, A, how

10. 15:10 – 16:43

    The Voice Marketplace: Empowering creators to earn

    1. MSMati Staniszewski15:10

       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, um, different styles. Um, so we launched Voice Marketplace, where you, you could create your voice and then, um, share it, and when the voice is shared, you earn money in the return. Today, we have almost ten thousand voices. We paid ten million dollars back to the people in the community. There are some crazy stories from the voices. Our-- One of our first voices was a, a, a deep Spanish voice, and the, the magic of the technology is that the same voice now is available on all different languages in the same way. So it's thirty different languages at the time. Now it's seventy. But thirty 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-

    2. JLJennifer Li16:02

       [laughing]

    3. MSMati Staniszewski16:03

       ...because of that deepness. And now it's our top three voice for all the use cases. So, uh, hidden messages, you can all register to our Voice Marketplace and maybe earn some money too. Um, the-- So that's the, the, I think the second important thing is, like, figuring out how we can be part-- h-how we can bring the industry together to disrupt together rather than just to disrupt. And with labels, I, I think I'm still learning how to, uh, interact. The, the-- So we, we worked with labels, uh, the, the Merlin and Kobalt, so four of 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. Um, that was

11. 16:43 – 18:05

    Challenges in licensing and 18-month negotiation process

    1. MSMati Staniszewski16:43

       a hard process. It took us eighteen months to figure out the agreement that works. And in the end, I think the main thing, uh, was, was adding sort of forcing functions or forcing timings to, um, to find, uh, uh, uh, effectively a trigger of like, "Okay, this is when we do it, and we either do it together or we do it separately." And, um, and those forcing functions really helped add urgency. Then we, we needed to move that forcing function a few times, but, but it still worked to, to a large extent, uh, to, to go after that. And then two is, of course, the, you know, the finding the compromise wasn't, wasn't, wasn't, wasn't easy. Um, but then in our case, working with the, with the, with the labels there was, um, kind of protecting what they are caring about. And they, of course, also care about how, um, how they continue doing well by their members, by their artists that they work with. Um, so we would spend a lot of time working with their members, speaking about how we think 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 really helpful.

    2. JLJennifer Li17:56

       And maybe tying back to the earlier question, as you are navigating, like, this landscape, um, how do you think about, like, bringing the right talent that can

12. 18:05 – 19:10

    Hiring in complex domains

    1. JLJennifer Li18:06

       help and lead some of these functions? And these are mostly unknown territories of how to navigate it. Like, where have you been seeing, um, success in bringing the right people?

    2. MSMati Staniszewski18:16

       So here, for the spaces that are ki-kind of completely new to us, so this and, like, legals and 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, uh, full-time in the past. But then would actually, um-Uh, a-adjust that with a lot of consulting, uh, people that would help us in a specific conversation. So in this case, in music, we had, uh, music lawyers that, uh, worked very closely with us that consult across few of them. And the good thing is that they know all the players, and they effectively were this, um, uh, bridging gap between, between both of us so, so we could speak the same language. And, um, and then that was, that was, that was really helpful.

    3. JLJennifer Li19:01

       Yeah. And you have had, um, a very specific taste for people that are risk, uh,

13. 19:10 – 20:45

    Finding risk-tolerant talent

    1. JLJennifer Li19:10

       tolerant enough and also understand the commercial and business opportunities to, you know, help guide the right chain of actions in each of those domains. I found that very fascinating.

    2. MSMati Staniszewski19:22

       Hundred percent. I mean, l-legal, I'm-- I, I don't know how many of you are trying to find a first legal counsel or have a number of those. For us, this was the, I think, one of the trickiest roles to hire for because you are, um, hiring into the space you don't know, you know very little about. And then, uh, and then we had the, like, hi- the first couple of legal people that, that were clearly not fit, so we separated paths. Then we hired a third person, and that person came from like a, a, a number of Fortune 500 companies and, uh, and they never worked in startup 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. Um, and it was really tricky to work because we-- it's like you kind of get risks, but you don't get risk advice of like, "Okay, and this is where we should draw the line." Uh, but everything was back to decision. And now we hired a person working previously in the, a number of companies as, as, as counsel. And don't poach them.

    3. JLJennifer Li20:23

       [laughs]

    4. MSMati Staniszewski20:23

       They are amazing. Uh, uh, and they understand the, the, the risk equation a lot better where, uh, where they are not only like a counterpart to figuring out, um, 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.

14. 20:45 – 21:48

    Transitioning from creator-first to enterprise adoption

    1. JLJennifer Li20:45

       For sure. Um, ElevenLabs, uh, started as more of a creator brand, um, everywhere from the individual creators to the, the creators that are building businesses. But now you have been having a lot of success moving into enterprise, um, not just started from the AI agent platform, but, you know, even with the, the, the text-to-speech, speech-to-text models. Um, how have you been navigating that transition? 'Cause 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.

    2. MSMati Staniszewski21:20

       So when we, when we launched, we had a lot of e-early inbound where when, when we started the kind of the classic PLG, a lot of inbound from enterprise. And I remember speaking with a16z team when, when they joined us, where our initial take was, of course, we want to be engineering company. We don't want salespeople. We would like to reinvent that and have, like, engineers do the sales. Uh, we, we, we did hire one traditional salesperson and one non-traditional salesperson, like an engineer.

15. 21:48 – 23:34

    Lessons from hiring the first salespeople

    1. MSMati Staniszewski21:48

       We told them like, "Do sales now." And that really, as you can imagine, didn't work out in this specific case. Um, but we learned our lesson, uh, and we, we now do invest in, in a combination of that. It's eighty percent sales, twenty percent engineering. Uh, so still a little bit of that. Um, but this was like super important lever of understanding who are the customers, what they care about, and working deeply with them to, to bring it back. Um, 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. Um, uh, Munjal from Hippocratic is, is, is here. He was one of the, uh, the earliest, uh, incredible use cases in the healthcare 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, uh, reminding them about the appointment that's happening. And, um, 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 twenty-twenty-three. But then we've seen this repeated pattern across, uh, across a number of, uh, other customers in customer experience space and, uh, and many others. And, um, 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, um, the combination of knowledge base inside a system. You need to help deploy that with telephony providers, whether it's Twilio to SIP trunking. Like, how do you do that in a templatized

16. 23:34 – 26:22

    Scaling orchestration, long sales cycles and cultural adjustments

    1. MSMati Staniszewski23:34

       and easier, easier way? 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 you version control? How you evaluate, monitor over time, fine-tune over time based on the results? And, and all of that is, has been, um, an, an, an, a big, big part. And underlying all, all, all of that, and we spoke a little bit with, with Matt before coming here, the, the foundation needs to be there, which is the security, the compliance, serving, serving the, the customers, um, a-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, um, uh, the four nines or five nines, hopefully one day will be, will be there, which is tricky in AI space. Uh, but the, the-- that's the, that's the goal. Of course, the, the difference between, um, 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, um, and I think that's where eagerness from our internal team was, was, was, was interesting to observe, where you had a lot of people that didn't work in a enterprise setting, and then you had other side of the company that did. And the side that didn't wasVery skeptic about going enterprise and like kind of waiting the six months or twelve months to results. And i- in the early days, we needed to shield them from that information and like, "Trust us, we'll do this, and it will work." Uh, but they were very skeptic, and then of course after twelve months it, it, it worked out. But that was probably the hardest culturally of how you kind of still keep everyone jumping on the same, on the same train.

    2. JLJennifer Li25:11

       That's exactly right. Um, a lot of companies actually, at least I observed, sort of slowed down after start adopting, uh, more of the enterprise sort of product launching and like building for the customer's request, um, that started to, thank you so much, um, to delay sort of the, the product launches. Um, is that something you're seeing, or is there still like a good balance of like we still wanna be able to put out demos and POCs and, um, early teasers quickly, but at the same time we'll get to, you know, deliver a very robust and reliable product?

    3. MSMati Staniszewski25:45

       So there are two parts. The, the first part is, um, so we have a, like a difference on the team structure, and then we have a difference on the kind of o- external product structure. On the external product structure, mm, we, we want to ship very quickly, but of course, if you are shipping to enterprise, you, you want to make sure that it's stable and reliable. So we delineate very clearly what's alpha, what's not alpha, um, and then we go through that transition through, through that period. Um, and then as we work with the customers, they can-- and, and our partners, they can decide whether, whether they want the access to alpha in the first place, and when they do, they-- that's clearly shown that this is an alpha product, it might not be as stable. And so they get a choice. And I think that

17. 26:22 – 27:55

    Customer choice in adopting early features

    1. MSMati Staniszewski26:22

       choice has been the, the most important lever, like do, do, do you want it or, or, or not? And, and some are, are, are, are, um, incredible on doing that, that, um, innovation and, and, and, and, and showing some of their work or experimenting with that work. Uh, Deutsche Telekom with John here is, is, is, is, is creating some of the incredible new podcast experiences, and that came from like testing early models of turning a, a text, a, a text into like a more NotebookLM style of a podcast with incredible voices that you can select for German-speaking voices, English-speaking voices that, um, that, that sound good. Um, and then there's a second, which is team structure piece, and that's something that we didn't do until, until later when we had, uh, more than a hundred of us, is that we delineate inside a company products that are pre-product market fit and pro- post-product market fit. Um, on the post-product market fit, you are working for the long term. You test and evaluate a lot before. You, um, you only deploy when it-- when that's, that's, that's truly ready. The pre-product market fit, your mission is, is to ship until you think we've hit the product market fit. And usually we give the six months period of like proving it out. If not, we kill the product, and we've killed product in the past, uh, this way. But that's like the, the main important piece of like, okay, until we know there is a big potential user base, we, we, we, we will continue iterating.

    2. JLJennifer Li27:44

       I've, uh, been able to observe some of those, uh, I guess, hard decisions in the moment, but it's the right decision later on to, to let go of some of the products. Um, this is one of my favorite questions.

18. 27:55 – 30:06

    Phases of company growth: product, sales, scaling

    1. JLJennifer Li27:55

       Uh, my partner Martín Casado always say, "Companies go through three phases. There is the product phase, there's sales phase, and there's a scaling phase." And given you have been through some of those phases, what has been the hardest transition for you as a CEO?

    2. MSMati Staniszewski28:10

       There's, there, there is a, a lot of, a lot of mini ones. Of course, I have my, my co-founder next to me across each of those, which is the, the-- I know him for fifteen years. He's my best friend since high school, so I have like the, the most luck, uh, to, to, to have, uh, that combination. Of course, uh, you, you Jennifer and all the, all the partners to help us through those transitions, which is, which has been incredible. Um, but I think the, the, the, the recent, uh, like recent realization was when we, we are now a three hundred and fifty people company. And, uh, and of course, that means our go-to-market team and the incentive structure around that has evolved pretty, pretty strongly. And what wasn't clear to me, and now in hindsight it's obvious, is that the, um, i- in early days, everybody would just operate on a passion basis. They would just operate what they think is best for the company. As our go-to-market team enlarged, we realized that the incentive structure really matters if you are building that machine. And, um, and that transition where you shift from, from, from a lot of, a lot of the people that are helping create that machine, are part of that machine, those incentive structures will eve-eventually drive the behaviors, which might be slightly different to what you had in mind if you don't make it extremely clear. And in some ways, the, the quota, the commissions are a effectively a lagging indicator of strategy. And then, um, and then strategy, um, uh, uh, is kind of leading of what will happen in the future. So you need to find a way to resolve those two together, where you want to make sure the quota and commissions and the strategy that you want to drive are closer together, and they, and the, the, kind of the disparity as close as possible. And, uh, so, so here, the-- for me, the biggest realization was that we are becoming a bigger company because there are clear behaviors that happen based on the commissions. And then two, to actually resolve those, we need to be very upfront in terms of, um, of, uh, of making it explicit that sometimes even if commissions are just this

19. 30:06 – 31:21

    Turning down licensing to a competitor

    1. MSMati Staniszewski30:06

       and you think it's the wrong thing, come back to us. Let's speak about it and adjust course. So now we are explicit with all our sales teams that if they are seeing a deal that let's say might be competitive in nature, and our pricing table would suggest that they can go very low and earn higher commission, but they think it's wrong, it's better to come to us. We are happy to still grant commission but kill the deal and, um, and, and go address. We had this case recently where one of our foundational level comp-co-competitor came to us wanting to license our models for demos. And, um, and of course, the incentive would suggest that you should sell to them, but luckily, luckily we didn't.

    2. JLJennifer Li30:43

       Yeah. [laughs]

    3. MSMati Staniszewski30:44

       We granted commission, though.

    4. JLJennifer Li30:46

       [laughs] Yeah, in early days you can definitely like-

    5. MSMati Staniszewski30:48

       And adjusted that now it's in the policy.

    6. JLJennifer Li30:50

       [laughs]

    7. MSMati Staniszewski30:50

       So we cannot sell to the foundational model companies.

    8. JLJennifer Li30:53

       So it's clear, clear to-

    9. MSMati Staniszewski30:54

       Yes

    10. JLJennifer Li30:54

        ... to all the-

    11. MSMati Staniszewski30:55

        Very clear. [laughs]

    12. JLJennifer Li30:55

        ... all the team internally. Um, that was incredible, Mati. Thank you so much for, for sharing all the lessons and learnings with us. Let's give a round of applause to, to Mati.

    13. MSMati Staniszewski31:04

        Thank you. [audience clapping] [outro music]

Episode duration: 31:29