The world of voice AI, with Mati Staniszewski of ElevenLabs

20:22Mati Staniszewski:So how you orchestrate that. So like that's probably the most common thing we see as we work with some of the companies out there is you want those systems to orchestrate extremely well, where if it's a conversational use case, pretty simple. You can root the agent to speak with. But if you need to authenticate, if you need to pull additional information from the database, what do you do? How do you handle that graciously? That's where you're going. And yeah, and to that extent, I would agree, that's just getting, getting, getting there. And we'll hopefully see that our goal is to pass the voice Turing test in all those cases, or the Turing test for all conversational agents outside of voice too.

20:56Mati Staniszewski:and I hope we'll all be there in the next year or so.

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21:37Are you guys working on personalized voice transcription, where it feels like part of the way we're making it hard for ourselves is when I speak to Siri, I have a bit of an accent, and so it sometimes has a hard time understanding me, but my accent doesn't change. And so it could just get good at listening to John. But my understanding is it's not. It's just like running the global voice recognition model. And I'm guessing it's the same for 11 Labs where you're running the global voice recognition model. But again, you have an accent. And so if someone's understanding, like if you walked up to someone in a coffee shop and said two words, they might have a hard time understanding it because they're not putting it through their matty Polish accent filter.

22:17And so where's this going with like actually interpreting the person that you know to exist on the other side?

22:24Mati Staniszewski:Yeah, I have a very tricky one to detect. So my voice is frequently used in the test. Ah, you're a part of the test suite. For text-to-speech, for speech-to-text, for like everything. Yeah, yeah, yeah. I'd be like, yeah, it's pretty, pretty. But again, trying to parse your voice in a global model is just making life hard. It's like have a Matty-specific model. Yeah, so on the speech-to-text by transcription, exactly. Like the big part now that we are bringing in is you have two parts. One, effectively like a person or a voice-specific detection. which is true for the accent side, but it's also true for a crowded room.

22:57So that's where we have an incredible research team

23:00Mati Staniszewski:that's able to continually do both the accuracy high, but also add things like speaker detection, of course, noise reduction. But then the second part is also keyword detection. So there are specific words that you would want to say in those settings that you want to effectively monitor for. So we spoke about, let's say I'm going to the coffee shop and order things. The set of actions the coffee shop would expect me to do is pretty limited. There's information theory. It's like they can just listen out for the coffee words. Exactly, and then try to match it to the closest proximity. So both things will help.

23:35Mati Staniszewski:In a setup where you have my voice, perfect. You can decode it and code it on that. If you don't have my voice, or even if you want to double amplify it, we already support effectively a keyword detection, which is useful for real-time setting and async setting. So back to the cheeky pine transcription, you're going to effectively pre-generate that from the previous podcast and look for a set of words that you would use traditionally in that. And so how hard, okay, so you did the keyword detection already, but how hard are the, I want to get superhuman transcription performance by feeding it an hour of Matty audio before it listens to Matty, and then it should be able to do a much better job transcribing.

24:16Is that just a really hard research problem? No, solvable.

24:20Mati Staniszewski:We think we can roll it out in one of the next versions, which is hopefully in the next months. Oh, so you think this year you're doing person-specific transcription? Person-specific transcription. We can already diarize speakers extremely well. So if we are speaking, we can, of course, dissimilate who is speaking when, which is like in transcription side, apart from accuracy, diarization is one of the harder problems. And we do that extremely well. And now it's going to be effectively what you were saying, fine-tuning based on the speaker that I want to listen to, which we know will be important.

24:53Mati Staniszewski:I mean, in healthcare setup, such an important part. You're in an operating room, you're a doctor, you want to say a command, then you want to really be able to listen to that one person-specific piece. You have a hardware device at home, let's say it's a pilot, that helps you control the TV. Here too, you will want that to listen to you versus, let's say, the family roaming around. Or maybe you want it to everyone. So you could decide that, but in many cases, you want to be able to specify that. Okay, that's really exciting. It's great because there's still so many unsolved research problems.

25:27There's just breakthrough after breakthrough coming in the domain of voice models. How about on the flip side when it comes to speech generation, the Zoom touch up my appearance feature? I've always thought about that in the context of voice, where should you offer a de-accenting filter for voices? or even this one podcast that I like to listen to, but the voice is a little mumbly, and I always thought they should put it through a de-mumbling filter just to make the enunciation a little better. But all these things, again, like Photoshopping an image, there's no reason that the... Have you thought about voice-to-voice, basically, rather than voice-to-text or text-to-voice?

26:07Mati Staniszewski:Yeah, so there are kind of two big parts. One, on the speed generation side, similar. So many ovations still there. there's like a wider piece and that's like the we released the v3 model that i kind of we're solving that for the first time is like can you control speech so you can have the text to speech you generate something that sounds emotionally great previously until until end of last year effectively you would rely on model to decide what's the best performance you could regenerate it but that ultimately model decides the best performance so that's where the controllability came in where we can finally give it cues of say it in a slower way or change how you deliver the dramatic pause or kind of any cues that you give and to be able to do that you need the architectural changes and the data that we kind of created over time where you annotated what was said and how it was said so you can actually train the model to do that so today finally you can have both speed generation or entire voice agent experience um with with what we call expressive mode where the agent knows the emotions on the other side so if the person is stressed, it can react and be reassuring.

27:11Mati Staniszewski:And that's generating a limb response on the reassuring side and response in that set of emotions too. And that breakthrough was super hard to do. And that, of course, stretches to a lot of what you said. It could be some version of speech enhancement, either real time or in a post setup to change how that's delivered. And that's relatively recent innovation. And we know it can still be so much better. The edge cases of how you want to describe it is pretty large. So that's one. And then the second part of the question, which is a huge question, that's speech-to-speech models. So as you said, our approach, as you think about voice agent, conversational agents, is effectively a cascaded approach.

27:52Mati Staniszewski:You use transcriptional speech-to-text, LLAM, text-to-speech, and orchestrates all of that together. And then you have a speech-to-speech, which kind of goes directly from speech, and there's a speech response on the other side. When I say speech-to-speech, is that the idea that it doesn't go through text as an encoding in the intermediate set? Oh, interesting. For performance reasons, for accuracy reasons? You usually do it for latency. Okay. For latency. It is faster to run a model that does not have to transcribe and then generate. Exactly. It's quicker, but on the flip side, you lose reliability.

28:23Mati Staniszewski:Yes. You lose all visibility into the parts of the pipeline. In emotionality, we think you can deliver both on both sides extremely well, and maybe you can make it more controllable too. So today we are optimizing heavily on a cascaded approach. I'm sorry, a cascaded approach is? Is the speech-to-text, going through the text layer. Oh, okay. Going through the text layer. And as we work with all of the businesses and enterprises, they will need that visibility into what happens. They will want to execute certain tasks on top of that. They want a good visibility into each of the steps and great accuracy of all the models.

28:55Mati Staniszewski:But beyond that, they can abstract away what's the LM layer, what's the intelligence layer. The integrations are easier in that system. So that's where we are betting a lot of the research work of how you can make that great. And we think we can make that great. And speech-to-speech, as you think about maybe more of a companion version of the applications, that's where that will flourish because maybe the hallucinations aren't as important, but the latency is a little bit more and maybe hallucinations are even a feature. And maybe in the future, just to finish that part, you will have some version of combination of the models.

29:25Mati Staniszewski:That for low-complexity, easy models, you will have speech-to-speech. And for higher complexity, you will have the cascaded. Okay, so I was going to ask about this. You know the way there is research on how the invention of writing changed humans' brains and just changed the neural pathways in ways beyond the actual written language. Do you observe that speech-to-speech models think differently than cascaded models? It sounds like they're dumber. They are definitely dumber. You need smaller model. You cannot. But that's interesting, right? That like forcing models to reason about text. I mean, I know they just have much more in there as well, but they're smarter.

30:10Mati Staniszewski:Yeah, but it's like, you know, like if you are going speech to speech, usually you will use smaller models so it's still quick. So you can like say that. I see. So it's also just a model size thing. Okay, but are there interesting differences beyond like correlates like size? What I can say is like slightly different to your question. The people interacting through voice and the performance we see for how they interact with the business changes just by nature of interacting with voice. A good example, you can contact 11labs and register for your interest. You go through the form. And at the end of that, we supplemented that.

30:45Mati Staniszewski:Instead of going through the form process, you can speak with our agent and leave more details. And what happened there are two things. One, people were actually much more keen to leave the forms through speaking with the agent. So we would go through the form a lot easier. But second, they would be a lot more open-ended in terms of what the use case are. So they would start giving us information about the wider set of use cases, the complexity of the use case. So the writing out was tedious and tricky. Because it's like an open-ended adventure game. Open-ended. You could ask follow-up questions.

31:15Mati Staniszewski:You could clarify. But people were just more at ease and could trust the system while doing that, that it's working. And that kind of helped us a lot. and then free, which maybe is more of a technological barrier, it also works across all languages. So now we have leads from all parts of the world coming in and leaving their details. So we did that use case, and now we have a few different companies building their HDR versions of that too to help them capture the leads coming in from banks all the way to actually one of the automotive companies that leaves that where people are just more keen to speak through voice.

31:50I want to ask about this kind of a second order effect. You have, you know, you've talked in the past about how growing up in Poland, I guess the dubbing of TV shows, they were cheap and so they would only have one voice actor for a TV show. So no matter all the parts, male and female, they're like, I love you, I love you too. You know, there's like one voice actor doing all of them. And now, you know, thanks to better voice models, you'll be able to just have like really good voices, AI generated for all the dubbing. Because again, it's not like it's taking jobs from great dubbing that was happening previously.

32:21It was like awful dubbing happening in Poland previously. So that's like one example of the second order effects. What are the other second order effects you're seeing of ubiquitous good text to speech, speech to text? It seems like across a broad array of languages, because whatever about in English, just this didn't exist in Polish or Irish or, you know, pick your language.

32:44Mati Staniszewski:One, breaking down the language barrier, you know, kind of the inspiration came from the movie side. But it also applies in any communication setup. Like, could in the future, could I travel to another country and speak Polish or speak English? And that language isn't being understood in the local native language. Like from Hitchhiker's Guide to Galaxy, this version of the Babelfare. Exactly. That you can actually understand the world. And voice, of course, will be an interaction layer. But similarly, all of us will have our own kind of extension and voice agents that can help on our behalf. And there is like very clear and great examples of that of people that lost their voice and can get it for the first time back.

33:23Mati Staniszewski:We see that everywhere, whether that's people that lost it due to ALS or throat cancer that can get it back. Just recently, there was an example of a patient that had Neuralink. We worked with them to bring the voice that that person could speak with their own voice back with the family around. We worked with the lady that lost her voice before she got married. And then finally technology became possible. We were able to recreate that voice. And for the first time, she could replicate the marriage ceremony and speak the vows together, which was like such a heartfelt moment. Probably like the most important from all the work that we do.

34:03When you guys talk about voice agents, is a voice agent just the idea that you have some long-running or persistent agent that is going out and interacting with the world through voice? And so customer service being one example of it. In the other direction, your claw going and making you a restaurant reservation and actually calling up the restaurant. Is that kind of how I should think about voice agents?

34:30Mati Staniszewski:That's right. It's exactly, whether it's like the reactive side of being able to interact with the customer or the proactive to call it back. We recently had a very interesting one, Topical, because it was a Guinness-related one, where there was a developer developing a Gindex, effectively. Oh, I saw that. They were calling all the pubs in Ireland to check on the price of a pint. You could ask that or report information. The Gindex is built with 11 Labs technology. It was built with 11 Labs, too. So people could actually do both sides, could proactively reach out, reactively reach out, all was captured through voice.

35:05Mati Staniszewski:And then kind of 3 ,000 different entities could report their prices and get that across. Have you, by the way, hooked up your OpenClaw to 11 Labs? Is the OpenClaw-11 Labs combo something that a lot of people at 11 are doing? So, as you know, the OpenClaw will kind of look for the most popular tools frequently where it tries to hook up. So 11 Labs is one of the recommended ones. It's the top option for voice. Can you tell me a bit about the business of voice models? I think people have an intuition around big LLMs, where there are these very expensive training runs. And yes, they kind of appreciate it quickly, but there's so much usage that all of the models trained to date have paid off their training runs and then some.

35:51And then there's this kind of ever larger CapEx going into, I mean, a lot of it is inference these days, but also training. And so people have some intuitions from the LLM world. I'm curious just how I should think about voice. For one, how expensive is training the voice models? Is the expense in the researchers? Is the expense in the training runs? And I mean, the economics is presumably kind of simple, where it's just per usage. But yeah, just talk us through the business.

36:21Mati Staniszewski:Yeah, definitely cheaper than the LLM and image video models. It's a really smaller models. Okay, so the models are smaller. Smaller. What's a parameter count for a leading-edge voice model? A few billion to low-tenths of billion parameter models. And for context, I think the, I mean, kind of like, you know, CPUs moved away eventually from gigahertz as, like, the metric as they moved to more cores. I think we've mostly moved away from just raw parameter count, but I think the leading-edge LMs are in the hundreds of billions of parameters. I think the leading ones, yes, but, of course, you know, you have the variations that you will use at lower scale.

36:58Mati Staniszewski:So CapEx is still pretty high. We've, of course, raised recently a half a billion at 11 billion valuation. Makes sense. Makes sense. To continue being able to build the best models in the world. Researchers, you know, of course, you want the best people in the world. I think we have those people working in audio and my co-founder who is leading that work. So that's definitely a big piece of like, not financially, but even like how you keep the ambitious deployment. So you kind of continue building leading models, helps you attract more talent and building that. And then on the how we serve it.

37:37Mati Staniszewski:So of course, inference is correlated with how the models are used. And for us, like we've seen incredible, incredible growth across the work. Mostly this is charged per, if it's input text or text to speech, it's usually per text token. If it's voice agent or transcription, then it's per minute. And we see that kind of being the bigger part. But usually, like broadly, it's per token basis. And of course, as we work with businesses, it's like an annual agreement. The bigger the spend, the bigger the comments, the bigger the discount to get that gross. The way we usually do is like when we have a new model, we try to give it at cost to a lot of the customers so they can experience the best.

38:17Mati Staniszewski:It's still usually like not as reliable. And the newest thing is often the most expensive, whereas you make the newest thing the most economically attractive one? We try to make it attractive so the customers are, like, you know, like, it's more expensive for us than any previous generation. We don't, like, the quality is higher, so we try to keep the prices still competitive to that. I see. You subsidize it, but it's inherently more expensive. Exactly. It's a bigger model. Exactly. Exactly. And over time, we might do some tricks to optimize it, but, like, we want the customers to, like, experience.

38:44Mati Staniszewski:Because of research, the big thing that we've seen is the reliability of the model in the early days might not be there. And then two, people don't even know what's possible with that model. So you kind of want the widest set of distribution so people can show the world what's possible. So you can have it, of course, as a distribution mechanism, learn yourself what to improve, what to change, and then get it out there. Are the voice models just getting bigger and bigger? Like, will we have voice models in the hundreds of billions of parameters? Or have we found, like, it seems like for certain types of model architecture, there's like an upper limit on like the natural size.

39:24Have we found that upper limit for voice models?

39:26Mati Staniszewski:It feels like for specific use cases, like say audiobook narration, you probably found that size. You probably don't need to stretch it too much bigger to make the quality as much higher. But for certain use cases, that will probably grow. The thing that's, you know, like I hesitated on the question is, in a cascaded approach, you probably will not see like dramatic size changes. You inherently want the models to be quick and reliable. You want to orchestrate them in a smart way. In a fused approach, probably that will get into like tens, hundreds, billion-parameter models because you kind of combine, of course, the LM side and the voice side.

40:03Mati Staniszewski:So that will get bigger. But on the just voice, I think it will keep being small. Okay. But there are certain domains where we'll see bigger models. That's so interesting. It is amazing how, it does seem fun from a research point of view, how there are still these various unsolved aspects and how you guys are just making technical breakthroughs and then releasing them down the product pipeline. That's like a really fun stage of a company's lifecycle. For sure. It's fun because it feels like we can do innovations on both sides. There's so much on research side, so much on product side. And then ultimately the biggest path is how we deploy it to the customers.

40:41Mati Staniszewski:Where SMB will have a very different dynamic than the enterprise. It's not vendor-sass relationship where you just give the product out there for the biggest companies out there. But you are more of a partner in their AI transformation part. So you want the resources to work alongside them to work on the frequently very new use cases that were impossible to help create and bring those voice agents to production. So that's like a big, big, big shift. But the biggest focus is how we bring the conversational agents out there to the businesses around the world. So when you say bring conversational agents is the biggest priority, is this for customer service type use cases?

41:21What are the most popular use cases for conversational agents?

41:25Mati Staniszewski:Yeah, we want to be a partner for full interactions between businesses and their customers or their audience. I'm saying their audience because that will apply in support. Support is the easiest one because that's where it's most ready. And that's maybe the big difference to how we see ourselves to some of the other companies in the space is this can also apply to sales. You can have the proactive side of reaching back. You can have AISDR versions of that. And then you can have all the way to the marketing use cases where we are your partner for working on even outside of like the conversational agent space of how you create a great marketing campaign.

42:01And so how does this break down between, you know, we had Dev Trainer from Intercom on here and they have Finn, their agent, and it's a thing in the website that you can go talk to. And he described a very similar phenomenon that you described, which is you start maybe thinking, oh, this will help me answer customer support queries. But it becomes like a generic UI for the website, where it's a box you can type in to go do things and understand things. And so why wouldn't you read the docs and design your integration that way? You know, whatever. And so will I have like one for text and then one for voice?

42:38Will you guys do text too? Will just, how does that? Because it seems like this is also succeeding at the text level with Finn and Sierra and all these things.

42:48Mati Staniszewski:The places where we know we will be able to provide the biggest value is like where ultimately today you will have either a big portion or most of the interactions coming through voice. So if that kind of intersection is there, that's where we can provide higher value. And of course, like if you need a text chatbot there, that's like if you fix the voice agent, you'll have fixed text piece or like inherently as well. But the place where we do optimize today is going to be like, how do you select the right voice for the right customer interaction? How you pull that in the pretty complex case of what you mentioned earlier of like how you orchestrate that to pause or look for something deeper into the docs, how it can be extension of entirety of the business.

43:31Mati Staniszewski:So not only in support, but across entire of the user journey. But the bottom line is like, We want to be able to provide you across entirety of the interactions. Voice is usually a big part of those interactions. And yes, we need to solve the integrations. We need to solve the knowledge. We need to solve text as part of that. But we wouldn't, for example, go into what I think will happen in a lot of those cases, very deeply into a reasoning version of those use cases where you maybe need to multitouch. Yeah, yeah, yeah. And a lot of complex actions. A lot of financial analysis. of like, that would be not something we optimize for.

44:08Can we talk about your revenue ramp, where you're just one of the fastest growing startups period of the past few years. What's your most recently announced revenue figure?

44:16Mati Staniszewski:Most recently announced was end of 2025. Whatever number you want to give us. So most recently announced was 350 at the end of 2025. Yeah. But the best proof of the technology working, so recently we announced our work with Deutsche Telekom and T-Mobile, with Revolut, with Klarna, with Meta, with IBM, a wide set of use cases. And this quarter was kind of one of the best for enterprise growth, where we had the first quarter hit 100 million in an additional ARR growth, which is crazy. In net new ARR. In net new ARR. Okay, so if you're saying this quarter was 100 million in net new ARR and 350 million at the end of the year.

44:52I'm no mathematician, but it's up in the 450 million range. And that's versus this time last year. That's a several fold increase. Just what's working? Like from the outside, I would assume that there is really strong cohort growth within accounts. And then you seem to have self-serve and enterprise businesses that both contribute a lot. I don't know how big self-serve is, but as a user, I like to be able to fiddle with 11 labs and not have to go talk to sales. But maybe you can just talk about what works to reach 450 million plus of ARR so quickly.

45:28Mati Staniszewski:Yeah, so exactly. So we are over 50 % is now sales led on enterprise. And I think largely that technology that powers a lot of their agentic interactions just became reliable at the same time as high quality over the last year, year and a half. So frequently you know this extremely well. You will start the account and then of course it continues expanding. And we see there's definitely land and expand motion across 11 apps. What does that expand look like? Is it like new departments? Is it just the usage starts taking off when a customer expands? Both. But usually the first part, too, it's like we try to make it very easy for our customers.

46:11Mati Staniszewski:Maybe that kind of against ourselves where we give the technology a pretty attractive economics because we so much believe in the technology providing value. So you can actually try it and test it. And then within that one apartment. And you think you'll make it up in usage, basically. Exactly. That usage, the kind of content continues increasing because you know it's providing value. And then it's so much easier to make that a choice. Yes. And then, of course, cross-department pollination is there too. And it's like, you know, our work with digital comes sort of marketing side. So we did Magenta work and Pogta's generation.

46:44Mati Staniszewski:Then it kind of expanded to customer support. And then it expanded to us working in agent across the entirety of the network so people can call in and have the agent. So you could see those step changes across. But we are now 470 people as a company. So we keep on growing. But some of the things that stay consistent is small teams. So we have less than 10 people teams for each of the product or research initiatives. Or even as you think about sharding some of our go-to-market strategy, those will be smaller teams understanding the industry in depth, understanding the market in depth, and going independently and going quickly.

47:23Mati Staniszewski:So that definitely contributed largely to that. Two, especially on the biggest enterprises, what we found works is, and it's like we have the full spectrum, self-serve, PLG motion that helps drive distribution, drive kind of awareness of 11 labs. And on the completely other spectrum, we have the high-touch forward deployed engineering working side by side with the customers to customize the entirety of their work together. Why did you guys do self-serve? Because I presume you have a lot of competitors where they have tech and it's behind a contact sales form and you have to go talk to an SDR and then talk to an AE, blah, blah, blah, blah.

48:01And you guys just offer the tech available on the site. And I'm a huge believer in this. I mean, a huge part of Stripe's growth has been driven by the fact that we just made Stripe available to anyone and built a lot of product around that adoption pattern. But so many companies seem to skip it.

48:17Mati Staniszewski:So I'm curious how you guys came from. So many reasons. So many reasons. I think the quick ones that come to mind is feedback loop. You can have immediate understanding of how good your technology is. Two, which is an extension of that. We stand behind our tech. We believe it is the best in the world for models, for voice agents, for deployment. So we want people to experience that. And I think you do that the same in Stripe, where the best version of the technology is available to everyone, which is so attractive to actually try it out. We always try to make everything we build for the highest end use cases, bring it back to the ecosystem free.

48:53Mati Staniszewski:Frequently, the newest of the use cases, for enterprise, you will need reliability, you need compliance, you need the scale which we deliver. So frequently, as you develop new technology, it might not be ready for a lot of those parameters, but it's definitely ready for developers and SMBs. and we love what they are doing because they are showing us the future and effectively helping us find a trajectory of where 11 apps should go. I'm totally convinced, I'm just always amazed that more companies don't pursue it or it feels like they're really shooting themselves in the foot by not, like did you guys self-serve on Stripe or did you?

49:27We self-serve on Stripe. Yeah, for example, you know, 11 is a huge company and yet you started on Stripe on a self-serve business.

49:33Mati Staniszewski:You kind of like initially, and it's like, you know, we were two of us at the beginning. You try to see what's working in the industry, but you try to think from first principles. So you want to try it out. You want to understand how it works. So the more friction elements before you're trying it out, the less you trust whether it's available, whether it will be additional payment that's hidden behind some of those steps. So you don't want to go through them. So it's so much. Speaking of Stripe, do you have any Stripe feedback for us? Anything you want us to fix? My most common feedback until recently is like, why don't you give us pay-as-you-go user-based billing type version?

50:07Mati Staniszewski:but one of our finance lead, Maciek, I know I was speaking with your team and that was the day before he was great, he was like thinking about it for a long time, he's great He said like, you guys should buy Metronome You should buy Metronome, and then the next day Metronome acquisition was announced so now you have it, so that was my most common feedback, and we'll be launching that's a good announcement for this for this podcast, we'll be launching user-based billing to everyone. I'm shocked you Oh, as in previously you had it on an enterprise basis, but everything on the self-serve basis was like plans?

50:42So we had the subscriptions, yeah.

50:44Mati Staniszewski:Subscription plans, you can go over them, but now we are launching a full pay-as-you-go experience. So you can just try out voice engine, which is effectively this whole orchestration loop all the way through to any of the models directly. Going back to self-serve, I think a new thing in AI is that all self-serve products should have pay-as-you-go as an option. Maybe you want to have like a subscription with some unlimited tiers, but I don't know if you had the experience of like you're using Cloud and like you're typing away your queries and eventually you hit some rate limit and it's like, sorry, you've hit your usage limit and you want to be able to do the thing that you can do a Cloud code, which is just pay per API.

51:20It's like, I'll pay for it. And it's kind of very funny as a consumer to not have the option to pay more, to use the product more. And so, yeah, I think every AI product will need, you know, they probably want to have some all you can need, most of what you can need is subscription with limits. and then the ability to pay for overages. So it sounds like that's what you're doing.

51:37Mati Staniszewski:Yeah, exactly. That's what we're doing. The only thing I wanted to ask you about is, I feel like all CEOs of larger companies today are trying to figure out how do all these AI advancements change the nature of the organization, and how do you redesign your organization a bit around all this new intelligence? And so that could be about what the scaling factor is of like the number of people you need to do the work. But it also should be like, do you need more senior people because they're better able to direct the AIs and the AIs are maybe you can do the work of, well, previously would have been junior people.

52:15Do you need more junior people because they're going to be more AI native in how they work? Do you want smaller teams? Do you want bigger teams? How do you actually go do the process engineering of your finance team should be using Cloud extensively? But like finance teams do not historically, you know, have a lot of home-built software. And so there's all these questions that are floating around. And you have very rapidly built a much more AI-native company. And so I'm curious what lessons we should all be learning from Eleven Labs as a large business recently built. And so without the baggage of decades of how we've always done it.

52:52Mati Staniszewski:Yeah. Yeah, we started in 2022, which is a year when the two topics of the day were crypto and metaverse. So just before. And then, of course, AI flow started. Exactly, exactly. But we could like had the privilege of like kind of scaling through the world when it was all happening. For us, Woodworks, and we really believe in that being the big part of the future. The first is small teams, like keeping the teams small and super flat. So like, can you have, both me and my co-founder will have over 15 direct reports each that we'll work with. And most of those people will have that same scale of direct reports.

53:28Okay, so your span of control is way larger than the traditional company. Normal would be eight. Exactly. You have double that. And obviously that's an exponential.

53:34Mati Staniszewski:Exactly. And of course, there are some teams which in the short term might not do that. But ultimately, that's where we think it's going to be headed. It's like roughly 10 team size within each of those work items. And startups. Pretty close. No offense, but startups often have pretty wacko management ideas. There was a funny tweet, Laura Grant made the confidence of an early stage startup founder blogging about their management theories. But you think this is not a startup effect? this is an AI effect where basically no it's definitely a little bit of startup effect I think it's like it's hindsight hindsight benefit I'm cancelling our stripe changes yeah no no it's like I need to pre-end that I'm not kind of you know it's the hindsight of this may be working we'll see in the next five to ten years in their places much flatter org much flatter org so it works for us it might not work for all the companies and there are some parts where like go to market we still are trying to figure out what's the best way but smaller teams flatter org and i think they're two paradigms but like generally people being more technical or if not technical even in non-technical teams having a technical resource so you know we will have a person in ops or in talent that will we have effectively a tech lead for that team yes that helps them automate a lot of that that that work and helps up level the rest of the team too yes so there are kind of two parts that are helping okay so talking through this in talent or something like that.

54:55Is it that you are building your own software where other companies might have bought software like a Workday or a Greenhouse or something? Is it that they are using the existing software you have better? Is the process that would be spreadsheets in a traditional company are built with software? How do you kind of use the software in these sorts of organizations?

55:16Mati Staniszewski:Yeah, like sometimes, but we still use a lot of like the traditional vendors. Like one pattern is of course, is LM-ifying everything, like making the data explorable for you to be able to interact with it, of like who's in the pipeline, what worked, who does the best references, like all of that works, so you can double down on that. But two, it's frequently things that you manually do that a lot of the current, like there is a gap between where the agents are today versus what you could do if you have the technical skill set. And a good example is like, how do you scrape all the right profiles to be able to reach out to the right candidates?

55:52Mati Staniszewski:so you like analyze whether it's you know how much i should want to say but but uh the like try to detect specific things that we know worked so you'll bring that across to the to the to the to the people on go to market side like there's just so many things you can do with with with additional amplifiers you know it goes from understanding what case studies are relevant and creating a good pre-read for you before you go to the meeting, through creating the AISDR experience that we spoke about, to creating an entire deck experience. So you have a pre-populated deck with the right numbers that is customized to that customer, which you want still the person to go through and develop, but ultimately is in there.

56:35Mati Staniszewski:So there's plenty of those additional things that you know will amplify the work of the people around, potentially replace some of those easier tasks that are done. And then there's like, you know, we wanted for people to explore the culture at 11labs. So we created a voice agent that people can speak with and see what's the culture, but also get prepped for the interviews. I think across many of those teams, like additional benefit of what they can do. Interesting piece. So, of course, in Ukraine, with ongoing work, they need to rethink a lot of how their development, their systems, their support works for the citizens across the country.

57:11Mati Staniszewski:And people are in the war zone. They don't have the same access to the information. They cannot rely on the same phone lines. They cannot rely on the same physical services around the country. So they've developed effectively a central... So your employees in the Ukraine? We had a few, but they reached out because they were developing their central map called Dia. They developed it over the years, but now with war, they were doubling down on how this can be a way of supporting the citizens. And of course, there's an easy part of how you create a first-agenda government where you have help with the benefits and what's happening on the front line or education.

57:47Mati Staniszewski:So that's delivered to everyone. Or healthcare, so you can book your checkup or appointment. So how you create all of that. And of course, we traveled to Kiev. We worked with them on bringing that and making that available for voice so everybody can access it. But the thing we've learned while being there was that model of what we speak about where you have technical resources in each of the teams. They actually have the same in every of the ministries. So every ministry had technical resources working on creating that agentic version of their work. And then it was like a central digital transformation team that would like assemble this all together to deliver that for the central citizen support, which I thought was brilliant.

58:25Mati Staniszewski:That's very tacked forward by Ukraine. So take forward, like the most advanced set of work we've seen. So we got a little bit validated, like, okay, maybe technical resources in each of the teams is a good idea. And that works heavily for us. And, you know, you mentioned some of the other parts, like do you hire the senior or younger? Like main thing we try to filter for, of course, the culture piece is so important. You can scale people, but scaling culture is much harder. So, like, you want to optimize for that being right. In our case, it's first principles, taking ownership, striving for excellence, but staying humble.

58:58Mati Staniszewski:And the main thing that's kind of in that ownership part that I think works well for the AI world is agency. Like if you have that agency to explore, regardless of where you are in the experience cycle, it's going to be a tremendous amplifier to your work. My biggest takeaway from all this has been that around agency, where I feel like high agency people are the winners of the advances in AI and within organizations, low agency people will lose out. Yeah, completely agree. Probably the most proud thing that Piotr and I are is as we scale the 11 labs, the people that are at 11 labs is being like just the culture and seeing the expansion of the culture, where culture builds the company now rather than any single person or any single product builds the company.

59:45Mati Staniszewski:That was probably the biggest validation and happiness. and there is kind of the other angle of that where I think people are like striving to be incredible in their craft and their work but at the same time have fun in a lot of their work and that kind of combination of agency and just enjoying what you do is probably the best thing we've been able to do today at 11 Labs. Well, it sounds like a really fun stage like we were saying. Interesting research breakthroughs, really fast growing business so I'm sure you're enjoying it. Andy, thank you. John, thank you so much. A red light car with a Bikram Jake In the riddle Ken дорage11 Canondin 11 If they aren't 28 Canondin subtitles

1:00:38subtitles Thank you.