Jason: You're on a bit of a heater, huh?

Mati: It's the best time to be building.

Jason: and revenue has surged, but you face really intense competition. Let's go right at that to start. 350 million in what 2 or 3 years and I'm hearing numbers five or 600 million now. Tell us about the revenue ramp of the company from the moment you released the software to today. the product's been in market for 40 months, 50 months, you tell me.

Mati: Spot on. We started company 2022. First year was all about building the research and the product to really kickstart the work. We built the first texttospech model that finally could sound human. Released it in 2023, beginning of 2023. Then it took us roughly 20 months to get to the first 100 million in ARR. Roughly 10 months to get to 200, 5 months to get to 300. Um and um and that's how we closed end of the last year and now we are at 600.

Jason: You're at $600 million in revenue. This is just extraordinary. Um how many employees now? Because the company's obviously hit incredible valuations, but you have to fill in that valuation and you you're competing at a very high level for talent. So, so tell us about how many employees you have now and how you maintain the culture of the company when revenue is ripping, investors are throwing money at you, showing up at your doorstep. I mean, quite literally. Um, but you've got to run the company. You've got to build a culture. So, how many employees now and how are you dealing with these competing uh priorities?

Mati: Yeah, that's that's the key element of how you all for us the the element of like how we can maintain the culture despite the quick growth is is is kind of critical and how we optimize both the interview cycle how we are bringing people on board how we on board them with 600 people today um so also very quick growth on that people's side and as a company we combine research and product so we we we are building a communication platform for AI on the research side this includes everything across audio generating speech transcribing speech, orchestrating speech for interactions on the product. This is how we can complete the entirety of the customer journey from marketing and creating assets and localizing them internationally through customer support with voice agents to proactive enablement of how voice agents can help in operations, training and sales. So this requires a lot of different talent um and and and and a part of that revenue growth is actually reflection of the functions we've grown over time. So from the original team very research very engineering heavy from the first 10 people we had zero attrition everybody is uh uh still at the company from those core research and engineering talent building together with us so so far been able to out compete and I think the common Fred and and then credit to my co-founder who is incredible researcher himself we've been able to assemble the team that is truly excited about solving audio solving interaction and building that research and if they if they are looking for an opportunity out there and looking for a company to join and solve that we are we are one of the the the leading if not the leading place to do that

Jason: and you started before AI was so impactful at making software

Mati: right

Jason: so when you were starting four years ago 5 years ago and working on this building software was limited to low percentage of the population of planet earth you know the number of people could write code and now here we are, you know, went from vibe, we had a no code moment, then vibe coding, and now we actually have people building production code who are not developers. You have developers going 10x and token maxing. How has building software changed internally? And how do you deal with making sure that the code is really high quality? because people are paying you this money uh but they're going to demand really high quality product since they're spending so much money with you.

Mati: Yeah, that's it's also true that 2022 was still the year where topics of the day were crypto and um and metaverse. So with the building there was also the best time to start because we could actually take a take a bit of time to focus on what we thought is the future. Um but the the way we are structured is a lot of small teams especially across the product engineering but also in how we think about go to market optimized for specific industries telco financial services healthcare. So every unit is very tightly knit together. Um and we do that across the company. Uh so it's usually five to 10 people teams that that that run ahead. And inside of each of those teams the decision we took which is slightly different than how it's usually structured. We embedded engineers in in in every place and even in the places which aren't engineering. So our talent team will have an engineer, our legal team will have an engineer, our revenue engineering or go to market engineering have engineers embedded all across and those people will have two roles. One is of course creating automations and bringing the software inside of that team, but second is actually helping everybody else do what you said, which is make sure that people are adopting AI, but also there's a security check for everything they deploy because ultimately if if you're not using a lot of the coding software, a lot of the co-working software, then you're probably in the wrong spot. If you're using too much of it, that is also a flag because you maybe are not doing that in the right way. Um and of course as you start bringing that into the sites of the organizations that never were exposed they frequently can create but not necessarily review whether that's actually doing behind the scenes all the secure ways or anything. So that's an essential role in the in the in the in the company.

Jason: Yeah. We the it's fantastic that everyone can build software until you put it into production and you have a leak.

Mati: Yeah. uh or that person leaves the company and people forget they built that software and it's just deprecating uh on its own. The other thing that seems to have changed is management. When you had 10 developers in your pod or six, you had a UX designer, you might have a pure graphic designer, you'd have a product manager, they rolled up and then suddenly, you know, we watched over the past 3 years. Oh, hey, this is pretty good at um summarizing what happened on the call. Oh, it is actually creating action items and it's telling us what to do next. Oh, and it's, you know, doing uh all the different stories in our conbon board. And now, how do you think about product managers and management as the CEO and as the co-founder?

Jason: you fired them all, right?

Mati: We don't we don't have any PMs,

Jason: right? Did you ever or did you have you never did? Okay.

Mati: Never did. thought it's it's a little bit of what you mentioned was also before the true AI impact started which was ideal ideal person in that role can code can understand the customer can understand design of course that's very hard to find there's no truly that many people that are expert in any all all of those fields at the same time so we optimize for profiles that are experts in at least one of those fields but understand at least one other field really well to your point what we are seeing now there's if you can do a little bit of all with AI you can maybe step change from being an amateur to being a advanced level maybe not an expert level so suddenly you are not bottlenecked on all the other functions to do your work in u in growth phenomenal part growth engineering a person can design experiment ship an experiment it's working and bring it back um we also have the privilege where we are using a lot of our product ourselves so to be able to do that ultimately to help everybody else create voice agents we ourselves need to create voice agents too. Um so we are seeing that also in the non-traditional functions even in go to market like you need to be able to create a version of that if we are offering that to the customers too and and we do we created our inbound AI SDR agent that in addition to the form that you fill on the website you have an agent that you can call

Jason: and uh and people are um of course e can give all the information in a much easier and quicker way but the second thing that happen is people also leave a lot more information so you can get connected to the right problem and right person a lot a lot quicker. Um so we are seeing that kind of phenomena all the time where actually using a lot of tooling makes you yourself better in your job overall and in 11lapse in our specific tooling that we are solving for customers. Yeah, it seems like the use case of calling on the phone and talking to a computer or previously going through voice jail and and it was incredibly arduous and painful and annoying. made you just say operator and hit the zero button like as fast as possible. But now it seems to have turned a corner where talking to a human you I almost feel bad talking to a human where I'm like I am so sorry I'm wasting your time with this and the AI is just so much more precise and the fidelity is so great that when you tell them what you're looking to do and you cut them off you don't feel bad you don't have to make small talk. Is that what you're seeing in your customer base in terms of the ability in real time to interrupt the agent to interrupt the uh you know conversation and just move faster has made consumers and companies basically embrace the technology.

Mati: Yeah, it's it's slowly becoming that you will be asking for a give me an AI agent effective call AI operator. But uh we we are seeing a transition. We're suddenly and that's you know the the the biggest fuel of the recent growth for us is enterprises sales team just doing incredible work but then finally the product combines the reliability that's core with the orchestration for a lot of the AI models but also the knowledge and the integrations to provide you the right experience. And yeah, I think it it was a step change in the last 12 months and especially in the last six of of how good that experience became where it's like this golden era of consu for for the consumers out there customers and and customers is coming where you're going to actually open a website um um call an agent and have um the agent have information from your past interactions and deliver that help. And I think we'll see this kind of interesting phenomena combining your previous question and this where now of course you are reaching frequently when you have a problem and you're asking for help but ultimately a the whole interface will change and morph depending on how you are operating with that inter interface with voice being helping you in the background find that information will shift from reactive to proactive to help you get uh that help before you potentially ask for it. Um and we are seeing those examples those examples too

Jason: seemed to me that speech to text um had a major blocker again in fidelity 10 years ago lawyers would put on dragon dictate if you remember that terrible software they get a headset and it seemed like the the big blocker was you felt like an idiot talking to a computer in an office, right? And so people who did it quietly in their office, you know, they they kind of got away with it. Um, but now we see something very different. The whisper in the office. People very quietly talking to their computer, giving it a prompt, you know, and talking to their agents. And now there's a ring out. You can press it. And uh I use a really cool product called Whisper Flow. I don't know if they use 11 Labs on the back end. I

Mati: they they use they use and and a few others uh as well. And they they are doing phenomenal work too.

Jason: Whisper flow is just a tremendous product. And then I got a pedal. Does anybody here use a pedal on their computer? Raise your hand if you're a com There's one dork, two dorks. Any others? Raise it high. Oh, she's half dork. Okay, so there's about three and a half dorks here. Next year, this is going to be Do you have a pedal?

Mati: I don't. I I have I

Jason: Have you considered a pedal?

Mati: I I should consider a pedal. I love the devices that you can wear and the

Jason: I have the Plaude. It's incredible. Plaude Pocket. Phenomenal. Like so good. And especially in events like this, I feel

Mati: if you pre pre preempted that you're recording, of course, but how incredible would it be that all the signal on the conversations that otherwise disappear, you maybe tap tap few notes here and there to try to get signal afterwards if you can just have that automatically fill your specific notes and make sure you do your follow-ups. Phenomenal.

Jason: All right, so let me make the case for the pedal. Okay, I have a I have three pedals under the desk and I I think I'm trying to figure out what the company is, but with Whisper Flow, you press down, it turns on, and uh you talk and then you let it go. And one of the annoying parts of working with an LLM is typing. And you're kind of like exhausted when you're giving it the prompt. So, you stop prompting. But if you're a professional artist like me and a talker, this is like incredible because when I press the pedal down, I just give a stream of consciousness now. And it turns out what these LLMs actually do really well with is taking a massive stream of consciousness where you just keep talking and talking and talking. So I'll give it a one to twominut prompt. Then I let go and it has changed everything. Everything. It's it's you know like the whole experience is changing so much a similar version of what we see happen is you know how you have a you want to say a thought and then you're like okay I actually want to change and say something else now you have those two context combined and the experience you get as an answer is so much better uh so we already see that as an experience but even the the previous example of like people are adjusting how they speak to AI versus how they speak to human people are asking how so yeah what what how should you speak to the LLM. We saw Sergey Brin say threaten it with bodily harm. It's a very effective technique if you haven't tried it, but what are the things that are different when you're talking to the LLM?

Mati: The the the a specific emotional example, we we work with a lot of um financial services companies uh uh uh Revolute, Clara, Pogbank. And some of the frequent case not in all of them is of course how you remind people about payment or that you collect the de that from the people that aren't answering and frequently people would naturally feel ashamed of telling the real situation with AI people are much more open to share what actually happened give the information and suddenly this emotional block of like in front of other human I don't want to be able to to say all of that is is is very different so that's different um usually people are more snappy with AI voice agent. It's like, you know, like quick responses.

Jason: Yeah. You don't mind cutting it off.

Mati: Exactly. So, you can like kind of go through to to the point you want much quicker. Um, which you needed to like change a little bit of the interaction model too, which which is working. Uh, but we'll work on the pedal and whether whether whether we should we should do

Jason: a little bit about celebrities on the platform. You have some celebrities who are on there. Um, you also have um an issue with impersonation. Um, I know this because somebody was like, "Oh my god, I love your bulldog videos." Many people know I'm a big fan of bulldogs. I currently have three. Um, and I said, "I'm sorry, I don't know what you're talking about." And they sent me a channel where somebody had created a bunch of dogs telling jokes and they made one and I guess they were looking for a podcaster. So they used the this weekend startups archive and 11 lab to create my voice and do this huge channel. And I contacted them and I said, "Oh my god, it's very flattering. How how did you do this? This is like a year or two ago." And they said, "Oh, I used 11 Labs." So I I think I emailed you about it. I'm like, "H how do you protect against this in advertising in the law in the United States? I'm not sure about here in France. I'm sure they have 17 laws for this. We have one. Um, you guys are great at regulations unless no offense. And the French guy over here is like, "Oh, Mond shal um the that's my French angry developer guy." Um, I cannot smoke in the Lou. Um, this is crazy. Um, and so is super like interesting. um with this right to privacy and I think you've got a quick education on this because you've had a couple people I'm sure write you a legal letter what it basically means is you you can't take somebody's voice and use it to you know do commerce in the world you can use it for parody there is fair use I can do a Donald Trump impersonation up here if I like we're going to take about 5% of 11 lab stock is okay with you put them in Trump accounts sounds good okay and for that you get to come to the White Okay, thank you. Um, nasty guy. Wouldn't give 5%. Loves socialism, but not America. It's the problem with the Nordics. Um, nasty, nasty socialism. Then I noticed when my guys wanted to clone my voice so that they could fix the ads where I mispronounce something or I do the wrong promo code, use the code jal 20. They like were like, "It's 25, dummy." And I'm like, "Okay, I have dyslexia." and then they redid it and it was like I'm sorry you cannot uh clone Jason's voice and then it's like I have to go in there and do it and you put a bunch of protections in there. So explain what's happening in that regard in terms of people's you know concerns around this and then the other side which is the opportunity because I think you got Jamie Fox and some other folks actually that you paid for their voices.

Mati: Yeah. No, the the the the voice is identity and IP. It's like you know when you when you speak a certain way people recognize it can feel that emotion and you know to some extent it was it it was a a um it could be a problem could be opportunity before I mean as you did impersonation of of of the President Trump it's of course similarly uh uh u something that is possible even of a human uh not specifically AI but um for us on the safeguard side you know over over last years We took the role as we are leading on our development development. We also need to lead on a lot of the safeguards. So that's like a critical element. We do three things. One, trace everything that's generated so we can take action when needed. Two, now we moderate both on the voice and text level. So if you were to input something that would be commercial in nature or uh would try to scam someone that gets flagged, we can block it. And now free because over last years we've seen the development of those models more broadly. how how can we create systems for the wider world so people can upload a sample and get information whether it's AI or not immediately um and we do it for 11 Labs but we also do it for other open source models the interesting part given that it's such a good um IP um and and part of your your your your element it opens up new opportunities so we partnered with Matthew McConna on creating a world

Jason: all right all right

Mati: and across languages and it's the first

Jason: I haven't got paid a lot of money for these independent films but oh 11 lab stock is juicy. The

Mati: The

Jason: yum yum. Could you do it? Could you do it in Spanish?

Mati: It's a it's it's a fugazia fugazi. Yeah. But the the crazy thing with the with AI technology open is that now the voice can be carried not only English but also in Spanish and Italian and Portuguese and you can still have exactly that element of emotions coming through. Um so that that's kind of a a good example there but we've seen that with master.

Jason: What do you pay these guys? What does it cost to get Matthew McConn? Is this like an eight figure deal, seven figure deal? You give him a little equity?

Mati: Always depends. Uh so like you know the masterclass for example is a good example where they worked with talent directly. And here you have u previously a static content that you would learn from. Now um you have interactive content. So you have Gordon Ramsey teaching you how to how to cook in the kitchen. He can scream at you if you're not doing raw scallops raw. So, so, so that is that is definitely a

Jason: So, they're doing characters now or or AI instances using 11 Labs. So, you can interact with them as part of your subscription.

Mati: Exactly. But as a company, what we now do and this from the beginning, we created a marketplace where people can create their voice. We authenticated, you can share it, and you earn money. Today, we paid back over $22 million back to the community of of of talent.

Jason: Really? uh which so those voiceover actors now who got paid as hourly workers sometimes they get a little backend if they were doing a commercial or something now they can spend an hour reading create an 11 labs voice and then license it out

Mati: 100% and then

Jason: do they get to pick their price or you pick the price

Mati: the depends on the model we do we do both so you can you can either give it a default that lets us distribute that slightly more optimally or you can pick yours and the use case is going to be going to be different and like you opens up a set of incredible opportunities in the dynamic context in other languages. Uh but maybe a last one on that like voice is such a big part of identity and probably probably our most important work was actually working with people that lost their voice due to ALS due to throat cancer and working on bringing that voice back. So he worked with congresswoman in the US Jennifer Jennifer Wexon who lost it and wanted to continue inspire others that you can do incredible work despite that and uh and was the first speech delivered in in in in in Congress or more recently I think this was my the most uh like heart uh warming story. There was um there's a woman that um that's wanted to get married lost her voice before she could get married. And then they decided to redo the marriage together. do the vows again and do the vows and you could see the whole family just for the first time hearing the vows. It was just uh you could you could feel the emotions that you can see in any other any other way because the voice is such a connecting thing.

Jason: Yeah. And and you've done it for some iconic voices. My understanding is the estate of James Earl Jones. I'm not sure if they did he pass? Is James Earl Jones alive? Can somebody pass? He he passed, right? Yes. But before he passed, I think he did a deal with Disney and he said, "Listen, for my family, I would like to license the Darth Vader voice for all time to Disney." They gave him some incredible deal. And then they were left with, "Well, how do we actually do this? Do we get a voice impersonator, but instead they went to you?" Talk a little bit about that deal and how it went down. And is that what they used recently, you know, in in uh some of the new films with Darth Vader? There's a new Darth Maul series um where they have Darth Vader and did you power that?

Mati: I don't know what I can say about the new things but definitely the big use case that that big big completely new experience uh was in the gaming space where yes Epic Games game Fortnite launched um Darth Vader which people and players could interact with live in partnership with the state in partnership with Disney. So every player after reaching a certain stage could have a Darve interact and help you solve the missions. And we are seeing that kind of mode coming up more and more often of how you can effectively extend extend your likeness your like you said publicity into interactive use cases bring it across the world uh together. Uh so that was exactly that model and now we are working on on a on a on a one of the public one is headspace.

Jason: So Headspace has a great meditation. This is the uh second uh greatest meditation app right behind Comm which you are an investor of. I am I didn't realize you're right. I did and it was a $4 million company

Mati: is incredible. I think their their team but anyway you were you were working with the the um the second place. Exactly. So they not exactly the second place but exactly to the working part. They so they localize a lot the content and and calm I think is trying some of the interactive elements. Could you have a meditation lesson that's personalized to you? Which uh which we we would uh hopefully love to to to and like imagine just you know so many voices.

Jason: David Saxs is defending Trump. Take a deep breath in. Breathe out. Breathe in. Breathe out. Maybe you should license the voice to come. I I mean that would be interesting.

Jason: Let's talk a little bit about um being up against some of the greatest entrepreneurs ever who want to take your business from you. Specifically, Daario and Anthropic, Sam from OpenAI. They they want your business. They've been pretty clear about it. Um, and I think you have used the frontier models in your product, but you must be thinking, my lord, am I enabling my own demise by partnering with them? And there's all these open-source models. So, um, how do you think about your partnerships with those type of frontier models and the fact that they want to kill your company?

Mati: So on on the on the first part the given we create a platform we try to provide all alms out there. So our customers can pick entropic open AI open source uh Google models um and that agnostic being agnostic to the specific model is actually helpful because customers can they make sure that they build a harness build the agent orchestration create the voice element of how that agent interacts with the world how the marketing interacts with the world but they are not dependent on any model. So for us that part is is is um is actually good because we can provide that to the customers. On the on the kind of the second big part of like of course the the space is overlapping increasingly models our platform platform are application everything is becoming a little bit more more fuzzy for us. The still the defining piece was focusing on that one layer of like how does interaction look like? How does communication look like? And we've been able to out compete them on voice models um both on text to speech, speech to text, on the turn taking on music and um and we've you know here our research team is is is is a a set of magicians that are able to continuously do it time and time again. Um and I think part of the reason is it's on the research side. It's the architecture that matters, not the scale. You really need to change how the model operates. Two, you need very specific data that there's of course a wide set of data out there, but it's unlabelled data and where we spend a lot of time. So we build a internal team of over thousand contractors that label all those audio assets to make them to make them good. So that's on the research side. And then as you think about the rest of product stack, we want to create a fully verticalized solution for that communication angle. The product understanding the right workflow in financial services is very different to healthcare, very different to telos. We spend all of our product team to figure out how that works and those companies don't. And then ultimately last piece is the ecosystem. Can you build the wider set of integrations voices that you use templates for the agent authentication that you can benefit from instead of starting from scratch? And so far we've been we've been able to create a new model for that.

Jason: Certainly though you must be concerned about hey the reinforcement learning the data leakage. They say they're not using your data, but they're kind of using your data. And so, do you have an open-source project internally as the like in case of Glass, we got to break this? And when do you think you'll be able to discontin working with them if you had to?

Mati: We we we we know that some companies are continuously trying to figure out how to distill and use the data. So that is uh that is a existing problem and we have few mechanism to to to stop it. Um or slow it down not stop it. Um but um but on the open source question or like creating our own um um uh versions we are we are looking a little bit closer on like how we could use our expertise of how does like you know we won't focus on knowledge work. we won't focus on coding but any interaction and how you can combine all those pieces together and make sure this is this is great. Yeah, we want to own. So we are spending more time there. Uh but it's also just great to be in the arena and compete with those guys and uh and and every so often show that we can do it and do it better.

Jason: Yeah, it's pretty clear in my estimation that that's where you you will wind up and the ability to make your own language model today, especially with all these great models out there that are now open-sourced. It's going to be a pretty easy for a company with your level of resources. So, why wouldn't you at least offering it as an option? And then I guess there's cost. I mean, you must be shipping tens of millions of dollars to the Frontier models every year.

Mati: ship good amount. Um we are good partners good partners with them. Uh um but but it's um it's ultimately you know showing up in the value we can create too. So like a lot of what we spoke at the beginning of how we can elevate ourselves as organization too is is definitely helpful. So I think they they done tremendous work on on building uh it's it's almost crazy that each of us has like a a a Turing uh like you know if you if you were to chat with an agent now it feels like um the Turing test will be complete it's as smart as another human. Uh and we hope this year we'll do that same thing for voice uh where any conversation feels like you are speaking with another human. It will be

Jason: yeah I think you're there. It just depends on the application and like what question you ask. But it definitely passes it. I mean it if we were to look at the tests that were created to define artificial general intelligence or just to define artificial intelligence, we passed all of those. These were tests that were created 30 or 40 years ago. We need a new set of tests right now. I think the new test is like can this be more intelligent than every single person on the planet times 10. And if we get anything less than that, we we're kind of like, oh yeah, it's not smart. I mean, these things, we're kind of there on AGI, don't you think that we've kind of achieved it, we just haven't deployed it. I

Mati: I am I there are definitely places where where we did achieve it.

Jason: Yeah, for sure. All right. Continued success. Let's give it up for Mati from 11 Labs. Well done. Thanks for coming out. I'm going all in.

Jason: (Oracle Ad...) You're growing also at a very significant clip.

Max: Exponentially.

Jason: Is it exponential? No, it's not

Max: exponential. Oh, it's a sustained 50% quarter over quarter for the last seven quarters.

Jason: 50% quarter over quarter. Last seven quarters. Yeah, that's that's pretty

Max: darn fast. So I think we actually just became as of the close last week on Tuesday one of the fastest enterprise company with a direct sales motion to go from one to 100 150 beating Sierra with one quarter.

Jason: Amazing. Um and so um people I mean there's a couple of things in life that people really hate and paying lawyers is like way up on the top of the list. um with your tools obviously you got your contemporary and Harvey and people it's just a it's a small company um in the in the states um and then you also have I guess Claude and other folks also want to be in your business so this is a big prize um to take I don't know 80% of what we pay lawyers for and compress it by 90% like what what is the realistic power law here in terms of making for startups in the audience, your legal bills dramatically drop in costs. Yeah. And I'm seeing it already in the startup space. I had an I had one firm, one startup that hit a million in revenue. They had closed multiple rounds of funding. Um, multiple obviously large number of employees, a decent couple dozen employees. They didn't have a corporate lawyer. And I said, "Whoa, whoa, whoa, whoa, whoa, whoa. You think I had a million dollars in revenue? Like, somebody should review the contracts?" And they're like, "Chat CPT, bro." And I'm like, "What about the cap table?" They're like, "Chp, bro." And I was like, "Okay." And HR. And they're like, "Same thing, bro." And I'm like, "Okay,

Max: it's going to be a fun diligence target one day."

Jason: Well, that's what I said. I said, "Hey, you know, when you do the series A, they're going to ask that like some of this stuff be reviewed. like, "Do you guys have like IP assignments?" They're like, "Yeah." I'm like, "How did you know to do IP assignments?" First-time founders are like, "We asked chat GPT." And I'm like, "Okay, wow. I just turned into unk like I guess." Yeah. So, so take us through what you you think is happening out there. This is not uncommon, right? What I scenario

Max: but but a a seedstage startup operates very differently from, you know, one of the biggest banks in the US. And so the way to think about the market or at least the way that we like to is you have this enormous bucket of legal services which today is being done manually. It's a trillion dollars every year into legal services which is very fragmented. But the software spend into legal technology is about 40 billion. So it means there's 4% software 96% service which is bananas. The software piece should be much bigger than that. And so the software piece naturally will grow into um the service revenue but also legal is a very supply constrained market. The demand for legal services is much larger than what there are lawyers or legal services available. And so many of the legal service providers are now using technology to serve new use cases, new market segments and to actually package new products and you will not make

Jason: what's an example of that like

Max: so so an example of that is Kulie actually they started serving startup founders directly with a sort of software uh platform that you just log onto the platform they pumped it full with their material and their precedent and then you have the startup material there and they've embedded workflows that reviews the contracts. Um, and what I think is interesting by that is it it starts to um break this model where you charge out associates for very high hourly rates and you have a billable hour model. And actually, if you look in law firms, the way that that business model works is you overcharge for the associates and you actually undercharge for the partners. I don't know if they're under charge. I mean, I got a bill recently and it was 1,800 an hour, right? But

Jason: for a senior person, I think the associates were 800.

Max: Well, you know, at Kirkland, it can go up to 4,000 an hour. But the but the thing is when a when a Kirkland if so, let's say, you know, 30 minutes of a Kirkland partner's time when it really matters can be worth a lot more than that. Like a lot more than that. If it's bet the company litigation or you avoid a pitfall that would have costed the company tens of millions of dollars,

Jason: well worth it. Yeah.

Max: Right. Exactly. And but the only way they know how to price that is to overcharge for the associates. But as you're saying, the enterprises are looking at this and they're going, "Huh, we're spending a lot of dollars on legal services. Let's take this in-house." And

Jason: Oh, really?

Max: Absolutely. I mean, we're doing this partly at Lora. We acquired four businesses so far this year. We did the diligence inhouse the with our own tool and the fastest transaction we did was 12 days from LOI to closing

Jason: because your motivation as the founder is to get the deal done, right? The motivation of the lawyer is to not have you sue them if they up the deal, right? And to make as much money as possible, which means to drag it out,

Max: which means their incentive is to even if they don't say it explicitly, it is to drag it out. your incentive is to close it as quick as possible. Yeah. Yeah. And so, you know, I think a lot of law firms are also experimenting with different pricing models where you do a fixed fee for a transaction or for a fund raise. Um, in litigation, you can take a part of the success fee when you win the deal. Yeah. Or win the case. And so, I think it's just very interesting how, you know, one of the biggest industries in the world now is being completely transformed and reshapen as a consequence of the tech. And are those law firms feeling like they're being disrupted or this is a huge opportunity and and and did that switch at a certain point in time or has it switched for them?

Jason: (restated in previous block)

Max: Um there's a lot of anxiety and a lot of fear and you know these law firms are enormously profitable and big businesses. Kirkland Ellis turns around $10 billion a year.

Jason: How many lawyers did it have?

Max: Four or 5,000. Wow. I mean, per partner, they make it between 5 and 10 million every year in profits. And so, when something like AI comes along, that poses um existential threats and existential opportunity. And I that's actually a big part of my job to help articulate with the leadership teams that we work with because we will only be as successful as our customers are. And so we actually have a very unique role at Legora as well which is called the legal engineer. So in the same way that Palanteer has forward deployed engineers, we have forward deployed lawyers and their job is to sit down with the Kirkland partners and help them transform their business from a preAI to a post AI world. A and it's sort of like document management and PCs were but 20 or 30 years ago when they were printing out and keeping drafts in a in a library in a in a storage facility and they had to sort of walk them through and handhold that.

Jason: Absolutely. But I think the difference

Max: the difference is those were you know mild productivity gains. Yeah. this can do a lot of the work and so it's really reshaping what it also means to be a junior lawyer going into this occupation.

Jason: What does it mean? Are those jobs going to still exist or a lot of the lawyers who are coming out of school going, "Oh my god, was this a good idea or a bad idea?"

Max: The job will exist. The tasks will be different, right? Um, in order to have a partner-driven model, you need to bring people up the ranks, right? In the same way as you do with software engineers. You need to have junior engineers so that one day you can have senior engineers who know what they're doing. Um, but the way to get there is very different. The way of getting there today will not be lock yourself in the physical data room, read through every single document, mark the errors, and you know, go fax it, right? It's and it's also no longer just look in the virtual data room and control F. It's orchestrating the agent that will be doing that work.

Jason: And and when you look at that work, you you have a global backdrop attorneys obviously very famously localized, right? And is this going to create attorneys who can operate across borders in a way that didn't exist? And you're starting to see that and is that something that's built into the product? So when you're doing even in the United States, it's a it's state level certification obviously. Um and doing a non-compete in the Northeast is very different than doing it in California. They're not very enforceable or enforceable at all in California as people know, but they're quite enforceable if you're in Boston.

Max: Yeah. So, so talk about that. Uh because that seems to be a place where there could be massive gains from AI. 100%. And it it's really two things. I mean the data that Legora sits on top of is on one hand side the firms and enterprises own data their precedent their organizational data and secondly we do the hard work of gathering all the cases all the legislation all the regulatory updates for every jurisdiction in the world and that is very painful but once you start to do that at scale it builds a real data mo. and so in the system if you are the GC of a company in California and you just landed your first customer in South Africa, right? Legora can be adapted to the local legislation in South Africa. And we actually had a case of this where, you know, instead of having to call a lawyer who then knows a lawyer in that region who will respond to the query, they can get an 80% accurate response immediately that they can start working off out of. And the better that gets um the more um the more interesting things I I I believe you can do because this data has really never been structured before and there are so many people who are working with setting policy and building regulation and and this is a enormous inefficiency in society

Jason: and Lexus Nexus has been a juggernaut and the legacy player in you know all the case law and regulations. They have a massive data moat. They but they only make a couple of billion dollars a year. And if you put your revenue and Harvey's revenue together, you guys are probably already just that the two of you uh you're both making hundreds of millions of dollars. So, they must be looking in the rearview mirror at you like the Tyrannosaurus Rex in Jurassic Park and going, "Holy shit." Like, are they coming for our business? And then here you are on stage saying, "Hey, we're doing all the manual hard work of getting that information into our what I assume is a proprietary language model." We'll get to that in a second. Um, are you going to just try and buy Lexus Nexus? I know it's part of a larger enterprise, or are you just going to kill it?

Max: Well, I think that some of the existing uh providers and the sort of legacy players have a really hard time pivoting into becoming AI native businesses. And they have a really hard time meeting and and catching up to the tempo that we run at. They can't get the talent. They don't work our hours. And they're so political in their organizations that it's just hard to move. Um, I think at the outset of AI, many believed and made a bet that those organizations who had all the data was going to be the winners. As we're starting to see in the market, that's no longer the case. I think there's a real opportunity for us to partner with content providers. And we're already doing this in many of the smaller jurisdictions like in Germany, in France, in Spain. The US is is is peculiar because it's such a duopoly on legal research as a West Law is the other one. West law and Lexus Nexus. Exactly. But um yeah, if you look at how their stock is doing, um I think

Jason: or are they getting priced in with the AI uh certainty? Yeah. Yeah, that's one way of putting it. Yeah, they're getting crushed. Uh and I

Max: Yeah. Yeah, that's one way of putting it.

Jason: I would assume there's some power law here. you know, they might have an incredible breath of, you know, old case law that they scanned in and went to the courouses and did all that work on, sent to India to be double blind typed in. Like they literally had two different people type in the cases or OCR them, then check them, look for the differences. I mean, cuz you can't get it wrong.

Max: You're right. That's what you have to do. Nope.

Jason: But today with the AI tools, the AI tools are really good at doing what they did manually. Yes. Um, you still have to ship the books because you have to physically scan. This is very strange in the US, but uh, West Law basically has a monopoly with the American government to report on the cases. So, they're not owned by the public in a way. They're owned by the company. You guys are very good at capitalism. Sometimes too good.

Max: Sometimes too good. But sometimes too good. But I mean, Harvard has a project. There's the court law court listener.

Jason: They're trying. They're trying. Yeah, it's it doesn't work. Um or rather put it this way, you cannot build a legal research solution that doesn't have all of the data. Because if you go to Wachtell, Lipton, Rosen & Katz（全球最顶尖的诉讼律所之一），里面的资深合伙人要用你们的系统去打一场涉及上百亿美元的世纪诉讼或为埃隆·马斯克（Elon Musk）辩护，你绝对不能缺失哪怕一条极其边缘的判例法。所以这与长尾效应相反：在法律领域，你不能只拥有头部 80% 的常规数据，你必须拥有 100% 的全部数据。

这也就意味着你们可能依然需要派人去各地的地方法院去复印和扫描那些老旧的案卷，并为每页支付 10 美分的复印费。

麦克斯: 的确有其他方式可以拿到这些数据，但在实际操作中确实面临着类似的过程。你需要把大量的文献典籍运送并扫描，因为你必须在后台精准匹配每一条“页面引用规范”（page citations）。以前在大学里我从来没想过自己会对法律数据如此狂热，但现实就是这样。

以往的第一代法律数据库仅仅是一个“关键词搜索”工具，律师输入词条，找到判例，然后还是需要自己在一页页文本里寻找逻辑；而随着 Claude 3.5 / 4 等大模型的发布，现在的 AI 代理不仅具备强大的长上下文理解能力，更可以直接为律师制定复杂的案件战略（case strategy）。

AI 能够直接在底层将证人证言（witness statements）、财务报表证据和历史判例法交织融合在一起，直接撰写出一份极其专业的、端到端的案件辩护书。这标志着 AI 从简单的“效率辅助”彻底转变为“具备闭环执行力”的生产力实体，你日常工作的核心将转变为去编排和管理这些法务 AI 代理。

我知道你们与 OpenAI 和 Anthropic 同样有紧密的合作，每年要向他们支付大笔的 Token 账单。但如果他们未来也试图涉足这个行业并推出官方的“法务 Claude 助手”，你们会感到威胁吗？

麦克斯: 他们目前在我们的特定应用层面上完全不构成任何竞争关系。

从表面上看，Claude 等平台可能推出了针对法务方向的功能，但这仅仅是把一些 Markdown 技巧和几个基础的法条搜索接口捆绑在一起的极其浅层的体验。

大语言模型厂商的做法反而在帮我们教育整个市场——它让所有保守的传统律师立刻意识到 AI 对法务工作的巨大颠覆价值。当用户开始在日常工作中使用它并迅速碰到大模型泛化能力的“天花板”时，他们就会意识到大模型在专业法务深度和本地合规上的巨大短板，这时他们就会立刻拿起电话求助于 Leya。因此，前沿大模型厂商的行为实际上扮演了我们 Leya 的极佳销售漏斗和客源管道（pipeline generator）。

专业化垂直微调才是出路

杰森: 刚才我们和 ElevenLabs 的 Mati 也聊到了自主微调模型的话题。对于 Leya 来说，你们会在未来利用开源底座去分叉（fork）并训练完全属于你们自己的法务垂直大模型吗？

麦克斯: 我完全不相信针对法务这种强逻辑领域去微调或构建一套全新的通用人工智能（general intelligence）模型会带来任何优势。在我看来，那完全是对资金和研发时间的毁灭性浪费。

我坚信高度专业化和窄领域模型（narrow models）在特定场景下的深耕价值，这是降低系统延迟和 Token 成本的唯一出路。

以我们平台深受客户喜爱的 Tabular Review（多维合同审查） 功能为例。该功能需要瞬间在几百份合同里交叉核对几百个法务合规提示词，这意味着每次操作背后都对应着上万次大模型 API 调用。

如果我们针对这种合同数据提取等极其单一的窄任务，训练并部署一个完全微调的极小模型，它的速度会快百倍，成本会降低万倍，表现也会远远超出那些通用的庞大 LLM。而我们的某些竞争对手试图去从头训练一套“通用法务人工智能模型”，这在商业和技术逻辑上是行不通的。

杰森: 最后一个问题，你们是如何在和这些顶级客户合作时，打消他们对于商业机密和“数据泄露”的恐慌的？法律毕竟是一个对数据合规和客户隐私要求极度严苛的古老行业，一旦发生核心诉讼机密在云端泄漏或被其他人训练的事故，律所就会面临毁灭性的诉讼索赔。

麦克斯: 数据合规和极致的安全信任就是我们的生命线。

这也是为什么在法务科技这一垂直赛道上创业的门槛极高。市面上有上百家自称“法律 AI”的初创公司，但绝大多数根本无法通过这些世界级银行和顶级律所严格到变态的安全和合规审查（IT security audit）。

我们很早就集中整个工程力量解决了这个合规门槛。一旦你成功突破了这个安全壁垒并入驻成为他们的合格供应商，后续的业务扩张和跨部门增购就会变得顺理成章。这也是为什么我们会实施通过 Leya 直接并购其他垂直工具的 M&A 发展战略。

今天，即便是一些掌握着国家级安全机密的军工武器制造商的合同，以及多国政府的敏感草案数据，也完全托管和运行在我们的 Leya 平台上。

杰森: 这意味着你们必须像传统厂商那样，为他们提供昂贵的私有化部署（On-prem）服务吗？

麦克斯: 我们坚持不提供任何私有化本地部署。

如果你妥协去为每个客户在 VPC（虚拟私有云）中进行孤立部署，这会给后期的软件版本升级、漏洞修复和持续集成制造噩梦般的依赖泥潭，这会彻底拖垮你的技术路线图和极速奔跑的步伐。我们通过了最顶级的公有云合规安全架构打消了他们的疑虑。

杰森: 商业逻辑非常清晰。再次祝贺你们取得的指数级业绩增长！非常感谢 Max 接受我们的对话。

大家掌声送给 Max！

我将 All In 到底！