The Trillion-Dollar Industries AI Is Disrupting: Voice, Law & the End of the Billable Hour

You're on a bit of a heater, huh? It's the best time to be building. And revenue has surged, but you face really intense competition. Let's go right at that to start. I'm doing all of you! If you were building a global financial system from first principles today, you wouldn't build it on 50-year-old legacy rails. You'd build air wallets. One AI native platform for global accounts, cards, and payments is designed to make the entire world feel like a local market. Others are bolting AI onto broken infrastructure, but air wallets was built for the intelligent era from day one. Stop paying the legacy tax and start building the future at airwallets.com/allin. Air wallets built for the future. $350 million in what, two or three years? And I'm hearing numbers five or six hundred million now. Tell us about the revenue ramp of the company from the moment you released the software today. The product's been in market for 40 months, 50 months, you tell me. We started company 2022. First year was all about building the research and the product to really kick-start the work. We built the first text-to-speech 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, five months to get to 300. And that's how we close. And of the last year and now we are at 600. You're at $600 million in revenue. This is extraordinary. How many employees now? Because the company is obviously hit incredible valuations. But you have to fill in that valuation and you're competing at a very high level for talent. 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'm quite literally. 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 priorities? Yeah, that's the key element of how you're for us, the element of how we can maintain the culture despite the quick growth is critical. And how we optimize both the interview cycle, how we are bringing people on board, how we onboard them. We're 600 people today. And so also very quick growth on that people's side. And as a company, we combine research and product. So we are building a communication platform for AI. On the research side is includes everything across audio, generating speech, describing 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. 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 nutrition. Everybody is still at the company from those core research and engineering talent building together with us. So far, we've been able to compete. And I think the common Fred and I'm 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 are looking for an opportunity out there and looking for a company to join and solve that, we are one of the leading, not the leading place to do that. And you started before AI was so impactful at making software. So when you were starting four years ago, five years ago, and working on this, building software was limited to low percentage of the population of planet earth. A number of people could write code. And now here we are, we had a no code moment and 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, but they're going to demand really high quality product since they're spending so much money with you. Yeah, it's also true that 2022 was still the year where topics of the day were crypto and metaverse. So the building there was also the best time to start because we could actually take a bit of time to focus on what we thought is the future. But 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 optimize for specific industries, the alcohol financial services, health care. So every unit is very tightly needed together. And we do that across the company. So it's usually five to ten people teams that that that run ahead. And inside of each of those teams, the decision we took, which is slightly different on how it's usually structured, we embedded engineers 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, engineers embedded all across. And those people have two roles. One is, of course, creating automations and bringing the software inside of that team. And 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 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. 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 whatever. So that's an essential role in the in the company. And the it's fantastic that everyone can build software until you put it into production and you have a leak. Or that person leaves the company and people forget they built that software and it's just deprecating 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 three years, oh, hey, this is pretty good at 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 all the different stories in our con bond board. And now how do you think about product managers and management as the CEO and as the co founder? Yeah, we so we don't fired them all right. We don't have any pms. So it's you ever or did you have never you never did I've never did thought it's a little bit of what you mentioned. I was also before the true AI impact started. It was ideal ideal person in that role can code can understand a customer can understand design. Of course, that's very hard to find. There is no truly that many people that are experts in any 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 fields 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 growth. And growth phenomenal for growth engineering present can design experiment ship an experiment. It's working and bring it back. 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. So you're seeing that also in the non traditional functions even 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 we do we created our inbound a is the art agent that's in addition to the form that you fill on the website. You have an agent that you can call and people are of course can give all the information much easier and quicker way. But the second thing that happened is people also leave a lot more information so you can be connected to the right problem and right person a lot quicker. So we are seeing that kind of phenomenal the time where actually using a lot of tooling makes you yourself better in your job overall and in 11 laps in our specific tooling that we are solving for. Yeah, it seems like the use case of calling on the phone and talking to a computer or previously going through voice gel and it was incredibly arduous and painful and annoying. It 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 conversation and just move faster has made consumers and companies basically embrace the technology. technology. Yeah, it's slowly become that you will be asking for, give me an AI agent effect. >> Give me the agent. >> Yeah, AI operator. >> But we are seeing a transition. We're suddenly, and that's, you know, the biggest fuel of the recent growth for us is enterprises, sales, teams, 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. >> Yeah, I think it was a step-chance in the last 12 months, and especially in the last six of how good that experience became. Where it's like this golden era of consumers, for the consumers out there, customers and anti-customers is coming, where you can actually open a website, call an agent and have the agent have information from your past interactions and deliver that help. I think we'll see this kind of interesting phenomenon combining your previous question in 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 more depending on how you are operating with that interface with voice, being helping you in the background find that information, those shift from reactive to proactive to help you get that help before you potentially ask for it. And we are seeing those examples, those examples too. >> Seem to be that speech to text, 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 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, they kind of got away with it. But now we've seen something very different, the whisper in the office. People very quietly talking to their computer, giving it a prompt and talking to their agents and now there's a ring out, you can press it. And I use a really cool product called Whisper Flow. I don't know if they use 11 labs on the backend. >> They use us and then a few others as well. And they are doing phenomenal work too. >> 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, there's one door, two door, (laughing) 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 gonna be, do you have a pedal? >> I don't. I have, have you considered a pedal? >> I should consider a pedal. I love the devices that you can wear. And it runs-- >> I have the plot. It's incredible. >> Plot, pocket, phenomenal, like so good. And especially in events like this, I feel if you pre-pre-pre-empted that you are recording, of course. But how incredible would it be that all the signal on the conversations that otherwise disappear? You maybe tap a few notes here and there to try to get the signal afterwards. If you can just have that automatically fill your specific notes and make sure you do your follow-ups. >> If not, all right. So let me make the case for the pedal. >> Okay. >> I have three pedals under the desk. And I think I'm trying to figure out what the company is. But with Whisper Flow, you press down, it turns on, and 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 bulls-dars, 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. Would you just keep talking and talking and talking? So I'll give it a one to two-minute prompt. Then I let go. And it has changed everything. Everything. >> It's the whole experience is changing so much. It's similar version of what we see happen is, you know how you want to say a father and then you're like, "Okay, I actually want to change and say something else." Now you have those two contexts combined. And the experience you get us in answer is so much better. So we already see data as an experience. But even the previous example of people are adjusting how they speak to AI versus how they speak to human. People are asking like, "How so? Yeah, how should you speak to the LLM?" We saw Sergey Brin say, "Fretnet 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? >> The specific emotional example. We work with a lot of financial services companies, a Revolut, Clare now, a Pogbank. And some of the frequent case, not in all of them, is of course how you remind people about payment or how you collect 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 say all of that is very different. So that's different. Usually people are more snappy with AI voice agent. It's quick responses. >> Yeah, you don't mind cutting it off. >> Exactly. >> So you can go through to the point you want much quicker, which you need to change a little bit of the interaction model too, which is working. But we'll look in the pedal and whether we should do it. >> Let's talk a little bit about celebrities on the platform. You have some celebrities who are on there. You also have an issue with impersonation. 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. 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 podcast. Or so they used the this week in 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 did you do this?" This is like a year or two ago. And they said, "Oh, I used 11 labs." So I think I emailed you about it. And I'm like, "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. You guys are great at regulations and less no offense. The French guy over here is like, "Oh, Mondo, Chican." The, that's my French angry developer. I cannot smoke in the Louvre. This is crazy. And so, it's super like interesting 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 I write you a legal letter. What it basically means is you can't take somebody's voice and use it to, you know, do commerce in the world. You can use it for parry. 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 labs. Is it okay with you to put him in Trump accounts? Sounds good, okay? And for that, you have to come to the White House. Great. Okay, thank you. Nasty guy wouldn't give 5%. Love socialism, but not America. It's the problem with the Nordics. 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, jcal20. They like, we're like, it's 25 dummy. And I'm like, okay, I have dyslexia and then they redid it. And I was like, I'm sorry, you cannot 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 concerns around this. And then the other side, which is the opportunity because I think you got Jamie Foxx and some other folks actually that you paid for their voices. Yeah, no, the voice is identity in the IP. It's like when you speak a certain way, people recognize it, can feel that emotion. And to some extent, it was a, it could be a problem, could be opportunity before. I mean, as you did a personation of the present Trump, it's of course, similarly, something that is possible even over human, not specifically AI. But for us on the safeguard side, you know, over over last years, we took the role as we are leading another development. We also need to lead on a lot of the safeguard. 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 would try to scam someone that gets flagged, we can block it. And now free because over last years, we've seen that the development of those models more broadly, how can we create systems for the wider world so people can applaud a sample and get information whether it's AI or not immediately. 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 IP and part of your element, it opens up new opportunities. So we partnered with Mafia Makona, he on creating a world called Radar Radar Radar. And across languages. And it's the first time I've paid a lot of money for these independent films, but oh, 11 labs, stock is juicy. Yeah. Yum, yum. Could you do it in Spanish? It's a fugazia, fugazia. >> Yeah. But the crazy thing with the AI technology open is that now the voice can be not only English, but also in Spanish and Italian and Portuguese, and you can still have exactly that element of emotions coming through. So that's kind of a good example there, but we've seen that with Masterclass. >> What do you pay these guys? What does it cost to get Matthew McConaughey? Is this like an 8-figure deal, 7-figure deal? You give him a little equity? Always depends. So like, you know, the Masterclass, for example, is a good example where they worked with Italian directly. And here you have previously a static content that you would learn from. Now you have interactive content. So you have Gordon Ramsay teaching you how to cook in the kitchen. He can scream at you if you're not doing-- >> [bleep] >> [laughs] >> Scalops of raw. >> So that is definitely-- >> So they're doing characters now, or AI instances using 11 labs so you can interact with them as part of your subscription. >> Exactly. >> So what's happening with we now do this from the beginning, we created the marketplace where people can create their voice. We authenticated, you can share it, and you're in money. Today we paid back over $22 million back to the community of talent. >> Really? >> So those voice-over actors now who got paid as hourly workers, sometimes they get a little back end 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. >> 100%. >> And do they get to pick their price or do you pick the price? >> Depends on the model, we do both. So you can either give it a default that lets us distribute that slightly more optimally, or you can pick yours and the use cases going to be different. And like I said, opens up a set of incredible opportunities in the dynamic context and other languages. But maybe the last one on that voice is such a big part of identity, and probably our most important work was actually working with people that lost their voice due to a loss due to fraud cancer and working on bringing that voice back. So you worked with congresswoman in the US, Jennifer Wexton, who lost it, and wanted to continue inspire others that you can do incredible work despite that. And was the first speech delivered in congress. Or more recently I think this was the most hard warming story. There was a woman that wanted to get married, lost her voice before she could get married. >> Wow. >> And then they decided to redo the marriage together. >> Do the vows again? >> I do the vows. And you could see the whole family just for the first time hearing the vows. It was just that you could feel the emotions that you couldn't see in any other way because the voice is such a connecting thing. >> Yeah, 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 as James Earl Jones alive. >> He passed. >> 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 some of the new films with Darth Vader, there's a new Darth Maul series where they have Darth Vader and did you power that? >> I don't know what I can say about the new things, but definitely the big use case that big, big, completely new experience was in a gaming space where-- >> Yes. >> -- Fortnite. So Epic Games, not more game Fortnite launched Darth Vader, which people and players could interact with live in partnership with this state, in partnership with Disney. So every player after reaching a certain stage could have a Darth Vader 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 your likeness, your life's publicity into interactive use cases, bring it across the world up together. So that was exactly that model. And now we are working on one of the public ones, headspace. So headspace has a great meditation app right behind Calm. >> Which you are investor of. >> I am. I didn't realize you were right. I did. But it was a $4 million company. >> That Calm is incredible. I think they're team-- >> But anyway, you were working with the-- >> The second place. >> Exactly. So they're not exactly the second place. But exactly to the working part. So they are localized love the content. And Calm, I think, is trying some of the interactive elements. >> Could you have a meditation lesson that's personalized to you, which we would love to interact with. >> That would be amazing. >> And imagine just, you know, you still have many voices. >> David Sacks is defending Trump. Take a deep breath in. Breathe out. Breathe in, breathe out. >> Maybe you should license the voice to calm. >> I mean, that would be interesting. Let's talk a little bit about being up against some of the greatest entrepreneurs ever who want to take your business from you. Specifically, Dario and then Propic, Sam from OpenAI. They want your business. They've been pretty clear about it. 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 how do you think about your partnerships with those type of frontier models and the fact that they want to kill your company? >> So on the first part, the given we created a platform, we tried to provide all our lamps out there. So our customers can pick. Anthropic, OpenAI, OpenSource, Google and that agnostic to the specific models is helpful because customers can make sure they build the harness, build the agent orchestration, create the voice element of how the agent interacts with the world, how the marketing interacts with the world, but they're not dependent on any model. So for us, that part is actually good because we can provide it to the customers. On the second big part of the space is overlapping. Increasingly models are platform, platform application, everything is becoming a little bit more fuzzy. For us, the still-defining piece was focusing on that one layer of how does interaction look like, how does communication look like. And we've been able to compete them on voice models, both on text-to-speech, speech-to-text, on the turn-taking, on music. And we've been out here, our research team is a set of magicians that are able to continuously do it time and time again. And I think part of the reason is it's on the research side. It's the architecture.metters, not the scale. You really need to change how the model operates, too. You need very specific data that there's of course a wide set of data out there, but it's unlabeled data and where we spend a lot of time, so we build an internal team of over a thousand contractors that label all those audio assets to make them good. So that's on the research side. And then as we think about the rest of the 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 Telcos. We spend all of our product team to figure out how that works and those companies done. And then ultimately last piece is the ecosystem. Can you build the wide 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 able to create a new model for that. 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 discontinue working with them if you had to. We know that some companies are continuously trying to figure out how to distill and use the data. So that is that is an existing problem and we have few mechanisms to stop it. Slow it down, let's stop it. But on the open source question are like creating our own versions, we are looking at what tells around how we could use our expertise of how does you know we want to focus on knowledge work, we want to focus on coding, but any interaction and how you can combine all those pieces together and make sure this is this is great. We want to on so we are spending more time there, but it's also just great to be in the arena and compete with those guys and and every so often show that we can do it and do it better. Yeah, it's pretty clear in my estimation that that's will 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 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. We are good partners with partners with them, but it's ultimately showing up in the value we can create to so like a lot of what we spark at the beginning of how we can elevate ourselves as organization to is definitely helpful. So I think they then tremendous work on building. It's almost crazy that each of us has like a cheering, like you know, if you were to chat with an agent now, it feels like the cheering test would be completed. It's us smart, us and other human. And we hope this year we'll do that same thing for voice where any conversation feels like you're speaking with another human. - Yeah, I think you're there. It just depends on the application and like what question you have. But it definitely passes. I mean, 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 that 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'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? Like we've kind of achieved it. We just haven't deployed it. - I am, there are definitely places where we did achieve it. - Yeah, for sure. All right, continued success. Let's give it up for Mati from 11 left. Well done. - So much. Thanks for coming out. ♪ I'm doing all of you ♪ - The AI companies building the future run on Oracle Cloud Infrastructure, training and deploying at scale on one of the world's largest AI infrastructures. The same Oracle AI platform gives enterprises access to leading models, AI grounded in their own data and the security to move from pilot to production. Learn more at oracle.com/ai or experience it live at Oracle AI Experience Live. (upbeat music) ♪ I'm doing all of you ♪ - You're growing also at a very significant clip. - Exponentially. - Is it exponential? No, it's not exponential. - Oh, it's a 50, it's a staying 50% quarter over quarter for the last seven quarters. - 50% quarter over quarter, last seven quarters. Yeah, that's very darn fast. - So I think we actually just became as of the close last week on Tuesday, one of the fastest enterprise company with the direct sales motion to go from one to 150, bidding Sierra with one quarter. - Amazing. And so 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. With your tools, obviously you got your contemporary and Harvey and people. - Right, it's just a small company in the States. And then you also have, I guess, quad and other folks also want to be in your business. So this is a big prize. Two, take, I don't know, 80% of what we pay lawyers for and compress it by 90%. To like, 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 a one firm, one startup that hit a million in revenue. They had closed multiple rounds of funding. Multiple, obviously, large number of employees, a decent couple of dozen employees. They didn't have a corporate lawyer. - No. - And I said, whoa, whoa, whoa, whoa, whoa. You think I'd have a million dollars in revenue. Like somebody should review the contracts and they're like, chat GP2, bra. And I'm like, what about the cap table? They're like, chat GPT, bra. And I was like, okay, and HR and they're like, same thing, bra. And I'm like, okay. - It's like they have fun diligence target one day. - Well, that's what I said. I said, hey, you know, when you do the series A, they're gonna 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 they do IP assignments? First time founders are like, we asked chat GPT. And I'm like, okay, wow, I've just turned into a punk. Like, I guess, yeah. So take us through what you think is happening out there. This is not uncommon, right? What I- - No, but a seed stage startup operates very differently from 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 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- - What's an example of that, like a- - So an example of that is, "Coolie" actually. They started serving startup founders directly with a sort of software platform that you just log onto the platform. They've pumped it full with their material and their president. And then you have the startup material there. And they've embedded workflows that reviews the contracts. And what I think is interesting by that is, it starts to 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 undercharge. I mean, I got a bill recently. - But it was 1800 an hour. - Right. But we're a senior person. And I think the associates were 800. - Well, you know, Kirkland can go up to 4000 an hour. But the thing is, when a Kirkland, 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 cost the company tens of millions of dollars. - Well worth it, yeah. - 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, "Ha, we're spending a lot of dollars on legal services. Let's take this in-house." - Oh, really? - Absolutely. I mean, we're doing this part of the Atlas Gora. We acquired four businesses so far this year. We did the diligence in-house with our own tool. And the fastest transaction we did was 12 days from L-O-I to closing. - 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 f*** up the deal. - Right. - And to make as much money as possible. - Which means to drag it out. - 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, 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 fund raise. In litigation, you can take a part of the success fee when you win the deal, or win the case. And so, I think it's just very interesting how, one of the biggest industries in the world now is being completely transformed and reshaped as a consequence of the transaction. - And are those law firms feeling like they're being disrupted or this is a huge opportunity? - And did that switch at a certain point in time, or has it switched for them? - There's a lot of anxiety and a lot of fear. And these law firms are enormously profitable and big businesses. Kirkland, I least turns around $10 billion a year. - How many lawyers did I have? - 4,500. - Wow. - I mean, per partner, they make it between five and 10 million every year in profits. And so, when something like AI comes along, that poses existential threats and existential opportunity. And 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 the Gore as well, which is called the legal engineer. So, in the same way that Palantir 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 pre-AI to a post-AI world. And it's sort of like document management and PCs were, but one year, 30 years ago, when they were printing out and keeping drafts in a library and in a storage facility, and they had to sort of walk them through and hand hold that. - Absolutely. But I think the difference is, the difference is those were mild productivity gains. 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. - What does it mean? Those jobs gonna 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?" - The job will exist. The tasks will be different, right? 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. Thank you. engineers so that one day you can have senior engineers know what they're doing. 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 go facet, right? And it's also no longer just looking at the virtual data room and control F. It's orchestrating the agent that will be doing that work. And when you look at that work, 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 state level certification, obviously. And doing the non-compete and the Northeast is very different than doing it in California. They're not very enforceable or enforceable at all in California, as people will know, but they're quite enforceable if you're in Boston. So talk about that because that seems to be a place where there could be massive gains from AI. 100%. And it's really two things. I mean, the data that LaGora sits on top of is on one hand side. The firms and enterprises own data. They're precedent, they're 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 mode. 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? LaGora 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, the more interesting things I believe you can do because this data has really never been structured before. And there's so many people who are working with setting policy and billing regulation. And this is a enormous inefficiency in society. 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 mode. 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, you're both making hundreds of millions of dollars. So they must be looking in their review mirror at you like the Toronto Soros Rex in Jurassic Park and going, holy s**t. 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 that gets it out in a second. 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? Well, I think that some of the existing providers and the sort of legacy players have a really hard time pivoting into becoming AI native businesses. Sure. And they have a really hard time meeting 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. And I think at the outset of AI, many believed and made a bet that those organizations without 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 peculiar because it's such a duopoly on legal research. West law is the other one. West law and Lexus nexus. Exactly. But yeah, if you look at how their stock is doing, I think they're, oh, they're getting priced in with the AI certainty. Yeah, that's one way of putting it. Yeah, they're getting crushed. And I would assume there's some power law here. They might have an incredible breath of old case law that they scanned in and went to the court houses and did all that work on sent to India to be double blind typed in. They literally were doing right. That's what you have to do. Yeah, they literally had two different people type in the cases or OCR them, then check them, look for the differences. I mean, because you can't get it wrong. But today with the AI tools, the AI tools are really good at doing what they did manually. Yes. You still have to ship the books because you have to physically scan. This is very strange in the US, but 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 a company. You guys are very good at capitalism. Sometimes too good. But just I mean, Harvard has a project. There's the court. Listener, they're trying. They're trying. That's it doesn't work. 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 Wachtel and a litigator at Wachtel, the best law firm in the world says, I'm going to use this to go after Elon or do a billion dollar case. You better make sure you have all the cases. So it's the opposite of the power law. You don't just need the top 80 percent. You actually need all of it. All of it. Which means you have to go to court houses and ask them for a copy and print it out and pay them 10 cents a page. Well, there's other ways of getting it. But in practice, yes, you have to physically get the books all the way to India. You need to open them. You need to scan them. Because you get what's called page citations. I never thought in college, I would get this nerdy about legal data. But here we are. And what's interesting is that these previous generation of databases were very much searched in the database, find the case and then the lawyer, you know, does their work. What's really interesting about especially the agents following the release of Opus 4.5 and 4.6 is they can now start to do really intelligent case strategy. And they can actually start to combine the witness statements, the cases, and they can really do end-to-end work, which is I think moving us from a world where AI is just augmenting to AI is actually really doing things. And your job becomes to orchestrate and to manage those agents as we're seeing in coding. And so you have partnerships with I'm assuming Anthropic and Open AI, yes. And you spend millions or tens of millions of dollars on two. Absolutely. And they are also competing with you on the margins. They are not competing in our product category at all. From for now. Yeah. Well, you know, from the outside, you know, Claude has a legal offering, which is basically a bundling of markdown skills files and a couple of integrations. And so I think what's really helpful about that is that it illustrates to everyone how applicable AI is in law. What it also does is it drives a lot of initial usage there and then you hit the ceiling or you understand how shallow it is. And then you call us. Right. So it's actually a big pipeline generator for us. So they start experimenting. We were just talking with the CEO of 11 labs about, hey, building your own models is, you know, pretty, pretty doable these days. And every six months, it gets easier and easier. So are you working on your own models using open source to then fork it and make your own models? Is that the future for your firm? So I don't believe in fine-tuning or building any general intelligence models. I think that's total waste of time and money. I do believe in very narrow models for narrow use cases that you also drive a lot of scaling. So you can drive both cost and latency down. An example of this for us is with a big feature called tabular review, which is basically the number of documents times the number of prompts. So 100 documents, 100 prompts, 10,000 API calls. If you make a fine-tune model at extracting contract data, it's very applicable there. But it doesn't make sense to build a general legal intelligence model like some of our competitors are attempting. Yeah. And how do you mitigate against the data-lequage issue with your customers, these, you know, or highly regulated industries with a lot of stake? So putting in, you know, this recent case you're working on in an litigation, if any of that were to seep into a language model and and then come out the other end. - This is disastrous. You have a higher level of trust. - Trust, and compliance is our currency. And so it's actually one of the reasons why it's really hard to sell into law. There's a lot of legal AI companies and very few are making it through. And not because it's hard to build stuff. It's actually quite easy to understand where you can build value, but getting into the customer is very hard. But that's something we cracked pretty early on. And once you're in, it's much easier to expand. So that's also one of the driving forces behind our M&A strategy. But yeah, I mean, we're hosting national secrets, weapons manufacturers with their contracts on LaGoura. And we work with governments. - Does that mean you have to put it on prem as well? - We don't do on prem. I, that's on the roadmap or? - No, I mean, deploying in a VPC is very time consuming. And it creates a lot of dependencies which slow down your roadmap and the execution forward. - All right, continue success, Max. - Thanks for taking some time for us. ♪ I'm doing all of you ♪ (upbeat music) ♪ I'm doing all of you ♪