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

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

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It's the best time to be building

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and revenue has surged but you face really intense competition. Let's go right at that to start.

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I'm going all in.

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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 Airwallocks. 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 on of broken infrastructure. But Air Wallix was built for the intelligent era from day one. Stop paying the legacy tax and start building the future@airwallocks.com Allin Airwallocks built for the future. 350 million in what, two or three years and I'm hearing numbers 5 or 600 million. Now tell us about the revenue ramp of the company from the moment you release the software to today. The product's been in market for 40 months. 50 months, you tell me.

0:16

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 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 closed end of the last year and now we are at 600.

1:00

You're at $600 million in revenue. This is extraordinary. How many employees now because the company's 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 mean quite literally. But you've gotta run the company, you've gotta build a culture. So how many employees now and how are you dealing with these competing priorities?

1:31

Yeah, that's the key element of how you, for us, the element of like how we can maintain the culture despite the quick growth is kind of critical. And how we optimize both the interview cycle, how we are bringing people on board, how we onboard them with 600 people today. 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. 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 and a part of that revenue growth is actually a 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 still at the company from those core research and engineering talent building together with us. So so far I've been able to outcompete and I think the common thread and 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, if not the leading place to do that.

2:11

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, the number of people could write code. And now here we are, 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, but they're going to demand really high quality product since they're spending so much money with you.

3:44

Yeah, it's also true that 2022 was still the year where topics of the day were crypto and metaverse. So they're building. It 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 optimized for specific industries, telco, financial services, healthcare. So every unit is very tightly knit together and we do that across the company. So it's usually five to 10 people teams 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 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 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 you're not using a lot of the coding software, a lot of the coworking 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 sides 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 other things. So that's an essential role in the company.

4:35

Yeah, 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 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 doing all the different stories in our Kanban board. And now how do you think about product managers and management as the CEO and as the co founder?

6:16

Yeah, you fired them all right. We don't have any PMs.

7:11

Right? Did you ever or did you have.

7:15

Never.

7:17

You never did?

7:17

Never did. It's a little bit of what you mentioned also. Before the true AI impact started, it was ideal person in that role can code, can understand the customer, can understand design. Of course that's very hard to find. There's not 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 field really well. To your point, what we are seeing now there is if you can do a little bit of all with AI, you can maybe step change from being an amateur to being advanced level, maybe not an expert level. So suddenly you are not bottlenecked on all the other functions to do your work in growth phenomenal growth engineering. A person can design experiments, ship an experiment, it's working and bringing 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 we are seeing that also in the non traditional funct even in go to market 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 AI SDR agent that in addition to the form that you fill on the website, you have an agent that you can call and people of course can give all the information in a much easier and quicker way. But the second thing that happened is people also leave a lot more information so you can get connected to the right problem and right person a lot quicker. 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 elevenlabs in our specific tooling that we are solving for customers.

7:18

Yeah, it seems like the use case of calling on the phone and talking to a computer or previously going through voice jail 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, 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.

9:04

Yeah, it's slowly becoming that you will be asking for a give me an AI agent effective call, give me the agent AI operator. But we are seeing a transition. We're suddenly and that's 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 was a step change in the last 12 months. And especially in the last six of how good that experience became. Where it's like this golden era for the consumers out there, customers and 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. And I think we'll see this kind of interesting phenomena 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 morph depending on how you are operating with that interface. With voice being helping you in the background find that information will shift from reactive to proactive to help you get that help before you potentially ask for it. And we are seeing those examples too.

9:58

Seemed to me that speech to text had a major blocker again in fidelity 10 years ago. Lawyers would put on drag and 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, they kind of got away with it. But now we see 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 really cool product called Whisperflow. I don't know if they use Elevenlabs on the back end.

11:15

They use us and a few others as well. And they are doing phenomenal work too.

12:01

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 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?

12:06

I don't.

12:27

Have you considered a pedal?

12:28

I should consider a pedal. I love the devices that you can wear and it transforms.

12:30

I have the plod. It's incredible.

12:34

Plaud Pocket. Phenomenal. Like so good. And especially in events like this. I feel if you pre preempted 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, 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.

12:36

All right, so let me make the case for the pedal.

12:58

Okay.

13:01

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 bull artist like me and a talker, this is 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, of consciousness where 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.

13:03

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, 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 contexts combined and the experience you get as an answer is so much better. So we already see that 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, how.

13:54

So, yeah, 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?

14:21

The specific emotional example we work with a lot financial services companies, Revolut, Klarna, Parkbank 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 debt 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 like quick responses.

14:34

Yeah, you don't mind cutting it off.

15:18

Exactly. So you can kind of 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 work on the pedal and whether we should do an integration there.

15:20

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 podcaster. So they used the this Week in Startups archive and elevenlab 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. 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 no offense, the French guy over here

15:33

is like,

16:40

That's my French angry developer.

16:46

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, write you a legal letter. What it basically means is you can't take somebody's voice and use it to 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 it okay with you? Put them in Trump accounts. Sounds good. Okay. And for that, you have to come to the White House. Okay, thank you. Nasty guy Wouldn't give 5%. Loves socialism, but not America. That'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 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 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.

16:50

Yeah, the voice is identity and ip. It's like when you speak a certain way, people recognize it, can feel that emotion. And to some extent, it could be a problem, could be opportunity. Before, I mean, as you did impersonation of President Trump. It's, of course, similarly something that is possible even with a human, not specifically AI, but for us, on the safeguard side, over last years, we took the role, as we are leading another development, we also need to lead on a lot of the safeguards. So that's 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 the development of those models more broadly, 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? 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 Matthew McConaughey on creating a World Cup.

18:23

All right, all right, all right.

19:45

And across languages, and it's the first.

19:46

I haven't gotten paid a lot of money for these independent films, but 11 lab stock is juicy. Yum, yum.

19:48

Could you do it? Could you do it in Spanish?

19:54

It's a Fugazia Fugazi.

19:57

But the crazy thing 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. So that's kind of a good example there. But we've seen that with Masterclass.

20:00

What do you pay these guys? What does it cost to get Matthew McConaughey? Is this like an eight figure deal? Seven figure deal? You give them a little equity?

20:17

Always depends. So, like, you know, the Masterclass, for example, is a good example where they worked with talent 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

20:24

not doing raw scallops. Raw.

20:42

So that is definitely.

20:46

So they're doing characters now or AI instances using elevenlabs. So you can interact with them as part of your subscription.

20:48

Exactly. But as a company, what we now do, and from the beginning, we created the 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 talent.

20:55

Really. 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 ElevenLabs voice and then

21:08

license it out 100%. And then do they get to pick

21:23

their price or you pick the price?

21:26

Depends on the model. We do both. 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 different. Like you said, opens up a set of incredible opportunities in the dynamic context in other languages. But maybe a last one on that voice is such a big part of identity. 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 U.S. jennifer Wexner, 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 heartwarming story. There's a woman that wanted to get married, lost her voice before she could get married.

21:28

Oh, wow.

22:23

And then they decided to redo the marriage together.

22:23

Do the vows again.

22:26

Do the vows. And you could see the whole family just for the first time, hearing the vows, it was just. You could feel the emotions that you can see in any other way. Because the voice is such a connecting thing.

22:27

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? Is James Earl Jones alive? Can somebody pass? He passed, right?

22:41

Yes.

22:52

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 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?

22:53

I don't know what I can say about the new things, but definitely the big use case that completely new experience was in the gaming space where Fortnite, so Epic Games Fortnite launched 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 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, like you said, 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 one is Headspace. So Headspace has a great meditation app.

23:27

Yes, this is the second greatest meditation

24:17

app right behind Calm, which you are an investor of.

24:21

Oh, I am. I didn't realize. You're right. I did invest in Calm, but It was a $4 million company.

24:24

But calm is incredible. Their team.

24:28

But anyway, you were working with the second place.

24:31

Exactly. Not exactly the second place, but exactly to that working part. So they localized a lot of the company 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 hopefully love to.

24:34

That would be amazing.

24:51

And like imagine just so many voices.

24:52

David Sachs is defending Trump. Take a deep breath in. Breathe out. Breathe in, breathe out.

24:56

Maybe you should license the voice to come.

25:03

I mean, that would be interesting. But 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 at anthropic, 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?

25:05

So on the first part, given we create a platform, we try to provide all LLMs out there so our customers can pick anthropic OpenAI open source Google models. And that being agnostic to the specific model is actually helpful because customers can make sure that they build a harness, build the agent orchestration, create a 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 actually good because we can provide that to the customers. On the second big part of of course the space is overlapping. Increasingly models are platform, platform, our application. Everything is becoming a little bit more fuzzy for us. There's still the defining piece was focusing on that one layer of how does interaction look like, how does communication look like. We've been able to outcompete them on voice models, both on text to speech, speech to text, on the turn taking on music. Here our research team has 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 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 unlabeled data and where we spend a lot of time. So you build an internal team of over 1,000 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 product stack, we want to create a fully verticalized solution for that communication angle. The product understanding the right workflow and 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 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 able to create a new model for that.

25:52

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?

27:56

We know that some companies are continuously trying to figure out how to distill and use the data. So that is an existing problem and we have few mechanisms to stop it or slow it down. Not stop it, but on the open source question or like creating our own versions, we are looking a little bit closer on how we could use our expertise of how does we want to 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 Great. We want to own, so we are spending more time there. But it's also just great to be in the arena and compete with those guys and every so often show that we can do it and do it better.

28:25

Yeah, it's pretty clear in my estimation that that's where 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.

29:12

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

29:41

Yeah, I think you're there. It just depends on the application and like what question you ask, but it definitely passes. 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'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.

30:23

There are definitely places where we did achieve it.

31:04

Yeah, for sure. All right, continued success. Let's give it up for Mati from 11 Lab. Well done. Thanks for coming out. I'm going all in. The AI companies building the future run on Oracle cloud infrastructure training and deploying at scale on one of the world's largest AI infrastructure. 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@oracle.com AI or experience it live at Oracle AI Experience Live. You're growing also at a very significant clip, exponentially. Is it exponential? No, it's not exponential.

31:07

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

31:50

50% quarter over quarter. Last seven quarters? Yeah. That's pretty darn fast.

31:56

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 grow from one to 150. Bidding Sierra with one quarter.

32:00

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 who. It's just, it's a small company in the, in the states and then you also have, I guess Claude 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%. 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. I'm seeing it already in the startup space. I had 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 dozen employees. They didn't have a corporate lawyer.

32:13

No.

33:23

And I said, you think at a million dollars in revenue, like somebody should review the contracts. And they're like, chat GPT, bruh. Yeah. And I'm like, what about the cap table? They're like chat GPT bruh. And I was like, okay. And hr. And they're like, same thing, bro.

33:23

And I'm like, okay, a fun diligence target one day.

33:39

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. Like we asked ChatGPT. And I'm like, okay, wow, I just turned into unk. Like I guess. Yeah. So. So take us through what you think is happening out there. This is not uncommon, right. What I scenario.

33:43

But a seed stage startup operates very differently from, you know, one of the biggest banks in the U.S. 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.

34:09

What's an example of that.

35:15

So an example of that is Cooli, actually they started serving startup founders directly with a sort of software 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. 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.

35:16

I don't know if they're undercharging. I got a bill recently and it was 1,800 an hour.

36:01

Right.

36:04

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

36:05

Well, you know, Kirkland can go up to 4,000 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 costed the company tens of millions of dollars.

36:08

Well worth it.

36:30

Yeah, right, exactly. 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.

36:30

Oh, really?

36:44

Absolutely. I mean, we're doing this partly at Ligura. 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 LOI to closing.

36:45

Because your motivation as the founder is to get the deal done.

37:01

Right?

37:04

The motivation of the lawyer is to not have you sue them if they up the deal.

37:05

Right.

37:10

And to make as much money as

37:11

possible is to drag it out.

37:13

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.

37:15

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 fundraise. 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 reshapen as a consequence of the tech.

37:23

And are those law firms feeling like they're being disrupted or. This is a huge opportunity. Did that switch at a certain point in time or has it switched for them?

37:47

There's a lot of anxiety and a lot of fear. And these law firms are enormously profitable and big businesses. Kirkland ellis turns around $10 billion a year.

37:59

How many lawyers do they have?

38:14

Four or five thousand. Wow. I mean, per partner, they make between 5 and 10 million every year in profits. And so when something like AI comes along, that poses existential threat 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 Ligor 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.

38:15

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 library in a storage facility, and they had to sort of walk them through and handle that.

39:02

Absolutely. But I think the differences.

39:18

Revisions, right? Yeah.

39:19

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.

39:20

What does it mean? Are those jobs going to still exist? Or are a lot of the lawyers who are coming out of school going, oh my God, was this a good idea or a bad idea?

39:36

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 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. 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 fax it. 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.

39:45

When you look at that work, you have a global backdrop. Attorneys, obviously very famously localized. 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. 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 don't know. But they're quite enforceable if you're in Boston.

40:28

Exactly.

41:06

So talk about that because that seems to be a place where there could be massive gains from AI 100%.

41:07

And it's really two things. I mean, the data that Ligora 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 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, Legora can be adapted to the local legislation in South Africa. And we actually had a case of this where 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 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 are so many people who are working with setting policy and building regulation. And this is a enormous inefficiency in society.

41:13

LexisNexis has been a juggernaut and the legacy player in all the case law and regulations. They have a massive data moat, 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 you, they must be looking in the rearview mirror at you like the Tyrannosaurus rex in Jurassic park and going, holy, 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. Are you going to just try and buy LexisNexis, I know it's part of a larger enterprise, or are you just going to kill it?

42:31

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. 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. 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 peculiar because it's such a duopoly on legal research as Westlaw

43:25

is the other one.

44:23

Westlaw and LexisNexis, exactly. But yeah, if you look at how their stock is doing, I think.

44:24

Are they getting priced in with the AI certainty?

44:31

Yeah, yeah, that's one way of putting it. Yeah.

44:35

They're getting crushed. And I would assume there's some power law here. You know, they might have an incredible breadth of, you know, old case law that they scanned in and went to the courthouses and did all that work on. Sent to India to be double blind, typed in. Like they literally.

44:37

You're right, that's what you have to do.

44:55

Yeah.

44:57

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.

44:57

Yes. And you still have to ship the books because you have to physically scan. This is very strange in the U.S. but Westlaw 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.

45:11

That's crazy.

45:28

A company. You guys are very good at capitalism.

45:29

Sometimes too good. But.

45:33

Too good.

45:34

But I mean, Harvard has a project. There's the Court Law. Court listener.

45:34

They're trying.

45:39

They're trying.

45:40

Yeah. 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.

45:40

So it's the opposite of the power law. You don't just need the top 80%, you actually need all of it.

46:06

All of it.

46:11

Which means you have to go to courthouses and ask them for a copy, to print it out and pay them 10 cents a page.

46:12

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 need to 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 search in the database, find the case, and then the lawyer does their work.

46:18

Right.

46:50

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.

46:51

And so you have partnerships with, I'm assuming, Anthropic and OpenAI. Yes. And you spend millions or tens of millions of dollars on tokens.

47:26

Absolutely.

47:37

And they are also competing with you on the margins.

47:38

They are not competing in our product category at all. At all from.

47:43

For now.

47:48

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. So it's actually a big pipeline generator for us.

47:49

Got it. So they start experimenting. Boom. We were just talking with the CEO of ElevenLabs about, hey, building your own models is 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?

48:22

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 we have 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 tuned 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.

48:49

Yeah, and how do you mitigate against the data leakage issue with your customers? These are highly regulated industries with a lot at stake. So putting in this recent case you're working on in a litigation, if any of that were to seep into a language model and then come out the other end, this is disastrous. You have a higher level of trust

49:35

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 it to the customer is very, 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 and A strategy. But yeah, I mean, we're hosting national Secrets weapons manufacturers with their contracts on Ligora and we work with governments.

50:04

Does that mean you have to put it on PREM as well?

50:48

We don't do on prem.

50:51

That's on the roadmap or no.

50:53

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.

50:55

All right, continued success. Max, thanks for taking some time for us.

51:08

I'm going all in.

51:27