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

(0:00) ElevenLabs' $600M ARR Ramp, 600 Employees & Life Without PMs (15:34) Celebrity Voice Deals, Deepfake Impersonation & Racing OpenAI and Anthropic (31:42) Legora's Hypergrowth, Disrupting Law Firms & the Billable Hour (42:31) LexisNexis Decline, Legal Data Moats & Legora's Narrow AI Models Thanks to our partners for making this possible! Airwallex is a leading global payments and financial platform for modern businesses, offering trusted solutions to manage everything from business accounts, payments, treasury, and spend management to embedded finance. https://airwallex.com/allin Oracle powers AI at every scale—from frontier labs to enterprise production. Your data. Leading models. No lock-in. One platform, architected for AI. Built for business. Visit https://www.oracle.com/artificial-intelligence/ai-experience/?source=:ex:sn:::::AllIn&SC=:ex:sn:::::AllIn&pcode= https://www.oracle.com/artificial-intelligence/ai-experience/?source=:ex:sn:::::AllIn&SC=:ex:sn:::::AllIn&pcode= Follow Mati: https://x.com/mati Follow Max: https://x.com/MaxJunestrand Follow the besties: https://x.com/chamath https://x.com/Jason https://x.com/DavidSacks https://x.com/friedberg Follow on X: https://x.com/theallinpod Follow on Instagram: https://www.instagram.com/theallinpod Follow on TikTok: https://www.tiktok.com/@allin Follow on LinkedIn: https://www.linkedin.com/company/allinpod Intro Music Credit: https://rb.gy/tppkzl https://x.com/yung_spielburg And a special thank you to Raise Summit for hosting us in Paris

Speaker names marked ? are inferred from speaking time and the show’s known hosts — they are not voice-verified.

• 0:00Jason Calacanis?

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

• 0:02Speaker 01

  It's it's the best time to be building.

• 0:04Jason Calacanis?

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

• 0:16Jason Calacanis?

  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 Airwallex, one AI-native platform for global accounts, cards, and payments. It's designed to make the entire world feel like a local market. Others are bolting AI onto broken infrastructure, but Airwallex was built for the intelligent era from day one. Stop paying the legacy tax and start building the future at airwallex.com/allin. Airwallex, built for the future.

• 0:44Jason Calacanis?

  350 million in, what, two or three years? And I'm hearing numbers five or 600 million now. Tell us about the revenue ramp of the company from the moment you released the software to today. The product's been in market for 40 months, 50 months? You tell me.

• 1:00Speaker 01

  Spot on. We started company 2022. First year was all about building the 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 300.

• 1:31Jason Calacanis?

  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 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?

• 2:11Speaker 01

  Yeah, that's the that's the key element of how you're 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. We're 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, it 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

• 2:53Speaker 01

  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, we'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 if they are looking for an opportunity out there

• 3:38Speaker 01

  and looking for a company to join and solve that, we are we are one of the leading, if not the leading, place to do that.

• 3:44Jason Calacanis?

  And you started before AI was so impactful at making software.

• 3:51Speaker 01

  Right.

• 3:51Jason Calacanis?

  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, you know, the number of people could write code. And now here we are, you know, when from vibe, we had a no-code moment, then vibe coding. And now we actually have people building production code who are not developers. You have developers going 10x and token maxing. How has building software changed internally? And how do you deal with making sure that the code is really high quality? Because people are paying you this money, but they're going to demand really high-quality product since they're spending so much money with you.

• 4:35Speaker 01

  Yeah, that's 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, 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

• 5:20Speaker 01

  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.

• 6:01Speaker 01

  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 company.

• 6:16Jason Calacanis?

  Yeah, we 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.

• 6:59Jason Calacanis?

  Oh, and it's, you know, doing all the different stories in our Kanban board. Now, how do you think about product managers and management as the CEO and as the co-founder?

• 7:11Speaker 01

  Yeah, we

• 7:12Jason Calacanis?

  You fired him all, right?

• 7:13Speaker 01

  We don't. We don't have any PMs.

• 7:15Jason Calacanis?

  Right. Did you ever or did you have?

• 7:17Speaker 01

  Never.

• 7:17Jason Calacanis?

  You never did.

• 7:18Speaker 01

  Never did. Thought 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 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 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, our growth engineering,

• 8:03Speaker 01

  a person can design, experiment, ship and 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 functions, even in go-to-market. Like you need to be able to create a version of that if we are offering that to the customers too. And when we do, we created our inbound AI SDR 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 in a much easier and quicker way.

• 8:46Speaker 01

  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, 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 in 11 labs in our specific tooling that we are solving for customers.

• 9:04Jason Calacanis?

  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, 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

• 9:45Jason Calacanis?

  the ability in real time to interrupt the agent, to interrupt the, you know, conversation and just move faster? Has made consumers and companies basically embrace the technology?

• 9:58Speaker 01

  Yeah, it's slowly becoming that you will be asking for a give me an AI agent effectively.

• 10:02Jason Calacanis?

  You give me the agent.

• 10:03Speaker 01

  AI operator. But we are seeing a transition where suddenly and that's, you know, 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

• 10:20Speaker 01

  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 other customers is coming, where you can actually open a website,

• 10:44Speaker 01

  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 and this, where now, of course, you are reaching frequently when you have a problem and you're asking for help. But ultimately, A, the whole interface will change and morph depending on how you are operating with that interface, with voice being helping you in the background find that information. It will 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.

• 11:15Jason Calacanis?

  It seemed 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, you know, 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, you know, and talking to their agents. And now there's a ring out, you can press it. And I use a really cool product called Whisperflow. I don't know if they use 11 Labs on the backend.

• 12:00Speaker 01

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

• 12:06Jason Calacanis?

  Whisperflow is just a tremendous product. And then I got a pedal. Does anybody here use a pedal on their computer? Raise your hand if you're a... There's one dork, two dorks.

• 12:18Jason Calacanis?

  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?

• 12:27Speaker 01

  I don't.

• 12:28Jason Calacanis?

  Have you considered a pedal?

• 12:30Speaker 01

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

• 12:34Jason Calacanis?

  I have the Plod, it's incredible.

• 12:35Speaker 01

  Plod, pocket, phenomenal, like so good. And especially in events like this, I feel if you preempted that you are recording, of course. But how incredible would it be? All the signal, all the conversations that otherwise disappear. You maybe tap a few notes here and there to try to get signal afterwards. If you can just have that automatically fill your specific notes and make sure you do your follow-ups, phenomenal.

• 12:58Jason Calacanis?

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

• 13:01Speaker 01

  Okay.

• 13:02Jason Calacanis?

  I have three pedals under the desk. And I think I'm trying to figure out what the company is. But with Whisperflow, 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 like incredible, because when I press the pedal down, I just give a stream of consciousness now. And it turns out what these LLMs actually do really well with is taking a massive stream of consciousness, where you just keep talking and talking and talking.

• 13:45Jason Calacanis?

  So I'll give it a one to two-minute prompt. Then I let go. And it has changed everything, everything.

• 13:54Speaker 01

  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 like, people are adjusting how they speak to AI versus how they speak to human. People are...

• 14:21Jason Calacanis?

  How so? Yeah, what, how should you speak to the LLM? We saw Sergey Brin say, threaten it with bodily harm. It's a very effective technique, if you haven't tried it. But what are the things that are different when you're talking to the LLM?

• 14:35Speaker 01

  A specific emotional example. We work with a lot of 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... 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 like, you know, like quick responses.

• 15:18Jason Calacanis?

  Yeah, you don't mind cutting it off.

• 15:20Speaker 01

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

• 15:32Jason Calacanis?

  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 podcaster, so they used the This Week in Startups archive and 11 Lab to create my voice and do this huge channel.

• 16:15Jason Calacanis?

  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 less, no offense. And the French guy over here is like, "Oh, mon Dieu, j'ai gueule."

• 16:45Jason Calacanis?

  The... That's my French angry developer guy.

• 16:50Jason Calacanis?

  "I cannot smoke in the Louvre."

• 16:53Jason Calacanis?

  "This is crazy."

• 16:58Jason Calacanis?

  Is 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, you know, do commerce in the world. You can use it for parody. There is fair use. I can do a Donald Trump impersonation up here if I like.

• 17:22Jason Calacanis?

  "We're going to take about 5% of 11 Lab stock. Is it okay with you to put them in Trump accounts? Sounds good, okay." "And for that, you have to come to the White House?" "Great, okay, thank you."

• 17:34Jason Calacanis?

  "Nasty guy, wouldn't give 5%. Loves socialism, but not America. What's the problem with the Nordics?

• 17:44Jason Calacanis?

  Nasty, nasty socialism." Then I noticed when my guys wanted to clone my voice so that they could fix the ads where I mispronounced something or I do the wrong promo code, use the code JCal20. They like, were like, "It's 25, dummy." And I'm like, "Okay, I have dyslexia." And then they redid it. And it was like, "I'm sorry, you cannot clone Jason's voice." And then it's like, "I have to go in there and do it." And you put a bunch of protections in there. So explain what's happening in that regard in terms of people's, you know, concerns around this. And then the other side, which is the opportunity, because I think you got Jamie Foxx and some other folks actually that you paid for their voices.

• 18:23Speaker 01

  Yeah, no, the voice is identity and IP. It's like, you know, when you speak a certain way, people recognize it, can feel that emotion. And, you know, to some extent, it was, it was a...

• 18:37Speaker 01

  It could be a problem, could be opportunity before. I mean, as you did impersonation of the President Trump, it's of course similarly something that is possible even of a human, not specifically AI. But for us, on the safeguard side, you know, over last years, we took the role as we are leading another development. We also need to lead a lot of the safeguards. So that's like a critical element. We do three things. One, trace everything that's generated, so we can take action when needed. Two, now we moderate both on the voice and text level. So if you were to input something that would be commercial in nature or would try to scam someone, that gets flagged, we can block it. And now three, because over last years, we've seen the development of those models more broadly.

• 19:21Speaker 01

  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...

• 19:45Jason Calacanis?

  All right, all right, all right.

• 19:46Speaker 01

  And across languages. And it's the first...

• 19:48Jason Calacanis?

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

• 19:54Speaker 01

  Could you do it in Spanish?

• 19:56Jason Calacanis?

  It's a fugazia, fugazi.

• 20:00Speaker 01

  But the crazy thing with the AI technology open is that now the voice can be carried not only in English, but also in Spanish, in Italian, in 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.

• 20:17Jason Calacanis?

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

• 20:23Speaker 01

  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 not doing-

• 20:42Jason Calacanis?

  It's fucking raw! Scallops are raw!

• 20:45Speaker 01

  So that is definitely-

• 20:48Jason Calacanis?

  So they're doing characters now, or AI instances, using Eleven Labs, so you can interact with them as part of your subscription?

• 20:55Speaker 01

  Exactly. But as a company, what we now do, and this 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.

• 21:08Jason Calacanis?

  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 Eleven Labs voice, and then license it out?

• 21:24Speaker 01

  Hundred percent.

• 21:25Jason Calacanis?

  Do they get to pick their price, or you pick the price?

• 21:27Speaker 01

  Depends on the model we do. We do both. So you can either give it the default that lets us distribute that slightly more optimally, or you can pick yours and the use case is going to be different. And like you said, opens up a set of incredible opportunities in that dynamic context in other languages. But maybe a last one on that, like, 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 ALS, due to throat cancer, and working on bringing that voice back. So I worked with Congresswoman in the US, Jennifer Wexner, who lost it and wanted to continue inspire others that you can do incredible work despite that, and

• 22:08Speaker 01

  was the first speech delivered in Congress. Or more recently, I think this was my, the most heartwarming story. There was this woman that wanted to get married, lost her voice before she could get married.

• 22:23Jason Calacanis?

  Oh, wow.

• 22:23Speaker 01

  And then they decided to redo the marriage together.

• 22:26Jason Calacanis?

  Do the vows again?

• 22:27Speaker 01

  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't see in any other way, because the voice is such a connecting thing.

• 22:41Jason Calacanis?

  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 ask? He passed, right?

• 22:52Speaker 01

  Yes.

• 22:53Jason Calacanis?

  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 use 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?

• 23:27Speaker 01

  I don't know what I can say about the new things, but definitely the big use case that, that big, big, completely new experience was in the gaming space, where-

• 23:36Jason Calacanis?

  Ah, yes.

• 23:37Speaker 01

  Fortnite, so Epic Games, not a game, Fortnite, launched Darth Vader, which people and players could interact with live, in partnership with the estate, 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, right, publicity into interactive use cases, bring it across the world 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-

• 24:17Jason Calacanis?

  Yes, this is the second greatest meditation app, right behind Calm.

• 24:22Speaker 01

  Which you are an investor of.

• 24:24Jason Calacanis?

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

• 24:28Speaker 01

  But Calm is incredible. I think their team-

• 24:31Jason Calacanis?

  But anyway, you were working with the second place?

• 24:34Speaker 01

  Exactly. Not exactly the second place, but exactly to the working part. So they localized a lot of 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-

• 24:51Jason Calacanis?

  That would be amazing.

• 24:52Speaker 01

  And like, imagine just, you know, so many voices.

• 24:55Jason Calacanis?

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

• 25:04Speaker 01

  Maybe you should license the voice to Calm.

• 25:05Jason Calacanis?

  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 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

• 25:47Jason Calacanis?

  models, and the fact that they want to kill your company?

• 25:51Speaker 01

  So on the first part, the, given we create a platform, we try to provide all of the labs 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 they make sure that they build a harness, build their agent orchestration, create a voice element of how that agent interacts with the world, how the marketing world interacts with the world, but they're not dependent on any model. So for us, that part is, is actually good, because we can provide that to the customers. On the, kind of the second big part of like, of course, the space is overlapping increasingly. Models are platform,

• 26:35Speaker 01

  platform are application, everything is becoming a little bit more fuzzy. For us, there's still the defining piece was focusing on that one layer of like, how does interaction look like? How does communication look like? And we've been able to outcompete them on voice models, both on text to speech, speech to text, on the turn-taking, on music. And we've, you know, 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 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 we build

• 27:19Speaker 01

  a internal team of over 1,000 contractors that label all those audio assets to make them, to make them good. So it's on the research side. And then as you think about the rest of product stack, we want to create a fully verticalized solution for that communication angle. The product, understanding the right workflow in financial services, is very different to healthcare, very different to 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 wider set of integrations, voices that you use, templates for the agent authentication, that you can benefit from, instead of starting from scratch? And so far, we've been, we've been able to create a new model for that.

• 27:56Jason Calacanis?

  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?

• 28:22Speaker 01

  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 a 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, we are looking a little bit closer on like, how we could use our expertise of, of how does, you know, we won't focus on knowledge work, we won't focus on coding. But any interaction, and how you can combine all those pieces together and make sure this is, this is great-

• 29:01Jason Calacanis?

  Yeah.

• 29:01Speaker 01

  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 self, and show that we can do it and do it better.

• 29:11Jason Calacanis?

  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?

• 29:41Speaker 01

  Ship a good amount. We are good partners, good partners with them. But it's, ultimately, you know, showing up in the value we can create too. So like, a lot of what we spoke at the beginning of how we can elevate ourselves as an organization too, is definitely helpful. So I think they've done tremendous work on building. It's almost crazy that each of us has like a Turing, like, you know, if you were to chat with an agent now, it feels like the Turing test will be completed. It's 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.

• 30:23Jason Calacanis?

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

• 31:03Speaker 01

  There are definitely places where we did achieve it.

• 31:07Jason Calacanis?

  Yeah, for sure. All right. Continued success. Let's give it up for Matty from Eleven Labs.

• 31:11Speaker 01

  Thank you so much.

• 31:12Jason Calacanis?

  Well done. Thanks for coming out.

• 31:17Jason Calacanis?

  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.

• 31:44Jason Calacanis?

  You're growing also at a very significant clip.

• 31:47Speaker 02

  Exponentially.

• 31:49Jason Calacanis?

  Is it exponential? No, it's not exponential.

• 31:50Speaker 02

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

• 31:56Jason Calacanis?

  50% quarter over quarter last seven quarters. Yeah, that's pretty darn fast.

• 32:00Speaker 02

  So I think we actually just became as of the close last week on Tuesday, one of the fastest enterprise company with a direct sales motion to grow from one to 150, bidding Sierra with one quarter.

• 32:14Jason Calacanis?

  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.

• 32:27Jason Calacanis?

  With your tools, obviously you got your contemporary in Harvey and people.

• 32:31Speaker 02

  Sorry, who?

• 32:32Jason Calacanis?

  It's a small company 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 to take, I don't know, 80% of what we pay lawyers for and compress it by 90%? 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 one firm, one startup that hit a million in revenue.

• 33:13Speaker 02

  Yep.

• 33:14Jason Calacanis?

  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.

• 33:23Speaker 02

  No.

• 33:24Jason Calacanis?

  And I said, whoa, whoa, whoa, whoa, whoa, you think at a million dollars in revenue, like, somebody should review the contracts? And they're like, ChatGPT, bruh. And I'm like, what about the cap table? They're like, ChatGPT, bruh. And I was like, okay, and HR? And they're like, same thing, bruh. And I'm like, okay.

• 33:41Speaker 02

  It's gonna be a fun diligence target one day.

• 33:43Jason Calacanis?

  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 to do IP assignments? First time founders are like, we asked ChatGPT. And I'm like, okay, wow, I've just turned into Unc. Like, I guess, yeah. So take us through what you think is happening out there. This is not uncommon, right?

• 34:07Speaker 02

  No, no. But a seed stage startup operates very differently from, you know, one of the biggest banks in the US. And so the way to think about the market, or at least the way that we like to, is you have this enormous bucket of legal services, which today is being done manually. It's a trillion dollars every year into legal services, which is very fragmented. But the software spend into legal technology is about 40 billion. So it means there's 4% software, 96% service, which is bananas. The software piece should be much bigger than that. And so the software piece naturally will grow into the service revenue.

• 34:48Speaker 02

  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.

• 35:15Jason Calacanis?

  What's an example of that?

• 35:16Speaker 02

  So an example of that is Cooley, 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.

• 36:01Jason Calacanis?

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

• 36:04Speaker 02

  Right.

• 36:05Jason Calacanis?

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

• 36:08Speaker 02

  Well, you know, Kirkland, it 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's 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.

• 36:30Jason Calacanis?

  Well worth it, yeah.

• 36:31Speaker 02

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

• 36:44Jason Calacanis?

  Oh, really?

• 36:45Speaker 02

  Absolutely. I mean, we're doing this partly at Legora. 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.

• 37:01Jason Calacanis?

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

• 37:04Speaker 02

  Right.

• 37:05Jason Calacanis?

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

• 37:10Speaker 02

  Right.

• 37:11Jason Calacanis?

  And to make as much money as possible.

• 37:14Speaker 02

  Which means to drag it out.

• 37:15Jason Calacanis?

  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.

• 37:22Speaker 02

  Yeah. And so, you know, I think a lot of law firms are also experimenting with different pricing models, where you do a fixed fee for a transaction or for a fundraise. In litigation, you can take a part of the success fee when you win the deal.

• 37:36Jason Calacanis?

  Yeah.

• 37:37Speaker 02

  Or win the case. And so I think it's just very interesting how, you know, one of the biggest industries in the world now is being completely transformed and reshaped as a consequence of the technology.

• 37:47Jason Calacanis?

  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?

• 37:59Speaker 02

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

• 38:14Jason Calacanis?

  How many lawyers did it have?

• 38:15Speaker 02

  Four or 5,000.

• 38:16Jason Calacanis?

  Wow.

• 38:17Speaker 02

  I mean, per partner, they make it between 5 and 10 million every year in profits. And so when something like AI comes along, that poses 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 Legora 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.

• 39:02Jason Calacanis?

  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 handhold that.

• 39:18Speaker 02

  Absolutely. But I think the difference is.

• 39:20Jason Calacanis?

  Revisions, right, yeah.

• 39:20Speaker 02

  The difference is, those were, you know, mild productivity gains.

• 39:25Jason Calacanis?

  Yeah.

• 39:26Speaker 02

  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.

• 39:36Jason Calacanis?

  What does it mean? Are 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?

• 39:45Speaker 02

  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. 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, you know, go fax it, right? And it's also no longer just look in the virtual data room and Ctrl+F. It's orchestrating the agent that will be doing that work.

• 40:27Jason Calacanis?

  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 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 will know, but they're quite enforceable if you're in Boston.

• 41:06Speaker 02

  Yeah, exactly.

• 41:07Jason Calacanis?

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

• 41:12Speaker 02

  Hundred percent. And it's really two things. I mean, the data that Legora sits on top of is, on one hand side, the firms and enterprises own data, their precedent, their organizational data. And secondly, we do the hard work of gathering all the cases, all the legislation, all the regulatory updates for every jurisdiction in the world. And that is very painful. But once you start to do that at scale, it builds a real data mode. And so in the system, if you are the G C of a company in California and you just landed your first customer in South Africa, right? Legora can be adapted to the local legislation in South Africa.

• 41:54Speaker 02

  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, 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 billing regulation. And this is a enormous inefficiency in society.

• 42:30Jason Calacanis?

  And LexisNexis has been a juggernaut and the legacy player in, you know, all the case law and regulations. They have a massive data mode. 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 Tyrannosaurus Rex in Jurassic Park and going, holy [bleep]. 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

• 43:12Jason Calacanis?

  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?

• 43:25Speaker 02

  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.

• 43:37Jason Calacanis?

  Sure.

• 43:38Speaker 02

  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 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 U.S. is peculiar because it's such a duopoly on legal research.

• 44:22Jason Calacanis?

  Westlaw is the other one?

• 44:24Speaker 02

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

• 44:30Jason Calacanis?

  Oh, are they getting priced in with the AI certainty?

• 44:34Speaker 02

  Yeah, that's one way of putting it.

• 44:37Jason Calacanis?

  Yeah, 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 would do.

• 44:56Speaker 02

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

• 44:57Jason Calacanis?

  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.

• 45:05Speaker 02

  Nope.

• 45:06Jason Calacanis?

  But today with the AI tools, the AI tools are really good at doing what they did manually.

• 45:11Speaker 02

  Yes. 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.

• 45:28Jason Calacanis?

  That's crazy.

• 45:29Speaker 02

  A company. You guys are very good at capitalism.

• 45:32Jason Calacanis?

  Sometimes too good, but there's.

• 45:34Speaker 02

  Sometimes too good.

• 45:35Jason Calacanis?

  But Harvard has a project, there's the Court Law, Court Listener.

• 45:39Speaker 02

  They're trying.

• 45:40Jason Calacanis?

  They're trying.

• 45:41Speaker 02

  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, you know, go after Elon or do a billion-dollar case, you better make sure you have all the cases.

• 46:05Jason Calacanis?

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

• 46:11Speaker 02

  All of it.

• 46:12Jason Calacanis?

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

• 46:17Speaker 02

  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.

• 46:29Jason Calacanis?

  Oh, my lord.

• 46:30Speaker 02

  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 this previous generation of databases were very much search in the database, find the case, and then the lawyer, you know, does their work.

• 46:50Jason Calacanis?

  Right.

• 46:51Speaker 02

  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.

• 47:25Jason Calacanis?

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

• 47:37Speaker 02

  Absolutely.

• 47:38Jason Calacanis?

  And they are also competing with you on the margins?

• 47:42Speaker 02

  They are not competing in our product category at all.

• 47:47Jason Calacanis?

  For now.

• 47:49Speaker 02

  Well, you know, from the outside, Claude has a legal offering.

• 47:54Jason Calacanis?

  Yep.

• 47:55Speaker 02

  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 know, you understand how shallow it is, and then you call off.

• 48:19Jason Calacanis?

  Right.

• 48:20Speaker 02

  And so it's actually a big pipeline generator for us.

• 48:22Jason Calacanis?

  Got it. So they start experimenting, boom. We were just talking with the CEO of Eleven 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?

• 48:49Speaker 02

  So I don't believe in fine-tuning or building any general intelligence models. I think that's a 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.

• 49:34Jason Calacanis?

  Yeah. And how do you mitigate against the data leakage issue with your customers? These, you know, are highly regulated industries with a lot at stake. So putting in, you know, 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, I mean, this is disastrous. You have a higher level of responsibility.

• 50:04Speaker 02

  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 it to 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 Legora, and we work with governments.

• 50:48Jason Calacanis?

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

• 50:51Speaker 02

  We don't do on-prem.

• 50:52Jason Calacanis?

  That's on the roadmap or?

• 50:55Speaker 02

  No, I mean, you know, deploying in a VPC is very time-consuming and it creates a lot of dependencies which slow down your roadmap and the execution forward.

• 51:08Jason Calacanis?

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