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

Summary

This is a two-part live CEO interview hosted by Jason Calacanis at the RAISE Summit. In the first half, Jason sits down with Mati Staniszewski, co-founder and CEO of ElevenLabs, the AI voice company that has ripped from launch to roughly $600M in ARR in about three years. Mati walks through the revenue ramp (20 months to $100M, then 10 months to $200M, then 5 months to $300M), how the company keeps its culture across 600 employees, and the unusual org design: no product managers, ever, and engineers embedded in every team including legal and talent. They dig into the shift from painful “voice jail” phone trees to AI operators, why people are often more candid with an AI voice agent (debt collection, shame), the tension between voice as personal identity and the impersonation problem (Jason’s own cloned voice, celebrity deals like MasterClass, and Fortnite’s interactive Darth Vader with the James Earl Jones estate), and how ElevenLabs stays ahead of OpenAI and Anthropic by betting that “architecture, not scale” plus proprietary data wins in voice.

In the second half, Jason interviews Max Junestrand, CEO of Legora, a legal-AI platform growing 50% quarter-over-quarter for seven straight quarters — which Max claims makes it one of the fastest enterprise companies with a direct sales motion to go from $1M to $150M, edging out Salesforce by a quarter. They frame legal as a trillion-dollar bucket of manual services sitting on top of a tiny software spend, and discuss how AI compresses the billable hour, why law firms feel both existential threat and opportunity (Kirkland & Ellis partners reportedly earning $5–10M each per year, rates up to $4,000/hour), and how Legora used its own tool to do M&A diligence in-house, closing one acquisition 12 days from LOI. Max explains the legal data moat held by LexisNexis and Westlaw (“you need all of it, not the top 80%”), why Claude’s legal offering is actually a pipeline generator for Legora, and why he believes in narrow, low-latency models rather than fine-tuning general intelligence.

The through-line across both interviews: incumbents in voice and law are being reshaped by focused, fast-moving startups that treat proprietary data and trust/compliance as their real moats, while frontier-model labs are simultaneously partners, suppliers, and looming competitors.

Highlights

”20 months to get to the first 100 million in ARR”

“Then it took us roughly 20 months to get to the first 100 million in ARR. Roughly 10 months to get to 200, 5 months to get to 300, and that’s how we closed end of last year, and now we are at 600.” — Mati Staniszewski, 1:00

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”We don’t have any PMs”

“Yeah, we we so we don’t we don’t have any PMs.” — Mati Staniszewski, 7:12

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”We paid back over $22 million back to the community of talent”

“Today we paid back over $22 million back to the community of talent.” — Mati Staniszewski, 21:00

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”It’s the architecture that matters, not the scale”

“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.” — Mati Staniszewski, 27:00

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”One of the fastest enterprise companies… beating CRM by one quarter”

“We actually just became… one of the fastest enterprise company with a direct sales motion to grow from 1 to 150, beating CRM with one quarter.” — Max Junestrand, 31:59

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”You hit the ceiling… and then you call us”

“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 us. And so it’s actually a big pipeline generator for us.” — Max Junestrand, 48:00

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Key Points

• ElevenLabs revenue ramp (1:00) - 20 months to $100M ARR, then 10 months to $200M, 5 months to $300M, now ~$600M.
• $600M ARR, 600 employees (1:33) - Maintaining culture while revenue rips and investors show up at the doorstep.
• Zero attrition from the first 10 hires (3:00) - Core research and engineering team fully intact.
• Engineers embedded in every team (4:31) - Even legal, talent, and go-to-market have an embedded engineer for automation and a security check on everything shipped.
• No product managers, ever (7:12) - Optimize for people who are expert in one field and strong in another; AI closes the rest.
• From “voice jail” to AI operators (9:58) - Enterprises and sales teams are the biggest fuel of recent growth.
• People are more candid with AI (14:34) - On debt collection with Revolut/Klarna/PagBank, callers feel less shame with an AI voice agent.
• Voice as identity and IP (18:23) - The impersonation problem and the opportunity of licensed celebrity voices.
• $22M paid to voice talent (21:00) - Authenticated marketplace where actors license their own voice.
• Fortnite’s interactive Darth Vader (23:27) - Live-interactive character with Epic, Disney, and the James Earl Jones estate.
• Model-agnostic platform (25:53) - Offers all LLMs while out-competing OpenAI and Anthropic on the voice layer.
• Architecture, not scale + proprietary data (27:00) - Internal team of 1,000+ contractors builds specialized labeled data.
• Distillation and data-leakage defenses (28:31) - Mechanisms to slow (not stop) companies trying to distill ElevenLabs’ data.
• Legora’s hypergrowth (31:51) - 50% QoQ for seven quarters; fastest direct-sales enterprise from $1M to $150M, beating Salesforce by a quarter.
• Legal is a trillion-dollar manual market (34:06) - Enormous services spend sitting on a tiny software spend.
• Legora ran its own M&A diligence (36:45) - Four acquisitions this year; fastest was 12 days from LOI to closing.
• Kirkland & Ellis economics (37:49) - ~$10B/year firm, $5–10M profit per partner, rates up to $4,000/hour.
• The junior lawyer role is reshaped (39:26) - The job survives, the tasks change; the path up the ranks looks different.
• Legal data moats need all of it (45:37) - You can’t build legal research with only the top 80%; Westlaw effectively monopolizes reporting on US cases.
• Claude’s legal offering as pipeline (48:00) - Initial usage hits a ceiling, then customers come to Legora.
• Narrow models, not fine-tuning (48:50) - Narrow models for narrow use cases (e.g. Tabular Review) drive cost and latency down.
• Trust and compliance is the currency (50:03) - Hard to sell into law; Legora deploys in the VPC but not on-prem.

Mentions

Companies

• ElevenLabs (1:00) - Mati’s AI voice company, ~$600M ARR.
• Legora (31:43) - Max’s legal-AI platform, formerly referenced as Leya.
• Airwallex (0:14) - Episode sponsor (global payments platform).
• Oracle (31:14) - Episode sponsor (Oracle Cloud Infrastructure / AI).
• Revolut, Klarna, PagBank (14:34) - Financial-services customers using AI voice for payments and collections.
• MasterClass (20:06) - Interactive celebrity content (Gordon Ramsay example).
• Epic Games / Fortnite (23:27) - Live-interactive Darth Vader.
• Disney (22:43) - Licensed the Darth Vader voice from James Earl Jones’ estate.
• Headspace / Calm (24:00) - Meditation apps; personalized/localized voice.
• OpenAI, Anthropic, Google (25:53) - Frontier labs; both partners and would-be competitors.
• Harvey (32:11) - Legal-AI competitor (“Harvey who?”).
• Salesforce (CRM) (31:59) - Growth benchmark Legora claims to beat by a quarter.
• Cooley (35:17) - Firm serving startups directly via a software platform.
• Kirkland & Ellis (36:08) - ~$10B/year firm; rates up to $4,000/hour.
• Wachtell (45:47) - “The best law firm in the world” example for needing all case data.
• LexisNexis / Westlaw (42:31) - Legacy legal-data incumbents with the case-law moat.
• Court Listener / Harvard project (45:29) - Open efforts to free case law (“They’re trying”).

Products & Technologies

• Text-to-speech model (1:00) - ElevenLabs’ first human-sounding TTS, released early 2023.
• Voice marketplace (21:00) - Authenticate, share, and license your voice for payment.
• Whisper Flow (12:06) - Push-to-talk dictation product Jason praises.
• Plaud (12:29) - Wearable recording/transcription device.
• Dragon Dictate (11:18) - The “terrible” legacy dictation software of 10 years ago.
• ChatGPT (33:32) - First-time founders using it for legal, cap table, and HR (“ChatGPT, bruh”).
• Tabular Review (48:50) - Legora feature (documents × questions) suited to narrow models.
• Opus 4.5 / 4.6 (46:52) - Enable intelligent, end-to-end case strategy for legal agents.
• Claude legal offering (47:44) - “A bundling of markdown skills files and a couple of integrations.”

People

• Mati Staniszewski (1:00) - Co-founder and CEO of ElevenLabs.
• Max Junestrand (31:51) - CEO of Legora.
• Jason Calacanis (0:00) - Host and interviewer.
• Dario (Anthropic) & Sam (OpenAI) (25:06) - Frontier-lab founders eyeing the voice business.
• Sergey Brin (14:22) - Referenced for the “threaten it with bodily harm” prompting quip.
• Jamie Foxx (18:00) - Celebrity in a paid voice deal.
• Matthew McConaughey (20:18) - Referenced for a cross-language voice deal.
• Gordon Ramsay (20:25) - MasterClass interactive-cooking example.
• James Earl Jones / Darth Vader (22:43) - Estate licensed the voice to Disney.
• Donald Trump (16:58) - Used in Jason’s impersonation / fair-use bit.

Surprising Quotes

“Harvey who?” — Max Junestrand, 32:30

“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.” — Max Junestrand, 48:50

“Trust and compliance is our currency. And so it’s actually one of the reasons why it’s really hard to sell into law.” — Max Junestrand, 50:03

“We hope this year we’ll do the same thing for voice where any conversation feels like you are speaking with another human.” — Mati Staniszewski, 30:00

“You cannot build a legal research solution that doesn’t have all of the data.” — Max Junestrand, 45:37

Transcript

Note on speaker labels: this is a Jason Calacanis-hosted 1-on-1 interview (first with Mati Staniszewski of ElevenLabs, then Max Junestrand of Legora). The other besties are not present. A handful of segments below are labeled “David Friedberg,” “David Sacks,” or “Chamath Palihapitiya” — these are almost certainly automated-diarization misattributions of Jason or the guest. Similarly, in the Legora half (after ~31:42), some lines labeled “Mati Staniszewski” are likely Max Junestrand, since Mati had already left the stage. Labels are reproduced as-is from the transcription output.

Jason Calacanis: 0:00 You’re on a bit of a heater, huh?

Mati Staniszewski: 0:02 It’s, it’s the best time to be building.

Jason Calacanis: 0:04 And revenue has surged, but you face really intense competition. Let’s go right at that to start. I’m going all in! 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. I’m going all in! 350 million in what? Two or three years? And I’m hearing numbers 500 or 600 million now. Tell us about the revenue ramp of the company from the moment you released the software to today. The product’s been in market for 40 months, 50 months, you tell me.

Mati Staniszewski: 1:00 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, 5 months to get to 300, and that’s how we closed end of last year, and now we are at 600.

Jason Calacanis: 1:33 You’re at $600 million in revenue. This is just 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?

Mati Staniszewski: 2:12 Yeah, that’s the, that’s the key element. Of how we, for us, the element of like how we can maintain the culture despite the quick growth is, 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 side. And as a company, we combine research and product. So we’re building a communication platform for AI. On the research side, this includes everything across audio: generating speech, transcribing speech, orchestrating speech for interactions. On the product, this is how we can complete the entirety of the customer journey, from marketing and creating assets and localizing them internationally, through customer support with voice agents to proactive enablement of how voice agents can help in operations, training and sales. So this requires a lot of different… talent, um, and, and, and 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, um, still at the company from those core research and engineering talent building together with us. So far being able to out-compete and I think the common, uh, thread and, and credit to my co-founder who is an incredible researcher himself, we’ve been able to assemble the team that is truly excited about solving audio, solving interaction and building that research. And if they, if they are looking for an opportunity out there and looking for a company to join and solve that, we are, we are one of the, the, the leading if not the leading place to do that.

Jason Calacanis: 3:44 And you started before AI was so impactful at making software.

Mati Staniszewski: 3:48 Right.

Jason Calacanis: 3:49 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 who could write code. And now here we are, you know, went from vibe, we had a no-code moment, then vibe coding, and now we actually have people building production code who are not developers. You have developers going 10x and token maxing. How has building software changed internally and how do you deal with making sure that the code is really high quality? Because people are paying you this money, um, but they’re going to demand really high quality product since they’re spending so much money with you.

Mati Staniszewski: 4:31 Yeah, it’s, it’s also true that 2022 was still the year where topics of the day were crypto and, um, metaverse. So the building then was also the best time to start because we could actually take a, take a bit of time to focus on what we thought is the future. Um, but the, the way we are structured is a lot of small teams, especially across the product engineering, but also in how we think about go-to-market optimized for specific industries, telco, financial services, healthcare. So every unit is very tightly knit together. Um, and we do that across the company. Uh, so it’s usually five to 10 people teams that, that, that run ahead. And inside of each of those teams, the decision we took, which is slightly different than how it’s usually structured, we embedded engineers in, in, in every place. And even in the places which aren’t engineering. So our talent team will have an engineer, our legal team will have an engineer, our revenue engineering or go-to-market engineering have engineers embedded all across. And those people have two roles. One is, of course, create automations and bringing the software inside of that team. But second is actually helping everybody else do what you said, which is make sure that people are adopting AI, but also there’s a security check for everything they deploy. Because ultimately, if, if you are not using a lot of the coding software, a lot of the co-working software, then you are probably in the wrong spot. If you’re using too much of it, that is also a flag because maybe you’re not doing that in the right way. And of course as you start bringing that into the sights 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 thing. So that’s an essential role in that in that in the company.

Jason Calacanis: 6:16 Yeah, we the it’s fantastic that everyone can build software until you put it into production and you have a leak. Uh, 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, uh, summarizing what happened on the call. Oh, it is actually creating action items and it’s telling us what to do next. Oh, and it’s, you know, doing all the different stories in our Kanban board. Now how do you think about product managers and management as the CEO and as the co-founder? You fired them all, right?

Mati Staniszewski: 7:12 Yeah, we we so we don’t we don’t have any PMs.

Jason Calacanis: 7:19 Right. Did you ever or did you have them?

Mati Staniszewski: 7:21 Never. Never did. I thought it’s a little bit of what you mentioned also before the the true AI impact started, which 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 no truly that many people that are experts in any all of those fields at the same time. So we optimize for profiles that are experts in at least one of those fields but understand at least one other fields really well. To your point, what we are seeing now, there’s if you can do a little bit of all with AI, you can maybe step change from being an amateur to being a advanced level, maybe not an expert level. So suddenly you are not bottlenecked on all the other functions to do your work. In growth, phenomenal for. Growth engineering, a person can design experiment, ship an experiment, it’s working and bring it back. We also have the privilege where we are using a lot of our product ourselves. So to be able to do that ultimately to help everybody else create voice agents, we ourselves need to create voice agents too. Um, so we are seeing that also in the non-traditional functions, even in go-to-market, like you need to be able to create a version of that if we are offering that to the customers too. And even 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, um, and people are, of course, easy can give all the information in 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 a lot quicker. Um, so we are seeing that kind of phenomena all the time where actually using a lot of tooling makes you yourself…

Max Junestrand: 9:00 …matter in your job overall and in ElevenLabs, in our specific tooling that we are solving for customers.

Jason Calacanis: 9:04 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 and it made you just say operator and hit the zero button like as fast as possible. But now it seems to have turned a corner where talking to a human, you, I almost feel bad talking to a human where I’m like, I am so sorry to bother you, you’re a human, but the AI is so good. When do you think we get to that use case where I’d rather talk to the AI than a human because I know the AI is going to solve my problem?

Mati Staniszewski: 9:58 Yeah, it’s slowly becoming that you will be asking for give me an AI agent effectively like a- an AI operator. But we are seeing a transition where suddenly and that’s, you know, the the biggest fuel of the recent growth for us is enterprises, sales teams 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, so you can create the right workflows, connect it to the systems, and finally the voice element that we can bring to life to make it feel like you’re talking with another human. And that combination now works. So we are seeing across telcos, across financial services, across healthcare, incredible work where people are calling and are being served by the agents. And of course you still have a fallback to a human in the moments where it doesn’t work. But it’s becoming better and better across those places. So I do think it’s this next 12 months where you’ll see that transition happening across more and more places.

Jason Calacanis: 11:18 Seemed to me 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’d get a headset. And it seemed like the the big blocker was you felt like an idiot talking to a computer in an office, right? And so people who did it quietly in their office, you know, they kind of got away with it. But now we see something interesting, which is people are talking to their phone, they’re talking to their computer, and Whisper Flow being one of these products where you talk and it types. Do a lot of people use ElevenLabs to do speech-to-text now?

Mati Staniszewski: 12:00 They use us and and a few others as well.

David Friedberg: 12:03 And they are doing phenomenal work.

Jason Calacanis: 12:06 Whisper Flow is just a tremendous product. And then I got a pedal. Does anybody here use a pedal on their computer? Raise your hand if you’re a computer - there’s one dork, two dorks. Any others? Raise it high. Oh, she’s half dork. Okay, so there’s about three and a half dorks here. Next year, this is going to be- Do you have a pedal?

Mati Staniszewski: 12:27 I don’t. I have-

Jason Calacanis: 12:28 Have you considered a pedal?

Mati Staniszewski: 12:29 I should consider a pedal. I love the devices that you can wear and it transcribes. I have the Plaud, it’s incredible.

Max Junestrand: 12:37 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 the follow-ups. It’s phenomenal.

Jason Calacanis: 12:58 All right, so let me make the case for the pedal. 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 able to just hit the pedal and go, and then here’s the seven things I want to happen, and here’s the constraints, and I want you to check with me before you do it, all of a sudden you get a better result because you gave the LLM more information, correct?

Max Junestrand: 13:54 It’s you know, like the whole experience is changing so much. A similar version of what we see happen is you know how you have a, you want to say a thought and then you’re like, okay, I actually want to change and say something else? Now you have those two 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 you mentioned, so much of what we do now is talk to a machine and the machine talks back.

Jason Calacanis: 14:22 How so? Yeah. What are the things that are different when you’re talking to the LLM? We saw Sergey Brin say threaten it with bodily harm. It’s a very effective technique if you haven’t tried it. But what are the things that are different when you’re talking to the LLM?

Mati Staniszewski: 14:34 The specific emotional example, we work with a lot of financial services companies, Revolut, Klarna, PagBank. And some of the frequent case, not in all of them, is of course how you remind people about payment or 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 another 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, quick responses.

Jason Calacanis: 15:19 Yeah, you don’t mind cutting it off.

Mati Staniszewski: 15:20 Exactly. So you can like kind of go through to the point you want much quicker, which we needed to like change a little bit of the interaction model too, which is working.

Jason Calacanis: 15:29 But we’ll lock on the pedal and whether we should, we should do an integration. Let’s talk a little bit about celebrities on the platform. You have some celebrities who are on there. You also have an issue with impersonation. I know this because somebody was like, ‘Oh my god, I love your bulldog videos.’ Many people know I’m a big fan of bulldogs, I currently have three. And I said, ‘I’m sorry, I don’t know what you’re talking about.’ And they sent me a channel where somebody had created a bunch of dogs telling jokes and they made one, and I guess they were looking for a podcast host so they used the This Week in Startups archive and ElevenLabs to create my voice and do this whole 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 ElevenLabs.’ So I think I emailed you about it and I’m like, how do you protect against this? In advertising, in the law in the United States, I’m not sure about here in France, I’m sure they have 17 laws for this. We have one.

David Friedberg: 16:31 You guys are great at regulations, no offense.

Jason Calacanis: 16:35 The French guy over here is like, ‘Oh mon dieu, J-Cal!’

David Friedberg: 16:40 The—

Jason Calacanis: 16:42 That’s my French angry developer guy. ‘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, you know, do commerce in the world. You can use it for parody. There is fair use. I can do a Donald Trump impersonation up here if I like. We’re going to take about 5% of ElevenLabs stock. Is that okay with you? Put them in Trump accounts? Sounds good. Okay.

Chamath Palihapitiya: 17:11 And Friedberg, you have to come to the White House?

Jason Calacanis: 17:13 Friedberg, okay, thank you. Nasty guy, wouldn’t give 5%. Loves socialism, but not America. It’s the problem with the Nordics. Nasty, nasty socialism. Then I noticed when my guys wanted to clone my voice so that they could fix the ads where I mispronounced something or I do the wrong promo code— —‘Use the code J-Cal 20.’ They’re like, ‘It’s 25, dummy.’ And I’m like, ‘Okay, I have dyslexia.’ and then they redid it and it was like I’m sorry you cannot uh clone Jason’s voice and then it’s like I have to go in there and do it and you put a bunch of protections in there so explain what’s happening in that regard in terms of people’s you know concerns around this and then the other side which is the opportunity because I think you got Jamie Foxx and some other folks actually that you paid to license their voice, correct?

Mati Staniszewski: 18:23 Yeah and the the the voice is identity and IP it’s like you know when you when you speak a certain way people recognize it can feel that emotion and you know to some extent it was it it was a it could be a problem could be opportunity before I mean as you did impersonation of of the President Trump it’s of course similarly a a something that is possible even with a human uh not specifically AI but you want to protect against the malicious use cases and enable the good ones. So from the beginning we built a set of moderation tools that on one hand side no-go voices that you cannot create, so any of the politicians, any of the famous people that we know we would block from being created. And then a set of tools that allow you to verify the voices, so if you want to create your own voice, you need to read a specific text so we know it’s really you. And then on the opportunity side, that’s the exciting part, right? You can now bring these voices to life across a lot of the use cases.

Mati Staniszewski: 19:46 Alright, alright, alright. And across languages and it’s the first time… I haven’t got paid a lot of money for these independent films but oh ElevenLabs stock is juicy, yum, yum.

Jason Calacanis: 20:01 Could you do… Could you do it in Spanish?

Mati Staniszewski: 20:04 It’s… it’s a fugazi, fugazi. But the crazy thing with the what AI technology open is that now the voice can be carried not only English but also in Spanish in Italian in Portuguese and you can still have exactly that element of trans- emotion coming through. Um so that’s that’s kind of like a good example there but we’ve seen that with MasterClass…

Jason Calacanis: 20:18 What do you pay these guys? How what does it cost to get Matthew McConaughey’s… is it like an eight-figure deal, seven-figure deal? You give them a little equity?

Mati Staniszewski: 20:25 Always depends uh so like you know the MasterClass for example is a good example where they worked with talent directly and here you have uh previously a static content that you would learn from now uh you have interactive content so you have Gordon Ramsay teaching you how to how to cook in the kitchen he can scream at you if you’re not doing it… it’s fucking raw! Scallops are raw!

Jason Calacanis: 20:48 So they’re doing characters now or or AI instances using ElevenLabs so you can interact with them as part of your subscription.

Mati Staniszewski: 20:57 Exactly. But as a company what we now do this from the beginning we created the marketplace where people… People can create their voice. We authenticated it, you can share it, and earn money. Today we paid back over $22 million back to the community of talent.

Jason Calacanis: 21:08 Really? So those voiceover actors now, who got paid as hourly workers, sometimes they get a little back-end if they were doing a commercial or something, now they can spend an hour reading, create an ElevenLabs voice and then license it out?

Mati Staniszewski: 21:24 100%. And then…

Jason Calacanis: 21:25 Do they get to pick their price or you pick the price?

Mati Staniszewski: 21:28 Depends on the model, we do both. So you can either give it a default that lets us distribute that slightly more optimally, or you can pick yours and the use cases going to be different. Then like you said, opens up a set of incredible opportunities in that dynamic context, in other languages. But maybe a last one on that, voice is such a big part of identity and probably our most important work was actually working with people who lost their voice. So we worked, for example, with a person who lost their voice ahead of the wedding and were able to recreate it just from a snippet of the voice they had. And imagine the wedding day, they could give the speech to their now wife.

Jason Calacanis: 22:29 Do the vows again?

Mati Staniszewski: 22:30 And do the vows again. 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 voice is such a connecting thing.

Jason Calacanis: 22:43 Yeah, and you’ve done it for some iconic voices. My understanding is the estate of James Earl Jones—I’m not sure if he—did he pass? Is James Earl Jones alive? Can somebody ask? He passed, right? Yes. But before he passed, I think he did a deal with Disney. And he said, ‘Listen, for my family, I would like to license the Darth Vader voice for all time to Disney.’ They gave him some incredible deal.

Mati Staniszewski: 23:27 I don’t know what I can say about the new things, but definitely the big use case that big, completely new experience was in the gaming space where Fortnite, so Epic Games’ 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 interaction and you could effectively extend, extend your likeness, your like you said, right, from this publicity into interactive use cases, bring it across the world all together. So that was exactly that model and now we are working on on on a… one of the public one is Headspace. So Headspace has a great meditation app-

Jason Calacanis: 24:17 Yes, this is the second greatest meditation app right behind Calm, which you are an investor of. Oh, I am? I didn’t realize. You’re right, I did. I did Calm when it was a four million dollar company.

Mati Staniszewski: 24:28 But Calm is incredible. They’re-

Jason Calacanis: 24:29 Calm is incredible. But anyway, you were working with the second place-

Mati Staniszewski: 24:32 Exactly, so they… not exactly the second place. But exactly to the working part. They localize a lot of the content and Calm I think is trying some of the interactive elements. Could you have a meditation led that’s personalized to you? Which, which we would hopefully love to interact and bring to life.

Jason Calacanis: 24:54 That would be amazing. And like imagine just you know Namaste. David Sacks is defending Trump. Take a deep breath in, breathe out. Breathe in, breathe out.

David Friedberg: 25:05 Maybe you should license the voice to Calm.

Jason Calacanis: 25:06 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 using them, or is it more coopetition? How do you frame using their models and being on great terms with them, but also being incredibly competitive with them?

Max Junestrand: 25:53 So on the first part, the 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 agnostic being agnostic to a specific model is actually helpful because customers can then make sure that they build the harness, build the agent orchestration, create the voice element of how that agent interacts with the world, and they’re not locked in into a specific model.

Mati Staniszewski: 26:30 On the, the kind of second big part of like, of course the space is overlapping, increasingly models are platform, platform are application, everything is becoming a little bit more fuzzy. For us the still the defining piece was focusing on that one layer of like, how does interaction look like, how does communication look like? And we’ve been able to out-compete them on voice models, both on text to speech, speech to text, and now on the voice agents, and actually do it time and time again. Um, and I think part of the reason is it’s on the research side, it’s the architecture that matters, not the scale. You really need to change how the model operates. Two, you need very specific data that there’s, of course, a wide set of data out there, but it’s unlabeled data. And where we spend a lot of time is we build an internal team of over a thousand contractors that helps us build the specialized data on top, so we can then train the model in a specialized way.

Jason Calacanis: 27:57 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?

Mati Staniszewski: 28:31 We we we we know that some companies are continuously trying to figure out how to distill and use the data. So that is a that is an existing problem, and we have a few mechanisms to to to stop it um or slow it down, not stop it. Um, but um, but on the open source question, or like creating our own um uh versions, we are we are looking a little bit closer on like how we could use our expertise of of building the best models to also create the models that we could use ourselves in the places where it makes sense. So this is definitely on the roadmap and something we’re exploring.

Jason Calacanis: 29:13 Yeah, it’s pretty clear in my estimation that that’s where you’ll 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, is going to be a pretty easy for a company with your level of resources. So why wouldn’t you, at least offering it as an option? And then I guess there’s cost. I mean, you must be shipping tens of millions of dollars to these companies, tokens.

Mati Staniszewski: 29:45 Should be good amount. Um, we are good partners, good partners with them. Um, uh, but 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 is definitely helpful. 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 would be complete. It’s as smart as another human and we hope this year we’ll do the same thing for voice where any conversation feels like you are speaking with another human and it will be…

Jason Calacanis: 30:23 Yeah, I think you’re there. It just depends on the application and like what question you ask, but it definitely passes the… 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 set of tests would be, can it do a job, can it do a task end to end, can it work for a week straight.

Mati Staniszewski: 31:05 I mean, there are definitely places where we did achieve it.

Jason Calacanis: 31:08 Yeah, for sure. All right, continued success. Let’s give it up for Mati from ElevenLabs. Well done. Thanks for coming out.

Mati Staniszewski: 31:13 Thank you so much.

Jason Calacanis: 31:14 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. You’re growing also at a very significant clip. Exponentially.

Max Junestrand: 31:51 Is it exponential? No, it’s not exponential. Oh, it’s a sustain 50% quarter over quarter for the last seven quarters.

Jason Calacanis: 31:57 50% quarter over quarter last seven quarters. Yeah, that’s pretty darn fast.

Max Junestrand: 31:59 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 1 to 150, beating CRM with one quarter.

Jason Calacanis: 32:11 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…

Max Junestrand: 32:30 Harvey who?

Jason Calacanis: 32:31 It’s just 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 cost. Yeah, and I’m seeing it already in the startup space. I had one firm, one startup that hit a million in revenue.

David Sacks: 33:14 Yeah.

Jason Calacanis: 33:14 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.

David Sacks: 33:23 No.

Jason Calacanis: 33:24 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…

David Sacks: 33:32 ChatGPT, bruh.

Jason Calacanis: 33:33 Yeah. And I’m like, what about the cap table? They’re like…

David Sacks: 33:35 ChatGPT, bruh.

Jason Calacanis: 33:36 And I was like, okay. And HR? And they’re like, same thing, bruh. And I’m like, okay.

David Sacks: 33:42 That’s gonna be a fun due diligence target one day.

Jason Calacanis: 33:44 Well, that’s what I said. I said, hey, you know, when you do the Series A, they’re gonna ask that 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. They’re 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 actually do at Legora and how it’s different from just asking ChatGPT.

Max Junestrand: 34:06 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 very small. And what we’re seeing now is you can take these legal services and you can productize them into software. And the interesting thing is that the law firms themselves are starting to do this.

Jason Calacanis: 35:15 What’s an example of that? Like a…

Max Junestrand: 35:17 So an example of that is Cooley, actually. They started serving startup founders directly with a software platform that you just log onto the platform, they’ve 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 sell time. Because if you sell software, you’re not selling time anymore. And that’s a really interesting shift because the whole leverage model of a law firm is that you have junior lawyers billing a lot of hours and the partners take a margin on that. But the biggest law firms in the world are undercharging for their partners.

Jason Calacanis: 36:00 partners. I don’t know if they’re undercharging, man. I got a bill recently and it was 1,800 an hour.

Mati Staniszewski: 36:03 Right.

Jason Calacanis: 36:04 But but but for a senior person, I think the associates were 800.

Mati Staniszewski: 36:08 Well, you know, Kirkland can go up to 4,000 an hour. But the thing is, when a Kirkland- if, 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-

Jason Calacanis: 36:30 Well worth it, yeah.

Mati Staniszewski: 36:32 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.

Jason Calacanis: 36:44 Oh, really?

Mati Staniszewski: 36:45 Absolutely. I mean, we’re doing this partly at Legura. 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.

Jason Calacanis: 36:59 Because your motivation as the founder is to get the deal done. Right. The motivation of the lawyer is to not have you sue them if they fuck up the deal, right. And to make as much money as possible. Which means to drag it out. Which means their incentive is to, even if they don’t say it explicitly, it is to drag it out. Your incentive is to close it as quick as possible, yeah.

Mati Staniszewski: 37:23 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, for 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, you know, one of the biggest industries in the world now is being completely transformed and reshaped.

Jason Calacanis: 37:40 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?

Mati Staniszewski: 37:49 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 dollars a year.

Jason Calacanis: 38:03 How many lawyers do they have?

Mati Staniszewski: 38:04 About 4,000 to 5,000.

Jason Calacanis: 38:05 Wow.

Mati Staniszewski: 38:06 Yeah. 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 Legora where we’re helping these firms transition from a pre-AI to post-AI world.

Max Junestrand: 39:00 AI to post-AI world.

Jason Calacanis: 39:02 And it’s sort of like document management and PCs were what 20 or 30 years ago when they were printing out and keeping drafts in a library and in a storage facility and they had to sort of walk them through and hand hold that.

Max Junestrand: 39:18 Absolutely. But I think the difference is…

Jason Calacanis: 39:20 Revisions, right? Yeah.

Max Junestrand: 39:22 The difference is those were, you know, mild productivity gains.

Jason Calacanis: 39:25 Yeah.

Max Junestrand: 39:26 This can do a lot of the work. And so it’s really reshaping what it also means to be a junior lawyer going into this occupation.

Jason Calacanis: 39:36 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?’

Max Junestrand: 39:46 The job will exist, the tasks will be different, right? Um, in order to have a partner-driven model, you need to bring people up the ranks, right? In the same way as you do with software engineers. You need to have junior engineers so that one day you can have senior engineers who know what they’re doing. Um, but the way of getting there is very different. The way of getting there today will not be sitting and doing very manual document review for two years. It will be orchestrating agents, checking their work, understanding the strategy, and doing the higher-level work much earlier.

Jason Calacanis: 40:27 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? I know you’re starting to see that and is that something that’s built into the product? So when you’re doing even in the United States, it’s a state level certification obviously, um, and doing business in different states you have to have counsel in each state. Is that something that AI can help bridge?

Max Junestrand: 41:08 Yeah, exactly.

Jason Calacanis: 41:11 So talk about that, because that seems to be a place where there could be massive gains from AI.

Max Junestrand: 41:14 100%. And it’s really two things. I mean, the data that Leya sits on top of is on one hand side, the firm’s and enterprise’s 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 an incredible data asset. And you can imagine a world where a lawyer in one jurisdiction has a query and they can route it to a network…

Max Junestrand: 42:00 …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’s so many people who are working with setting policy and building regulation and this can really change how that works.

Jason Calacanis: 42:31 And Lexus Nexus has been a juggernaut and the legacy player in, you know, all the case law and regulations. They have a massive data moat. They—but they only make a couple of billion dollars a year and if you put your revenue and Harvey’s revenue together, you guys are probably already just that, the two of you, you’re both making hundreds of millions. So they must be looking in their rearview mirror going, oh my god, these startups are going to lap us. Are they able to pivot or are they too committed to their existing business model and their existing data set?

Max Junestrand: 43:25 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 of them made a lot of noise and did a lot of marketing, but as the technology has matured, I think it’s become clear who’s actually building the real products.

Jason Calacanis: 44:35 Westlaw is the other one.

Max Junestrand: 44:37 Westlaw and Lexus Nexus exactly. But yeah, if you look at how their stock is doing, I think they’re—

Jason Calacanis: 44:44 Oh are they getting priced in with the AI uncertainty?

Max Junestrand: 44:47 Well, yeah, that’s one way of putting it.

Jason Calacanis: 44:49 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—

Mati Staniszewski: 45:00 the cases or OCR them, then check them, look for the differences. I mean, because you can’t get it wrong.

Jason Calacanis: 45:06 Nope.

Max Junestrand: 45:07 You’re exactly right, that’s what you have to do. But today with the AI tools, the AI tools are really good at doing what they did manually.

Jason Calacanis: 45:09 Yeah, they would literally have two different people type in—

Mati Staniszewski: 45:11 Yes. And you still have to ship the books, because you have to physically scan. This is very strange in the US, 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 a company.

Max Junestrand: 45:27 That’s crazy. You guys are very good at capitalism.

Jason Calacanis: 45:29 Sometimes too good. But there’s, I mean Harvard has a project, there’s the Court Law, Court Listener…

Mati Staniszewski: 45:36 They’re trying.

Max Junestrand: 45:37 They’re trying. 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.

David Friedberg: 45:46 Mm-hmm.

Max Junestrand: 45:47 Because if you go to Wachtell, and a litigator at Wachtell, the best law firm in the world, says ‘I’m going to use this to, you know, go after Elon or or do a billion dollar case’, you better make sure you have all the cases.

David Friedberg: 46:02 Hmm. So it’s the opposite of the power law. You don’t just need the top 80%, you actually need all of it.

Max Junestrand: 46:09 All of it.

Jason Calacanis: 46:10 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?

Mati Staniszewski: 46:17 Well, there’s other ways of getting it, but in practice, yes. Um, you have to physically get the books all the way to India, you need to open them, you need to scan them, because it’s what’s called page citations. I never thought in 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, you know, does their work. Right. 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.

Jason Calacanis: 47:30 And so you have partnerships with I’m assuming Anthropic and OpenAI, yes? To—and you spend millions or tens of millions of dollars on tokens?

Max Junestrand: 47:39 Right. Absolutely.

Jason Calacanis: 47:41 And they are also competing with you on the margins?

Max Junestrand: 47:44 Um, they are not competing in our product category at all. Um, for now. Um, well, you know, from the outside, uh, 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 know, you understand how shallow it is, and then you call us. And so it’s actually a big pipeline generator for us.

Jason Calacanis: 48:22 Got it. So they start experimenting, boom. We were just talking with the CEO of 11 Labs about this. 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?

Max Junestrand: 48:50 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 scale in, 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 questions, and you can imagine you run that at enormous scale, so there it makes a lot of sense to have a very optimized model.

Jason Calacanis: 49:38 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.

Max Junestrand: 50:03 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. 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 through, you have a very strong position because the trust is hard-won.

Jason Calacanis: 50:49 Does that mean you have to put it on-prem as well?

Max Junestrand: 50:53 No, we don’t do on-prem. I…

Jason Calacanis: 50:56 Is that on the roadmap or…

Max Junestrand: 50:57 No, I mean, you know, deploying in the VPC is very… It is time consuming, and it creates a lot of dependencies which slow down your roadmap and the execution forward.

Jason Calacanis: 51:08 Alright, continued success, Max. Thanks for taking some time for us.