Vertical AI Innovations: Abacus LLM in Finance & Synthesia's Video Avatars | E2112

24:47Yeah. And besides, you care much more about the monthly than the one time. Exactly. One time is a sweetener for you guys. There you go, Alex. So it really wrote about the monthly, so the one time gives us flexibility in our pricing in a way that we wouldn't have otherwise. Honestly, I mean, like, pigs get fat, hogs get slaughtered, but, like, if you can go ahead and get paid twice, huzzah! I mean, look, I'm not going to stare gross profit in the face and say no to it, but I just realized something interesting about your 15K a month price point. because this is on-prem, you're not eating enormous public infra cloud costs.

25:25So your COGS must be low and your gross margin must be just ludicrously lovely. Exactly right. So we are not paying an open AI or anything on a per query basis, which is why our fees can be fixed, right? We can say, okay, for the contract, 10 ,000 or 12 ,000 or 15 ,000 a month, banks and credit unions like that. They do not like annuities that are variable. They will shut that down every time. They want a fixed price. And so they can ask 10 ,000 queries a month or a million queries a month. It doesn't matter, right? The price never changes. So we can undercut a lot of our competition on price, first of all, when it comes to these sorts of Asians and AI deployments.

26:01And then secondly, as you mentioned, you know, the one-time fee that we're charging, you know, because everything's on-prem, everything is built in-house. So, you know, we can really price it in a way that makes sense for the client. has anyone ever come to you and said listen we want you we want to be on prem we don't know how to set up our own you know racks have you ever like gotten the screwdriver out like put the gpus together for someone else to run the model on their own metal yeah it's funny you mentioned that so a lot of credit unions and banks maybe even the one you mentioned top of the call from your hometown they actually have a server room right actually in the building right and so we've actually gone to physical server rooms before spent a few days helping them install abacus so maybe not quite a screwdriver, but we have been in the room in the vicinity of the actual servers that run the computational data for the bank or the credit union.

26:52So we're not afraid to be there on site if we have to. I'm now frantically Googling credit unions, Corvallis, Oregon, trying to figure out which one it was. It's the one over by my old dentist. You know the place, right? You've been there. Of course. Yeah, yeah. A hundred percent. I want to drill down on some of the tech things and get away from the money side for a minute. And response control, hallucination control, clearly when you're talking about regulated industries, can't be spitting out BS. How did you guys actually go about combating hallucinations? Because I think that most models have made progress here.

27:22I think there's a good trajectory, but I wouldn't say it's something that's been resolved. And so I'm kind of curious, what was your approach? Yeah. So we have what we call chain of validation, right? And it's a three-tiered system, Alex. And that really helps us ensure that Abacus is always giving the right answer. So as you know, with LLMs, hallucinations is a big problem, right? How do we make sure that the answers that an LLM gives is accurate and grounded in real facts and data? So our chain of validation has three steps. The first is triangulated retrieval, right? So Abacus will look up.

27:55So say you ask a question like, how much is a late? And Abacus gives the answer. A late fee is$25. dollars she will look to cross-reference three different sources of information to validate that a late fee is actually 25 so she'll look at their online website make sure that the fee listed on the website says 25 she will look at their truth and lending disclosure to make sure the disclosure says 25 and then she'll look at their internal policy documents to say that shows that the late fee is 25 when all three she here is abby the agent that's right that's right abby the agent And so if all three of those sources match, the data passes the first step in chain of validation, which is triangulated retrieval.

28:36Then there's a second step in chain of validation, which is claim decomposition engine, right? So we take the logical structure of a claim and we decompose it, right? And make sure that that's valid with the fact. So let's take our example. A late fee costs$25. We're talking about a thing, a late fee. We're talking about a fee. And then we're talking about amount, right? We take the thing, the fee, the amount is the logical claim that we're trying to validate, and we validate that claim against the information. So with an LLN, they give a lot of very colorful answers, answers that are meant to sound very human-like in quality.

29:14We're extracting just the logical claim that's being made. The thing is saying it costs this much. Is that true? Customers are allowed to sign into online banking and set up alerts. are customers allowed to sign into online banking? And once they're in there, are they allowed to set up alerts? That's a logical statement, right? That we're trying to validate. Okay. Why wouldn't you do that before you did the triangulation of the$25 price point for this late fee that we're discussing? Because to me, it sounds like you would want to get to the absolute nuts and bolts of the logical progression and then check that against the facts.

29:49Yeah, it's a good question. Because if you check the logical progression of a statement, right, Right. And it is valid, but the validity. So if I said, for example, all cats are dogs, that is a valid logical statement. It's not a true logical statement, but it's valid, right? All right. Everyone knows that CRM isn't just software. It's basically the heartbeat of your business. But it can get ugly quick if your data isn't organized and you're dealing with a messy tech stack. That's why I love HubSpot for startups. It's the all-in-one customer platform. So you don't need a Franken-side of tools.

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31:03They've been around those sharp turns and they can tell you how to navigate them. HubSpot was built by scrappy founders. I know them. And they understand every dollar counts. That's why hundreds, thousands of startups trust HubSpot to scale their businesses. Here's an amazing call to action. So generous from my friends at HubSpot. 75 % off. That's right. Seven, five, not 7 % off, not 5 % off, 75 % off HubSpot for startups. You're going to get three months of perplexity AI for free. That's a great pot sweetener. Head to HubSpot.com slash startup. We're not just checking the validity of the logical statement.

31:40We're also checking the truthfulness of the statement. So the first is we want to make sure that the information going into that logical statement's true. Once we validate that it's true, then we validate the actual logical claim, right? Is the claim valid? So if something were to say a late fee is negative$100, that's not a logical claim, right? So that's not a valid logical claim. So we know immediately something's wrong. So that's why we do it in that order. But then we have the third piece, which is what we call fact backpacking, right? So every single answer Abby gives comes with the actual document that provided the answer.

32:12Where in the document that answer came from and a full citation. So document ID, paragraph level hash. So if that person using Abacus wants to see, okay, Abacus saying it's$25. I just want to make sure that's right. You know, for my own sanity check, they can click to see the actual physical document. Okay, this is where it came from. This is the exact paragraph. Oh yeah, it says$25 there. I know the answer is right. Is the person here that's checking this, the end user or the company itself who's vetting their own system? The end user. So we empower all of the end users, no matter if they're a teller or an executive vice president, if they receive any answer that Abacus gives and they want to make sure that that answer checks out.

32:52It sounds about right, but I don't know about that. Is it really$25? I thought we changed it to$35 last month, right? They can click on the actual answer, right? And it'll show them, oh yeah, in policy 568, where it talks about late fees in this paragraph, I can see right here, Abbey highlighted exactly where it came from. Can I just say that if your credit union is charging you a$35 late fee, get a new one. That's worse than Citibank, who I hate. Yeah, no, credit unions are really good about late fees. I'm just making up numbers here, but they - I know, I'm just - They're really fantastic. Yeah, no, but I agree with you.

33:26Late fees can get completely out of control. I'm curious, with this three-part system, do you get hallucinations down to zero, down to a de minimis level or down to a merely acceptable, but still improving level. We have a 98 % accuracy rate because of this system, right? Anything that the system flags, it kicks out, right? Okay. And because we train on financial data, you know, we know the kinds of questions they're asking, you know, we can be very, very precise. So we have a 98 % accuracy rating. Hallucinations are incredibly rare if they happen. Okay. We're able to get it down to a level so low that they're not material.

34:03And that's where the trust comes in. And so once clients learn to trust Abby, you know, sky's the limit as to what you can do. That's amazing. I'm so happy that we've gone from, you know, attention is all you need to chat GPT to this level of deep vertical integration in, you know, less than a decade. I mean, that's why technology is awesome. Like, I mean, my God, I do hate that I feel so incredibly behind every single day because I feel like I'm sticking my head into a fire hose of news, but then you see where the rubber meets the road and it's pretty exciting because this is actually going to make smaller financial institutions more competitive more efficient and just more viable so that that brings me joy um david now about your company though how fast are you growing we've grown tremendously fast we have more interest than we're able to service right now so we're we're really trying to you know move the needle on uh our fundraising around and and you know build up some infrastructure here so we can do that.

34:59Just last year alone, we went from 25 ,000 in ARR to 125 ,000, or excuse me, MRR, 25 ,000 in MRR to 125 ,000 in MRR by the end of the year. So we have grown incredibly fast so far. When I think about venture capital, I know this is this big in startups, not this big in venture capital, but it often feels like the same thing. A lot of founders need to raise money because they need to build, they need to reach a certain milestone, hire a certain number of people, buy a certain technology or whatever. And so it makes a lot of sense to have risky capital in there. But going from 25K MRR to, you know, north of a million ARR in a year, I mean, it feels like you probably have so much more income to play with that you're probably not as capital constrained as your average Joe in the startup game.

35:44So why is it the right move for you guys to go out and try to raise more money versus just self-funding? Because it feels like you have a fork in the road here, if you will. You're right. When I started the business, I started the business and I wanted to run it like a real business. We're cashflow positive. We don't have a burn rate because I'm from Chicago. I'm from the Midwest. I'm not one of these fancy San Francisco boys. So I started it the old fashioned way, my business, and worked at it slowly as we've made progress. To me, making money was the definition of, okay, now I have a business.

36:20We have net income. So you're right. We do have capital. We do have money coming in the door. So why venture capital? And the answer, Alex, is as much as I hate to say this, to win in this space, you have to be first, right? The company that these credit unions or banks or insurance companies adopt first is going to win because once they're in there, they're very hesitant to change, right? Some of these banks and unions have systems from the 80s and they don't want to change it because it works. So the same thing is going to be true of whatever AI system they adopt. So we need to get there first.

36:51To get there first, we need a large infusion of cash so that we can move very, very quickly, make sure that we're capturing as much real estate as possible as the AI space is building out and companies are starting to adopt this technology. And so that's why the venture capital is coming in. So the gates open, you're running across the field to get the best spots at the concert, and you want to run even faster to beat all the nerds out. So you raise money. Okay, I'm here for that. But if you do end up raising some money, going out there and then going back to profitability. Well, that would make my Chicago roots very happy to hear because I love net income.

37:24Those are my two favorite words in the English language. All right, David, before we go, one, what's the website? And two, what is a role that you are hiring for? You're having a hard time landing the candidate. Yeah. So goadvocist.co is the website. And then a role that we're hiring right now, sales, sales, sales, sales. And we need people that have expertise in that industry. So banking, insurance, credit unions. So those are the roles we're really looking to fill right now. So if there's anyone out there listening that's interested, please let me know and go to the website and hit contact us.

37:54But we're looking for people with that industry experience and that sort of sales point of view or disposition. So all my cool salespeople out there that know those industries, let me know if you're interested. All right. Well, David, thank you so much. And when you, I don't know, whatever your next milestone is, 2 million air or whatever it is, I'd love to have you back on to learn more about it because companies that grow as fast as yours are, are onto something. So in the meantime, good luck. Enjoy the Chicago summer. It'll be tank top season for a solid three months before it freezes again.

38:23We'll talk to you soon. Thank you, Alex. Thank you very much. I told you that was going to be fun, but let's take it one step further. Let's go talk to Synthesia, not Synthesia. Come on, get it right, people. And let's learn more about AI-generated avatars, training videos, video models, and all that good stuff. Let's go. We have been talking about AI so much on this show, I'm sure you want us to be quiet about it. Who wants to see more text generated by a robot? Well, good news today, we are going to stay on AI, but we're going to move away from the written word and instead focus a little bit more on video.

38:54Now, regular viewers of the show will recall that some time ago, I heard tell of a neat new product called Synthesia, and I wanted to play with it. So I made a video, brought it to the show, showed it off to Jason. Well, today we are bringing the company on the show to talk about what they're building because i have added them to the twist 500 partially because they raised a big round earlier this year but partially because they're growing very very quickly and we'll get to that so please welcome to the show it's victor ripperbelli the ceo and co-founder of synthesia hey man good to be here thank you for being here uh for folks who are watching the video it's actually quite late over in london where he is he's been very generous with his time but first of all let's start right there building an ai company in the uk i've been told victor that if you're not building within three blocks of one part of San Francisco, you're doomed and yet you're absolutely not.

39:41So what's it like to build an AI company over in Europe? There's pros and cons. I think if you weigh up everything at once, it's probably still better to build an asset for the US. But there's a lot of benefits actually for building in Europe. One of them I think is actually talent, right? So the price of talent in the Bay Area is extremely high compared to the rest of the world. And it's not just about the cost. Actually, I think there's also a loyalty element to this, which has two sides to it. In the Bay Area, most people are building their own personal stock portfolio. And they're very quick to jump ship if you miss a couple of quarters, right?

40:18In Europe, there's a different way you relate to your work. It's less transactional in a sense. People care a lot about the mission, the company they work in. And they don't care so much about stock options, which definitely has its downsides as well. But my general sense is that people are less jumpy in Europe. And I think that's, of course, beneficial if you're the founder of a company, right? You know, when we started the company back in 2017, the capital markets were not nearly as global as they are today. I think that has kind of like equalized today, more or less. I don't think it's particularly harder to waste money in Europe than it is in the US.

40:49You're less close to the ecosystem. I think that matters a lot if you're building, if you're customers at other tech companies. And that's one of the things that we aren't. That's not really a thing for us. It's not even a top five industry for us. But obviously, if you're building developer tooling, you probably want to be in the Bay Area. That's just proximity to other tech companies who are ultimately your customers is much better. But I think building in Europe, building in the UK is getting better and better and better. There's also the benefit of a company like us, who are one of the companies in Europe that are doing really well.

41:19If you're a great European engineer, there isn't that much choice, right? In SF, there's like a thousand cool startups who've laid lots of money and do very interesting things. so i mean pros and cons but overall i think europe and the uk is actually become a pretty good place to build i've uh don't tell anybody but i've actually scooted the uk back into the eu mentally i'm just preparing for a post brexit feature so we'll see how long that takes but i think we can kind of see the writing on the wall uh okay victor what i did there was actually not start with what the hell you're building so synthesia in my understanding is a tool that i can use to create essentially AI avatars in videos.

41:56And these are aimed not at the consumer use case, I'm not making a 30 second clip of Gandalf walking around with Frodo. But instead, I'm creating video materials for a corporate environment. So one, how close was that and to narrow it down for me? Not bad. But I think a lot of people have this perception that we're an AI avatar company. And that's definitely the way we started, like, let's start it. But that's, that's kind of how we We initially went to market, the hit product market fit four or five years ago in 2020. I think by now we're much more than that. Avatars is a feature in our platform, but really what we are is an AI video platform for the enterprise.

42:31We cover the entire life cycle of the video. We help you create the content which starts with the AI models, the avatars, right, which replace the need for a camera. But it's also a fully fledged video editor, sort of like using Canva or PowerPoint. It's a modern collaborative platform that can support enterprise with thousands of people creating things simultaneously. organize themselves around that. It's a content management system for translation, updating, versioning. It's a publishing platform. We have our own AI video player that's built to show the videos you make in Synthesia and deliver your insights and analytics back.

43:01So really, it's about the entire value chain. It's not just about creating clips of advertising, even though that's where we started. You're right on the use cases. We're not targeting entertainment. We're not targeting advertisements, really. And the best way of thinking about Synthesia and the market we serve today is people today want to watch and listen to content they don't want to read that much anymore right in our private lives everyone i think will agree with that we're recording a podcast with a video right now and most people prefer to consume information this way and that's because when we have free choice that's what we do but when we go to work right we don't have free choice most of the time you have to basically consume a lot of text a lot of emails a lot of slides and that's just a much worse way of communicating and to your customers employees your partners that could be anything product marketing to customer support to internal trainings, it's a much worse format if you're using text.

43:48If you want to communicate with most efficiency, you need video. And what we've built is a platform where enterprises can essentially use our tooling to communicate in the most effective way, which is with video, but at the speed and scale of text, right? You don't have to use cameras. You don't have to use actors. You're going to need to be great at video editing. It all just comes out the box in a PowerPoint-style experience. And the markets we serve, five years ago, when we launched the first iteration of the platform, it was very much internal-focused learning and development workout training-oriented use cases.

44:18What we've seen is that as the avatar and voice technology gets better and better, and you unlock more and more tan. So today, it's 52 % of all videos generated that are external-facing, which is like customer support. Oh, so over half now. It's over half now, right? And a lot of that is like product marketing, for example. But it's not like the fancy meta ad that attracts you initially. It's the mid-funnel content, right? It's like you're interested in a product, you go to the website, and you want to learn how does my product compete against a competitor. That can now be a video instead of a long page of text.

44:48Your consumer and your transaction out of mortgage used to be faced with a long page of text that very few consumers can sit down and comprehend. Now you can watch a five minute video instead. So it's not like the top level awareness stage of the funnel. It's much more in the kind of information sharing, how-to style of content that we found on mission. Now, when I showed the demo, we used a model of a woman sitting on a couch wearing a striped shirt, for example, that's kind of the thing you have on your front page. Can I use Synthesia to make a video or an AI digital twin of myself? Or am I always going to be working with a model that you guys have provided and also, I presume, tuned and tweaked to fit your voice algorithms?

45:29So you can make your own avatar, a very popular feature on the platform, one of the most popular ones. It's super simple, you can use your webcam, it takes five minutes, you basically just recite a script that we give you. You can also, if you want higher quality, you can go to a studio, you can record it with a camera and essentially the quality you input to the system is the quality that you get out. These technologies are magical now, right? Should we just try cloning your voice at some part in time? Actually, I haven't yet because I think my voice is very annoying and I wouldn't want to listen to it more so it actually hasn't occurred to me.

46:01But I will play with that, yeah. Because you can speak 30 languages at the click of a button and it is pretty magical to listen to your own actual tone of voice, speaking in Spanish, German, Italian, whatever language you want to speak. That actually, that is freaking cool. And I presume, because we're talking about multiple languages here, that the service itself does support much more than just English. We support 140 different languages, which is also a key part of our value proposition, especially for global companies, right? We need to communicate across borders all the time. So let's talk just a little bit about training data, because you guys recently announced a deal with shutterstock to ingest some of their visual information to help build out your express 2 model but when i'm thinking about how to nail an irish accent i mean is there a repository you can go out there and like train against you to collect all that yourself how did you go about supporting that many different languages and speaking styles so that particular part is separate from the the the deal with the shutterstock right if we start to count the video side of things, which is really where Shutterstock is kind of relevant here.

47:08Basically, what's happening in the world of AI, as you probably know, right, the models get bigger and bigger and bigger and bigger, and they get more and more generalizable. LLM is kind of like the first iteration of this, where we built something which was essentially highly generalizable and could just do a lot of different things. It wasn't like a model that was like really good at doing just one thing. In the world of video and avatars, if you look at what the product is capable of today, it's essentially people talking to the camera, and the quality has gotten like really high. It's kind of edging on looking like a real video, essentially.

47:38But there's a lot more things what the avatars do. They want avatars that can laugh, avatars that can cry, avatars that really use their body language in the correct manner. So when you look at me speak right now, I use my hands. There's a beat to what I'm saying. The avatars are not there yet. They will be with these new flagship models. And essentially what you want is for our AI models that centers on human speaking, we want them to see lots of different scenarios, lots of different types for people interacting in many, many different ways. And that requires lots of training data. So we both spend a lot of money on procuring our own data sets, working with actors all around the world and studios, 3D data and so on.

48:13But it's also really helpful with these models to see kind of a glimpse of the world from Shutterstock, which has like a lot of content on their platform. So much. Okay, so it sounds like this is very, very hard to do well. So you're using both data from other people, getting your own data from actors and voice and motion capture and so forth. How much better will Express2, the model I believe you're currently training right now, be compared to what I can currently see via the Synthesia website? It'll be a lot better. I think what we're getting to now is maybe Self-Dubes just to outline how I view the kind of avatar space and video in general, right?

48:53So I think these technologies started five years ago. We were the first ones to launch the property. And back when we launched the first iteration of them, they're pretty crap, to be honest, right? The devices sounded like you're kind of talking to a GPS. The video was kind of stilted, but it was kind of like good enough for some use cases. And we were some of the first to discover what those use cases were. And they were definitely not Super Bowl ads at all, right? But for internal training videos, where the alternative was asking people to read 10 pages of PDF documents, these sort of crappy avatars actually was a much better experience, right?

49:24Now, what has happened since then is the quality has gotten better and better and better and better. but we're still in the realm of what I would call educational how-to, very kind of utilitarian practical content, right? It's about like me delivering some information to you in the most engaging way. It's not yet about storytelling. Storytelling to me is making you feel something, making you laugh, making you sad, making you happy, right? That's what a real actor does. That's what you look like a great app, right? It makes you feel something. That's what you see in entertainment content that makes you feel something.

49:50And I think with these new models, we'll actually begin to cross into that because what's going to happen is that the avatars can and begin to perform their lines like a real actor would. When I speak to you right now, my voice kind of slows down and speeds up. And I emphasize a specific word. I use my hands to underline something, right? And that's how we speak as humans. That's what's most natural to us. And when we can teach these avatars to do that, we are going to be able to create content that's going to be even closer to the real thing. And it's going to have all the emotions and explosivity you would have from a real actor.

50:18So I think with these new models, I think we'll break through into storytelling. and you'll begin to see the first iterations of creating content that's not just meant to inform, but also kind of entertain, storytell, make you feel something. And I think that's going to be a pretty interesting watershed moment. We've seen that in other modalities, something like if you just stick with just the voice part, essentially before a couple of years ago, voice technology was only used for assistance in your phone or for GPSs, right? You would never listen to an audio book that was done with an AI voice.

50:48It was just way too bad. It was terrible. Now you have companies that basically take real books and turn them into audiobooks, and it's a great listening experience because all the emotional exclusivity is conveyed. But we haven't had that moment for video yet, and I think that will be a pretty big watershed moment that just unlocks so many use cases. So I think it's pretty exciting. So what I just heard is that when the Express 2 model comes out from Synthesia, we're going to cross over to almost a new generation of AI video quality. It's going to unlock a lot of other use cases, both in the corporate world and I presume also at some point in time in the consumer domain as well.

51:23Absolutely. Yeah. That's incredible. So I'm going to go off topic here and talk about where I see this going and I'll bring us back to the corporate world and how you're doing and so forth. but I recently was playing with OpenAI's voice mode, just chatting with my AI. And we had a talk, I gave it a name. It was kind of weird. I almost felt like I was like crossing some sort of like ethical boundary. We were just talking about like the stock market, but it still felt different in an interesting way. Almost like I was talking to a person, even though I knew better. If I take that moment of like, oh wow, this is actually really good and apply it to what you just told me about what you're going to pull off with the Express 2 model video.

52:00i mean how long until i have like my ai is is not only persistent in terms of memory and knowing me but also has like a a look to them that i would expect to persist across my digital environment it feels like to me you're building the real front end for ai here in a way that other people haven't yet and they're still trying to get me to type into a chat box so to me what you're describing feels like it opens up a cambrian explosion of possibilities versus just making middle funnel corporate materials that much more beautiful for sure i mean i think we're so early in all this technology and as to your point right i mean these things will be real time they'll be the fidelity will be 10 times what it is today and you will be at some point right probably have like ais that can tell jokes you'll laugh at which sounds weird we may even at one point have you coming home after a long day of work and sitting down on your couch and instead of turning on netflix to entertain yourself you may actually begin talking to an avatar go through some interactive kind of experience, right?

53:01It's kind of hard to imagine. Five years. Yeah, I mean, maybe even before that, but definitely in five, 10 years, I think this is going to be a completely normal part of living our lives. And it'll be weird. There'll be lots of challenges. But ultimately, I think what this promise is, right, is that the interactions we have with our computers are going to be much more natural the way our brains are wired, process information, right? Like using a keyboard and a mouse, it's natural to all of us today because we've, you know, we kind of grew up with it. But everything really is a proxy for how we actually prefer to interact with each other in the real world, right?

53:35Which is, we talk to each other, we see emotions, we use our hands, we show people things. I think we can just, we can get what goes on. And it sounds like you had the same experience, but the first time you tried something like OpenAzModicSmart, right? It is kind of like a holy shit kind of moment where like you almost cannot believe it's real. But like the first time you used ChatGPT, right? You just, and it's truly magical technology. the flip side of that is it's also very interesting how quickly we just get really used to things right like with chat gpt it's like if you took chat gpt back like 40 years in time you'd get burned at the state right oh it's like someone invented uh it's like this is like insane but now everyone's like it can't like solve the entire all it can't solve all by homework in one prompt right i have to do in two prompts that's all we get used to this very quickly yeah but i mean we get used to it quickly because our expectations rise so fast.

54:27So the first time that I saw a Synthesia video, I thought to myself, this is 90 % of what I could want. How dare they not give me 100? Which is an insane perspective to have, given that, you know, not that long ago, this was, as you said earlier, poor voice, stilted video, kind of crappy, but definitely a great first step progress. But compared to today, I mean, it's mind-blowing how much better things are, and I almost feel like I should be more enthused. Like, I should be more excited day-to-day, but instead, tools become rote very quickly when you actually go about using them exactly and I think a great analogy here is like visual effects in movies or computer games if you go back and watch a movie you watched when you were 10 years old you were like mind blown as the realism and you're back then you're thinking like this cannot get better we've reached like the apex of like visual effects and then when you go back and watch it today it looks like crap right so we just get used to this stuff so quickly Yeah, it's also interesting, especially when you work with humans, which we do, right?

55:25Humans is the hardest thing to synthesize and make AI videos about, right? Because we're so sensitive to even like the smallest inconsistencies, right? Like when you look at a human, we have so many like micro interactions, the micro movements that goes on all the time. And we're incredibly good at spotting if something is like just a little bit off. Much better than if you're just making a video of like waves in the sea or something like that, where I think in a lot of AI models now are capable enough to do that. But it is a bot, it's kind of a moving goalpost, right? Which is kind of like very fascinating.

55:57Okay, let's bring it back to today. So actually, as this video comes out, we're recording this just a little bit early. You guys are announcing that you've reached 100 million in annual recurring revenue. One, congrats. And two, and this is a stat that actually blew me away more, is that 70 % of the Fortune 100 are now customers, and that's up from 40 % about two years ago. you're getting pretty darn close to having every single of the largest companies out there a customer for you so i'm curious what can you tell me about what they're using synthesia for today has the use case changed at all is it where it was even though you're going into the largest companies in the world or do they want something else out of you it has definitely changed over the years you know and we're very fortunate to have worked with lots of our customers over many years and cutting into you know larger and larger contracts because more and more people in those companies use Synthesia.

56:45And as I kind of described a bit earlier, it still revolves mainly around practical content and utilitarian content. It's about informing someone of something. It's not storytelling, ads, and those kinds of things yet. But what we have seen is that a lot of our customers that started working with us a couple of years ago, maybe they started with internal training and learning. But then since then, now they're using us for customer support. They're using us for product marketing. They're using us for partner integrations and a whole bunch of other things so i'm curious about your costs because the technology is awesome you guys did just raise 180 million dollar series d i think you said it was a 2.1 billion dollar valuation that's a big chunk of capital so i do you guys have very high compute costs is is training very expensive um or is that money more earmarked for you know people marketing and so forth so it's always been very important to us uh to build a great business and not just in terms of top line growth, but in terms of unique analysis.

57:41So it's a really, really solid business, kind of like a marketing perspective, retention metrics, and those kind of things, which is something I'm really proud of, almost more proud than a Hollywood and AR figure. Now that said, of course, we do spend a lot of money on training AI models, right? Like it's important for us to stay the leader of the field, have the best models and so on and so forth. But I generally take a very practical approach to how we train models. You know, I don't want to just train models for the sake of training models. There's a lot of companies out there that start with the technology, right?

58:09So let's train And an AI video model that could do absolutely anything. If you want an AI video model that could do absolutely anything, that's going to be a very big training one. It's going to be very expensive. And your model is going to be kind of okay-ish at a lot of things. Yeah. But not really, really good at one thing. And for some use cases, that's the thing you want, right? Sure. Because I'm saying that that's not great. What we're doing, though, is we're focusing purely on humans talking to the camera. So that's a much more narrow domain than all the videos in the world. That also means that we can be smart around how much training we need to do.

58:38We can work with open source models. We can add a lot of our own data. But of course, a ton of old algorithms. So even though we do spend a lot of money on AI models, that's not the predominant reason for raising this capital, right? That is predominantly going into headcount and all the usual things you'd see in a SaaS company. But I think it's kind of interesting how the margin profile of AI companies, right? It's like they look very different depending on what kind of model you are. In general, it looks like it looks better if you're like workflow driven and use case driven as opposed to being like in the model layer.

59:08Because when you build a workflow on a platform around your model, you're not really charging for the model. You're charging for the workflow you're selling to our customers. And for us, what that means is that, to the point I made earlier, yes, the avatar is a feature in the product, but they are a part of the product. What people pay us for is not just avatar videos. They pay us for translation, versioning, video editing, collaboration. They pay to use our video player and analytics suite that comes with that. And I think that means you can build a different shape of business, of course, like being a foundational model company.

59:41And it's amazing, like looking at open air and probably all these guys, right? They're going to be great companies, but it's going to be very few of those. And I think if you're not going to be one of those truly big winner-takes-all kind of companies, I think you want to be very smart about what models you train and how much money you pour into that, right? Because if you use$200 million to train models and you still don't have the best model on the market, you're basically kind of screwed. So we think a lot of, we think backwards from the use case and for the workflows. And when we need to train our models, we do our own models to do that.

1:00:09If we need to integrate other models into our ecosystem to create the best experience for our customers, we'll do that. And we're not religious about like having to do everything ourselves, right? And I think that's, that's been a key part of our strategy for many years. Hey, if OpenAI, Anthropic, Google, XAI, and Mistral all want to spend a hundred billion dollars making something awesome and then beat each other up on price and then offer it to me, cool, I'm in. Exactly. I'm happy as a cat because I don't have to lose all that money and I get the best stuff. So I do really appreciate that. Now, we talked about the Fortune 100, clearly biggest companies out there in the world.

1:00:42I'm curious about the next like 5 ,000. So are you guys seeing smaller, more mid-market companies have a similar appetite for what Synthesia offers, essentially video AI technology? I mean, we have customers that are of all sizes, right? From just individuals all the way up to the world's absolute biggest companies. and we have a self-service product we have a freemium product which i think you've interacted with yourself right so all that is is of course also very important for us but to my point earlier around focus and we're building for the enterprise you know and that means we're building for businesses and that means we make a bunch of trade-offs that makes our product amazing for the enterprise and for bigger businesses and and maybe sometimes means that we're not always like priors as we need to like a small business that said you know we work with lots of small businesses who love the product and we love working with them.

1:01:28And there's going to be tools like Synthesia. I think the best way of thinking about the ultimate TAM of something like what we're building right now is basically PowerPoint, right? Every office worker in the world today creates PowerPoints. And in 10 years' time, they're going to be creating videos instead of interactive videos. They're not going to be creating PowerPoints. I think that's for sure. And so our tool is enterprise-focused in terms of like the company strategy, but the product in itself can be used by literally anyone, right? And of course, when you have a PLG-driven model like we have, where you can go in and try out the product and sign up a credit card and go for it, you get amazing.

1:02:05It's just amazing. You get everyone in, right? And everyone who wants to play around with it and use it can use it. It'll be interesting to see if the split between enterprise companies using it for internal knowledge sharing and smaller companies using it for marketing external-facing creations, if that holds as the models improve over time or if it becomes much more blended. I presume it'll become much more the same across the size of customer, but the question I guess then is just how quickly but I guess things are moving so fast in AI, maybe soon, I guess is my answer to that everything feels like it's about 6-18 months away where are we going?

1:02:44It's going to be nuts it's going to be nuts but I think also, I think what you'll find is if you think of like the total market for all this as being any communication touchpoint, if it's text, the PowerPoint, the video, whatever, as the market that we're targeting, right? I think the shape of the different types of markets will be different. So for example, in internal facing communication, you'll have a lot of videos that'll all be very different because they're all talking about some different topic, right? Which makes sense, right? You're teaching your partner how to do things, you're teaching your customers how to use the product.

1:03:12There's just so much communication that happens in an enterprise and with the kind of close stakeholders. Now, if you take advertisement for a job, right? That's probably going to be different to shape. That's probably going to be more, I'm going to spend a lot of time on this one video and I'm going to make it really good. And then I'm going to iterate like 10 ,000 variations of that video. I'll change out the avatar to be slightly older, slightly younger. I'll change the hook, right? I'll change the way the product is positioned in there because that's a conversion rate optimization game, essentially, which is what you're doing on meta, AdWords, et cetera.

1:03:45So I think that's going to be more about You create a couple of great base ads, and then the system creates many, many, many different iterations of that to figure out which ones works the best. And this is actually exactly what has happened today, especially in text. With AdWords today, when I was a teenager, I did AdWords. You would have to write all the different AdWords iterations yourself. What you do with AdWords today, you can't just give it a theme. It's like, this is my product. It does X, Y, Z. This is my competitors, whatever. And then AdWords just automatically generates, I mean, thousands or hundreds of thousands of different variations to figure out which one performs the best, right?

1:04:22Now, that's not possible with video today because we can't really programmably create video at that level of scale. But that is going to be possible very, very soon, right? So I think it's got very interesting to see the difference between this kind of algorithmic creation of video for conversion rate optimization. On the other hand, we'll probably have more kind of PowerPoint style creation inside companies where you'll still want to have an onboarding video for the sales team. You'll have a specific video explaining how partners should integrate with your platform and the pricing policy and all those different sorts of things.

1:04:52Which is still very early, but there'll be so many different shapes of products that'll come to rise in the Z-Cosystem. This is mind blowing because I just realized that eventually, you know, run the tape out far enough. Instead of having text ads that are targeted for my demographics, my age, my location, my education level, whatever. It's going to be like, here is the best video avatar to sell X to Alex. And here's the best video avatar to sell Z to Victor. And I'm really curious what that is for me. Like, I don't know. Is it like an old man? Is it like a woman my age? I have no idea, but someone's going to math that out and I'm going to get told a lot about myself based on how I'm sold to.

1:05:35And I don't know if I like that. That just feels... Is that therapy or is that advertising? Yeah. As I said, it's going to be weird and it's going to get weird real quick. And there's lots of ethical considerations. Sure. Culturally, right? I think the fact that these technologies are developing so quickly is very exciting. but humans are much slower to change and to understand new technologies and what's happening in the world than technologies developing right now right and that definitely creates that creates um some problems or some potential challenges that we have to make sure we kind of square up to all right well victor an absolute treat uh when express 2 comes out i want you to come back on the show and show it to me and i'm curious to see how far things can go but in the meantime, I'm going to go make your AI avatar tell me more heavy metal facts because I can.

1:06:25What is the URL for people to go check it out themselves? And quickly, what's a role you're struggling to hire for? Right. www.synthesia.io. Go in, create a free account, make an avatar of yourself. Right now, we're struggling to hire and we're hiring across all roles. And we're building an amazing go-to-market team across North America and Europe. And we're building amazing engineering teams in Europe. So if you're in one of those two camps, and you want to join a company that's become a$100 billion company one day, then you know where to go. All right. Thanks, Victor. I absolutely love talking to founders.

1:07:01It actually just never gets old. I've been doing this for, what, a decade and a half now. And every time you talk to a new founder, you leave pretty darn energized. We're back tomorrow with our live news team with Jason and Lon. But I do really enjoy getting the chance to sit down with founders, dig a little bit more deeply into what they're working on and just riff, just learn what's working in the market, what's not, where is the capital really flowing? So expect a lot more of these. I have a bunch in the can. We're recording a lot more this week, so there's a lot more twists coming your way.

1:07:30I'll see you tomorrow. Bye.