The Twenty Minute VC (20VC): Venture Capital | Startup Funding | The Pitch
00:22:12 · Harry Stebbings, Brendan Foody
[00:22:12] S1: Do you think we'll go through a period of consolidation? Because there are a huge amount of them where you'll actually end up buying the medical data provider, because it's a really important part, medical data. Do you [00:22:21] S0: think you will have that period of consolidation? I think there will. I think in most markets, when the markets are so frothy and anyone can get funding and run negative margins, of course, there's gonna be this proliferation of companies that pop up, and when markets come back to earth and there are natural corrections, that's when there's periods of consolidation. And so we view having over $500,000,000 in cash and a super profitable business as a significant asset and allowing us to be prepared for when there is a market correction to make sure that we consolidate market share. You're profitable today? Very profitable. [00:22:57] S1: How long have you been profitable for? [00:22:58] S0: We've never really burnt cash. We burnt $500,000 after our seed round. And then from there, we've pretty much been profitable ever since. We have more cash than we've ever raised. And it's just because the business has grown so quickly that we obviously try to redeploy capital as fast as we can to invest in growth, but yeah, the business has grown so fast that we haven't been able to redeploy capital commensurate with that.
00:22:58 · Brendan Foody, Harry Stebbings
[00:22:58] S0: We've never really burnt cash. We burnt $500,000 after our seed round. And then from there, we've pretty much been profitable ever since. We have more cash than we've ever raised. And it's just because the business has grown so quickly that we obviously try to redeploy capital as fast as we can to invest in growth, but yeah, the business has grown so fast that we haven't been able to redeploy capital commensurate with that. [00:23:24] S1: Can I ask a Myth Buster one, which is after we had Adash on the show, I think first time, people were like, oh, the revenue's not real revenue? It's like GMV. When we understand your revenue and... What's the revenue say? [00:23:36] S0: I can't show the exact revenue number, but it's, you know, dramatically higher than whatever's been posted publicly. [00:23:41] S1: Let's give a ballpark, I'll use my simple numbers, just genuinely, not, like a billion, just it's an easy [00:23:46] S0: That's much more than that, but yeah. [00:23:48] S1: Okay, let's say a billion because it's easy for my brain. So we have a billion, is that like sales for Airbnb and then they get 20% of that?
00:23:48 · Harry Stebbings, Brendan Foody
[00:23:48] S1: Okay, let's say a billion because it's easy for my brain. So we have a billion, is that like sales for Airbnb and then they get 20% of that? [00:23:57] S0: So the revenue is between a 3040% gross margin, but the key distinction and why it's not GMV, but it's revenue, is that the experts are actually only one part of the broader value chain that we deliver to customers. So when a customer comes to us, they're generally buying tasks where they would say, Hey, they'll pay $1,000 for this task that delivers model improvement. And then we do the end to end process associated with how do we find the experts? How do we hire the experts? How do we build the platform that the experts work on so the experts can do the work? How do we have our AI project manager manage the experts to automate all of the coordination of helping to produce this data? How do we have automated quality checks, etcetera, to produce the end product of the task that we're delivering for our customer? And so that's the large distinction of how we're powered by a talent network in the same way that Uber is powered by a driver network, but that's not the end product in the same way as some of those marketplace businesses. [00:24:57] S1: What's so interesting for me, and you can tell me if this is bullshit or not, it's like you've seen the evolution of this business from like, hey, we provide raw data back to the largest models in the world, was how it started. And now it's like end to end, we provide it fully and then we send it to you, we make sure everything's ready and it's full stack.
00:24:57 · Harry Stebbings, Brendan Foody
[00:24:57] S1: What's so interesting for me, and you can tell me if this is bullshit or not, it's like you've seen the evolution of this business from like, hey, we provide raw data back to the largest models in the world, was how it started. And now it's like end to end, we provide it fully and then we send it to you, we make sure everything's ready and it's full stack. [00:25:16] S0: Exactly, very vertically integrated. Well, because so many parts of the downstream signal inform the upstream signal, right? Like we can use the quality checks on how high calibers each of the individual data points to understand exactly what are the types of experts that we should be onboarding to achieve the data that drives the most model improvement. And there's oftentimes this very power law nature of data that drives model improvement, and that out of a dataset of 10,000 tasks, the top 2,000 tasks will create majority of the value. And so it allows vendors that are extremely high quality to be super differentiated insofar as pricing power, because quality is the X factor that becomes dramatically more valuable than any other dimension. [00:26:00] S1: What tasks are super high value?
00:26:00 · Harry Stebbings, Brendan Foody
[00:26:00] S1: What tasks are super high value? [00:26:02] S0: Is it like the medical, the financial modeling style? It corresponds extremely closely to economic value. So think if you go through the top five domains that we serve, it would be software engineering, it would be finance, medicine, law, consulting, etcetera, and the super long horizon tasks within those. And so I think we're moving away from the paradigm of how do we get a investment banker to prepare a financial model, and moving towards the paradigm of how do we get a banker that can talk with five different colleagues and wait to hear back their responses and prepare an entire slide deck with a deliverable that includes the financial model, the analysis in a multi week long project. Those are the kinds of tasks that we need to be building to push the frontier of research and evaluation, so that those are the capabilities that people are able to use in the models in six to twelve months. [00:26:59] S1: Can I ask, which segment are we underserved in? In terms [00:27:02] S0: of model capabilities? In terms [00:27:04] S1: of like, we don't have enough medical data, we don't have enough financial modeling data, we don't have... Is there a segment where like, you know what, if we were to acquire a company in this space to plug a hole in our data supply?
00:27:14 · Brendan Foody
[00:27:14] S0: Well, I would say maybe I'll give it from Merkur's perspective, and then I'll give it from the lab's perspective. We tend to be now so good at mobilizing experts that we're able to access pretty much any domain. There's always going to be some degree of like these niche pockets of oncologists or whatever it is that have a particular background, but generally we can fill those fairly quickly. And it's more about people that actually are very acclimated to the frontier of AI, because it's the people that both have the expertise in oncology, but also are power users of ChatGPT or Claude that are able to find where the model makes mistakes and help the model learn from those mistakes. And so that's from the Merkor perspective. From the perspective of the labs, it seems like it's all encompassing. It's just like the barrier to automating everything that you can do in say Google Workspace is how do we cover the full distribution of all of the context, I. E. Messages, Slacks, Slides, Excel sheets, and all the tasks, prompts and outputs that correspond to everything that you do in your job. And that applies to every individual and every domain throughout the economy. And so there's this enormous mobilization of hundreds of thousands and soon millions of people to build out the full distribution of everything that you could pass into Google Workspace and everything that you could want out on the other side in every job category throughout the economy. Before we dive into a tweet that