The Twenty Minute VC (20VC): Venture Capital | Startup Funding | The Pitch
00:19:48 · Harry Stebbings, Brendan Foody
[00:19:48] S1: I'm sorry for digging down, but you said reasoning capabilities will allow enterprises to clean data more efficiently. Why? [00:19:54] S0: Well, the reason is that if a model is able to, for example, read through every message written in Slack over the last six months, the model can presumably structure a table of here are all the different customer conversations that happened in the CRM, etcetera. And so I don't expect humans to be doing like that type of stuff of how do we structure data, how do we classify it, etcetera. But I do think that humans will do the things that models inherently can't do, such as the test of knowledge. [00:20:24] S1: When we look at the market for being a data provider to some of the largest models in the world, it's such a large market that you're seeing the unbundling of it into such verticals. I met the other day a medical, real world medical data provider to them, where basically they have surgeons that kind of, I don't know, have video cameras on and they record all the real world data. Do we see the mass unbundling of the data providing market? Is that how
00:20:48 · Brendan Foody
[00:20:48] S0: it plays out? It's interesting. We're doing a ton of data collection in the physical world as well, especially across skilled domains, where you have electricians and mechanics and scientists strapping cameras to their head to record things. I think that there's always going to be some degree of value in some of these niche vendors that are able to go really deep in a specific vertical. But what we're finding is that there's enormous value to aggregation and economies of scale, and that when we have this talent network of over 5,000,000 people that are able to refer their friends, it's just so much easier for us to find the marginal doctor, because we have that enormous talent network that can refer us to their friends. And even more importantly, that the kind of data shapes that we would build for a lawyer are often very similar to the kinds of data shapes that we would build for a doctor. And so all of the tooling that we build is very, very cross applicable, and that's the way that most labs have been scaling out their data quite horizontally. And so for that reason, we are finding that the labs tend to prefer partnering with a very horizontally capable vendor that is able to flex across all of the different verticals and scale extremely quickly, rather than working with 100 different vendors that they have to train for the same data shape in 100 different domains.
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?