The Twenty Minute VC (20VC): Venture Capital | Startup Funding | The Pitch
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
00:28:48 · Harry Stebbings, Brendan Foody
[00:28:48] S1: you did, which slightly terrified me, to be quite honest. So you said like 30 to 40% is kind of how [00:28:53] S0: we think about like our revenues from that? Generally, yeah. [00:28:56] S1: Okay. So if we take the the rounds that we've raised, which round felt most uncomfortably high? Good question. Well, so [00:29:04] S0: I'll talk through the valuations of each, of the revenue of each. So our seed round... Did Phantom not fly you in the chopper? That was serious ice. So our seed round was in September 2023, we were at called a million in revenue run rate, or just shy of that, and I initially didn't wanna raise because I wanted to bootstrap the company, but Adarshan Suriya's condition on dropping out was that we needed to raise money, and so we met General Catalyst eight a. M. On a Sunday morning. They gave us a term sheet within thirty six hours for $2,300,000,000 at a $23,000,000 post money valuation. [00:29:40] S1: Pretty [00:29:40] S0: gross. So that was pretty reasonable insofar as price And this was was Max [00:29:44] S1: and him on?
00:29:44 · Harry Stebbings, Brendan Foody
[00:29:44] S1: and him on? [00:29:45] S0: This was Max and Nico. And then at our Series A, the business didn't grow that much from the seed to the Series A, but we found the market was a key differentiation. And we met Victor when we were at, call it, 1,500,000 in revenue run rate in May 2024. And Victor got super excited. Initially, I refused to take a second meeting, but then he said, Oh, have you ever been in a helicopter? And so Peter took us on the helicopter flight and Benchmark really wanted to work with us. And so by the time that they gave us a term sheet, were at, call it, 2,500,000 in revenue and they gave us $250,000,000 post money valuation. Did that feel uncomfortable? [00:30:25] S1: Because that's a big jump, 02/23 post to $2.50. [00:30:28] S0: So keep in mind, the time, this sounds crazy because we were at 2,500,000 in revenue, but I was projecting 50,000,000 in revenue run rate by the end of the year and 500,000,000 by the end of next year. And so it felt [00:30:40] S1: like a bargain. Dude, you do know all founders project that day, [00:30:45] S0: we in the never the project. Sure. [00:30:47] S1: It just doesn't happen often. Yeah, yeah, yeah.
00:30:47 · Harry Stebbings, Brendan Foody
[00:30:47] S1: It just doesn't happen often. Yeah, yeah, yeah. [00:30:49] S0: And then four months later, we met Felisa's, and we never would make a slide deck or take investor meetings. And so Lisa sent us an email saying, Hey, we know your co founder, Surya, really likes Ferraris. Do you want to go racing Ferraris with us? Then I replied and I said, You caught my eye. Tell me more. And they said, We'll meet at the airport in Hayward and go on Aiden's private jet to Las Vegas to race Ferraris around F1 track. And so I was like, We're available in three weeks on a Sunday. And so we do this. We race Ferraris. We're at $20,000,000 in revenue. They ask us what valuation do we think makes most sense? And I say $1 to $2,000,000,000 So they give us a term sheet at a $2,000,000,000 valuation. And at the time, that's 100 times revenue. And everyone thinks that that's a high valuation. Meanwhile, was an incredible investment. [00:31:41] S1: So I'm gonna be honest, this is when I interviewed Adash at that time. And at the end, I was like, dude, I would love to invest. Please let me invest. And you very kindly let me put a small check-in. And I then spoke to several of the biggest investors in the world. And no offense, they chuckled at me like, Dude, that was such a high price. It's such a high price. Well, so here's