The Twenty Minute VC (20VC): Venture Capital | Startup Funding | The Pitch
00:09:45 · Harry Stebbings, Adarsh Hiremath
[00:09:45] S1: Take me to the take me to the seed round, dude. How did it go? Do you remember getting the term sheet? Just take me to that. Because you're 18, 19 at the time? [00:09:53] S0: We were 19 at the time. So that was that was just surreal. So what ended up happening is, initially, we thought we wanted to base the company in New York. So I'll take credit for making the the wrong call there. I very, very quickly realized that it was the wrong decision. But what ended up happening is we had moved to New York before raising the seed round. For me, actually, the more surreal moment wasn't actually when the money hit for for the seed round. It was actually when we changed our, like, salaries in Gusto to $500 a month. I felt like we made it at that time. I was like, amazing. You know, we just moved to New York. We changed our salaries to $500 a month. And then afterwards, we closed our seed round. And then when the money was wired, we were just looking at the account, like, you know. [00:10:39] S1: How was that process? Like, did you pitch many venture investors? Did the round come quickly? How much did you raise? Just take me to it. It's a special moment. [00:10:48] S0: So so we raised over 3,000,000, and it came very, very quickly. So General Catalyst led the round and really, really enjoy working with Max and Nico. So
00:10:48 · Adarsh Hiremath, Harry Stebbings
[00:10:48] S0: So so we raised over 3,000,000, and it came very, very quickly. So General Catalyst led the round and really, really enjoy working with Max and Nico. So [00:10:57] S1: You are one of the fastest scaling companies in Silicon Valley in The US in startups in general. It was 50,000,000, I think, of ARR in November. I quoted it wrongly. It's you may be able to correct me, but it's much more now. With 30 people at the time of the 50,000,000, and I've heard a little rumor on the grapevine that you do nine nine six, so 9AM to 9PM, six days a week. Can you unpack if that's true, why you do it, and how that works in reality? [00:11:26] S0: It's really funny. A lot of people ask me this question about the nine nine six thing. The only reason we actually just floated those numbers out is because we didn't want our team working on Sundays. So I like to think of the nine nine six stuff as more of, a side effect than an objective. We've just really, really carefully selected for working with people who care deeply about the mission. The side effect about that is they don't wanna wait until Monday to to move the company forward. So people really do it just because they enjoy being in each other's presence. They enjoy what they're working on. [00:11:56] S1: Do you worry about creating a hustle culture with nine nine six?
00:11:56 · Harry Stebbings, Adarsh Hiremath
[00:11:56] S1: Do you worry about creating a hustle culture with nine nine six? [00:12:00] S0: This is not something unique to Merkur. It's like all the successful companies have had pretty intense cultures historically, and it's just a function of a startup. Right? You gotta work harder than everyone else. The one thing I will say about that is that momentum is very, very energizing. I think when we select for people to work at Merkor, one realization that we've come to is that you can teach people a lot of things, whether it be, you know, technically or going to market or whatever. But the one thing that you can't quite teach people is to care. And that's one thing that we index on pretty heavily in our hiring process and something that we really look for. [00:12:36] S1: I heard that you've been growing 50% month on month continuously for quite a while now. That growth to keep up is is insane. How does that feel internally? And what's the first thing or two to break? [00:12:50] S0: The way I like to think about that level of growth is, it's basically a perpetual stress test on the business. Things are constantly breaking, whether it be process, or, you know, you might need to hire people to fill in gaps quicker than you might ordinarily need to do or whatever. But I think the main thing is everyone in the company needs to keep outgrowing themselves. Right? Redefining what's possible for for them, taking on new roles. [00:13:13] S1: What does no one tell you about scaling that you wish they had told you?
00:13:13 · Harry Stebbings, Adarsh Hiremath
[00:13:13] S1: What does no one tell you about scaling that you wish they had told you? [00:13:16] S0: Scaling culture is harder than scaling software. When you're adding people to the team very, very quickly, there's this dynamic that the culture that you create with the first, you know, 20 people is in some ways the strongest the culture is ever gonna be. Ensuring that that culture stays strong as the company grows, does new things, and new people enter the company is really, really challenging, but in some ways, the most important part of building a legendary company. [00:13:42] S1: We mentioned scale earlier. One of your investors said to me that you are mostly doing data labeling for foundation models. Do you think that's fair? And is that a niche market or a wedge into a much larger market in your mind?
00:13:56 · Adarsh Hiremath
[00:13:56] S0: Actually, insight about the market is that human data and talent assessment have actually become the same thing. Where, you know, I can take you back five years where when we think of this data labeling or human data stuff, it's essentially a crowdsourcing problem. Let's say Waymo wants a bunch of their images labeled. You get a bunch of people across the world to draw boxes around stop signs to make the model better at classifying stop signs. Fast forward to today, and the nature of human data work has changed a lot. Now it's GPT four point or whatever model is not good in a particular domain, so we actually need an expert to make the model better in that domain. And figuring out who that expert should be is 100% a talent assessment problem and is a perfect application of the platform. With a lot of the labs that we work with, we're able to figure out who are the exceptional people in very, very specific domains how those people work with the labs. And the interesting thing about this is that it's essentially a forcing function on our long term objectives. Right? When you think about Mercor building this global, unified labor market, what do we need to make this happen? We need tons of smart people on the platform, and we need the ability to predict job performance and figure out what those people should be doing, which happens to be the exact set of problems that a lot of AI labs are having.
00:15:15 · Harry Stebbings, Adarsh Hiremath
[00:15:15] S1: Can I ask you then? When we think about, like, the AI labs today, I heard through the grapevine that as you mentioned that you work with some of the top AI labs. Merkle experts, how does that fit into these labs? What does that partnership look like? Just help me understand this. [00:15:29] S0: It looks exactly the same as placing someone to work at any company. So just like Merkor might work with startups making their first hires or companies hiring in a more traditional full time capacity, it's the exact same thing for, a lot of the large AI labs. They'll hire people through the Merkor platform to essentially help with post earning models. [00:15:48] S1: When you look at today, what is the satisfaction on a higher basis? Is 90% of hires successful? Is 60%? What are the metrics that you place? And what is the one core metric that you use for the success of the business? [00:16:01] S0: Customers keep growing their relationships with us. Net retention is over 100% by by a large margin. You know, as long as they keep expanding, it means that we're doing a good job [00:16:11] S1: at finding the right people. In terms of an infrastructure basis, what models are we sitting on top of today? [00:16:16] S0: It's interesting because the model landscape is just changing so so quickly. But we we leverage a variety of models and have been particularly thrilled with the OpenAI models. [00:16:28] S1: Have we always been on OpenAI predominantly? [00:16:30] S0: We've always used OpenAI in some capacity.