20VC Exclusive: Mercor Raises $100M at a $2BN Valuation: Scaling to $70M in ARR in 24 Months | 9-9-6: 9AM-9PM - 6 Days Per Week: The Most Intense Culture in Silicon Valley | The Future of Programming, Models and Data with Adarsh Hiremath

0:00The round is 100 million and the price was, it was at 2 billion. I think we'll live in a world with many, many models with different use cases. We're already seeing this with a lot of application layer companies where they all have these specialized use cases for how they want to leverage the models. I think being a recruiter is the highest prestige position in any company. The recruiter is the one who controls the talent inflows and outflows of every company and pretty much you can gather all you need to know about a company from seeing the talent in flows and uploads, the businesses that succeed in a world where software costs approach zero will be built on network effects.

0:35This is 20VC with me Harry Stabbings and today we have an exclusive. One of the fastest growing companies in Silicon Valley right now, McCall, has closed a $100 million round at a $2 billion valuation, led by Felicis. They are exceptional for many reasons. They scaled to 70 million in Aero in just 24 months. They are famed for working 6 days per week, 9am to 9pm. All of their founders are teal fellows, and with this laces round, they are also the youngest unicorn founders ever. Today I sit down with McCall's co -founder Adash Hyamath. But before we dive in today, here are two fun facts about our newest brand sponsor, Kajabi.

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4:34You have now arrived at your destination. Adash, I am so excited for this dude. Listen, I've heard so many good things from Pat Grady, from Nico, from Anil, from Scott Sandel even, so thank you so much for joining me. Thank you for having me, really, really big fan of the pod. That is very, very kind of you do, but I did my stalking beforehand and everyone told me about your mastery of debating you and your co -founder, Syria. So you would debate champions. How did debate prepare you for founding a company? Let's start there. One thing about Brendan's Syria and I is that we actually go quite a ways back.

5:09So I actually first met Syria when I was 10 years old. And the reason we got along so well is because we were pretty much the only elementary schoolers who wanted to compete in high school debate. And then we ended up at the same high school, which is also where I met Brendan. All three of us were on the debate team together. Syria and I decided to do policy, so we ended up being debate partners together and then going on and competing in all these national tournaments. But debate is a lot like founding in a lot of ways, right? Like I like to think of my debate partnership with Syria as sort of my first startup, just because we had 50 -50 equity in each other's success.

5:42If one of us were to mess up, it would tank the odds for both of us. There's like this constant feedback loop after every debate round about whether you won or lost, picking the right debate partner is like the most important decision you can make in policy debate and similarly picking the right founding team is the most important decision you can make while starting a company. So there's that parallel and then there's just the immense amount of ownership, right? You know, we both have a stake in each other's success. Yeah, at the time you were like 18, 19 and you can correct me if I'm wrong that.

6:10But you, Brendan Surrier, get interested in labor market. How does that happen? Actually, so Brendan and Surrey and I started working together without any business ambition. We just started a Devshop together. So we were like, cool. You know, let's learn how to build software really, really quickly. Let's go to these startups, let's figure out things they want built, let's build it together. And what we ended up doing is recruiting these really, really exceptional folks from India to help us out with our Devshop. And then very, very quickly we realized, you know, the software was one thing, but we had found some really, really exceptional people.

6:43and it was more about the people than the software. So then we were like, okay, we found these people in a completely manual way, can we automate this? And that's how the automated candidate side of the platform was born. Very quickly realized, Brendan Sir, and I couldn't scale well by doing sales manually. So then we had to automate the other side of the platform too, the company facing platform and that's how the marketplace was born. Okay, so the marketplace is born. We've automated both sides of the platform. How exciting. except you're at Harvard at the time, I think. And now you have this very vibrant and working platform.

7:18Take me to that moment and the decision between whether you drop out or whether you stick to the traditional course. Well, it's funny that you say I was at Harvard. I was definitely there physically, not sure about mentally. I was pretty much doing everything I could to avoid going to classes. And I actually have a pretty funny story about this. So, Brandon would visit me pretty frequently at Harvard. And my roommate at the time Artemis had this like really really weird sleep schedule He would just like go to the engineering building and pretty much be nocturnal So the routine that we would typically follow is Brandon would visit me He would just crash and Artemis is bad because he'd be in the engineering building just working on problem sets And then he'd come back wake Brandon up and then we get to work together and then he would go to sleep during the day And then you know fast forward today Artemis has joined the the record team.

8:03So it really was the typical dorm room story When you're then deciding, shit, am I actually going to leave Harvard? It's one thing to say, it's another thing to do. Can you just take me to that moment? At the time, it wasn't obvious at all that we should drop out, and I really, really sympathize with my parents at the time for, you know, not approving because here I was, we hadn't raised our seed round, we hadn't raised the Series A, there was no Teal Fellowship, there was one side of the marketplace that had a little bit of revenue, and I was telling them that I wanted to bend in my degree program.

8:35So it wasn't an obvious decision at all, but I think, you know, like most of these decisions, you just make them completely emotionally, and I just knew I wanted to work with my best friends. Can I ask, for a lot of students who are wanting to start a business, who have a business already, how do you advise them on whether to drop out or whether to state to the traditional path? It's an emotional decision. Like, you can try to rationalize dropping out or starting a company or try to figure out the exact set of prerequisites that you have to do. But for me, for example, the moment I knew that I wanted to drop out was actually back when we had an office in Paul Walto.

9:13The office had exactly three desks, one for Brendan, one for Syria, one for me. I was like, Syria, man, should we drop out? And then he just looked at me and he was just like, dude, how hard could this be? Wasn't a logical argument at all, but in that moment, I was just like, let's do this. Let's let's drop out of school. Where was the business out at this point just to frame it? No seed round, a little bit of revenue, no teal fellowship, nothing. We were just three friends working in a small office in Palo Alto with our amazing team in India. Take me to the take me to the seed round dude. How did it go?

9:49Do you remember getting the term sheet? Just take me to that because you were 18, 19 at the time. 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's changed our like salaries and ghosted of $500 a month I felt like we made it at that time.

10:27I 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? 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. So we raised over three million and it came very, very quickly. So general catalyst led the round and really, really enjoy working with Max and Nico. So you are one of the fastest scaling companies in Silicon Valley in the US, in startups in general.

11:03It was 50 million, I think, of Aero in November. I quoted it wrongly. You may be able to correct me, but it's much more now. With 30 people at the time of the 50 million, and I've heard a little rumor on the grapevine that you do 9 .96. 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? It's really funny. A lot of people ask me this question about the 9 .96 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 9 .96 stuff as more of like a side effect than an objective.

11:41We'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 want to wait until Monday to move the company forward. So people really do it just because they enjoy being each other's presence, they enjoy what they're working on. Do you worry about creating a hospital culture with 9 .96? This is not something unique to Mccore. It's like all of the successful companies have had pretty intense cultures historically and it's just a function of the startup, right? You got to work harder than everyone else. The one thing I will say about that is that momentum is very, very energizing.

12:16I think when we select for people to work at Mercur, 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. I heard that you've been growing 50 % month or month continuously for quite a while now. That growth to keep up is insane. How does that feel internally and what's the first thing or two to break? The way I like to think about that level of growth is it's basically a perpetual stress test on the business.

12:56Things are constantly breaking whether it be process or 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 them, taking on new roles. What does no one tell you about scaling that you wish said to you? 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 going to be.

13:31ensuring that that culture says 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. 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? Actually, our insight about the market is that human data and talent assessment have actually become the same thing, where 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.

14:10Let'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 today and the nature of human data work has changed a lot. Now, it's GPT, foro, 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 % Italian 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 and have those people work with the labs.

14:51And the interesting thing about this is that it's essentially a forcing function on our long -term objectives, right? When you think about my core 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 the AI labs are having. Can I ask you, when we think about the AI labs today, I heard through the great vine that as you mentioned there, you worked with some of the top AI labs.

15:23Mercool experts, how does that fit into these labs? What does that partnership look like? Just help me understand this. It looks exactly the same as placing someone to work at any company. So just like Mercor 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 Mercore platform to essentially help with post -training models. When you look at today, what is the like, satisfaction on a higher basis? Is 90 % of higher successful? As 60 % what are the metrics that you place?

15:57And what is the one core metric that you use for the success of the business? Customers keep growing their relationships with us. Met retention is over 100 % by large margin. You know, as long as they keep expanding, it means that we're doing a good job at finding the right people. in terms of an infrastructure basis. What models are we sitting on top of today? It's interesting because the model landscape is just changing so, so quickly. But we leverage a variety of models and have been particularly thrilled with the OpenAI models. Have we always been on OpenAI predominantly? We've always used OpenAI in some capacity.

16:33If improved in terms of like any aspect of model air, what would make the biggest improvement on the business in the product today for you with McCall? I think a concrete example would be the AI interviewer. We've built the product in such a way that whenever the model's improved, the experience for applicants on our platform also improves pretty significantly. In general, this is something that's been on our mind, right? There's like this huge wave of models getting better and better and better. And can we ride that wave to make our product better and better and better? So to summarize, well, we leverage LLMs and all these models throughout our product, the whole product gets better as the models get better.

17:09What do you think will be the next generation of models in terms of what they look like first before we get to training data? The whole market is shifting to reinforcement learning. You're already seeing this with O1, O3, the deep seat models. As a result, I think we're going to see really, really powerful models in specific domains that can reason extremely well and I think that'll be really, really exciting and unlock just a huge number of use cases across a variety of different industries and domains. Do we live in a world of many, many specialized models but very fragmented? Or do we live in a world of monoliths with one or two very horizontal platforms?

17:53I think we'll live in a world with many, many models with different use cases. We're already seeing this with a lot of application layer companies where they all have these specialized use cases for how they want to leverage the models. For us, it's hiring and beating the expert hiring manager for another company that might be financial analysis in a specific domain. Across each of these use cases, I think these companies will need to make their models better for their own purposes. How fed do you think the analogy is that the model landscape will be very much like the cloud landscape in terms of It's real for juggernauts and it being very hard to switch out of Do you agree that it's hard to switch out of them or do you think given the model transients?

18:37It's actually much easier and much less defensible in our respect There will only be a couple of companies that are able to build these foundation models that everyone builds off of that analogy roughly holds. I don't anticipate there being 20 companies, trading foundation models that can all be leveraged in the same way someone might leverage OpenAI, for example. In terms of the post -training data side, how much will be human data versus how much will be synthetic data moving forward? I think a lot of it will be human data going forward. I think a great example of this is eVALS. EVALS for models definitely have to be outside of model capability.

19:16In order to see whether model is doing well at a particular task, you need to have an eValset created by humans that is better than the model at that particular task. And humans are going to play a huge role in that, for example. And I think they're a whole set of other use cases, whether it be SFT, RLHF, RL environments, like how the models of tomorrow are being trained, that all require these expert humans to essentially teach the model how to get better. To what extent would you say that data is the bottleneck that prevents model improvement more than compute or algorithms? Data is the bottleneck.

19:50I think there would be an accurate statement. Why then does so many people tell me, including Jonathan at GROC, that actually synthetic data is often more high quality? It doesn't involve the drags of the internet like Reddit. In a lot of cases being included. And actually you'll see this exponential increase in model performance due to actually mostly using high quality synthetic data, not low quality human data. Why is that wrong? So the first thing is that it's not zero sum. Even in a world where human data is super important for the next generation of models, it doesn't mean that synthetic data will also be important.

20:26So synthetic data will certainly be a part of the equation. But in a lot of ways, the bottleneck to unlocking and unleashing the next level of intelligence will be expert humans, which brings me back to the question and phrase that you used low quality human data. Low quality human data certainly won't push the models to be better at anything. High quality human data will, again, that's a talent assessment problem. The biggest lever on data quality for creating these post training sets, for example, is finding the right people, which again is really, really hard to do. In terms of like, compute now algorithms, how do you think about where we're at today in terms of them being a bottleneck?

21:02We mentioned data as a bottleneck. I'll compute an algorithm too. How do you think about that? All of them are pieces of the same puzzle, but the error that we're entering requires really, really expert humans to make models better at very, very specific use cases. How long will that be the case for? For a very, very long time. There's this huge long tail of tasks that models can't do. Light what? Just help me understand. You're teaching me. Well, maybe we could take a step back. If we reach the point a couple of year, a hundred years from now where the models are able to do every single job and Humans no longer have any work to do society is gonna look really really different Well, we're all gonna be living on a UBI.

21:45We're all gonna be playing video games all day Whatever it may be but until that point comes there's gonna be a whole set of tasks that the models cannot do Whether they be specific, you know economically valuable tasks like maybe the job that a consultant could do or or maybe a specific category of engineering, or even more niche things, right? Maybe it's making the model better at some specific hobby and we're always gonna need to fill in the gaps particularly in that long till. And Harry, the other thing I'll say is, I think people are really, really in this mindset of this unidirectional relationship between humans and AI, right?

22:23Where I can't do something, I give it to the AI, the AI takes it to completion. But I think the more realistic breakdown is the AI for a specific use case might be able to get us 60, 70, 80 % of the way there. But for that remaining 40, 30, 20%, you're going to need a human to be able to take you all the way there. And the reality of the situation is finding that human will become harder and more about able to do. If you get further and further up the spectrum of or able to 70, 75, 80, 85, 90. Do you not need fewer and fewer humans because the frequency is much less as you move closer and closer to perfection?

23:04That's a great question. And I think that begs the question of, what do labor markets look like later on? I think the key thing is that the market will move towards specialty and sophistication. Meaning the types of work that we see 50 years from now will be more specialized. And oftentimes we'll require people with kind of like a higher level of sophistication in that specific thing. Can I ask you, when you sell to clients, what is the moment where they're like, wow, shit, we've got to use my call. When we're able to find exceptional people at the cost of software hundreds of times over. But when you're in that sales cycle with them today, when are they going, yeah, we've got to sign up.

23:45Is it when they see the AI interviewer? Is it when you show them the price? Is it when they meet a candidate? Like, when is that while moment for them? It's usually when the first couple candidates start working with them. What is that buying process? They buy one at a time? Is it on a talent basis? Is it on a timeline basis? How does a deal with McCool work? One interesting thing about McCool, we don't have a sales team. There's not a single person who works on sales at McCool, outside of, you know, the founders. These days, what we're seeing is mostly customer inbound. Folks have heard great things about Mccore from other people who have hired a thermocore and then reach out to us, and then we go from there.

24:26So right now, it's more of a bandwidth thing than any tactical or coordinated sales motion, I would say. What percentage of highs is end to end done by software versus has human in the loop? On our end, the entire process is automated. This is everything from a candidate hearing about Mercor and going on to the Mercor platform via job listing, us pulling in their resume, their salary expectations and whatnot, administering a personalized interview based on both their background and the role, allowing them to get paid for their work. That entire process is automated. What does a take look like on a per candidate basis?