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

Adarsh Hiremath is the Co-Founder and CTO @ Mercor, an AI recruitment platform and one of the fastest-growing companies in technology. They have scaled to $70M in ARR in just 24 months. They are famed for working 6 days per week, 9AM to 9PM. All of their founders are Thiel fellows, they are also the youngest unicorn founders ever with the fundraise announced today raising $100M led by Felicis at a $2BN valuation. In Today’s Episode We Discuss: 04:36 How Debating Makes The Best Founders 06:05 Do People Treat You Differently When a Unicorn Founder 10:58 Scaling to $70M ARR in 24 Months 13:42 How Culture Breaks When Scaling So Fast 23:49 The Future of Foundation Models 24:05 OpenAI vs Anthropic 24:32 Data: Synthetic vs Human 27:10 The Future of Programming and AI 28:15 The Impact of AI Tools on Software Development 28:51 Why Software Will Become Commoditised 29:55 Network Effects and Marketplaces 33:13 Raising From Benchmark After a Helicopter Ride 37:30 Quickfire Round: Insights and Reflections

Speaker names marked ? are inferred from speaking time and the show’s known hosts — they are not voice-verified.

• 0:00Speaker 00

  The round is a hundred million. And the price was? It was at two 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 inflows and outflows. The businesses that succeed in a world where software costs approach zero will be built on network effects.

• 0:35Harry Stebbings?

  This is 20 VC with me, Harry Stebbings. Instead, we have an exclusive. One of the fastest growing companies in Silicon Valley right now, McCrory, has closed a $100 million round at a $2 billion valuation led by Felices. They are exceptional for many reasons. They scaled to 70 million in ARR in just 24 months. They are famed for working six days per week, 9:00 AM to 9:00 PM. All of their founders are Thiel fellows. And with this latest round, they are also the youngest unicorn founders ever. Today, I sit down with McCrory's co-founder, Adarsh Hyamath. But before we dive in today, here are two fun facts about our newest brand sponsor, Kajabi.

• 1:16Harry Stebbings?

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• 2:01Harry Stebbings?

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• 2:40Harry Stebbings?

  Remote never charges hidden fees, just best-in-class global employment solutions for a low flat rate. The world's top remote companies love Remote. GitLab, the world's largest all-remote organisation, trusts Remote to run their global team. Remote is funded by Index Ventures, Sequoia Capital and the host of the greatest podcast ever, Harry Stebbings and 20VC. Ready to learn more? Head over to remote.com/20vc, that's two zero vc, and begin hiring within minutes. Enjoy 10% off your first three months by using the promo code 20vc at checkout. Now that your team is up and running worldwide, make sure your finances work just as hard with Brex, the ultimate financial stack for startups.

• 3:23Harry Stebbings?

  So when Brex was founded, it wasn't just about creating another financial product, it was about solving the really gritty challenges that founders face daily. Let's be honest, building something from the ground up is hard enough without dealing with clunky outdated banks that pile on fees and leave your cash idle. Brex is different, it's the financial stack that scales with you no matter where you are in your journey. From corporate cards to maximising your runway to earning yield on your cash. Brex was designed with founders in mind to make every dollar go further so you can focus on building. And here's what really stands out to me. Brex combines the best of checking, treasury and FDIC insurance in one powerhouse account. You can send and receive money globally at lightning speed.

• 4:05Harry Stebbings?

  Earn yield from day one and still access your funds whenever you need. Plus, with 20x the standard protection through programmed banks, your cash is not just working harder, it's working safer too. It's no surprise that one in three venture-backed startups in the US, with companies like Anthropic, Coinbase and Robinhood. I mean, my god, these companies are incredible. Trust Brex to help them grow. If you want to join the smartest startups on the planet, head over to brex.com/startups and see what they can do for you.

• 4:33Speaker 00

  You have now arrived at your destination.

• 4:36Harry Stebbings?

  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 Sandell even. So thank you so much for joining me.

• 4:46Speaker 00

  Thank you for having me. Really, really a big fan of the pod.

• 4:50Harry Stebbings?

  That is very, very kind of you, dude. But I did my stalking beforehand and everyone told me about your mastery of debating, you and your co-founder Surya. So you were debate champions. How did debate prepare you for founding a company? Let's start there.

• 5:06Speaker 00

  One thing about Brendan, Surya and I is that we actually go quite a ways back. So I actually first met Surya 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. Surya and I decided to do policy, so we ended up being debate partners together and then going on and competing in all of 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 Surya as sort of my first startup, just because we had 50/50 equity in each other's success. If 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

• 5:49Speaker 00

  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.

• 6:05Harry Stebbings?

  Yeah, at the time, you were like 18, 19, and you can correct me if I'm wrong there. But you, Brendan, Surya, get interested in labor markets. How does that happen?

• 6:15Speaker 00

  Actually, so Brendan, Surya and I started working together without any business ambition. We just started a dev shop 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 dev shop. And then very, very quickly, we realized, you know, the software was one thing. But we had found some really, really exceptional people and 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, we realized Brendan, Sir and I couldn't scale well by doing sales manually.

• 7:00Speaker 00

  So then we had to automate the other side of the platform too, the, the company facing platform. And that's how the marketplace was born.

• 7:06Harry Stebbings?

  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. Take me to that moment and the decision between whether you drop out or whether you stick to the traditional course.

• 7:24Speaker 00

  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 Brendan 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 Brendan would visit me. He would just crash on Artemis's bed because he'd be in the engineering building, just working on problem sets. And then he'd come back, wake Brendan up, and then we'd 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 McCour team. So it, it really was the typical dorm room story.

• 8:07Harry Stebbings?

  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?

• 8:16Speaker 00

  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 abandon my degree program. So 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.

• 8:44Harry Stebbings?

  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 stick to the traditional path?

• 8:55Speaker 00

  It's an emotional decision. Like, you can try to rationalize dropping out or starting a company, or try to figure out the exact, you know, set of prerequisites that you have to do. But like, for me, for example, the moment I knew that I wanted to drop out was was actually back when we had an office in Palo Alto. And the office had exactly three desks, one for Brendan, one for Surya, one for me. I was like, Surya, man, should we 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.

• 9:31Harry Stebbings?

  Where was the business at at this point, just to frame it?

• 9:34Speaker 00

  No seed round, a little bit of revenue, no Teal Fellowship, nothing. We were just three friends, uh, working in a small office in Palo Alto with with our amazing team in India.

• 9:45Harry Stebbings?

  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 were eighteen, nineteen at the time?

• 9:53Speaker 00

  We were nineteen 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 uh, like salaries in Gusto to five hundred dollars 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 five hundred dollars a month. And then afterwards, we closed our seed round. Um, and then when the money was wired, we were just looking at the account like, you

• 10:38Speaker 00

  know.

• 10:39Harry Stebbings?

  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.

• 10:48Speaker 00

  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.

• 10:57Harry Stebbings?

  You are one of the fastest scaling companies in Silicon Valley, in the US, in startups in general. It was 50 million, 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 million. And I've heard a little rumor on the grapevine that you do 996. 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?

• 11:26Speaker 00

  It's really funny. A lot of people ask me this question about the 996 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 996 stuff as more of like 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 want to wait until Monday 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.

• 11:56Harry Stebbings?

  Do you worry about creating a hustle culture with 996?

• 12:00Speaker 00

  This is not something unique to Mercor. 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 Mercor, 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.

• 12:37Harry Stebbings?

  I heard that you've been growing 50% month on 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?

• 12:50Speaker 00

  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.

• 13:13Harry Stebbings?

  What does no one tell you about scaling that you wish they'd told you?

• 13:16Speaker 00

  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. 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.

• 13:42Harry Stebbings?

  We mentioned scale earlier. One of your ambassadors 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?

• 13:56Speaker 00

  Actually, our 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 O 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.

• 14:41Speaker 00

  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 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 the AI labs are having.

• 15:15Harry Stebbings?

  Can I ask you then, when we think about like the AI labs today, I heard through the grapevine that as you mentioned there, 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.

• 15:29Speaker 00

  It looks exactly the same as placing someone to work at any company. So just like Merkle 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 Merkle platform to essentially help with post-training models.

• 15:48Harry Stebbings?

  When you look at today, what is the satisfaction on a hire basis? Is 90% of hires successful? At 60%, what are the metrics that you place? And what is the one core metric that you use for the success of the business?

• 16:01Speaker 00

  Customers keep growing their relationships with us. Net retention is over 100% by a large margin. You know, as long as they keep expanding, it means that we're doing a good job at finding the right people.

• 16:12Harry Stebbings?

  In terms of an infrastructure basis, what models are we sitting on top of today?

• 16:16Speaker 00

  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.

• 16:28Harry Stebbings?

  Have we always been on OpenAI predominantly?

• 16:30Speaker 00

  We've always used OpenAI in some capacity.

• 16:33Harry Stebbings?

  If improved in terms of like any aspect of model there, what would make the biggest improvement on the business and the product today for you with Merkle?

• 16:41Speaker 00

  I think a concrete example would be the AI interviewer. We've built the product in such a way that whenever the models improve, the experience for applicants on our platform also improves pretty significantly. In general, this is something that's been, you know, 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, while we leverage LLMs and all these models throughout our product, the whole product gets better as the models get better.

• 17:09Harry Stebbings?

  What do you think will be the next generation of models in terms of what they look like first before we get to training data?

• 17:16Speaker 00

  The whole market is shifting to reinforcement learning. You're already seeing this with O1, O3, the deep-seek 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.

• 17:43Harry Stebbings?

  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?

• 17:53Speaker 00

  I think we'll live in a world with many, many models with different use cases. Right? We're already seeing this with a lot of application layer companies, right? Where they all have these specialized use cases for how they want to leverage the models, right? For us, it's hiring and beating the expert hiring manager. For another company, it 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.

• 18:22Harry Stebbings?

  How fair do you think the analogy is? That the model landscape will be very much like the cloud landscape in terms of three or four 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 transience, it's actually much easier and much less defensible in that respect?

• 18:41Speaker 00

  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.

• 18:58Harry Stebbings?

  In terms of like the post-training data side, how much will be human data versus how much will be synthetic data moving forward?

• 19:06Speaker 00

  I think a lot of it will be human data going forward. I think a great example of this is evals. Evals for models. Definitionally have to be outside of model capability. In order to see whether a model is doing well at a particular task, you need to have an eval set 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 there are 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.

• 19:42Harry Stebbings?

  To what extent would you say that data is the bottleneck that prevents model improvement more than compute or algorithms?

• 19:49Speaker 00

  Data is the bottleneck. I think that would be an accurate statement.

• 19:52Harry Stebbings?

  Why then does so many people tell me, including Jonathan at Grok, that actually synthetic data is often more high quality? It doesn't involve the dregs 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?

• 20:16Speaker 00

  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 won't also be important. So 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 model 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.

• 20:56Harry Stebbings?

  In terms of like computing algorithms, how do you think about where we're at today in terms of them being a bottleneck? We mentioned data is a bottleneck. All computing algorithms too. How do you think about that?

• 21:07Speaker 00

  All of them are pieces of the same puzzle, but the era that we're entering requires really, really expert humans to make models better at very, very specific use cases.

• 21:18Harry Stebbings?

  How long will that be the case for?

• 21:20Speaker 00

  For a very, very long time. There is this huge long tail of tasks that models can't do.

• 21:27Harry Stebbings?

  Like what? Just help me understand. You're teaching me.

• 21:30Speaker 00

  Well, maybe we could take a step back. If we reach the point a couple of 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 going to look really, really different. We're all going to be living on a UBI, we're all going to be playing video games all day, whatever it may be. But until that point comes, there's going to be a whole set of tasks that the models cannot do, whether they be specific economically valuable tasks, like maybe the job that a consultant could do, or maybe a specific category of engineering, or even like more niche things, right? Maybe it's making the model better at like some specific hobby. And we're always going to need to fill in the gaps, particularly in that long tail.

• 22:14Speaker 00

  And Harry, the other thing I'll say is, I think people are really, really in this mindset of like, this like unidirectional relationship between humans and AI, right? Where 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 sixty, seventy, eighty percent of the way there. But for that remaining forty, thirty, twenty percent, you're going to need a human to be able to to take you all the way there. And the reality of the situation is finding that human will become harder and more valuable to do.

• 22:50Harry Stebbings?

  If you get further and further up the spectrum of we're able to seventy, seventy-five, eighty, eighty-five, ninety, do you not need fewer and fewer humans? Because the frequency is much less as you move closer and closer to perfection.

• 23:04Speaker 00

  That'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 fifty years from now will be more specialized. And oftentimes will require people with kind of like a higher level of sophistication in that specific thing.

• 23:27Harry Stebbings?

  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 core?

• 23:34Speaker 00

  When we're able to find exceptional people at the cost of software hundreds of times over.

• 23:39Harry Stebbings?

  But when you're in that sales cycle with them today, when are they going, yeah, we've got to sign up? Is 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 wow moment for them?

• 23:53Speaker 00

  It's usually when the first couple candidates start working with them.

• 23:57Harry Stebbings?

  What is that buying process? They buy one at a time? Is it on a per talent basis? Is it on a timeline basis? How does a deal with Mercor work?

• 24:05Speaker 00

  One interesting thing about Mercor, we don't have a sales team. There is not a single person who works on sales at Mercor outside of, you know, the founders. These days, what we're seeing is mostly customer inbound. Folks have heard great things about Mercor from other people who have hired through Mercor and then reach out to us. And then we go from there. So right now, it's more of a bandwidth thing than any like tactical or coordinated sales motion, I would say.

• 24:32Harry Stebbings?

  What percentage of hires is end to end done by software versus has human in the loop?

• 24:38Speaker 00

  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.

• 25:02Harry Stebbings?

  What does a take look like on a per candidate basis?

• 25:06Speaker 00

  It all comes back to quality. You know, I briefly talked about Uber and just going back to that example, right? When I get into an Uber, there isn't that much of a difference between the 4.8 star driver and the 4.9 star driver. Because the unit of work is not exponential. But with something like Mercor, there's a huge difference between the top 0.1% and then the 80th percentile person. So usually for customers, it's not a question of price. It's a question of quality. If we're able to find those 0.1% people reliably at the cost of software, what we take is often a second thought.

• 25:43Harry Stebbings?

  And so I'm sorry, what is that take then? Is it like a standardized take? Is it like on a case by case basis? What does that look like?

• 25:51Speaker 00

  It's on a case by case basis. For some customers, it can be, you know, over thirty percent. For some, it can be less.

• 25:56Harry Stebbings?

  Over thirty, that's great. Amazing. Well done. When you look at candidate completion rates, how much of that is India versus rest of world today? I know you specialize in finding amazing talent in India specifically.

• 26:09Speaker 00

  So the reason we started with India is because our parents immigrated from India, Suri and I, so they went to these amazing schools. So we like started these recruiting campaigns from those schools specifically. And actually, like, one of the things that got us really, really excited about, you know, labor markets in general and the inefficiencies associated with it was just because like, one of the best engineers I've ever worked with on our team, we found through a Facebook ad and I manually interviewed him. Actually, he didn't pass the interview. But the reason that we ended up hiring him is he sent me a really, really long message about what exactly he got wrong in the interview and how to correct it. And I just felt like we got to work with him. It was sort of that that prompted us to start in India.

• 26:50Speaker 00

  But if you fast forward to today, actually, the number one place that, you know, workers on the Mercor platform who, you know, have jobs through us are from is actually the United States.

• 27:02Harry Stebbings?

  Percent wise, is it sixty percent US style?

• 27:05Speaker 00

  It's high up there, yeah.

• 27:07Harry Stebbings?

  And client wise, all US too?

• 27:09Speaker 00

  Mostly US, yeah.

• 27:10Harry Stebbings?

  A lot of young exceptional people are being told today that they shouldn't maybe study CS anymore because actually, CS is becoming so automated. Forty-one percent of code is now written by AI. In five years' time, that'll be extortionately higher. Do you agree with that advice? And how do you think about whether or not young people should learn programming today?

• 27:31Speaker 00

  My take is that programming is actually more important today. It's just going to happen at a different level of abstraction. One could argue that the leap from assembly to Python was actually maybe even a bigger leap than the leap from Python to natural language. My answer there is that the way we define programming will look very, very different. It may be a person who, you know, has like average skills by today's standards and in computer science, who's orchestrating thousands of superhuman coding agents to achieve more than we thought was even possible. But that skill set, which, you know, we can define as programming at a different level of abstraction, programming in English is going to be super important.

• 28:11Harry Stebbings?

  Can I ask, how has the way that you program changed over the last two years?

• 28:15Speaker 00

  I definitely use a lot of the AI tools. They've gotten really, really good. A great example is Cursor. A lot of members of our team use Cursor and love it. It makes doing things that would take a lot of time just so simple and elegant, right? A great example is testing. With a couple of prompts, you can just generate a more thorough test suite than, you know, anyone could have imagined for your application, for example. And that just wasn't possible before. Maybe it's like bringing the same consistency from like one part of the codebase and refactoring it for another part of the codebase. You can basically snap your fingers in Cursor and it'll get done. And I think like the implication for software is that software is going to get commoditized very, very quickly as these coding agents get really, really good.

• 28:59Harry Stebbings?

  What does a world look like where software is commoditized? What does that mean?

• 29:02Speaker 00

  It means that people will be able to build applications much faster than was historically possible. It also means that the businesses that succeed in a world where software costs approach zero will be built on network effects. The companies that could even give away their entire codebase and they'd still be alive. The marketplaces and companies like Meta, you know, and Airbnb that have built these really, really strong network effects will be the ones that thrive.

• 29:30Harry Stebbings?

  Do you agree with people who say like, ah, SaaS is dead because companies will just build their own software? Or do you think differently?

• 29:37Speaker 00

  I think what we consider SaaS will change. The next era of SaaS will be replacing entire services, whether it's the end-to-end process of a recruiting agency like Mercor or another, you know, different service or that is incredibly manual and incredibly repeatable.

• 29:55Harry Stebbings?

  You said about network effects there. If I were to push you to the strongest network effect that you have within Mercor today, what do you think it is?

• 30:04Speaker 00

  It's a it's a great question and I would break it down as in two categories. So one is the network effect of a marketplace that you might see i- in a labor marketplace like Uber, where every additional company that hire through Mercor strengthens the marketplace and every additional candidate on Mercor strengthens the marketplace as well, because there's like a higher pool of really, really exceptional people to choose from. And the second network effect, or like data flywheel, is around this job prediction piece. We're able to see who is performing well on jobs and the specific reasons why they're performing well on jobs and use that end-to-end data on people's outcomes to to make it really, really easy to surface the person that might be the best for for a given role,

• 30:48Speaker 00

  even if they themselves don't know it.

• 30:50Harry Stebbings?

  How do you think about building stickiness and switching cost and making sure that 50 million is really fucking sustainable?

• 30:58Speaker 00

  It all starts with quality. A lot of the greatest products or companies of our generation have been usage-based. Stripe is a is a great example of this. The reason that that revenue is really, really sticky is because you're able to create these like six-star experiences for customers and candidates. And I think that's one thing that has resulted in our very, very quick revenue ramp, right?

• 31:21Harry Stebbings?

  When you think about the product today, what would you most like to change that Brandon and Surya would most not let you change?

• 31:28Speaker 00

  Maybe running our entire internal hiring process for Mercor in a completely automated way, meaning Brendan, Surya and I don't even talk to someone when they come into the office. We walk in the conference room to meet them the first time and we're just like, wow, this person is awesome. We couldn't have found this person, even if we spent all day, every day trying to find this person. We're getting there and that's just super, super exciting for us.

• 31:54Harry Stebbings?

  How do you think about the future of remote and remote versus in-person? I don't think you can do 996 and do it effectively unless you're in person. I think that motivation, that intensity, you feel in the same room.

• 32:08Speaker 00

  Yep, and that's exactly why we do, you know, in-person in San Francisco. Brendan, Surya and I all get super energized by being around people. A lot of our best ideas from Mercor have come when we weren't even in a meeting, right? We were just sitting around, chilling, discussing things, and then you have that aha moment. And I think there's something really, really special for in-person.

• 32:27Harry Stebbings?

  What was the worst product decision you made?

• 32:30Speaker 00

  At one point, Brendan Sari and I all thought that chat was the future of all UI. So one of the initial iterations of the Mercor product was just built around a chat interface, like there was no other way to hire people unless you used the Mercor chatbot, because we were so bullish on chat. I think we've come around to that. We now like mix chat with with other things where applicable or leverage LLMs in other ways. But for a while, we thought like the concept of a web app tomorrow would be dead and the way you would interface with all web apps would just exclusively be with chat. So it wouldn't even be, you know, you clicking a button to hire someone, it would be you telling the chatbot to hire the person. I think, you know, it's possible down the line, but we may have mistimed a little bit.

• 33:13Harry Stebbings?

  In terms of funding, dude, you've got some of the best on your cap table. You mentioned GC at the start. Towards me, you've raised quite a few rounds in quite quick succession. How did you think about that? And do you agree when the money's on the table to take it?

• 33:27Speaker 00

  An interesting dynamic about a ball of our, you know, fundraising rounds is just that like, we didn't intend on on doing the fundraising at the time. And it sort of just came to us. You know, going back to the example about Benchmark, someone introduced Brendan to Victor. Brendan said we were heads down and then Victor, you know, convinced Brendan to to have a conversation with him and then the rest was history from there.

• 33:53Harry Stebbings?

  How did that process go down? So Brendan meets Victor and then you guys meet Victor and you have a chat. How does that go down?

• 34:00Speaker 00

  The way it went down was Brendan had the initial chat with Victor. And then, you know, afterwards Brendan was like, okay, gonna gonna get back to work. Afterwards, Victor asked Brendan if he'd ever been on a helicopter. And Brendan said, no. Before he knew it, Brendan was on a helicopter with Peter Fenton from Benchmark. And we knew that they were the firm that we wanted to work with very, very quickly.

• 34:22Harry Stebbings?

  And so Brendan comes back and goes, hey, guys, they took me on a helicopter. Let's do it.

• 34:27Speaker 00

  Had a couple more conversations with with Victor and the Benchmark team. And it was clear that they were. They were the best. We wanted to be in business with them and and work with them and they've just been phenomenal.

• 34:37Harry Stebbings?

  Then how many months later is the next round?

• 34:40Speaker 00

  The next round is about over, what, six months later?

• 34:45Harry Stebbings?

  Six months later. You don't need the money at that point. Talk to me about that round. How did you think about taking the money then?

• 34:51Speaker 00

  It's interesting because we we again weren't focused on on fundraising, right? We had built a business that was doing a lot in revenue, you know, we're paying out tens of millions to.

• 35:02Harry Stebbings?

  How much is it doing in revenue at this point?

• 35:04Speaker 00

  Eight figures in in revenue, right? And and we were we were just like, okay, let's be heads down. But just like we we felt with Benchmark, we we wanted to be, you know, in business with Sandeep and Felices and and the amazing team there, which made it a no brainer.

• 35:18Harry Stebbings?

  Do you enjoy fundraising?

• 35:20Speaker 00

  Ah, not really. Not really, yeah.

• 35:24Harry Stebbings?

  Do you have a board, Adarsh?

• 35:27Speaker 00

  We do. It's it's, you know, Brendan, Suri and I and and Benchmark on the board.

• 35:31Harry Stebbings?

  Is that it?

• 35:32Speaker 00

  That's it, yeah. And yeah, we we don't enjoy fundraising. I think the thing that we really, really enjoy is moving the business forward. That that's always the thing that that founders enjoy the most. So we've been, you know, laser focused on that and sometimes it just makes sense to do the round.

• 35:48Harry Stebbings?

  You said about eight figures in revenue there when I think you raised that one of the rounds. Were you aware of how fast the revenue scaling was? Like, were you guys looking at each other going, this is unbelievable?

• 36:00Speaker 00

  We definitely had that moment. At the time we raised that round, we didn't realize how much the growth was going to accelerate. We knew of it, we were confident in it, but just the fact that it even exceeded our expectations, you know, wrapping up Q1 of of this year, uh, is something that we're we're all, you know, really, really excited by.

• 36:19Harry Stebbings?

  And talk to me about this new fundraiser. Was this new fundraiser the Felices round?

• 36:23Speaker 00

  Yep. So the new fundraiser was led by by Felices with, you know, other amazing investors, including GC, Benchmark and others participating as well.

• 36:33Harry Stebbings?

  And how much was this round?

• 36:34Speaker 00

  The round was a hundred million.

• 36:36Harry Stebbings?

  And the price was?

• 36:37Speaker 00

  It was at two billion, yeah.

• 36:39Harry Stebbings?

  What a phenomenal round, dude. Like, no, really, like, it's brilliant round. In terms of dilution, you dilute 5%, you get 100 million on the balance sheet. Phenomenal round for a company to do.

• 36:50Speaker 00

  Thank you. Yeah, we're, we're really, really excited to be in partnership with Sandeep and the Felices team. They're, they're amazing.

• 36:58Harry Stebbings?

  Do you need the money? Like, what are you gonna spend 100 million on? You guys make a load of money. You, you raised not long ago. What are you gonna do with 100 million?

• 37:07Speaker 00

  I think it gets really dangerous when people raise the money and then think that they just have to spend it immediately because they raised the money. Our goal isn't to deploy 100 million dollars tomorrow. But Harry, I think the thing about our business is that labor aggregation and building this unified labor market, it's gonna take a long time. We just wanna make sure that we have a balance sheet that uh is kind of commensurate with that long term.

• 37:30Harry Stebbings?

  Listen, I wanna do a quickfire round. So I say a short statement, you give me your immediate thoughts. Does this sound okay?

• 37:35Speaker 00

  Let's do it.

• 37:36Harry Stebbings?

  Dude, what do you believe that most around you disbelieve?

• 37:39Speaker 00

  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. Pretty much, you can gather all you need to know about a company from seeing the talent inflows and outflows. The recruiting function of a company i- is the most underrated and undervalued.

• 37:58Harry Stebbings?

  Does the whole "we should do more with less" efficiency on a per-human basis not go against McCrory and the importance of recruiters?

• 38:07Speaker 00

  Actually, i- it goes with it, right? Because efficiency is only possible if you find the right person. Solving that matching problem and finding the right person is really, really hard, especially with manual processes that don't scale.

• 38:19Harry Stebbings?

  Who do you think is the best person in the world at what you do? And what have you learned from them?

• 38:24Speaker 00

  It's interesting. I've had this conversation with some members of the McCrory team before. I think one thing that we like to joke about is that company execs are a lot like athletes in a lot of ways, where there's like this drive and desire to, to win. Maybe, you know, I had, I had dreams of being a basketball player a while ago. Definitely not what I do today. But, you know, one person who I think really embodies that winning mentality is LeBron, uh, and I like him a lot, so.

• 38:48Harry Stebbings?

  Execs are like athletes. How do you treat yourself as an athlete?

• 38:52Speaker 00

  Think there's an element of pushing yourself to win and focus on the right things and getting better every day. And that's just something that I think about, right? How can I be the best version of myself tomorrow and be an even better version the next day and continue that and have that compound for 10, 20 years?

• 39:08Harry Stebbings?

  What have you changed your mind on in the last 12 months?

• 39:11Speaker 00

  It's an interesting question. Part of it is the, you know, the SaaS answer I gave you a little bit earlier, that over time, it's become pretty obvious to me that the next generation of SaaS will replace entire services end to end. And I think that realization has sort of been part of the reason that we've built Mercor in this way.

• 39:30Harry Stebbings?

  What's one thing that you're doing today that you should stop?

• 39:33Speaker 00

  I have to be honest with you. I think probably my lime ride to the office when I'm running late for morning stand up. You know, we start at 9am every day and sometimes it's like I'm leaving my apartment at 8:55 and I'll just take a lime, go straight down the hills of San Francisco in the most unsafe way possible. So I should probably stop that.

• 39:52Harry Stebbings?

  What do you know now that you wish you'd known when you started Mercor?

• 39:56Speaker 00

  I would say just how hard it would be to to build a business like this. I told you, you know, back when we decided to start Mercor, it was like a complete, you know, emotional decision where Suraj just looked at me and said, hey, man, how hard could this be? You know, Brendan came in with his optimism and we just did it. And I'm thankful for that. But I didn't really have grasped my mind around how hard building a business like this would be.

• 40:18Harry Stebbings?

  You can have anyone on your board. Who do you have?

• 40:21Speaker 00

  I would have to pick Sam Altman.

• 40:23Harry Stebbings?

  You can ask Sam Altman any question. What do you ask Sam?

• 40:26Speaker 00

  I would probably ask him more about what AGI looks like.

• 40:30Harry Stebbings?

  I know Sam. Sam would turn it back on you and go, why didn't you tell me first? What do you think AGI will be?

• 40:36Speaker 00

  When we achieve AGI or sort of like think about AGI, it will certainly involve, you know, doing more economically valuable work, right? So when more and more and more of economically valuable work has been automated to some extent, you know, research has been automated to some extent, I broadly put that in the bucket of AGI.

• 40:54Harry Stebbings?

  It's 2035. Okay. Final one. Where is Mercor then? Paint that picture for me of how big you are, how many people you've placed. Where is Mercor?

• 41:06Speaker 00

  I have to work backwards a little bit, right? So how many job seekers are there? You know, roughly put it in a couple of billions. How many? Jobs do does each person take on, right? People change their roles. You know, maybe we factor out all the jobs Mercor creates for AI agents and like roughly focus on just the jobs for people, create a couple dozen jobs for each person. Mercor has created 100 billion jobs and has built the the unified labor marketplace, meaning that anytime a company wants to hire a person for a specific job or task, they do it through Mercor. And anytime a candidate wants to consider a company for a specific job or task, they do it through Mercor.

• 41:47Speaker 00

  And Mercor is able to solve the matching problem across every role, across every company in a seamless way.

• 41:55Harry Stebbings?

  Would you love for Mercor to be a public company?

• 41:57Speaker 00

  One day.

• 41:58Harry Stebbings?

  Listen, Adarsh, I've peppered you with questions. Thank you so much for putting up with my very wayward approach to a schedule, but you've been fantastic. So thank you, man.

• 42:07Speaker 00

  Thank you for having me. It was really fun.

• 42:11Harry Stebbings?

  I have to admit, I'm not sure I've seen a company operate with the intensity that Mercor and Adarsh does with the 996. I absolutely love the work ethic. You can find the show on YouTube by searching for 20 VC. That's 20 VC on YouTube. But before we leave you today, here are two fun facts about our newest brand sponsor, Kajabi. First, their customers just crossed a collective $8 billion in total revenue. Wow. Second, Kajabi's users keep 100% of their earnings, with the average Kajabi creator bringing in over $30,000 per year. In case you didn't know, Kajabi is the leading creator commerce platform with an all in one suite of tools, including websites, email marketing, digital products, payment

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• 43:35Harry Stebbings?

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• 44:18Harry Stebbings?

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• 45:03Harry Stebbings?

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• 45:48Speaker 00

  As always, we so appreciate all your support and stay tuned for an incredible episode coming on Monday on 20VC.