# Mercor: From $1M to $500M in 17 Months: The Fastest Growing Company in the World How to Think About Margins and Revenue Sustainability in AI · Why Evaluation Benchmarks in AI are BS Today with Brendan Foody 20VC · Sep 15, 2025 · 61 min · 11,218 words Speakers: Brendan Foody, Harry Stebbings Source: https://www.996.fm/episodes/20vc--ep-4aff35df/ ## Cold open **Brendan Foody** [0:00]: We were already at a 9 figure revenue run rate, and the company quadrupled since the the Scale acquisition. We scaled the business from 1 to 500,000,000 in revenue run rate in the last seventeen months, which is the fastest revenue growth of all time, one month faster than Cursor's time from one to 500. We have the demand to, like, double overnight if we can meet capacity. RL environments will subsume the entire economy. **Harry Stebbings** [0:29]: This is 20 VC ## Intro **Harry Stebbings** [0:30]: with me, Harry Stebbings, and today we have the fastest growing company in history on the show. So they've scaled revenue from 1,000,000 to 500,000,000 in just seventeen months. Their business has quadrupled since Scale was acquired. This is an insane story, and I'm thrilled to welcome Brendan Foody, co founder and CEO of Mercor, to the hot seat. Now I did not pull any punches in this show. Brendan was amazing answering some very direct questions here. This one was fantastic. But before we dive into the show today, ## Sponsor read **Harry Stebbings** [1:00]: I love seeing the team come together to make this show happen. What I don't love is trying to keep track of all the information, the data, and the projects that we're working on across dozens of platforms, products, and tools. That's why we use Coda, the all in one collaborative workspace that's helped 50,000 teams all over the world get on the same page. Offering the flexibility of docs with the structure of spreadsheets, Coda facilitates deeper teamwork and quicker creativity. 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You have now arrived at your destination. ## Conversation **Harry Stebbings** [4:34]: Brendan, dude, I've been so looking forward to this. I just had the best chat to Victor who gave me the best intel, so you should be really quite nervous at this point. But thank you for joining me. **Brendan Foody** [4:43]: Thank you for having me on. I'm I'm not sure what to expect with that, but I'm excited to jump in. **Harry Stebbings** [4:49]: I think mothers are the most important things in the world. And Victor told me that I had to start with your ability to sell early and why your mother was nervous about it. Can we just start there? **Brendan Foody** [5:01]: Absolutely. So I had a dozen different side hustles when I was growing up selling things in one form or another. But one of my favorites is that in eighth grade, I love selling donuts where I saw that Safeway was selling donuts for $5 a dozen. And so I would buy Safeway donuts, I would bike to my middle school and sell them for $2 each. And I saw it was working. So I wanted to scale it up. So I asked my mom to drive me to Safeway, she said that she didn't want any giveaways. So she would charge me $20 to drive me in her minivan to Safeway, buy 10 dozen donuts, go to my middle school, sell them for $2 each. I had all sorts of things happen where competition popped up selling Chuck's donuts, which if people aren't familiar, has like a $1 cost basis, but they're higher quality donuts. And so I dropped my prices to $1 for two weeks to run them out of business because I knew that middle schoolers would care more about price as the comparative advantage. I had my principal called me into their office to try to shut down my donut stand, saying that, you know, I wasn't allowed to sell food on school campus. And so I moved my donut stand 20 feet over off of school campus so that they couldn't police me, so to speak. And tying back to your question, Harry, after my mom saw all of this when I was in eighth grade, she was very nervous that I would start selling drugs, right? Because it's like, you know, small jump from donuts to drugs. And so she insisted that while I'm not Catholic, I should go to Catholic high school to make sure that I stayed in touch with my values and and met my cofounders there. So I guess she was right all along. **Harry Stebbings** [6:38]: Was that actually why she sent you to Catholic high school? Because she wanted you on the straight and narrow? **Brendan Foody** [6:43]: That was exactly why. Because I'd gone to public school through eighth grade. My siblings had all gone to public school all the way till college. But the the primary motivation was that she didn't want me to get into trouble. **Harry Stebbings** [6:53]: As a principal, you're just you're the child that just pisses you off no end, aren't you? You're like the no adult who, like, moves it just outside the boundaries. Can I ask Brendan, did you always know you'd be successful? And what I mean by that is very specifically, when I interview the best founders, they have a duality, which is they have this superiority complex. They think that they are better than everyone. They don't admit it because it sounds dickish, but they do. And then they have this inferiority complex where they are not happy with their current state, and they wanna do more and more and more. Do you have that? **Brendan Foody** [7:24]: I would say that I definitely had grand ambitions growing up of all the things that I wanted to do, but I don't think it was nearly at the scale of what we're doing today nor how fast it would happen because those two dimensions are nearly impossible to predict. I was definitely ambitious. I I don't think that I had a a perfect sense for what that would look like, though. **Harry Stebbings** [7:48]: They told me about your not wanting to go to college. Before we dive into Mercor and the market itself, because there's so much for us to unpack, I'd just love to understand how you thought about college, not wanting to go, and how it informs how you advise other young people on college. **Brendan Foody** [8:03]: I'll start with the other story that I like to tell around my side hustle in high school, which tees up why I didn't want to go to college. But I initially was reselling sneakers as a lot of people my age would do in that generation. And I realized that all of these sneaker resellers were eligible for AWS credits, but they weren't claiming the AWS startup promotions. And they were instead just paying big AWS bills. And so I started a consulting agency where I would help the sneaker resellers create websites for their startups, them apply to get credits. And some of those became actually like venture scale companies. And I made hundreds of 1000s of dollars when I was in high school. And so when I, you know, was starting to think about whether I wanted to go to college, I was thinking, why would I go to college to get some job and like a Fang company or in consulting or whatever it is where I'm making way less money, I would love to just like, go full time on the things that I love doing. And so I had a big argument with my parents about whether or not I should go to college. And eventually I appeased them and applied to colleges like ten days before the application was due. **Harry Stebbings** [9:12]: How do you advise other young people stay on the value of college, given what you've seen and experienced now in reality? **Brendan Foody** [9:19]: So much of the reason that college is no longer valuable from an educational standpoint is that that information is all available online. Like my parents preconceived notions is that they didn't have YouTube, they didn't have the internet and all of this access to information at their fingertips. And so they needed to learn it from professors, right? Versus for me, I listened to almost every Stanford GSB lecture when I was in high school. And I just like loved consuming information online and like listening to all of your podcasts, Harry. I've been doing that since I was little. And I think that AI only exaggerates that and making it easier to organize that information to understand it to learn. There's So still value to college from a social standpoint. Like, I had a lot of fun, but I don't think there's too much value from an educational standpoint. **Harry Stebbings** [10:07]: Listen, I totally agree. I went to university for about four weeks before I dropped out. **Brendan Foody** [10:12]: Oh, I didn't know that. **Harry Stebbings** [10:13]: Yeah, yeah, I went for four weeks and then a sponsor offered me a $100,000. And I went to my law professor and I was like, how much do you earn? And he was like, $82,000. I was like, great, I'm out of here. Like, this is not and I hated law as well. Dude, I wanna start with something that Edwin said on the show. He said that everyone in the space is simply body shops. Is that a fair summarization of the space, or do you push back and say, Mercor is not just a body shop? **Brendan Foody** [10:43]: I don't think it's fair at all. I mean, we operate as close research partners to all of our customers and helping them to mobilize some of the highest caliber people in the world to push the frontier of model capabilities. But I think so much of our insight on the market is radically different and understanding how important high caliber people are, rather than leaving them out of the narrative. And I'll give the backstory of sort of how we really got involved in the market in the first place. Scale AI came to us, they used our platform to hire 1000s of people. And we realized that there was this enormous transition underway, moving away from this crowdsourcing paradigm that Scale and Surge pioneered of how do you get low and medium skilled people that write barely grammatically correct sentences for early LMs, and very quickly moving towards this sourcing and vetting paradigm of how do you find the Goldman bankers, the McKinsey analysts, the Fang software engineers, the top doctors and lawyers that can work directly with researchers to help them build the highest complexity data on Earth and understand what that data is. Because when we were dealing with undergrad level math problems, it meant that the researchers could easily look at the math problem and understand why the model was making a mistake. But when we're dealing with the kind of work that a Goldman associate would do in their fifth year, it means that the researchers can't interpret the evals, and they can't interpret all of the data that they need to hill climb and ultimately improve model capabilities. And so it's really that trend around a different engagement model and higher caliber work that caused us to take off and really catalyze this meteoric growth. **Harry Stebbings** [12:23]: If we extrapolate that out further and further with the advancement of models, your supply side becomes narrower and narrower if you think about it. As models become smarter and smarter, the ability to do what you do requires smarter and smarter people, and they are just by nature less and less. How does that evolve to its ultimate destination then as we run out of really smart people? **Brendan Foody** [12:44]: The total addressable market is limited by the amount of things that humans are better at than models. And so I'll give an example that helps to contextualize this. I remember when we started working on a high complexity RL environment project, where the model would use one tool and interface with it in a task that would take a human a few hours to do. We started this became a famous product eventually, but we started out with 100 people. And it was easy to stump the model, it's easy to find mistakes that it was making. And over time, only 20 people could contribute to it, the exact dynamic that you're describing. But then we started adding other degrees of complexity of how do we get the model to use other tools, like accessing your Google Drive, like accessing your calendar, your Gmail, your Slack, all these different things. How do we get it to do the trajectories that, you know, a human might spend ten hours, one hundred hours on, and all of a sudden, everyone else could contribute to the project again, because they could stump the model. And what it goes to show is that so long as there's things that the human is able to do, the model is not able to do. And we want those capabilities in the model, whether it's to schedule a meeting or write emails for you or whatever it is, we need humans that help to create those verifiers and help to measure that frontier to ultimately improve model capabilities. **Harry Stebbings** [14:02]: Dude, I had the founder of Cohere on the show the other day, and he said that we are absolutely seeing the reaching of scaling laws being questioned and that GPT-five focusing on efficiency really is an embodiment of that. Do you agree that we're hitting scaling laws being achieved and we're reaching a period of plateauing, so to speak, in terms of progression? **Brendan Foody** [14:22]: I don't think that models are plateauing. Like, if we look at the last twelve months of progress in models, I've been blown away. To his point, we're definitely seeing a difference in the way that people improve model capabilities and that it's no longer shoveling a lot of low caliber medium scale data into the model. Right? It's much more these curated datasets with extremely high caliber people that are built in a thoughtful way. And I think that that transition towards our own environments and all this, like, high complexity data has been one of the most important things underpinning the trajectory of Mercor. **Harry Stebbings** [15:01]: When we think about the supply side of that data, you're obviously one of the providers. There are many providers now, it would seem, including your Turing's, your Handshakes and Surge's. And how do you differentiate on the supply side of data in this way? **Brendan Foody** [15:15]: We saw the market shifting dramatically away from crowdsourcing towards sourcing and vetting. And once this happened, there were all these other labor marketplaces that caught on to that transition. Right? They saw our growth, and they wanted to chase after that saying the same things in podcasts and trying to position themselves in a similar way. But I think one of the largest things we've realized is that the outcomes of data and the people that contribute to it are extremely power law, similar to a company where if you have 100 people on a project, oftentimes, majority of the model improvement is coming from the top 10 to 20% of people, right, just like sort of majority of the value in a company will often come from the top 10 to 20% of people. And what that means is that when we're able to build proprietary advantages in the way that we have not only our supply base in the referral network to access them, but also the way that we match those experts with the opportunities where they're going to do phenomenal work, it creates so much value for customers that it's extremely difficult to compete against, right? When we're able to find those people that are the 10x contributors, it's very difficult to recreate. **Harry Stebbings** [16:28]: A lot of people have cited a criticism of the space being they're very good at facilitation, but not great at measuring the efficiency of the data that's produced. The challenges of being first on a show is you say all the quotes, and then I can use them. Edwin said that none of the competitors have algorithms to measure the quality of the data that they're producing. Is that right? **Brendan Foody** [16:48]: That's not true at all. In fact, we use all sorts of models and algorithms to assess the quality. We train on data to see how it's improving model capabilities. And we do function as a deep research partner to our customers. I think the difference is that I think about our business as at the intersection of labor marketplaces and AI research. And how do we leverage our core competency in finding world class people and pair that with the fact that we work with all of the top research labs at the frontier of model capabilities. And we're not like the crowdsourcing companies and that we try to hide all of the people on the platform, pay them low rates, etcetera. **Harry Stebbings** [17:29]: One of my friends is on the board of one of your competitors. And they said that labs are incentivized to ensure that no one company dominates. And so they intentionally spread business around to ensure no one becomes too powerful. Is that true? And can you just help me understand that dynamic? **Brendan Foody** [17:45]: I think that that has definitely happened in some cases. But ultimately, the thing that labs care about the most is how do they improve model performance? How do they get those top 10 to 20% of people that are driving the vast majority of the model improvement? And so that's how their spend allocation and investments, you know, ultimately get allocated is what are the vendors and strategic partners that are able to deliver those outcomes? And how do they work as deeply as possible with those partners. And we've definitely found those stories of customers where I think they start out multi vendor and working with a bunch of different vendors, but ultimately get to the point where they realize that they're going to be making a trade off in the performance of their model and the performance of the data sets if they are trying to diversify too much and lean very significantly into moving almost all of their work to us. **Harry Stebbings** [18:38]: That's so interesting. So you expect like a multi vendor approach that then concentrates over time. Is that how you think about like spend? **Brendan Foody** [18:45]: I do. And if you look at a lot of the analogs and markets, they often start very fragmented with many different players, but consolidate over time. And so much of that reason for consolidation is that there's structural advantages and economies of scale to being the first player and having this fixed cost investment in having the best professionals in the world, the Goldman bankers, McKinsey analysts, the network associated with them, as well as all the matching infrastructure of understanding exactly what tasks and jobs are these people going to do well at. And so it doesn't make sense for so many different companies to be making those redundant investments. And I think that the market being hot is what gives a lot of those companies more funding and more fuel. But consolidation generally happens as markets come back to earth a little bit and levels up. **Harry Stebbings** [19:38]: One thing that I worry about often is concentration of revenue. You saw it with Nvidia, where I think it was like 51% of revenue was two clients in one certain segment of their business. I think it was 36% in another segment of their business. What was your largest customer in terms of concentration of your revenue? **Brendan Foody** [19:54]: Our largest customer, I can't share the exact percentage, but the breakdown is relatively similar to Nvidia. And part of the reason is that concentration is relevant, but the high order bit is like building a phenomenal business that's creating a lot of value for the most important customers, right? And like, ultimately, Nvidia is worth trillions of dollars. And some of the best empirical evidence that it's okay to have a business that leans into a handful of customers, especially when those customers are the best customers in the world. **Harry Stebbings** [20:26]: You don't understand, Brendan. I I'm the Brit who basically takes incredibly talented Americans with insanely great businesses and then critiques them. It was when I said to Benny off the other day, Marc, the single digits growth is just not good enough. And Mark was like, dude, I have a $42,000,000,000 company. What do you have? And I'm like, you know, that's a very fair response. That's they I I I think you're right to respond with that. Can I ask you, when Scale got bored, did your phone just go off the hook? Did demand just go through the roof? **Brendan Foody** [20:57]: It did. I mean, we were already at a 9 figure revenue run rate, and the company quadrupled since the the Scale acquisition, to put that in in frame of reference. **Harry Stebbings** [21:09]: You were a 100. And then I saw yesterday you're four fifty now. **Brendan Foody** [21:13]: Well, there there's all sorts of news articles that that have come out without complete information. But I didn't **Harry Stebbings** [21:20]: mean that as a spoiler. **Brendan Foody** [21:22]: No, but what we're what we're sharing imminently is that we scaled the business from 1 to 500,000,000 in revenue run rate in the last seventeen months, which is the fastest revenue growth of all time, one month faster than Cursor's time from one to 500. **Harry Stebbings** [21:38]: How much of that do you think was fueled by Scale AI being bought? Like, was that a real tipping point where you saw an acceleration? **Brendan Foody** [21:45]: It was definitely a tipping point where we saw meaningful acceleration. In fact, the company is growing faster now at 500 than it's ever grown before. And so the growth continues accelerating. I mean, we had already been growing extremely quickly. And the fact that we were already such a deep partner to all of the Frontier Labs was one of the key things that positioned us so well when the Scale news happened to expand those relationships and support customers. **Harry Stebbings** [22:12]: When I speak to people in the space, they all kind of say that they knew Scale was shit for a while. I'm British and very direct, which is quite anti British, to be honest. But they say that they kind of all knew it for a while, and that actually it wasn't a surprise seeing now that other people think that they're shit too. Did everyone kind of know that they weren't a great quality provider? **Brendan Foody** [22:32]: I think people broadly knew. I think Alex was phenomenal at so many things in distribution and sales. But in some ways, Scale lost the focus on product, on scaling quality, And that was one of the largest challenges of the business. But actually, I had to choose the most important thing, it would be the internal link to quality, which is that having phenomenal people that you treat incredibly well is the most important thing in this market and getting those people to refer all of their friends and actually help to improve the frontier of models. And so I think that Mercor started out really with this obsession on phenomenally talented people, like our average marketplace pay rate is $95 an hour to put that in frame of reference, whereas Scale and Search generally pay about $30 an hour. And so it's just a radically different approach to the way that we think about what kinds of capabilities we want models to achieve and how we want to treat the people that ultimately help to achieve those capabilities. **Harry Stebbings** [23:39]: When we think about kind of the hourly rate there on the supply side from human created data Mhmm. One thing that challenges the model in my mind is the synthetic data creation and how that supplants the need for human created data. How do you think about the future where synthetic data creation removes the need for human data creation? **Brendan Foody** [23:59]: Well, it ties to what I was saying earlier about how the total addressable market is bound by the amount of things that humans are better at than models, which is that, of course, there's gonna be synthetic reviews, there's gonna be synthetic augmentation to make it more efficient to engage with humans. But ultimately, if you want to push the frontier to get the model to do something that the human knows how to do that the model doesn't know how to do, then you need some human stasis point to measure that. Every single time that, you know, there's been questions around, are we going to have super intelligence that's just able to teach itself and do everything that that's turned out to not be true. And we've turned out to continue scaling up the amount of experts that are contributing to improving these models, especially in all of the professional domains that are most economically valuable. **Harry Stebbings** [24:47]: In ten years, do the models still need humans to help train them? **Brendan Foody** [24:52]: I very much believe so. And the reason is that the question comes down to when we'll have superintelligence. Once we have superintelligence and models are better than humans at everything, then, of course, that means that humans won't be able to contribute to models and measure that frontier that models aren't able to do. But I still think it's a very long road. Like these models have gold medals in Olympiad math, and they're better than the best PhD at reasoning, but they can't draft an email for me, they can't schedule a meeting, they can't do so many of the basic things of just using a handful of tools to do a task that takes me a few hours. And that entire road to automating the entire economy and building agents for everything is paved with humans creating evals for all of those workflows. **Harry Stebbings** [25:38]: Do you think the current method of evals is bullshit? **Brendan Foody** [25:42]: How so? **Harry Stebbings** [25:43]: We train or we assess the effectiveness or efficiency of models based on, you know, humanity's last test and all this other crap, which doesn't actually determine practical usage in society. **Brendan Foody** [25:56]: We're releasing a lot of announcements on this soon. But I think that one of the largest inefficiencies in all of AI research is that the evals that people have been going on of Humanities last exam and PhD level reasoning or Olympiad math are wholly disconnected from the outcomes that consumers and enterprises actually care about, where they want the model that is able to build a financial model like a Goldman banker or build consulting research decks, like a consultant would do or build a web app, and the way that you would expect a thing engineer to be able to do. And so I think that that transition is going to be very meaningful. And one of the most exciting shifts in AI actually being useful in the economy. **Harry Stebbings** [26:42]: Okay, get you there. So then we think about evaluations are bullshit. What is the right way for evaluations to be done then? If I gave you a magic wand on Evals, what would you change to make assessment more effective? **Brendan Foody** [26:56]: The number one thing is bridging the divide in the real to sim gap. Like how do we make sure that the tasks that we're building Evals over and hill climbing as closely as possible reflect the distribution of the capabilities that people care about. So what I would say, Harry, is think about the things that you do in your day to day job, and how that could be evaluated for a model. Say, you do research of investment opportunities that you're considering where there's all sorts of like online research of cross referencing their pitch book data, and they're using their product, all these different things, right? Imagine you could create a rubric that similar to how a professor would create an essay, grades how well the model is going about doing all of the online research and the using the tools associated with doing that. And so I think that that will be one of the most important trends as we move away from the era of academic evals towards measuring the real capabilities that users care about. **Harry Stebbings** [27:57]: When you think about the scaling to 500,000,000 in revenue, and you said the speed just being much faster than anyone could have anticipated, you have to change as a leader very significantly. How have you changed most significantly as a leader? I'm relatively young. How old are you? I, am 22. I turned 22 in April. Fuck. Okay. When you raised it 2,000,000,000, people thought it was particularly crazy, if I'm being honest. Like, an investor, like, that was a punchy price. Now, I mean, it looks ridiculously cheap. How did you think about valuation when raising? **Brendan Foody** [28:29]: Too many people think about valuation through the lens of market comps and revenue multiples and not enough through the lens of what's possible with this company, what extraordinary thing can this company achieve, especially when you have such meteoric growth. And so I'll give you like a couple of revenue numbers at each of our valuations. When we met Victor, we were at $1,500,000 in revenue run rate, he gave us the term sheet, and we were at a little over $2,000,000 in revenue run rate. So over 100 x multiple on revenue, he paid two fifty evaluation. I'm sure the Benchmark partnership thought that was insane at the time that at the Series B, when Felicis gave us the term sheet, we were at 20,000,000 in revenue run rate. So it's 100x multiple on the revenue. But what they saw in talking to customers was the phenomenal experiences that we were creating, and that growth that we had was going to continue. And so now we're 25 times larger in revenue scale than we were at the Series B. But in a spot where the business is so profitable that we don't need to go out for financing or spend too much time thinking about financing while we get a lot of offers and interest. **Harry Stebbings** [29:41]: Dude, that is hilarious because I obviously met Adarsh, and he very kindly let me put in a small check. I had no idea you're at 20,000,000. I thought you were way bigger. **Brendan Foody** [29:51]: I think we let you put in a small check a little bit later. Like, because the company keep in mind, we were growing over 50% month over month, like we average 54 month over month growth for a while at that time period. And so it wouldn't shock me if, you know, it was a couple months after the round and we were at a meaningfully higher revenue scale. **Harry Stebbings** [30:12]: Dude, I'm thrilled. Otherwise, I was massively off to my partnership when I was like, yeah. Yeah. Yeah. They're way past there. Where they'll be looking at this again. What? Do you need to raise more money then? Again, I am direct to a fault. The rumors of a $10,000,000,000 valuation. You're like, if you're a 500,000,000 dude, that's only 20 x. And given your growth rate, that would be cheap. **Brendan Foody** [30:33]: Definitely what what I've been thinking about too. We honestly haven't given it much thought. Like, we have gotten a bunch of offers from existing investors. We haven't really shared any materials on the business. There's just been sort of like outside indulgence and and offers based on that. **Harry Stebbings** [30:49]: Is it a nice feeling? Or is it a hey, let me just focus and do my work? **Brendan Foody** [30:53]: I think there's parts of both. Like there's parts of it feeling validating, but also parts of it feeling distracting, and that we just want to focus on creating phenomenal experiences for our customers and for the experts in our marketplace. But I think that it's likely we'll do a financing soon to answer your question with low dilution largely because there's a lot of benefits to signaling ourselves as the market leader in RL environments and all of the high complexity data that we produce. And so we'll keep you updated, Harry. **Harry Stebbings** [31:27]: Do you think a big financing will do it? If you think about it, I'm just intrigued. Like, as you said, Surge, they have big revenue numbers. They're at over 1,000,000,000 now in revenue. Is it the financing that'll do it? **Brendan Foody** [31:39]: Obviously, the financing will so called do it, but I think it can definitely play a part from a a signaling standpoint, making more a little bit more noise about that and and what we do and and how we see the market developing over time could be interesting. **Harry Stebbings** [31:53]: If you had truly unlimited resources, what would you do differently? **Brendan Foody** [31:58]: This is a tricky question, because I feel like we're at a point where we're trying to invest as aggressively as possible, but the business is still profitable, and we're not trying to be profitable. And so I don't think that having another few 100,000,000 in cash would meaningfully change the way that we're investing. But I do think that having a fortress balance sheet has its benefits, having sort of like the new mark of of the company, etcetera. And so I don't think it would change the way we're investing too dramatically. **Harry Stebbings** [32:27]: You're gonna go, woah, Harry, what are you talking about? But at 500,000,000 and growing at the rate you are, you'll soon be at a scale where an IPO is very possible. Given public pricing now being better than private pricing in a lot of markets, do you wanna go public sooner rather than later? **Brendan Foody** [32:45]: It's not something I've given too much thought to because it's it's sort of surreal considering we started the company in January 2023. And all of my college classmates just graduated in May, but there's a lot of benefits to staying private. It's funny, like, remember when I was talking with Jack Dorsey before he invested, one piece of advice he gave me was that we should stay private as long as possible. **Harry Stebbings** [33:07]: What was his reasoning for that? Super interesting. **Brendan Foody** [33:09]: It allows you to stay very long term oriented, like public companies get so caught up even though founder led companies tend to be more resistant to it. Think public companies still get more caught up in the quarterly numbers, and aren't as focused as they maybe should be on all of the long term drivers of value and moats. And so I think that that is one of the core reasons allowing us to stay very long term oriented, especially when there's also so much access to capital in the private markets. **Harry Stebbings** [33:43]: Do you think there's too much cash in the private markets today? **Brendan Foody** [33:46]: I mean, I don't know, because it's it's sort of like a supply and demand question. If I were an investor, I would definitely think that there's too much cash at the the markets. **Harry Stebbings** [33:55]: But you could say there's a load of shit competitors who are getting funded to the tune of hundreds of millions that shouldn't be getting funded. **Brendan Foody** [34:01]: I think that's definitely the case. My heuristic for this is the age old saying of how or ties the idea of it at least that it's probably overestimated in the short term and underestimated in the long term. If we're evaluating things on a three year time horizon, it wouldn't shock me if like, we feel like things are frothy, and it's a crazy time. But if we're evaluating things on a ten year time horizon, all of these extraordinary businesses that are being built will look like a discount. And the challenge right now is just saying, you know, are we in 1996, 1997, or or some other time? **Harry Stebbings** [34:39]: Dude, you weren't born then, so you can't talk about that. Like, that's when I was born. Okay? I I have a FIFA game that was when you were born. That that really makes me feel old. Did you see the MIT study or release? **Brendan Foody** [34:53]: What did you make of that? I think it ties the exact point you were making earlier about how Evals are bullshit. Right? It's like when we start showing that we have Olympiad gold medals or PhD level reasoning, that doesn't mean that it's going to be useful to enterprises. In fact, in 95% of cases, right, we're seeing these failure cases. And the answer is that we'll need evals for every one of those implementations and examples, because evals are the way that we measure the truth that we have a stasis point of understanding what the models are capable of. And if we think about the model as the product, then the eval is the PRD. And so many people have been vibe spending on AI without actually writing the PRD of what do they want to implement and how do they measure that it's going to be successful. **Harry Stebbings** [35:40]: Dude, I need your help. You said vibe spending on AI. The revenue numbers that we see from some players application layer are just awe inspiring, like, in scaling in a way that we've never seen before in my history anyway. How do you think about the sustainability of revenue for the majority of AI companies, and how would you advise me, a friend, an investor? **Brendan Foody** [36:01]: I think the most important thing is looking at the numbers and anecdotes around retention, to see the revenue health and whether there's real value, right? If you meet an application layer company where ninety five percent of their pilots are failing, it's probably not going to be a good investment. But if you meet a business that has extraordinary unparalleled retention numbers, and you talk to those customers, and you hear about how much they love the product, then of course, it's a really exciting opportunity. And so I think that those signs of true market fit are the most important when there's sort of a lower friction to accessing initial pilots or contracts. **Harry Stebbings** [36:39]: The other element is margin. And the margins are pretty terrible in a lot of cases, especially when you take into account free user giveaways, which there's a lot of. Should we give a shit about margin structures given how early we are in the cycle? Or yes, we should. It's always fundamental. **Brendan Foody** [36:55]: The answer is yes. Like both of those matter. And it's very contextual. On one hand, I am a huge believer in capital efficiency, like we have very positive gross and net margins, unlike most AI companies. But on the other hand, like, I also see the case that if you're able to distill models and make them an order of magnitude more efficient in twelve months, then it could make sense to run really aggressive margins on serving models, it really comes down to the stickiness, and whether those subsidies today are driving large LTVs that make sense long term. But I think the case where I would be hesitant is when there's very competitive markets with low switching costs, so that people are pumping hundreds of millions in subsidies, maybe billions in subsidies, and then all of a sudden, the customers are switching over to a competitor if those subsidies dry up. **Harry Stebbings** [37:49]: Often, bank are concerned that the level of CapEx is concerning because of the requirement on revenue generation that's required to make up that CapEx. Do you share that concern, or do you think this is a super cycle? Of course, the investment is required and the revenue will show itself like Master Sun believes it will. **Brendan Foody** [38:10]: I'm less concerned about the broader CapEx cause I think that if you have a ten year investment horizon, the market generally will look like it's at a discount. But I think that there are definitely cases of exuberance. Right? People need to just be thoughtful about which investments are gonna have those positive ten year horizon ROIs and which don't make as much sense. **Harry Stebbings** [38:34]: What segment do you think is most overhyped, over exuberant? Not company, just, segment. **Brendan Foody** [38:40]: Nothing jumps out to me on that. Because obviously, like, I think the things with the most hype are code and foundation models and maybe starting to be use cases and finance. And I feel like the value being created is also very real. The amount of utility that our engineers get from Cursor and Cloud Code and Cognition is incredible. **Harry Stebbings** [39:03]: Do you use all three internally? **Brendan Foody** [39:05]: Yeah. We we let people choose. And so various people use different products. **Harry Stebbings** [39:10]: What's the distribution? **Brendan Foody** [39:11]: I think it's a lot, especially the most cursor usage closely followed by Cloud Code. But it's hard because it's very dynamic, like the market is changing so fast, the products are improving so quickly that I think some of that distribution will change over time. **Harry Stebbings** [39:28]: Do you think that switching costs between those? **Brendan Foody** [39:31]: There are surprisingly low switching costs. Definitely, some of these products are moving the direction of adding more switching costs, right, with understanding how you interact with the platform and having data flywheels around that or custom models for your code base. But I think a lot of those sources of defensibility are taking, you know, more time to develop. And right now, the market is very competitive, which has framed a lot of the sort of negative gross margins that we've seen companies have in the coding space. **Harry Stebbings** [39:59]: In five years time, will you have more or less engineers? **Brendan Foody** [40:03]: I think more. And the reason is that engineering is such an elastic role, right, where if we could build 100 times more software, or say we make engineers 10 times more efficient, we would probably build 100 times more software, And so far as maybe not unique platforms, but the amount of features those people would ship and the iterations on every ranking algorithm, etc. And so I'm a huge believer in the fact that AI, especially in domains like software engineering, will be an amplifier in making people more productive and making people more valuable rather than diminishing their value. **Harry Stebbings** [40:42]: You mentioned code there being one. You mentioned models being another. Do you think the biggest model providers have been created already, or do you think some of the biggest in the future are yet to be created? **Brendan Foody** [40:53]: The largest model creators already exist, but I'm not a 100% sure about that. Like, I I definitely have, you know, some airbars about it. My expectation for why the largest model builders exist is just obviously the extraordinary CapEx in terms of both data and compute investments that go into that, as well as building out all the teams of researchers that has quickly become phenomenally expensive. But at the same time, I think that there, you know, may be other breakthroughs that help to enable more model progress, and those could play a role coming from startups. **Harry Stebbings** [41:30]: I love the kind of dual sided mindset there. You mentioned the expense of talent. Mhmm. Is the expense and the economics around talent today in AI, in SF, just nuts? **Brendan Foody** [41:42]: It definitely is. I mean, certainly also beyond my wildest imaginations a couple of years ago. But I think what it's really amplifying is the importance of having a really strong purpose, more so than just paying people well, because lots of companies can pay people well. And I think that **Harry Stebbings** [42:03]: I really like you, Brendan. You're awesome. But like, come on, dude. When Zuck puts 100,000,000 down, you're like, okay, yep. I'm out of here. **Brendan Foody** [42:11]: Look, I agree. You still need to obviously reach parity with respect to like the economics of things. And, of course, giving people a lot of upside in the business. But part of purpose isn't only the mission of the company, but also the economic upside associated with that mission. And not sure startups can pay someone $100,000,000 in liquid cash, but we can give people equity grants that are appreciating extraordinary quickly as part of the vision of the company to help people capture upside in this purpose. And so I I do think that that is increasingly important in having an employee base of missionaries, not mercenaries, and people that are in it for the long haul. **Harry Stebbings** [42:55]: Will Zuck spend work, do you think? He's got all the mercenaries together, which are very talented, brilliant people. Does that work? I think so. **Brendan Foody** [43:05]: I think that there's an extraordinary team there, and so it'll it'll be fun to see what they build, but these things are always always hard to say. **Harry Stebbings** [43:13]: Which team do you think is underappreciated that doesn't get the love that it deserves? **Brendan Foody** [43:18]: It's interesting because OpenAI gets a lot of the love of, like, ChatuchiBT being the brand that everyone talks about. I feel like Anthropic gets a lot of the love around code and and Cloud Code. XAI, definitely much more so on the consumer side as well. I definitely feel like a lot of the Gemini flash models are also extraordinary and underappreciated on on Evals, especially their small models. I'm always amazed with. So if I had to choose maybe not a company, but especially set of models, think the DeepMind team did a really phenomenal job on a lot of those smaller models. **Harry Stebbings** [43:52]: Totally agree with you there. I think Google's massively underestimated. Do you think we live in a world of many unbundled specialized models or fewer monolithic generalized models like the providers you mentioned? **Brendan Foody** [44:03]: I used to be more in the camp of a lot of specialized model very much in the camp of a lot of specialized models. Models. Now I think it'll be a lot of both. What **Harry Stebbings** [44:12]: changed to cause that change of mindset? **Brendan Foody** [44:15]: The amount of generalization that we're seeing, especially like o three blew my mind and just how phenomenal a model was and how well it generalized. And g p five as well as a phenomenal model. And so I think that when there's still so much headroom in these like foundational capabilities, it feels structurally more efficient to have those as individual investments to improve model capabilities. We're just in the first inning of model customization of every enterprise wanting models to know how to use their own set of tools of knowing how to use all of their own like knowledge bases, etc. And the processes that they've codified. And that'll be another, you know, huge area of investment over the coming decade. **Harry Stebbings** [44:57]: Do you buy sovereignty as a reason why a model provider wins? You know, we've got Mistral in Europe, you have Cohere in Canada. Is sovereignty a reason why a model provider wins? **Brendan Foody** [45:08]: Maybe wins in a scoped part of the market. Like I could see why, for example, there would be a lot of benefits to having Mistral be an expert in European law that might have nuances from other kinds of law. And they've just invested far more in having the best model there where it doesn't make sense to use other models. But I don't think that the largest companies per se are going to be those that invest in a specific geography. Think it's going to be a broader set of capabilities and the general purpose models that people use every day to code or to build products or do their day to day work. **Harry Stebbings** [45:49]: I don't know if you know this, but I'm particularly disliked in Europe because of my affiliation or affection towards the nine nine six work culture. No. Truly, my DMs are basically a war zone nowadays. Nine nine six is a model that you very much espouse to. Can you talk to me about why you are nine nine six bullish first? **Brendan Foody** [46:11]: Well, not exactly. I I need to offer a key clarification, which is that we've actually never mandated ours. It was more so when we were talking about nine ninety six, it was a description of how the early team worked. In fact, the reason we talked about nine ninety six was because people were working so much more than that, that we wanted people to go home a little bit early so that they could be well rested, etc. That intensity is, of course, extremely important in building a generational business. But at the same time, I think we've become less focused on like the in person elements of that intensity and recognizing that it can be expressed through outputs. Because when the market for talent is so competitive, it especially makes sense to just optimize for working with the best people less so than optimizing for FaceTime. **Harry Stebbings** [47:01]: Fascinating. So now you're at the stage where you need to bring in execs, where you need to make the language with which you speak more conservative. **Brendan Foody** [47:10]: Well, I I don't know exactly. I love **Harry Stebbings** [47:12]: it. I I work with so many companies where they're nine nine six nine nine six nine nine six, and then suddenly it's like, shit. We need to bring in that CPO. And he's never gonna be nine nine six because he's, like, stellar CPO from big company. And you're like, what? No. No. No. It's all about impact. It's about impact. And the language changes to be a lot more neutral. My lesson is actually you need to do that. **Brendan Foody** [47:34]: The thing is, when we were all, you know, there was like 20 of us in a room working with our India team as well. It was just like everyone loved what they do. And if people left to like go home for dinner or had something else going on, like we wouldn't bat an eye. **Harry Stebbings** [47:51]: No, you're just just just fire them, give them their box and say, of the bell. You don't need to come in tomorrow. **Brendan Foody** [47:57]: I think the truth is that all along, it's been much more about hiring people that, like, give a shit and love what they do and are obsessed with it and the way that we are, rather than specific hours. And early on, those were highly correlated. But I I think that as the company expands, they're not always as perfectly correlated. And there's definitely exceptions. **Harry Stebbings** [48:20]: I just wanna ask one final one before we do a quick fire. I got asked this brilliant question the other day that's really just, like, stuck in my head. What would you do if you weren't scared? An example for for me, just so you have a framing, would be, I'd move to Silicon Valley. I'd compete in the Colosseum Of Technology rather than sitting in London being happy being a big fish in a small pond. What would you do if you weren't scared? **Brendan Foody** [48:43]: It's an interesting question because I feel like I live in a very, like, risk on way of always trying to, you know, make big bets, maybe one ties to capital efficiency. And maybe it's for better or for worse, right? Part of the reason that we've run the business in a very capital efficient way is that I've always been very thoughtful about, you know, how will markets develop over time? And how do we ensure that we're building a super durable, sustainable business that will be around in ten years, but I often wonder if maybe we should just start burning hundreds of millions of dollars a year. But my question to you is like, could you? I think I think we could find a way. But like how do **Harry Stebbings** [49:25]: they spend it on talent? **Brendan Foody** [49:27]: On subsidizing either supply or demand side of the marketplace of how do we get great people on the supply side? Or how do we subsidize customer projects? I think the business certainly doesn't need to do these things. And you know, we have the demand to like double overnight if we can meet capacity, and we have a supply base that that loves us and is growing phenomenally quickly. But at the same time, I do think that if I were trying to burn $100,000,000 I could figure out a way to do that. We can go away for a weekend. I'll show you how to burn on the windows. Harry, what do you think? As as an investor, how how would you handle that? Would you be scared and capital efficient, or would you be maximally aggressive about burning money? **Harry Stebbings** [50:09]: I don't live the competitive landscape that you do. If I'm feeling continuous pressure from competitors that I feel are good and I have an ability to undercut them in a way that they don't undercut me, I would absolutely leverage cash reserves to subsidize it, be a loss leader until I can bluntly strangle them out of market. Interesting. But it depends. If you don't feel that competitive pressure, which is it's clearly not showing in your numbers, I would I would not. Cash can actually be a bit of a problem at certain stages. You know, when you look at large companies, you need to make your cash work for you. And, you know, you really have to buy the growth in a lot of cases. You just don't wanna get to that stage. **Brendan Foody** [50:50]: I totally agree. And I think that's why we've always erred on the side of capital efficiency and fundamentals. **Harry Stebbings** [50:57]: What does Peter say? **Brendan Foody** [50:58]: I think Peter is more on the side of of capital efficiency. You know, he's seen how these things play out and the ups and downs of markets. So, yeah, he's been more in that camp. And don't get me wrong, I'm still like incredibly bullish on the market and AI, I think we're much more like '96 or '97. But focusing on fundamentals at least does buy you a lot of durability and long term, just having the right values and culture that it can be easy to lose sight of in this one way door of not being efficient. **Harry Stebbings** [51:28]: Final final one. Promise to end the quick. You said you could like, there was double the demand than the supply. It's that much of a constraint supply that if you had the resources or reserves on the supply side of data, you could double the business. Definitely. We turn down **Brendan Foody** [51:43]: projects every day. And the reason is we're we're very focused and disciplined about working with the best customers in the world and doing phenomenal work for them. And so the capacity is how do we, you know, scale up our ability to do that. That's my biggest focus right now. **Harry Stebbings** [51:58]: Dude, what does your mom say? **Brendan Foody** [52:01]: It's evolved over time. When I dropped out, she was very upset. Now I think she's come around. Have you done secondaries? Very small amount. Do you advise founders to take them not take them? I think the most important thing is like making sure it's not distracting, right? Because ultimately, the vision that we're selling the company or you know, selling everyone is that we are fully committed. And I want to demonstrate that in every aspect of the word that this is our life's work. And the thing that we plan to spend the next decades on, you know, showing that on every dimension. **Harry Stebbings** [52:36]: I wanna do a quick fire round. So I'm gonna say a short statement, you're gonna give me your immediate thoughts. Does that sound okay? Sounds great. What's one widely held belief about AI that you're like, God, that's so wrong. Just please stop. **Brendan Foody** [52:48]: That will have super intelligence in three years. That's better than humans at everything. I think it's totally wrong. **Harry Stebbings** [52:55]: You can be the CEO of OpenAI for a day. What would you do that they're not doing? **Brendan Foody** [53:00]: I think model customization is a really exciting opportunity because API will have low switching costs, not much pricing power. It's not a good business. And focusing more on model customization is a really exciting opportunity. **Harry Stebbings** [53:15]: Do you think OpenAI win the consumer, have ChatGPT as a Trojan horse, and Anthropic win business and enterprise, and have claw code and win that segment? Certainly seems like that. What question should every AI company be asking themselves that they aren't? **Brendan Foody** [53:31]: I really like the thing Sam Altman says of, will models being dramatically better in one to two years improve your business or worsen it? I think that that is, in so many ways, the most important question to see if you're building a business that's durable and well positioned for the future. **Harry Stebbings** [53:49]: He said it first on our show. Oh, really? Yeah. And that was the show where he took a 20 VC jumper and he put it on. Brendan, I was like, yes. This is, like, unbelievable brand. **Brendan Foody** [53:59]: Well, Sam wore one of our our Mercor jackets the other week, which I I was over the moon about. **Harry Stebbings** [54:05]: Yeah. I was I was too. Okay? And then he gets he gets on camera, Brendan, and do know what he says? What did he say? Startups, we're going to steamroll you. And I am a startup investor. Okay? My job is to inspire entrepreneurs. I'm like, oh, no. Oh, no. But yes. Dude, what have you changed your mind on in the last twelve months? **Brendan Foody** [54:26]: You know how I talked about how I thought there would be this is a little contradictory. I thought there'd be a lot of model customization. I think I've indexed more on a lot of generalization. And just like foundation models will be huge, huge businesses. Well, I still think they should invest more in the customization as well. **Harry Stebbings** [54:44]: What investor do you not have that you would most like to have? It doesn't need to be a fund, it could be a person, it could be anyone. **Brendan Foody** [54:51]: I think Jeff Bezos, I've admired Amazon so much and just like the early clarity of thought in the business and long term focus. And I think there's a lot of analogs, so I would love to learn from him. **Harry Stebbings** [55:02]: Why do you not have him? With the cap table you have, getting him would not be impossible at all. I haven't met him. I'll I'll have to I I haven't put too much time in it. I've been meaning to. You can give yourself one piece of advice going back to January 2023, starting Mercor. What do you know now that you wish you had told yourself back then? **Brendan Foody** [55:23]: I'd say focus on Foundation Model Labs. I didn't understand the scale of the opportunity with Foundation Model Labs in January 2023. And I think being the first company to realize that, especially in how our marketplace fit into it was one of the most impactful things. And if I'd realized that nine or twelve months sooner, that would have been even more exciting. **Harry Stebbings** [55:43]: How penetrated into their spend are we? You know, when you look at them, they are absolutely destroying a lot of their economics to win this race. They can only do that for so long. How penetrated are we into their spend? **Brendan Foody** [55:56]: So there's different buckets within their human data spend. Like, there's the RLHF buckets, which we don't do as much of. Like, Surge is the largest player in RLHF. But then there's the new data types that everyone's moving towards called RL environments, where we're call it rough estimate is like 50 to 60% of the market. And so doing quite well on that and expanding market share quickly. **Harry Stebbings** [56:18]: And do you think like market share is still continuously expanding? Like how much room does the market itself there have left to expand? **Brendan Foody** [56:25]: I've talked to multiple executives, CEOs at leading labs that believe that RL environments will subsume the entire economy, because it doesn't make sense that humans would be doing monotonous redundant work, redundantly researching different companies each week or guests for your podcast, it makes way more sense for humans to build the framework of how to do that, so that models can then learn how to do it and do it for us. And so I think that that is going to be a ridiculously exciting transition. **Harry Stebbings** [56:59]: Dude, I've so enjoyed having you on the show. This is why I don't send questions. We all this ahead of time. None of it has been covered because this was way more interesting. Thank you so much for being so flexible with my questions, and you've been fantastic, dude. No. I love it. **Brendan Foody** [57:14]: Thanks for having me on, Harry. **Harry Stebbings** [57:17]: Honestly, I think I just have the best job in the world. I get to sit down and learn from incredibly talented leaders in the space and just follow my curiosities and interests. I hope you like the show. Please let me know how I can make it better for you. You can email me Harry@20bc.com. But before we leave you today, ## Sponsor read **Harry Stebbings** [57:34]: I love seeing the team come together to make this show happen. What I don't love is trying to keep track of all the information, the data, and the projects that we're working on across dozens of platforms, products, and tools. 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