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Mercor CEO on Why Application Layer Companies Have No Defensibility & Token Spend Exceeds Salaries

Brendan Foody is the Founder and CEO @ Mercor, one of the leading data providers to the largest labs on the planet including OpenAI. In the last two years, Brendan has scaled the company to $1.5BN in ARR and a valuation of $10BN. ----------------------------------------------- Timestamps: 00:00 Intro 01:13 True or False: Mercor lost Meta & OpenAI as a customer with the hack? 05:52 Are We Entering a Golden Age of Cyber? 11:06 AI, Jobs & Layoffs: How Do Humans Fit Into the New Economy? 21:17 Rejecting a $30B Acquisition 27:39 The Fundraising Story: Helicopters, Ferraris & $10B Valuation 32:50 Infrastructure Will Win Over Application Layer 35:52 Is SaaS Dead? When Network Effects Are the Only True Moat 42:12 Token Spend on Agents Now Exceeds Employee Headcount 54:40 Competing for Talent When Meta Offers $20M Per Year 01:01:56 Do Sovereign AI Models Actually Matter? 01:07:17 Does HR Slow Companies Down? Brandon Pushes Back 01:09:31 Quick-Fire Round ---------------------------------------------------------------------------------------------- Subscribe on Spotify: https://open.spotify.com/show/3j2KMcZTtgTNBKwtZBMHvl?si=85bc9196860e4466 Subscribe on Apple Podcasts: https://podcasts.apple.com/us/podcast/the-twenty-minute-vc-20vc-venture-capital-startup/id958230465 Follow Harry Stebbings on X: https://twitter.com/HarryStebbings Follow Brendan Foody on X: https://twitter.com/BrendanFoody Follow 20VC on Instagram: https://www.instagram.com/20vchq Follow 20VC on TikTok: https://www.tiktok.com/@20vc_tok Visit our Website: https://www.20vc.com Subscribe to our Newsletter: https://www.thetwentyminutevc.com/contact ----------------------------------------------- #20vc #harrystebbings #mercor #ai #ceo #aimodels #saas #cybersecurity #hiring

Brendan FoodyguestHarry Stebbingshost
Jun 1, 20261h 14mWatch on YouTube ↗

EVERY SPOKEN WORD

  1. 0:00 – 1:13

    Intro

    1. BF

      Building defensibility in the software layer on top of the models is going to be incredibly difficult. We have the demand to double overnight, we just don't have the capacity.

    2. HS

      Joining me in the hot seat today, we have Brandon Foody, co-founder and CEO of Mercor, one of the fastest growing AI companies valued at over $10 billion today, doing over a billion dollars in revenue.

    3. BF

      Over the last two years, everyone has increasingly realized that the model is the product. I think we're seeing in real time that services are getting automated.

    4. HS

      This is the most revealing interview that Brandon has ever done. Discussing is revenue really revenue in this business? What does that look like moving forward? Would he rather invest in OpenAI or Anthropic?

    5. BF

      Right now, we're spending more on tokens for our internal agents than we are on employee headcount.

    6. HS

      How much does it cost to hire a high-quality AI researcher?

    7. BF

      Oftentimes, it would be in the tens of millions of stock per year.

    8. HS

      Ready to go? [upbeat music] Brandon, it is so good to have you in the studio, dude. Thank you so much for joining me in person.

    9. BF

      Super excited to be here. Thanks for having me,

  2. 1:13 – 5:52

    True or False: Mercor lost Meta & OpenAI as a customer with the hack?

    1. BF

      Harry.

    2. HS

      So I was thinking about how we're gonna structure this, and I was like-

    3. BF

      Mm-hmm

    4. HS

      ... "You know what? There's, there's quite a lot of myths or rumors around Mercor." And given it's our second time-

    5. BF

      Mm

    6. HS

      ... I thought I could kind of break the ice and just go straight for them. So myth number one that we're gonna tackle, okay, is there was a, a hack-

    7. BF

      Mm-hmm

    8. HS

      ... or a leak or whatever, I don't know how you, kind of terminology you call it, but a hack, and, um, revenue's been flat. What's really happening with Mercor? Uh, true or false?

    9. BF

      So there was an incident. All of the other parts are false in that we obviously handled it very quickly, we were in touch with customers, we moved incredibly fast at, uh, engaging Mandiant and a bunch of other security consulting firms, and the company's been crushing it ever since. We've expanded our relationships with all of the frontier labs and added 300 million in net new ARR in the last 60 days.

    10. HS

      300 million in 60 days. Fuck me. Okay.

    11. BF

      It's been pretty crazy, yeah. [laughs]

    12. HS

      Well, well-

    13. BF

      Keeping us busy.

    14. HS

      I'm sorry, I just have to ask, where were you when you found out about the hack, and what did you do?

    15. BF

      Well, it was a Saturday, so I was in the office, uh, and I [laughs] was talking with our engineering team. And I think the initial thing is, of course, like, um, you know, how, how are we communicating this to customers and trying to be very proactive about understanding exactly what happened, what was accessed, et cetera. And then how do we communicate this to the experts and, um, a- and just moving, uh, containing it, uh, moving quickly on the comms. And then from there, of course, making sure that we put in place all of the right, uh, things so that it never happens again.

    16. HS

      You know, there's a brilliant poem, uh, and poet, Rudyard Kipling, who said, you know, kind of, uh, essentially you have to keep your head when all about you are losing theirs.

    17. BF

      [laughs]

    18. HS

      That is a time when everyone is losing theirs.

    19. BF

      Definitely.

    20. HS

      I, I, I don't by no means wanna be patronizing. We're both young. You're younger than me. Uh, what do you do to stay calm when that is an oh shit moment?

    21. BF

      Well, it's interesting 'cause I feel like throughout the lifetime of the business, I have been through a lot of very stressful moments.

    22. HS

      [laughs]

    23. BF

      That was definitely stressful, but it definitely w- wasn't close to the most stressful one.

    24. HS

      Seriously?

    25. BF

      [laughs] Yeah. I mean, there's been pl- times when, you know, I'm freaking out about, uh, making sure we get something right with a customer or whatever it is, but I think part of it is that there was this broad perception on Twitter that was much more exaggerated than what actually happened within the business. And so having a thorough un- understanding of what actually happened and having really strong relationships with customers gave us a lot of confidence that we would get out through... get through it, be on the other side even stronger. Um, and we used to have six values as a company, but we added a seventh value as security, um, to make sure it's very ingrained in the culture. But I think that, um, yeah, just that, that confidence that we, um, know what's going on, and that there's sort of this echo chamber on X that we need to, uh, hedge against a little bit, um, as we talk to the team.

    26. HS

      Do you pay attention to it, and do founders need to pay attention to it or no?

    27. BF

      Definitely. I mean, I think founders need to pay attention to it. Like we had an all hands with a company where we just laid out, "Here's exactly what's happening. Here's the trajectory of the business," and I think that was very helpful to the entire team. Um, but it was def- definitely annoying that there were all of these people saying things that didn't actually happen, and we couldn't quite speak out against them too explicitly, otherwise there's, you know, going to be the Twitter mob circling and, um, y- you know, all these recommendations from lawyers, et cetera.

    28. HS

      The hard thing is there are often a lot of people with economic incentives behind the scenes-

    29. BF

      Totally

    30. HS

      ... who will absolutely trounce you and be very negative because they are aligned to a competitor or, you know, we're in a YC company that's been through a lot of shit in the last few days-

  3. 5:52 – 11:06

    Are We Entering a Golden Age of Cyber?

    1. BF

      I think so. I mean, we're even seeing this on the customer side, where our customers obviously are very focused on how do we improve the model cyber defensive capabilities so that we can have the best AI security engineer that is able to defend every enterprise from all of these attacks.Um, because in our, our incident, it was the attacker that used a swarm of coding agents to help get access to the system, as is happening in a lot of these. Um, and so I think there's going to be an enormous boom in AI security engineering tools and, uh, various forms of defense that are able to help protect companies against, um, all of the increasing waves of cyber incidents that are just getting started.

    2. HS

      Can I just be very naive and dumb here? How do swarms of coding agents make for such dangerous and malicious actors? Like, how does that actually work?

    3. BF

      So the reason is that when a normal attacker is trying to find vulnerabilities, they can only review so much code and go through, um, you know, a certain portion of it at a human speed bound by the amount of people on their team. Versus a... Y- when they're using swarms of agents, they're able to be very exhaustive in reviewing the entire code base, looking at, um, you know, the entire front end, all the different things that they've accessed. And so that has allowed a lot of these attackers to just move much more quickly. And so we've been, uh, exploring various collaborations with customers and how we can strengthen their cyber defensive capabilities, um, to hedge against exactly this type of attack as well.

    4. HS

      Got you. In terms of those various customers, uh, true or false, you lost OpenAI and Meta as customers in the hack?

    5. BF

      False. Our relationship, um, with OpenAI is stronger than ever, obviously. I can't speak, uh, too much to specific customer relationships, though.

    6. HS

      Can I push on Meta?

    7. BF

      Of course. I mean, I think that Meta, um, you know, like, currently the relationship is still paused. Every other one of the Frontier Labs has grown their relationship with us since, um, and the company has been crushing it. Um, but they're the only one that is paused-

    8. HS

      And it would be paused-

    9. BF

      ... which is public

    10. HS

      ... because of, just because of the security?

    11. BF

      Well, there's other things happening there. Um, like, obviously I think that, uh, Meta's a unique customer because of the Scale acquisition, and so naturally, um, they're going to work with Scale more, but I don't wanna speak too much to, um, the specifics of a customer.

    12. HS

      'Cause I thought when you saw, like, Handshake's revenue just, like, parabolically go up, it was just, like, Meta shifting spend from you to them. Is that not true?

    13. BF

      That's not true.

    14. HS

      Interesting.

    15. BF

      Mm-hmm.

    16. HS

      What is that then?

    17. BF

      I probably shouldn't speak too, [laughs] uh, granularly to that. Uh, but yeah.

    18. HS

      Uh, totally cool.

    19. BF

      [laughs]

    20. HS

      Um, okay, but, but, okay, so we have lost-

    21. BF

      I'll speak to everything except customers. [laughs]

    22. HS

      But, but we have lo- lost OpenAI. Got you.

    23. BF

      Yeah.

    24. HS

      Okay, cool. Um-

    25. BF

      Stronger than ever.

    26. HS

      'Cause I got told by many of your [laughs] investors before the show, "They're definitely having..." Great, good. Thank you, you're wrong. Um, you've been... I, I read this article. You've been trying to poach MicroOne team members with signing packages in the millions.

    27. BF

      We have not extended a single offer to someone from MicroOne.

    28. HS

      So no millions?

    29. BF

      No millions. [laughs]

    30. HS

      Bugger.

  4. 11:06 – 21:17

    AI, Jobs & Layoffs: How Do Humans Fit Into the New Economy?

    1. HS

      How humans fit into the economy.

    2. BF

      Mm-hmm.

    3. HS

      Fascinating. When we look at the news, we see Intuit lays off 16,000, Meta lays off 8,000 at 4:00 AM, LinkedIn 1,000, Coinbase da-da-da-da, ClickUp now 22% going. It's hard for people to see how humans are gonna fit into that new economy.

    4. BF

      Totally.

    5. HS

      Do you share that concern?

    6. BF

      I think to some extent. I believe there's certainly going to be many more jobs in 10 years than there are today, but there's also gonna be a lot of job displacement along the way. And amidst all of these layoffs, I think the most important question is understanding what jobs is AI able to do and what jobs is AI not able to do. And so we're building a ton of initiatives such as the AI Productivity Index, or Apex, that are becoming the industry s- standard in answering that question of measuringAcross all the different popular job categories that people talking about, ranging from consultants to investment bankers to lawyers to software engineers, what are the actual tasks within those that AI can automate, and what are the tasks that it can't?

    7. HS

      With the greatest of respect, does that not change so quickly? You know, when you spoke- when you saw Andrej Karpathy talk-

    8. BF

      Mm-hmm

    9. HS

      ... about how he uses coding agents, it was like, "Oh, I use it for 20% of the work," and then it's like, "Oh, it does 80% and I do the final 20%," within a six-month period.

    10. BF

      Definitely. Well, even another example on that is on Apex, the frontier model right now is at about 40%, and 12 months ago, the frontier model was o1, which was scoring 1%. [laughs] And so that's the progress of the last 12 months, and obviously, we expect it to continue and be fairly significant. Um, but I think that the key thing is that everyone underestimates the elasticity for demand and increased productivity in the economy. Like, ultimately, o- over the last 250 years, we've increased productivity by 25X, equivalent to automating about 96% of someone's job. And during every technology revolution, ranging from the Agricultural Revolution to the Industrial Revolution to the computer revolution, people feared that there would be this enormous job displacement because of the lump of labor fallacy, where people assume that there was a fixed amount of things that had to be done, and when we made people more productive, that would all of a sudden mean that there were fewer jobs. Yet 250 years later, there's more jobs than ever before, and, and it's because we have no shortage of problems to solve as a society, right? We still need to solve climate change and cure cancer and do all of these other new things, and so-

    11. HS

      I, I buy that completely. What I don't buy is the speed of transition.

    12. BF

      Mm-hmm.

    13. HS

      And what I mean by that is when you look at Industrial Revolution, Agricultural Revolution, it took multi-decade cycles to implement and train new technologies to do what humans did.

    14. BF

      Yeah.

    15. HS

      Now with NanoBanana Pro, I can get rid of all designers in my media company pretty much overnight.

    16. BF

      Well, the thing I agree with you is about displacement. I agree there's gonna be a s- very significant amount of displacement, but I also think that the economy is becoming much more effective at creating new job categories and allocating new labor. Like, a great example is what we do in that now we're paying out over $3 million a day in the fastest job category ever created in history, and I expect that's gonna continue growing exponentially from here. And I think that there's going to be so many new job categories created across everything within AI, such as training agents for deployed engineering, building data centers, all the way to all of the problems that we otherwise wouldn't have been able to address as a society, like how do we, um, build solutions to climate change? How do we, you know, have more people working on rockets to explore space, et cetera?

    17. HS

      Totally get you. You said 3 million per day paid out?

    18. BF

      Mm-hmm.

    19. HS

      What is that in 12 months' time?

    20. BF

      In 12 months' time, that's probably about triple that.

    21. HS

      9 million?

    22. BF

      Mm-hmm.

    23. HS

      Do you think you're being ambitious enough?

    24. BF

      [laughs] Maybe it's quadruple that. Uh-

    25. HS

      [laughs]

    26. BF

      ... we have, we have, uh, internal projections that are always much more aggressive than our external projections, but we, we almost doubled our, our projections last year. [laughs]

    27. HS

      What new role will we have in five years that does not exist today?

    28. BF

      One of the largest things that people underestimate, both in the context of AI labs as well as within the enterprise, is how significant of a job category it is gonna be to train agents. Like, what we're seeing is that all knowledge work is converging on training agents because it is structurally more efficient to do something once. Instead of having a customer support representative that is redundantly responding to hundreds of tickets, they're going to train an agent how to do that once. Instead of having a lawyer that is redundantly doing dozens of similar redlines on commercial contracts, they're gonna train an agent how to automate that. And even probably when you're playing around with Claude, you see that there are so many repetitive workflows of how you prepare for a meeting or draft emails or whatever it is, where it's just much more efficient for you to train the agent how to do that activity so that you can amortize that over the entire useful life cycle rather than doing it redundantly yourself. And so I think that there's going to be this enormous paradigm shift as agents enter the workforce and everyone begins to manage them.

    29. HS

      Can I ask you, when we think about that enterprise adoption, I think one of the biggest problems that we have is data structures and data cleanliness.

    30. BF

      Mm-hmm.

  5. 21:17 – 27:39

    Rejecting a $30B Acquisition

    1. BF

      and it's just because the business has grown so quickly that we obviously try to redeploy capital as fast as we can to invest in growth, but, um, the mar- yeah, the business has grown so fast that we haven't been able to redeploy capital commensurate with that.

    2. HS

      Can I ask a myth buster one?

    3. BF

      Mm-hmm.

    4. HS

      Which is, um, after we had Adarsh on the show, I think first time, um, people were like, "Oh, the revenue's not real revenue. It's like GMV." When we understand your revenue and, uh, what's the revenue today?

    5. BF

      I can't share the exact revenue number, but it's, you know, dramatically higher than whatever has been posted publicly.

    6. HS

      Let's give a ballpark, just 'cause my simple numbers. Like it's genuinely, I'm not... Like a billion. Just, it's an easy number.

    7. BF

      It's much more than that, but yeah. [laughs]

    8. HS

      Okay. Let, let's say a billion 'cause it's easy-

    9. BF

      Mm-hmm

    10. HS

      ... for my brain. Um, so we have a billion. Like, is that like sales for Airbnb and then they get 20% of that?

    11. BF

      So the revenue is between a 30 and 40% gross margin, but the key distinction in why it's not GMV but is revenue is that the experts are actually only one part of the broader value chain that we deliver to customers. So when a customer comes to us, they're generally buying tasks where they would say, hey, they'll pay $1,000 for this task that delivers model improvement, and then we do the end-to-end process associated with how do we find the experts, how do we hire the experts, how do we build the platform that the experts work on so the experts can do the work, how do we have our AI project manager manage the experts to automate all of the, ah, coordination of helping to, ah, produce this data, how do we, ah, have automated quality checks, et cetera, to produce the end product of the task that we're delivering for our customer. And so that's the large distinction of how we're powered by a talent network in the same way that Uber is powered by a driver network, but that's not the end product, ah, in the same way as some of those marketplace businesses.

    12. HS

      What's so interesting for me, and you can tell me if this is bullshit or not, it's like you've seen the evolution of this business from like, hey, we provide raw data back to the largest models in the world. Like was, like how it started.

    13. BF

      Mm-hmm.

    14. HS

      And now it's like end to end we provide it fully, and then we send it to you. We make sure everything's ready, and it's full stack.

    15. BF

      Exactly. Very vertically integrated.

    16. HS

      Yeah.

    17. BF

      Well, because so many parts of the dro- downstream signal inform the upstream signal, right? Like, we can use the quality checks on how high caliber is each of the individual data points to understand exactly what are the types of experts that we should be onboarding to achieve the data that drives the most model improvement. And there's oftentimes this very power law, uh, nature of data that drives model improvement in that out of a data set of 10,000 tasks, the top 2,000 tasks will create majority of the value. And so it allows vendors that are extremely high quality to be super differentiated insofar as pricing power because quality is the X factor that becomes dramatically more valuable, uh, than any other dimension.

    18. HS

      What task is super high value? Is it, like, the medical, the financial modeling style?

    19. BF

      It corresponds extremely closely to economic value. So think if you go through the top five domains that we serve, it would be software engineering, it would be finance, uh, medicine, law, uh, consulting, et cetera, and the super long horizon tasks within those. And so think we're moving away from the paradigm of how do we get a investment banker to prepare a financial model and moving towards the paradigm of how do we get a banker that can talk with five different colleagues and wait to hear back their responses and prepare, uh, an entire slide deck with a deliverable that includes the financial model, the analysis in a multi-week long project. Those are the kinds of tasks that we need to be building to push the frontier of research and evaluation so that those are the capabilities that people are able to use in the models in six to 12 months.

    20. HS

      Can I ask which segment are we underserved in?

    21. BF

      In terms of model capabilities?

    22. HS

      In terms of, like, we don't have enough medical data, we don't have enough financial modeling data, we don't have... Is there a segment where, like, you know what? If we were to acquire a company in this space to plug a hole in our data supply-

    23. BF

      Yeah. Well, I would say maybe I'll, I'll give it from Mercor's perspective, and then I'll give it from the lab's perspective. Like, we tend to be now so good at mobilizing experts that we're able to access pretty much any domain. There's always going to be some degree of, like, these niche pockets of oncologists or whatever it is that have a particular background, but gene- generally we can fill those fairly quickly, and it, it's more about people that actually are very acclimated to the frontier of AI because it's the people that understa- that both have the expertise in oncology but also are power users of ChatGPT or Claude that are able to find where the model makes mistakes and help the model learn from those mistakes. And so that's from the Mercor perspective. From the perspective of the labs, it seems like it's all-encompassing. It's just like the barrier to automating everything that you can do in, say, Google Workspace is how do we cover the full distribution of all of the context, i.e., messages, Slacks, Slides, Excel sheets, and all of the tasks, prompts, and outputs that correspond to everything that you do in your job, and that applies to every individual and every domain throughout the economy. And so there's this enormous mobilization of hundreds of thousands and soon millions of people to build out the full distribution of everything that you could pass into Google Workspace and everything that you could want out on the other side in every job category throughout the economy.

    24. HS

      Can I ask you before we dive into a tweet that you did which slightly terrified me-

    25. BF

      [laughs]

    26. HS

      ... um, to be quite honest. So you said, like, 30 to 40% is kind of how we think about, like, our, our revenues from that.

    27. BF

      Generally, yeah.

    28. HS

      Okay. So if we take the, the rounds that we've raised, which round felt most uncomfortably high?

    29. BF

      [laughs] Good question. Well, so I'll, I'll talk through the valuations-

    30. HS

      Yeah

  6. 27:39 – 32:50

    The Fundraising Story: Helicopters, Ferraris & $10B Valuation

    1. BF

      [laughs] That was series A. So our seed round was, uh, in September of 2023. We were at call it a million in revenue run rate, uh, or, or just shy of that. And I initially didn't wanna raise 'cause I wanted to bootstrap the company, but Adarsh and Surya's condition on dropping out was that we needed to raise money. And so we met General Catalyst, uh, at 8:00 AM on a Sunday morning. They gave us a term sheet within 36 hours for $2.3 billion at a $23 million post-money valuation. Uh-

    2. HS

      Pretty good.

    3. BF

      [laughs] So that was, that was pretty reasonable-

    4. HS

      Yeah

    5. BF

      ... uh, insofar as price for the time.

    6. HS

      And this was Max and Hemant?

    7. BF

      This was, uh, Max and Niko. Um, and then at our series A, we... The business had- didn't grow that much from the seed to the series A, but we found the market was the key differentiation. And we met Victor when we were at call it one and a half million in revenue run rate, um, in May of 2024. And Victor got super excited. Um, initially I refused to take a second meeting, but then he said, "Oh, have you ever been in a helicopter?" And so Peter took us [laughs] on the, the helicopter flight. And then Benchmark really wanted to work with us, and so, um, we were... By the time that they gave us a term sheet, we were at call it 2.5 million in revenue, and they gave us a $250 million post-money valuation. And then just four months-

    8. HS

      Did that feel uncomfortable? 'Cause that's a big jump, two, you know, 223 post to 250.

    9. BF

      So keep in mind at the time, this sounds crazy 'cause we were at 2.5 million in revenue, but I was projecting50 million in revenue run rate by the end of the year, and 500 million by the end of next year. Uh [laughs] and so it felt like a bargain. [laughs] And then-

    10. HS

      Dude, you do know all founders project that, right? Like [laughs]

    11. BF

      But, but we beat the projections

    12. HS

      It never happens. Sure.

    13. BF

      [laughs]

    14. HS

      Just doesn't happen often.

    15. BF

      Yeah, yeah, yeah. And then four months later we met Felicis, and we never, like, would make a slide deck or take investor meetings. And so Felicis sent us an email saying, um, "Hey, we know your co-founder Surya really likes Ferraris, so do you wanna go racing Ferraris with us?" And then I replied and I said, "You caught my eye. Tell, tell me more." And they said, "We'll meet at the, uh, we'll meet, uh, at the airport in, uh, Hayward and go on Idan's private jet to Las Vegas to race Ferraris around the F1 track." [laughs] And so I was like, "We're available in three weeks on a Sunday." And so we do this. We race Ferraris. We're at 20 million in revenue. They ask us what valuation do we think makes most sense, and I say $1 to 2 billion. So they give us a term sheet at a $2 billion valuation. And at the time, you know, that's 100 times the revenue, and everyone thinks that that's a high valuation. Meanwhile, it was an incredible investment.

    16. HS

      So I'm gonna be honest, this is when I interviewed Adarsh at that time

    17. BF

      [laughs]

    18. HS

      And at the end I was like, "Dude, I, I would love to invest. Please let me invest." And, and you very kindly let me put a small check in. Um, and I then spoke to several of the biggest investors in the world, and no offense

    19. BF

      [laughs]

    20. HS

      ... they, like, chuckled at me. Like, "Dude, that's such a high price [laughs] You, such a high price."

    21. BF

      Well, so here's the thing-

    22. HS

      So keep it going

    23. BF

      ... we, we'd been growing 50% month over month for the prior six months, and I think what none of them really realized was that it would contin- continue for the subsequent, you know, 12 plus months.

    24. HS

      Yeah.

    25. BF

      And so then that compounded more and more. By September we were at a... September of 2025, uh, or, or say October, we were at call it 400 million in revenue run rate. And then Felicis was like, "We wanna invest more." Um, and so they gave us a term sheet at a $10 billion valuation. We didn't really wanna spend much time on a financing 'cause the business was growing 50% month over month, and so we were very preoccupied. Um, and so that was about 25 times.

    26. HS

      Yeah.

    27. BF

      Um, and you know, the business has almost 4X'd since then.

    28. HS

      So review, which one felt most uncomfortable? If you were to choose any.

    29. BF

      If I had to choose any, I would say that the series B priced in the most, like, the furthest ahead of our growth. Um, uh, that or the series A. I think it was probably the series B, um-

    30. HS

      The 2 billion

  7. 32:50 – 35:52

    Infrastructure Will Win Over Application Layer

    1. BF

      The reason I believe that is that the application layer company's businesses are not far removed from th- the foundation model company's businesses. Like, it is not a far leap for Claude Cowork to add capabilities across medical and legal. Obviously they did it with software engineering and, and do that, can do that across finance. And so I feel like building defensibility in the software layer on top of the models is going to be incredibly difficult. Whereas on the other side of things, in the infrastructure side it feels like there are meaningful moats that are getting built. Um, like we're compounding enormous network effects in the business and a pretty significant data moat as we build out the inventory for our customers. Um, compute companies obviously are able to, um, build moats through these very long R&D [laughs] cycles. Um, and so I think that there are going to be high margins that get achieved at the infrastructure layer, and, uh, sort of sustainable profitable businesses in a way that it's less immediately clear at the application layer.

    2. HS

      I mean, you saw Nabis. I don't know if you saw this, but they increased their pricing by 30%-

    3. BF

      I didn't. No

    4. HS

      ... across support [laughs]

    5. BF

      Wow

    6. HS

      ... which, uh, will have absolutely no impact on demand. I mean-

    7. BF

      That's insane

    8. HS

      ... isn't, isn't that absolutely nuts? So you increase price by 30%, zero impact on demand.

    9. BF

      It, it's probably the same for us honestly. Like, we have the demand to double overnight, we just don't have the capacity. And so it's mainly a question of how effectively can we scale to mobilize people to build out these environments much more quickly.

    10. HS

      Do you do pricing elasticity tests? Because if you can double price and, uh, double the business...

    11. BF

      We maybe can't double prices. Um, we could double capacity. We could probably increase prices by 30% without much of an impact. But the other thing you need to consider is that pricing is not merely a question of optimizing for the next six months. It's optimizing for a structure that wins the market over the next decade, right? And for that reason, we're very focused on how do we do what's best for customers, how do we do what's best for experts, and how do we build a sustainable business while we're doing it but make sure that we're not leaving oxygen in the market? Because high margins invite competition.

    12. HS

      Okay. I am an investor in several application layer companies

    13. BF

      Mm-hmm

    14. HS

      ... downstream, like a Lagora, which you mentioned there. Um-You know, we, we see the Legalora versus Harvey battle. I think everyone actually is coming around to the fact that they shouldn't be fighting each other. They should be wary of Anthropic, to your point.

    15. BF

      Totally.

    16. HS

      Um, but then I look at it and go, there is incredible defensibility. It's a very deep product, specifically suited to the workflows of lawyers. Anthropic would have to build out whole separate product teams, divisions to come after them. They'd have to build out GTM teams, customer success teams, a- adoption teams. It's a different freaking company. The defensibility is there. Argue back.

  8. 35:52 – 42:12

    Is SaaS Dead? When Network Effects Are the Only True Moat

    1. BF

      Maybe I would say two things. First is that I think over the last two years, everyone has increasingly realized that the model is the product. That, like, we can build so many of these different abstractions of trying to stitch together API calls and having all this like, uh, patchwork logic where people used to have all these drag and drop agent builders.

    2. HS

      Mm-hmm.

    3. BF

      And then they just realized that like, if we give the model the end goal, and we train it to accomplish that end goal, it has outperformed every other solution in almost every case that we go after. And that bodes incredibly well for those that are training models end to end. The second thing to consider is that software layers are able to get recreated very quickly now. Like, we're building out an eval set that measures how effectively agents can build end-to-end sa- SaaS applications, where 2025 was the year of how do you get a model to make a PR in a code base? And 2026 is the year of how do you get the model to clone Slack end to end? And those capabilities are going to exist in the models in the next 12 months. And so that means very significant things for companies that are betting on software modes sustaining their businesses.

    4. HS

      If we take that extrapolated further, how effectively can we build Slack internally agent-led-

    5. BF

      Mm-hmm

    6. HS

      ... entirely? That would very much concur with the SaaS is dead, because if you're a large company needing maybe small customizations, integrations, say you're a real estate company and you need very specific integrations to pricing providers, you'd build your own.

    7. BF

      I generally agree. I think that the caveat is when those companies have network effects, there's probably a significant moat that isn't being priced in fully. Uh, for example, Salesforce has tons of companies that are building integrations on top of their platform that creates this, uh, almost marketplace and network effect around it, or Slack has Slack Connect, right? And I think even Card is another great example of, right, this whole network effect of the people that use it and wanna use the same platform across all of their companies. Um, I think that the companies that have network effects will be able to, in some ways, generate more value because they can iterate ten times faster while leveraging those network effects to create more value for their customers and therefore, um, build more valuable products, charge more money, et cetera, and increase revenue. The companies that don't have network effects are going to struggle very significantly because then there's not really a defensible moat in the pure software associated with the products that they build. And so to me, that is the litmus test that determines whether this company is going to become worthless or whether this company is going to gain dramatic value from their ability to ten X product velocity.

    8. HS

      You said we're learning more and more that the models are the product.

    9. BF

      Mm-hmm.

    10. HS

      What if I push back and say the go-to-market is the product? When you're selling to law firms, it's about being in the room with, uh, you name your biggest law firms, your Cooley's, your Goodwin's, your White and Case, your Clifford Chance, building the relationship with the buyer and then the CS and the adoption, and it's actually in the go-to-market, not in the product.

    11. BF

      So I agree with this in part, but the caveat I would give is I think it's arguably more the forward deployed motion rather than the go-to-market. Uh, and forward deployed motion being the post-sales, go-to-market being the pre-sales. Because ultimately, say you're just really good at sales and then you provide a SaaS product, and you have a savvy customer who's spending a million dollars a year on the SaaS product, and they realize they could just like tell Claude to copy it, and they'll get the same exact thing. It feels very difficult to maintain your pricing power, even if you're the best in the world at sales. Whereas on the other hand, if you have a great forward deployed motion where you're going deep with a customer, you're training the agents based on all of this tacit knowledge within the company so that it understands how to perform effectively, that feels incredibly differentiated and hard to recreate. And that's also the reason that we see obviously the labs OpenAI and Anthropic investing so much in this forward deployed motion. And so I think that the Sequoia article that services are the new software resonated [chuckles] a lot in that these software modes are, are whittling away, and it's the ability to layer services on top of software to meet the customer where they're at and go the last mile that is creating stronger defensibility.

    12. HS

      Do you buy this new sexy category? I mean, venture investors are wonderful people, but like this new sexy category that like AI-enabled services is like the future goldmine.

    13. BF

      I think in a large way I do. Uh-

    14. HS

      You do?

    15. BF

      I think, I think the key thing is that you need to make sure that they're actually going to leverage AILike, I think there are a lot of companies that are just, like, building services and not gaining a significant competitive advantage from AI and using that. That's the thing you've gotta be careful about, but I think it's very rational. Like, I'll, I'll give an example in the context of Mercor, which is that we, within this process of turning human time from the talent network into building these super rich environments that mirror everything that people could do in their jobs, there is a lot of human coordination of how do we answer people's questions, how do we track the KPIs of the project and manage it effectively, how do we build the bespoke tooling for that project? And we have about 100 people, uh, or call it 150 people in our delivery organization that do that for deployed work of helping to go the last mile for the customer. But now we have an AI project manager that just completed its first project managing that entire thing end to end, where it's able to hire the experts, it's able to answer their questions, it's able to build the annotation tool using its coding tools, uh, within our platform and produce the end data type. And the experts all had a really good experience on the project reporting to the AI project manager that was running it. And so I think we're seeing in real time that services are getting automated, and that that is going to be this extraordinary transformation in the economy.

  9. 42:12 – 54:40

    Token Spend on Agents Now Exceeds Employee Headcount

    1. HS

      One thing that powers, obviously, the agents that we use is the tokens that power them, and I thought the whole point was that we have increased token efficiency and token costs come down. Um, token costs are rising for everyone.

    2. BF

      Mm-hmm.

    3. HS

      Help me understand how you see token costs changing in the next six to 18 months and why that is.

    4. BF

      Well, it's a fascinating case study in Jovan's paradox-

    5. HS

      Mm.

    6. BF

      -similar to what we were talking about in the context of making humans more efficient, leading to more jobs, right? When we make models improve by 10X year over year, that has just been causing the total consumption of the models to go up and up and up as the cost per performance go down. Um, I think insofar as how it's gonna develop is that this trend is going to continue very, very significantly before we start seeing any leveling off of token consumption within the enterprise. Like, right now we're spending more on tokens for our internal agents than we are on employee headcount, and I think most businesses are gonna look like that in-

    7. HS

      Wait, you're spending more on tokens for agents than you are on headcount?

    8. BF

      Exactly.

    9. HS

      So your token spend on agents is more than salaries?

    10. BF

      That's correct. It's, it's pretty incredible. And so what we... The way we manage it is that we have a variety of these key workflows throughout the company where we have an AI project manager, as I was describing, that manages operations. We have our interview question agent that, where we've done over 5 million interviews and asked all the questions in the interviews. Uh [laughs] , we have our interview ranking or the f- broader candidate ranking, where it helps to assess all of the candidates and figure out who we should be hiring. We have agents for accounting automation. We have agents for fraud detection, et cetera. And corresponding to each of these agents, we have an eval that tells us which model is best to use for this given use case and what is the Pareto frontier of price performance for that specific use case. And that eval allows us to make the decisions around where should we be allocating our inference spend, what provider should we be using, et cetera. And I believe that over time, this is going to develop to look very similar across every Fortune 500, where they'll need to have this system of record for evaluating and specifying agent behavior across every workflow in their business, and they're going to use that to commoditize the model layer because they want to enable perfect competition at the, for the models having zero switching costs. And so we've been growing extremely quickly with the enterprise and helping them to populate the system of record and building out those evals for each of the use cases that they have throughout their business.

    11. HS

      Do you think you will see that commoditization at the model layer whereby enterprise clients are able to really efficiently package the workflows that they do so it does commoditize the model layer? 'Cause right now it's not commoditized quite.

    12. BF

      Yeah. So I think the key distinction is that I think the API layer will get commoditized. The, uh, you can definitely build stickiness in workflows that people have on top of those APIs. Like, for example, I have all of these, like, routines running in Cloud Code, and I feel like it would probably be difficult, or I at least wouldn't put in the time to move those routines over, and I have a bunch of similar things running in ChatGPT. So I think that there's going to be various ways that people can build stickiness, but for pure API-based products, where it's like if we are just spending $10 million a year on a specific workflow, obviously we're gonna have an eval for that, and every time a new model comes out, we're gonna benchmark that and understand exactly how we should be hot-swapping between models and distilling models.

    13. HS

      Why does the API layer get commoditized?

    14. BF

      Because the switching costs are zero. Like, when the switching costs are zero, that means that... A- and there's a new frontier model every two months, that [laughs] means that we very quickly are going to, uh, swap them out, right? And ultimately, the decisions that we make boil down to the score on the eval corresponding to that workflow. And so it's very easy to compare model to model one for one in a perfectly, like, hot-swappable way, which is, uh, almost the definition of a commodity.

    15. HS

      I'm still reeling from your token spend with agents more than headcount-

    16. BF

      Yeah

    17. HS

      ... 'cause actually, uh, Marc Benioff said the other day that they spend 300 million on Anthropic, which seemed like a lot of money, but actually when you bait it down, it worked out to be about 3.8% of developer salaries is being spent on Anthropic, which actually is much less than one would think.

    18. BF

      Yeah.

    19. HS

      What do you think that is in 24 months' time?

    20. BF

      For Salesforce?

    21. HS

      Yeah.

    22. BF

      I think I don't know about 24 months' time, but I would bet that in five years, the average enterprise spends more on compute than headcount. And the reason for that is that the models are just becoming so capable that it seems like there's just enormous ROI to being able to, um, ha- have models do something for 100K a year, uh, that is going to continue compounding at an exponential rate in a way that human intelligence is not going to. And so humans will still play an important role at the things models can't do, but I expect that, uh, cost of inference, cost of compute will exceed that. The reason that that's so interesting to me is that having an eval for your specific workflow, like say we take the case of Salesforce, having an eval for how good is a specific model at code generation in their use case is often a 10X lever on the price performance of that model because they can distill the model, they can have an open source model that is performing as well if not better for a dramatically lower cost. And so as we see this enormous shift towards compute and significant inference spend across every workflow in the enterprise, they're going to need to have evals that act as a source of truth for whether those workflows are being done correctly, and whether they're using the right workfl- uh, whether they're using the right models to accomplish that.

    23. HS

      With the greatest of respect, are evals today not relatively, um, unhelpful? It's like, you know, how good are you at, you know, driving around the corner for the driving test in this very specific way, but actually it's not how it works in the real world, and it's actually not very practical.

    24. BF

      That's exactly the problem, right? Is that we used to have this paradigm of all of the academic benchmarks that were totally disconnected from the outcomes that enterprises actually care about, where people were building everything ranging from GPQA for PhD-level reasoning to IMO for Olympiad math to humanity's last exam for this long tail of academic problems no one really cares about. And now they're focused on, how do we get the model to do this end-to-end workflow coordinating with multiple colleagues for a financial model or a slide deck like we were discussing? How do we get the model to build an entire SaaS application end to end? And that's why there's this enormous build-out in pushing the frontier of evaluation as a critical research problem for the next frontier of model development.

    25. HS

      Okay. Next frontier of model development. If I listen to everything that you just said, I, I would draw two conclusions. One, shit, we should just invest all of our money into OpenAI and Anthropic, and then the realization dawned on me that the majority of startups, and you can shoot me down. Again, shoot me down.

    26. BF

      Mm-hmm.

    27. HS

      Um, is the majority of startups today, especially on the West Coast, use frontier models to see where they can go and how far they can push them, and then they use open source, often Chinese models, to get as close to that as possible at a much better cost basis. In which case OpenAI and Anthropic are inherently challenged by that much more cost-efficient open source model. Right or wrong?

    28. BF

      I think both are true. Like, there's going to be man- many orders of magnitude more demand in five years than there are today. Maybe four or five orders of magnitude more demand. But there's also going to be increased competition with people just distilling and having fine-tuned open source models that accomplish their workflows. Ultimately, I think OpenAI and Anthropic are incredible investments, and it seems like there's starting to be consensus around that, uh, in a way that there wasn't just a couple of years ago. But at the same time, I think that majority of inference in five years is going to be using a open source or custom fine-tuned or distilled model, not using a frontier model.

    29. HS

      Okay, interesting. You said that obviously incredible investments. Um, where will they be in five years' time?

    30. BF

      Valuation-wise?

  10. 54:40 – 1:01:56

    Competing for Talent When Meta Offers $20M Per Year

    1. BF

      towards a world where there's increased job displacement, increased uncertainty around how many jobs are there gonna be, especially for the bottom half of Americans, I think that this is going to become extremely problematic. And so I would suggest that we move towards a paradigm where we instead focus on taxes of things that aren't necessarily going to have a negative impact on incentives in the economy. Like, one great example is capital gains, where, like, I'm going to invest money in assets regardless. And so if there's, like, higher capital gains tax, it's not like I'm just gonna, like, not invest, right? Um, and so I think that taxing capital gains, especially short-term capital gains, which I think is probably not as beneficial for the economy as long-term capital gains, would probably be structurally much better off than taxing income.

    2. HS

      With the greatest of respects, if you increase the tax on capital gains, you will disincentivize those investors to take risk. Why the fuck should I pay more? I'm already taking a risk. I'm already investing in innovation when other people won't, when banks won't, when all the data tells me not. Now you wanna tax me more for doing that, for taking the risk? Of course, you will disincentivize investment.

    3. BF

      The thing is, when investors are taking very high risks, it's generally in an aggregated way in a portfolio. And so you would tax the gains on the portfolio overall. And so even if you have a portfolio of like... And I know that you don't like to hear the capital gains tax, Harry, but [laughs]

    4. HS

      No, no, no, no, no. I, I think... and, and, and I say this with the nicest respect.

    5. BF

      [laughs]

    6. HS

      It's just wrong.

    7. BF

      [laughs]

    8. HS

      Like, 'cause you just move.

    9. BF

      But I, I agree that you n- the main thing you need to be careful about is if people would move to other geographies, because obviously, that, uh, creates problems. But I, I think, like, capital gains is one option. I think-

    10. HS

      But I... Sorry, I'm so sorry to be a dick, dude.

    11. BF

      [laughs]

    12. HS

      And you can say, uh, with- withdraw. Like, that creates problems. Yeah, that's kind of the whole point. You fuck off to somewhere that doesn't have capital gains, and then you lose all the tax revenue completely. I w- sorry, forgive me. I'm- we live in-

    13. BF

      No, go ahead

    14. HS

      ... we live in the UK where there's the Green Party, which there's this idealist movement that's like, "Oh, increase our..." Well, yeah, then we leave, and then you have nothing.

    15. BF

      I, I agree. I think that there needs to be sensitivity analyses associated with [laughs] how does the increased amount of taxation cause people to just leave and reduce overall government revenue. But I think that another way of going about it is also taxing consumption of items that probably aren't the best. Like, it's crazy to me that instead of taxing carbon, we tax the bottom half of Americans. Like, but we... Why don't we tax carbon, right? That's, like, a very clear negative externality in the economy, at least in the US, that's not taxed. Um, and so I feel like there is a lot of low-hanging fruit with respect to things that we could tax without damaging incentives in a perverse way or causing people to flee the country that would be far better than taxing the bottom half of Americans. And the other thing is that it's only 3% of government revenue. Like, the fact that, uh, it's only 3% feels like it's a very easy decision for policymakers to make in the grand scheme of the impact that it would have on people, but-

    16. HS

      Would, would you tax prediction marketplaces? It's, it's gambling.

    17. BF

      I probably would. There's probably some value of having good prediction marketplaces for allowing people to have effective predictions of the future and hedge things within their lives and investment portfolios, but it's likely okay to tax. The thing on that point of, around taxing the bottom 50% is Jeff Bezos retweeted me, which I was ecstatic about. And so-

    18. HS

      That's pretty cool.

    19. BF

      [laughs] It was, it was pretty great, yeah.

    20. HS

      Who's the coolest person you've met?

    21. BF

      I really like Jensen, and I really like Satya. I mean, so many incredible people. Um, obviously Dario and Sam are incredible. Um, but if I had to choose one person, I mean, Jensen's so cool, right? Like, the jacket, uh, his style, he's always on point. Uh, so I would say Jensen is probably one of the coolest.

    22. HS

      The fascinating one I would love to ask you, and, and you shouldn't give the answer to this, but I, I think this, and people have asked it of me before, it's very hard to answer, is who did you think would be amazing who was surprisingly underwhelming?

    23. BF

      [laughs]

    24. HS

      And don't answer that.

    25. BF

      I can't answer that one, yeah. [laughs]

    26. HS

      But, no, but it's a really good one.

    27. BF

      It is an interesting question, yeah.

    28. HS

      And I have met a couple-

    29. BF

      [laughs]

    30. HS

      ... where you're like, "Wow, that gives me confidence that I can do that too." [laughs]

  11. 1:01:56 – 1:07:17

    Do Sovereign AI Models Actually Matter?

    1. HS

      Do you buy the sovereignty argument of we need sovereign models because we don't want our data going to US or China or wherever that is?

    2. BF

      Maybe in some cases. Like, there is value in localization, and I'll give an example, which is that oftentimes labs will come to us and say they need their models not just to be good at American law, but also to be good at British law, or good at French law, or whatever the jurisdiction is in the world. And I think that that is going to be an important last mile in making the models useful in whatever jurisdiction that they're operating in. That said, the labs are just gonna hire 10,000 people in France to teach the models how to be better at French law, and I don't think that there's so much that others are gonna be able to do to stop that because the transfer learning capabilities from all of the other domains that they're focusing on are just so powerful.

    3. HS

      And when you say about hiring 10,000 people, the thing that's just astonishing me is the, the wave of cash, uh, for me, I'm sure OpenAI is the same, but I've seen it specifically with Anthropic. I mean, insane levels of comp.

    4. BF

      Totally.

    5. HS

      How do you compete against that?

    6. BF

      It's definitely one of the things that's most top of mind, in particular because the markets for people founding companies are so hot, where, like, we've had three employees that have founded companies worth in excess of $100 million. Um-

    7. HS

      I saw your tweets where you, you know, you do the Mercor Mafia tweets.

    8. BF

      Yeah, exactly.

    9. HS

      Yeah, yeah.

    10. BF

      And so, a- and we're a very young company, right? Um, and I think that it's difficult for a variety of reasons. A lot of people probably don't have a full understanding of just how hard it is [laughs] to build a company, and, uh, as, as you know well, Harry, um, and how low the probability of success is, and how fortunate we were, and how lucky we got along the way. Um, and so I think that that's definitely one of the large challenges, and even, like, there was someone I was hiring the other day and he had an offer for $20 million in cash per year from TBD, and, like, that's the kind of stuff we run into on a regular basis.

    11. HS

      T- TBD? What do you-

    12. BF

      Uh, Meta's, uh, super intelligence, uh, group.

    13. HS

      20 million in cash?

    14. BF

      Per year. Or it's in stock, but liquid.

    15. HS

      That's hard to compete against.

    16. BF

      [laughs] It's hard to compete against, yeah.

    17. HS

      Does that change? Do, does that just continue to escalate?

    18. BF

      So I think that it'll probably continue to escalate for the people that... for a smaller group of people. But I also suspect that as more people gain knowledge of how these labs operate and what the capabilities of, uh, how to train a frontier model, that means that there's going to be more supply in the market for people that, um, have that skill set and, and thus a little bit more reasonable pricing. And so I, I expect there to be some craziness that continues, but hopefully sort of the 99th percentile at least, um, it, within the market will balance itself out.

    19. HS

      What is the hardest role to hire for today?

    20. BF

      Researchers.

    21. HS

      Just because of supply?

    22. BF

      Because of supply and demand. It's just this market where there's 10 times more demand than there is supply, and that makes it very difficult. We've been building out an incredibly strong research team. Um, like Edward Hu, the first author on, uh, lawyer, uh, on Laura, who was previously at OpenAI, is working with us, and a bunch of other top researchers. But the market is definitely, uh, getting very hot.

    23. HS

      How much does it cost to hire a, a high-quality AI researcher?

    24. BF

      Oftentimes it would be in the tens of millions of stock per year. [laughs] For, for the really good people, yeah

    25. HS

      I remember when researchers weren't paid very much

    26. BF

      [laughs]

    27. HS

      This was like 10, 15 years ago. They were like the underpaid but brilliant people in society.

    28. BF

      Yeah.

    29. HS

      Now I feel like that's relatively-

    30. BF

      Totally flipped

  12. 1:07:17 – 1:09:31

    Does HR Slow Companies Down? Brandon Pushes Back

    1. BF

      The caveat I'll give is that I think it's really important. Like, we definitely had challenges in scaling culture when we went from 40 people to 400 people ex-

    2. HS

      How does that show up?

    3. BF

      ... extremely quickly. Well, it's so many things, ranging from making sure that we keep a really high talent bar, to making sure that people are bought into the mission of the company, to even the tactical things of making sure that managers are communicating to their team about their performance review and how they're doing [laughs] so that they're never surprised by a performance review. And when we have a young team with a lot of first-time managers, that just creates culture challenges of people that aren't used to giving feedback and maintaining all of the values and commitment to the mission of the team. And so I think that to some extent I agree, and I think that some of the big tech companies probably go too far on empowering HR, but I also think that it's important and one of the large lessons we've had over the last 18 months or so is that it's critical to really get these foundations in place as you scale headcount, otherwise it creates problems.

    4. HS

      Culture challenges. Before the show we said that, um, after the show with Adarsh, a couple of people thought that like 996 was the way that like Mercor is run, and it's like clock in, clock out. Um, why is that not true, and how do you think about that?

    5. BF

      So the reason it's not true is that we've never mandated hours at the company. And obviously I work extremely hard. A lot... Adarsh works extremely hard. We work from when we wake up until we sleep pretty much all the time, aside from like maybe working out, but I'm still work- thinking about work during that time. And most of our leadership team, of course, does as well, but at the same time majority of my leadership team has kids, and we want them to be able to like go home and see their families and all of that. And so I think that it's some combination of knowing that building a legendary company requires immense dedication to the mission of the business while also recognizing that we need to ensure that it's a sustainable environment for the best people in the world to do their

  13. 1:09:31 – 1:14:01

    Quick-Fire Round

    1. BF

      life's work.

    2. HS

      Are you ready for a quick-fire round?

    3. BF

      Of course.

    4. HS

      Would you like to go public?

    5. BF

      Definitely.

    6. HS

      When?

    7. BF

      In the next few years. I think that all legendary companies eventually go public, and so it's an important part of the journey and maturing and having a much larger company than we have today. But I think that it's not something we're rushing to do this year or next year, in part because we dropped out of college less than three years ago at this point, and it's, uh, still a very young business where we want to make sure that we properly actualize everything that we're working on on the enterprise side especially before going public.

    8. HS

      Don't laugh. Do you ever like lie in bed at night and just go like, "Wow, like it's pretty wild"?

    9. BF

      I'm always pinching myself, and I feel extremely grateful for the team and Adarsh and Surya and how all of them made it possible because I could have imagined 100 things that would have gone differently and we'd be in a totally different circumstance.

    10. HS

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

    11. BF

      I used to have some questions around whether the foundation model labs would be the largest businesses in the world because of the exact things you asked about in the context of how much those models are gonna be able to maintain pricing power amidst a competitive environment. But I think that as we've seen the sheer revenue ramp of these businesses, I've gained immense conviction that they will be the most valuable companies in the world.

    12. HS

      You can invest in OpenAI or Anthropic. Which one?

    13. BF

      Oh, I can't respond to that. I would choose that. [laughs]

    14. HS

      [laughs] Um, who do you not have as an investor in the company yet that you would most like to have?

    15. BF

      I really admire Jeff Bezos. I think he's so disciplined about the culture of Amazon. That's one of the things that's always stuck with me. Everyone there just understands the values and, um, i- is steering in the same direction, is such a strategic business leader. I've never met him, but I've always wanted to

    16. HS

      Which competitor do you most respect and why?

    17. BF

      I admire that Edwin from Surge has done a really good job in, um, staying super close to research, and it's something that we've obviously been doing a lot of as well. But I think that's probably, uh, the lar- one of the largest things that differentiates both us and Surge is our ability to train models to hire some of the best researchers in the world, and I, um, admire them for execution on that front.

    18. HS

      What percent of data providers are just respectfully transactional talent marketplaces?

    19. BF

      In terms of volume or number of competitors?

    20. HS

      Number of competitors.

    21. BF

      About half.

    22. HS

      Half?

    23. BF

      Yeah.

    24. HS

      Uh, what would you most like to change about your role today?

    25. BF

      I would say that there's a decent amount of HR things that get escalated to me, and so we're looking for a really strong head of people that is able to handle a lot of this.

    26. HS

      Final one for you, dude. What's the kindest thing that anyone's ever done for you?

    27. BF

      One that really stuck with me is I remember... A- and I'll, I'll probably attribute this to the entire Prod community, uh, namely especially a couple of people like Rob Walken, Ben Spector, and Richard DeHaan. But Prod was this nonprofit that got started at MIT and Harvard, and I was sort of a blow-in 'cause I didn't get into those schools, but I went to Georgetown. And for the first year of the business, like, they would meet with us every week. Ben became a big customer. Richard would give us, like, tons of money just as to float working capital, and, uh, Rob gave incredibly valuable advice, and they had nothing in it for them. They took no equity. I tried to give them equity, and they, they wouldn't, wouldn't accept it. And Mercor wouldn't exist if it weren't for any of those individuals, I would say. And I think that that is something that I'll always be grateful for for the rest of my life.

    28. HS

      Dude, I have to say, I loved having you on the show last time. It was incredible to do this in person. I'm so thrilled with how this conversation went, and you've been amazing.

    29. BF

      Thanks so much for having me, Harry. Always great to come back

Episode duration: 1:14:12

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