Pod Insights The Twenty Minute VC (20VC): Venture Capital | Startup Funding | The Pitch 20VC: Mercor CEO on Why Application Layer Companies Have No Defensibility, The Model is the Product | Token Spend Will Exceed Headcount Spend in 5 Years | The True Cost of Hiring AI Researchers in the Valley Today with Brendan Foody About this episode 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. AGENDA: True or False: Mercor lost Meta and OpenAI as a customer with the hack? Mercor has been poaching competitor talent, paying them millions? Mercor revenue is not real revenue and is only GMV? 12:56 Would Brendan sell Mercor for $30 billion? 14:23 Why everyone is wrong that AI will lead to labor displacement? 15:59 We will create many new jobs that do not exist with AI. 16:59 Why training agents will be a massive labor category that does not exist today 19:51 Will we see the data provider market unbundle and specialize into verticals? 22:24 Is the stated revenue really revenue or is it really GMV? 27:55 How a 1 million ARR company secured one of the best investors in the world with a helicopter ride 29:41 How Felicis secured the deal of the decade with a race track and a set of Ferraris 32:59 Which investment round felt like the highest price to grow into? 34:49 Why will value accrue to the infrastructure layer, not the application layer, in the next 12 months? 35:46 Why the model is the product and why application layer companies should be scared as a result 37:22 Why network effects will be the determinant of value creation 38:46 Why the forward-deployed motion, not the GTM motion, will determine true value creation. 41:59 Why token spend within organizations is going to continue to increase 43:54 Why agent evaluation to commoditize the model layer will be a massive business for enterprises? 51:13 Why we should have increased capital gains tax 01:01:31 How to compete with $20 million a year from Meta? 01:08:49 Will Mercor go public and when?
Transcript 14,094 words · 2 speakers
Speaker names marked ? are inferred from speaking time and the show’s known hosts — they are not voice-verified.
0:00 Speaker 00 Building defensibility in the software layer on top of the models is going to be incredibly difficult. I think over the last two years, everyone has increasingly realized that the model is the product. We have the demand to double overnight. We just don't have the capacity. Like, right now, we're spending more on tokens for our internal agents than we are on employee headcount. I think we're seeing in real time that services are getting automated. I could definitely see one of them being a $10 trillion company, maybe even significantly higher. How much does it cost to hire a high-quality AI researcher? Oftentimes, it would be in the tens of millions of stock per year.
0:41 Harry Stebbings? This is 20VC with me, Harry Stebbings. Now, joining me in the hot seat today, we have Brandon Foodie, co-founder and co-CEO of McCor, one of the fastest-growing AI companies valued at over $10 billion today, doing over $1 billion in revenue. Now, Brandon's done quite a few shows before, and so my question was, how do I get answers that he's never given before? How do I push and ask questions that no one's ever pushed him to ask before? This is the most revealing interview that Brandon has ever done, discussing core elements like, is revenue really revenue in this business? What does that look like moving forward? Would he rather invest in OpenAI or Anthropic? We do not shy away from the spicy question in this show, and Brandon was incredible and more than delivered.
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2:55 Harry Stebbings? Air Wallets brings you an intelligent financial operating system that powers how global businesses operate and grow, allowing you to manage and automate banking, treasury, payments and spend. The most exciting part for me, they're heavily investing in agentic finance. If you're scaling globally, you need a banking and finance platform that's borderless, real time and intelligent. Check out Air Wallets today and see how they're helping thousands of businesses like Canva, McLaren and Deal. Scale at airwallets.com/20vc. Terms and conditions apply. Your monies are safeguarded, not FSCS protected. See airwallets.com for more details. While Air Wallets helps your money move globally, Vanta helps your security keep up. What's one thing in business that's spreading as fast as AI?
3:40 Harry Stebbings? AI risk. Every new tool your team signs up for, every vendor that turns on AI features, every new integration. Each one, I'm sorry to say, is an opportunity for something to go wrong. And most security programs weren't built for AI's pace of growth. Well, that's where Vanta comes in. Vanta is the number one agentic trust platform used by over 16,000 fast-moving companies like Ramp, Cursor, Harvey and more to ensure they're always audit-ready. And now Vanta's helping companies like yours. Watch for the risks that show up between audits across your vendors, your AI tools and your whole environment. How? Well, the Vanta agent works like a 24/7 GRC engineer in the background, finding issues, drafting fixes. And cutting vendor agreements time by up to 50%.
4:23 Harry Stebbings? Whether you're a fast growing startup or a global enterprise, Vanta's here to help you automate your security and your compliance and earn and prove trust. My listeners get a special offer, oh yes, a special offer, $1,000 off Vanta at vanta.com/20vc. That's V A N T A dot com slash 2 0 V C for $1,000 off. You have now arrived at your destination. Brendan, it is so good to have you in the studio, dude. Thank you so much for joining me in person.
4:52 Speaker 00 Super excited to be here. Thanks for having me, Harry.
4:55 Harry Stebbings? So I was thinking about how we're going to structure this and I was like, you know what? There's, there's quite a lot of myths or rumors around McCour. And given it's our second time, I thought I could kind of break the ice and just go straight for them. So myth number one that we're going to tackle, there was a hack or a leak or whatever. I don't know how you can terminology you call it, but a hack. And revenue's been flat. What's really happening with McCour? True or false?
5:20 Speaker 00 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 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.
5:43 Harry Stebbings? 300 million in 60 days. Fuck me.
5:46 Speaker 00 It's been pretty crazy. Yeah. Keeping us busy.
5:48 Harry Stebbings? Where were you when you found out about the hack and what did you do?
5:52 Speaker 00 Well, it was a Saturday, so I was in the office and I was talking with our engineering team. And I think the initial thing is of course, like, 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, and just containing it, moving quickly on the comms. And then from there, of course, making sure that we put in place all of the right things so that it never happens again.
6:21 Harry Stebbings? You know, there's a brilliant poet, Rudyard Kipling, who said, you know, essentially, you have to keep your head when all about you are losing theirs. That is a time when everyone is losing theirs.
6:30 Speaker 00 Definitely.
6:31 Harry Stebbings? I don't by no means want to be patronizing. We're both young, you're younger than me. What do you do to stay calm when that is an oh shit moment?
6:37 Speaker 00 Well, it's interesting because I feel like throughout the lifetime of the business, I have been through a lot of very stressful moments. That was definitely stressful, but it definitely wasn't close to the most stressful one.
6:48 Harry Stebbings? Seriously?
6:48 Speaker 00 Yeah. I mean, there's been times when, you know, I'm freaking out about making sure we get something right with a customer or whatever it is. But 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 understanding of what actually happened and having really strong relationships with customers gave us a lot of confidence that we would get through it, be on the other side even stronger. And we used to have six values as a company, but we added a seventh value, a security, to make sure it's very ingrained in the culture. But I think that, yeah, just that confidence that we know what's going on and that there's
7:32 Speaker 00 sort of this echo chamber on X that we need to hedge against a little bit.
7:36 Harry Stebbings? Do you pay attention to it and do founders need to pay attention to it?
7:40 Speaker 00 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. But it was 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 all these recommendations from lawyers, et cetera.
8:07 Harry Stebbings? The hard thing is there are often a lot of people with economic incentives behind the scenes.
8:12 Speaker 00 Totally.
8:13 Harry Stebbings? 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 and their competitor has a lot of people behind them through various different means and the alignment is not obvious, but it really sounds out on Twitter.
8:30 Speaker 00 That is. Is exactly what happened. And like, I can even think of one person that's very prominent, who's invested in multiple competitors and just like made this tweet about how all of our data was getting accessed by China, when it was totally untrue.
8:44 Harry Stebbings? You mentioned adding security as a seventh pillar there. We've seen so many hacks, it's almost become normalized, as awful as that sounds.
8:54 Speaker 00 Yeah.
8:54 Harry Stebbings? Are we about to enter a golden age of cyber, given the new threats awakened by AI?
8:59 Speaker 00 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's 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. Because in 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. And so I think there's going to be an enormous boom in AI security engineering tools and various forms of defense that are able to help protect companies against all of the increasing waves of cyber incidents that are just getting started.
9:41 Harry Stebbings? Can I just be very naive and dumb here? How do swarms of coding agents make for such dangerous and malicious actors? How does that actually work?
9:49 Speaker 00 When a normal attacker is trying to find vulnerabilities, they can only review so much code and go through, you know, a certain portion of it at a human speed bound by the amount of people in their team, versus when they're using swarms of agents, they're able to be very exhaustive in reviewing the entire code base, looking at 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 exploring various collaborations with customers and how we can strengthen their cyber defensive capabilities to hedge against exactly this type of attack as well.
10:27 Harry Stebbings? In terms of those various customers, true or false, you lost OpenAI and Meta's customers in the hack?
10:34 Speaker 00 False. Our relationship with OpenAI is stronger than ever. Obviously, I can't speak too much to specific customer. Relationships, though.
10:42 Harry Stebbings? Can I push on Meta?
10:43 Speaker 00 Of course. I mean, I think that Meta, currently the relationship is still paused. Every other one of the Frontier Labs has grown their relationship with us since, and the company has been crushing it, but they're the only one that is.
10:55 Harry Stebbings? And it would be paused because of, just because of the security?
10:58 Speaker 00 Well, there's other things happening there. Like, obviously, I think that Meta's a unique customer because of the Scale acquisition, and so naturally, they're going to work with Scale more. But I don't want to speak too much to the specifics of a customer.
11:12 Harry Stebbings? Because I thought when you saw like Handshakes revenue just like parabolically go up, it was just like Meta shifting spend from you to them. Is that not true?
11:19 Speaker 00 That's not true.
11:20 Harry Stebbings? Interesting. What is that, then?
11:22 Speaker 00 I probably shouldn't speak too gradually to that, but yeah.
11:26 Harry Stebbings? Totally cool. But, okay, so we have.
11:28 Speaker 00 I'll speak to everything except customers.
11:30 Harry Stebbings? But we have lost OpenAI. Got you. Okay, cool. Because I got told by many of your competitors before the show, they're definitely having a great, good, thank you, you're wrong. You've been, I read this article. You've been trying to poach Micro One team members with signing packages in the millions.
11:46 Speaker 00 We have not extended a single offer to someone from Micro One.
11:50 Harry Stebbings? So no millions?
11:51 Speaker 00 No millions.
11:52 Harry Stebbings? Bugger. Why does that come about? Because I read this article.
11:56 Speaker 00 So the reason for the article was that someone on our team sent an outbound to some people at Micro One saying that we were hiring a variety of people with these very high signing bonuses. I think one of them said $500,000 is a potential signing bonus. And they took first meetings, but we didn't move forward with offers in anyone. And obviously, the way that gets framed to the press is, oh, these are offers that are going out. Where there is a giant distinction from one of our employees sending, you know, a message to one of their employees versus actually sending out a legal offer letter.
12:31 Harry Stebbings? Love it. Press is a wonderful thing, huh?
12:33 Speaker 00 Totally.
12:34 Harry Stebbings? Okay, next MythBusters. I'm enjoying this. This should be a new show, MythBusters. You might get uncomfortable with this one. I heard a rumor that Amazon tried to acquire you for $13 billion. True or false?
12:45 Speaker 00 That one is false. I obviously can't speak too much to like other acquisition. Kind of stuff, so I'll reserve any comments on future acquisition questions there.
12:57 Harry Stebbings? Would you sell for thirty billion?
12:58 Speaker 00 No, I wouldn't. I mean, ultimately, we've gotten a lot of acquisition interest and we could walk away with, like, I could walk away with billions of dollars in cash. The thing is, that's just not what motivates me. Like, I'm very motivated by how do we solve this incredibly important problem in the world of how humans fit into the economy? I feel like we have the opportunity to build a legendary company and creating this new category of work. And our probability of executing on that vision wouldn't be as high if we weren't an independent company.
13:34 Harry Stebbings? How humans fit into the economy? When we look at the news, we see Intuit lays off sixteen thousand, Meta lays off eight thousand at four AM, LinkedIn a thousand, Coinbase, click up now twenty-two percent going. It's hard for people to see how humans are gonna fit into that new economy.
13:51 Speaker 00 Totally.
13:51 Harry Stebbings? Do you share that concern?
13:53 Speaker 00 I think to some extent. I believe there's certainly going to be many more jobs in ten years than there are today. But there's also going to be a lot of job displacement along the way. 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 standard and answering that question of measuring across 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?
14:37 Harry Stebbings? With the greatest of respects, does that not change so quickly? You know, when you saw Andre Kepathi talk about how he uses coding agents, it was like, oh, I use it for twenty percent of the work. And then it's like, oh, it does eighty percent and I do the final twenty percent within a six-month period.
14:51 Speaker 00 Definitely. Well, even another example on that is on APEX, the frontier model right now is at about forty percent. And twelve months ago, the frontier model was a Which was scoring 1%. And so that's been the progress of the last 12 months. And obviously, we expect it to continue and be fairly significant. But I think that the key thing is that everyone underestimates the elasticity for demand and increased productivity in the economy. Ultimately, 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,
15:35 Speaker 00 people feared that there would be this enormous job displacement because of the lump of labor fallacy, where people assumed 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 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
16:04 Harry Stebbings? I buy that completely. What I don't buy is the speed of transition. 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.
16:17 Speaker 00 Yeah.
16:18 Harry Stebbings? Now with Nano Banana Pro, I can get rid of all designers in my media company pretty much overnight.
16:23 Speaker 00 Well, the thing I agree with you is about displacement. I agree there's going to be a 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 going to 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.
17:07 Speaker 00 Like, how do we build solutions to climate change? How do we, you know, have more people working on rockets to explore space, et cetera?
17:14 Harry Stebbings? Totally get you. You said 3 million per day paid out?
17:16 Speaker 00 Mm-hmm.
17:17 Harry Stebbings? What is that in 12 months' time?
17:19 Speaker 00 In 12 months' time, that's probably about triple that.
17:22 Harry Stebbings? Nine million.
17:23 Speaker 00 Mm-hmm.
17:23 Harry Stebbings? Do you think you're being ambitious enough?
17:26 Speaker 00 Maybe it's quadruple that. We have internal projections that are always much more aggressive than our external projections, but we almost doubled our projections last year.
17:36 Harry Stebbings? What new role will we have in five years that does not exist today?
17:41 Speaker 00 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 going to be to train agents. 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 red lines on commercial contracts, they're going to 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
18:25 Speaker 00 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 lifecycle 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.
18:44 Harry Stebbings? 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. I interviewed a guest the other day and they said we'll have like data cleaner as one of the most important jobs in the next five years. Is data structure and data cleanliness the biggest barrier to enterprise adoption?
19:01 Speaker 00 Well, I agree in part. I think that certainly the models need to have access to data to perform their jobs effectively, but the caveat is that they'll be able to clean the data themselves fairly effectively as reasoning capabilities go up. The thing that humans will need to contribute to is all of the tacit knowledge within the organization that isn't written down. Because I've found that when I try to get agents to do all of these workflows throughout Mercor, there's just an enormous amount of context that lives in people's heads that the agents need to have access to to perform effectively. And so So much of that is going to be the new job of employees of how do we codify all of this knowledge?
19:41 Speaker 00 How do we train agents so that they're able to perform these tasks effectively across every function in the organization?
19:48 Harry Stebbings? I'm sorry for digging down, but you said reasoning capabilities will allow enterprises to clean data more efficiently. Why?
19:54 Speaker 00 Well, the reason is that if a model is able to, for example, read through every message written in Slack over the last six months, the model can presumably structure a table of here all the different customer conversations that happened in the CRM, et cetera. And so I don't expect humans to be doing like that type of stuff of how do we structure data? How do we classify it, et cetera? But I do think that humans will do the things that models inherently can't do, such as the task of knowledge.
20:24 Harry Stebbings? When we look at the market for being a data provider to some of the largest models in the world, it's such a large market that you're seeing the unbundling of it into like such verticals. I met the other day a medical, real world medical data provider to them, where basically they have surgeons that kind of, I don't know, have video cameras on and they record all the real world data. Do we see the mass unbundling of the data providing market? Is that how it plays out?
20:49 Speaker 00 It's interesting. We're doing a ton of data collection in the physical world as well, especially cross skilled domains where you have electricians and mechanics and scientists dropping cameras to their head to record things. I think that there's always going to be some degree of value in some of these like niche vendors that are able to go really deep in a specific vertical. But what we're finding is that there's enormous value to aggregation and economies of scale. And that when we have this talent network of over 5 million people that are able to refer their friends, it's just so much easier for us to find the marginal doctor because we have that enormous talent network that can refer us to their friends.
21:33 Speaker 00 And even more importantly, that the kind of data shapes that we would build for a lawyer are often very similar to the kinds of data shapes that we would build for a doctor. And so all of the tooling that we build is very Very, very cross applicable and that's the way that most labs have been scaling out their data quite horizontally. And so for that reason, we are finding that the labs tend to prefer partnering with a very horizontally capable vendor that is able to flex across all of the different verticals and scale extremely quickly, rather than working with 100 different vendors that they have to train for the same data shape in 100 different domains.
22:12 Harry Stebbings? Do you think we'll go through a period of consolidation? Because there are a huge amount of them where you'll actually end up buying the medical data provider, because it's a really important part, medical data. Do you think you will have that period of consolidation?
22:22 Speaker 00 I think there will. I think in most markets, when the markets are so frothy and anyone can get funding and run negative margins, of course, there's going to be this proliferation of companies that pop up. And when markets come back to earth and there are natural corrections, that's when there's periods of consolidation. And so we view having over 500 million in cash and a super profitable business as a significant asset and allowing us to be prepared for when there is a market correction to make sure that we consolidate market share.
22:54 Harry Stebbings? You're profitable today?
22:56 Speaker 00 Mm hmm. Very profitable.
22:57 Harry Stebbings? How long have you been profitable for?
22:58 Speaker 00 We've never really burnt cash. We burnt a half a million dollars after our seed round. And then from there, we've pretty much been profitable ever since. We have more cash than we've ever raised. 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 yeah, the business has grown so fast that we haven't been able to redeploy capital commensurate with that.
23:24 Harry Stebbings? Can I ask a Mythbuster one, which is after we had Adarsha on the show, I think first time, people were like, oh, the revenue is not real revenue. It's like GMV. When we understand your revenue, what's the revenue today?
23:36 Speaker 00 I can't share the exact revenue number, but it's dramatically higher than whatever has been posted publicly.
23:41 Harry Stebbings? Let's give a ballpark, just because of my simple numbers, it's genuinely, I'm not like a billion, just it's an easy number.
23:46 Speaker 00 It's much more than that, but yeah.
23:48 Harry Stebbings? Okay, let's say a billion because it's easy for my brain. So we have a billion, like, is that like sales for Airbnb and then they get 20? Twenty percent of that.
23:57 Speaker 00 So the revenue is between a thirty and forty percent gross margin. But the key distinction and 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 a thousand dollars 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 the coordination of helping to produce this data? How do we have automated quality checks, et cetera,
24:41 Speaker 00 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 in the same way as some of those marketplace businesses.
24:57 Harry Stebbings? What's so interesting for me, and you can tell me if this is bullshit or not, is like you've seen the evolution of this business from like, hey, we provide raw data back to the largest models in the world. That was like how it started. And now it's like end to end, we provide it fully. And then we send it to you. We made sure everything's ready and it's full stack.
25:16 Speaker 00 Exactly, very vertically integrated. Well, because so many parts of the downstream signal inform the upstream signal, right? Like, we can use the quality checks on how high calibers 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 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 than any other dimension.
26:00 Harry Stebbings? What tasks are super high value? Is it like the medical, the financial modeling style?
26:05 Speaker 00 Corresponds extremely closely to economic value. So I think if you go through the top five domains that we serve, it would be software engineering, it would be finance, medicine, law, consulting, et cetera, and the super long horizon tasks within those. And so I think we're moving away from the paradigm of how do we get an 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 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
26:50 Speaker 00 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.
26:59 Harry Stebbings? Can I ask, which segment are we underserved in?
27:02 Speaker 00 In terms of model capabilities?
27:03 Harry Stebbings? 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?
27:14 Speaker 00 Well, I would say maybe I'll give it from Merk Core'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 generally, we can fill those fairly quickly. And it's more about people that actually are very acclimated to the frontier of AI. Because it's the people 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
27:58 Speaker 00 and help the model learn from those mistakes. And so that's from the Merk Core 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
28:41 Speaker 00 and everything that you could want out on the other side in every job category throughout the economy. Before we dive into a tweet that you did, which slightly terrified me, to be quite honest. So you said like 30 to 40% is kind of how we think about like our revenues from that. Generally, yeah. Okay, so if we take the rounds that we've raised, which round felt most uncomfortably high? Good question. Well, so I'll talk through the valuations of each and the revenue of each. So at our seed round. Did Fanta not fly you in the chopper? That was Series A. So our seed round was in September of 2023. We were at, call it, a million in revenue run rate or just shy of that. And I initially didn't want to raise because I wanted to bootstrap the company.
29:24 Speaker 00 But a Darshan series condition on dropping out was that we needed to raise money. And so we met General Catalyst 8:00 AM on a Sunday morning. They gave us a term sheet within 36 hours for $2.3 million at a $23 million post-money valuation. Pretty good. So that was, that was pretty reasonable insofar as price of the time. And this was Max and Hemon? This was Max and Eco. And then at our Series A, we, the business 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 in May of 2024. And Victor got super excited. Initially, I refused to take a second meeting,
30:07 Speaker 00 but then he said, oh, have you ever been in a helicopter? And so Peter took us on the helicopter flight. And Benchmark really wanted to work with us. And so by the time that they gave us the term sheet, we were at, call it, 2.5 million in revenue, and they gave us $250 million post-money valuation. Did that feel uncomfortable? Because that's a big jump, 223 posts to 250. So keep in mind, at the time, this sounds crazy because we were at 2.5 million in revenue, but I was projecting 50 million in revenue run rate by the end of the year and 500 million by the end of next year. And so it felt like a bargain. Dude, you do know all founders project that day. But we beat the projections. Sure, just doesn't happen often. Yeah, yeah, yeah.
30:50 Speaker 00 Four months later, we met Felisa's and we never would make a slide deck or take investor meetings. And so Felisa sent us an email saying, hey, we know your co-founder Surya really likes Ferraris, so do you want to go racing Ferraris with us? Then I replied and I said, you caught my eye, tell me more. And they said, we'll meet at the airport in Hayward and go on Idan's private jet to Las Vegas to race Ferraris around the F1 track. 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 one to $2 billion. So they give us a term sheet at a $2 billion valuation.
31:33 Speaker 00 And at the time, that's 100 times revenue and everyone thinks that that's a high valuation. Meanwhile, it was an incredible investment.
31:41 Harry Stebbings? So I'm going to be honest, this is when I interviewed Adash at that time. And at the end, I was like, dude, I would love to invest, please let me invest. And you very kindly let me put a small check in. And I then spoke to several of the biggest investors in the world. And no offense, they chuckled at me like, dude, that's such a high price, you use such a high price.
31:58 Speaker 00 Well, so here's the thing is, 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 continue for the subsequent, 12 plus months.
32:09 Harry Stebbings? Yeah.
32:10 Speaker 00 And so then that compounded more and more by September of 2025, or say October, we were at, call it 400 million in revenue run rate. And then Felisa's was like, we want to invest more. And so they gave us a term sheet at a $10 billion valuation. We didn't really want to spend much time on a financing because the business was growing 50% month over month. And so we were very preoccupied. And so that was about 25 times. And the business has almost 4x since then.
32:37 Harry Stebbings? So review, which one felt most uncomfortable?
32:39 Speaker 00 If I had to choose any, the Series B priced in the most, like the furthest ahead of our growth, or that or the Series A, I think it was probably the Series B because both were 100 times revenue, but it's very Different to be 100 times revenue when you're at 2.5 million in revenue versus 20 million in revenue. So that was probably the largest one, but obviously both were great investments in hindsight.
33:02 Harry Stebbings? What is the next rounder?
33:04 Speaker 00 We'll see, probably a much higher valuation. We're getting a lot of offers at meaningfully higher valuations, but the company is fairly profitable and so we're taking our time to see who the right partner is.
33:14 Harry Stebbings? We're also just going through modes of transport, aren't we? We had the chopper, we had the Ferraris, we've had.
33:20 Speaker 00 That's a good observation.
33:21 Harry Stebbings? You need a warship now to get the series, do you?
33:24 Speaker 00 I totally agree. I've never been on a warship before, but that sounds a lot of fun.
33:28 Harry Stebbings? There you go, Sequoia line up the warship. The next 12 months will be dramatically better for infrastructure companies upstream of Anthropic and OpenAI than for application layer companies downstream of them. This was your tweet. Why do you believe that?
33:44 Speaker 00 The reason I believe that is that the application layer companies' businesses are not far removed from the foundation model companies' businesses. Like it is not a far leap for Claude Co work to add capabilities across medical and legal. Obviously, they did it with software engineering and 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 modes that are getting built. Like we're compounding enormous network effects in the business and a pretty significant data mode as we build out the inventory for our customers.
34:27 Speaker 00 Compute companies obviously are able to build modes through these very long R and D cycles. And so I think that there are going to be high margins that get achieved at the infrastructure layer and sort of sustainable, profitable businesses in a way that it's less immediately clear at the application layer.
34:46 Harry Stebbings? I mean, you saw Nebius, I don't know if you saw this, but they increased their pricing by 30%.
34:50 Speaker 00 I didn't know.
34:51 Harry Stebbings? Across the board, which will have absolutely no impact on demand. Isn't that absolutely nuts? So you increased price by 30%, zero impact on demand?
34:59 Speaker 00 It's probably the same for us, honestly. Like we have the demand. And 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.
35:13 Harry Stebbings? Do you do pricing elasticity tasks? Because if you can double price and double the business.
35:19 Speaker 00 We maybe can't double prices. We could double capacity. We could probably increase prices by thirty percent 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. 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.
35:52 Harry Stebbings? Okay, I am an investor in several application layer companies downstream like a Logora, which you mentioned there. You know, we see the Logora 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.
36:08 Speaker 00 Totally.
36:08 Harry Stebbings? 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, adoption teams. It's a different fricking company. The defensibility is there. Argue back.
36:31 Speaker 00 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 patchwork logic where people used to have all these drag and drop agent builders. 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. 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.
37:12 Speaker 00 Like, we're building out an eval set that measures how effectively agents can build end-to-end SaaS applications, where 2025 was the year of how do you get a model to make a PR and a codebase? And 2026 is the year of how do you get the model to clone Slack end-to-end? Those capabilities are going to exist in the models in the next 12 months. That means very significant things for companies that are betting on software moats sustaining their businesses.
37:40 Harry Stebbings? If we take that extrapolated further, how effectively can we build Slack internally, agent-led 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.
38:00 Speaker 00 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. For example, Salesforce has tons of companies that are building integrations on top of their platform. That creates this 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 want to use the same platform across all of their companies. I think that the companies that have network effects will be able to, in some ways, generate more value because they can iterate 10 times faster while leveraging those network effects to create more value for their customers
38:43 Speaker 00 and therefore 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 10x product velocity.
39:11 Harry Stebbings? You said we're learning more and more that the models are the product. 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 Name your biggest law firms, your Cooley's, your Goodwin's, your Wyden 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.
39:34 Speaker 00 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 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.
40:19 Speaker 00 That feels incredibly differentiated and hard to recreate. And that's also the reason that we see, obviously, the labs opening 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 a lot and that these software boats 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.
40:46 Harry Stebbings? 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.
40:55 Speaker 00 I think in a large way I do.
40:57 Harry Stebbings? You do?
40:58 Speaker 00 I think the key thing is that you need to make sure that they're actually going to leverage AI. Like 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 to... That's the thing you've got to be careful about, but I think it's very rational. Like I'll give an example in the context of Mercor, which is that 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?
41:41 Speaker 00 And we have about 100 people 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 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.
42:26 Harry Stebbings? 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. Token costs are rising for everyone. Help me understand how you see token costs changing in the next six to 18 months and why that is.
42:43 Speaker 00 Well, it's a fascinating case study in Jevon's paradox, 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 costs per performance go down. I think insofar as how it's going to 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 going to look like that in.
43:26 Harry Stebbings? Well, you're spending more on tokens for agents than you are on headcount.
43:29 Speaker 00 Exactly.
43:30 Harry Stebbings? So your tokens spend on agents is more than salaries.
43:32 Speaker 00 That's correct. It's pretty incredible. And so 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 five million interviews and asked all the questions in the interviews. We have our interview ranking or the 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?
44:14 Speaker 00 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 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.
44:54 Harry Stebbings? Do you think you will see that commoditization of the model layer whereby enterprise clients are able to really efficiently package the workflows that they do? So it does commoditize the model layer because right now it's not commoditized quite.
45:07 Speaker 00 Yeah. So I think the key distinction is that I think the API layer will get commoditized. You can definitely build stickiness and workflows that people have on top of those APIs. Like, for example, I have all of these like routines running in Claude 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 going to have an eval for that. And every time a new model comes out, we're going to benchmark that and understand exactly how we should be hot swapping between models and distilling models.
45:49 Harry Stebbings? Why does the API layer get commoditized?
45:52 Speaker 00 Because the switching costs are zero. Like, when the switching costs are zero, there's a new frontier model every two months. That means that we very quickly are going to 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 hot-swappable way, which is almost the definition of a commodity.
46:20 Harry Stebbings? I'm still reeling from your tokens band with agents more than headcount. Because actually, Mark Benioff said the other day that they spend 300 million on Anthropic, which seemed like a lot of money, but actually, when you bated 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.
46:39 Speaker 00 Yeah.
46:40 Harry Stebbings? What do you think that is in 24 months' time?
46:43 Speaker 00 For a Salesforce?
46:43 Harry Stebbings? Yeah.
46:44 Speaker 00 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. The reason for that is that the models are just becoming so capable that it seems like there is just enormous ROI to being able to have models do something for 100K a year. 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 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,
47:24 Speaker 00 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 models to accomplish that.
48:03 Harry Stebbings? With the greatest of respect, are evals today not relatively unhelpful? It's like, you know, how good are you at, you know, driving around the corner for the driving test in a very specific way, but actually, that's not how it works in the real world? And it's actually not very practical?
48:16 Speaker 00 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 humanities 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? That's why there's this enormous build-out in pushing the frontier of evaluation
49:00 Speaker 00 as a critical research problem for the next frontier of model development.
49:04 Harry Stebbings? Okay, next frontier of model development. If I listen to everything that you just said, 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, 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?
49:41 Speaker 00 I think both are true. Like, there's going to be 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 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.
50:22 Harry Stebbings? Okay, interesting. You said that, obviously incredible investments. Where will they be? Be in five years' time.
50:28 Speaker 00 Valuation wise, revenue wise?
50:30 Harry Stebbings? That's on, valuation wise.
50:31 Speaker 00 Valuation wise, wow.
50:32 Harry Stebbings? If we put them both at a trillion today, give or take.
50:34 Speaker 00 Yeah, this is hard to imagine.
50:36 Harry Stebbings? This is one I'll play back to in five years' time and we'll both look back and go, oh, either we were very prescient or just completely wrong.
50:42 Speaker 00 I could definitely see one of them being a $10 trillion company, maybe even significantly higher. It feels like the opportunity associated with being the frontier model is so large. It will just like eat up so much of the other demand within the economy because that also means that when you have the frontier model, you can use that as a teacher model, distill your own models, to have the best small models, et cetera. So I would guess at least one of them is worth more than $10 trillion.
51:10 Harry Stebbings? My next assumption was when you talk about orders of magnitude more, when you talk about spending more on compute than you will on salaries. Why didn't we just put all of our money in Nvidia? I know it sounds supercilious and glib.
51:22 Speaker 00 I think it's not a crazy idea. Nvidia is obviously a phenomenal business that will continue to execute super well. The only caveat is that it feels like we're starting to move towards a multi-chip future, where obviously Cerebras is executing well, I'm good friends with the Edge guys, most of the labs are building in-house chips. And so I would guess that in five years, it doesn't feel like Nvidia has quite the same monopoly. But that's okay, because even if they only have 30 or 40% market share in the largest market in the world by far, that is the world's most valuable company.
51:57 Harry Stebbings? Speaking of the world's most valuable company, you're seeing this concentration of value towards like the top eight names more than ever before. 84% of the year-to-date rally was driven by the top 10 names. Do you worry about the concentration of value to such a small number of players?
52:12 Speaker 00 Maybe to some extent. I definitely worry about how do we smooth out the benefits to society? Like, how do we ensure that every enterprise and every individual is able to reap the full benefits of AI rather than just like a handful of people in San Francisco? But ultimately, I also think that there is some natural dynamic associated with capital allocation where it is. Going to be more valuable to give the compute to a anthropic where they have the marginal demand and can use that right away versus a less successful company that might not be able to create the most value with that. So I think that it's probably good from a capital allocation and efficiency standpoint,
52:55 Speaker 00 so long as we are able to manage the societal implications of increasing inequality.
53:02 Harry Stebbings? Speaking of increasing inequality, you wrote an essay about, and this is taken from your Twitter, how we should eliminate income tax for the bottom half of Americans. Talk to me about that.
53:12 Speaker 00 Well, I believe this very strongly. I actually wrote this essay when, as a research paper, when I was a freshman in college. It was one of the few productive things I did in college. Essentially, the thesis of this was that the largest positive externality in the economy is jobs. People talk about all these like economic theory of how we have negative externalities like carbon, or like smoking, or whatever it is, we should tax those. But on one hand, the largest positive externality is jobs. Yet on the other hand, the way that most economies structurally collect income is by disincentivizing jobs, both on the income tax side by taxing the individuals, as well as on the payroll tax side of taxing the companies.
53:54 Speaker 00 And as we move towards a world where there is increased job displacement, increased uncertainty around how many jobs are there going to 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 is like higher capital gains tax, it's not like I'm just going to like not invest, right? And so I think that taxing capital gains, especially short-term capital gains, which
54:37 Speaker 00 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.
54:46 Harry Stebbings? 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 want to tax me more for doing that, for taking the risk? Of course you will disincentivize investment.
55:09 Speaker 00 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...
55:24 Harry Stebbings? No, no, no, I think, and I say this with the nicest respect, it's just wrong, like, because you just move.
55:33 Speaker 00 I agree that you, the main thing you need to be careful about is if people would move to other geographies, because obviously that creates problems. But I think like capital gains is one option.
55:43 Harry Stebbings? But I, sorry, I'm so sorry to be a dickhead and you can say withdrawal. 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 wa, sorry, forgive me, we live in, we live in the UK where there's the Green Party, which there's this idealist movement that's like, oh, increase, well, yeah, then we leave and then you have nothing.
56:07 Speaker 00 I, I agree. I think that there needs to be sensitivity analyses associated with 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. 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
56:48 Speaker 00 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 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.
57:06 Harry Stebbings? Would you tax prediction marketplaces? It's gambling.
57:10 Speaker 00 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 OK to tax. The thing on that point around taxing the bottom 50% is Jeff Bezos retweeted me, which I was ecstatic about.
57:33 Harry Stebbings? That's pretty cool.
57:35 Speaker 00 It was pretty great, yeah.
57:36 Harry Stebbings? Who's the coolest person you've met?
57:38 Speaker 00 I really like Jensen and I really like Satya. I mean, so many incredible people. Obviously, Dario and Sam are incredible, but if I had to choose one person, I mean, Jensen's so cool, right? Like, the jacket, his style, he's always on point. So I would say Jensen is probably one of the coolest.
57:55 Harry Stebbings? The fascinating one I would love to ask you, and you shouldn't give the answer to this, but I think there's some 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? And don't answer that.
58:07 Speaker 00 I can't answer that one, yeah.
58:08 Harry Stebbings? But it's a really good one.
58:10 Speaker 00 It is an interesting question, yeah.
58:11 Harry Stebbings? And I have met a couple where you're like, wow, that gives me confidence that I can do that, too.
58:16 Speaker 00 You know, actually, I will say this one thing, which is that I remember when I went to Georgetown, I didn't get into Harvard, and I was like, wow, you know, the people at Harvard are probably dramatically smarter than me. And I went to this nonprofit called PROD, where it was a bunch of kids from Harvard and MIT that were all building startups. And like, they're very smart, don't get me wrong, but I do think that most of us have this very equalizing feeling, that like, majority of people that accomplish extraordinary things, when you spend more time with them, you realize that they're sort of just a normal person to a significant, not all of them, but most of them, to a significant extent.
58:57 Speaker 00 And I think that makes you feel like, like, when I saw Ethan Thornton from MOC raising $70 million as a 19-year-old, I'm like, wait, Ethan? Finn is like a chill guy and a good friend and like, maybe I could do something like that one day. It just gives you the sense of being able to accomplish so much more.
59:15 Harry Stebbings? It's so interesting you said that, that kind of dispersion effect from seeing your friends achieve. And I think it's one thing that's held Europe back in many ways. You work with some of the largest model providers in the world. How do you feel about Europe's inability to compete slash provide leading models to the world? Like, when you look at the benchmarks, I mean, Mr. All might make an entry at, you know, seventy-two. It's like the Eurovision Song Contest kind of at the bottom. I love off, I love this, I'm very proud of it as a, but shit, we haven't delivered on the model side.
59:45 Speaker 00 Yeah.
59:45 Harry Stebbings? Does Europe improve that? Does that matter?
59:48 Speaker 00 I think that it's going to be difficult to change because there's just so many strong network effects around talent, right? When we have the best talent, even I know so many brilliant French researchers that go to work at OpenAI, Anthropic and DeepMind, right? Because when we have the best talent at those labs, that's where they all aggregate. And then that compounds to them having more capital, more compute, more impact, et cetera. And so I expect that trend to continue and to be one of the largest, not only economic, but geopolitical advantages that the US has.
1:00:22 Harry Stebbings? So if you're Europe today, do you just go, you know what, sort it, we've lost that model race. But we can still be a dominant energy provider, if you're Norway where I'm from. Actually, we do pretty well on Norway providing energy. Is that, do we just accept that?
1:00:35 Speaker 00 I would accept that, yeah. I think that maybe it's worth having some post-training capabilities because there is going to be value to distillation and some of the work that happens after foundation models are built. And there's definitely going to be some value in applications. But I don't know if I would lean aggressively into how do we compete head to head with Anthropic.
1:00:57 Harry Stebbings? 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?
1:01:05 Speaker 00 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 lab. But also be good at British law or good at French law or whatever the jurisdiction is in the world. 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 going to hire 10,000 people in France to teach the models how to be better at French law. And I don't think that there is so much that others are going to 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.
1:01:49 Harry Stebbings? And when you say about hiring 10,000 people, the thing that's just astonishing me is that the wave of cash. For me, I'm sure OpenAI is the same, but I've seen it specifically with Anthropic. I mean, insane levels of comp.
1:02:00 Speaker 00 Totally.
1:02:00 Harry Stebbings? How do you compete against that?
1:02:03 Speaker 00 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 an excess of $100 million.
1:02:18 Harry Stebbings? I saw your tweets where you do the McColl mafia tweets.
1:02:20 Speaker 00 Yeah, exactly. And so, and we're a very young company, right? 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 to build a company, as you know well, Harry. And how low the probability of success is and how fortunate we were and how lucky we got along the way. 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.
1:02:54 Harry Stebbings? TBD?
1:02:55 Speaker 00 Meta's superintelligence group.
1:02:58 Harry Stebbings? 20 million in cash?
1:02:59 Speaker 00 Per year. Or it's in stock, but liquid.
1:03:02 Harry Stebbings? As hard to compete against.
1:03:03 Speaker 00 It's hard to compete against, yeah.
1:03:05 Harry Stebbings? Does that change? Does that just continue to escalate?
1:03:08 Speaker 00 It'll probably continue to escalate 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 how to train a frontier model, that means that there's going to be more supply in the market for. People that have that skill set and thus a little bit more reasonable pricing. And so I expect there to be some craziness that continues. But hopefully, sort of the 99th percentile, at least, within the market will bounce itself up.
1:03:41 Harry Stebbings? What is the hardest role to hire for today?
1:03:43 Speaker 00 Researchers.
1:03:44 Harry Stebbings? Just because of supply?
1:03:45 Speaker 00 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, like Edward Hu, the first author on. Laura, who was previously at OpenAI, is working with us and a bunch of other top researchers. But the market is definitely getting very hot.
1:04:06 Harry Stebbings? How much does it cost to hire a high-quality AI researcher?
1:04:10 Speaker 00 Oftentimes, it would be in the tens of millions of stock per year for the really good people, yeah.
1:04:14 Harry Stebbings? I remember when researchers weren't paid very much. It's like 10, 15 years ago, they were like the underpaid but brilliant people in society.
1:04:21 Speaker 00 Yeah.
1:04:21 Harry Stebbings? Now I feel like that's relatively changed.
1:04:24 Speaker 00 Yeah.
1:04:25 Harry Stebbings? Is it harder than ever to run the company?
1:04:27 Speaker 00 I don't think so. Like, to give a frame of reference, we were 40 people and 50 million in revenue run rate last year, at the start of last year. Since then, we've 7 or 8x headcount and we've increased the broader scale of the business by 25, 30x. It's definitely been very stressful to keep up with the growth along the way. But I think that now we have the supporting functions. Like, we have HR, or not really HR, but we have finance and we have legal and we're building out HR. And that brings some sense of stability where I don't have to deal with all of these little escalations.
1:05:09 Speaker 00 And I'm able to just spend my time focusing on building great products, research and time with customers. And that, I think, has made it easier, significantly easier to run the business.
1:05:20 Harry Stebbings? I get in a lot of shit for everything I say these days, which is wonderful. My team just go, oh, no, Harry. The trouble is, I don't deliberately rage bait, but people just hate me. Which is the worst thing. But HR, I tweeted after a show with Adam at AppLovin, no great CEO. That I've met and it's true loves HR. They slow you down, they implement policy and procedure and it's just a pain. Do you agree with me?
1:05:45 Speaker 00 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 forty people to four hundred people.
1:05:56 Harry Stebbings? How does that show up?
1:05:58 Speaker 00 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 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.
1:06:39 Speaker 00 But I also think that it's important and one of the large lessons we've had over the last eighteen months or so is that it's critical to really get these foundations in place as you scale headcount. Otherwise, it creates problems.
1:06:51 Harry Stebbings? Culture challenges. Before the show, we said that after the show with Adarsh, a couple of people thought that like nine nine six was the way that like McCour is run. And it's like clock in, clock out. Why is that not true and how do you think about that?
1:07:05 Speaker 00 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 of 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 working, thinking about work during that time. 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 we need to ensure that it's a sustainable. Environment for the best people in the world to do their life's work.
1:07:50 Speaker 00 Are you ready for a quick fire round? Of course. Would you like to go public? Definitely. When? 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 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 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. Don't laugh. Do you ever lie in bed at night and just go like, wow, pretty wild?
1:08:32 Speaker 00 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. What have you changed your mind on in the last 12 months? What have I changed my mind on? 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 going to be able to maintain pricing power amidst a competitive environment.
1:09:12 Speaker 00 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. You can invest in OpenAI or Anthropic. Which one? Oh, I can't respond to that. I would choose that.
1:09:30 Speaker 00 Who do you not have as an investor in the company yet that you would most like to have? 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 is steering in the same direction. As such a strategic business leader, I've never met him, but I've always wanted to. Which competitor do you most respect and why? Good question. I admire that Edwin from Serge has done a really good job in 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 one of the largest things that differentiates
1:10:14 Speaker 00 both us and Serge is our ability to train models, to hire some of the best researchers in the world. And I admire them for execution on that front.
1:10:23 Harry Stebbings? What percent of data providers are just respectfully transactional talent marketplaces?
1:10:30 Speaker 00 In terms of volume or number of competitors?
1:10:32 Harry Stebbings? Number of competitors.
1:10:33 Speaker 00 About half.
1:10:34 Harry Stebbings? Half?
1:10:35 Speaker 00 Yeah.
1:10:35 Harry Stebbings? I'd love that. What would you most like to change about your role today?
1:10:39 Speaker 00 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.
1:10:51 Harry Stebbings? Final one for you, dude. What's the kindest thing that anyone's ever done for you?
1:10:55 Speaker 00 One that really stuck with me is I remember, and I'll probably attribute this to the entire prod community, 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 because 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 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 wouldn't accept it. And Mercor wouldn't
1:11:39 Speaker 00 exist if it weren't for any of those individuals, I would say. I think that that is something that I'll always be grateful for, for the rest of my life.
1:11:47 Harry Stebbings? 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.
1:11:56 Speaker 00 Thanks so much for having me, Harry. Always great to come back.
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Previous 20VC: Corgi Insurance: The Most Intense Workplace Culture in America: 7 Days Per Week, Founder Sleeps in Office, Corgi Cafe Open 24 Hours a Day, 60% of First 30 Employees Have Corgi Tattoos | The Journey from $0 to $2.6BN Valuation in Just 2 Years Next 20VC: Anthropic Files to Go Public | Token Budgeting Panic Hits Corporate America | Cognition Raises $1BN at $26BN Valuation | Apollo Warns PE Software Returns Will be Disastrous | The 9-9-6 Work Ethic: Performative Theatre or Startup Reality? Page updated October 26, 2026 · transcript and summary are machine-generated
