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

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 a 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 quite quality air researcher? Oftentimes it would be in the tens of millions of stock per year. This is 20 VC with me, Harry Stebnings. Now joining me in the hot seat today, we have Brandon Fudy, co-founder and co-CEO of McCore, one of the fastest growing AI companies valued at over $10 billion today, doing over a billion dollars 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 open AI around Thropic? We do not shy away from the spicy question in this show and Brandon was incredible and more than delivered. But before we dive into the show today, did you know the industry average for booking a business trip is 45 minutes as a massive waste of your team's time? Or would an Avan your employees can book a trip in just seven on average? Avan is the AI-powered travel and expense platform, designed for companies that value efficiency. It drives real business impact through high employee adoption and automated policy control. 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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.com/20VC for $1,000 off. You have now arrived at your destination. Brandon, it is so good to have you in the studio, dude. Thank you so much for joining me in person. Super excited to be here. Thanks for having me, Harry. So I was thinking about how we're going to structure this. And I was like, you know what? There's quite a lot of myths or rumors around McCall. And given it's our second time, I thought I could break the ice and just go straight for them. So myth number one that we're going to tackle. But there was a hack or a leak or whatever. I don't know how you can tell me what do you call it, but a hack. And revenues been flat. What's really happening with McCall? Ture false. So there was an incident. All of the other parts are false. And that we obviously handled it very quickly. We were in touch with customers. We moved incredibly fast at engaging Mandean 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. 300 million in 60 days. Fuck me. It's been pretty crazy, yeah. Keep you going, Susie. Where were you when you found out about the hack? And what did you do? Well, it was a Saturday, so it 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 just containing it moving quickly on the comms? And then from there, of course, making sure that we put in place all the right things so that it never happens again. You know, it's a brilliant poet, Roger Kippling, who said, you know, kind of essentially, you have to keep your head when all about you are losing theirs. That is the time when everyone is losing theirs. Definitely. By no means one of you patronizing we're both young, you're younger than me. What do you do to stay calm when that is an oh shit, man? 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. Seriously. Yeah. I mean, there's 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 as 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 sort of this echo chain, brand X, that we need to hedge against a little bit. Do you pay attention to it? And to founders need to pay attention to it? 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 there's definitely a knowing that there were all of these people saying things that didn't actually happen. And we couldn't quite speak out against them to explicitly otherwise there's, you know, going to be the Twitter mob circling and all these recommendations from lawyers, et cetera. The hard thing is there are often a lot of people with economic incentives behind the scenes. Totally. 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. That is exactly what happened. 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 access by China when it was totally untrue. You mentioned adding security as a seventh pillar there. You know, we've seen so many hacks. It's almost become normalized as all flips that sounds. Are we about to enter a golden age of cyber given the new threats awakened by AI? 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. 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 going to start. Can I just be very naive and dumb here? How do the swarms of coding agents make for such dangerous and malicious actors? How does that actually work? When a normal attacker is trying to find vulnerabilities, they can only review so much code and go through a certain portion of it at a human speed, 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. In terms of these various customers, true or false, you lost open AI and matters customers in the hack. - Boss, our relationship with open AI is stronger than ever, obviously I can't speak too much to specific customer relationships though. - Can I push on matter? - 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. - And it would be paused because of just because of the security? - 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 wanna speak too much to the specifics of a customer. - 'Cause I thought when you saw like hand shapes revenue just like power roll, like we go up, it was just like meta shifting spend from you to them. - Is that not true? - That's not true. - Interesting. - What is that in? - I probably shouldn't speak to a granularly to that, but yeah. - Totally cool. - Okay, so we have-- - I'll speak to everything except customers. But we have lost open AI, got you? - Yeah. - Cool. 'Cause I got told by many of your closest before the day. Definitely have 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. - We have not extended a single offer to someone from micro one. - So no millions. - No millions. - Bucker. Why does that come about? 'Cause I've read this article. - 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 as a potential signing bonus. And they took first meetings, but we didn't move forward with offers in any one. And obviously the way that gets framed to the press is oh, these are offers that are going out with there's a giant distinction from one of our employees that dig a message to one of their employees versus actually sending out a legal offer letter. - Love it. Press is a wonderful thing, huh? - Totally. - Okay, next, I'm enjoying this. This should be a new show, Mythbusters. You might get uncomfortable with this one. I had a rumor that Amazon tried to acquire you for $13 billion. - True or false? - That one is false. I obviously can't speak too much to like other acquisition kind of stuff. I don't know, reserve any comments on future acquisition questions. Would you sell for $30 billion? - 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 in 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. - How humans fit into the economy? When we look at the news, we see intuals of 16,000, medalists of 8,000, 4am, LinkedIn, 1000, Coinbase, that will click up now 22% going. It's hard for people to see how humans are going to fit into that new economy. - Totally. - Do you share that concern? - 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 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? - With the greatest of respects, does that not change so quickly? You know, when you saw André Capathy talk 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." - 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 01, which was scoring 1%. So that's for 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, 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 it's because we have no shortage of problems to solve this society, right? We still need to solve climate change and cure cancer and do all of these other new things. And so, 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. Now with Nanobonana Pro, I can get rid of all designers in my media company pretty much overnight. Well, the thing I agree with you is about displacement. I agree there's gonna 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 and 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 build solutions to climate change? How do we have more people working on rockets, ticks for space, et cetera? Totally get used to 3 million per day pay down. What is that in 12 months time? And 12 months time, that's probably about triple that. Nine million. Do you think you're being ambitious enough? 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. What new role will we have in five years that does not exist today? 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 red lines 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's 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. - 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 clanniness. I interviewed a guest the other day and they said, we'll have data cleaner as one of the most important jobs in the next five years, is data structure and data clanniness the biggest barrier to enterprise adoption? - 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 found that when I try to get agents to do all of these workflows throughout Mark Orr, 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 much of that is going to be the new job of employees of how do we codify all of this knowledge? How do we train agents so that they're able to perform these tasks effectively across every function in the organization? - I'm sorry for digging down, but you said reasoning capabilities will enterprise us to clean data more efficiently. - Why? - Well, the reason is that if a model is able to, For example, read through. every message written in Slack of the last six months. The model can presumably structure a table of, "Here are all the different customer conversations that happened in the CRM, etc." 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, etc?" But I do think that humans will do the things that models inherently can't do such as the test and knowledge. When we look at the market for being a data provided some of the largest models in the world, it's such a large market that you'll see the unbundling of it in today, 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? 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 and some of these 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. 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 cross-applicable. And that's the way that most labs have been skilling 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 and 100 different domains. 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 product because it's a really important part medical data. Do you think you will have that period of consolidation? 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 in a super profitable business as a significant asset in allowing us to be prepared for when there is a market correction to make sure that we consolidate market share. You're profitable today. Very profitable. How long have you been profitable for? 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 the business has grown so fast that we haven't been able to redeploy capital. It comments right with that. Can I ask you Mythbuster 1, which is after we had a Dasha 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 say? I can't show the exact revenue number, but it's dramatically higher than whatever has been posted publicly. Let's give a bull photo to you, because my simple number is genuine. I'm not like a billion. Just as easy enough. That's much more than that. But yeah, let's say a billion, because it's easy for my brand. So we have a billion. Is that like sales for Airbnb, and then they get 20% of that? So the revenue is between a 30 and 40% gross margin. But the key distinction and why it's not GMV, but it's 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 the coordination of helping to produce this data? How do we have automated quality checks, etc. to produce the end product of the task that we're delivering for a 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. What's so interesting for me, and you can tell me if this is bullshit or not, is that 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?" 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. Exactly. Very vertically integrated. Well, because so many parts of the downstream signal and form 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 in so far as pricing power, because quality is the X factor that becomes dramatically more valuable than any other dimension. What does the super high value? Is it like the medical, the financial modeling style? It corresponds extremely closely to economic value. So think if you go through the top 5 domains that we serve, it would be software engineering, it would be finance, medicine, law, consulting, etc. 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 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. Can I ask which segment are we underserved in in terms of model capabilities? In terms of like we don't have enough medical data, we don't have enough financial modeling data, is there a segment where like you know what, if we were to require a company in this space to plug a hole in our data supply? Well, I would say maybe I'll give it from my course perspective and then I'll give it from the lab's perspective. Like we tend to be now so good at mobilizing experts that were 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 our power users of chat GPT or cloud that are able to find where the model makes mistakes and help the model learn from those mistakes. And so that's from the more 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 and say Google Workspace is how do we cover the full distribution of all of the context i 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. 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 up. 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 of the revenue of each. So at our seed round did Fountain not fly you in the chopper? That was serious I so our seed round was in September of 2023 we were at called a million in revenue run rate or just shy of that and I initially didn't want to raise because I want to bootstrap the company but a Darshan series condition on dropping out was that we needed to raise money and so we met general catalyst 8 a.m. on his Sunday morning. They gave us a term sheet within 36 hours for $2.3 billion at a $23 million post money valuation. I think that's so that was that was pretty reasonable in so far as much of the tax in him on this was Max and Eko 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 a key differentiation and we met Victor when we were at 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, but then he said, "Oh, have you ever been in a helicopter and so Peter took us on the helicopter flight?" And benchmark really won to work with us. And so by the time that they gave us a term sheet, we were at call it 2.5 million in revenue and they gave us $250 million post money valuation. - Did that fail uncomfortable? 'Cause that's a big jump, you know, to 23 posts to 250. - So keep in mind, at the time, this sounds crazy 'cause 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. (laughing) - Dude, you know who found this project, right? - But we didn't have a project. - It's short. - It just doesn't happen often. - Yeah, yeah, yeah. And then 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, Suria, "really likes Faris. "Do you want to go racing Faris 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 Ida's private jet to Las Vegas "to race Faris 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 Faris, we're at 20 million in revenue. They ask us, "What valuation do we think makes most sense?" And I say, "One to two billion dollars." So they give us a term sheet at a two billion dollar valuation. And at the time, that's 100 times revenue. And ever think that that's a high valuation. Meanwhile, it was an incredible investment. - So I'm gonna be honest, this is when I interviewed a Dosh at that time. And at the end of the day, 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 in the world. And no offense, they like chocolate. They like, "Dude, that's such a high price." You eat such a high price. - Well, so here's the thing is, we'd been growing 50% month of our month for the prior six months. And I think what none of them really realized was that it would continue for the subsequent, you know, 12 plus months. - Yeah. - And so then that compounded more and more by September of 2025, or say October, we were at called 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 of $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, you know, the business has almost four Xs and then. - So review, which one felt most uncomfortable? - 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 and hindsight. - What is the next round done? - We'll see, probably a much higher valuation. We're getting a lot of offers at meaningfully higher evaluations, but the company's fairly profitable. And so we're taking our time to see who the right partner is. - We're also just going through modes of transport, aren't we? We had the chopper, we had the Ferrari's, we've had, we had to get our simulation and get our ship out together. - Seriously. - Yeah. - I totally agree. I've never been on a warship before, but that's a lot of fun. - There you go. - So we'll line up the warship. - The next 12 months will be dramatically better for infrastructure companies, upstream of anthropic and open AI than for application layer companies, downstream of them. This was your tweet. Why do you believe that? - 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 cloud 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 and the infrastructure side, it feels like there are meaningful modes that are getting built. Like we're compounding enormous network effects in the business in a pretty significant data mode as we build out the inventory for our customers. Compute companies obviously are able to build modes through these very long R&D cycles. And so I think that there are going to be high margins that get achieved at the infrastructure layer in sort of sustainable profitable businesses in a way that it's less immediately clear at the application layer. - I mean you told Nebius, I didn't know if you saw this, but they increase that pricing by 30%. - I didn't know. - Of course support. - Which will have absolutely no impact on demand. - Isn't that absolutely not? So you increased price by 30% zero impact on demand? - It's probably the same for us honestly. Like we have the demand to double overnight. And so it's mainly a question of how effectively can we scale to mobilize people to build out these environments much more quickly? - You do pricing elasticity tests because if you can double price and double the business. - We maybe can't double prices. 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. 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. - Okay, I am an investor in several application layer companies downstream like a LaGora, which you mentioned that we see the LaGora 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. - Totally. 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 that GTM teams, customer success teams, adoption teams. It's a different fricking company. The defensibility is there. I'll give you back. - Maybe I would say two things. First as though I think over the last two years, everyone has increasingly realized that the model is the product. That 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 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 undone. 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 and to end SAS applications where 2025 was the year of how do you get a model to make a PR on a code base? And 2026 is the year of how do you get the model to clone Slack and 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 modes sustaining their businesses. If we take that extrapolated further, how effectively can we build Slack internally agent led entirely? That would very much concur with the SAS's 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. I generally agree. I think that the caveat is when those companies have network effects, there's probably a significant mode 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. And I think even Cart is another great example of this whole network effect of the people that use it and one 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 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 mode 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. 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, you name your biggest law firms, your coo-leas, your good wins, your widening 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. So I agree with this in part, but the caveat I would give is I think it's arguably more of the forward deployed motion rather than the go-to-market. And for 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 realized they could just like tell Quad to copy it. And they'll get the-- 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, open-air andthropic 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, is that these software modes 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. You buy this new sexy category, I mean, VentrumS is a wonderful people, but like this new sexy category that like AI-enabled services is like the future gold mine. I think in a large way I do. 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 gating 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 Mercore, 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, and we have about a hundred people or called 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 a real time that services are getting automated and that that is going to be this extraordinary transformation in the economy. 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 arising for everyone. Help me understand how you see token costs changing in the next six to 18 months and why that is. What's a faster encased in Javon's paradox? Some wonder what we were talking about in the context of making humans more efficient leading to more jobs, right? When we make models improved 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. I think in so far 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. Well, you're spending more on tokens for agents than you on headcount. Exactly. So your token spend on agents is more than salaries. 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 the candidates and figure out who we should be hiring. We have agents for accounting automation. We have agents for fraud detection, etc. 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 prediffrent tier 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, etc. 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. 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? Because right now it's not commoditized quite. 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 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 chat CBT. 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. Why does the API layer get commoditized? Because of 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 like hot swappable way, which is almost the definition of a commodity. I'm still reeling from your token spend with agents more than headcount because actually not many of 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. Yeah. What do you think that is in 24 months time? First sales force. Yeah. 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 100 K 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 e-vail for your specific workflow, like say we take the case of sales force, having an e-vail 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 e-vails 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. With the greatest of respect, e-vails today are not relatively unhelpful. It's like how good are you at 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. 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 GPUA for PhD level reasoning to IMO for Olympiad Math to humanity's last exam for this long tail of academic problems known really cares about and now they're focused on how do we get the model to do this end and workflow coordinating with multiple colleagues for a financial model or a slide deck like we're 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 as a critical research problem for the next frontier of model development. 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 open AI 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 stay 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 open air and anthropic are inherently challenged by that much more cost efficient open source model right or wrong. I think both are true. 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 accomplished their workflows. Ultimately, I think OpenAI and Anthropic are incredible investments and it seems like they're starting to be consensus around that. It 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. Okay, interesting. You said that, obviously, incredible investments. Where will they be in five years time? Valuation wise. That's on. Valuation wise. Valuation wise. If we put them both at a trillion stake, it will take. Yeah, this is hard to imagine. This is one I'll play back in five years time. We'll both lay back and go, "Oh, either we were very prescient or just completely wrong." I could definitely see one of them being a $10 trillion company, maybe even significantly higher. It feels like the opportunity of so-shed with being the frontier model is so large. It will just 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 to steal your own models, to have the best small models, etc. So I would guess at least one of them is worth more than $10 trillion. 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 super-cilious and glib. I think it's not a crazy idea. NVIDIA is obviously 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 Srebris is executing well. I'm good friends with the Etch guys. Most of the labs are building in-house chips. 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. Speaking of the world's most valuable company, you're seeing this concentration of value towards 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? Maybe to some extent, I definitely worry about how do we smooth out the benefits to society? How do we ensure that every enterprise and every individual is able to reap the full benefits of AI rather than just 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 an 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. So long as we are able to manage the societal implications of increasing inequality. Speaking of increasing inequality, you wrote an essay about, and this is taking from your Twitter, how we should eliminate income tax for the bottom half of Americans. Talk to me about that. Well, I believe this very strongly. I actually wrote this essay when I was 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 economic theory of how we have negative externalities like carbon or smoking or whatever it is, we should tax those. But on one hand, the largest positive externalities 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. 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. One great example is capital gains where I'm going to invest money in assets regardless. And so if there is 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 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. 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, when banks, 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. 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 area, but no, no, no, no, I think I say this with the nicest respect. It's just wrong. Like, because you just move. I agree that 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 that capital gains is one option. But I'm so sorry to be addicted and you can say I withdraw. 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 want, sorry, forgive me, we live in the UK where there's the green party, which is this idealist movement. It's like, oh, increased. Oh, yeah, then we leave. And then you have nothing. I agree. I think that there needs to be sensitivity analysis 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 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 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 and the grand scheme of the impact that it would have on people. Would you tax prediction marketplaces? It's gambling. I probably would. There's probably some value of having good prediction market places 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. It's pretty cool. It was pretty great. Yeah. Who's the coolest person you've met? 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 is style. He's always on point. So I would say Jensen is probably one of the coolest. The fascinating one I would love to ask you and you shouldn't give the answer to this, but I think this and people have asked it from me for us right how to answer is who did you think would be amazing? Who was surprisingly underwhelming? I don't answer that one. I get it. It's a really good one. It is an interesting question. I have met a couple of way. You were like, wow, that gives me confidence that I can do that too. 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, the people at Harvard are probably dramatically smarter than me. I went to this nonprofit called Prod where it was a bunch of kids from Harvard and MIT that were all building startups. They're very smart. Don't get me wrong. But I do think that most of us have this very equalizing feeling, that majority of people that accomplish extraordinary things, when you spend more time with them, you realize that they're just a normal person to a significant, not all of them, but most of them to a significant extent. I think that makes you feel like when I saw Ethan Thornton from mock raising $70 million as a 19-year-old. I'm like, wait, Ethan is like a chill guy and a good friend. Maybe I could do something like that one day. It just gives you the sense of being able to accomplish so much more. 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/provide leading models to the world? When you look at the benchmarks, I mean, Mr. All might make an entry at 72. It's like the Eurovision Song Contest. I love it. I'm very proud of it as you, but shit, we haven't delivered on the model side. Does Europe improve that? Does that matter? 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 research. that go to work at OpenAI and for Opinion DeepMide, 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 to continue and to be one of the largest, not only economic, but geopolitical advantages that the US has. So if you were 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 born. Actually, we do pretty well on Norway providing energy. Is that what do we just accept that? 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, had to have within Throboc. Do you buy this sovereignty argument of we need sovereign models because we don't want our data going to US or China or whatever that is? 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. I think that that is going to be an important last mile in making the models useful and 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's 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. And when you say about high in 10,000 people, the thing that's just astonishing me is the wave of cash. For me, I'm sure open-air is the same, but I've seen it specifically with Anthropic. I mean, insane levels of comp. Totally. How do you compete against that? 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. I saw your two's where you do them McCormack. Yeah, exactly. 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 who's 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. TBD met as a super intelligence group. 20 million in cash per year. Or it's in stock, but liquid. As hot to compete against. It's hard to compete against, yeah. Does that change? Does that just continue to escalate? 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 present all at least within the market will bounce itself out. What is the hardest role to have for today? Researchers just because of supply. 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, who the first author on Laura, who was previously at OpenAI, is working with us in a bunch of other top researchers. But the market is definitely getting very hot. Oftentimes it would be in the tons of millions of stock per year. For the really good people, yeah. I remember when researchers weren't paid very much. 10, 15 years ago, they were like the underpaid but brilliant people in society. Yeah. Now I feel like that's relatively changed. Yeah. Is it harder than ever to run the company? 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 seven or eight X had count. 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, 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. 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 around the business. I get in a lot of shit for everything I say these days, which is wonderful. My team just go, oh no, hi. The job is I don't deliberately rage bait, but people just hate me. I'm actually just the worst thing. But HR, I tweeted after a show with Adam and Ab Lovin, no great CEO that I've met and it's true, loves HR. They slow you down, implement policy and proceed. You're in it's just pain. Do you agree with me? The caveat I'll give is that I think it's really important. Like we definitely had challenges and scaling culture when we went from 40 people to 400 people. How does that show up? 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. But I also think that it's important in 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 head count otherwise it creates problems. Culture challenges. Before the show, we said that after the show with the dash, a couple of people thought that like 996 was the way that like McCore has run and it's like clock in, clock out. Why is that not true and how do you think about that? 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 a darsh 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 thinking about working 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. Are you ready for a quick far out? 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 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 have a light lie in bed at night and just go like, wow, pretty wild. I'm always pinching myself and I feel extremely grateful for the team and a darch and surreine how all of them made it possible because I could have imagined a hundred things that would have gone differently and we'd been a totally different circumstance. What if 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. 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. 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 staring the same direction as such a strategic business leader. I've never met him but I've always wanted to. Which competitive do you most respect? I'm why. Good question. I admire that Edward from search 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 both us in search is our ability to train models to hire some of best researchers in the world. And I admire them for execution on that front. What percent of data providers are just respectfully transactional talent marketplaces? In terms of volume or a number of competitors? Number of competitors. Not half. Half. Yeah, I'd love that. What would you most like to change about your role today? 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. Final one for you, dude. What's the kindest thing that anyone's ever done for you? 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, Richard DeHon. But prod was this nonprofit that got started at MIT in 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. They wouldn't accept it. And more core wouldn't 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. 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. Thanks so much for having me, Harry. Always great to come back. But before we leave you today, did you know the industry average for booking a business trip is 45 minutes as a massive waste of your team's time? 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