Investing Billions - E255: How to Hire the Top 0.1%

Episode Date: December 4, 2025

What does it take to recruit the top 0.1% of engineers in the world — and why has talent become the ultimate constraint in AI? In this episode, I’m joined by Chris Vasquez, Founder & CEO of Quant...um Talent, one of the most in-demand technical recruiting firms in the AI ecosystem. We discuss why elite engineering talent has become the core bottleneck in AI, how companies can actually attract S-tier builders, what founders get wrong about hiring, and why talent density—not headcount—is the strongest predictor of outcomes in today’s startup environment.

Transcript
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Starting point is 00:00:00 So, Chris, you run a recruiting business. Give me a sense for the scale of quantum today. Today, we're about 36 people. We partner with about 250 technology startups concurrently. We've built or helped build about 300 technology companies to date. Been around for a little bit less than six years. And we partner with 80% of the Tier 1 Metro Capital Firts out there. 80%.
Starting point is 00:00:23 So last time we chatted, you said the AI founders are at war and we're the armed dealers. That's what you mean by that? Great metaphor. Look, I think we're in a talent war, and there's always a talent war of some sorts, but right now it's probably the most competitive one we've ever seen, which is why you see Zuckerberg spending a billion plus on individuals, which we've never seen in history. And so effectively, wherever technology is accelerating and there's innovation happening, there's going to be a war for a very small subset of talent that can produce asymmetric outcomes.
Starting point is 00:00:53 And so while these AI founders are at war with each other, we are the arms dealers. We're the talent dealers. because once you have capital, once you have your idea, your main constraint is talent. And so we specialize in helping founders build those 0.1% teams that can actually out-compete and produce remarkable outcomes. You mentioned 0.1%. Some people refer that to S-tier engineers. What's an S-tier engineer?
Starting point is 00:01:20 And give me some indications that somebody's S-tier. So when we're looking at recruiting, I think you want to have a simple mental model for how to evaluate both the clients, the candidates. It's like if I'm an 18 year old guy working at Starbucks, I'm not going to go pull some crazy supermodel. It's just not going to happen. So you have to understand how attractive I as an employer and my opportunity and how attractive of a candidate kind of land. And so when you're looking at tiers, if you will, it's basically a mental model to assess someone's pedigree or caliber. So an S-tier individual is somebody who has exceptional traits. They have very clear evidence of greatness. And so if you look at engineering, for instance, an S-tier signal could be they were
Starting point is 00:01:59 a 4.0 from Waterloo, which is arguably the best university in the world. Or it could be they were a Y Combinator founder and hit Series A. And then you bring them back in as an I see in your company. But they've been an ex-founder. They've been a founding engineer at a notable company. They've won Math Olympiads. Like, what is it about this person where they're so spiky in a particular area where their resume just oozes greatness? And the reality is very few companies can actually land those individuals. You have to build traction and become. worthy of attracting those people. It's very challenging. But more or less, the idea is when you're building a startup, the thing you care about is talent density. Talent density is king. If you run a
Starting point is 00:02:40 service-based business, your team literally is the product. So the caliber of the people in your company determine the fate of your company. If you're building a product company, it's the people that are building the product. So again, the quality of the product that you can build and sell is determined by the quality of individuals that can build it. And so everything at the end of the today, if you simplify it, it comes down to talent density. Why was PayPal such a legendary company? They had 14 barrels, as Chief of Boyce says. They had 14 monumental S-tier people that after PayPal all went on to build billion-dollar-plus companies, in some case, you know, Elon might be the first trillioner, but they had insane density of talent. And that's
Starting point is 00:03:17 what allowed them to solve all these novel problems at scale. And so when you're building a company, that's the main priorities. How do I build the most talent-dense team? It's not like 21, 22, where everyone's bragging about, I managed a thousand person company and we did this, this and that. It's about good business. It's about revenue and profit per employee. That is the key metric. When you look at Mercor, when you look at Sir J.I, who has in between 50 and 100 full-time people doing over a billion in revenue, that's impressive. And so you don't need a billion people anymore. You need better people that can leverage AI. And again, talent density wins. The best, most talented, nimble team typically wins in business.
Starting point is 00:03:56 And AI companies don't seem to be constrained by capital. They seem to be constrained by talent. And that's obviously what you work with. But there's this new paradigm of talent over capital in the marketplace. How do startups incorporate that into an operating mechanism? Capital is almost a commodity at this point. If you're remotely talented and you're building an AI, you can probably raise capital for venture capital firms.
Starting point is 00:04:21 And so the problem is, is not capital. You have to look at the system and say, where is the constraint? Capital is not the constraint. We have billions and billions flowing into AI. The constraint is there's not enough talent. It's very new technology. How many people have commercialized successful LLM agentic applications? Not that many companies.
Starting point is 00:04:38 And so there's a very small subset of talent that has proven they can do these things. And so everyone's fighting over a much, much smaller pool of available talent. And then the salaries because of that demand go through the roof. And so companies, if you want to compete at the highest level, either you have incredible investors and incredible founding team. And there's a clear story you can tell just from the investors and the founding team and the idea. Or if you don't have an ex-unicorn founder, you don't have 20 million from Andreessen. You have to figure out how to tell a damn compelling story and then get as much traction as fast as humanly possible. Right.
Starting point is 00:05:13 That is what's going to allow you to attract that next caliber of person. One of the difficulties of the space is that AI keeps on improving, that LLLL. to keep on improving. What are the second order effects of recruiting in the space where AI keeps getting better? It's an interesting point. When it comes to AI getting smarter, my common sense says you're going to need less engineers in the future since one person with AI can do much more. So you need much more sophisticated people and companies with great design taste, great product sense, commercial aptitude, architecture skills, but the actual AI can do a lot more work as we continue to progress. If you have today mid-jurney that has, you know, worth billions of
Starting point is 00:05:53 dollars, has dozens of employees. In the future, you might have somebody with billions of dollars with five employees. And is this software genius? Look at like the probably best in class model right now. It's the enterprise forward deployed model, that Pallenture coin. You know, every company is repeating the Pallenture Playbook right now. We are for deploying engineers as vertically focused on specific customers and customizing workflow. And they're using AI, but I talked to a founder last week, he's trying to disrupt the entire Ford Deployed model. Or he's saying that these four deployed engineers, it's still 80% engineers, only 20% actually using AI. But he's saying when we improve reinforcement learning, and these machines actually get really good at learning from each other and from themselves, eventually even Ford deployed engineers, 80% and 90% of their jobs can be automated.
Starting point is 00:06:39 So I don't know how far the rabbit hole goes or where things will evolve to. I think what's interesting from my angle being a recruiting firm is we don't. specialize in one particular technology we specialize in the future so wherever the puck is going no matter how technology evolves there's going to continue to be a refined and constrained set of individuals that are capable of innovating and pushing the future forward and so that's that's the game that we play is how we always know where that puck is heading so that as these technologies and skill sets continue to evolve that we have those relationships and we can uniquely place those people within startups. Tell me about the Sequoia founder whose company you fired as a customer and
Starting point is 00:07:22 what happened there. Yeah, I mean, this happens all the time. I think with early founders, you usually have to have a little bit of reality distortion where you think you're the hottest chick in the bar. And then you realize there's a lot of hot chicks in the bar and that your company is not the only amazing mission. And so you're able to recruit a lot of early founding team members at really competitive rates and it worked really well. And then, you know, you realize at some point that doesn't always keep trending. So at some point, what got you here won't get you there and you have to completely reshift how you look at things.
Starting point is 00:07:51 And the biggest mistake I see a lot of founders make is they're very constrained on cash and compensation and equity. And you've got to just, you have to understand the realities of the market. And so there was one company in particular that was looking to hire a lot of people. And we said, you have to basically be here in these ranges. They said, no way, we're going to figure this out. We said, okay, we'll try it out.
Starting point is 00:08:14 seven offers later, seven offer declines. We had no results in three to four months. We said, look, you have to come up to these bans or otherwise we can't help you. So we ended up parting ways with the client. They came back three months later, hadn't made a single hire yet. And we said, hey, are you ready to change the bans? And so they did. And we were able to place about 17 of their next 20 people that ended up unlocking a billion dollar valuation.
Starting point is 00:08:38 And so for them, they had distribution. They had a great founding team. They had it all figured out. But engineering and velocity of product building was their only condition. strain. And so we were able to help them maneuver through that by very simple thing, just fixing recruiting processes to accelerate time to close. So we have a faster recruiting process and obviously making sure the comp bands were in alignment with the caliber of people that they were targeting. And then once that problem was solved, it was unicorn status for these guys.
Starting point is 00:09:07 It was an interesting A-B test where it was upstream, they already had were Sequoia backed, And then it was upstream, they A, B, test their own strategy, which is paying up for talent or not paying up for talent. They got to test both in one company. Yeah. It happens all the time, man. I think a lot of founders get very bottleneck. Well, I recruited this guy who was a founding engineer from Uber at 200K base. It's like you probably knew that guy. You had a relationship. So people think because they're able to land a couple incredible hires at really competitive rates that they can somehow scale that strategy. And what you do initially just does not scale. So you have to change your mentality. and your approach as you continue to grow your company to ensure that you're going to win. Rank tier, what S-tier engineers, the top 0.1%, even higher their tier one, what they look for and what they prioritize. Give me a rank rankings of their preferences. When you're looking at S-tier individuals, they're looking to join S-tier companies.
Starting point is 00:09:58 And so there's a myriad of things that could be. But when I look at the tiers and how we evaluate that, one is their backing, who are their investors? More importantly, who is the founding team? If the founding team is all from tier two companies, they're like, we want S-tier companies. I had this problem actually with two founders that had raised $20 million and they're really smart guys, and they're building a foundation model.
Starting point is 00:10:19 But neither of them actually came from research backgrounds. And it was nearly impossible for them to recruit an S-tier researcher because the researchers didn't respect any of their backgrounds, even though they had great business sense, amazing founding. So the caliber of the team they're going to assess, they're going to say, do I, am I really inspired by the density of talent here? Do I feel like this is a privilege to work alongside people? That's going to be very big.
Starting point is 00:10:38 I would say the market size, the opportunity, the traction. Look, they're going to want to be excited about the mission. Some people have a very particular mission. They're excited about a domain space, a problem space they're excited about. And a lot of people are more practical. They're like, I'm really good at this particular skill set. I want to apply my skills to a company with the greatest commercial upside where I can have a giant win. So each person's going to have different core motivators.
Starting point is 00:11:01 But the DNA thread that connects them all is A players want to work with A players. S tier people want to work with other S tier people. So they want to work with people that are really fucking impressive, really obsessed with what they do, where they can have giant outcomes. And because of this talent density, you mentioned probably the most famous case of the PayPal Mafia where these dozen of people have become so successful. Does it sometimes make sense to overpay for the first couple of hires knowing that it's not sustainable so that you could create this talent density? Or is it something that you have to keep on paying up for? I think you are fine overpaying. My perspective is this.
Starting point is 00:11:34 You can't afford, nor do you need, S-tier P. people in every category from top to bottom of your business. It's not possible. And so the way I think through this is figure out which areas in your business that you need to compete on and be the best at. What areas in your business that if you spiked the hardest in would allow you to dominate that much more. And so for some companies, it's going to be product. For some, it's going to be design. For some, it's going to be engineering. For some, it's going to be more back-end, high, you know, high-throughput low latency, you know, like hedge fund type stuff. You don't need beautiful UI work there so you don't need the front and engineer from Figma or linear, right? So figure out
Starting point is 00:12:10 what areas of your company are truly critical to you becoming the leader in the market and then over index there. And then anyone who's in leadership roles, who's leading your back end, who's leading your design, you want to try to have the leader or the barrel who's kind of scaling and owning that division. You want that person to be as high caliber as possible because it's highly unlikely you hire a tier two or tier one individual that's somehow able to recruit these tier so the density up front really matters there has to be somebody's standard bear in every single part of the business every function yeah you need someone leading each function that's truly the best at that function and then figure out where you are going to need to out
Starting point is 00:12:53 compete people and make sure you have the absolute best in class people there like for ramp for instance, they definitely, I would say they compete more on product than engineering. They've got some damn good engineers. Don't get me wrong, but there's, I think, deeper talent density and other companies in engineering, but their product is amazing. And I would say they're underwriting, their capital markets team that does all these creative financing deals. I think that's a big wedge they compete on. And so those people have to be extremely elite. And so again, you don't need to be remarkably elite in every area. Like a lot of these companies that have PLG products that just sell themselves. Like they don't even have great sales teams. They can get to hundreds of millions
Starting point is 00:13:30 at ARR without even bring in big sales teams because the product itself is where they need to spend that time making sure they have the absolute best people in the world. So bottom line, I think it's just really important to try to optimize for density, hire the best people you possibly can early. You want to pay attention to pedigree. You want to pay attention to what are the signals that make this person truly elite. Schools can be overrated. I would say they're very important if it's a junior candidate not as important later on but like do they come from exceptional backgrounds that they've done exceptional things that companies be respect it de-risks you because you know they've seen greatness before but then don't over-index on pedigree over-index on the human being like
Starting point is 00:14:06 do they genuinely get excited by the work are they going to be pulled by the work or are they just here for a commercial outcome and their heart's not really in it right do they fit in with your work style do they fit in with your energy your culture those things are equally important but the key here is that like you want to build a great team you have to have great human beings and you're better off hiring fewer people that are truly world class. One of the reasons I wanted to get you in on the podcast is you're in the eye of the storm. There's an AI storm and you are at the eye and upstream of everything, which is talent. Yeah.
Starting point is 00:14:38 And one question is, it's almost a thought experiment for most people, but for you, it's actually your lived experience. And what percentage of times can somebody have this S-tier talent density and not find a product and not be successful from a product side if they have the right talent in the room? Is that even possible, or is that common? Great question. I think it's still common. I think it's still common.
Starting point is 00:14:58 Look, I don't think any amount of talent can find product market fit, right? Like, the founder, in my opinion, is still responsible for finding PMF. I think the team is an accelerant. I think that's really on the founding team to find PMF. Now, I still think that talent matters, but I think there's plenty of remarkably talented teams with the most insane people in the world. The products just don't hit. And just because I've worked with a number of people who've built billion,
Starting point is 00:15:22 and deck a billion-dollar companies that assemble great teams and build their next product, it just doesn't work because what you did in the past has no say in what you do in the future. And so I think talent alone isn't the answer, but if you do have a good idea and you can find PMF, the fruition of your potential is going to be almost entirely dependent on that. But again, if you have a great founder, that founder is the tip of the spear when it comes to the talent density. And so good teams, I will say, even if the product sucks and they, they'll pivot, right? Twitter from audio pivoted to Twitter. Curser was a pivot. Thigma was a pivot. All these companies are pivot. So great teams, I think, pivot if they're not in the right
Starting point is 00:16:03 model and can usually figure things out. But again, venture is a very risky bet. It's a gamble. No matter how smart you are, no how many billion dollar companies you built. So it's just a matter of probabilities. And I would say your probability of success dramatically increases based off the caliber of your team. Is the opposite true, which is no matter how good of an idea or maybe you've got an early product market fit that with a bad team, you just can't go to the distance? I think a bad team, you're probably screwed. But I think, in my opinion, an average team in a really hot market can still crush it
Starting point is 00:16:35 or an incredible team in an average market will be average at best. So I think the market matters a lot. Like, let's take what we're doing right now. AI is hot. It's not hard to bring in business. if you're a good recruiting firm right now right it's there's way too much demand so i see companies that are very average actually doing pretty good and we're doing exceptionally well but again we have a fantastic team and we're benefiting from this incredible wave and we just happen to have a big boat
Starting point is 00:17:03 to to ride that wave but average teams can still win and perform now long term i don't know if they're you know i think they're they're less defensible but um they can still win i think the market you're in and the directional correctness is actually more important. Ever wanted to explore the world of online trading, but haven't dared to try, the futures market is more active now than ever, and plus 500 futures is the perfect place to start. Plus 500 gives you access to a wide range of instruments, S&P 500, NASDAQ, Bitcoin, gas, and much more. Explore equity indices, energy, metals, Forex, crypto, and beyond. With a simple, intuitive platform, you could trade from anywhere, right from your phone.
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Starting point is 00:18:26 to recruit the top talent? From a weapons standpoint, like we use basic technology. We don't have any technology differentiator. But the way I look at it is if we're going to war and you've got machine guns and grenade launchers and I just have pistols, you're going to have a huge advantage, even if I'm a more skilled operator. And so the arms for me, again just goes back to the quality of the talent, right? If you're trying to build a foundation model, it's going to go compete with one of the big dogs. And I'm, and I can pull you a researcher from Open AI are Anthropic. That's an expert in the steel that you're going to dive deeper in. And, you know, the other person's competing with some random research that's unproven from a
Starting point is 00:19:02 random university. It's like, what's the probability this person is going to win? It's, so you just, you want to be as stacked as humanly possible going into war, right? You want to make sure you're stacked with the absolute best equipped team to solve your business problems. And so I just look at it as a very rudimentary metaphor of like, yeah, you just want the best people. The people are your arms, right? Because the people are going to be the ones building the product and selling it. And so you just have to be ruthless with your standards for who you are. And then again, you need to write partner who if you can, if you can't, if you can't, you need to pay for a partner. you need like there is almost no amount of money there's certainly in a flushing point where it stops
Starting point is 00:19:42 making sense but there's almost a amount of money that you could overpay for the right people if they can create billions and tens of billions of enterprise value with a small team given the leverage of AI you can't afford not to try to find those people and so that point you're willing to pay Navy SEAL teams to come in there and help you land those people and perhaps this perhaps this is a dumb question, but you gave this example of the open AI researcher and anthropic researcher and some random researcher at a university. Let's say that random researchers at Harvard, similar IQ. Why is it that this open AI and anthropic researcher has such a leg up on that person? Is it because of their learnings and unpack why that's
Starting point is 00:20:24 very specific experience is worth that 10x in common? Look, I don't want to oversimplify it because this random researcher from this random university could have some really in-depth experience or a mental model of the world, they could see something. Oh, again, there are these outlier hires you don't want to overlook and simply pedigree hunt. Oh, just because they're from open AI, they're going to be fantastic and they're going to make me win. That is not how talent works. But in general, you want people that have seen greatness before. It de-risks you, right? You want people that have evidence that they have built something equally complex that you are looking to build or more complex. You want to see that they've been able to thrive, get promotions. You
Starting point is 00:21:02 You just want a story. Like, the more experienced someone has, the clearer the story is. And when we're hiring people, we're trying to fill in the end of the story. We're trying to assess what we have in front of us and write the end of the story, but we don't know it. And so the more story there is, the more data we can actually start to read and see patterns. Okay, this person went here. Every place this person goes, they seem to find the hardest problem and solve it. That tells us something.
Starting point is 00:21:24 When someone's not as experienced, you're really betting on future potential. It's riskier. They can pay out big. But there's just less evidence to make a conclusive. decision and they just they have less experience they're going to come in and in some cases that might be better because you need to completely reimagine something and experience can actually be a bottleneck but in a lot of cases having experience having seen multiple different environments you understand the problems that you're going to face you understand where this company needs to be in 18 months
Starting point is 00:21:50 you've already seen the future because you've lived it at another company and so that company's buying that experience that they can accelerate their guarantee or probability of of solving that as fast as possible so experience is just beneficial but I always say look if you're building a startup, hire for two people. One is an expert that brings deep expertise in a particular area. And two is high slope 20 something year olds. Like either find people that are the best their particular craft that bring in an intense amount of specific knowledge that you need or hire really smart, really high slope 20 something year olds that can work 80 plus hours a week and just muscle your way to victory. Right. Those are two great archetypes of people.
Starting point is 00:22:30 Which is essentially the same thing in five years, right? If you have somebody starting high and still with a nice slope and you have somebody starting lower, but with a high slope in five years, they'll cross. Yeah, exactly. What does it mean? Why is it so important that that engineer has seen greatness? What does that mean and why is that important? Look, I think in general, if you're trying to build a great team, you need great people.
Starting point is 00:22:50 How do you know if you have a great person, right? You have to look to the history, right? My odds of picking a great person if I can see and evaluate their history versus me going and blind without knowing their history and just evaluating a human being in an hour, it's going to be way harder to do. And so I just want to look for any signal. If it's a junior candidate, right? Maybe I'm looking at everything in their life where they like, were they a D1 athlete, you know, where they, an immigrant that came from nothing and had to start working when they were 12 years old to take care of their parents and their family. Like what what qualities is
Starting point is 00:23:23 this human have that shows me they have asymmetric levels of grit, tenacity, ambition, sharpness, et cetera. Now, if they're more experience, I'm trying to build a core banking operating system for a new company, right? I want to hire someone I know has done that at a high quality because if I fuck that up and I hire someone who hasn't built something as beautiful and complex and sophisticated, I hire someone who's done it at a little shitty startup versus like a company that did that at successful scale, I'm in a huge, I'm in massive levels of risk. There's a very high probability that person doesn't know how to do that. And so the reason you're paying is to bring in talent to de-risk your ability to execute. And so I would rather bring in to either talent
Starting point is 00:24:01 that is remarkably talented with evidence they've done the exact thing I wanted to do at my level or greater, or someone that has remarkable intelligence, still has signals of greatness, but they might not have done the exact thing I need to do, but they're so brilliant and there's enough there where I'm betting on that person for the long term. Despite all the hooplets, not just the extreme upside that you're hiring for, you also want them not to torpedo the project and not to be so bad that actually creates a contagion within the group. Yeah, and that's a big mistake a lot of early founders make that I have made way too many times is hiring two junior on the founding team.
Starting point is 00:24:35 They're missing adults in the room and they're missing people that are great architecture and have good product sense. And you get a bunch of like 22-year-olds that can run through walls, but they didn't have enough expertise up top to give them the correct direction. And so, look, there's a lot of different ways. Like Mercor, obviously, that average age in that company is probably 24. And they're obviously a decadour. And so there's different things that work for different people.
Starting point is 00:24:55 But I wish it was less nuanced, but it is. What differentiates a good recruiter from an S-tier recruiter? We'll call it top quartile versus top 1%. Well, if we look at very basics, the best recruiters in the world are, if they're an agency, they're making well over a million a year. So you can just look at someone with performance and they're all right, well, how do you perform against other people? But I think, because basically their reputation spreads to the industry and, and, you
Starting point is 00:25:23 Yeah, they just put up results. Like, how do you know the best people in any industry? How do you know the best VP of sales? Right? How do you know? They make the most money, right? They're the most successful. But is there a difference between them making the most money and then placing the best candidates or it becomes an efficient market?
Starting point is 00:25:40 It's a good point. I think money alone is an indicator. Like, you look at someone's performance. You can say, okay, these people perform. I'll say this. Just because you were great in one recruiting industry and you crush it doesn't mean you're going to be good in another. They're different sports. So recruiting for giant 14.
Starting point is 00:25:53 500 companies and doing high volume sales and engineer people like that's different than doing founding people so the best person at that that might actually suck at this and so you have to know what sport you're playing right but the best recruiters in my opinion they have very high IQ and they're very good at pattern recognition they can see small details other people can't see they have velocity man when you're in recruiting you have to move fast in my opinion the best people they're extremely clear on what it is they need to do and who are the right people for respect to clients because they ask very deep questions. They go 10 layers deeper.
Starting point is 00:26:29 They're in the details. So they're able to clarify what is the absolute perfect hire for this company contextually that's going to produce the outcome. And they're able to reverse engineer the people based on that. It's much more first principles thinking versus just trying to do basic company and title pattern matching. And then they're able to go out and produce pipelines of those people at scale. So you need high IQ.
Starting point is 00:26:52 you need to be moved. Your brain has to move very fast because I'm talking a thousand to two thousand messages a week, you know, 50 plus calls a week. Eventually you're managing hundreds of candidates in parallel. So the best recruiters, they move with crazy velocity, but they move with surgical precision. They're trying to understand each client, each role with a deeper context. And so the best way I could put it is like a good recruiter would be like a doctor and an S tier recruiter would be like a brain surgeon. So if we were to remove the top of my school here and look at my brain and I'm a doctor, looking into my brain, I could probably point out a handful of distinctions. There's your prefrontal
Starting point is 00:27:27 cortex. There's your amygdala. There's your brain stem. But if a brain surgeon were to look at that, he or she could point out 500 little details about the brain. A general doctor just wouldn't know. And so I think this goes into the idea of mastery for anything you do. The masters can simply look at hundreds of data points and make more refined decisions because of their increased awareness and consciousness in that space, which allow them to make very precise decisions that lead to much better outcomes. And then the best, the best, have that cognitive ability and they can work 70, 80 hours a week and Elon Muskett, right? So part of it is just intellect and pattern recognition. And the other piece of it is how much of an engine do you have? I have a mental model for this.
Starting point is 00:28:12 It's kind of like Lewis and Clark when they're going across the country, they pave a certain path. And just to get to that same part of the country, it's easy. They just follow the path. And spending all their mental energy on the next realization or the next, the next forest and the next place to pave. And the best have just spent so much time that they get to that, like all the obvious realizations that these people are still trying to pay the path on on the most obvious things to them, they get to go deeper and deeper and deeper. And every day they go deeper and deeper into the forest. Exactly. Yep. I've heard this recruiting heuristic, which is you want to pay 20% higher and get people that produce five times more. Are there any
Starting point is 00:28:48 kind of general ideas in terms of compensation and efficiencies that are easy to grasp and nuance? I would say this. There's a caveat there. Pay 20% extra for the 5x production, but make sure they can do the 5x production. I'm not a fan of just paying extra to land talent. I'm a fan of paying people their market rate for their value. And so if you have like this insane engineer with an insane pedigree and they've got two years of experience and they truly can create that level of output, pay that person like a 10-year engineer. Like that's, or if you need to spike in a particular area, pay two, three X the typical salary. If there's a one particular area, like you need the best research in the world, it's worth it.
Starting point is 00:29:30 Look, Zuck is paying 100 and 1,000 to 1 salaries for particular people because he understands this. All these big bureaucratic HR companies, sorry, we have this, we have this comp structure. Everyone is always at this. They have seven years of experience. They're at this. It's just like, it's so, it is so generic. and antithetical to landing great talent because you have to pay based off merit, not based off of years of experience.
Starting point is 00:29:55 And so I would say, yes, overpay for certain clients. I would say especially overpay in equity, but make sure you're giving market rate in cash, overpay them in equity, so they're more bought in. You don't want to have these guys that are like trying to get Citadel level-based salaries at startups. That's ludicrous, right? So you don't want these mercenaries ever, right? but you do want people that are remarkably talented and they are going to have offers for the people in the market and pay the option impact carda they're not going to tell you what the salaries are their other offers are it's the actual real market data and that's the problem with a lot of these comp structures like if you use carda like what do i pay these guys carda and these things are indexing every company in the market so they're indexing tier three tier two tier one s tier all that's bunched in and so what they what they can't tell you is that a mid-level engineer at ramp is going to be
Starting point is 00:30:44 making more than a staff level engineer at Webflow. And it doesn't factor in the pedigree tax that you have to pay for landing better people with different companies. And so the best data is going to be almost anecdotal where it's like, all right, well, what other offers do they have? Because that's what we're competing. It's not card or pay. And so you have to decide, do you want to pay a $250,000 base salary for a two-year engineer who's a freaking genius? Or is that overkill? And it's not even worth it. And you're like, why am I overpaying for this talent? and I'm not even overly competing in this wedge of my product. So, yeah, it's hard to give anything comp related there that's going to be sticky because it's so contextual.
Starting point is 00:31:21 How do CEOs politically navigate paying somebody three to four times higher because they need spikiness in a certain function? A lot of times it'll come in at like Series A or something. Like, well, these guys are getting paid more than everyone we had at C. I'm like, well, cool, those guys have way more equity. So they need to be okay with that. Like, they need to know we're going to bring in a lot more people. they're getting paid more money than all of us. But they're not going to have nearly as much equity, right?
Starting point is 00:31:44 And that's why they're the founding team or the early team. So one is just making that distinction. It's like you guys are paid more equity for a reason. And then two is like you have to just have a practical standpoint where like, what is everyone in this company's goal? We want to IPO for the maximum money we possibly can. Right. So if we need to do that as a team,
Starting point is 00:32:03 we have to uniquely find these individuals and pay them more. And people, if they're smart, going to be okay with that. Again, if they're still making more equity, right? The founding team still has the most equity, which is the most valuable thing in the company. And so I think you just have to set expectations there is like everyone wants to win a lot of the founding team that has the most equity. They're not even that impressive often. A lot of these people aren't like massive proven executives and founding team members prior. And so like, yeah, of course you're going to bring in experience talent. You're going to have to pay a premium. That's going to make everyone else's
Starting point is 00:32:35 equity worth a lot more money. Yeah, as long as the rationale is incorrect. smart people will understand the rationale if they're focused on that one piece of timeless advice that you wish you would have had before you started as a recruiter that would have accelerated your career if you wouldn't hire 100 of that person don't hire one of them the easiest thing to do as you're scaling a company is to compromise on your talent density you're constantly fighting talent entropy because the more successful you are the more demand you have for your product or service, you want to just keep throwing bodies at problems because you're passing up on so much money. And so you're very likely to at some point compromise on the quality
Starting point is 00:33:22 of that talent. And then you end up having delusion of culture, delusion of results, delusion of brand and reputation, which destroys companies. And so what I realize is a good juristic for bringing someone on your team is, would you want 100 of this person your team? Are they so awesome, you'd hire a hundred of them. Right? A lot of times we make concessions, like, we can have one of these guys. You can just sit on his island and do his thing because he's good. And so you start making these concessions,
Starting point is 00:33:44 but if you start thinking through that heuristic of what I want 100 of these people on my team, what would the company look like if every hire we made was exactly like this person? You're like, oh shit, I might not want to hire this person. And so that's been a good heuristic for me because I think now we've really tightened up the caliber of people we allow into the company.
Starting point is 00:34:01 And even if that means we lose millions and millions of dollars, we realize that density is the most important thing. And so as we continue to scale our company, the number one heuristic for us of success is the caliber of people we continue to bring on. And so that question or that frame helps me stay focused on what's important. That's so good. Is there ever an edge case on that? Is there ever you just need to make a hiring decision? Yes. And every time I regret it. So every time it seems urgent and every time it was a mistake. Absolutely. Yeah. And I've done it. I've had people that in the short term,
Starting point is 00:34:38 solve these problems and we're starting to increase revenue and then 12 months later, they're gone. And that's why we're launching multiple new divisions right now in the company. I've been recruiting for go-to-market to open up this new division for eight months. I've interviewed over 70 people. I have not made one hire yet. And I have competitors that are building lots of traction and momentum and go-to-market. I'm choosing not to because I know that with two to three of the right people that are at my bar, I can out-produce 15 of their team. and over time they won't be able to keep up with it. And so I'm careful about when I enter a market is do I have the people,
Starting point is 00:35:13 the S-tier individuals that can eventually allow us to become number one. But if we have a bunch of seven, eights out of tens at the beginning, it's going to be hard to ever be better than that. And so I want tents to start new practices. And I'm willing to leave millions in the table to ensure that we have a higher work product, which just means we have a better team. Well, Chris, you're one of the most excellent and focused on excellence people I ever I've ever met, and that is a huge statement
Starting point is 00:35:38 given the people on the show. So thanks so much for being my friend. Thanks for jumping on podcast. Thanks, David. Love this, dude. That's it for today's episode of how I invest. If this conversation gave you new insights or ideas, do me a quick favor. Share with one person in your network who'd find a valuable or leave a short review wherever you listen.
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