Pod Insights How I Invest with David Weisburd E255: How to Hire the Top 0.1% About this episode 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 Quantum 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 Speaker names marked ? are inferred from speaking time and the show’s known hosts — they are not voice-verified.
0:00 David Weisburd? So Chris, you run a recruiting business. Give me a sense for the scale of Quantum today.
0:04 Speaker 01 Today, we're about thirty six people. We partner with about two hundred and fifty technology startups concurrently. We've built or helped build about three hundred technology companies to date. Been around for a little bit less than six years and we partner with eighty percent of the tier one venture capital firms out there.
0:23 David Weisburd? Eighty percent. So last time we chatted, you said the AI founders are at war and we're the arms dealers.
0:30 Speaker 01 That's a 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. 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 point one percent teams that can actually out compete and produce remarkable outcomes.
1:13 David Weisburd? You mentioned point one percent. Some people refer that to S tier engineers. What's an S tier engineer? And give me some indications on somebody's S tier.
1:23 Speaker 01 So when we're looking at recruiting, I think you want to have a simple mental model for how to evaluate both the clients and the candidates. Right? It's like if I'm, you know, an eighteen year old guy working at Starbucks, I'm not going to go pull some crazy supermodel. It's just not going to happen. Right? So you have to understand how attractive am I as an employer and my opportunity? And how attractive of a candidate can I 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. Right? 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 a four point oh 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.
2:07 Speaker 01 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 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 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.
2:51 Speaker 01 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 day, if you simplify it, it comes down to talent density. Why was PayPal such a legendary company? They had 14 barrels, as Chief for Boys says, they had 14 monumental S to your people that after PayPal all went on to build billion-dollar-plus companies. In some case, you know, Elon might be the first trillionaire, but they had insane density of talent. And that's what allowed them to solve all these novel problems at scale. And so when you're building a company, that's the main priority is 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.
3:36 Speaker 01 That is the key metric. When you look at Merckor, when you look at Sergey AI, 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.
3:56 David Weisburd? 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.
4:08 Speaker 01 Yeah.
4:08 David Weisburd? How do startups incorporate that into an operating mechanism?
4:13 Speaker 01 Capital is almost a commodity at this point. If you're remotely talented and you're building an AI, you can probably raise capital from venture capital firms. 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. 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,
4:58 Speaker 01 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 man entries in, you have to figure out how to tell a damn compelling story and then get as much traction as fast as humanly possible, right? That is what's going to allow you to attract that next caliber of person.
5:16 David Weisburd? One of the difficulties of the space is that AI keeps on improving, that LLMs keep on improving. What are the second order effects of recruiting in the space where AI keeps getting better?
5:26 Speaker 01 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 in 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.
5:49 David Weisburd? If you have today mid journey that has, you know, worth billions of dollars, has dozens of employees, in the future, you might have somebody with billions of dollars with five employees and this software genius.
6:01 Speaker 01 Well, if you look at like the probably best in class model right now, it's the enterprise forward deployed model, that Palantir core. You know, every company is repeating the Palantir playbook right now. We are forward 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 forward deployed model where he's saying that these forward 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, you know, forward deployed engineers, eighty percent, ninety percent of their jobs can be automated. 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,
6:45 Speaker 01 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 do we always know where that puck is heading so that as these technologies and skillsets continue to evolve, that we have those relationships and we can uniquely place those people within startups.
7:18 David Weisburd? Tell me about the Sequoia founder, whose company you fired as a customer and what happened there?
7:24 Speaker 01 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's 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, it 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. 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
8:08 Speaker 01 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. 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 bands 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 bands? And so they did. And we were able to place about seventeen of their next twenty people that ended up unlocking a billion dollar valuation. 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 constraint. And so we were able to help them maneuver through that. By very simple things, just fixing recruiting processes to accelerate time to close.
8:53 Speaker 01 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.
9:07 David Weisburd? 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.
9:18 Speaker 01 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.
9:45 David Weisburd? Rank tier, what S tier engineers, the top 0.1%, even higher than tier one, what they look for and what they prioritize. Give me a rank rankings of their preferences.
9:55 Speaker 01 When you're looking at S tier individuals, they're looking to join S tier companies. And so they're probably, 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 were their investors? More importantly, who is the founding team? Right? If the founding team is all from tier two companies, they're like, ah, 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, 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.
10:39 Speaker 01 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, 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.
11:14 David Weisburd? 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?
11:31 Speaker 01 I think you are fine overpaying. My, my perspective is this, you can't afford, nor do you need S tier 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 backend, 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 end engineer from Figma or Linear, right? So figure out what areas of your company are truly
12:14 Speaker 01 critical to you becoming the leader in the market and then overindex there. And then anyone who's in leadership roles, who's leading your backend, 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 S tiers. So the density upfront really matters.
12:42 David Weisburd? There has to be somebody standard bear in every single part of the business, every function.
12:46 Speaker 01 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 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 in other companies engineering, but their product is amazing. And I would say their underwriting, their capital markets team that does all this, 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, 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 of ARR
13:31 Speaker 01 without even bringing 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? Have they done exceptional things that companies be respect? It de-risks you because you know they've seen greatness before. But then don't overindex on pedigree. Overindex on the human being. Like, do they genuinely get excited by the work? Are they, are they going to be pulled by the work? 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
14:16 Speaker 01 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.
14:29 David Weisburd? 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.
14:37 Speaker 01 Yeah.
14:38 David Weisburd? And one question is, it's almost a thought experiment for most people, but for you, it's actually your lived experience. In 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?
14:54 Speaker 01 Great question. I think it's still common. I still, I think it's still common. 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 you've, I've worked with a number of people who've built billion and deck of 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, 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,
15:40 Speaker 01 the fruition of your potential is going to be almost entirely dependent on that. But again, if you have a great founder, that, 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 like they'll pivot, right? Twitter from Odeo pivoted, pivoted into Twitter. Cursor was a pivot. Sigma was a pivot. All these companies are pivots. So great teams, I think pivot if they're not in the right 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 matter 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.
16:20 David Weisburd? 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.
16:28 Speaker 01 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, 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.
16:45 Speaker 01 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.
16:56 Speaker 01 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 to ride that wave. But average teams can still win and perform. Now, long term, I don't know if they'll ever, you know, I think they're, they're less defensible. But they can still win. I think the market you're in and the directional correctness is actually more important.
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18:05 David Weisburd? And to continue the analogy we started with, where AI recruiting wars and you're the arms dealer, what are the weapons that are available to you to recruit the top talent?
18:14 Speaker 01 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 that's going to go compete with one of the big dogs, and I'm, and I can pull you a researcher from OpenAI or Anthropic, that's an expert in this field that you're going to dive deeper in. And, you know, the other person's competing with some random researcher that's unproven from a random university. It's like, what's the probability this person is going to win? It's, so you just, you want it, you want to be as stacked as humanly possible going into war.
18:57 Speaker 01 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.
19:17 Speaker 01 And then again, I need the right partner who if you can, if you can produce this yourself, awesome. If you, if you can't, you need to pay for a partner. You need like, there is almost no amount of money. There's certainly an inflection point where it stops making sense, but there's almost no 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, 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.
19:49 David Weisburd? And perhaps this is a, perhaps this is a dumb question, but you, you gave this example of the OpenAI researcher and anthropic researcher and some random researcher at a university. Let's say that random researcher is at a Harvard, Stanford, similar IQ. Why is it that this OpenAI and anthropic researcher has such a leg up on that person? Is it because of their learnings? And unpack why that's very specific experience is worth that 10x in comp?
20:14 Speaker 01 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 OpenAI, 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're looking to build or more complex. You want to see that they've been able to thrive, get promotions. You want, you just, you just want a story. Like, the more experience 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
20:59 Speaker 01 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. When someone's not as experienced, you're really betting on future potential. It's riskier. They could 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 gonna face. You understand where this company needs to be in 18 months. You've already seen the future because you've lived it at another company. And so that company is buying that experience.
21:41 Speaker 01 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 at 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.
22:16 David Weisburd? 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.
22:24 Speaker 01 Yeah, exactly.
22:26 David Weisburd? What does it mean? Why is it so important that that engineer has seen greatness? What does that mean and why is that important?
22:33 Speaker 01 Look, I think in general, if you're trying to build a great team, you need great people. 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 in 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, were 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 does this human have that shows me they have asymmetric levels of grit, tenacity, ambition, sharpness, et cetera?
23:15 Speaker 01 Now, if they're more experienced, 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 a successful scale. I'm in a huge, uh, I'm, 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 that is remarkably talented with evidence. They've done the exact thing I want them 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.
24:00 Speaker 01 But they're so brilliant and there's enough there where I'm betting on that person for the long term.
24:06 David Weisburd? Despite all the hoopla, it's 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.
24:15 Speaker 01 Yeah, and that's a big mistake a lot of early founders make that I have made way too many times is hiring too junior on the founding team. Or they they're missing adults in the room and they're missing people that are great at 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, but there's a lot of different ways, like Merck or obviously that average age in that company is probably 24. And they're obviously a deck of corn. So there's different things that work for different people, but I wish it was less nuanced, but it is.
24:45 David Weisburd? What differentiates a good recruiter from an S tier recruiter? Call it top quartile versus top 1%.
24:53 Speaker 01 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's performance and they're all right. Well, how do you perform against other people? But I think.
25:05 David Weisburd? Because because basically their reputation spreads through the industry and and.
25:09 Speaker 01 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? Like they're the most successful.
25:21 David Weisburd? But is there a difference between them making the most money and them placing the best candidates? Or it's it's becomes an efficient market?
25:27 Speaker 01 It's a good point. I think money alone is an indicator. Like you look at someone's performance, you can say, okay, these 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 Fortune 500 companies and doing high volume. Sales and engineer people like that's different than doing founding people. So the best person at 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.
26:08 Speaker 01 And who are the right people for respective clients because they ask very deep questions. They go 10 layers deeper. 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, uh, then they're able to go out and produce pipelines of those people at scale. So you need high IQ, you need to be moved, your, your brain has to move very fast because I'm talking a thousand to 2,000 messages a week, you know, 50 plus calls a week. Eventually you're managing hundreds of candidates in parallel. So the best recruiters,
26:50 Speaker 01 they, 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 skull 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 cortex. There's your amygdala, there's your, you know, 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, a general doctor just wouldn't know. And so I think this goes into the idea of mastery for anything you do.
27:30 Speaker 01 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 Musk, 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?
27:57 David Weisburd? I have a mental model for this. 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. Follow the path and they're 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 pave 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.
28:26 Speaker 01 Exactly. Yep.
28:27 David Weisburd? 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 kind of general? Ideas in terms of compensation and efficiencies that are easy to grasp and non nuanced?
28:40 Speaker 01 I would say this as a caveat there, pay 20% extra for the 5X. Production, but make sure they can do the 5X production. I'm not, 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't create that level of output. Pay that person like a 10 year engineer. Like that's, or if you, if you need to spike it 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. Look, Zuck is paying a hundred and a thousand to one salaries for particular people because he understands this.
29:21 Speaker 01 All these big bureaucratic HR companies, sorry, we have this, uh, we have this comp structure. Everyone is always at this, they have seven years of experience there 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. And so I would say, yes, overpay for certain clients. I would say especially overpaying equity. Um, 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. Like 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 from the people in the market.
30:06 Speaker 01 And pave the option impact, Carta, 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 you use Carta, like what do I pay these guys? Carta and these things are indexing every company in the market. So they're indexing tier three, tier two, tier one, S tier, all of that's bunched in. And so what they, what they can't tell you is that. Uh, uh, mid level engineer at Ramp is going to be 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 at 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 against, not Carter or Pave. And so you have to decide, do you want to pay a $250,000 base salary for
30:50 Speaker 01 a two-year engineer who's a frickin genius? Or is that overkill? And it's not even worth it? And you're like, why am I overpaying for this talent when I'm not even overly competing in this wedge of my product? So yeah, it's hard to give anything cop-related there that's going to be sticky because it's so contextual.
31:07 David Weisburd? How do CEOs politically navigate paying somebody three to four times higher because they're they need spikiness in a certain function?
31:15 Speaker 01 A lot of times they'll it'll come in at like Series A or something like, well, they, these guys are getting paid more than everyone we had at seed. 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? 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, we have to uniquely find these individuals and pay them more. And people, if they're smart, they're going to be okay with that. Again, if they're still making more equity,
31:59 Speaker 01 right? Like, 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. It's 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, you know, executives and founding team members prior. And so like, yeah, of course, you're going to bring in experienced talent, you're going to have to pay a premium. That's going to make everyone else's equity worth a lot more money.
32:24 David Weisburd? Yeah, as long as the rationale isn't corrupt, smart people will understand the rationale if they're focused on that one.
32:31 Speaker 01 Correct.
32:32 David Weisburd? What's what's one piece of timeless advice that you wish you would have had before you started as a recruiter that would have accelerated your career?
32:38 Speaker 01 If you wouldn't hire 100 of that person, don't hire one of them.
32:45 Speaker 01 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,
32:55 Speaker 01 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 of that talent. And then you end up having dilution of culture, dilution of results, dilution of brand and reputation, which destroys companies. And so what I realize is a good heuristic for bringing someone on your team is would you want 100 of this person on your team? Are they so awesome you'd hire 100 of them? Right? A lot of times we make concessions like, ah, we can have one of these guys. He can just sit on his island and do his thing because he's good, but like, and so you start making these concessions, but if you start thinking through that heuristic of would 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?
33:40 Speaker 01 You're like, oh shit, I might not want to hire this person. And so that, 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. And even if that means we lose millions and millions of dollars, we realize that 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.
34:07 David Weisburd? That's so good. Is there ever an edge case on that? Is there ever, you just need to make a hiring decision?
34:13 Speaker 01 Yes, and every time I regret it.
34:17 David Weisburd? So every time it seems urgent and every time is a mistake?
34:21 Speaker 01 Absolutely. Yeah. And I've done it. I've had people that in the short term 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 outproduce 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, the S tier individuals that can eventually allow us to become number one?
35:04 Speaker 01 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 tens to start new practices and I'm willing to leave millions on the table to ensure that we have a higher work product, which just means we have a better team.
35:18 David Weisburd? 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, given the people on the show. So thanks so much for being my friend. Thanks for jumping on the podcast.
35:29 Speaker 01 Thanks, David. Love this, dude.
35:31 David Weisburd? That's it for today's episode of How 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 it valuable or leave a short review wherever you listen. This helps more investors discover the show and keeps us bringing you these conversations week after week. Thank you for your continued support.
Previous E254: How to Build a 100-Year Venture Firm Next E256: What Fintech Will Look Like in 5 Years - Steve McLaughlin (FT Partners) Page updated October 31, 2026 · transcript and summary are machine-generated