# Palantir Founder: How to Reach the Top 1% *Joe Lonsdale* · https://www.youtube.com/watch?v=nQ2vho89H1w ## Summary In this short conversation, Joe Lonsdale discusses what separates the very best engineers and builders from merely strong ones, arguing that the top performers are a different universe rather than a small step up. He explains that you need to be within a couple standard deviations of elite talent to even recognize it, which is why large institutions often fail to identify or retain such people. He credits the American tech ecosystem—its culture, capital, and high turnover—with nurturing and managing top talent in a way other countries struggle to match. ## Key points - The very best people in the world have a brain structure and problem-solving approach that is fundamentally different, not just slightly better than the second best. - You have to be within a couple standard deviations of elite talent to even appreciate it, which is why institutions staffed by people one standard deviation above average cannot tell the difference. - Lonsdale says he has a specialized ability to pick and manage some of the best engineers in the world, which larger institutions struggle with. - He argues you don't learn to win by failing; you learn to win by working with people who have ups and downs and then win. - The American commercial context has plasticity of learning—CIOS who wouldn't buy their product five years ago now do. - Identifying top tech talent is compared to music: very few people can hear the blue note, and almost no one can play it. - A very talented normal person will pick the moderately talented candidate out of a room, whereas Palantir or his VC would pick the truly exceptional one. - Bob, one of the people referenced, went on to do important work at OpenAI. - Lonsdale says managing these exceptional people is his karma after others had to deal with him when he was young. ## Notes ### Elite Talent Is a Different Universe - Lonsdale argues the very best in the world are not one standard deviation above the second best—their brain structure and way of solving problems is entirely different. - He points to the people who built PG (PostgreSQL), including Bob, as examples: very few people could have pulled that off, though they would also have done very well in an academic context. - Bob went on to do very important work at OpenAI. ### You Must Be Close to Talent to Recognize It - To appreciate elite talent, you need to be within a couple standard deviations of it. - Institutions like the Pentagon struggle because people who are only one standard deviation above average cannot tell the difference between four and five. - Lonsdale believes he has a weird, specialized ability to consistently pick and manage some of the best engineers in the world. - Institutional cultures can even reject people who are extremely good. ### The American Advantage - The US tech scene nurtures top talent, allows them to be managed, provides capital, and teaches them. - Lonsdale pushes back on the idea that you learn to win by failing: you learn to win by working with people who have ups and downs and then win. - The American commercial context has plasticity—CIOS who wouldn't buy their product five years ago now do, something other countries struggle with. ### Hearing the Blue Note - Identifying top tech talent is like music: very few people can hear the blue note, and almost no one can play it. - Roll three crazy people into a room and a very talented normal person will pick the moderately talented one; Palantir or his VC would pick the truly exceptional one. - Lonsdale jokes that managing these exceptional people is his karma after others had to deal with him as a kid, and reflects that those times together were some of the best of his life. ## Transcript [00:00] the very very best in the world are the brain structure is different and one of the things that our institutions underestimate but we're we have an advantage in America because we have this tech scene is the people think that there's like you know they're with a standard deviation of the best if you're the second best but it's not it's just a different universe it's not true and you see this in software and building software products the very best in the world the people who built PG uh the the brain structure the way of solving problems it is not at all similar to how uh you know um if you I don't want to mention their last names because I think one of them is pro happily not famous but uh >> I think my picture comes out for him [00:50] when he search online >> uh on PG with Bob they're very few people who could could have pulled that off and I don't and I think convers and and I would say if you took Ryan and Bob and put them in academic context I don't think Bob Mcgru minds I'll leave Ryan's last name out but they would have they would do very very well. >> Yeah. Well Bob obviously went on to open AI to do very important work there. It's an interesting point about about talent that I think you you you you framed in a way was very helpful to think about that you actually have to be within a couple standard deviations to even appreciate this. And so I think a big problem with a lot of our institutions for example around the Pentagon is if you have [01:26] people who are only you know one standard deviation above they can't even tell a difference between four and five they have no idea what's even going on at those levels. So you need people who are good enough to even appreciate that >> we have a huge advantage and let's just take US commercial. This is a really true thing and really not understood. Um that to appreciate talent you need to be within standard deviation of that talent. Unless you have some specialized weird this is where for whatever reason I think I have some kind of weird ability where you know I have picked some of the best and managed some of the best engineers in the world consistently. larger institutions struggle in some cases because they think [02:07] they can't they can't tell the difference between >> well I I think it's almost like those cultures reject these people who are extremely good right >> but that's and that's but but but but again to your American optimism thing which I share and we have a culture that nurtures them allows them to be managed provides capital teaches them like you know one of the things I think people get wrong is you don't learn to win by failing. You learn to win by working with people who have ups and downs and then win. So there's like a deep understanding whether they work with you at HVC or they work with others, some of whom I like and some of whom I don't, but they're they you you do learn what it means to build business. And in the [02:50] American commercial context, uh especially, you have this high turnover. though it's it's like people really do the people running these institutions on the tech side like it 5 years ago CIOS didn't buy our product now they do because it's like there's this like plasticity of of learning in the US which is something that I think other countries struggle with that it is true that identifying the difference between um you know Bob and Ryan and any very strong normal engineer is something that takes specialized quite Frankly, I think it's very similar to music. Very few people can hear the blue note. Almost no one can play it. To be what we do in tech is we hear the blue blue note on the tech talent side [03:36] >> which very few people it's like you roll three crazy people into a room >> and a very talented normal person will pick the one person who's moderately talented. Whereas if you roll those three people into Palier or to your VC will be like no we want that one. And that's crazy. Then you have to be able to manage that person. >> That's also >> basically that's like my my karma to have to manage these people now after you had to deal with with us when we were kids. So [laughter] that's how we're doing it. >> I have a lot of stories about our times together, but those our times together are some of the best of my life.