· David Senra, Alexandr Wang
[00:00:00] S0: Talk me through the difference between how you were as... You're the founder, you're running Scale. Now you're refounding this lab, working with Mark. The different ways that you approach your work in those two environments. [00:00:00] S1: At Scale, one of the things I really internalized, and I don't even remember where this advice came from, but it was kind of one of these ideas that if you want to be a good founder, you have to be able to do every job. Had a very strong point of view about how every little thing at the company should happen and should work, and that resulted in a company that was very much so a product of my effort and my point of view, and it expressed itself in all sorts of ways throughout the company, but also made me the bottleneck in very big ways. There's pros and cons of that for a company. Definitely, I think that's if you were to start a company and it's your first time starting a company, you should definitely take that approach because it's much more likely to be successful than if you don't take that approach. [00:00:00] S0: Was Scale your first company? [00:00:00] S1: Scale was my first company, yeah. [00:00:00] S0: Incredible.
· David Senra, Alexandr Wang
[00:00:00] S0: Incredible. [00:00:00] S1: Yeah. But at Meta, it was a very different assignment because we had to accomplish so much in so little time. I didn't have the luxury of being able to, quote unquote, "do every job" within the lab. I certainly am not an AI researcher. I'm also not a product designer. I'm not all these things that are totally pivotal to the organization being successful. So at Meta, very much so, I adopted this philosophy around just how do we create an environment where we're going to hire incredible people? It was job number one, hire brilliant and incredible people who are at the top of their field across every discipline. [00:00:00] S0: Why do you think you're able to do that at Meta? It's just because you have more resources? Why can you do that?
· David Senra, Alexandr Wang
[00:00:00] S0: Why do you think you're able to do that at Meta? It's just because you have more resources? Why can you do that? [00:00:00] S1: I think one of the things that was very charismatic and attractive about the founding moment of MSL was there was an opportunity to build a lab from scratch with a really, really small team, and we had a strong point of view of where we're going, personal superintelligence, that I think resonated with people. I think everyone who works on AI wants it to help people, wants it to ultimately mean something to their mom or to their grandma or to their grandpa, wants it to be something that's meaningful to every single person in the world. Because of the fact that we're doing this with the resources of Meta, there was an opportunity to... We weren't going to be bottlenecked by compute. We weren't going to be bottlenecked by infrastructure. We weren't going to be bottlenecked by distribution. We weren't going to be bottlenecked by the things that startups often are bottlenecked, but it was an opportunity to really build something that you could put your stamp on and have a lot of influence over and really shape. I think that was quite exciting for a lot of people. [00:00:00] S0: And so that's why he chooses to spend an insane amount of money, what looks like an insane amount of money, getting a handful of top talent, because then he knows that those top talent will recruit, in turn, other top talent. But you need that as a starting point, correct?
· Alexandr Wang, David Senra
[00:00:00] S1: Yeah, and I think one thing that is maybe somewhat underappreciated about that specific part is that the stock prices of OpenAI and Anthropic ran up a lot. So, it just happened to be the case that- If you looked at how much these people were making if they stayed at OpenAI or Anthropic, it was a lot of money. So, I think for a lot of the talent, it ended up being kind of net neutral in many ways, comp-wise. But obviously, I think it's kind of like the same thing. Like, oftentimes, a lot of us don't think about how much the early employees or the early researchers at these labs that have had immense stock value appreciation, what their comp looks like. We started this fully on the belief of having and hiring the most brilliant people. That's one. Setting clear North Star, personal superintelligence, those two. How do we create the environment where people do their best work? At Meta and in MSL, I'm doing much more of, quote-unquote, "traditional management," so to speak. Because these are the things I'm thinking a lot about, is like, how do I create the environment where all these brilliant people who are exceptional and very, very special can all operate and achieve the greatest expression of their talent? I'm thinking a lot more almost like a coach in some ways, versus at scale, it was very different. I was the auteur. I was the person whose point of view had to be expressed in everything that we did. [00:00:00] S0: Say more about that you feel like a coach.
· David Senra, Alexandr Wang
[00:00:00] S0: Say more about that you feel like a coach. [00:00:00] S1: I think that my job is to, A, help set the North Star and the directionality of where we want to go, which again, personal superintelligence, winning on consumer AI, building AI that means something to every person. And then spotting in the team special talent, and then giving people the environment and resources to be able to express that maximally. I think one thing that big companies often mess up is that there's exceptional people at most big companies, but they just are not empowered to do their best work, and that's why they often leave to go to smaller companies or start startups or whatever it might be. [00:00:00] S0: You mentioned Jeff Bezos' shareholder letters earlier. That's something he would repeat as well in all the books I read about him, but also in shareholder letters. Like, if you have great people that can't build, they're going to leave.
· Alexandr Wang
[00:00:00] S1: Yeah, exactly. And AI is such a special thing. I think this term research now, it feels like it almost doesn't mean anything. But by definition, humanity doesn't know the limits of what these models are capable of and what are the things you can do with them. They really are these unknown artifacts that we don't understand super well. So, it really is science and research. We are exploring the limits of what you can do with this technology and what are the things that can be built. And it is a process that requires lots of trial and error. It requires brilliant people to have incredible insight. It's one of these things that you can't think about this process like a very operationally intense process. People need space and time and resources to be able to do their best work. And I think about this in many dimensions, right? I think Nat Friedman is absolutely brilliant. And as I mentioned, so much of Muse, if there were a single person whose single point of view came through on Muse, it is Nat. It's important for him to be in a position where he can fully express all of that. And then we have some brilliant researchers. They have very, very exciting ideas. How do we create an environment where they are able to explore those? And most of them won't work out. Some of them will work out incredibly well. The cases where that works out justify all the investment. So, there's two kinds of problems, so to speak, and that analogy is like diamond mining or building skyscrapers, and it depends on what the distribution of the payoff of the things that you're working on are. So, there's some areas where the distribution is super power law. Most of the things you do will be kind of useless, but then some things you do can be literally a million times more valuable than everything else. VC, I think, is a great diamond mining kind of industry, to give a sense. It's just all about being able to identify the special ideas and see those through.