00:09:24 · Aditya Agarwal
[00:09:24] S1: Yeah. That's interesting. You know, you talked about lived context and personal point of views on things. Right? And a bunch of folks who have been building for a while, right, are... Which is the other part of the code that I referred to earlier. It feels like bewildering and surprising. You feel at sea, right? And primarily because you found joy doing a certain set of activities. And some of those activities seem to be disappearing, right? I was just listening to Greg Brockman's interview where he said that I used to achieve flow state while coding twenty four... Twelve, twenty four hours. Right? But maybe many of people today or many of us who possibly had that in the past are never going to see that again because the world has changed, right? So for people who have spent a lot of time building a certain point view on the world the way it exists and their life experiences might be in areas which seem to be in the blast videos of labs, how should they think about the same statement that you just, the set of postulates that you just gave us on what is, how do they find their purpose and meaning today?
00:10:38 · Srinivas Narayanan
[00:10:38] S0: Yeah. Yeah. I mean, one change is hard, but, you know, I would say we as software engineer engineers have been blessed in many ways in the last several decades to be the people who have caused so much change for the rest of the world. So we've inflicted a lot of change on lots of other people. So for us to now complain that change is happening is actually not fair. We have to we have to deal with this change. I think the positive side of it, I'm not denying this change is hard, but, you know, the the... On the flip side, we have so much more ambition for what you can accomplish. Right? Yeah. I think you should see that as a huge positive side. And, yes, the mechanism by which you used to build and what you find joy might have changed, but you can find so much more purpose in being able to imagine and dream higher, which is I think gonna be incredibly exciting. And I think somebody else had a quote that I found pretty pretty interesting. You know? If you think change is hard, what do you think about extinction? Right? So, you know, you have to embrace change, and I think there is always change. I I think also one thing... One frame that might be helpful for people is, you know, think about the outcomes you wanna achieve. Right? Ultimately, software is a tool to create outcomes that are positive for the world. And if you really focus on the outcomes, I think you will enjoy getting to the outcomes in much faster ways through all these new technologies that we never had before.
00:12:02 · Aditya Agarwal
[00:12:02] S1: No. I think that that's a powerful one because Buddha Bedikhi to dream and bring those dreams to fruition, both of them seem to be getting unlocked. Right, though they might not be visible today. And that brings me to the next question. Both at Facebook and OpenAI, right, if I look at what you've been building, there was no set playbook Because frankly, it's unchartered waters, non defined markets, opportunities. And those categories themselves did not exist. And I'm not even sure that even in OpenAI's case, that category is fully manifested yet. And every founder, some point in time, feels that there's no playbook. So I'm just curious on when you're flying without a compass or without the GPS coordinates, how do you figure out which is the most interesting direction to go? And how do you point resources and personal bandwidth and attention?
00:12:54 · Srinivas Narayanan
[00:12:54] S0: Yeah. I mean, oftentimes at OpenAI, we kinda feel like we are building the plane as we were flying it. You know, everything was growing like crazy. Facebook, I had some experience of dealing with hyperscale, but the hyperscale at AI was just of the even different magnitude that was exposed to. We had to break a lot of the playbooks. You know, conventional software... Sorry, startups would tell you, you know, focus on one thing. Right? But AI is so broad. Like, we had a consumer product. We had a developer product. We had an enterprise product. We had a business product, and, you know, we had to deal with all of these things. So so doing... Being able to do so many different things at the same time, we just had to do it. Like, there is no way to do that. No way around it. Yeah. We had to scale at 10 times what anybody else had gone through, you know, in in our infrastructure, in our systems, in our process, in people, everything. I think one thing that really, really works is just hiring incredibly good people, not just talent, but also people with very, very high agency. That's one of the ways I think we were able to deal with. I think OpenAI was... Had one of the most talent dense, you know, places that I've ever been at. I had the incredible privilege of working with these people. And so... And, you know, I've also had the privilege of building these teams. I think when you're dealing with so much change and so much chaos, you need an amazing team, and we had an amazing team because you can then lean on all of the other people to be able to go solve these difficult problems. So I think that was extremely important. We also had to mature extremely fast compared to any other startup. Right? Because you're dealing with Fortune 500 customers. They want security. They want reliability. How do you give reliability when there is so much change and you're growing at 10 times? Right? All the systems you're relying on are breaking. So I think the other thing that helped us is somehow figuring out some balance between how do you build the guardrails while also trying to innovate. You know, we found some ways to do that. You know, when do you... How do you build and how do you buy? Because you don't have the time to build a lot of these things.
00:14:53 · Srinivas Narayanan, Aditya Agarwal
So you have to make really informed and smart decisions about, okay, what systems are okay buying and not building. Right? So it's just a combination of all of these things that we had to go through. [00:15:04] S1: I think both of them are interesting points. Right? I actually want to talk about the first one first. You know, at early stages, often, there's no personal belief that the team is the company. Right? And there's been a lot written about maybe the drama of people leaving some of the labs. But I think what's more interesting is why do people come to these labs, right? What is about the culture and the mission and the ability to make an impact? And how do you communicate and get the right kind of people? I'm curious about that. Because when I look at the kind of people who are there, it's completely mind blowing across the board. And I'm not only talking about like, OpenAI, Anthropic, but even if I look at the world model companies across the board, I think the quality of people who are who are going to all of these places are amazing. So what what has been the fundamental unlock? Right? And specifically in OpenAI's context, what really worked?
00:15:58 · Srinivas Narayanan
[00:15:58] S0: Yeah. I think the most important thing is building an extremely highly talent and steam and small teams that are... Where everybody respects each other for what they bring to the table. And so you have to just put a lot of effort into your hiring process, hiring culture, who do you bring in. And if something doesn't work out, you know, it's not that that person isn't great, but it's just not a great fit for the company. Be willing to make those decisions, hard decisions if things aren't working out, and just maniacally focusing on what's working. I think you also have to have a good understanding of what is right for your company. Right? For OpenAI, there was a whole bunch of things about, you know, really deep passion for why you're here, the passion for AI, the ability to move really quickly, the ability to make decisions in uncertainty, the ability to have a lot of agency, you know, having curiosity. So there's a bunch of things that, you know, we really, really... Ability to collaborate really well with others. And, you know, the profile that I would say really worked for OpenAI is very, very entrepreneurial people. Sometimes you hire for what is in a resume. That is not necessarily what is right for the company. You know, I think just being able to, one, incredibly smart, but also being extremely entrepreneurial and getting things done very quickly, these are extremely important. I think that is sort of true for a lot of startups. So I think we were incredibly lucky. We're also... I mean, you just look at the impact AI is having, especially OpenAI and other labs are having. I think it also brought a lot of people into the table. So we were very lucky in that way, which is that everybody wanted to work there. So we didn't necessarily have a problem of trying to attract. We had very good people who wanted to come here. And then, of course, you know, you have friends, you have referrals, etcetera. But you have to spend a lot of time on figuring out what's the right set of values, what's the right sort of people you want for your company. And every company is unique. And so that's very, very something you have to craft a lot.