00:39:42 · Nikesh Arora
[00:39:42] S0: Great question. Look, two and a half years ago when LLMs came about and all of us, people chasing the idea of AI trying to figure out what the hell should we do, There's this whole debate around, oh great. If you remember the early announcements, the company says, we're building our own model. Remember the announcements of smart companies? I'm gonna build my own model. So was like, come on, I'm unique. Like you just told me, I gotta have my own model. So I went off to Google, my friends there, and said, hey, guys, do you think we should have our own cybersecurity model? And that was kind of an interesting debate I had with Sundar and Thomas Kurian. And I walked away from that conversation figuring out that, at least in my domain, the model was not going to be better than the super brain they were going to build as the big LLM, whether you want to call it Gemini or Opus 4.7 or pick your favorite, whichever one of your fancies. But it came to the conclusion I will never have the amount of compute training capability that I need to build my own model. That was my conclusion at that point in time. That doesn't mean there may not be specialized models in the future or specialized stacks, perhaps. I don't want to call them just models. I think models are part of the stack, as well discovered. There comes training data, vector debits, etcetera, etcetera, you guys know better. I think there will be specialized stacks for certain tasks. And the parallel I would draw is, when we were at Google doing Google search, there was a debate. Should we have Google Local be its own stack and own model? Should we have Google Maps have its own stack, own model? And Google over indexed on the universal search box and said every search has to be done there, is a commerce search, finance search, map search. Now you have to decide the trade off. If I put everything there, it doesn't lend itself to a specialized solution. I think the areas where specialized solutions, one, where you needed incremental domain expertise, domain data, domain connections, and domain APIs, and to the extent of domain fulfillment. We take e commerce. Amazon built a better stack, built Google Search, couldn't be the better e commerce engine because we didn't have the domain data, we didn't have the fulfillment, we didn't have the payment capability with Amazon. Or take Expedia, right?
00:41:51 · Nikesh Arora, Aditya Agarwal
Google stopped at giving you lead gen. They didn't close the transaction because it would have been competitive with that certain segment. So or take OpenTable to work. So there were people who built specialized stacks, for lack of a better word, using the same idea and same technology without being beholden to the overall universal model. And that's where you have to decide, where do you expect the models have so much more incremental capability that your specialized stack will not be able to differentiate versus where can you build a deeper stack where you can differentiate from the model, which usually means proprietary data, usually means proprietary connections, proprietary network effects, and proprietary fulfillment, if that makes any sense. [00:42:32] S1: It makes complete sense. You're pretty intense. You're pretty direct. You wear that on your sleeve, which I really admire. And also, in my research, it sounds like you do Zoom calls with 50 random employees at Palo Alto Networks. What do you hope to learn from that? What is the goal there?
00:42:52 · Nikesh Arora
[00:42:52] S0: If you think about any system at scale, we have 20,000 employees. If a system at scale has to produce output, you have to make sure that the last person in the chain understands why they're doing and they can make a decision on their own accord as long as they understand the why of it, not the what of it. And at that scale, 30% of the job becomes communication. Like I can sit here and discuss missiles. The question is, can 20,000 people at Palo Alto discuss missiles? Now the problem is, I get smarter every time I talk to my head of product, I talk to my head of incident response. I talk to somebody in Dario's team. I talk to Sam. I get smarter every time. They tell me stuff. The problem is the more I hoard it, the less the 20,000 employee knows about it, the less likely they will be able to do it as well as I want to do it. So you discover when you're running a scale organization, communication becomes 30% of your job. So part of my when I started the pandemic, to be honest, I started that because people are nervous. They don't know if they have a job or not. I didn't know what the problems were, so I was talking with them. And then they just continued. I've been through 7,000 to 8,000 people by now. But I learned when they tell me I just asked them, what's going really well? What do you wish you knew more? What do you do differently? And then you end up chatting with these people. They tell you all kinds of stuff. They discover sometimes discover, wow, we have that function at Palo Alto? Oh my god, this is what they do over there? Why do we do that? So I come back and say, holy shit, why are we doing it this way? So I just learn how people implement things, not always do they end up being happy things. They end up being not so happy for some people in the end, so that goes back to the intensity part of it. But I just learned so much more. Otherwise, I can get protected by 10 people who sit around my table and tell me what I wanna hear.
00:44:39 · Aditya Agarwal
[00:44:39] S1: You know, you also are very active in the kind of startup ecosystem. What are you looking for when you talk to a founder? Like, within the first kind of five, ten minutes, when, you know, early stage, it's all about just a founder, more or less. What are you looking for?
00:44:55 · Nikesh Arora
[00:44:55] S0: I just spoke to 10 of your friends, so be careful. The question becomes, look, we all have our mental models. I'm not sure right or wrong. What's your mental model? Many companies I wish I had invested in, wish I was like, it's never going to work. So don't think I have the magic sort of answer. You do your thing. Depending on how early you're looking at it, I have never met a company succeed whose CEO founder wasn't intense. So I'm perfectly fine being intense. The biggest challenge in life, I remember, is when I had my first job is you get promoted, you get promoted for doing a great job, which means everything you did was great. Hence, you're promoted. Then you now have three more people working for you. Then your job is to understand how to get those three people to be as effective and efficient as you. So you say, oh, now it's not just me. Now we're three and onetwo together because I'm managing three to a higher quality. When that gets to 20,000, it becomes a bigger problem. So the question is, which one of these founders is likely to scale to 10, 20, 30, 40 people? Because the first failure point is you get a genius who's not able to execute with 10 more people through that effect. And then it's not just about how smart they are. It's about being able to extract the best out of the 10 people, which is a different problem. 10 of us can go build a bridge tomorrow, that's one bridge. Can 10 of us build a business that builds bridges, that's a different story. And you can pick up your analogy, whatever analogy you want for that. So I think part of it is the founder's passion, their ability to understand it, they will execute. I'm a sucker for hard problems and big problems. I am not into features and incrementality. My eyes gloss over and you'll know before the meeting is over that I didn't get it or this is usually what they tell me. You didn't get it because never that they don't have a good idea. I didn't get it, which is fine. I'm perfectly fine with that. So that's usually where it goes by still my share of hits and misses Sure. In that bucket.
00:46:50 · Aditya Agarwal, Nikesh Arora
[00:46:50] S1: No. I actually really like that answer because I think that's not one that most early stage investors at least talk about. Like, we often are talking about core things that are around depth, clarity, creativity, but not about what will their ability to lead a team at even... Not forget 20,000, but even, like, 20 people. It's not a common, I would say, vector of evaluation that I see very often. So I appreciate that answer actually. [00:47:13] S0: Look, as I I usually say this in not for profit context, it's true instead of cause. I never met bad ideas really, often met bad execution. [00:47:22] S1: Yeah, sure. [00:47:22] S0: So everybody has a reasonable idea. And if they pressure test it well enough and they talk to enough people, the ideas are reasonably good. The challenge is almost all of you will pivot multiple times in your startup journey. [00:47:37] S1: At least, yeah.