00:00:00 · Nikesh Arora, Aditya Agarwal
[00:00:00] S0: This might come as news to you, but humans have been writing bad code for a very long time. No company knows how many models they have in place right now. Nobody has any idea how safe those models are, how secure they are, if they have backdoors or not. [00:00:12] S1: Tell us about Mythos. [00:00:12] S0: In every enterprise, you'll find vulnerabilities in vendor deployed software, custom code, and open source. This thing's gonna find all of them. We will find vulnerabilities which should have taken us ten years to find in six months. [00:00:23] S1: What does it mean to have agentic security when we don't even know what agents are? But all of us are building agents all the time. [00:00:28] S0: In cybersecurity, there's generally two modes. One mode is [00:00:33] S1: Welcome everyone to SPC. Very excited to have all of you here. Obviously, very excited to have Nikesh here to tell us about his own minus one journey and also to get a window into what I think are bound to be really interesting times in our industry, but also in cybersecurity in particular. Nikesh, just rewind a little bit. You were telling me before the talk here about some of your incredible origin story. [00:00:59] S0: I don't know about incredible, but sure.
00:00:59 · Nikesh Arora, Aditya Agarwal
[00:00:59] S0: I don't know about incredible, but sure. [00:01:03] S1: And I think it was... What I found really interesting was the audience at SPC, a lot of them are here trying to figure out what to work on next, trying to figure out kind of that in between phase that we call minus one, how to really, I would say, step on the gas in terms of ambition and find that interesting spot between being patient and impatient and letting, in some ways, the bigger idea come to you. So you've had a number of incredible transitions in your own career, leading to some incredible opportunities. How have you navigated some of those times? How have you found inspiration? How have you really taken, in some ways, your time to find like the big thing because you have an incredible, I would say, success rate?
00:01:47 · Nikesh Arora
[00:01:47] S0: Well, first of all, thank you for having me here. Appreciate it. And thank you all for braving the rain and showing up here. I hope this is useful for you. I think in terms of inspiration and in terms of how to bide your time until the right thing hits you, one of the challenges with extremely smart people is they can apply themselves to any problem and put their heart and soul into it and do a great job for it. Many people, I'm guessing everybody in this room believes they're smarter than the average person. That's why you're here. I think everyone. Okay, well, don't forget they're here. Eventually, if everybody believes they're smarter than the average, there's a problem, but that's a different mathematical problem. We'll deal with that later. But if you were to apply yourself to something, and if I said, here are options, three options one, two, and three, you could say, I could do all of them. I can start the world's best restaurant. I can start the world's best airline. I can build the best video model with beating Sora, L and M, etcetera. So the question really becomes is, what can you connect with and how tractable the problem is and how patient are you? I think to your point about patience and patience, you said earlier when we were talking, there are too many companies being started. I think we sometimes get waylaid or trapped by the idea that somebody else is getting traction so quickly. Therefore, I must get traction quickly. And then the risk is then you end up building a feature. You end up building a part solution to the problem. You're not solving the real problem. In our times, can always point to Elon Musk and say, look, he didn't take an easy problem. He said, we're going to go to Mars. Sounds like a hard problem. They said, I'm going build an electric vehicle or go build an autonomous vehicle or Waymo did that. Those are hard problems. They're ten, fifteen, twenty year horizon problems requiring tons of capital. And you almost feel like, holy shit, how am I going to even get started in that direction and solve something that big? That's at one extreme. And the other extreme, you think you can go program something really quickly, and in six months, you'll have a product market fit. In twelve months, you'll have 100,000,000 ARR. So the answer is somewhere in the middle.
00:03:51 · Nikesh Arora, Aditya Agarwal
I'm not sure that there's a moat for a company that built 100,000,000 ARR in the first seventeen months or twenty months. Maybe that moat may exist, may not exist, may get obliterated by the next person. Or maybe it becomes a competitive advantage because you got out first and you captured all the market and that became... You became the winner in that space. [00:04:12] S1: You know, it's interesting, Nikesh. If I look at, for instance, when you when you joined Google, it must have been 30,000,000,000 in market cap? [00:04:20] S0: 18, but it was counting. [00:04:21] S1: 18 was... Exactly. So it's kind of... You know, it's obviously close to what? $3.54000000000000 today, something like that. And then when you joined Palo Alto Networks? [00:04:32] S0: Coincidentally, '18. [00:04:33] S1: '18. Coincidentally, On its way to the big one. So in some ways, when you joined them, they were kind of like a fraction of the size that they were, but they had some scale. So were you joining them for what they already had? Were you joining them for, in some ways, the latent potential energy, some combination? And what was your version of patience and impatience as you got to those? Maybe Palo Alto Networks, I think you've talked a little bit about this. You kind of came in as not knowing a ton about cybersecurity. What did you see kind of before you joined?
00:05:08 · Nikesh Arora
[00:05:08] S0: Well, I think Google, I joined because I needed a job. And it turned out to be an amazing company and has gone to $4,000,000,000,000 in market cap or thereabouts. And so there was no more genius. In hindsight, I can justify it. I can tell you all the great things. But at that point in time, I need to get out there, get myself a real job, and that was a great job I got and it was amazing. I think it had a market opportunity bigger than most. At that point in time, digital advertising was 2% of advertising. Today, 60% to 70% of all advertising. So you could see that there's going to be huge opportunity. You could see consumption. You could think about commerce, etcetera. And I think we're at the beginning of a similar trend with AI. I think for the next fifteen years, every industry will get transformed with AI. So whatever you're thinking of is not big enough. Whatever you're thinking of is too small and too specific. There's tons and tons of opportunity. I think that's the parallel you can draw. I think it just had tremendous amount of tailwinds. And then I think Google's secret sauce is they were able to execute in multiple dimensions across the board. In Palo Alto, similarly, when I started, we were 1% market share in our sector. And generally, it's 1% market share in this sector. And the sector is going grow at 10% a year for the next foreseeable future. Then you have the opportunity of getting from 1% to something bigger. And if you do the math of enterprise, for those of you who are interested in enterprise companies, the math of enterprise generally tells you that if you get critical mass, get escape velocity. You get to be big enough, then you get a field force that is big enough that allows you to have a larger and larger share of wallet of a customer. So we earn from 1% approximately 6% or 7% now. And there's no reason why we shouldn't be 20%, 30% market share in our segment. And if that's true, that makes us a much larger business.
00:06:53 · Aditya Agarwal
[00:06:53] S1: Perhaps to come back to to Google for a second before we inevitably turn to AI and cybersecurity, it's interesting. I I always believe that the best antidote for any kind of organizational problem is winning. If you're winning, it kind of solves and kind of papers over all cracks. And it's often hard to know what an organization is from the outside. And most of the external narratives, they're not wrong. It's just kind of very hard to kind of capture the zeitgeist of a company internally. But if you, from your benefit of kind of the amazing run that you had there, what are some non obvious, like, non kind of externally talked about reasons why Google continued to be so good for so long. Like, the longevity in terms of innovation is also quite rare. Right? Like, oftentimes, you can build a good thing, you can milk it for a long time, have a good company, but they kept on building good things. And what lessons can you draw from that for entrepreneurs?