00:34:13 · Aditya Agarwal, Nikesh Arora
[00:34:13] S1: be a lot of work. So... But coming back to something that you said, is it feasible that the only way to really in some ways not avert sorry, the only way to deal with the volume of changes we will need to make is through AI itself? [00:34:27] S0: Sure. Look, I think your question to ask is how should people in this room think about it? You guys are blessed. You're going to build new stacks. This is great. And you're going to see the problem in front so you'll know what the new world of open source is going look like when all these vulnerabilities come to bear. What is the new process for open source? How are we going make sure that open source gets patched quickly? How are we going to make sure there are certified artifacts that you can use, etcetera, etcetera. So hopefully that paves a new way for this to happen. There's going to little bumpy ride for a short period of time, but I think we're to power through it. [00:34:56] S1: So your take is that probably there's about three, four, six months maybe before this becomes essentially the capabilities that Mythos has. Everybody's catching up quite quickly.
00:34:56 · Aditya Agarwal, Nikesh Arora
[00:34:56] S1: So your take is that probably there's about three, four, six months maybe before this becomes essentially the capabilities that Mythos has. Everybody's catching up quite quickly. [00:35:06] S0: Yeah. No, look, the capabilities will happen. I think the challenge will be some technology infrastructures are not as nimble in their ability to upgrade batch. There's tons of regulated industry. They have to go through a bunch of other hoops. So that's where the challenge is going to be. I don't think the challenge will be in the mainstream. It will be in the edge case problem. There's a physician's office in Nebraska which is using some piece of SaaS software, and the guys wrote it fifteen years ago, and the guy's gone. Nobody knows how to patch it. Those are the edge cases you're gonna find. Those have to be fixed, or people have to go through a technology refresh cycle of some sort.
00:35:06 · Nikesh Arora, Aditya Agarwal
[00:35:06] S0: Yeah. No, look, the capabilities will happen. I think the challenge will be some technology infrastructures are not as nimble in their ability to upgrade batch. There's tons of regulated industry. They have to go through a bunch of other hoops. So that's where the challenge is going to be. I don't think the challenge will be in the mainstream. It will be in the edge case problem. There's a physician's office in Nebraska which is using some piece of SaaS software, and the guys wrote it fifteen years ago, and the guy's gone. Nobody knows how to patch it. Those are the edge cases you're gonna find. Those have to be fixed, or people have to go through a technology refresh cycle of some sort. [00:35:42] S1: I'm curious, Nikesh, is there a larger lesson to be taken away for the broader applicability of these Gen AI models as we use them in more areas? Maybe self driving, maybe some other areas. I'm not just talking about the security implications, But we clearly have entered an interesting loop where it's able to do things that we are not able to obviously do at human levels. And then we're coming up with reactive ways of essentially reacting as an industry. So do you anticipate similar meta arcs, like perhaps in medicine, perhaps in self driving cars? Do you think that this will be a more common pattern, that the AI is now able to do a bunch of things that we need to figure out very quickly how to react to?
00:36:27 · Nikesh Arora
[00:36:27] S0: So let's abstract that for a second. So in 2004, when I was selling Google Search, the big promise was the democratization of information. Now everybody, a farmer in India, somebody in Nigeria, we all have access to the same information. Because traditionally, historically, information was power. The priests had more information. The Brahmans had more information. So you actually, the ability for you to have information allowed you to be smarter, act, arbitrage, all kinds of stuff. And the big was inner is about. Everybody has access the same information. You can't arbitrage information. If you abstract that notion, what AI allows you to do when it'll get into version three or version four in the next year, two years, three years, is the democratization of intelligence. This is much more profound and perhaps depending on which side you look at it, glass half full or half empty, is a much more optimistic or scary scenario. And what I mean by that is today, in any function, if I hire 1,000 people, I am captive to the intelligence of each of those 1,000 people to create the right outcome. I have a bunch of processes to train people, see if all 1,000 can perform at peak capacity to get the same outcome, whether it's in customer support, marketing, finance. My weakest link is the least smartest person in that cohort doing the same thing. If I can make that average 95% and I get all 1,000 people to perform at the ninety fifth percentile, I have a winning proposition. The AI is going to do that, that if you actually get your model to work right, every one of the thousand people using version of a Copilot or an agent which does the work is now of a higher intellectual capacity to perform the task, which means the output is going to be that much better, which means I don't need so many humans of varied capability to get that done. I probably need 10 experts and 90 agents to create the same outcome. So that's the transformation we're getting to. If you can apply that mental model to any function, any industry, any job, that is a transformation we're going to go through in the next ten years. And that's the bigger prize. The bigger prize is not building a small piece of functionality, which is incremental for what we do today because the models hallucinate and I can't go all the way.
00:38:47 · Nikesh Arora, Aditya Agarwal
The idea is to go think about that five years out or ten years out and say, if I built this, those 5,000 people who do this thing, I can do the work of 5,000 people with 10. It's not a labor argument. It's more of a quality of output, speed of output, efficiency of output, and the higher capacity that you have that becomes the opportunity. [00:39:09] S1: Nikesh, you were talking earlier about some of the parallels between Google Search and today's foundation model ecosystem and how everyone was saying one search engine would subsume all of the functionality. Could you perhaps expand on that for this room? How should one think about which of the things that I might build will be subsumed by kind of the larger foundation models versus what seem to be good secular in some ways? Like, perhaps at least not immediately in the eye of Soron kind of product ideas.
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?