00:17:55 · Aditya Agarwal
[00:17:55] S1: Got that. The other part that you talked about, Fortune 500 CEOs and companies adopting AI. Right? And a bunch of our founders and both founders here and globally are building for the largest scale adoption whenever it happens, right? What is your view on the rate at which adoption has been happening in Fortune 500? And especially as a bunch of us have moved our workflows to agents and some of us are taking to it like fish at sea. Right? And I'm just curious, what are the specific engineering problems that need to be solved for some of these Fortune 500 CEOs and CFOs to believe that, hey. You know what? It's time to hand over some of the jobs to be done here or maybe go completely autonomous in some areas.
00:18:40 · Srinivas Narayanan
[00:18:40] S0: I mean, as we all know, we are seeing tremendous adoption in areas like coding where the the output is fairly verifiable. I think in more complex or maybe in other functions where things are less verifiable, it's harder. It's harder for a whole bunch of reasons, especially in large companies. One, how do you get the right context to the model? Because a lot of the way people make decisions at companies, there's institutional knowledge. Things aren't written down clearly. Yeah. So you can't really just get an AI to go and do that. Right? Like, an analogy that I've been saying is it's it's a little bit like hiring an extremely smart person into your company, them having almost a blindfold. You know, you can't talk to anybody else. You don't know what is where, but you have to go do your job. You're incredibly smart, but most people will fail. Right? You won't know how to do. The way you onboard a person in a company is by having them talk to others, understand how a company works. Yes. It's all building that institutional context. So that, I think, is not trivial to do today. This is why you see enterprise adoption being slower. I think that's one big challenge we have to do. Being able to learn on the job. You know? If you make one mistake as a new person in a company, people say, oh, don't do that. Do it the other way. And then we register that. Right? So how do you turn that into a repeatable and trustworthy process for an AI agent or job? That's the second problem we have to solve, just learning on the job. I think the third problem we have to solve is is security. Mhmm. I think there is a fair amount of concern about how agents can go rogue. Can they do all these things that, you know, do I trust them? So we really, really have to solve the trust problem is to pull guardrails around them, to actually have deterministic controls on what your agent can do and cannot do. And maybe there are certain risky decisions that you're not comfortable offloading to an agent, but you can start small. And I think gradually, the risk profile for what you let an agent do will probably grow... Will grow over time.
00:20:31 · Aditya Agarwal, Srinivas Narayanan
[00:20:31] S1: Yeah. It almost also feels like, you know, you need to rewire the entire org structure because both incentives, motivations, and the way some of these orgs have worked historically, even holding information, holding holding power. And all of those things play out in various ways where a complete agent take Yeah. And a human workplace Yeah. I still feel feel some distance away. Right? [00:21:00] S0: That's true. I know I was saying this earlier. Right? I think there's a famous quote about, you know, we humans have paleolithic emotions, medieval institutions, and godlike technology. And it's sort of true. All the organizations are designed for humans doing things during the last hundred years. I think if you want to... So this is why you're seeing individual productivity is changing because of use of assisted tools. But the way most organizations work is actually how work gets drawn across teams, And that has a whole bunch of business processes and customs and things that you're used to and designed for humans being at the center. You have to redesign them for AI. I don't think that transformation will happen before that. I think there's good analogies in how electricity panned out as well. Factories had to be redesigned to actually accommodate electricity. Right? Like, couldn't just... You know, they weren't seeing the gains when electricity first happened until they completely rewired the the... How the factories were set up. So I think there is some process for that also, which all, I think, companies have to go through.
00:21:59 · Aditya Agarwal
[00:21:59] S1: One of the rewiring organizational rewiring that a lot of people are worried about is the fact that most orgs historically have worked with you referred to it earlier. New people come in. They go into this apprenticeship model, right? And they learn and they become effective. And just seems that today with this chunk of intelligence available, that apprenticeship model seems to be at risk, right? And a lot of younger folks are worried about what a meaningful career means for them, right? And this is more a personal curiosity as a parent. I'm just wondering that, and even for someone who talks to a lot of people who are early in their careers, how should people think about this specific access?
00:22:46 · Srinivas Narayanan
[00:22:46] S0: Yeah. I will acknowledge that I think the jobs of the future are... There's a lot of uncertainty that we don't quite know what they are. We know it's going to change. So, you know, like to our previous conversation, we have to be prepared for change. The the things that I think we can say are, one, be extremely good at learning and using all the AI tools. I think Jensen or somebody said that, you know, the risk is you not being replaced by AI, but you being replaced by a person who knows how to use AI. 100%. So definitely don't do that. Right? Just get extremely comfortable with that. I think the other things we have to do is, you know, resilience, agency, curiosity. Those, I think, attributes fundamentally, like, you know, building relationships with people. I still believe these things are gonna be extremely important. And so I think continuing to invest in that and continuing to be curious about learning and being adaptable to what the future holds, I think, has to... It's it's gonna be extremely important.
00:23:42 · Aditya Agarwal
[00:23:42] S1: I do agree that all of these form factors or all of these powers are, like, absolute superpowers with with massive payoffs. We'll turn to OpenAI a little. I'm curious about a few things, right? At close to a billion users now, right, or maybe more. You know, it almost feels like one of the most exciting consumer companies, right? But of course, at times it doesn't feel like a consumer company because we're talking about a bunch of other things here. For a bit, at least for a few quarters, it felt like OpenAI was losing the battle on the enterprise side, and this is just an outside in POV. How did you change that playbook internally when you look at that fist fight?