Lenny Rachitsky: belief

Transcript context

…we are an AI-first company. And this happened, we were working towards it even prior to ChatGPT, but ChatGPT became that seminal moment in November of '22 that we actually did that. So, that was one. Number two was we had to make sure that we defined what success looked like. The way that individual success was defined was everyone wanted to be a GM at Cisco. They wanted to own their own fiefdom, be a general manager. Because they felt like, "In order for me to move up the ranks, I need to be a general manager, which means I need to have my own sales team. I need to have my own marketing team. I need to have my own product team. I need to have my own engineering team. I'm going to make sure I run my own silo." And if you're a 40 billion business in product revenues, 45 billion whatever we were at the time, and then all of a sudden your goal is that you're going to just run a bunch of $40 million businesses and break it up into a series of 40 million businesses, that's actually not a good thing for the company. So, the thing we did was we said, "We have to become not a holding company of 251 acquisitions and thousands of different products, we have to become a platform company." And the characteristic of the platform is you have to be tightly integrated where the customer feels the same emotion, no matter what product of ours they use. There's the same set of expectations that can be served, reliability, trust, elegance and design, solving a problem in the most efficient way. Those are the things we want to strive to do. But you don't have to buy everything all at once, because we also want to be realistic about the fact that not every customer only uses Cisco top to bottom. There's an ecosystem. So, loosely coupled, but tightly integrated. You don't have to buy everything all at once, but boy, when you do buy two things together, they work like magic. So, that was the second big thing we did. And then the third one we did was we said, "Let's make sure that we have a mental model shift in the company." And we did this about five, five and a half years ago when I first joined. This was a very deliberate decision, which was, we cannot operate in a walled garden. We have to make sure that we operate in an open ecosystem, which means we have to be completely comfortable with having a competitor that we're going to partner with. And that's okay. We don't have to think about this in a zero-sum manner. In order for me to win, someone has to lose. We can partner, because if a customer has made a choice of going with company A and company B and we happen to be one of those two companies, we owe it to the customer to invest in their success in that other company because if the customer succeeds, that success has a flow through rate to you that's going to be pretty high. And so, that's what we did, and I think that's been those principles of building great products, but making sure that it operates like a platform and having an open ecosystem, I think has been kind of central. And then not being confused about the fact that we'll be AI-first from the top down. I want to take a tangent and make sure people understand what Cisco even does these days. I think as a lay person, you'll think about Cisco and you're like, "Okay, they WebEx, yes. They make maybe some routers." You guys are key to this massive AI infrastructure build-out that's happening right now. You're a major player in this. I don't think people realize this, people listening to this podcast. Give us just a quick glimpse into how Cisco fits into this massive build-out and just what does Cisco these days? Cisco is a critical infrastructure company for the AI era. What does that mean? But if you think about where the constraints are right now, if you think that AI is going to be one of the biggest movements, and then you ask yourself the question, "What could hold AI back?" There's three things where we feel like we can have a direct impact that can hold AI back. Number one is there's an infrastructure constraint. There's just not enough power compute and network bandwidth in the world to go out and satiate the needs of AI. Number two is, there's a trust deficit. If people don't trust these systems, they're not going to use them. And right now there's a lot of mistrust in these systems. Hallucination is a feature when you're writing poetry, but when you're trying to go out and run predictable systems, hallucination can be a bad thing. And these models are unpredictable, they're non-deterministic, and so they have to make sure that they have safety and security factored into them. And then the third area is a data gap. So far we've trained these models with human-generated data publicly available on the internet, but we are running out of human-generated data publicly available on the internet to train the models. And every company is going to differentiate based on their own proprietary enterprise data being used to train the models, synthetic data and machine data, which is where the most amount of growth is. And the third category of machine data we can play a massive role in at Cisco. So, what does Cisco do then? If you think about a GPU, which is what everyone now is very clear because of the great job that Jensen has done that here's what a GPU's core contribution is to AI. If these GPUs aren't networked together, you don't have AI, because it used to be that you could train a model on a single GPU, but then what happened was the model got too big to be put on a single GPU. So then you had a server with eight GPUs that got connected together. So, you could train a model with eight GPUs. But then that wasn't good enough. So, then what happened was you said, "I'm going to have a rack of servers that I'm going to network together." That at some point wasn't big enough. And so then they said, "I'm going to have a cluster of racks that are connected together." And that connected together is the operative word. That's what we end up doing is NVIDIA makes the GPUs and we connect those GPUs together. AMD makes the GPUs, we connect them together. And now what's happened, Lenny, is you have these data centers that might be hundreds of kilometers apart that need to operate like one coherent cluster, which means that they're completely in sync. Every GPU is in sync with each other when you're doing a training run. And that requires a very sophisticated set of technologies that we build to make sure that you could have two data centers, 800 kilometers apart, but boy, they run completely in sync with each other. And that's what Cisco does.…

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