Lenny's Podcast: Product | Career | Growth
00:09:40 · G2 Patel
[00:09:40] S0: Innovation in my mind is a choice. I always find it interesting when people say, "Well, you're a large company, you can innovate. You're a small company, you can innovate." It's like, "No, it's just a choice. Every day you come into work and you can choose to be thinking about being creative, or you can choose to not be creative." It's like a little binary ... It's a binary choice You can make every hour, every minute of every day. And so, we made that choice that says, Cisco is going to be not just an iconic company. And Chuck Robbins, our CEO says this very eloquently. He's like, "I want Cisco not just to be an iconic company. I want Cisco to also be an iconic and innovative company." And so, we got to make sure that we are actually innovating with the set of constraints that we are dealt with. Every company has their own set of constraints, and we have our own set of constraints, and we have to make sure that given those constraints, we have to actually innovate really well. Now, what are the two or three things that have happened that have really helped us out? One was, being very clear on what is up for debate and what is not up for debate. Because what can end up happening is you can always have a pocket veto in a large company where if you ask enough number of people, people say no. If you're a large company, you ask enough number of people, someone's going to say no, right? And so, when you have conviction about something that's happening, that is going to be a bet that you need to place. What most people think in large companies is large companies don't experiment. That is in fact not true. Large companies experiment a lot. What large companies don't do is when an experiment works, they don't go all in and double down. They try to keep hedging. We didn't hedge on AI. We said we were going to go all in. That was number one. What that meant was we had to get people to understand that their personal success and the success of the company are very aligned in us getting dexterous with the use of AI. That means that if they feel like for some reason AI is going to take their job or AI is going to be negative for them, we had to reassure them that that was not the case.
00:11:52 · G2 Patel
But the reverse was guaranteed to be the case, that if you didn't choose AI, if you weren't going to be dexterous in whatever job function you're doing, then your job is probably not going to be that relevant over here in the long run. So, that was the first thing that we did, that was a, I'm not a big fan of top-down hierarchy of going out and doing things. In fact, deep down inside I don't respect hierarchy as much. I feel like it can constrain you at times. But I wanted to make ... On this one we were very, very deliberate. The entire company is on the same page, 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.
00:14:13 · G2 Patel
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.
00:15:34 · Lenny Rachitsky
[00:15:34] S1: 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?
00:16:03 · G2 Patel
[00:16:03] S0: 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.
00:18:18 · G2 Patel, Lenny Rachitsky
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. We provide the networking, we provide the optics technology, we provide the safety and security technology, we provide the observability, we provide the data platform, all of those things together for making sure that we provide critical infrastructure for the AI era. [00:19:10] S1: So, being on the inside of this massive investment that is happening across the world, what do you think isn't being priced in into where things are heading into how much life will change, or just the scale of this build out?