Lenny's Podcast: Product | Career | Growth
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?
00:19:25 · G2 Patel
[00:19:25] S0: Years ago I'd had a chance to meet with Ray Kurzweil. He's the chief scientist at Google for a while, and I think he still is. And he had talked about, he was writing this book called Live Long Enough to Live Forever. And so I was talking to him, I'm like, "What is the impact to human population if all of a sudden you can have 15 generations living simultaneously, because we have an indefinite span of life? Because now all of a sudden everything has to change. How does housing work, housing work? How does agriculture work? How does transportation work? How does ... Everything changes?" And he looked at me and he had the most profound answer and he said, "Most people can't think exponentially, because they always think exponentially maybe on a single dimension." But what ends up happening in these things is you can sometimes you have to keep in mind that exponentiality happens across multiple dimensions all at once. So, if you do have an indefinite span of life, you have to assume that humans are creative enough that they're going to find a way to have a three-day crop cycle. And they probably will have 5,000 story skyscrapers, and there will be a bunch of things in society that we have assumed are not solvable that'll now be solvable. So, when you go back to your question and say, what changes in this entire equation that has not been factored in well? I think today AI is looked at largely as a productivity tool and an aggregation mechanism. I have data all over and I'm going to be able to make sure that language can be used to compose the data in a way that I can give you, Lenny, the answer to the question that you're looking for that I think is the 0.0001% of the tip of the iceberg. The reality is, is we will have original insights generated that don't exist in the human corpus of knowledge, and we will have the physical world get augmented to language where capacity is augmented to humans. And what we have to be careful of is that that capacity is working on behalf of humans, but if that capacity is augmented to humans, you can now do things that you really care to do, and not do things that you don't care to do. And so our biggest realization that we had when we were using Codex, for example, when we were writing a code with OpenAI's kind of model and development tool, was the first three months we were screwing around with this.