Cisco Systems, Inc. (CSCO) Earnings Call Transcript & Summary

Welcome. We are thrilled you're here, and you should be thrilled you're here because there's a lot of Super Bowl traffic, even though the Super Bowl is in Santa Clara, but there are a lot of events going on in the city. So we're really glad that you were able to make it. This is our second annual AI Summit, and I think the lineup we have this year is incredible. And last year's event was a great event. But today, we're bringing a special group of people together to really talk about the entire AI ecosystem, AI visionaries. And I also want to just recognize the people that are watching this online in addition to who's in the room with us today. We appreciate you spending time with us, and hopefully, you really enjoy the event. Just so you know, this is all thought leadership and all discussions about what's going on, all things AI. There's no product announcements, just candid conversations, no sales pitches, right, Jeetu?

No sales pitches.

All right. Good. And -- but I want to talk a little bit about what's going on in business and society and where things are happening with AI. I think that last year was a big turning point, as we all know. And for all the enterprise customers who are here this week, we all believe 2026 is going to be a turning point for AI. And we believe that this will be the year of agentic applications, and we believe that all of us know that the impact on what we do every day is going to change significantly. And whether we're talking to our enterprise customers or governments around the world, we know we have to embrace this. Many of us believe it's the biggest transition that we've ever seen, and I'm old. I've been through a lot of them. And this, I do believe, will be more revolutionary, and it's moving faster, obviously, than anything that we've ever seen. There are lots of questions and discussions about what does it mean to your enterprise infrastructure? What does it mean to your security posture? What does it mean to application development cycles? All those things are really important. And all of us in this room know that those of us who embrace AI will ultimately be the winners. And so that's what this is all about today. And we hope that we can be a little bit -- a little part of helping you actually take advantage of what's going on in this industry right now. One other topic I just want to touch on quickly is trust. You'll hear Jeetu talk about the fact that one of the deficits that we have in deploying AI, one thing that bothers us is trust, whether it's trust in what's going to happen to my data, trust in the models, trust in your infrastructure, trust in the agents, trust in the partners that you're working with. This is a very big deal, and it was front and center in Davos. I know some of you in this room were in Davos. There were a lot of discussions about trust for lots of reasons. But particularly as it relates to AI, whether it's geopolitical dynamics, sovereign dynamics, it's a big topic that we'll also cover a bit today, but it's a conversation that we'll want to continue as we leave here. And I'm proud of the trust that we have had over the years with our customers and with governments around the world, and we continue to plan to operate in a way that makes you feel good about working with us and other partners that we all bring to the table to actually make this a reality. We all know that it's moving fast, and none of us can do it alone. Therefore, trust is really imperative. We've been working a lot on the infrastructure and how your infrastructure will have to change to adapt to this. We know that as you deploy agents, your traffic flows are going to change. The latency requirements are going to change. Your security architecture is going to have to change. And we have been working a great deal, whether it's with the hyperscalers on building out infrastructure, whether it's sovereign cloud providers. We're really seeing the enterprise start to pick up. We're doing this through partnerships with NVIDIA, AMD, OpenAI, Anthropic others as well as HUMAIN, G42 and other partners around the world where we're learning a lot about how this is being done. And we hope to be able to bring that to you as we move forward and bring not only the trust but also the understanding of how AI is being deployed all around the world. I hope you learn a lot today. I hope you have fun. I hope you feel inspired, and hopefully, you'll leave here believing that this was a really good use of your time. Thank you again for being with us because your time is the most valuable thing you have. And we truly appreciate you coming and spending your time with us. And with that, I will hand it off to Jeetu Patel.

Thank you, Chuck. Good morning, everyone. And this is -- let me start with some bad news is I have no idea what we're going to do next year with the lineup. It's got a little bit of a problem. But AI is advancing faster than any of us had ever anticipated. And the reality is we're going into the next big phase of AI. So we started with these intelligent chatbots back in '22 in November, where you ask it a question, it gave you back an answer that felt like magic. Now we are squarely in this phase of agentic AI. 2025 was a year of experimentation. A lot of people experimented with agentic AI. In '26 you're going to see a lot of kind of ROI getting realized, and you're going to see a lot of these applications get to production. And then shortly thereafter, as we are starting to see the developments happen at such a rapid pace, what you're also going to see is physical AI is not too far. And so we're starting to see robotics and you're starting to see large world models and all those things are starting to come together. So at Cisco, as we thought about this, what we wanted to do was make sure that we assembled the builders of AI and have conversations rather than just talk about pitching products and talk about what specifically needs to happen so that we can all get the most amount of harness from this movement that's going on right now. And as we thought about that, the one question that we constantly keep asking ourselves is what are the constraints? What are the impediments that actually hold AI back? And we think there are three major ones. The first one is that we have an infrastructure constraint. We just don't have enough power, compute and network bandwidth, now memory, you start to think about data center shells. To go out and say that the needs of AI. So that's going to be an area that we are spending billions on at Cisco, and I think the industry is spending trillions in making sure that we can go out and fulfill the needs of AI. So that's the first area. The second is the one that Chuck talked about, which is a trust deficit. We currently -- we need to make sure that these systems are trusted by the people that are using them because if you don't trust these systems, you'll never use them. And this is the first time that security is actually becoming a prerequisite for adoption. In the past, you always ask the question whether you want to be secure or you want to be productive. And those were kind of offsets of each other. And now what you're starting to see is if people don't trust these systems, they will never use them. So the second area is a trust deficit. We need to make sure that we trust not just using AI for cyber defense, but we trust AI itself, so you got to secure AI itself. And then the third area is this notion of a data gap. And what I mean by the data gap is these models that get trained, get trained on human-generated data that's publicly available on the Internet. We are now at the point where kind of -- we're running out of human-generated data publicly available on the Internet. And what you're starting to see happen is synthetic data has gotten to be extremely potent in training these models. And then you're also starting to see that the highest amount of growth of data is happening with machine-generated data. And as agents get more and more prolific and as you have these agents working 7 by 24, you will see continued amounts of acceleration and exponential on machine-generated data. And at Cisco, it turns out, we are at the center of all of this stuff. And what we're doing is working very hard to make sure that we can build out the critical infrastructure for the AI era. And so that's the goal. The past year has been nothing short of incredible in the amount of kind of progress that the team has made. And I think we are supercharging some of the world's largest data centers with the entire stack. We've got Martin Lund here, who runs our silicon business, and we start from that because we want to make sure that we're actually starting with silicon. So if you look at what we've done over the course of the past year, we started with the G200 chip, which was used for going out and making sure that within the data center, you can have clusters that get networked together because if you have a GPU that's not networked, you don't really have an entire application that can be run. So you have to make sure that -- the network is an essential ingredient. So the G200 chip was for the scale out. And then we said, what's happening now is these models are getting bigger where they don't just fit within a single data center. You don't have enough power to just pull into a single data center. So now you need to have data centers that might be hundreds of kilometers apart that operate like an ultra-cluster that are coherent. And so that requires a completely different chip architecture to make sure that you have capabilities like deep buffering and so on and so forth, so that you need to make sure that these data centers can be scaled across physical boundaries. And so that's the second area, and we actually built a chip called the P200 chip, but we didn't just build the chip. We also built the entire system with the 8223 Router. And so that allows you to make sure that these data centers are going to be hundreds of kilometers apart today, but eventually, they'll get to continental scale, right? And then lastly, you have these -- we are reaching the physical limits of copper and optics and coherent optics especially, are going to be extremely important as we go start building out this data center infrastructure. That's an area that you're starting to see a tremendous amount of progress being made. Now the reality is, is only a small number of firms can do this entire full stack. And we've been working with hyperscalers that you'll hear a lot from today. We've been working with neo clouds. We've been working with sovereign clouds that you'll hear from today. We're working with service providers. And we're also redefining the AI stack for the enterprise. And you'll hear this with Chuck and Jensen later in the evening, but we've actually got a tremendous partnership with NVIDIA, where we're building secure AI factories. And we want to make sure that we continue to have enterprises find infrastructure to be plug and play because it's far too complicated today. Now the reality is, why is it important for the enterprise? Because I think token generation is going to be one of the core currencies of every company and every country, because your ability to economically and efficiently generate tokens will be directly proportionate to national security as well as to economic prosperity. And so we need to make sure that we can actually make that as efficient as possible, as productive as possible for the generation of tokens. But token generation shouldn't be just limited to data centers. We should also have token generation happen at the edge. And so as you start having a distributed architecture, what we wanted to do is we've also provided this capability of a Unified Edge where you can have these branches, and the edge where you can start having inference processing occur rather than just being done in the data center because sometimes, the kind of latency requirements that are needed for applications, you might not have the time to go and do a round trip to the data center. So regardless of where you are wanting to generate tokens, we want to make sure that we can actually participate, which is why we're also pioneering AI safety and security, because one of the things we want to do is make sure that we are not just making sure that these networks are connected, they're securely connected. And so this notion of not just using AI for cyber defense, but also securing AI is a pretty important dynamic. And last year, at this stage, we launched a product called AI Defense. And this year, I'm delighted to announce that we're actually starting to get the world to be safer because companies like CVS and companies like NEC, they're starting to use these products within their environment so that they can actually secure AI itself. And then the other thing that we've done is we've made sure that we want to make sure that this becomes economical. So we're building our own foundation security models and making sure that a lot of those are available in the open source community, and we will continue to keep doing that. So a combination of products that we build as well as the foundation security models are going to be pretty important. But we're also reimagining how agents will simplify operations because one of the big areas that you're starting to feel a tremendous amount of pressure is this infrastructure is getting very complicated, and it actually has to be managed in an extremely low friction manner. And agents can really help materially lower the cost of complexity for organizations. And so we've built this entire apparatus and scaffolding for AgenticOps, where these agents can go out and make sure that they can detect when something goes wrong before it goes wrong and then proactively remediate and fix it so that you don't spend majority of your time investigating an issue during an outage. You spend majority of your time responding and remediating to that outage as quickly as possible. And we want to make sure that, that once again, happens with models that are built that are very bespoke for the infrastructure that you're building. And so these are the kind of investments that we continue to keep making. But the point in time in the industry that we are at right now is a really interesting juncture because AI is itself helping us accelerate the use of AI. And last year, when we talked about this, it was -- it seemed like a far-fetched goal, but 70% of all AI products now at Cisco are using code that's generated by AI, right? 70%. And I would say that what it's not going to -- within the year 2026, we will have at least close to half a dozen products that will have 100% of the code written by AI rather than written by humans. Now humans will still have a very valuable role to play because they're going to make sure that they're writing specs and they're making sure that they're actually going out and reviewing the code. But the bottleneck is no longer going to be around the writing of the code activity, the bottleneck is going to be around the reading and reviewing of the code activity. And so as we see this progress, it's extremely exciting, and we want to make sure that the builders of all of these technologies are here today. And what we want to do is have some candid conversations. We have not overly prepared the conversations. We want to make sure that they flow and they actually go in the directions that people wanted to go. That's why we wanted to do it in a fireside chat format rather than making sure that people came in and did keynotes. And as AI innovation actually continues. One of the challenges that we have that we have to collectively as a community address is AI is moving at a pace much faster than organizations have the capacity to absorb it. And that's actually something that has to get solved for. And that's a change management exercise. That's a cultural exercise. That's something that we have to make sure that we collectively as a community work together on because just building bigger, faster technology that doesn't actually keep in mind how that technology can be absorbed is not really going to go out and get us what we need. So it's truly a paradox of progress. On one hand, everyday AI is solving harder problems. On the other hand, you could start to see that we are struggling with articulating concrete impact on ROI with these technologies on a consistent basis, uniformly distributed across the entire globe. And so to resolve this paradox, we have to answer some really tough questions, and that's what we want to make sure that we actually have these conversations with. And by the way, I apologize from the beginning, it's going to be a long day. I didn't know -- we didn't know if a way to make it any shorter, but this will test your stamina. But we want to make sure that we actually ask some tough questions. So for example, where is the ROI for AI going to come from? Is it going to come largely from efficiency? Is it going to come from us being able to solve problems that we have never been able to think about being solved before that are going to be some things that are going to be in there. In this increasingly nationalistic world that we're starting to live in, is sovereignty more important than raw intelligence? Because every country, every company might want to actually make sure that they are actually focused on resilience. And sovereignty is a proxy for resilience. Our language models going to be enough to get us to the movement of AGI, or are you going to need more? Are they going to be large world models is physical AI going to need to be a necessary ingredient as we actually start to work these things together. And if physical AI is something that we believe is going to be a necessary ingredient for us to get to AGI, then what does that ChatGPT moment look like for physical AI? How far are we from that? These are the kind of questions we need to make sure that we ponder. So what we don't want to do is actually talk about individual technologies but talk about the larger trends that are happening in the market. Now it's questions like these and many more that hopefully we'll explore today, and that's what we've built Cisco AI Summit, and we're going to stay true to that form as we continue these conversations over the years.