Nikolai Tangen: Hi everyone, I'm Nikolai Tangen, CEO of the Norwegian Sovereign Wealth Fund, and today I'm here with someone who is very important for AI, namely Jayshree Ullal, CEO of Arista Networks. Jayshree has over 40 years of experience in networking, so no one can talk about this better than her. Jayshree, it's great to see you.

Jayshree Ullal: It's great to be here, Nikolai. I have a bit of a cold, so please excuse me if I sound nasal.

Nikolai Tangen: No problem, let's start from the beginning. I don't have to sing on this podcast anyway, so everything will be fine, right?

Jayshree Ullal: Yes, no singing. So, let's start from the beginning. What does Arista do?

Jayshree Ullal: Yes, that's a difficult question. So, Arista is building the world's most demanding mission-critical networks, which are high-performance, high-scale, low-latency, high-reliability, high-availability, high-automation, high-telemetry, meeting the needs of modern data centers and AI. So, there are very high demands at different levels. And especially in the last decade, Arista has emerged as both a pioneer and a leader in this field.

Nikolai Tangen: I've heard that if you think of chips as engines, then networking is the highway between them, right?

Jayshree Ullal: Yes, that's the right way to look at it. I believe chips drive a lot, especially in the world of AI, where processing happens – GPUs, APUs, XPUs, or whatever you want to call them. But when you process it, you have to worry about whether you're fully utilizing it. And so, if a car has a speed of 100 miles per hour, but it can only go 30 miles per hour because the infrastructure can't help it go further, then you're underutilizing the car, or in this case, the GPU. So, yes, we are the information highway for all accelerator users, mission-critical workloads, whatever name you give it.

Nikolai Tangen: So, let's say I'm sitting in my office now. I type a prompt into ChatGPT. How do I use your products? How does this service work?

Jayshree Ullal: Yes, so we mostly work behind the scenes. Like my parents always used to say, "What do you do?" because they can't see what we actually do. And when you type a prompt for Arista, at that point, we're basically connecting everything to everything. This conversation happening on this podcast is also not possible without it. So, high-intensity traffic patterns, whether it comes from a user, device, workload, machine, server, storage, or now, increasingly, from AI killer applications. We connect, and this happens through a set of switch and routing platforms. We call them Leaf-Spine architectures, where multiple leaves connect to an aggregate spine, and nowadays, it can also be an AI spine. These are some of the world's largest high-scale platforms. But to do all this, they need power. The platforms and chips need to be powered with the right software. And this is where Arista's specialty has been its Extensible Operating System (EOS), where we power these platforms and networking connectivity with more and more types of data. We can utilize structured data, unstructured data, flow data, and all of these with our specially built software.

Jayshree Ullal: And how is AI networking different from the systems that came before? If we go back a bit and look at networking about 20 years ago, the internet started with the research community. At that time, protocols and algorithms like TCP/IP came from research projects, especially ARPANET. Today's modern internet is built on this foundation, where research took the form of practical networking systems. At that time, there were many protocols like XNS, AppleTalk, OSI, and IP. But in the 2000s, TCP/IP became the common language for internet communication worldwide.

Jayshree Ullal: By 2015 to 2020, TCP/IP alone was no longer sufficient because networking needs became more complex. During this period, people started building the world's largest cloud platforms, such as AWS, Azure, GCP, Google, and many others. They needed an unprecedented level of scale to reach far more companies and users. So, systems had to be multi-tenant because they were supporting not just one company but thousands of companies. So, an ordinary CIO of a mid-sized company has stopped building their own things and started depending on the cloud to run their mission-critical assets, whether it's an e-commerce application, a database, or something else.

Jayshree Ullal: Now, as AI has come in the last few years, it's bringing a new twist because cloud traffic is very different from AI traffic. AI traffic, and we've all seen this with the launch of ChatGPT and all the other language models, is bringing a complexity and search that is thousands or millions of times deeper and larger than anything seen before. So, this required even faster traffic patterns, lower latency, and higher performance. So, typically, what we were doing before AI, we called it the front-end network, which connected standard CPUs, storage, general-purpose queries, indexing, searching, and things like that.

Jayshree Ullal: With the advent of AI, we were suddenly connecting hundreds, if not thousands, then millions of accelerators. And as I said, this GPU traffic required a very different type of traffic. Like, understand it as the quality of traffic, the intensity of traffic, the durability of traffic. It's very intensive in terms of peak bursts. So, we often call it the back-end network or scale-up and scale-out networks. We almost had to create different types of parameters and metrics. And until we came along, I would say most of it was like islands of little things. There was an InfiniBand network, a PCI network, a CSI network. And Arista has succeeded in bringing Ethernet and IP and standards-based capabilities to the back-end for AI, just like we had in the cloud.

Nikolai Tangen: What is now the biggest bottleneck in terms of building these networks?

Jayshree Ullal: Yes. So, I would say the biggest bottleneck for the entire industry is power. Because the power consumption of NGPU and networks and cables and optics and everything is absolutely not like before. Before, we used to talk about megawatts. Now we talk about 10 gigawatts in a data center. To put it in perspective, right? It's bigger than a football field, and you're powering it with gigawatt capacity. So, our AI providers or cloud providers are not able to find this level of power. If you're looking for that kind of power today, it might take you three to five years to find it. Space and power are probably still the biggest problem. But once you find it, that's where we come in to help you prepare it the right way.

Nikolai Tangen: Exactly, and so if it's found, you'll get the call. But now there's a lot of money pouring into AI infrastructure. How do you see this? Is this development happening too fast?

Jayshree Ullal: Yes, that's right. This change has happened very quickly. If you look at the old cycles of networking, it used to take almost 5 years to go from 100 megabit to 1 gigabit, then 10 gigabit, and 100 gigabit. But now, the migration from 100 gigabit to 200, 400, 1.2, and further to 3.2 terabit is happening almost every 12 months. That means changes that used to take 5 years are now happening in 12 to 18 months. This means that where we reached 400 gig last year, this year we are already thinking about 800 gig and 1.6. So, there is a very high demand for speed scale because you have to aggregate all these throughputs, and for the anticipated latency, all this is coming at us at once.

Nikolai Tangen: You were at Cisco during the internet boom, right?

Jayshree Ullal: I was, I was.

Nikolai Tangen: How does what you saw then compare to now?

Jayshree Ullal: The internet boom happened in a very, very stable way. There was a peak in 1999-2000 due to the dot-com boom and bust. I would also mention Y2K. But mostly, it took 15 years for that internet to develop. I was there from the first acquisition of Crescendo in 1993, when the company was less than a billion dollars. And when I left in 2008, it was a 40 or almost 50 billion dollar company, where the Catalyst Switch, which I think is still Cisco's most successful product line, was contributing at least a third.

Jayshree Ullal: But the thing was, it happened slowly and steadily. But there was a peak. There was a surge for two years. This feels very different for many reasons. The biggest reason is that the 15-year process we were talking about is now compressed into just five to seven years. Secondly, its scale aspect is growing exponentially, almost 1000 times more. And third, perhaps most importantly, because people like to compare: are we in a bubble? I think these are very responsible companies, and so we are not in a bubble, or if we are in a bubble, we are in a long bubble. These are not companies like Selex that came, got funding, and then shut down. These are responsible companies like Microsoft, Google, Meta, and Oracle that are taking the responsibility to build all this. Along with this, there are also new clouds like OpenAI and Anthropic that also need to be mentioned.

Jayshree Ullal: So I would say this feels more like a tremendous mega-trend that's here to stay for some time, rather than a bubble.

Nikolai Tangen: We had overbuilding during the dot-com boom. So, how can we be confident that this won't happen again?

Jayshree Ullal: Because all of this is not available. Neither is there enough power nor enough capacity. It will take time to build. Our desire is far greater than our ability to execute. So, there will still be almost a 3-year execution phase, unlike 1999, when everyone rushed to build companies, thinking if you build it, they will come. This time, the situation is reversed. Here, the thought is: please build it quickly, we need it now. This is the biggest change. But again, this doesn't mean I won't correct myself and say it's not a bubble. Rather, it's important to say that it's happening very quickly and suddenly on a large scale. But I know, it's far more than the steady state we've all gotten used to. But I don't think it's being done for a one-year factor. It's a three to five-year job, and it's being done by responsible companies who are voting with their money because they see the demand. That's the biggest difference: there is demand.

Nikolai Tangen: Speaking of responsible people, your biggest clients are hyperscalers, right? Cloud companies, Microsoft, cloud giants?

Jayshree Ullal: Yes, yes.

Nikolai Tangen: How concentrated is the customer base?

Jayshree Ullal: Well, they always have been concentrated. As an example, Microsoft and Meta have each accounted for more than 10% of our revenue since we went public in 2014. And I'm not sad for this concentration. I'm happy and grateful for that partnership. So, they remain 10% of our revenue. But obviously, as the denominator gets bigger, we are diversifying into a lot of specialty cloud providers, enterprise campuses. But it would be a joke if I didn't tell you that they are very, very critical to Arista's strategy, and the partnership has never been stronger.

Nikolai Tangen: When it comes to building AI capabilities, which country are you most impressed with?

Jayshree Ullal: Which country? Hmm, hmm. Of course, the United States, and we are also leading. There are some things happening in other countries too. But in terms of scale, and most of the companies we work with are in the US today.

Nikolai Tangen: Are there any other countries you think are doing a good job?

Jayshree Ullal: Absolutely. I think we're seeing parts of it in the Nordic countries, in the Middle East. There's some in the UK as well. There's some in Korea and India. So, there's no doubt that it's rapidly expanding to many other international locations.

Nikolai Tangen: When you look at where the investments are going, are you concerned that the world will become even more divided than before?

Jayshree Ullal: No, most of the investments I see are not dividing the world, but rather connecting the world more. So, thankfully, they are not government-sponsored. So, they are all about how to advance AI and technology and communication. I was in the Middle East a month ago, and I'm very impressed with the pace of progress there, where they're investing a lot of their oil money into necessary AI investments and capabilities. So, I see it as unity, not division.

Nikolai Tangen: Speaking of being impressed, you now have 21% of the market for AI data center networking, which is tremendous. How did you get there?

Jayshree Ullal: Is that all we have? I was hoping it would be more! But then we have to work. We've come from zero to 21 beautifully. I think if you look at the high-speed market, we're probably around 41. But if you look at the general speeds of all switching, we're probably around the number you mentioned. Look, this is a labor of love. The company was founded in 2008 and started shipping products. So we didn't do it suddenly. We did it slowly but surely. And truly, I think to optimize the culture of this company, which was started by Andy Bechtolsheim and Ken Duda, I have to say, they laid the right foundation for our software, our hardware infrastructure, and kept making it better. So our market share actually comes from our customers appreciating our innovation. We are built by engineers for engineers. We pay a lot of attention to quality architecture and support our customers by doing the right thing for them. Because we sit at the heart of a mission-critical environment, where if we go down, everyone knows. If we don't go down, we remain the world's biggest secret, and I have no problem with that. So I believe we've done it in a very methodical way and haven't rushed any product to market before it was ready.

Nikolai Tangen: You spent 15 years at Cisco building their data center business. So I just want to know, what was it like working at Cisco during its peak?

Jayshree Ullal: It was more than just the data center business. I loved my time at Cisco. When I joined Cisco, the company's revenue was about 650 million, and its market cap was 6 billion. So it wasn't a startup, but it was a young company. I got an opportunity, and at that time, the company was mostly building routers. It was during that time that I got the opportunity to contribute to and develop the Catalyst brand of switches. First, it was the Catalyst 1000, then 5000, 6000, 500, 4000, 3000. So, this switching brand was zero when I first came. And I still remember, the first year it was 40 million, then the second year 365 million, and then the third year it became 1 billion. And things like this cannot happen without the World Wide Web and the internet, no matter how great your products are. So I felt proud, I felt like I was part of a rocket ship. It was a fantastic company, and you know, but at some point, I felt, wait, I didn't want to be in such a big company. I wanted to do something smaller. But I remember those Cisco days and those 15 years very fondly.

Nikolai Tangen: What was the most important lesson you learned on that rocket ship?

Jayshree Ullal: I think we have the same thing here, which is customer focus. Cisco has a culture of customer focus. I think Arista doubles down on that, and on innovation, engineering, and quality. But I've attended calls from Cisco at 2:00 AM. As an executive sponsor, if something was serious, I would pick up that phone. You know, even though I value my sleep very much, at that time, that was more important. So I would say the culture of customers is very, very important.

Nikolai Tangen: You probably could have been the next CEO of Cisco. So why didn't you want to change spaceships?

Jayshree Ullal: You know, I don't know if I would have been the right CEO for Cisco because I was an accidental executive at Cisco. I never planned to be the executive that I was. I loved products.

Nikolai Tangen: But I think most people are accidental executives, aren't they?

Jayshree Ullal: No, some people aren't. I mean, it's not like you were five years old sitting in India thinking, "Hey, I'm going to be a superstar at Cisco." Oh, absolutely not. You can ask my parents how much of a non-superstar I was when I was five. But let's leave that aside for now. I think I would say I loved products. I loved working with engineers to build products for customers. And I was so immersed in technology that I didn't think I had bigger capabilities or aspirations to be the CEO at Cisco. So, when I actually left Cisco, the truth was I thought I would do something completely different, like clean tech or energy or solar or batteries. And I really wanted to come back to Silicon Valley and get back to what I loved, which was being an entrepreneur. And so I joined Arista when it had zero revenue and 30 engineers. And most importantly, what I loved about it was I loved working with those people.

Nikolai Tangen: So, let's say I have a startup. I have zero revenue. I have 30 people sitting in an office. How do I recruit a superstar like you in that time of complete uncertainty?

Jayshree Ullal: Yeah, no, I think Andy and I, when was this? We knew each other. So this is Andy Bechtolsheim.

Jayshree Ullal: Andy Bechtolsheim is the German co-founder of the company. When I was at Cisco, we acquired his company Granite. So we had worked together at that time. In addition, when he founded Sun Microsystems and I was building chips at AMD, we also worked together. Actually, we've known each other since 1987, which is about 35 years or even more. So I think when he saw that I was leaving my job and I didn't know where I was going, I just wanted to clear my head, he said, "Oh, I have this startup." I said, "Oh." And my first reaction was, "Oh, no, Andy, I don't want to do networking and switching again." But as I started to understand what the founders were trying to do and what the engineers were building, I became interested because it was networking and switching. But it was with a twist. It wasn't like the internet and Catalyst switching that I knew. It was a completely different thing.

Nikolai Tangen: What was it that attracted you to it?

Jayshree Ullal: Its software and software architecture were very unique. What do most networking companies do? They have five use cases, five operating systems, and 50 images for each, so they have to manage 250 things in total. But here, there was only one operating system and one binary image. It had a ton of class foundation, what we called a state-driven publish-subscribe model. In this, if something fails, it doesn't just fail. It automatically recovers, and the agent comes back up. And this was exactly the opposite of Christmas tree lights, where if one light goes out, all the lights go out. Here, we had built a system where even if one light failed, we would just change that LED, and the entire system would keep running. The customer wouldn't even know. It's because of this software that we got the opportunity to work with so many mission-critical and high-profile customers. And then we used that technology in different types of use cases. That technology attracted me, and the people did too. But to be honest, there was also a bit of nervousness because the year I joined was 2008. That was the time of the financial crisis. One of our early customers was Lehman Brothers, and you know what happened to them after that.

Nikolai Tangen: I know.

Jayshree Ullal: So my entrepreneurial career at Arista didn't start well, but it's been going strong for 17 years now. It's not that we haven't seen ups and downs. We absolutely have. The financial collapse was one of them. Our biggest competitor Cisco suing us was another thing. COVID and the entire supply chain crisis was also another thing. So, we have faced many difficulties to get to where we are today, and it's good to be here.

Nikolai Tangen: What does it take to compete with a big company like Cisco? You're a startup, thinking we have to beat them. What does it take?

Jayshree Ullal: I don't think most of my colleagues would tell you that I ever felt like I was competing with Cisco because initially we were so focused on very specific and unique use cases that Cisco didn't even want to be involved in or pay attention to. Like high-frequency trading and low latency. These were use cases that Cisco was not focused on at all. And our first 100 million in revenue mostly came from there. Our second use case was cloud, and Cisco called it MSDC, which is massively scaled data centers. But they didn't focus on that sector at all. They didn't even try to understand what customers wanted and why they did what they did. Actually, in the early days of AWS, Amazon, and Google, they used neither Cisco nor Arista because they built their own systems. They were quite dissatisfied with their requirements and the solutions available in the market. So, once again, I felt that we were doing something that big corporations, especially Cisco at that time, were not doing.

Nikolai Tangen: What is an example of excellent customer service?

Jayshree Ullal: There are many examples and many stories I can tell you. But let me ask you the same question. If you pick up the phone and call Arista support today, what do you think? And let's say you have a network problem and you had to call. What kind of response would you expect from them?

Nikolai Tangen: That you would come and fix my problem.

Jayshree Ullal: Typically, in most places, they first ask, "Who are you? Have you paid the bill?" If you're just asking questions, they say, "We don't have your account in our system." But we don't do that. We immediately ask, "What is the problem?" Our average resolution time is very fast. For us, it's like treating a patient in an emergency. You don't ask them 20 questions. You understand the symptoms and treat them. So our average resolution time is 25 minutes. This is an unheard-of record because our team is full of experts, and quality and support go hand in hand. When you build high-quality products, if there's a misconfiguration or a support problem, we take immediate action and are there. So we don't outsource it. We have a follow-the-sun model, meaning worldwide coverage all the time. We respond quickly, understand the problem, and solve it, and the average time is quite low. And it's a very high-quality experience.

Nikolai Tangen: How do you make sure that you stay ahead?

Jayshree Ullal: It takes a bit of personal time. I set aside time in my calendar where I just focus on thinking and don't take any meetings because everyone wants to meet with me. I always try to stay updated on technology. For example, in 2022 and 2023, I made it a point to stay updated on AI because I wasn't fully paying attention to it before. And when the ChatGPT moment came in November 2022, I felt that was a big milestone for the tech industry. I remember I had to give a talk at the Optical Fiber Communication Conference (OFC) in January 2023. I could have talked about optics, long haul, short haul, DWDM, co-packaged optics, DSP, etc. But instead, I used that conference to discuss AI as a driver.

Jayshree Ullal: And to do that, I had to learn and read myself because you're talking to a PhD audience who probably knows more about the theory than I do. And so I always take a lot of pride in learning, always pushing the boundaries as a person and with my peers in the industry, and also within Arista. We have a lot of top technical experts in our organization.

Nikolai Tangen: How far into the future can you predict?

Jayshree Ullal: Not that far, actually, I could be wrong. I can predict that some things might happen. But my best accuracy is within one to three years. After that, the situation will be a bit better.

Nikolai Tangen: What's going to change in the next three years?

Jayshree Ullal: I feel that the way we look at AI today is going to change quite a bit in the future. Right now, we're building a mainframe version of AI, where we talk about large clusters. Billions, trillions, and quadrillions of parameters and tokens. We also look at how all this manifests in teraflops, terawatts, or terabits of capacity. But in my view, even though all this will be important in the next three years, I'm not underestimating it. I think it's going to become a more distributed problem. We won't be able to fit everything into one size. And just as mainframes went to client-server and eventually, you know, distributed PCs and clients and phones, similarly, I think AI will also come in many different shapes and sizes, and you know, including this one.

Nikolai Tangen: And what would be the implications of that?

Jayshree Ullal: Quite significant because miniaturization is much harder than just building the biggest and most powerful system. This means we'll have to translate our training models into reasoning and inference, and do more with less resources in many use cases.

Jayshree Ullal: Because if you solve the problem just by increasing bandwidth capacity and compute power, then you can run the world's largest training workloads. But if that same capability is applied to reasoning and taken to general-purpose compute and storage in a more distributed way, then AI can reach every desktop. It won won't be limited to just large and powerful systems.

Nikolai Tangen: Are you concerned that the world of technology is increasingly being split into two between the US and China?

Jayshree Ullal: The way we are divided today also has a political aspect. I wish innovation and technology would advance on a level playing field. But I understand that we are all governed by different countries. So, my concern is that for the United States to stay ahead and be one step ahead, we have to keep innovating and keep partnering with the best talent. You know, I'm an example of an immigrant who came here and wasn't born here. Whether that talent is in China, India, London, or anywhere else, we need access to it, and we need to work together with the best minds.

Nikolai Tangen: Now let's talk about culture. How would you describe the culture of Arista?

Jayshree Ullal: In one word, it's do the right thing. Because as a public company, it's easy to just focus on the quarter or on one thing. And there's no doubt that every company has a business model that we have to focus on. But if you go beyond that and do the right thing, then all other things take care of themselves. I can give you one best example. Early in our career, we had a very big quality problem with one of the chip members in our hardware. We worked with them, they worked with us, and I had only one option, which was to put a small patch in the software. But I tried my best and told our customer, "Look, you're not seeing the problem right now, but it could gradually get worse, and we want to replace all your gear at our cost." This almost could have bankrupted us. But we chose to do the right thing. So more people remember that we did that, even though it was bad news at the time, rather than all the good news, right? So I think focusing on doing the right thing for your employees as a company, and as a culture, doing the right thing for your customers, that's the big thing I would highlight.

Nikolai Tangen: How do you make sure that this attitude is shared throughout the organization?

Jayshree Ullal: It's not easy, especially as you get bigger. But the best thing is that as a management team, you know, we've largely been the same management team for the last 15 years. And we sometimes lose people because of other goals or financial success. But I think when you have the same management team and leaders, and now we're bringing in the next generation of leaders, and you constantly reinforce that culture, it permeates throughout the organization. And I would also say that even though we have middle management, we see that they are not just professional managers, but they are also coaches and team players and actively contribute their ideas and skills every day. My president and CTO, Ken Duda, who is also the founder of this company, still codes himself. So this tells our employees that we are leading by example. We are not saying one thing and doing another.

Nikolai Tangen: How do you keep learning?

Jayshree Ullal: Maybe I don't learn enough. As I said, I have many sources of learning. When I meet customers, they teach me a lot because they see things differently. That's one way. I also spend a lot of time with my engineers to understand what they're doing and what problems they face. They always teach me something. Today you can go to Claude or Gemini or ChatGPT and become a software developer with very good efficiency, which makes it easy to understand complex topics. I only trust my weaknesses. But that's a good source of learning, and my favorite way to learn is actually reading, which I do less and very little. But that's my favorite way.

Nikolai Tangen: Jayshree, when you hire, what do you look for?

Jayshree Ullal: Yes. So, obviously, when you hire someone, it's very clear that they should be capable. So, there's an element of capability and asking them about their capabilities. But I look for capability, character, moral values, culture, and what kind of person they are. Are you a good person? Because you know, I want to make sure that this hire is not only capable of delivering results but also stays with us for the long term and embraces and blends into our culture. Okay? Again, coming back to doing the right thing. So we always think about the long term, not the short term. And so we look for people who think like us. Often people say that if you hire someone you know or a relative, that's nepotism. But I believe that also brings in a lot of good people. Because if you know that employee, chances are their relative will be similar. So, if we have openings, I'm always ready to offer internships to the families of Arista employees.

Nikolai Tangen: What percentage of your employees are of Indian background?

Jayshree Ullal: Um, I would say that because we have a large facility in India, it's around 15 to 20%.

Nikolai Tangen: Can we talk about your upbringing in India? You grew up in New Delhi. What were your dreams when you were five years old?

Jayshree Ullal: Yes, I was born in London, grew up in Delhi, and spent most of my life in the United States. My father was a teacher. So he thought about my education and made sure I did well. He started the first five IITs in India, as you probably know, they are very famous and have a good reputation. So I think that in a middle-class family, institutional education was a very big part. So doing well in school, you know, not just doing well, but doing very well in school, going to the best schools. My parents made sure they could give me the best education. So I never dreamed that I would do what I'm doing today. I focused on one step at a time, like how to do well in school and how to get to the best colleges. There was no long-term plan at that time. But I knew that my parents had very high expectations of me. Maybe at that time, I didn't have such high expectations of myself.

Nikolai Tangen: When so many expectations are placed on someone, what effect does that have on them?

Jayshree Ullal: Sometimes it forces you to perform better. Because it's a very interesting thing that whenever my father or mother went to the parent-teacher association to meet my teachers, they always said, "Jayshree has a lot of potential. But she's not fully utilizing it. She's not using her full potential." And so they would come back and demand more, and expect more. But it taught me a particular lesson. I had a very close friend in India, and like student rankings, she always came first, and I never came first. And I saw that she, you know, to do well in these exams, in terms of studying, she would study almost eight hours a day, and I would study only three to four hours a day. And for me, that was a good ROI, if you want to call it that. I got a rank that wasn't as good as hers, but I still did well. I was in the top 10%. And I tried to explain to my parents that I was playing the long game, and today that might sound right. But at that time, it was more of an excuse not to study eight hours a day.

Nikolai Tangen: You studied electrical engineering, which was not very common for girls at that time.

Jayshree Ullal: No, absolutely not.

Nikolai Tangen: So how did you do all that?

Jayshree Ullal: Yes, I got there by elimination. I liked physics, I liked chemistry, I liked math. I didn't like biology. I wasn't very good in the arts department. And I thought, what is it that can give me this intersection of problem-solving? And computer science was still a fairly rare field in my day. Perhaps if that opportunity had been more, I would have done more. I had to take my computer science classes in the math department, for Fortran, C++, and assembly. I took some classes, but electrical engineering was the real hard-core engineering, and now it's coming back again. I wish more and more people would do it now in this modern world of silicon and chip and hardware design. So I think, and I was influenced by my father, who started the IITs, who applied science to real-world problems, which I think is what engineering really is. That was something that excited me.

Nikolai Tangen: You're considered one of the hardest-working people in tech. How much do you work?

Jayshree Ullal: You know, I think when you love what you do, it doesn't feel like you're working hard or working long hours. You're enjoying it. At the same time, as a CEO, all the responsibility is on you, and I have a responsibility. So I would say, my mom also says, "You work too hard." And I say, "Mom, I love what I'm doing." So I never counted the hours. For me, it has always been important to balance my work with my health and family. But to be honest, I need some more hobbies. Maybe then I'll work a little less.

Nikolai Tangen: What are your hobbies? Do you have any hobbies?

Jayshree Ullal: Yes, you know, I used to run a lot, but I hurt my knees. So now I walk and do a lot of hikes. Our dog is a big part of it too. My family is connected to music in some form or another. Even though I don't have any plans for public singing, it's a lot of fun. The sound in the shower is always amazing. Anyone can sing well there. And nowadays, with the help of synthesizers, it sounds even better. I love all of this. I also really enjoy reading.

Nikolai Tangen: What do you read?

Jayshree Ullal: I read a lot of biographies. I haven't been able to read much lately. But whenever I do read, it's mostly during long-distance flights. And either I print out and publish some specifications and read some technical documents, or I read a very light novel, like a John Grisham type, where you don't have to think or use your brain too much. That's my downtime. And to be honest, I prefer reading to watching movies.

Nikolai Tangen: What are you most proud of in your life?

Jayshree Ullal: I am most proud of my family. I would say my daughters, my husband, and my extended family. They give me a lot of joy because at that level, it's not just about me, but about them. Recently, I also received an honorary doctorate degree from my alma mater, Santa Clara University. But my daughters really worked hard. One got a PhD, and the other got a doctorate in veterinary science. I'm proud of their values. I'm happy for their compassion, and also that they chose their own path to excellence.

Nikolai Tangen: What kind of advice do you give to young people, including your daughters?

Jayshree Ullal: I often give this advice because most young people today are very anxious about reaching a certain point quickly in their careers. I tell them, build your path to excellence. Each of us has a talent, which means we are good at some things and very bad at others. But if you can find the things you are good at, and make them better, and work to become excellent at them, and keep working on them, then I think everyone defines their own path to excellence, and it's not always measured by money. It should be measured by reaching the destination you consider success.

Nikolai Tangen: Jayshree, it seems your teachers in India were completely wrong. You have fully utilized your potential. Thank you very much for everything you do to keep the world connected, and I wish you all the best for the future.

Jayshree Ullal: Thank you, Nikolai. Thank you for these thoughtful questions.

Nikolai Tangen: Thank you.