← In Good Company with Nicolai Tangen
In Good Company with Nicolai Tangen
Arista Networks CEO: The AI Infrastructure Boom, Power Limits, and What’s Next
00:39:35
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- [00:01]
A
Hi everyone, I'm Nicola Tangen, the CEO of the Norwegian Sovereign Wealth Fund. And today I'm here with somebody who is absolutely vital to AI, namely the remarkable Jaishree Ulal, CEO of Arista Networks. Jaishree has more than 40 years background in networking, so nobody is better able to talk about this than her. So, Jaishree, super to have you on there.
- [00:24]
B
Great to be here, Nicol. I have a bit of a cold, so apologize if I sound nasal.
- [00:30]
A
No problem. No, let's start with a bit.
- [00:32]
B
I don't have to sing on this podcast, right? So we should be fine.
- [00:35]
A
No singing. Let's start with. Let's start with the beginning. What does Arista do?
- [00:50]
B
Yeah, that's a loaded question. So Arista is building the world's most demanding mission critical networks, which is high performance, high scale, low latency, high reliability, high availability, high automation, high telemetry. So it's a lot of demands across a lot of spectrums. And Arista has emerged especially over the last decade, to be both the pioneer and the leader in this.
- [01:23]
A
I've heard that if you think that kind of chips is the engine, then the networking is the highway between them. Right?
- [01:30]
B
Yeah, that's a good way to look at it. I think the chips fuel a lot of, especially in this world of AI processing with GPUs, APUs, XPUs, whatever you call them. But as you process this, you have to get worried about are you processing it to full utilization. If a car is rated at 100 miles an hour, but can only go 30 miles an hour because the, the infrastructure cannot support going further, then you're grossly underutilizing the car or in this case the gpu. So yeah, we are the information highway to all accelerators users, mission critical workloads, you name it.
- [02:11]
A
So let's say now I sit in my office, I do a prompt on ChatGPT, how do I touch your products? Just what happens with the.
- [02:19]
B
Yeah, so we are very much behind the scenes. This is something my parents always used to say, what do you do? Because you can't see what we do. And when you type in a prompt of Arista, basically what at that point we're doing is connecting everything to everything. This conversation on this podcast is not possible without it. So the traffic patterns of high intensity, whether it comes from a user, a device, a workload, a machine, a server, a storage, or now more and more the AI killer applications, we connect and we do that through a set of switches and routing platforms. We call them leaf spine architecture, where you can have multiple leaves that connect into an aggregate spine. And these days you can also have an AI spine. These are some of the world's largest high scale platforms, but they need to be powered platforms and chips need to be powered with the right software. That's where Arista's hallmark has been, its extensible operating system EOS where we fuel these platforms and networking connectivity with more and more types of data. We can deal with structured data, unstructured data, flow data and make that all work through our purpose built software.
- [03:35]
A
How is networking from AI different from what came before?
- [03:39]
B
Yeah, very different. So just to step back a little, the. If you go back even 20 years in networking, the Internet was born through a lot of in fact the old days. The TCPIP and all those algorithms came from the research community, the ARPANET. Then there were multiple protocols, things like XNS, Apple Talk, OSI, IP, TCP, IP became the worldwide language of Internet communication in the 2000 era. In the 2015, 2020 era, that wasn't enough. People were starting to build some of the world's largest clouds, whether you think of AWS or Azure or gcp, Google or so many more. And they needed the kind of unprecedented scale to touch so many more companies and users. It had to be multi tenant because they were not supporting one company, they're supporting thousands of companies. So a typical CIO of a midsize company stopped building their own things. They started relying on the cloud for their mission critical assets, whether it was E commerce applications or their databases or whatever. Now in comes AI the last couple of years and that's adding another twist because cloud traffic is very different than AI traffic. AI traffic and we all saw this with the onset of ChatGPT and all the other language models is conducting a class of complexity and search that is thousand or million times deeper and greater broader. And so this required even more intensity of the traffic patterns. Typically we were doing with before AI what we would call front end networks, connecting standard CPUs, storage, general purpose, query, indexing, searching, that kind of things. With the advent of AI, we were suddenly connecting hundreds, if not thousands or even hundreds of thousands of accelerators. This GPU traffic, as I said, required you to very different traffic. It's much more intensive in the fidelity of the traffic, the intensity of the traffic, the durability of the traffic, the peak burst. This is often called a back end network or a scale up and scale out network. We had to build almost a different set of parameters and metrics until we came along. I would say Majority of this was little islands of things. There was an infiniBand network and PCI network, CXL network. And Arista was able to bring Ethernet and IP and standards based capabilities like we had in the cloud into the backend for AI.
- [06:21]
A
What's the biggest constraint now in terms of building out these networks?
- [06:28]
B
I would say the biggest constraint for the industry at large is power, because the power consumption of these GPUs and the network and the cables and the optics and everything is just unprecedented. In the old days we used to talk about megawatts. Now we talk about tens of gigawatts in a data center. To put this in perspective, this is larger than a football field and you're powering that with gigawatts of capabilities. So our AI providers or cloud providers are unable to find this level of power. If today you're searching for that kind of power, it could take you three to five years to find it. The space and the power are probably the biggest problem right now. But once you do find it, you have to outfit it correctly. And that's where we come in.
- [07:13]
A
Absolutely. So if you find it, you have to, you have to call you guys. But now enormous amounts of money flowing into AI infrastructure right now. Just how do you, how do, how do you view this? Is it? Well, I think we're too fast.
- [07:27]
B
Yeah, it has happened very fast. When I look at the cycle of how NETWORKING progressed from 100 megabit to 1 gigabit to 10 gigabits to 100 gigabits, that process happened every five years maybe. But the migration from 100 gigabit to 200, 401.60 and eventually 3.2 terabits is literally all happening within 12 months each. So what used to be five year cycles has become 12 to 18 month cycles. So when we think, okay, we're just done with 400 gig last year, we're already thinking of 800 gig and 1.6 tera this year. So the insatiable demand for speed, for scale, because you've got to aggregate all of these throughputs and for predictable latencies all hitting us at once.
- [08:18]
A
You were at Cisco during the Internet boom. So how does this happen?
- [08:20]
B
I was, I was.
- [08:21]
A
How does this compare with what you saw at that stage?
- [08:25]
B
Much greater. The Internet boom happened in a very steady fashion. There was a period of time where there was a peak in 99, 2000 due to the dot com boom and bust, I would add, and then Y2K as well. But for most part, that Internet took 15 years to develop. I was there from the very first acquisition in 1993 of Crescendo, when the company was less than a billion dollars, to when I left in 2008. It was a 40 or close to $50 billion company where the catalyst switches, which I think are still Cisco's most successful product line, were contributing at least a third. The point though was it happened slowly and steadily, but there was a peak, there was a bubble for two years. This one feels very different for a number of reasons. First of all, that 15 years that I was talking about is collapsing literally into five to seven years. The time aspect of this is shrinking. Secondly, the scale aspect of it is just exploding in magnitude. It's a thousand x more. And the third, perhaps the most important one, because people like to make this parallel to are we in a bubble? I think it's a lot of responsible companies and therefore we're not in a bubble or if we are in a bubble, we're in a prolonged bubble. It's not CLECs that came in were funded and then they collapsed. These are responsible companies like Microsoft, Google, Meta, Oracle that are taking responsibility of building all of these, not to mention the NEO clouds as well, such as OpenAI and Anthropic. I would say more than a bubble. It feels like an explosive mega trend that is here to stay for some time.
- [10:07]
A
We did have overbuilding at the time of the dot com boom. How can we be confident that it doesn't happen again?
- [10:15]
B
Because it's not available. The power and the capacity is not. So nobody can really overbuild. It's going to take time to build it. The desires are greater than our ability to execute on it. I think it'll still be a three year execution rather than what happened in 1999 where everybody just hurried up, built these companies and said build it and it will come. Here there isn't a build it and they come it is please build it, we need it now. That's the biggest change. But again, that's not to say that I wouldn't correct myself and say it isn't a bubble, meaning it is happening in a furious sudden state so far greater than the steady state we've all been used to. But I don't think it's being done for a one year factor. It's a three to five years and it's being done by responsible companies who are voting with their money because they see demand. That's the biggest difference, that the demand is there.
- [11:10]
A
Talking about responsible people. So your biggest clients are the Hyperscalers, Right. The cloud companies, the Microsoft titans.
- [11:18]
B
Yeah.
- [11:19]
A
How concentrated is the customer base?
- [11:22]
B
Well, you know, they've always been concentrated. Microsoft and Meta, as an example, have been more than 10% of our revenue each since we went public in 2014. I'm not going to apologize for that concentration. I'm going to be pleased and grateful for that partnership. They continue to be 10% of our revenue. But of course, as the denominator gets larger and larger, we are diversifying to a lot of specialty cloud providers, to the enterprise, to the campus. But I would be kidding if I didn't tell you they're very, very integral to Arista's strategy. And the partnership has never been stronger.
- [12:01]
A
Which country are you the most impressed by when it comes to building the AI capabilities?
- [12:05]
B
Which country? Undoubtedly the United States. I think we're leading. There are pockets of things happening in other countries, but in terms of scale and the companies we're working with, majority of that is in the US today.
- [12:20]
A
Any other countries which you think are doing the right things?
- [12:25]
B
Definitely. I think we're seeing pockets of this in the Nordics, in the Middle east, some of it in UK as well, some of it in Korea and India. So no question it's coming to many of the other international locations as well.
- [12:45]
A
When you look at where investments are taking place, are you worried that it's going to split the world even more than before?
- [12:52]
B
No. Majority of the investments I'm seeing are not splitting the world, but connecting the world more. Thankfully they're not government sponsored, so they're all about AI and technology and how to increase the communication. I was just in the Middle East a month ago and I'm just so impressed with the rate of progress there where they are investing a lot of their oil currency into important AI investments and capabilities. So I don't look at it as a divide, I look at it as a unity.
- [13:21]
A
Talking about impressed. You have now 21% of the market for AI data center networking, which is incredible. Just how did you get there?
- [13:32]
B
Is that all we have? I was hoping it was higher, but we got work to do then. We've climbed our way beautifully from 0 to 21. I think if you look at the high speed market, we're more at like 41. But if you look at the general speeds of all switching, then we're probably more like the number you mentioned. Look, it's been a labor of love. The company was founded and started shipping products in 2008. We have not done it in a bolt of lightning. We have done it steadily but surely. Really, I think to epitomize the culture of this company that Andy Betrothein and Ken Duda started, I would have to say built the right foundation for our software, for our hardware infrastructure and keep improving it. Our market share really comes from the fact that our customers appreciate our innovation. We're built by engineers for engineers. We put a maniacal focus on quality architecture and doing the right thing for our customers and supporting them. Because we're sitting there in the heart of a mission critical environment where if we go down, everybody gets to find out if we don't go down, where the world's well kept secret. And that's fine with me. So I think we have acted in a very methodical fashion and not just rushed a product to market before it was ready.
- [14:56]
A
Now you spent 15 years at Cisco building up their data center business. Just what was it like being at Cisco during kind of peak levels? Right.
- [15:05]
B
It was more than the data center business. I loved my time at Cisco. When I joined Cisco, Cisco was approximately 650 million in revenue and 6 billion in market cap. It wasn't a startup, but it was certainly a very young company. I got a chance and the company was largely building routers. I got a chance to contribute in creating and building the Catalyst brand of switches. First it was catalyst 1200, then 5000-600050-04300, 3000. So this switching brand went from being zero when I first came in there to I still remember the first year was 40 million, then 365 million the second year and then a billion the third year. And those kind of things don't happen without the World Wide Web and the Internet taking off, no matter how great your products are. So I felt privileged to be part of a rocket ship. It was a great company. But at some point I felt like, wait. I didn't intend to be in such a large corporation. I wanted to do something smaller. But I fondly and nostalgically remember my Cisco days and those 15 years.
- [16:10]
A
What was the most important thing you learned from being on that rocket ship?
- [16:14]
B
I think the same thing we have here, which is that focus on customers. Cisco has a culture of customer focus. I think Arista doubles down on that as well as innovation and engineering and quality. But I've taken calls on behalf of Cisco at 2am as an executive sponsor. If something was serious, I'd pick that phone up. Even though I value my beauty sleep, that was more important at that point in time. So I would say the culture of customers is super important.
- [16:46]
A
Now you could probably have been the next CEO of Cisco. So why did you want to jump spaceship?
- [16:54]
B
I don't know if I would have been the right CEO for Cisco because I was an accidental executive at Cisco. I didn't plan to even be the executive I was. I love products I love.
- [17:07]
A
Don't you think most people are accidental executives? I mean it's not like, it's not like you are five years old and you sit in India and think, hey, I'm going to be a superstar in Cisco.
- [17:17]
B
Oh heck no. That, you know, you could ask my parents how little of a superstar I was when I was five years old. But leaving that for the moment, I guess what I'd say is I loved product. I loved working with the engineers to enable products to customers. I was too steeped in the technology interests to think I had broader capabilities or desire to be a CEO at Cisco. Actually, when I left Cisco, the fact of the matter was I thought I would do something entirely different like clean tech or energy or solar or battery. I really wanted to get back being in Silicon Valley to the roots of what I love, which is being an entrepreneur. I joined Arista when it was zero revenue with 30 engineers. The most important thing I loved about that, which is working with the people I enjoyed.
- [18:11]
A
Let's say now I have a startup, I got zero revenue, 30 people hanging out in an office. How do I recruit a superstar like you were at the time into the total uncertainty?
- [18:23]
B
Yeah, no, I think Andy and I go back to what was it? We knew each other.
- [18:29]
A
So this is Andy, Andy Dachtoshein, the.
- [18:32]
B
German co founder of the company. So we had bought his company Granite when I was at Cisco. So we had worked with each other. We worked also when he was founded Sun Microsystems and I was in AMD building chips. So basically we've known each other for, since 1987, 35 years or more. I think when he saw that I was leaving and I truly did not know where I was going, I did not want to make a decision on where I was going. I wanted to clear my head. He said, hey, I got this startup. And I said, oh. My first reaction was, oh no Andy, I don't want to do networking and switching again. But as I got to understand what the founders were trying to do and what the engineers were building, I was intrigued because it was networking and switching with a twist. It was not the Internet and catalyst switching. Like I knew it. It was a completely different ballgame.
- [19:23]
A
And what exactly was that? What was that twist that made you Interested the software.
- [19:27]
B
The software architecture was so unique. When you look at what all the other networking companies have done, they usually have five operating systems for five use cases times 50 images. You're managing 250 here. We had one operating system, one binary image with a lot of class foundation that we call the state driven publish subscribe model where if something fails, it doesn't just fail, it automatically recovers that agent. And it's the opposite of a Christmas tree light. Where the Christmas tree light fails, everything goes down. Here we built something where one light failed. We could actually replace that LED and keep everything up and the customer would never know it. That software has what has earned the right for us to be in all these mission critical, high profile customers you named. And then of course we've taken it to a variety of use cases. So I was drawn to that technology, I was drawn to the people. But I would be kidding if I didn't tell you there were moments of anxiety because the year I joined was 2008. It was in the middle of the financial collapse. One of our earliest customers that I won was Lehman Brothers. And you know what happened to them after that?
- [20:34]
A
I do.
- [20:34]
B
So I had an ominous start to my entrepreneurial success career at Arista, which is now 17 years and going strong. So it's not like we didn't go through ups and downs. We absolutely did. The financial collapse was one. Our largest competitor Cisco suing us was another Covid and the whole supply chain crisis was another. So we have fought a lot of adversity to be where we are and it's good to be in this position.
- [21:02]
A
What does it take to take on a giant like Cisco, be a startup and it's just like hey, we're going to beat them. Just wanna what does it take?
- [21:11]
B
Most of my colleagues would tell you, I don't think I ever felt I was taking on Cisco because initially we were so focused on very specific and unique use cases that Cisco didn't even want to be in or pay attention to, like high frequency trading and low latency. That was a use case they just didn't to want deal with. And our first hundred million came largely from that. Our second use case was the cloud and again Cisco used to call it msdc, massively scaled data centers. But they didn't really pay attention to that sector as a sector of customers and try to understand why they did what they did. In fact, the early days of aws, Amazon and Google, they, they didn't use Cisco, they didn't use Arista because they built their own because they were so dissatisfied with their requirements and what was commercially available. So once again I felt like we were fulfilling a white space that the large corporate corporations, and in particular Cisco, were not doing at that time.
- [22:17]
A
What is an example of outstanding customer service?
- [22:25]
B
There are many examples and many stories I could tell you. But let me actually ask you that question back. If today you picked up the phone and called Arista support, what do you think and you had a network problem and you had a what do you think the response or expectation from you would be?
- [22:44]
A
Well, that you came and sorted out my problem.
- [22:47]
B
Well, typically how it works is we'll say, okay, who are you? Have you paid your bills? If you're just a random asker of questions, they're like, wait a minute, we don't have an account on you. We don't do any of that. We immediately say what's the problem? And our average resolution time, it's like treating a patient in an emergency or ICU. You don't ask them 20 questions. You get to their symptoms and treat them. So our average resolution time is 25 minutes. It's an unheard of record. Why? Because we're filled with experts and our quality and support go hand in hand. You build high quality products. If somebody misconfigures them or there is a support problem, you've got to be right there. So we don't outsource this to anybody. It's a fall of the sun model. It's worldwide coverage, any day, anytime, any zone, and we respond and solve the problem. Some problems are solved in five minutes, some may take hours, but the average time is very short and it's a very high quality experience.
- [23:44]
A
How do you make sure you stay ahead?
- [23:49]
B
It takes some personal time. So I will block out think times in my calendar where I just don't do meetings because everybody wants me to do a meeting. I make a point of staying current on events and technologies. AI as an example, I made a personal point in 2022 and 2023 to come up to speed. I wasn't paying full time attention to it when the ChatGPT moment hit in November of 2022. I think it was a massive sort of milestone for the tech industry. And I remember I had to give a talk at the Optical Conference OFC in January of 2023 and I could have talked about optics and log haul and short haul and DWDM and co packaged optics and DSPs and all of that. But instead I used that optical conference to actually talk about AI as a Driver. And so to do that, I had to learn and study myself because you're talking to an audience of PhDs who probably know much more theoretically. I take great pride in always learning, always continuing to push the envelope, both as an individual and from my peers in the industry. And inside of Arista, we have a very rich organization of top technical experts.
- [25:13]
A
What's the furthest you can see into the future?
- [25:17]
B
Not that well, I'd be wrong. I can see far, far enough to say some things might happen, but I would say my accuracy is best in the one to three year. After that, it's a little more blue sky.
- [25:29]
A
And what is going to change over the next three years?
- [25:33]
B
I think the way we think of AI right now is going to change dramatically. So today we are building the mainframe version of AI. We're talking about how bad are the clusters? How many millions and billions and trillions of parameters and tokens can you put in there? What does that manifest in terms of teraflops or terawatts or terabits of capacity? But in my view, the next three years, while that is important, I'm not underestimating that. I think what's going to happen is this is going to become much more of a distributed problem. We're not going to be able to fit it all into one size. Just as the mainframe moved to client server and eventually distributed PCs and clients and phones, I think AI is going to take many shapes and sizes, including this one.
- [26:23]
A
What are the implications of that?
- [26:26]
B
Significant, because miniaturization is a lot harder than saying, let me just build the biggest baddest to everything. That means I've got to be able to translate my training models into reasoning and inference and do more with less in a lot of these use cases. Because if you can just throw bandwidth at the problem and throw capacity and compute capability, then yeah, you can process the world's largest training workloads. But if you can apply that to reasoning and extend that in a more distributed fashion all the way into your general purpose compute and storage, I think that'll be AI to every desktop, not just AI for the biggest, baddest.
- [27:12]
A
Are you worried that the technology world is splitting in two here between the US and China?
- [27:20]
B
There is a political aspect to how we are polarized today. I wish, I wish innovation and technology could be playing an even field. But I understand we're all countries governed by different. So I am worried that for the United States to stay ahead and a step ahead, we have to be constantly innovating and Constantly partnering with the best talent. I'm an example of an immigrant who came here who wasn't born and raised here. Whether that talent is in China or India or London or wherever, we need access and we need to be collaborating with the best of the pines.
- [28:03]
A
Let's move on to culture. How would you describe Arista's culture in one word?
- [28:09]
B
Do the right thing? Because it's too easy to be, especially when you're a public company focused on the quarter, focused on something. And no doubt every company has a business model that we have to focus on and all that, but if you look past that and do the right thing, then all the intermittent things take care of itself. One of the best examples I can give you is early in our career we had a huge quality problem with one of our chip vendors, with the hardware. We worked with them, they worked with us. I had a choice of just doing a minor patch on the software, but I went all out to explain this to our customers and say, look, you're not seeing the problem right now, but this could be a slow decay and we want to volunteer to replace all your gear at our costs. That could have nearly made us bankrupt at that time, but we chose to do the right thing so more people remember that we did that even though it was bad news at that time than all the good news. I think that focus on doing the right thing as a company for our employees, as a culture for our customers, is what I would describe as the one liner.
- [29:17]
A
How do you make sure that this attitude is shared across the organization?
- [29:21]
B
It's not easy, especially as you get bigger. But what's beautiful is as a management team, we have pretty much been the same management team for the last 15 years. We lose people occasionally due to alternate goals or financial success. But I think when you have the same management team and leaders, and obviously now we're bringing in next generation leaders, and you reinforce that culture, then that permeates throughout. The other big thing I would say is while we of course have middle management, we make sure that the middle management plays an important role in this culture and isn't just a professional manager, but a coach player that they're contributing with their own two days of coding. My president and cto Ken Duda, who's the founder of this company, he still codes. So that tells our employees that we're leading by example. We're not saying one thing and doing another.
- [30:20]
A
How do you keep learning?
- [30:21]
B
I probably don't learn enough. But as I said, one of the important my sources of learning are Many when I go to see customers they teach me a lot because they look at it through a different lens. So that's one aspect. I spend a lot of time with our engineers to understand what they're up to and what some of their pain points are. So they're always teaching me. Of course today you can go to Claude and become a software developer with great efficiency or Gemini or ChatGPT. Those tools are certainly making it easy to understand complex topics. I try not to rely on them except in my points of weakness, but that's certainly a source of learning. My most favorite part of learning is actually reading, which I do less and less of and but that's my go to.
- [31:10]
A
So Jashree, what do you look for when you hire?
- [31:13]
B
Yeah, so obviously when you hire somebody it's very clear they need to be competent. So there's an element of aptitude and quizzing them on their capabilities. But I look for competence, character, moral values, culture. And what kind of a human being are you a good person. Because I want to make sure that this hire is not only capable of producing results but can also be with us in the long run and embrace and permeate our culture. Again, back to doing the right thing. So we definitely enforce the look at the long road and so we look for like minded people. In fact, many people say, oh you know, if you hire somebody who's related to you, is it nepotism? I think there's a good chance you're going to get some really good people that way because you know the relative is good so they must be too. So I'm always willing to give internships to the Arista employees, extended family, if we have openings, of course.
- [32:20]
A
What proportion of your employees have Indian background?
- [32:27]
B
I would say because we have a large India facility, It's probably around 15 to 20%.
- [32:36]
A
Can we go back to your upbringing in India? You grew up in New Delhi, so what were you dreaming about when you were five years old?
- [32:45]
B
Yeah, well, yeah, I was born in London, raised in Delhi and then have spent majority of my life in the United States. My dad was an educator so he was dreaming about my education and making sure I do well. He started the first five IITs in India which as you probably know are very well known and high reputation famous. So I think education was a huge part of being a middle class family and institution. So doing well in school, excelling, not just doing well, excelling in school, school, going to the best schools. My parents made sure they could afford the best education so I didn't Dream what, what I'm doing now. I dreamt more one step at a time about how do I do well in school, how do I go to the best colleges. And there was no long term plans per se, but I, I could tell that my parents had high expectations of me. Probably me myself didn't have as many high expectations of myself.
- [33:45]
A
What does it do to a person to have all these expectations on you?
- [33:48]
B
Sometimes it forces you to perform better than you would. Because it's a very interesting thing that anytime my dad or mom went to meet my teachers at a parent teacher association, they'd always say, Jai Sree has a lot of potential, but she's not using it all, she's not fulfilling to her highest potential. And so they come back obviously demanding more, expecting more. But I think what it taught me was, you know, one of my best friends in India, you rank all the students. So she always came first and I didn't come first. And I noticed that she would work, you know, about eight hours a day in terms of studying and to do well in these exams, I would work more like three to four hours a day. And to me that, you know, rich roi, if you want to call it that, to get a rank that was not as good as hers, but still I did well. I was in the top 10%. You know, I tried to convince my parents that I was playing the long game. And today it may look like I was, but at that time it was more an excuse to not study eight hours a day.
- [35:00]
A
You studied electrical engineering, which was not very common for women at that time.
- [35:03]
B
No, not at all.
- [35:04]
A
So how come you ended up there?
- [35:06]
B
Yeah, I ended up there through elimination. I loved physics, I loved chemistry, I loved math. I did not like biology. I was not as 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 pretty rare field in my era. Probably if that had been there, I would have done more of it. I had to take my computer science classes in the math department for Fortran and C and assembly. So it was, I took a few classes, but electrical engineering was the hardcore engineering and it's coming back. I wish more people will do it now in this modern world of more and more silicon and chip and hardware design. So I think, and I had been influenced by my father starting the IITs that applying science to real world problems, which are what, which is what I think engineering really is, was something that excited me.
- [36:00]
A
Now you are considered one of the hardest working people in technology. Just how much do you work?
- [36:06]
B
You know, I think when you love what you do, it doesn't feel like you're working hard or working long. You're enjoying it. Also, as a CEO, the buck stops with you. And I have a responsibility. So I would say my mom says the same thing. You work too hard. You work too hard. And I say, mom, I love what I'm doing. So I haven't kept track of the hours because it has always been important to me to balance my work with my health and my family. But frankly, I clearly need a few more hobbies. Maybe then I'll work a little less hard.
- [36:43]
A
What are your hobbies? Do you have any hobbies?
- [36:45]
B
I used to run a lot, but I've hurt my knees, so I walk. I do a lot of hikes. Our dog is a big part of that. My family, we're all into music of some form or shape. So while I don't plan to do any public singing, it's still a lot of fun. The acoustics in a shower sound really good. Anybody can sound good. And these days you can use enough synthesizers to sound good too. So I love that. I love to read. I probably.
- [37:19]
A
What do you read?
- [37:20]
B
You know, I read. I read a lot of. I haven't read as much recently, but when I do read, it's usually on long distance flights. And either I will print and publish a spec and read a technical document, or I will read a completely, you know, brainless novel like a John Grisham or something where I don't have to think about things because that's my downtime. But I would rather read than watch a movie.
- [37:49]
A
When you look at your. When you look at your life, what are you the most proud of?
- [37:53]
B
I'm most proud of my family. I would say my daughters, my husband, my extended family. They give me a lot of joy because it's at that point, it's not just about me, it's about, you know, them. I recently I got an honorary doctorate degree from my alma mater, Santa Clara University. But my daughters really did a PhD and a doctorate in veterinarian science. So I'm happy for their values. I'm happy they're compassionate people and they're happy that they've pursued their path of excellence.
- [38:27]
A
What kind of advice do you give to young people, including your daughters?
- [38:31]
B
This is a lot of the advice I give because they all, they seem anxious to get to a certain point and a certain time in their career. And I said, look, pursue your path of excellence. You know, every one of us has a gift, which also means we're good at some things, we're very bad at some things. But if you can find the things you're good at and improve that and build upon that, and become excellent at it and keep working on it. I think everybody defines their path to excellence. And it isn't always measured by money alone. It should be measured by reaching that destination of what you deem to be success.
- [39:11]
A
Deshwi it seems like your teachers back in India were totally wrong. It seems to me that you have absolutely used your potential to the full. Big thanks to you for keeping the world connected. And all the best of luck.
- [39:25]
B
Thank you. Nikolai. Pleasure. Thank you for all the thoughtful questions.
- [39:28]
A
Thank you.