00:00 I went through that journey of how you entrepreneurial and define your business and how you sort of adapt and navigate. That is a time that I think is very parallel to what's happening with AI today. Being a network has never been so important. I think after 30 years, I think network has got its spotlight, if you will. How do you reimagine this entire process where you have an agentic AI that can solve the problems and can do all these tickets and backlog in the background without that being the focus. My journey and my destiny sort of led me to this space, right? As I said, I joined Exodus as a software engineer and Exodus changed his business model to a data center model. And so, I got to learn a lot about infrastructure. Then I went to SaaS applications, so I was learning a lot about SaaS. And then I was in cybersecurity for some time. And so, you get the breadth of skill set that you do. And then you do a lot of business transformations. And so, that's how you sort of equip yourself. So, you're a little bit more horizontal and then over a period of time, you get vertical, right? That's the journey I think of most CIOs. And you need to go between these being a startup company and being a large company back and forth, right? Maybe multiple times in a day. And I think that's what defines me because I can be thrown into a trenches and figure myself out.

01:19 All right, Venky. Thank you for being here with me at Atomic Conversations by Atomic Work. And good morning.

01:27 Thank you for welcoming me here. Thank you. Thank you. Good Good morning to you as well.

01:31 Yeah. So, so Venky, we we typically go over, you know, the life of a CIO and starting from the career to where we are and then where we're going. And going back to the roots of you, who you are, help me understand, you know, where you started, you know, your college Um and uh how you came here to US and what's the you know roots for you?

02:02 Sure. You know, I I grew up in Chennai um and uh in a state called Tamil Nadu in in India. And uh my fascination for computer started in high school. You know, we had uh we were the first grade 10th grade to have like high school mean computer science as an option, right? Oh, wow.

02:22 And uh so we were fascinated by sitting in front of this terminal typing in commands. It was basic programming at that point in time. And there was another selfish reason. You know, you know, Chennai is really hot. So being in a computer room mean you get air conditioned room. So we we we spent a lot of time in computer. So that's was my first fascination. So when when I left uh school when I graduated from school, my my I was I really knew that I really wanted to be in computer science. Um I I got different offers for engineering from different colleges. I said, "No, I wanted to be in computer science." Nice.

02:59 Uh and uh there was uh and and and it really neat well, you know, was fixed into like really what I wanted to do from a software perspective, right? And so I I did my undergrad in math um and I also did uh courses in computer science side by side at NIT, right? Got me to uh you know, familiarize uh myself with the latest things. Started with COBOL in those days. You know, like Yeah.

03:28 Got to write like, you know, 100 lines of code you know, to get some basic stuff done, right? And so started with COBOL programming, you know, proceeded in, you know, different softwares and stuff like that. And then I I had a choice between whether I wanted to do pursue my career in math going into like, you know, a masters in research, you know, or go into computer science and applications. So, uh my passion was was with software and my passion was with, you know, computer science at that point in time. And so, I chose computer science and I said, you know, even even during high school, during college days I used to work in part-time for companies, help them help entrepreneurs, you know, the stuff. I was By default, I was the IT person at home because I was like the person who sort of dabbled with a lot of gadgets and computers. So, you know, that's how that's what shaped shaped me and what I am today. You're a tinkerer.

04:26 You've been tinkering with all of this stuff. So, actually I did the it's a very very uh interesting, right? So, uh when I came out of college, the first um thing I also did was assembling PCs. Cuz to learn. I was fascinated by how all of that comes together. And uh and and uh before I even ventured into any job, I was like uh interning uh at a company in Chennai, actually. Um and uh well, I had so much fun cuz you have to package everything, build the whole piece, whole PC come together, and then deliver it. And then set it up, configure. And so, we used to do that uh and I really enjoyed it because you have to install the software, you have to go through troubleshooting, setting things up, and building the hardware pieces together, video controls, and all the things that need to be there because we used to kind of bring all of these things, different different pieces, and then put it together. So, yeah, it's fascinating in those days. those floppy disks

05:22 and you put them in there. Yeah, yeah.

05:24 A lot of the time you used to go to Microsoft Word and type in all these documents, and I remember uh because there used to be a lot of power outages in India. You had to keep saving these documents all the time, right? Every few minutes, save these documents.

05:36 these power UP UPA whatever they um the power batteries and kind of support those. And when the CD I remember when the CD-ROM came, we had to really uh create space for the floppy disk and then create another you know place for CD-ROM so you can insert it and then so now you have permanent writing and then like a lot more space and then

06:00 all of that so it's crazy.

06:02 a it's a fun evolution of where we were in almost 30 years ago to where we are today.

06:08 Yeah. I think the the the part about this career obviously you worked in so many great companies throughout and from Exodus for several years and then to Thompson Reuters and and then Anaplan and then here now at HP. What was the most interesting period for you in your journey?

06:30 I you know always look at my early part of career as things I really cherish because that's when you're learning a lot. You're like you're in the weeds.

06:39 You're in the weeds of things, right? So I was a young engineer working in Toronto. Right? And and I came to visit my sister in during December obviously a nice time Toronto to Bay Area, right? Yeah. Yeah.

06:52 Yeah and I said what the heck let me just apply for a job and in those days you got a fax in your resume. Right? In '96. So so faxed in my resume and I got a job as a Java programmer. So I was excited because I was reading James Gosling's book on Java or really early early programmer, right? And I joined Exodus a really small startup company you know about 20 30 people in Sunnyvale. At that point in time we were an ISP. We wanted to do like something with e-commerce because the head of CyberCash at the time was on our board. We wanted to do a lot of different things together, right? And I went through that journey of how you entrepreneurial and define your business and how you sort of adapt and navigate. And that is in that is a time that I think is is very parallel to what's happening with AI today. All right? And so

07:50 on one end and then Google on the other.

07:52 you know, we had when Hotmail was a startup company, right? And Hotmail wanted to put in, you know, uh uh these kiosks in airlines and give free email to the world. You know, little did they realize that US is also a great great market, right? Yeah.

08:08 And then, you know, it was very simplistic model of how Hotmail was a startup company, Yahoo was a startup, Google was a startup.

08:15 still the biggest at that time.

08:17 And if you look at lot of parallels with AI today, right? Yeah.

08:21 When the internet first started, right? People didn't realize what the internet model was all about. There was ISPs and we were on ISP too at that point in time. There was ISPs that were competing to sell access to Yeah. residences and businesses, right?

08:36 And ISPs were based on the uh the the bell model, the telephone model. Yes. Yes.

08:43 And the telephone model, it's it assumed that you're going to have like few pots lines or few telephone lines. And you're going to give a hundred extensions out and assume that not everybody's going to be on the phone at the same point in time. This is exactly the opposite of the internet model. Yeah.

08:58 Where you had these it's 24/7. The evening you had these search engines that had in massive crawlers that used to go go around the internet, index the uh picture of there.

09:10 Yeah. And during the day you had all these e-commerce companies that were doing businesses, right?

09:14 And, you know, yeah, so people used to move into the data center in the middle of the night. So we actually had a large search engine company move from UC Berkeley Yeah.

09:25 to Santa Clara, you know, with walkie-talkies and storage devices at 11:00 p.m. or so, right? So

09:31 are Sun storage arrays that they used to carry.

09:35 E10Ks we used to call those refrigerator servers right there. He's You talk about ecosystem that changed and we said this this internal access model is not going to work out. We need to do private peering. When internet first came out it it was all about public peering, right? There was a there was a public peering point in San Ramon. Yeah.

09:54 And there was one in Washington D.C. And if you look at how internet worked, everything had to go through San Ramon site and East Coast in D.C. And Europe did not have one. So, Europe had to come from, you know, wherever whichever country in Europe to Washington D.C. and then go back to another country.

10:12 Yeah, so that's where the gateways came later on, right?

10:15 Yeah, that's where the Yeah, yeah. And the funny story that is that the the internet access point in Washington D.C. actually was housed in a parking garage.

10:23 Right? And when the elevators went up and down, you know, you had some some sparks happen, voltage fluctuations happened. So, from there we had to say we had to reimagine, you know, obviously the internet infrastructure wasn't given that that much of importance. We had to reimagine this whole world and we came up with private peering. That was a key part as well. You don't need to go to a common exchange points. We have the routers and the switches and the network in here. You can come to we can do private peering. And both the data centers prior to that it was not called data centers. They were called cages like you have in zoos, right? Yeah, exactly.

10:58 So, Exodus was the first one to come up with the name internet data center and we grew like 40% every quarter. Like we we grew like 400% Sorry, 400% every year. Yeah.

11:11 We grew from $3 million to a billion dollars in like less than 4 years. I think it was the fastest growth company in the at at that at that period of time. We used to open data centers that were completely sold out, right? Yeah.

11:23 And that is that is the point where networking was really relevant. Yeah.

11:27 Because we used to get those routers that talked talked the, you know, BGP's that used to go out to the internet. We used to have these large switches. Yeah. So, that's where

11:39 1 Mbps to where we are.

11:41 So, you know, frankly, I was just talking to having a town hall to my colleagues in HPE. Yeah.

11:49 And I said, if you look at network when when we started off, you call ethernet, you know, 10 megabits. It's fast ethernet. Yeah. It's 100 megabits. yeah.

11:57 Today, we are at 1.6 terabits per second. We've come a long way from where it is. And I think today with the AI, that's why has also shrunk.

12:05 The size has shrunk, you know. We have liquid cooling. Back then, we had, you know, some of the things that I look back, it's kind of humorous, right? In those days in Exodus when we get data centers, we get power for free. Yeah.

12:18 And then, you know, you know, sort of 1999, 2000, PG&E went bankrupt, right? Yeah.

12:22 Almost Then, we realized, oh, power is the main source of the economic level for data centers, right? And today, if you go to a data center company, you say, how much power do you want, right? And so, the economics changed, and we didn't realize the early economics at that point as well. Coming back, you know, I think fast forward now with the AI world, right? You know, network has never been so important. I think after 30 years, I think network is is God. It's, you know, spotlight, if you will.

12:49 Yeah. I think the the the key is as we learn through all of these transformational journey at a different stage that we've gone through for the last 30 years. Um one of the most fascinating thing that I'm observing is is that we were always catching a different wave. Um I think we were lucky generationally being in the part and it's because we're at the right time at the right place in order to be uh have that opportunity to ride. Because you don't know when the wave comes, when it came, you're ready to jump on and take off.

13:25 And I think when you know, I think that's the great thing about being in the Bay Area and Silicon Valley is that you can catch the wave of change, right? I was uh talk about my Stratify, right? And Stratify came at the end of the dot-com era. It was called Purple Yogi before that. You know, really get a lot of fun year, you know, purple color shirts, purple color chairs, you know, a lot of chief yogi officers and stuff like that. But obviously the dot-com bust, things changed. Yes.

13:55 And they recalibrated their business model to e-discovery.

13:59 Kind of it's kind of gave a sense of realization that it's not all that kind of a thing that that it things can happen that you have to prepare for and an eventuality and reality and the bubble burst sometimes if you're in a bubble, yeah.

14:14 And if you can reconstitute and that's, you know, I think 2002 or 2003 is when Supreme Court passed email as an acceptable, you know, evidence in court.

14:23 And then, you know, we were there doing data processing and multiple litigations and we had the first uh cloud or SaaS model, right, for e-discovery, the first SaaS company for e-discovery. So that that is an interesting journey moving from a search engine to like an e-discovery SaaS company, early early forms of cloud or SaaS, if you will.

14:44 Yeah. And then here we are like we just started our journey from internet commerce to uh big data and cloud processing. Now combining all of that into AI because it requires everything that we've kind of built ourselves to to get here. Um but I think this time if everybody is feeling something is different. What what is it that you feel that is different with this AI um onslaught?

15:14 I think, you know, I think after 30 years, I think uh this is something transformation, right? If you looked cloud, cloud solved an economic problem. Yeah.

15:26 It was not um economically to scale. right?

15:30 profitable for you to run your own data centers. Yes.

15:33 That was the mantra in Exodus, right? Hey, why do you want to run your own generators? And why do you want to be in Actually, the first data center company came in because they didn't want to run the data center in Palo Alto, which is expensive real estate, right?

15:44 And so, you know, so that was the mantra during like dot com days, right? Then they said, you know, why do you want to run your own software when you have all these data centers that can do it? By the way, you have a better user experience, you have an easy to use So, the user interface changed, um the the place where the the software's hosted changed, but fundamentally the business process remained, right? I mean, that didn't change, right? Right. Yeah.

16:10 You talk about a CRM or ERP, they are as complex, right? It was just a different place and a different experience, but

16:17 Also, it's a same problem. It's same problem, but a different era, right?

16:21 Exactly. Same problem, different era. What AI and specifically Agent AI allows you to do is is rethink your software ecosystem. Yeah.

16:31 And where you have these agents that can solve a particular business problem or or automate a business process, irrespective of what's underlying software application is or where the data is stored. You know, that can be obfuscated, right? You can have a a layer of these independent agents, right? Yeah.

16:54 And I look at it, you know, in a way by which, you know, in the 1800s, if you go back a little bit, right? You had these steam engines, right? And all these factories were architected around steam engines. The steam engines ran in the basement, and you had these large, you know, belts and shafts.

17:15 Yeah. And the factory floor was organized by how these shafts and belts were there. You know, you have a big machine and it goes closer to the shaft. A lot of hardware.

17:23 Yeah, a lot of hardware, right? It is closer to the shaft because it needed more power. Your smaller machines or were far away. And all these uh manufacturing processes were closer to the belts, right? And they are vertical, you know, in multiple floors. Not not because people liked high-rise buildings, but because that was the most efficient for power. Yes.

17:42 Then came the electric motor and it took us, you know, quite a couple of decades to sort of re-architect ourselves and think, "Do you need all these layers?" Yeah.

17:50 I think that is where we are in is that now you have your your new motor is your AI, right? And how do we re-architect the enterprise to adapt this new model? Yeah.

18:03 Do you need all these processes? You know, I take a you know, since I'm an IT, I give you IT help desk process, right? Somebody comes to a portal, opens up a ticket. Hey, do you need a portal, right? Opens up a ticket. And then he says, "Thanks for opening up a ticket. Here is your ticket number." Yeah.

18:18 So, you and then you get an email. Like like a DMV model, right? You get a token, right? Uh and then you basically say, "What's the priority of the request? What's the category of the request?" And then you

18:30 And then you need to go to the wrong counter. You need to go to that counter.

18:34 It follows the same factory model, right? And then you are assigned a ticket and then, you know, by the way, that's just a beginning of process and the engineer goes does something. Yeah.

18:44 You know, although they are supposed to record all these things in the ticket, most of the time they don't, right? It's it resides in some email, some document over there. And then the result is closed and then you measure them on how fast they close the ticket. Exactly.

18:58 The question is, "What's your response time? What's your resolution time? What's your SLA?" That's your SLA, right? Yeah.

19:04 In the new world, you have AI that can solve the problem in a few seconds.

19:10 Yeah, instant it's real time, right? People, yeah.

19:12 How do you reimagine this entire process where you have an agent AI that can solve the problems and can do all these tickets and backlog in the background without that being the focus?

19:25 Yeah, absolutely. Because, you know, those systems were built for IT, not for the end users. Exactly.

19:34 And now, what's really happening is how do we bring experience through AI. So, that like we just talked about, how do you reduce that friction and delay and wait period between a ticket that is supposed to be a a token between the two people that are requesting. It's because everything is human to human. And inefficiency comes in is because we have a capacity limitation, capability limitation, memory limitation. And so, if you if somebody's opening a ticket, I'm like, "Hey, it doesn't have all the information. Can you take a screenshot? Send it to me. I'll look at it. Oh, I'm doing something else. I'll come back to you. You wait." Yeah.

20:17 So, I think those inefficiencies are because we as humans were limited in in our in our abilities to kind of transact how much we can do in a capacity. But then technology came and it tried to basically help routing these things. And but then it became very evident that it's not always the right way. It is also it has its own limitations. So, I think what's happening is is that we built all this time everything that we built is built for humans. In the sense, we built for a a department. We didn't build for employees. We built for the departments. And then IT comes in and basically, "Okay, now what do you want?" I will survey. Yeah. Yeah.

21:00 Okay? And that's how we've been prescribing the solution to the employees. And then when they ask too many things, we basically say, "No, no, no, that is not a priority. I can only give you this much right now." And so that is how we've kind of managed it because, you know, that IT is very small compared to the number of employees in the organization. So, the systems were built for IT to be efficient, employee to be the most inefficient. Yeah. Yeah.

21:25 Typically the way it is.

21:26 That is That's how organizations were structured.

21:29 Exactly, right? So, now we're flipping it, and that's what AI is able to do is like, "Okay, all of these inefficiency we are controlling from an IT point of view, let's remove the barriers. How can you bring that to the user where they are, and bring that experience to them, bring the data to them, serve them what they are looking for? So, that way you remove going to the portal, opening a ticket, cuz tickets are irrelevant for the user. Cuz ultimately what they're asking is, "How can I get out of this problem that I have?" Now, the ticket is a something that you want to track for compliance reasons and then what other reasons, whatever you want to know exactly how we did it for someone else to kind of look at it and then say, "Okay, do we need to automate this so that we can get rid of this ticket coming in the future?" That is all the IT's job. What does employee care about it?

22:19 Yeah, exactly. That's That's not That's not something that's a that uh that is uh you know, needed, right? For solving the problem, right? Exactly.

22:28 And going back to the steam engine analogy, right? Decades later, when the architect they said it's more efficient to have a electric motor for every machine out there. Yeah.

22:42 Rather than to have the centralized steam chamber that powers the entire plant. And so they got rid of the belts, right? That's the shift. Because they were slow.

22:51 You know, so the question is what belts are we going to get rid of? What workflows Exactly.

22:58 are we going to get rid of in this coming in in new AI paradigm. Yeah.

23:02 And and the new AI paradigm you sort of say every employee has an AI agent. Yes.

23:09 That you know can serve them, you know, whatever problems they have whether it be, you know, IT help desk related, whether it be their it's it's CRM or, you know, uh mobile or and, you know, the manufacturer, right?

23:25 Everything is within the possibility for them to really automate as much as and then they can bring the data to them. Yeah. Ideally.

23:32 I think I think that's that's the transformation that we are in and, you know, it's exciting to be a part of that. Yeah.

23:39 So I I know um we kind of take it in every transformation journey that uh as CIOs and CISOs put security and and IT and and all of the business systems and ecosystem whether it is uh infrastructure or uh a software that you're trying to bring um for the either customers or for the uh internal employees. Um we've been the prescribers of that. And obviously, even if they go and venture and do something, it'll end up coming back to the IT organization one way or the other. It's like a It's like a boomerang. Yeah. Yeah. Yeah.

24:15 So that's what happened with the internet systems and that's what happened with SaaS and now with AI, obviously all these agents are going to be deployed, but then ultimately who's going to manage these things, right? But then one thing is always been um clear even when we started almost 25 years ago even with internet era. The only thing that is common factor is data. Even then we're processing data, it's a transactions. Uh then we're actually understood, oh that data is valuable, so let's process the data to empower the businesses. And now let's take those processes and the data and then bring back uh the intelligence to drive those decisions across the board so that we can reduce the inefficiencies that we've already built ourselves. So, what is kind of telling me out of all of these things is is humans are the most inefficient beings. People because we kind of adapt to whatever is given to us. We build something. It's like, you know, you you built a home, you lived in it for 5 10 years, and now you find all kinds of faults that you don't like in that house. And like, "Okay, now I need to go get another house that is going to not have all of these things like I got some uh capability and some interests that I've developed by being in that particular scenario or moment and then and I I lived through that. Now, I'm not going to repeat that same mistake and I want to have certain things that I didn't think through. What do you think?

25:50 Yeah, I think it's, you know, uh that's that's very accurate how you sort of put it, right? I think we are in that era where we are re-architecting, you know, how the organization works and not just IT, but just how the enterprise works. Yeah. You know, you take a business process like lead to cash. Do you need to have lead, you know, contacts, opportunities, forecasting, probability, and and everybody has a percentage for forecasting. This workflow is what is what, you know, years of CRM Yeah.

26:27 legacy software taught us, you know, when when we were implementing Siebel back in Exodus days, we had the same thing then now we have the modern SaaS vendors that have the same thing, right? The business process is same same same thing, right? AI already knows the context, right? Yeah.

26:44 Uh they already know the background, they already know, you know, the incumbents, the competitor. It can It can be intelligent enough to forecast Yeah.

26:53 rather than a person putting a probability Yeah.

26:57 based on, you know, what they think it is, right? And so, I think all these business processes have to be rethought.

27:03 have to be like rethought, you know, in the AI era, right? Yeah.

27:07 What I really want to do is, you know, Step back.

27:10 influence and sell my products to the end customer. Yeah.

27:14 What is the best way of doing it, right? I don't need a conveyor belt either. I don't need those process steps 1 2 3 4. And then, you know, because humans are also designed on processes, right? Yeah.

27:26 Then you have a sales meeting that sort of says, I want to go to sales forecasting meeting, talk about all these probability, you know, forecast probabilities that are high percentages, right? You have humans that have adapted to inefficient process. Now, you need to sort of change how you sort of look at it and have new processes and cut the workflows.

27:46 Exactly. I mean, I think ultimately why we are mimicking a human to adopt AI in this situation is is because we are still the decision makers. And we knew whatever we had wasn't good enough. Yeah.

28:05 And now you got this new tool in our hand that we need to reimagine some of the things that we've done. Maybe this could completely change the way that you're actually doing. So, we go from like, you know, doing things for 1 week to 1 day. Yeah.

28:17 And maybe sometimes 1 day to 1 minute. And so, but that is a possibility that we are actually now witnessing by by thinking through what we're doing. So, that means you need to figure out like, okay, do I just completely step back and then say, you know, what? I don't have anything right now. What would I do? Yeah. Yeah.

28:36 I think that's that's the AI first mentality, right? You start off with, you know, how do I do it with AI first, right? Yeah.

28:43 I think we tend to focus on the wrong metrics. We focus on how much of code have you generated? How many software engineers we have, right? As opposed to what is the problem we we we need to solve? What is the product we need to develop? Exactly.

28:57 Whatever it takes to do it, it takes code, it takes hardware, it's a combination of you know, infrastructure and software and user interface and all those kind of stuff, right? That's all something that's all the building blocks. Yeah.

29:11 Right? You know, let let let AI figure that out. Yeah.

29:15 And let's focus on how do we help steer that AI. Yeah.

29:19 Uh to solve real-world problems. Yeah.

29:22 Which is why I think that, you know, you know, there's been a constant thing of, "Hey, you know, AI will you know, have a lot of job loss with AI." Yeah, yeah, yeah.

29:31 People said the same thing about radiologists. "Hey, you won't have a You will not This is an obsolete profession." People said that about, you know, uh the electrical, you know, specialist, right? And they're still very valuable. You know, we had a power outage in our area in in San Jose and we were out a few years ago. We had We were down for about, you know, a couple of days and we got to get the electricity company to fix it, right? And so, you are going to have those software engineering is always going to be in demand. Mhm.

30:04 Is it But, the question is, where in the stack are you? Are you in the stack of just churning out code? Then, Building?

30:12 you know, that's something that AI can do. Yeah.

30:14 Are you in the uh uh stack where you are trying to use the code to sort of produce new products, get new innovations, um you know, you know, and solve business problems? Then, you still need to have that, you know, human in the loop, right?

30:33 Yeah, yeah. No, I think so. One of the most fascinating things that happening is is that um AI is moving much more rapidly in terms of its capabilities. And uh we didn't have uh agents, but then we got agents, which is basically now automating certain tasks uh with workflows and whatnot. And now we're also going into managing several agents by a coworker automatically designating what each agent should do and how they need to do the work and then so that the human doesn't need to guide it. Then bring a supervisor, which is a human, and a supervisor agent basically So, now we're basically bringing a human equivalent uh assistant. So, like you will have a chief of staff or a CIO or a chief of staff or a CISO, a CTO. And your day-to-day, all you have to do is tell your coworker, in this case your uh EA, obviously, for technical work, to say, "Okay, prepare what I need to do today. And who am I supposed to meet, what I need to do? Tell me." So, now we're actually offloading the memory that we inherently need to use for doing everything to remember to have someone else prepare everything, and then you just basically look at it and I'm like, "Okay, I need to talk to this guy. What am I supposed to discuss?" So, it's almost like imagine we're going into driving ourselves to self-driving cars. Literally, you're not doing anything. Right? So, so to a point where we we might not even use our memory to do something. That means we have to now figure out what do we do with it? So, to make sure that this new tool that is coming in, now you have to also think more than what it is thinking to be able to use it. So, your creativity actually now has to be much better and bigger than what the AI can bring, and that's why this is an opportunity and a challenge to many. Like people that probably don't have any skills but a huge creativity can probably build an unimaginable solution than that highly capable person who has no creativity. You see what I'm saying?

32:54 Yeah, yeah, yeah. I think I think it's a it's a great transformation that we are in, right? And I think I think that you know few years down the line, maybe even few months down the line Yeah.

33:07 you know, every one of us will have, you know, a digital twin. Yeah.

33:11 And the digital twin will

33:12 You send it to the meetings.

33:13 set up meetings you know, it will you you you won't need to have no all these you know, Zoom Teams, you know, passwords and stuff like that.

33:23 And you don't have to go to New York to attend it.

33:25 Yeah, you don't have to go, you know, it's going to be so transparent. Yeah.

33:28 It you it will organize your meetings, it will set up agendas, it will, you know, the the the the lot of the, you know, work that a chief of staff, you know, does in terms of, you know, hey, I need to have QBR, I need to have my, you know, quarterly reports Yeah.

33:47 I need to have talent conversations. Now, your talent conversations will be interesting where it will be like, you know, agent

33:53 How is your agent performing?

33:54 Yeah, exactly. How's my agent performing, right? So, it's going to be an interesting

33:58 I think this is fascinating. This is what I'm actually looking at, you know. So, if everybody is given these co-workers now you have to manage your work through it so that you do uh 10x the work or 5x the work that you're doing today. Uh then comes to how well and how efficient are you in setting up those co-workers to work like you to really help the organization to go where it is going. So, I think there we're kind of getting into that different paradigm altogether, Right?

34:33 Yeah, it it increases your velocity, right? And it helps you do more uh you know, uh activities help in achieving better business outcomes faster. Yeah. Yeah.

34:46 Uh from a product side helps you innovate more and get products out faster, right? And so, I think we are in the cycle where, you know, it's not going to be years of getting a product out. It's not going to be, you know, uh you mean mean we did a lot of ERP that went about several years. Mhm.

35:03 Um and several CIOs as well, right? And so, we're not we're not going to have that year outcome again where people are going to be patient for years, right? Yeah.

35:12 And so, the cycles are going to change. It's going to be more frequent, uh more iterative as we sort of go along.

35:18 Yeah. Yeah. I know I think um the I think we're on a on a perfect time in in the in terms of uh really seeing through the next decade of evolution in in an AI era that could potentially change generational transformation that we are uh we're going to witness ourselves. And and um the part that I'm very interested in your unique journey that you, Venky, um came from Chennai to US going through this transformation. Looking back, how do you see it? Like, you know, when you say, "You know what? I think I'm happy with where I am. I think I am I've done proud for for everyone that uh been in my life and then you know what I wanted to. Obviously, you never thought you would become a CIO when you first started your career, but ended up adopting to become one. Did you ever think that you wanted to be a CIO?

36:20 No, it's just uh you know, uh just my journey and my destiny sort of led me to this to to to this place, right? As I said, I joined Exodus as a software engineer and Exodus changed data model yeah, his business model sorry to a to a data center model and so I got to learn a lot about infrastructure. Then I went to SAS applications so I'm learning a lot about SAS and then I was in cybersecurity for some time and learn a lot. So you get the the breadth of skill set that you do and then you you enter into a lot of business transformations and so that's that's how you sort of it could be a social so you're a little bit more horizontal and then over a period of time you know yeah you get vertical, right? I think I think that's that's the journey I think of most CIOs and to answer your first question, right? I think that look we're just scratching the surface. You know, I'm learning every day about what's the new things in in the AI world, right?

37:18 We're re-equipping ourselves again, right?

37:21 Yeah, I think I think this is the beauty, right? You know, we had client-server software and you know, mainframe computers and we went into PCs, then we went into cloud computing and now we are

37:35 you know, mobile, right? And so whoever thought this mobile device would be this so powerful, right? And then you know, now we are in the AI world and you know, we we we're learning, you know, how this is going to going to work and we're learning how to adapt ourselves and how to change using these new tools that we have. So it's always exciting to be you know, a CIO at this point in time because you're you you get a front seat into helping the the business transform itself.

38:10 So we were calling iPhone a smartphone. I think it's going to become an intelligent phone. Right? Cuz iPhone because that is where the next level of things is going to come. All of the apps, all of the data, all of the things that in in this is going to be intelligent. Yeah. And

38:33 You really need a phone number. Yeah.

38:35 Right? It's the You know the device has an IP address, it knows you have Bluetooth, whatever protocol they have, right? You don't need a phone number. Yeah.

38:43 I mean, just like do I do I need like an you know, your IP address is your phone number. identity. Yeah, identity.

38:50 It's just one identity that you create for the device and that is literally what we are anyway associating a phone. It's more like a medium in which you can carry that identity to another device and you know, so I I think this is very interesting.

39:05 And it can talk any any language. Yeah. I mean, I can't

39:09 Exactly. Now we have AirPods translating in other different languages if you

39:13 Yeah, no, I you know, it was fascinating because I got my new car uh you know, Tesla I bought a couple of weeks ago. I traded it with my old one and got a new one and and it was I had Grok as a part of that. Nice.

39:25 Now I can talk to Grok in Tamil or Hindi or any language. Yeah.

39:31 It actually changes it just it picks up I can talk for one sentence in Tamil and it's conversational, right? And you can see how many I it's fascinating for me because these these uh the navigation systems and these voice navigation systems were were very uh you know, uh unidirectional. Yeah.

39:49 You got to say I'm like, "Hey, give me this address to this place." And if the car didn't recognize you, say, "Hey, can you repeat it again? I didn't get it." And you got to repeat the entire sentence again. With a conversational AI, you can say, "Wait a minute, I'm searching for the address."

40:04 Or here here I have the address. And by the way, you know, I'm hungry. I need to get lunch on the way." Yeah. It can basically No, tailor customize

40:13 listening to you, can respond in whichever uh, language you want, can learns you as well. Yeah.

40:20 It's not going to say, "Hey, you have lunch for you." Yeah, you had lunch and you were like driving for 4 hours, you probably need to take a coffee break somewhere, right now? Yeah. Yeah.

40:27 You know, before you hit the highway, maybe you go to take a coffee. So, I think we're getting to that intelligence, you know, if it can come to the phone, to your car, and to your computer, right? And that's what makes us more efficient at the end of the day.

40:41 Yeah, no, I agree with you. And I think one interesting thing is we were building things for people. Now, we're trying to augment the people with the same, in, uh, you know, some in many places. And, uh, obviously, that is where the scare of replacing and all of that. But, then comes the whole point, right? People are still in charge, you know, of decision-making in the organizations. Eventually, we're going to augment some of those capabilities with AI. But, how do you see because, you know, most everything that we've done is centered around the people, even now. And then, we built things because of the capabilities of the technology and the requirements of the business. With the processes. We put it all together, we built things. Yeah.

41:31 Now, we're reimagining all of that. And in the middle, um, what is really important, as a CIO, when you look at these transformation journeys that you went through? What are the, you know, few things that you've learned about teams, people, culture, and and and how how do you see that as, uh, as a, you know, most important thing for in your success?

41:55 I think I think that's a critical, uh, piece where, you know, if you look at organizations, some people are early adopters. Yeah.

42:04 They run at 100 miles an hour, right? And they they grab stuff and they're, you know, in and you need those early adopters Yeah.

42:11 because they are the people who are going to be champions who kind of can take AI into their organizations and can champion the cause for AI, right? And then you you see people who are, you know, resistors, right? And sort of say, you know, I like this thing. This is probably another dot-com thing. This is possibly another gizmo that I have, you know, look at all the things that uh that the last 30 years, right? And you want to be able to prove and convince them to get along. But at the end of the day, a CIO's job is to really ensure that you bring the team along Yeah.

42:48 together, right? I mean, this is almost, you know, describe it as like like a like a football, right? Yeah, yeah.

42:55 You know, you're the quarterback and you have a team, you call the plays, but you bring the team along, you know, you're not a leader if you if you run at 100 miles an hour, but then don't bring the team along with you, right? This is not a 100-m sprint or this is this is not, you know, golf or any other sport where it's an individual sport, right? Yeah.

43:15 This is a team sport and your team is the entire organization. It's not just IT team, it's a it's your IT team and your business team, your business leaders, and their organizations you got to bring along in the journey. I think that's the that's the job and the value, you know, which I think, you know, AI will not replace, right? Yeah, yeah.

43:36 And uh and that's an organizational change that we all need to uh to bring about.

43:41 I agree. I agree. So, Venky, there is always um this notion, you as a CIO, you've built some credibility, you've built some uh character to your um office and and the office of CIO is is as, you know, it resembles who you are. And there's certain values and certain disciplines, certain things that uh that you brought. What what are the things that are most important for you when somebody looks at you and say, "Hey, when he is this?"

44:14 You know, I What shaped me meeting every human is is goes through their journeys, right?

44:23 And if you look at my journey, I was blessed to be in a high-growth Silicon Valley company in my first job, right? Mhm.

44:32 Everybody dreams of being being in a Silicon Valley startup, right? And Yeah.

44:37 my first job over here. So, I was I was I was lucky in some ways, right? I went through that journey and actually at some point in time, you know, I I wanted to retire at the age of 28 or so.

44:48 You felt like I could have

44:49 It felt good though, you know, we had an IPO in '98. We did really well. Wanted to retire, right? And I I really wanted to be an entrepreneur helping, you know, investors, helping startup companies, right? And then, you know, one day, you know, you know, one of the the lead person in Klein Perkins basically said there were more angels in Silicon Valley than they were in heaven at that point in time. Exactly.

45:13 You know, that got to thinking, yeah, right? You know, what am I trying to do over here, right? At the age of you know, 26, right? Yeah.

45:20 And I got back into then I moved into startup companies that grew to more entrepreneurial, right? Stratify was entrepreneurial. StarSight changed even in B2B. Yeah.

45:32 And Anaplan was entrepreneurial. Thomson Reuters Yeah.

45:36 Data gets commoditized. They want to change from a data company to a data platform company. You know, Anaplan, you know, wanted to take planning and keep it SaaS company again, high-growth company. And as you look at look at the journey of being an entrepreneur, HPE went through a lot of transformations with split with uh Hardware, software, enterprise, yeah.

45:58 You know, networking, all All kind of transformations that have happened, right? So, I to me I I am an entrepreneur first trying to solve a problem, whether it be a business problem or a technology problem. And then I apply the necessary learnings to sort of solve that, right? And I tend to be technical, you know, I tend to be tactical, I tend to be strategic. And you got to have all these three because if you're really just strategic and don't have the technology chops, you're not going to be able to know how to apply that strategy. And And you you need to have both these pieces and you need to go between these being a startup company and being a large company back and forth, right? Maybe multiple times in a day. And I think that's what defines me because I can I can be thrown into a into the into the trenches and figure myself out.

46:54 A lot of ambiguity can be a feeding feeding ground for you. is I welcome Yeah.

47:00 chaos because I look at chaos as an opportunity. Yes, exactly.

47:03 make it better, right? So, when I was at Anaplan on day one, you know, I was said that hey, we are you know, we don't have an admin for Workday and our HR people left and so you got to you got to learn how to do it temporarily. So, you know, there were there were there it was. I figured it out, right? Yeah.

47:19 And you know, we we had a data center that was also growing, you know, by leaps and bounds and I had to really scale the data centers, right? So, a lot of the times you need to go in Wear different hats.

47:31 wear different hats, figure it out. That's what That's that's the thing that I like about, you know, my job and that's the thing that I I really think about myself that defines me as a ability to go in and deal with chaos and hopefully come out better at the end and be that entrepreneur that can figure it figure out solutions to problems.

47:53 Yeah. So, if you would advise somebody in your team or someone outside looking at Venky, he's done amazing. Um what would you what would you recommend that worked for you that they should certainly would could potentially uh useful for them in their journey.

48:15 I think one of the key thing is be, you know, be very uh curiously inquisitive about, Yeah.

48:23 you know, uh the newer technologies, right? Mhm.

48:26 Try to unlearn before you learn. Yeah.

48:29 You know, that's very critical, right? And uh and look at yourself as a architect Mhm.

48:37 in how you solve. You don't have to have the title of the architect to be an architect. Mhm.

48:42 You know, human body is architecture. in there. Yeah. Yeah.

48:46 Human body is an architecture. You have a brain, you're network with all these nerves, you know, you have high-speed networking going on, right? You You have memory, right? And and you're you're programmed in that way, right? Yeah.

48:57 Look at it as end-to-end, right? I mean, you you just can't have strong legs, Yeah.

49:02 but but not a heavy upper upper body, right? You You just can't have, you know, you know, the mobility in one area and not the other, right? You need to have all these things to function, right? Yeah.

49:11 So so so have that mindset and and I think, you know, uh in your spare time, in you know, uh read up about what's next. I do a lot of uh uh LinkedIn blogs, read Mhm.

49:25 read in LinkedIn, read books. Uh I I tend to use more podcast and audio learning as opposed to books because just getting the time to buy a book and reading it just you don't get enough time, right? I agree.

49:38 Talk to a lot of people. I think people the you know, I'll tell you one fascinating note before we leave, right? Yeah.

49:44 Everybody's trying to replicate Silicon Valley, Yeah.

49:47 There was a person came in from architect came in from from China and said, "Hey, we have a Pudong University, we have a downtown and I'm going to make make it exactly like University Avenue in Palo Alto." Uh-huh.

49:59 The thing that you cannot replicate is the amount of passion, Mhm.

50:04 energy, and entrepreneurial spirit that you have,

50:09 you know, in in a given region. That's why you still continue to have the big AI players here. See, you you that's that is a unique DNA of multiple things.

50:19 Multiple things coming together, right?

50:22 Like Bangalore has become now.

50:24 Bangalore Yeah, Bangalore to to to some extent has become, right? It's a

50:28 It take a while, right?

50:29 Yeah, yeah, it it it takes a while to sort of get there, but you're seeing that as a innovation hub, right? And so, I think you need to you know, I think these are these are the things that really makes this area Yeah.

50:43 very very unique where you can have somebody who's entrepreneurial, somebody who who has that energy, the passion to change the world, uh to take risks, Yes.

50:54 right? And you know, you need to take risks because unless you take risks and you know, you're not going to figure out what's next and with risk come failure and you need to be able to accept the failure when that happens and move on, right? And so, that's critical.

51:09 you have to be curious, you have to be risk-taking, you have to be humble, right? And you have to be inquisitive and learning and unlearn to learn, right? And ultimately, the most important thing is is just build a DNA uh and that is required to adapt, Yeah.

51:29 right? I mean, ultimately, that is the way you see, you know, if somebody wants to be something uh just like you are. You got to you got to you got to think through and then be in those areas.

51:40 Yeah, ultimately, I'm I'm one person in you You uh hundreds of millions of people live in Silicon Valley, right? Yeah.

51:49 And I'm I'm one degree away if I get into a social gathering or a networking meeting, I'm almost one degree away from from eight or nine people in the

51:59 Yeah, that you know. Yeah. That you know or

52:02 that you know either in current current or or previous, right? Yeah.

52:05 And so when you have those conversations, when when you you are have the network that really thinks alike, that has the same DNA, Yeah.

52:14 something happens in that chemistry reaction that makes you a better person by by just breathing the air, by being here, right?

52:21 Yeah. Yeah. I appreciate you taking the time and be here on a Friday and and on a short notice and uh it's a it's a pleasure and then I'm I'm I'm really delighted to have this conversation with you. And I really enjoyed it.

52:36 Thank you for having me here. It was a enjoyable conversations to reflect in the past in the past but but more so for look looking forward to the future. A lot of things to come.

52:48 right. We're in a we're in the right time and the right space and so we're all lucky to be here. So I am I'm really Thank you. Thank you for being here. Yeah. Thanks.