Listeners and viewers, welcome to another episode of Disrupt Ed, and you know the drill here. We're gonna talk to those people who are truly disruptive in the work that they're doing. They are making a difference in this five gs wire, workforce augmented, technology interconnected, globally interdependent, AI accelerated, Web3 immersive, pandemic interrupted world out there. And I have an extraordinary, duo of people that we're gonna talk to today. And a shout out first to our shared alma mater, the University of Michigan. We are posting live this week on the heels of an historic win for the University of Michigan Wolverine basketball team, which bested the vaunted University of Connecticut by six points and won the national championship. And you know what? If I sound obnoxious, I'm gonna tell you there's math behind this. The last time Michigan won a national championship in basketball, I was twenty eight years old, which means mathematically, if it all goes the same way, I won't get another chance to celebrate until I'm one hundred and two. So I'm gonna make this week count. So that's why, and Arun is a a very passionate U of M Walgreens, so let's just let's just take a moment to bask in the glory of this week. Right? I am telling you. I'm telling you. I'll tell you, Ron, when when I went to school, when I was in school, we had the Fab Four. You remember the Fab Four? I do, absolutely. And yeah, it was so disappointing that we had to lose to Duke in the 'ninety two championship. This is redemption. You know, the same thing happened to me. I was in the Rose Bowl talking about Michigan we cannot ignore football. I was in the Rose Bowl and we got our butts handed out to us pardon my French. It was again redemption time when we won the championship last year against the same team. Same score they beat us, we beat them. So I don't think you'll have to wait until you're one hundred and two, Rod. No, no, no, no. We have now got a buck to trend. No. It's been exciting. You know, I think what was most exciting about it, just to delve into it for just a second before we get to the business at hand. You know, what was exciting to me is how powerful two things were powerful. One, the velocity of their wins. You know, to you know, they were projected to be a one point favorite against Arizona. They blasted them by eighteen points. They were dominant, and that team never got within ten points after the first ten minutes of the game. You know, Tennessee, the same. Alabama, the same. That was extraordinary point number one. Extraordinary point number two. Three of ties in with what we're gonna talk about, and that is the team, the team, the team. You know, to our listening and viewing audience, I met Arun Varadarjan, who is the cofounder of and the chief commercial officer for Ascendiant, a Juggernaut software engineering firm that is doing some incredibly powerful stuff. From a human point of view, this is what I love about them. And from the minute I met this company, I wanted them on my show because their tagline says it all, engineering that elevates life. I mean, I think sometimes when we talk about the disrupted world of AI and what it's doing, it's crushing in on this sense of anxiety that people have. Is my job gonna be eliminated? What am I gonna do? How am I gonna be successful in this very disrupted world? And these guys are looking at it really and truly from a how do we help people? How do we help better people's lives? And how do we use the tools in front of us to do that? So, Aron, I want you to just share with our listening and viewing audience a little bit about your origin story and where that compelling message started and originated. Oh, it's definitely, Ran. You know, when we So me and my buddy, who's now the chief executive officer of Ascendiant, we met when we were in this company called Cognizant. And all along, both of us, while we operate with our head, we operate with a lot of heart. That's one of the reasons why we continue to work together. We've been working together for almost fourteen, fifteen years in various companies. And our thought was very simple. We said, Listen, if we are not doing work that's going to impact lives, it is not going to be meaningful for us to do it. And I'll tell you, there's a little story here. So he and I, we were working with Kaiser Permanente, this was back in the days, and we did a very small project for them. It wasn't a massive project. I think we were building some very interesting dashboards and reports for them, but I'll tell you what those dashboards and reports were doing. It was about a two fifty ks project. It wasn't even big in terms of actual value, but the impact it had was phenomenal. This was during the height of the opioid crisis, if you remember. There was good news, people were overprescribing, people were refilling when they should not be refilling, and we actually provided real time visibility to Kaiser on what was happening to their patients in terms of Wow. Opioid use. And I told them, Listen, this is a significantly meaningful project for us. It may be only twelve Absolutely. That's a great story. I mean, is a great and powerful visual for all of us on what you're trying to do. So, you know, all of us in the tech world at some point in time, we want to do something of our own. So we said, listen, if we ever get together and do something of our own, let's make sure that we anchor it and hang our hat on the fact that our company is going to focus on impacting lives. So if you remember, if you cast your mind back to twenty twenty, so twenty twenty is when we kind of the whole notion of Ascendiant was born, And twenty twenty, guess what, was the height of the pandemic, if you remember. And we had also come out of this era called digital transformation, which I felt was a little underwhelming for many customers because tall promises were made and very little done. So when we started thinking about Essendean, we said, of course, impacting lives was our overarching, what do I say, overarching tenet to form the company. But when we kind of started reflecting on the past, we realized that there was why were we not able to deliver what we promised to our clients? Why were we taking so much time to spread the value of technology across the planet? And we started really digging deep into that, and we realized that there were two or three fundamental problems that we were addressing, that needed to be addressed, but not yet being addressed. One was the speed to develop technology. The speed was just not fast enough, right? It was taking while there was a lot of research going on, to make that research into tangible, applicable technology that can be used and consumed was a problem. It was too long and it was too expensive. It was too costly. So our ability to reach and impact a wider audience was limited by the fact that we had speed issues and we had fundamentally cost issues, and the third problem was there was no guaranteed predictable outcomes that we could be that we could stand by or underwrite. Because, you know, I'll tell you, Ron, I've been doing this for too long. There is not nobody who can commit saying, I can get this done for you in six months and stick to that commitment. Because the vagaries of building technology, the vagaries of building software is so innumerable and so labor dependent that it was almost impossible for us to guarantee outcomes for our clients. So these three reasons were the ones that prompted us to say we need a different sort of company to really achieve that higher order goal of impacting lives. We need to address these three friction points that were plaguing the industry. Well, you may be the first you may be the first. You're certainly the first person I've met who dreamed this big and actually made it happen. As I understand it, one of, your claims to fame was creating seven hundred thousand lines of code in less than three weeks. I mean, that's a demo a demonstrable proof of concept that you can take time out of the process, and you can take obviously, with that amount of time shrunk, you're obviously going to take programming costs out of the mix. So, hats off to you Arun, because you were one of the first to really see it in those terms. I think the other thing that I find fascinating, so if I can just geek out with you a little bit here. I think the other thing that's fascinating is the way you've approached it from a systems point of view. I mean, what you're essentially saying to your clients is, in the age of AI, an agentic AI, you no longer need to be working on one piece of the puzzle. Need to be working on the system or else that tech debt that's at the beginning of the process is going to carry forward and your system's going to crash and it's still going to be plagued with some of the same legacy problems. So if companies are looking at legacy modernization of their platforms, they really have to think differently about this, right? For sure, for sure. See, I'll tell you, interestingly, a lot of the parallels that we saw in terms of what needed to be done to software was quite honestly what was happening in Detroit with the auto industry, right? With the Japanese competition coming in and saying that I don't need it'll take me four hours to do a line change and therefore I could have multiple models coming out of the same factory, a flexible manufacturing kind of a concept, and a lean manufacturing concept that was there where I could guarantee predictable outcomes, that parallel was something that we were trying to see why can't it be applied to the software world, right? And let me tell you, I think only now is that realization dawned a lot of people. Now we have Claude, we have this, we have that, we have tons of people trying to solve this problem. We saw the opportunity five years ago to really systematize the entire software engineering process. Because what we realized, Ron, when we studied the entire process of software engineering right from idea through to realization and management of the software, there were tons of friction points from start to finish. Developers, engineers, product managers, testers, quality engineers, system reliability. You've got many actors in the lifecycle. We forget that. We always think of the developer and the coder. There are tons of these people need to come together to orchestrate and build a system. And we realized that this entire process was so disconnected, so messed up, and then on top of that this thing called Agile showed up. Now Agile became a convenient way for the engineers to say, not in this sprint, I'll do it in the next sprint. So what they were doing is that in the guise of saying I need to agile is a good concept because quite honestly, agile was all about saying that, listen, there's a lot of dynamism in the way people think about building technology. You need to be able to take that dynamism into account so that when you build the end product, it is not what you thought it should be two years ago, but it is current, right? It is taking into consideration the design changes, all the requirement changes that you have. So Agile had a very good philosophy. But the way Agile got implemented was where it messed up. So on top of having this very disconnected and convoluted approach to building software, you had Agile coming in and further delaying the delivery of output. So we said we've got to simplify this stuff. This is getting too complicated. How do we get to truly frictionless engineering? How do we get to having the engineering experience be a joy and not a pain? And that's when we started thinking about this whole notion of saying, why don't we just build a platform, right? Why don't we use AI? Why don't we use machine learning and build a platform that will allow our engineers and teach them how to do engineering better? But in the initial phase, Ron, when we looked at this platform, our initial thinking was how do we use AI and machine learning to understand where are all the pitfalls and learnings so that the engineers can do their engineering work better the next time around. That was our initial thinking. But that thinking quickly changed when generative AI started coming into play, which was what, about three years ago. And we realized the power of generative AI because we said, listen, the earlier notion was let's get AI and machine learning to assist the engineers and help them improve the way they engineer. That was the original design of our platform. But then we said, hey, why don't we turn it around? Why don't we get the platform to do the engineering and have the engineers make sure that the platform is doing it right? So we said we'll flip the roles. And that became a game changer for us in terms of our thinking. And today, if you look at our platform, our platform generates all of the artifacts, whether it's a design document, whether it's product roadmap or a requirement or an epic or a user story. I mean, these are things that form part of the engineering life cycle. Our platform generates all of that and our engineers are there just to say, yeah, that looks good, this needs to change, and the role of the engineer has changed dramatically in our world where you're not a generator of artifacts, but now you're a reviewer and validator of artifacts, and also a systems engineer now, just like you have in manufacturing, where I'm looking at do I have the right engineering system to deliver the end product, right? So I must say I borrowed a lot of the ideas the manufacturing world to think about it. That's why we call it lean software engineering. We call it many things today within Ascendiant, but essentially the whole idea is can I build a system that will autonomously build my software and I'm watching it build and making sure it's building it right, and if it's not doing it right, I'm turning dials and making adjustments to ensure that at the end of the day the end product is good? And that may be a much more scalable way for me to build software faster, cheaper, and better, and serve the broader planet and impact over the billions. Right? That is how split hairs, though, but when you talk about a scalable solution, you're talking about scaling a system. What you're not talking about is scaling the number of engineers required. So I think that's one of the things people have to consider is there is going to be a dislocation of people in roles because AI is going to be replacing a lot of people. And I think the sooner we get our arms around that, the better because then people have the opportunity to adjust. It's the uncertainty that plagues and fuels anxiety. It's not, oh, my job's going to go away. Well, when that happens, I'll find another one. But it's really that idea that this job won't be necessary. So people have to start asking a bigger question, and talent becomes a much more sophisticated discussion. What are the skills and traits that I'm looking for in a post agile environment where my system is going to be doing the work and those looking at it from a human engineering point of view are reviewing it for quality assurance. They're reviewing it for subject matter expertise. They're reviewing it to make sure the artifacts are intact and appropriate. They're doing it from a systemic approach, not, Oh, we've got a problem here, me go fix that. Correct. Correct. Exactly. Do that right? You have it absolutely right, Ron. So I actually believe in the theory of abundance. I don't believe in the theory of limitations, right? So our view of the world is that, yes, we had thousands of engineers doing this amount of work, but now I can have thousands of engineering doing this amount of work. Way more work. See, there was a notion I don't know, Ron, you know, there was a notion way back in the valley talking about a 10x engineer. Everybody spoke about it, but nobody knew who she was or who he was. And I can tell you today, if our engineers can start shifting their mindsets from skills to competency and capabilities, they will set themselves up because we have the systems now that can truly make them 10x engineers. See, today, unfortunately, if you look at the industry, people hire for skills. When I started my career in IBM thirty years ago or whatever, right, we all date ourselves, we were not hired on skills. We were hired on aptitude, on logical thinking, on reasoning, on programming concepts, not because you knew Java or Python or some nonsense like that, right? So the industry has to go back, in my opinion, to the basics and the foundation and first principles. I'm a big believer in first principles. And that's where we have missed the plot. We have all become skill based as opposed to be first principles based. So that is where I see that if you really want to be employable in the new world, I would strongly recommend you know, a lot of young people come and ask me, Hey, Arun, should we even send our kids to an engineering school? I said, Yeah, you must. Don't change that. Oh, you think computer science is dead? I said, no, it's not dead. What you're going to do is going to change. Ask your kids and ask your children to focus on learning the first principles. Today, the focus in education has been very skill oriented as opposed to competence That needs to change. So my submission, Ron, is that I think there are going to be even more job opportunities than before, right, but they're going to be asking different questions of the talent. I like this, Arun. Different asks of the talent. That's what we need to be prepared for. I think that's a great note to end our, first episode on, but we're gonna come back for more. So to our listening and viewing audience, you've been listening to Arun Vara Darijan, who is the cofounder of Ascendiant, software. And we're going to come back for episode two to talk about the future of talent because Arun and his colleagues have some very, very interesting views on this and how to manage for a future that's entirely disrupted. And so stay tuned for episode two where we're going to bring Wesley Pullen in ascendiant CTO to join the conversation. So stay tuned and come right back and get disrupted with us.
