The Main Scoop | Disrupting With Purpose: Learning from the Past to Innovate Forward

Discover the next chapter of mainframe evolution 💡

On this episode of The Main Scoop, hosts Daniel Newman, CEO and Chief Analyst at Futurum, and Greg Lotko, SVP & GM, Mainframe Software Division at Broadcom, are joined by Eduardo J. Ciliendo, CEO at 21CS, to unpack the mainframe's pivotal role in transforming the digital landscape.

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They explore how mainframe continues to evolve and discuss the merits of meaningful disruption vs. disruption for the sake of it.

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‍Tune in as they cover 👇

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✅ Why transformation needs a clear business goal, not just change for change’s sake

✅ The evolving role of AI in modernizing mainframe operations and improving accessibility

✅ How organizations can bridge the gap between existing mainframe expertise and new talent

✅ The critical role of historical data in driving AI-powered business insights and developing AI training models If you're navigating modernization, AI adoption, or digital transformation, this episode is packed with insights to help you disrupt strategically and build for the future.

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Watch the video above, and be sure to subscribe to our YouTube channel, so you never miss an episode.

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‍Disclaimer: The Main Scoop is for information and entertainment purposes only. Over the course of this webcast, we may talk about companies that are publicly traded, and we may even reference that fact and their equity share price, but please do not take anything that we say as a recommendation about what you should do with your investment dollars. We are not investment advisors, and we ask that you do not treat us as such.

Greg Lotko:
Hey, folks, welcome to the next episode of The Main Scoop. I'm Greg Lotko. I'm joined here by my co-host, Dan Newman, live and in person. Great to see you.

Daniel Newman:
It's always good to be with you, Greg. Another great episode of The Scoop here.

Greg Lotko:
Absolutely. And we're, we're kind of going to talk broad today, right. We're going to talk about IT trends and how businesses can use the latest and greatest technologies or where the motion is going to, to kind of bring advantage to their businesses.

Daniel Newman:
Look, the word transformation gets thrown out sometimes. Almost thoughtlessly.

Greg Lotko:
We're going to transform the business from what to what?

Daniel Newman:
And I think that's been the question. And I think with so much acceleration, momentum and demand and everybody knowing how deflationary and how much growth comes with technology, I think at times we use the word loosely, but we don't always know what it means. So while, yes, we might talk broadly, I think it's still a really important topic.

Greg Lotko:
And you want to disrupt for purpose, Right. To give yourself a competitive advantage to any of your competitors in the marketplace. It's not change for just change sake. But we have a guest, as always today. So we've got with us, Eddy Ciliendo. He is the CEO of 21st Century Software. Delighted to have you here, Eddy.

Eduardo Ciliendo:
Thank you, Greg.

Greg Lotko:
Are you nervous about being here with us now after hearing us ramble on?

Eduardo Ciliendo:
I mean, two geniuses like you two. I mean, of course.

Greg Lotko:
Wow, he's good.

Daniel Newman:
He's going to get a clip.

Greg Lotko:
Yeah, I like that. So what do you think? I mean, you've been in the IT space for quite a few years now. Now you're functioning as a CEO. I'm sure you talk to customers all the time. What do you see going on? How are people leveraging and transforming and disrupting in industries?

Eduardo Ciliendo:
Yeah, I mean, I really like something that you start off with. Right. Don't disrupt for disruption's sake. Right. I mean really have a purpose. Exactly. Have a purpose. And I mean, what I've seen over the past 20, 25 years that I've been in user in the industry is more often than not, people want to disrupt just for disruption's sake. We remember the dot com bubble. Everybody thought that's got to be the next great thing. Then we had those silly phone applications that didn't add any business value. I think when cloud computing first came up everybody jumped on the cloud train now with AI, So I think we always have to take a step back and really think, okay, so what can I improve in the business? What makes sense the way it is today? And what can we, again, disrupt that really helps the business go forward?

Greg Lotko:
Change. Transformation with a purpose.

Daniel Newman:
Well, there's almost like always these two different forces. There's being exposed to what technology might be able to do to drive your business, and then there's the practical implementation of those technologies and then seeing the outcomes. We're in another massive inflection right now. And our data is overwhelmingly saying that companies are cognizant that there's many options for something like an AI implementation, but they're stuck in PoCs. They can't get things done. They're not even able to figure out even what to measure, let alone measuring it. And so I think this. A lot of this comes down to Eddy's culture of innovation.

Eduardo Ciliendo:
True.

Daniel Newman:
And cultures of innovation have what I would say is sort of these two forces. The first force is identifying that tech can drive something forward. And then it's having a culture that can actually absorb that technology, put it to use and create outcomes.

Greg Lotko:
Apply it. Yeah.

Daniel Newman:
Applied. Yeah. But I like to use a lot more words because I'm an analyst. But seriously, what do you think about as a CEO, as a leader, someone you've worked for big companies, you're driving a software company now, kind of. How do you think about driving a culture of innovation?

Eduardo Ciliendo:
That's a great question. I'm not sure if I found the 100% answer yet. So I'd love to hear from you guys. Right. But yeah, I mean, especially in our space. Right. I mean, this is The Main Scoop. We're talking about mainframe. I think mainframe and culture of innovation sometimes don't necessarily go together because,, we have a spirit of extremely high quality. But in the spirit of innovation, you need to have a spirit of, okay, we can make failures, we can fail, we can fail fast, we can experiment. And that traditionally hasn't been really the mainframe model. Right. We always wanted to deploy stuff that has been tested for decades. And we only add a little feature not to. Not to shake the boat too much. So bring this culture, especially into the mainframe space where, hey, guys, gals, it's okay to make a mistake. Let's just have an experiment. Let's not talk too much about it. Let's see how it works, and then take it from there. I mean, one example that we had last year, we did at 21Cs, our first hackathon and the team that won was the team that is taking care of the VSC operating system. And honestly, I mean, that would have been the last team that I thought would do it. But we had a couple of really talented young people there. They were not afraid. They came out and sure enough they created a chatbot that interfaces with VSCN to do system tasks.

Greg Lotko:
So yeah, I feel like this is misunderstood in the mainframe space. So everything you said I would agree with on the surface and what people see. Right. So of course you don't see customers in their application space implementing a change and thrusting it in without having it well tested and hardened. And if you look at the technology that has come out in the hardware or from the vendors and the partners in the space, obviously we're not going to let it into the wild until it's hardened or until our customers can count on and trust that it's secure, that it's reliable, it's stable. And I, I, I see that many customers, people in the industry who don't get to see the engineering going on in our teams think that means that innovation isn't going on or risk taking isn't being done. We also do hackathons and the difference is that all happens in our shops, largely behind closed doors. We try things. Yeah, not everything works at the point where we release it into the wild, where we give people access to it. Our segment of the industry where mainframes are running the most important mission critical workloads in the world, they have to know that it's, that it's rock solid. But don't be confused folks. That innovation, that risk taking is alive.

Eduardo Ciliendo:
And well in our teams and it's, you're spot on. And I think that's kind of an interesting dichotomy in our industry. Where you have that stability that is demanded by customers, the quality and they have too.

Greg Lotko:
Having the utmost confidence in it and faith.

Eduardo Ciliendo:
Exactly. But at the same time, again, internally, you're spot on. Where we want to have this culture, hey, look guys, just try something out and see if it works. And if it doesn't, well, throw it out, Try something new, right? Yeah.

Greg Lotko:
That was like WD40, right? It got named WD40 because of the 39 failed attempts to come up with the formula beforehand. But everybody sees this great water displacing product, WD40. It's awesome, right? It took 39 failures to get there.

Daniel Newman:
A lot of innovation. That's how it happens, yeah. There is a lot of the appropriate application of data and we're going to come to that in a minute to see how we streamline these processes. You look at something like semiconductors that are in a really interesting place, but you look at thermals, how actually different materials connect to each other. And we've heard about the future of quantum use cases and we hear about supercomputers in the future, but in the end it's because when you have 10 factorial, different options of how compounds can come together to create a bound set of materials to create, it's almost impossible. So that's how you get to 40 and then 40,000. But in the end, I mean that's the data conundrum. The data conundrum inside of a business is we have more data than we know how to use. The AI era, Eddy, has brought us to this period of time in which we're all saying we got to have our data right? If you want to take advantage of Gen AI, well, guess what, 20 years ago we were saying you're going to have to have your data right if you want to get involved in big data. 20 years before that there were AI algorithms being created. This stuff is not new. But in your world, this influx of data is real. I'm curious kind of how you're seeing that evolution of the data influx being put to use in your world?

Eduardo Ciliendo:
No, I think that's critical. Right. And I mean you hear it so much. I mean, currently we see this stagnation in some of the gen models, right, where we simply don't have enough training data anymore to train those models. And I think that's one of the areas where the mainframe really could do more and participate more. Because no other platform out there has decades of customer interactions, whether it's banking information, insurance information, airline ticketing information, transaction data.

Daniel Newman:
Yeah, right. I mean, massive.

Eduardo Ciliendo:
So business leaders I think are amiss if they're not tapping into that huge vault of data that they haven't leveraged. Right. Because we see a lot of the training data comes from systems outside of the mainframe. So I think that's a huge untapped potential going back to how can business leaders get more out of the mainframe, have more innovation? The huge untapped potential in the mainframe is for me one of those areas.

Greg Lotko:
And you think about it, whether it be insurance companies, financial legalities, a lot of the data that has been processed has mandates or legislation that says you have to maintain seven years of data online, so why not make it work for you? Yeah, leverage it.

Eduardo Ciliendo:
Exactly.

Daniel Newman:
So in the spirit of going down the AI path, am I allowed to do this? Sure.

Greg Lotko:
Go. We joke, is this you or are you an AI?

Daniel Newman:
Me and AI. Oh yeah, I'm actually Dan AI. Okay, the Dan bot's gonna ask a question.

Greg Lotko:
The Dan AI.

Daniel Newman:
How do you see the role of those convergences between the mainframe and AIML happening? What's your sort of viewpoint on how mainframes and AI are gonna symbiotically become one?

Eduardo Ciliendo:
Yeah, I think that's the multi-billion dollar question. I think there's a ton of very valuable use cases. We just talked about all of the historical data that business leaders really should use to train their models. Right. That's one area. I mean, the other thing, and we hear IBM talking a lot about it. I mean, the mainframe still is the backbone of our economy with most meaningful transactions running on that system. Right. So AI inferencing really should happen at the point of transaction. But the one area that is probably not as exciting for business leaders, but it's very exciting for me as somebody who really strongly believes in the mainframe platform, is I think AI gen AI just will make it much easier for future talent to interact with the mainframe. Right. I mentioned this little chatbot example. We're doing autopilots right now where somebody without a technical background, certainly somebody without a mainframe background, can now interact with the system and ask, hey, was my application back up? What happened last night between 7pm and 3am on my system? It's those kinds of interactions that I believe will bring a lot of value, will streamline operations, but will also make it easier for new people to come into the mainframe arena too.

Greg Lotko:
And we've talked about AI a bunch.

Eduardo Ciliendo:
Right.

Greg Lotko:
And I know you always bring it up. I get that I've given you a hard time about it, which you might think I'm not a fan of.

Daniel Newman:
We're just having a little fun.

Greg Lotko:
I am, I am. So the reality is, it's funny some of the things we've talked about, the idea of not using technology for technology's sake. And we have all seen companies that chased cloud or chased whatever technology at a point, the balance, the dichotomy or the contrast there is you have to start doing something with it so that you can learn about it. So you know how to apply it. So AI, I worry about biases and models. I worry about the data sets that you're going over. I think there's cases where this technology can be applied where you have more of a finite model and inherently what's in it. And it can help you discover insights that you wouldn't otherwise know, like looking at our support data or troubleshooting or problem resolution. And the exercise of doing that with meaning, applying a technology for a purpose allows your team to learn about the technology and come up with ideas of how else to apply it.

Daniel Newman:
Well, it's interesting just hearing him provide that use case, but one of the things we've talked a lot about here on The Main Scoop is the sort of knowledge transfer that's going on that you, you're, you're recruiting in younger talent, you're trying to keep that fresh talent. But there is a ton of historical rich knowledge from engineers that have been running mainframes that did not come up the way they came up. That came up in a different era. AI seems like such a great opportunity for that data to be documented and be exposed and made available, where someone that maybe is more of a modern developer, modern DevOps can access that experience through a generative app, and then they can review historic code, why something was built a certain way, and then see how to build things into the future.

Greg Lotko:
And I think we're dipping our toe in the water in all walks or facets of life, right? I like to play with cars, right? So if I'm trying to troubleshoot something, I'll go out and look on YouTube and I'll look for a primer or a video that somebody else shot. I can see in the future an AI that might have ingested all that troubleshooting and be able to, hey, here's the problem I'm having, and suggest to me a path to go down or, or a potential resolution. And that's how we learn over time, not just technology learning, but how we interact with it 100%.

Eduardo Ciliendo:
And those are kind of the use cases that I now, as a CEO, find almost more interesting. I could be wrong, but I don't believe in those big revolutionary things where we're all going to be out of the job tomorrow because we now have AI overlords running.

Greg Lotko:
The world now, and AI is not going to eat a hamburger for me. I want to eat that burger.

Eduardo Ciliendo:
Very true, very true. Right?

Daniel Newman:
So many jokes.

Eduardo Ciliendo:
But there's a lot of, I think there's so many use cases like you just mentioned, right. You want to troubleshoot something. In the past, I'm also a bit of a car guy. In the past, I would have researched all tons of things. Now, you get the AI summary on top. Hey, by the way, this is what we suggest and we see this across the board. We had our marketing team today talk to an AI company about how generative AI could help them build PowerPoints going forward. I think it's those small use cases that will make us all so much more productive and take some of the tedious work out of our daily lives so that we can be more innovative. Going back to the topic of innovation and being more productive.

Daniel Newman:
So Eddy, what is helping customers be a disruptor of digital transformation? What does that mean to you? How does that happen?

Eduardo Ciliendo:
So I mean, my big caveat is that I always say I'm not a finance expert, I'm not an insurance expert, I'm not in travel, I'm not in government. Right. I'm an IT guy and I've always been in that mainframe world. Right. So I think my job is not to tell you on the business side how to do your job or how to do your job better. I'm here to listen to your ideas, how you want to structure your business, and to make sure that we put the technologies and processes in place or build them for you so that you can disrupt your industry.

Daniel Newman:
So then when it comes to modernization. 21 CS. Right. It's fun to think about a company with that kind of durability and that kind of success with your engagements. And the work you're doing at 21Cs though, is modernization. I mean, is that the big focal point for you right now?

Eduardo Ciliendo:
It is. And for multiple reasons. I mean, again, we've been talking about the next generation coming in, right. I mean, that's just one area where we have to modernize everything because we bring newcomers into the platform. They don't want to interact with green screen TSOs and in the future, they probably don't even want to interact with all those fancy UIs that we're building right now because again they will just want to have a chat prompt or something like that to interact with the system. Right. So I think that is an absolutely crucial piece for us to ensure the longevity of this platform.

Greg Lotko:
And it's funny. I think what's old is new and new is old. I mean, when we came into this space, right. You remember those days when we first started working on the platform and we had ideas of how to write more efficient code or more elegant algorithms, or whether it was modularizing our code. Some of the traditional guard that was like “No, no, no. That's not the way we did it.” And the reality is we learned from them, they learned from us, we learned together and got better over time. And it's the combination of technology and people that's driving us forward.

Eduardo Ciliendo:
Great point, by the way. Not sure if we're going a little off topic here.

Greg Lotko:
We do that a lot here.

Eduardo Ciliendo:
For me, it's a whole notion of that hybrid workforce. Right. Because yes, we've been talking about the newcomers and everything, but you're spot on, combining all of our, all of the expertise that you alluded to. Right. We have Sysprox, we have developers that have 30, 40 years of experience. I mean, they know what they're doing. How can we harness that knowledge, get it into some of the AI models. But then also how can I create this hybrid team of newcomers straight from college? They're all gen AI savvy, they know all of that stuff. How do I combine that workforce with very experienced people and it can't be either or it has to be a combination of both.

Greg Lotko:
Totally agree.

Daniel Newman:
So before we wrap things up, I'd love to just sort of ask you, so how do you disrupt yourself? How do you keep challenging yourself? How do you think big and draw the best talent and get them to come to you? The little back and forth, the sort of perception of maybe what isn't the most fast paced forward now. Despite the fact that Greg's convinced me over the years that this industry is really disruptive. But how do you keep the disruptive talent coming through and keep people excited?

Eduardo Ciliendo:
Yeah. I mean, on, on a personal level. All right, so how do I keep disruption almost to myself? So first of all, my motto is always stay, stay humble, stay hungry. I never think of myself as the smartest guy in the room. And if I do, I either ask my wife or my kids to slap me.

Greg Lotko:
To correct us.

Eduardo Ciliendo:
Yeah, exactly. So I think a lot of it has to do with surrounding yourself with the best talent, especially in the job of a CEO. Again, I don't have to be the Smartest guy in a room. But I have to get the smartest people into the room. Right. That's my job. And then our dad, just on a personal level, never stops learning. Right. So whenever there's a silly thing out there, I don't know, like for me it was TikTok, it was Instagram, what I have no clue about. So you just tip your toes in it. I just recently bought one of those VR headsets because I just said to myself, hey look, this is cool technology. I have to get in. And even though there's probably 12 year old kids that are kicking my ass right now on some online multiplayer game, it's just important for you to always stay, stay engaged with what's going on out there.

Greg Lotko:
I agree with them. I think it's about listening, being open to new ideas. Surround yourself with people that are smarter than you but equally as poor, as important whether they're smart or similar. People that are willing to speak up.

Eduardo Ciliendo:
Yeah.

Greg Lotko:
People that are willing to push back and that are willing to speak their mind and provoke thought in you so that you see things in different ways.

Daniel Newman:
Well, what do they say? If you're the smartest person in the room, you're probably in the wrong room. Then you never want to be the smartest person in the room. You always want to be pushing or you're not learning. And I think whether it's in technology, it's that we are constantly pushing ourselves. Eddy, thank you so much for joining us. There was a lot here today. Disruption is kind of a, it's technological, Greg, but it's also a bit of a state of mind. It's that kind of constant pushing your org, pushing yourself, pushing the technology, pushing the customer. But, it's always got to be purposeful.

Greg Lotko:
I agree. That's where I was going to go. We've talked about technology, we've talked about it being with a purpose, humility, always learning, having the application, but making sure you're aware of what's going on and then that will allow for creativity of how to apply it. And you got to do it all with resiliency and stability because people are counting on this technology.

DanielNewman:
And if I can add one more thing, you have to be able to measure it.

Greg Lotko:
Agreed.

Daniel Newman:
You cannot do it if you don't know where you're heading towards. We heard it from Eddy today, we've heard it from many guests on the show in the past and I think we're going to continue to hear this theme into the future. But it was a great conversation. Eddy, thanks so much for joining us. And thank you everybody for being part of the community here on The Main Scoop. We hope you will hit subscribe. Join us for all of our other episodes. We have a lot of great content here. Thanks for tuning in.

Eduardo Ciliendo:
See you all.

Greg Lotko:
Bye bye. Thanks a lot.