Techstrong TV June 2, 2025

Hey, everybody. Ai, it's getting real in DevOps. You're watching Texty.

Hello everybody, and welcome to today's edition of the Text Drawing Gang. It's a happy Monday, and we got a lot to talk about, and we got some new faces on the gang. But let me first introduce Tracy Reagan, who's one of our, uh, stalwarts.

Been on the show since almost the very beginning. Tracy is still in New Mexico. Good to see you.

Good to see you too, Mike. Glad to be here. And this is gonna be a fun day for a topic.

All right. Awesome. Also, joining us today is Jack Gold, who's been an industry analyst for as long as I can remember, and I've known Jack for, I'm thinking we're going on at least 30 years, Jack, is that right?

Uh, probably Mike, what's, well, we were both six at the time, so it it's okay. There you go. But Jack, introduce yourself and tell folks what you do.

Sure. So, as you said, Mike, I'm, uh, an industry analyst. I've been an industry analyst for about 30 years now.

Scary to think it's been that long. gold Associates, uh, looking at the various aspects of enterprise technologies, but also some of the consumer technologies that are gonna be used in enterprise and not too distant future after they become popular at the, uh, consumer space. And looking at all kinds of technologies, including, of course, ai, but also things like security Cloud, uh, the, uh, focus, uh, we have a focus on ROI and and TCO for enterprises especially.

So that's becoming very important for companies that want to make a decision on where they want to go with technology. And, and so there's a, a wide variety of technologies that we look at that, uh, help enterprises become more productive, more efficient, more capable. All right.

And also joining us today for the first time, and for me, I am meeting for the first time is Dr. Stacy Thayer. Am I saying that right, Thayer?

Yes, you are. Yes, you Are. Awesome.

Great to be here. Stacy, tell folks what you do. Sure.

Uh, so I'm cyber psychologist by trade. I am, um, program coordinator and professor at, uh, Norfolk State University, where we have a cyber psychology program. I have been in the security industry, um, since I was calling 26, you know, attending 2,600 meetings and calling BBSs and hanging out with the, the hacker crowd in Boston, um, back a long, long time ago.

Um, but so between my work as a cyber psychologist, and then I also work with, uh, various companies in the security industry, uh, a lot of times doing their events, working with conferences, uh, which is great because it helps me stay on top of, uh, all the latest trends, get to know speakers and meet people all the time, which is great. All right. Well, I've been known to positive theory and ask the panelists whether I'm crazy or not.

So it'll be interesting to see if I can actually get certified by you. All right, I, anyway, let's get started though. Um, there's been some amazing advances in AI and DevOps, or at least they've seem that way.

We'll see if they actually pan out. But in the last, uh, week and a half, we've seen enro talk about a new coding agent that can work for, I think they said seven hours. Uh, at the same time, harness has a MCP server that they've connected up to their CICD platform.

Uh, new Relic is talking about integration between its AI agents and the AI agents from, uh, GitHub. So you can create an issue and the GitHub agent or will resolve that issue for you. And then they, new Relic agent will check the work done by the GitHub agent.

And then finally, Amazon is out and extended its, uh, AI agents to do, um, essentially reverse engineer applications and refactor them. And it doesn't automatically do it, but it does a lot of the scut work underneath it, and it kind of makes it, you know, more feasible to do that kind of thing. 'cause frankly, as anybody who's ever been involved in one of those projects knows it's a career threatening decision.

Tracy, you've been looking at DevOps for a long, long time now. What do you make of all this? Well, most of the viewers have heard me complain about the fact that AI and DevOps hadn't been, uh, married yet.

And why and when is it gonna happen? Well, you know, let's mark the day May 30th. It is the birth of a new way of doing DevOps.

I think it's important to realize, you know, when I, when, when I was selling, uh, Meister products in the, in the early part of the DevOps world, I would tell people about automating builds. And their response would be, well, we don't need that because Jenkins automates our builds. It was like, no, Jenkins doesn't automate your builds your script automates your builds.

And Jenkins calls the build because what was, what is Jenkins and what is our what, our CICD server, they're job schedulers. That's it. They've always been just job schedulers.

They've never really automated anything except the scripts that it calls. You could have used Jenkins to, I don't know, automatically open your garage door and start your coffee pot if you wanted to. So now we have finally entered the world of AI and job scheduling.

So, anthropo, uh, I'm sorry, I forgot the name of the company. Philanthropic is killing it when it comes to AI and, and these new tools, they are the ones who in introduced us to cps. I got super excited then because there was a potential to get rid of plugins.

And now we have the potential to have a seven hour window of job scheduling that we can call and do anything we want with, which means that we have the potential to start evolving our DevOps pipelines to include more work. Because right now, companies struggle, really struggle with going and updating the thousands of, of workflows that we have to update just to add software bill of material, uh, generation, which is holding us back. So I believe that we are going to be, if we embrace this new technology and start thinking very different about how we can manage a job and schedule a job and what jobs, what, what needs to be included in that job in a new different way, we can now start really moving forward with not just DevOps, but the whole platform engineering space.

So yes, today is a really interesting day. And on top of that, when I first heard about, uh, CPS model contact protocols, I got super excited. 'cause plugins have held us back for quite a while.

Uh, the ortel, uh, open source team, they jumped right on it, and they started looking at creating CPS for GitHub and GitLab for doing pull request and issues. So it does not surprise me that harness has built an MCP server around this, because that is exactly how we're gonna get rid of plugins. So we have, today is a big step in the evolution of DevOps with those two new announcements.

And I'm looking forward to building a new DevOps platform around CPS and around, um, philanthropics, uh, you know, workflow, uh, load generation, because that's what it's doing. And I couldn't be happier, to be quite honest. I really couldn't.

Tracy, can I jump in for a second? Um, the, uh, you know, the notion of MCPS is great, but because it's, uh, tropic, do you think that there will be some hesitation from other players, some of the major big players to use cps? As far as I can tell, everybody's kinda agreed on a, it's a, it's a quasi defacto standard.

What the, the part that's unclear to me, Jack, is there's also talk about this agent to agent protocol, like Google wants to put on top of that. And I can't tell if that's a higher level of abstraction. I mean, when they launched it, they kind of positioned it as something complimentary to MCP.

But I talked to other folks in their kind of scratching their head going, if I have an MCP, why do I need that? So I don't know what your thoughts are there. Yeah.

Uh, and, and that's my concern, right? Because, uh, MCP is great if it get, it's seems to be adopted almost universally, but then you've got somebody like Google coming along, doing their own thing. Will AWS do something different?

Uh, I I just worry that if we get away from standards that the whole notion, and Tracy, you're, you're right on being able to just inter interact with everyone, all the, all the different ai AI systems, AI agents is critical. But if we get away from that, I mean, that's, that's a little bit like saying, you know, in the PC space, right? Everyone uses USB except you, or USB doesn't fit into my USB, right?

And, and, and that, that becomes a real issue. So that's why I kind of asked the question, Well, you know, it's all about adoption, right? It doesn't mean it's the best of solution.

It's what will the developers grab onto and start working with? And CPS went really fast. Everybody is talking about it, and, and everybody wants to develop something around it.

Everybody understands the impact it, it has. So developers have really latched onto cp. So I don't think you know that it's all about developer adoption.

It really is. It doesn't mean it's the best solution. It's the solution that IT developers started working with first and got comfortable with.

Yeah. And for all the talk about this great new thing, when you peel it back, as far as I understand that it's A-J-S-O-N based remote procedure call. It's not rocket science.

It's just kind of a, a standard way of doing something. But Tracy, I wanna dive in a little bit more on a point you're trying to make, getting rid of plugins. Does that also mean well, are we gonna get rid of the scripts too?

Because the scripts are the things that are brittle and the things that kind of break all the time and why the pipeline suddenly stops working. So how far are we going here? Let's pray.

Let's, all right, now take a moment and put a good vibe out in the universe for a script list DevOps platform, because the scripts themselves is what holds us back. Plugins and scripts. Plugins and scripts, constantly backward, compatible issues, constantly updating 'em, can't even update.

You know, we're taking, uh, 90 days to update of, uh, of CVE because we have to go update all the POM files. We should hope, as I think I said this on the last, um, on the, on last Monday, uh, call, we should hope that a script can be deleted and nobody will freak out. That should be our goal, because we should be able to have something, something generate our scripts perfectly.

Now, I know the question becomes, where is it gonna get the data specific to what you're trying to create? But there should be a way to pass those parameters in if that's what we have to, you know, we have a context window, we can do that. It's now.

So yes, it's, it should be the end of scripts. We should be moving away from these brittle, uh, scripts, um, and into something that's more generated. 'cause that's where the repeatability comes.

And that's also where the visibility comes. When you, when you can repeat something, you can more easily fix it. But scripts across three different applications at the same company can look very different.

And when a problem occurs, it's hard to then go and, you know, automate an update for it all because everybody's doing something different. So, yes, we need to get to a point where it's not just the workflow, um, not a, not just a seven hour, seven hour job scheduler workflow, but it should be the scripts that set themselves. Stacy, all of this stuff revolves around the idea that we are gonna have these AI agents that are quote unquote teammates on a working alongside humans.

Um, are we kinda, you know, as humans psychologically prepared for thinking about AI agents as a teammate? I mean, are we gonna put names to these things and, you know, are they gonna become pets or are they just kind? Are they just kind of random things?

Yeah, yeah. No, great. Great question.

And you know, Tracy, your point you thinking it's all about adoption. It's all about, you know, are are people willing to work with this? And I was thinking that as you were, you were talking, you know, how excited slash scared slash how much trepidation do we have about a, adopting these AI technologies, uh, and making change?

And the idea, uh, you know, if you've ever listened to somebody actually talk to chat beat GBT or talk to Gemini, or you, you thank your, uh, I was in a Waymo the other day. If you've been in one of those automatic driving cars, we all got outta the car and thanked the Waymo, right? And so I think it, there's a lot about us that, that don't know what we would replace how we interact with these AI interfaces with, right?

And so would they be our best work buddies? You know, maybe would we, we talk to them the way that we talk to our coworkers, what will that look like? And I think some people are more willing to adopt and experiment and accept, um, than others.

So nobody understands me, but my AI agent is kind of where you're going, right? Right. Which, which you're programmed, right?

I mean, if you talk to, to Gemini, it says, okay, here's who you are. And you can program your, your AI buddy to be who you want. There's, there's programs in psychology or apps of your virtual therapist, right?

And is it an actual therapist, or is, does it matter if it's real or if the perception is real, Right? So, Tracy, how will DevOps workflows evolve in your mind? 'cause I'm thinking about it this way, maybe.

So let's say I have 10 agents and you have 10 agents. How are our agents actually gonna get together and actually do something together that's meaningful? Because ultimately we are all responsible for different parts of the application, or a microservice or whatever it is.

But what is that collaboration gonna look like? I don't really think it's gonna change very much in terms of how workflows collaborate, to be honest. I, I really don't.

I think it's just gonna change the mechanics. We're gonna still be, uh, doing the same kind of DevOps work that we do now. We're gonna collaborate in the same way.

We're gonna have workflows that do different things. Maybe a testing workflow. You know, we could have, uh, you know, different, uh, uh, models that we're using.

But at the end of the day, what we're doing is we're making our lives so much easier by taking away all of the broken pieces in the DevOps pipeline. That's what I'm focused on. But I don't think it's gonna change the way we think about the, the, the software factory floor very much.

It's just gonna change the way we work. It's just gonna get rid of an old process for, uh, you know, job scheduling. 'cause that's really all these CICD tools are, to be honest.

They're job schedulers and some are better than others, and some have historical tracking and some don't. But in the end of the day, it's just job scheduling. And we got a lot of logs.

So I, I just don't think we're gonna change DevOps very much. And I've been thinking about this since I've read the article, and I got all excited this morning. Um, I just think that we're gonna do it better.

And some of the platform engineering, um, goals will be built into it. Will it change, Tracy? Sorry for interrupting, but will it change the skillset that people require to be good at DevOps now?

No. I, I think you still really have to have to be a good DevOps engineer. It takes more than just being able to write a good script.

It takes a solid understanding of the architecture of an application, um, and how pieces and parts work together and what you need to do to make sure that it's successful when it's deployed. What we can't see as DevOps engineers or platform engineers is why something broke, because much of the data is, uh, fragmented and there, uh, much of it's in logs in different places. So I would hope in this, in this process of having, um, this AI now ai DevOps pipeline, that we could start solving that problem as well.

Um, now what I don't see happening, and we'll see if, if it does in the future, every time you run a workflow, you capture data and versioning, versioning, that information is important. So you can map that data back to what's actually running in your production environments. So these kinds of steps will still need to be worked with.

It just means we're gonna have a better job scheduler, a more efficient one, one that has less scripting around it and fewer plugins. And that's what we desperately need. I would hope that AI adds the knowledge, as you stated earlier, right?

It's, it's, it's all about trying to find the errors, trying to find the inconsistencies, trying to find the differences. That should be what AI is really good at. And that should be, I wanna give huge help.

I wanna give Stacy the last word on this because as we've talked about on the show many times, cybersecurity is the reason we can't have nice things. So what happens when an AI agent gets hacked and takes, and somebody takes over an entire process, Right? That, and that's part of the, the question that concerns, especially with AI being such the buzzword now, and I mean, for good reason, it's not to invalidate that, but do we understand it well enough?

Do we understand the predictions of, of how that works? Right now, everybody is just jumping in to integrate everything, and, and that's great. There's a lot of great good things that AI can do, but we haven't mastered it because we don't really fully quite understand it yet.

Um, you know, I'm not too worried about, uh, you know, something going in and going Terminator on us at the moment. But, uh, but in terms of how we use this, what does it look like? Is this somebody that can take over aspects of your job?

To what point does it help? And then what's the long lasting impact of that, both within an organization, if it goes, if the AI goes down, if it decides to just stop working or doesn't understand the discrepancies in the code. You know, there's a lot of different things that's, that are going on that does require the skillset of understanding how to relate and under and translate effectively.

Uh, ai. All right, well, folks, think about it this way. AI is the undiscovered country, and we're all gonna have this exploration together.

And for good measure, that bridge that we just crossed to get there, and we just burned it on the wayside, there's no going back anyway. So here we go. We'll be back in a minute.

Hey guys, we're back. And there are these new devices in the Zoological Gardens of PCs. They're called AI PCs, and they have these things called neural processor units in them.

And, uh, Dell and hp and just about everybody who makes anything is building one of these. And they're kind of aimed at accelerating or running AI models or inference models on a desktop client. And Dell has one that's saying specifically AI data scientists and developers.

'cause you know, those folks need to work with models locally. But Jack, we've been talking about the rise of these devices now for it feels like the better part of a year or coming up on it anyway. Um, do we need them in the first place?

And BI mean, are they gonna become the devices, everything gonna be in A IPC? Or where are we on this adventure? So the answer to your first question is, yes, we need them and we need them for various reasons.

Um, and, and I can get into the second piece because of the first piece. So, um, AI works best in certain types of hardware. Uh, CPUs aren't great at ai.

You can do some simple tasks, AI tasks on CPUs, but you really need heavy duty parallel processing, which is, you know, NVIDIA's game, right? That's, that's why NVIDIA's doing so well in the AI space, uh, with their GPUs. Now, in the early days of early days meeting, you know, six to 12 months ago, uh, AI in a PC was really all around, uh, having an Nvidia, A-M-D-G-P-U installed on it, and you could do some inference modeling and inferencing capability as we moved on.

Now that, uh, most of the PC chips are including either a built-in NPU, you know, Intel and a MD and, and, and Qualcomm have built in NPUs in their devices or standalones, which are, which are higher performance from Qualcomm or a MD or, or whoever. Um, we're moving to an area where we can do a lot more AI processing in a short period of time. And so what's happening is we're seeing a, a split in the marketplace, uh, for, in the PC space.

Uh, we're seeing a split between consumers of AI and developers of ai. Let me start with the developer piece. In the past, I'm going back 30, 40 years now.

Uh, when, uh, developers first started, um, doing cad, doing a lot of different kinds of graphical systems, they needed to get on high in those days, high performance, meaning many computers that had probably 10 MIPS of processing capability, uh, to do their work. Then Sun came out with workstations. Workstations were standalone.

They were the PCs of their time, basically high powered PCs that let those developers, those designers do their work at a local environment, not having to timeshare on a larger machine. And there were a lot of advancements made because of that. And then it of course, moved down into the PC space over time as well, where we do a lot of CAD design, uh, on PCs.

Today, designers have high powered PC machines. We're seeing the same process play out with AI today. Most AI modeling is done at the cloud level, at, you know, on a hyperscaler or, or a large in-house on-prem system with lots of Nvidia GPUs in IT.

Development is very expensive on those machines. If you can deploy high powered, high powered for their capabilities, PCs that have the ability to, to, that you can give one to each of your engineers that allow them to do localized AI modeling, AI tuning AI capabilities, uh, that is a much more efficient process. And that's what we're starting to see with some of these new AI PCs coming out.

You know, Dell announcing one. Others have announced that they're gonna move in that direction as well. So you're gonna see a lot more AI flow down into the personal space.

You're also gonna see AI running on our machines. So to answer your earlier question, is this the future? I think within the next one to two years in the enterprise space at least less perhaps.

So in the comp compute in consumer space, because that's more price sensitive, most enterprises will have any new machine they buy will have, uh, NPU uh, AI capability built in. It's just gonna be there. Uh, you're not gonna have to buy it separately.

That will raise the price slightly, but not enough to, uh, offset the increased productivity that you're going to get. Consumers will take a little bit longer. So the AI space is high growth, it's gonna be very important.

That doesn't mean there aren't gonna be a lot of lower NPCs that perhaps don't have NPUs or, or gpu, big heavy duty ai GPUs in them, but they'll be relegated to the lower end of the space. And we're gonna have a lot of agents running out our PCs, we're gonna have a lot of AI capabilities. We're already seeing it with, you know, copilot and things of that nature.

But it'll, it'll only get more intense over, over time. Tracy, is this on your wishlist, or for all? I know you probably have one already, but, um, you know, is this on the top of your things I want to get?

And This is, it is absolutely on the top of the things that we need to get. So for, for, for example, for us to try to build a, a DevOps, um, small language model, right? We would go out and we would have to, um, use one of the services like a Google, and it's about 25 cents a GPU per hour, and we're gonna need that running for a month, two months.

We could buy some pc, we can buy some local PCs and never have to pay that price again, right? So it, it will help, um, it will help the developers change the way that they develop if this is right, available to them that they've already purchased and they're not having to set up an account in order to do it. That's how I see it.

It's gonna give me the tools that I absolutely need to adopt AI into the software that we're creating at a far lower cost way. Lower cost, because the, uh, you know, and, and I realize that companies like Google probably make quite a bit of money on this stuff, and it's gonna disrupt their, their model, but we need it cheaper and we need it at our fingertips. Well, Google will still do fine, as will AWS and Azure and everyone else, but they'll, they'll be more, more, much more segmented towards the high end of ai.

Uh, the kinds of things, Tracy, I think that you're talking about really work very well on a personal level, on a, on a local workstation. And so there is gonna be a, a division between high-end, very large models, and as you say, smaller models, SLMs, that will work great on some of these new workstations that are coming, uh, online. And by the way, as, as we all know, you know, the semiconductor model, Moore's Law, they're gonna get more and more powerful every year.

So we're gonna be able to do some pretty fantastic stuff on these machines. And a hundred billion parameters doesn't seem small to me, right? That's pretty, that's, you know, when we first started talking about this, I could imagine that, that, that, you know, that large of a system running on a smaller, uh, on a, on a, on a pc, Well, some of these PCs are gonna have what, you know, 500 flops or going forward or, you know, AI ops is probably more appropriate.

So yeah, I mean, it's inflation, right? It's everything goes up next year. It'll be puny, but this year it sounds big, right?

All right. No, that's actually my question, Tracy. I, I mean, is now the time to buy an AI ma machine is, or do you think they'll be, do you wait a year?

Is it like the next iPhone or you just wait for the next iteration and maybe that one's got a stronger capacity? Or is now the time it's like, yes, this is where you should start. Should companies be investing in this model versus, say, waiting a year two, three to see how things can grow?

You know, I think, you know, like when the internet first came out, I don't wanna be stuck on my 2,400 bo modem dialing up when now there's wireless and, you know, faster, uh, technologies. How long do you think if you had to give a timeline would take before you'd say, now's the time to buy when you need it, when you need it, right? When you we're at that point.

We, we need, we need it. So, you know, we're not gonna wait because we need it now. That, that's been the question forever with PCs, right?

And, and enterprises, do I buy it now or do I wait a year? Because I know there's gonna be a new chip from Intel or whoever, a MD, that that's gonna be more powerful. But Tracy, you're right, if I need it now, you know, I can't wait a year.

I gotta get the work done now. And, and that's important. There is another piece of this, by the way that, that we haven't talked about that I wanna address very quickly.

And that is that from a personal workstation's perspective, if I'm working in ai, in theory at least, security is much enhanced. I'm not putting a lot of stuff out in the cloud that people can tap into. It's all localized, or I can at least try to keep it localized.

And so, uh, security from an AI perspective is, is a very important aspect. Uh, anything we can do to enhance that. Uh, honestly, that's the part that concerns me most about AI in general, is how do we know that the models are not doing bad things to us?

How they, how do we know they're not hallucinating? There was an example in the press this week about, um, a model trying to bribe people to keep them from turning it off. I mean, that, that's getting pretty extreme.

We, we, we talked about that last week on the show. Yeah. Yeah.

I wanna Stacy know, going back to why we can't have nice things. I mean, Jack is saying maybe the endpoint is gonna be more secure than the cloud, and that's possibly so, but last time I checked, we weren't very good at securing endpoints in the first place. So, um, so I'm kinda looking at this going, how many of these things are there?

Because if I was a criminal, I would scan for a I PCs and go, those are the ones that are most interestingly target, Right? Right. And, and, and it comes interesting.

There's, there's of course the, the discussions around, uh, humans and the human factor being so susceptible to vulnerabilities and, and being oftentimes the, the most challenging aspect of a, of a security program. And I think what's interesting with AI is that we as humans, we were looking for patterns. We're looking for predictability, especially if we're trying to break into something or hack into it, how predictable can AI be?

If, if we all, if all four of us went in and, and asked the same question or tried to write the same, you know, enter in this code or did the same thing, would, is the output predictable? And if it is predictable, does that then become more susceptible to security vulnerabilities and flaws? Because it can be replicated or can, where, where does the human factor come in within these AI models?

'cause right now, there's still a very heavy dependency on the human part to be able to work within these functions, to be able to program the ai to be able to set it up. And do we understand it well enough to be able to build a strong security infrastructure around it? Do we know where the weak spots are?

Do we know where, uh, something could be vulnerable? Are we masters of it? And are we ahead of the game and we are ahead of the, the bad actors who may be able to take advantage of it?

And, and of course, there's also als always the issue of, you're right. But there, there's also the issue of can we use AI to hack ai, right? A AI security hackers are starting to use AI to try and find those vulnerabilities.

So it's a, it's a double-edged sword, And we can use AI to find the vulnerabilities ourselves, right? We, you know, we, we should be doing the hacking so we can do the correction. Yeah.

Yeah. And there's a lot of penetration testers and offensive security folks out there who are trying to understand that and trying to work with, okay, what, how does AI impact both offensively and offensively? I do think, though, going back to the, just for a minute on this topic, going back to the CPS that we talked about in the last segment, um, you know, we really never have gotten that good at API security in the first place.

So I think there is a lot of the vulnerabilities around the mcps, and I think that's a, an area that of, of, of security issue that we should be focused on. Just a thought, I'm gonna end this topic here, but I would point out one thing, the future of security might very well be, you know, me watching my AI agents beat up the bad guys, AI agents and vice versa, and then battle it out somewhere in the ether, and hopefully the good guys will win. But we'll see how it all plays out.

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Contact us today and tell your story to the world in the most powerful way with Textron Group. Hey, folks, we're back and we're gonna have a little chat about digital transformation. We've been on this topic now forever, and a lot of companies, I think there's probably no company at this point that hasn't at some point launched some sort of initiative, but they keep running into the same issues over and over again.

Technical debt or data misaligned goals. Um, Stacy, you have a, a a background in human psychology, and I can't help but wonder at this point how much of this issue is technical, or how much of this is just us and the way we as humans are kind of structured. And the problem is staring us in the mirror every morning.

So I I'm always gonna go on the side of the, of, it's always that person looking back at you, right? Because the technology is great, but as right now, it's only as good as the humans that are behind it, right? And, and the way that we can explain it, and I think there's a lot of, you know, people who are working technology that then they get it, they get in with their teams, and then they go to the boardroom, or they go to the executives and they're like, okay, how do I translate this into a good business case, a good business model?

I can explain everything that we need, need on the technical aspect that makes perfect sense to anybody that needs it about why this would be advantageous, how this would help, uh, create more efficient, uh, code or to check and, and, you know, do all the technical things we want it to do. But then how do you then encapsulate that into something that makes sense to people that may not be as technical or that may need to make business decisions, or that may be also be using it and to use it responsibly. Just like we were talking about humans and cybersecurity.

We need to have a good cybersecurity program. Well, now we need to have a, a good cybersecurity program that also integrates AI and best practices with ai, because it does come down to the people and how they're using it and how responsible the use is, and how well they understand it. Mm-hmm.

Tracy, you built your fair share of applications over the years, and I'm sure that they were all intended to be used magnificently, and, but when you present those to end users and it doesn't quite work out the way you had it hoped, or that they kind of just kind of stare at it and use maybe 2% of the features, what goes on there? I mean, is there some sort of disconnect between, I don't know, are developers left brain people and end users are right brain people and it's that simple? Or is there more at work here?

That is such a loaded question, Mike. Yeah, I mean, there is something called, you know, a drift in your functionality that somebody think would be really cool, and a developer goes ahead and puts it in and nobody uses it. There's, there's always that problem.

But, um, you know, we used to say, if we can get 70% of the functionality we've done that people really, really need, we're doing pretty good, right? And the problem for companies is keeping up with technology. It is so difficult to keep up with technology.

You write an application and then you have to turn around and rewrite it to meet the new demands. So let's talk about digital transformation shift for just one moment. Most companies are still trying to go through this.

They're trying to get rid of monolithic applications and move to something that's more easily scalable. A lot of what we've discovered, some of it happened during COVID. I was, during COVID, I was driving down the road listening to NPR and I don't remember what state it was.

Um, but they were talking about the fact that they had so many new applicants that their system completely shut down, and they couldn't bring on any new applicants until they got more Windows servers. They were gonna have to go out and buy more Windows servers in order to onboard new applicants because they couldn't handle the, the, the, uh, the workload. So if you are a, an insurance company or a bank or anybody trying to stay ahead of the competition and relevant, you're gonna have to take your old applications, maybe add some new functionality to 'em, but most importantly, build them so that they can be scalable, for example.

So you're constantly in this, this, I don't know, this cycle of trying to keep your software not new, new functions, not new features, just relevant for the new technology. And here we go again, we haven't even finished digital transformation, and now we're dealing with ai. So it, it is hard to keep a application up to date in terms of the new features that your customers want and keep the platform that it's running on relevant and able to handle the demands of the economy today.

It is a challenge. Sorry for interrupting. There's also another big piece because I talk to a lot of enterprises about digital transformation over the years.

And, and two things that really stand out about why people haven't been as successful as they, they think they could have been. Number one is they start with the technology instead of the business problem. Define the business problem that you're trying to solve first, and then try to find the technology that fits into it as, as opposed to the other way around.

Uh, and and that's very relevant to our discussion today, which is, do I really need ai? Well, what are you gonna do with it? How is it gonna help you?

The other piece of it is, if you can't define what you really need to your developers, how are they gonna develop a product that you want? And so one of the things that I, I advise clients about is, if you really want your developers to understand what you need, take 10%, 15%, 20% of your developers and stick 'em in the real business, uh, that, that you're doing. You know, if it's hr, put 'em in hr.

If it's customer service, put 'em in customer service. You know, they're not gonna be great at it. But if they do that for four or five weeks, they're gonna understand what the problems are and they're gonna develop much better code longer term.

Uh, so if there are ways to get better at this, I think what ends up happening to, from a lot of, uh, company's perspective and digital transformation is they think it's a buzzword as opposed to a real business process that they have to work at. Stacy, can we do that? Can we take all our IT folks outta their proverbial ivory towers and stick 'em in the business units and magical will happen?

I wish. No, no, I don't wish. But I mean, really, truly, it's some of them, yes.

Right? I mean, I think just like everything, people have strengths, they have weaknesses. They wanna be able to understand a deep dive into the technical components, or a deep, some of them are more interested in, in business.

I think in general, and why I say I, I wish I'm a big fan for people understanding the role that they play within the bigger organization. One, it just helps you understand your value and your contributions. So if you understand that you're, you're, you're in a, you're in it or you're, you're a developer, you're doing something and you're in your, maybe you're in a bubble, you're writing your code, but somewhere in that you're also impacting the business.

And what you do impacts other departments. And you know, it's all a connected system. And I think when you can step out and say, okay, well what is, how does the business practice work?

What is happening here? How did the decisions I make impact the business? And vice versa.

It allows you to see things one more holistically, and two, then to be able to really understand your, your value and the way that you do contribute and the way that the decisions that the fact that the decisions you make matter. One of the questions and the concerns that I often have with, with AI is the understanding behind the business decision and the buzzword. And, you know, I know walking the show floor at RSA, it was like everybody had to have ai, you know, and I've talked with, um, with investors and asking, you know, what companies are you looking at?

It's like, oh, no, they don't say they don't have anything with ai. We don't even look at them. com bubble, right?

Where it was like, you could have a company, but if you weren't online, you might as well have not had a company now that ended up look, you know, look what Amazon did. Like it was, put it, you, we need, need to lean into being online. You did need to have a website, but at what point are we leaning so much into these technologies that we are forgetting the business?

We are forgetting the why, the bottom line and the role that it plays. Like, yes, we wanna make this investment. This is worth it because it's gonna result in revenue, it's gonna result in product, it's gonna re, uh, result in better security.

So we don't have vulnerabilities. You know, why are we doing it? Not just can we, um, I'm gonna pull my, my, my Jeff gold bloom here, right?

We're so busy thinking about whether or not we can, you know, um, thinking about, you know, should we, and so that's usually the question that I have is does it make sense? Why, why are you doing it? It may be the best business decision in the world, and then go for it, but make sure you have the reason why so you can use it effectively.

Yeah. It's gotta be more than this fear of missing out, right? Right.

Exactly. It's AI tech, AI tech box. We all have FOMO when it comes to technology.

I swear I've talked to so many, you know, you talk to some high level exec right now and they're feeling like they're missing out if they're not doing something with ai, but maybe it's just a little early for them to do something with ai. And so maybe they should be looking at do they really even have a, an issue with digital transformation? Should they be going moving out of their monolithic apps right now?

Maybe they should be waiting. So sometimes stepping back and seeing if it's just FOMO might be a good idea. Jack, when I talk to business people though, then their whole frustration with IT is still pretty high.

And they look at it and they go, Hey, you know what? For all the noise in all the years, the productivity numbers are relatively the same. And the GDP isn't all that different.

And they're kind of like looking at it going, where's the math here? Yeah, where's the value? What, what value does it offer me right now?

And, and that's a real issue in many companies. Um, you, you're right, Mike, you know, they've been investing in technology for years. It's 10% of the budget, 15% of the budget and their sales are going up by 3%, right?

It, it, it's, it's not, you know, there's a, there's a disconnect there. I think what really is important for most large enterprises is that you have not just the dev, the, the developers and the, the, the worker bees, if you will, understanding what they need to do, but having the executives talk together, the best processes I've seen is where the, the C-suite actually moves back and forth, where if you have, you know, we're, we're used to having CIOs, CISOs, you know, CMOs, whatever. It's good to rotate those folks every once in a while have this, the, the, the IT director actually maybe not run the business, but it'd be at high level to the business so they actually understand the business.

So there's, and, and, and, and vice versa, you know, maybe the marketing guy or the HR person runs it for a little bit. Um, one of the ways that works, by the way, as well, uh, from a developer's perspective, and we've advised clients to do this, is take your developers and stick 'em on the help desk for a couple of weeks. They're gonna know where the problems are on those applications.

'cause people are calling up and saying, here's the problem I have. How do I fix this? So if you can do that interdisciplinary thing, if you can move people around so they better understand the overall workings of the organization as opposed to their individual silos, it works out much better.

And that's where you get the better investment. That's where you get the, the improvements. Stacy, 20 years ago I asked somebody, why did you get into it?

And he looked at me with a straight face and he said, well, I like machines, cats, and people in that order. Um, are we getting, you know, a new class of IT people who are kind of more engaged with it and people in technology? Or is that just wishful thinking?

I think so. I, I think, and, and part of it also is we're seeing a lot more, uh, the term digital natives, so to speak, right? Like, you know, 20, 20 years ago, years ago, it was, people were drawn to technology for a reason.

It was, I would rather work with technology than people. And then over years now, technology and people become more blended. Who knows?

With ai, I mean, we'll just go right back and it'll, we'll v off again, and now it's just technology. But, um, you know, when I do some of my, my organizational development of burnout is, you know, like a lot of times people don't go into working with technology because they like people, right? They computers can be frustrating.

They don't do what we want them to all the time, but there's a sometimes bigger connection than there is for people. Whereas for others, you give them a piece of technology and they don't know what to do with it. My, my mother, and I'm sure people's parents could tell you that I don't know what this technology does, and some people feel that way about people.

Um, that's Why you talk to six and seven year olds, they fix it for you. They're The best. Yeah.

Oh, my kids, they're, they know more than I do every day of the week. Uh, but yeah, I think now we're seeing where doing it, you're doing, most people one way or another are doing it for their family, at least in, in some way. It may just be, you know, I even do that.

It may just be a simple reboot. That's what I got. Just reboot it.

But sometimes that's enough. Uh, but yeah, I think at this point, as technology becomes more and more ingrained in our life, it also has to become ingrained in our skillset, which allows us to then merge those skill sets and, and say, okay, I have to work with people and I have to work with technology as well, and understanding the intersection of the two. And, you know, there's something too about, yeah, there, the, on the other side, if you look, if you're coming from this, from the, um, the chief level, right?

You know, Jackie mentioned that those folks need to learn more and understand better and maybe cross train, and I agree completely, but, you know, yesterday I was, I think it was on Anderson Cooper I think is where I saw it, but the, um, CEO of Tropic, um, Dario, um, ti I think it was, or E Modi, uh, is his last name. He was on, as you know, talking about how AI was going to potentially reduce employment or in increase the employment unemployment rate by 10 to 20%. Because we're gonna have so many jobs.

The jobs that are kind of repetitive and easy are gonna go away. Now, if I'm sitting there listening to it from a c from a CEO's perspective, I'm okay with that, right? Because I want to increase my profits.

So again, fear of missing out, I'm going to say, why are we not doing more ai? What can we, how can we apply AI so we can achieve a 10% decrease in our, in our costs, in our, in our labor costs, and still be, and be more productive? So the, again, as I said before, there are so many, um, there's so many parameters hitting, uh, the, the C level, a branch of businesses to try to make decisions around what technology to use and why to use them.

It may not be you're gonna get a dev better DevOps pipeline. It may be, what are we gonna deliver at the bottom line? You know, that's what they're looking at.

They're looking at cutting cost. They're not necessarily worried about delivering a better product to the end, the end user. Somebody else is worrying about that.

The product manager's worried about that. They're worried about how, how we can do it at a lower cost. So all of these, um, you know, digital transformation promise lower cost AI is now talking about a lower cost.

This is what's driving many of the execs to make changes. It's, it's, it's bottom line. If I could make about that, Tracy, number one is if from, from a lot of companies that I've talked to that are looking at ai, it's not just about lower costs.

It's about I can't hire enough people and I'm hoping AI will, will rescue me, especially in the development space, right? The second piece is, you know, specifically to the tropic CEOs saying all, all of this stuff, I won't use the term I normally use, but it's, it's garbage. Uh, we are very good.

Everyone's very good at making predictions about, uh, when things will come. They're usually off by a factor of 10. And, and how impactful it will be also off by a factor of 10.

You know, if it's, if he's saying it's in two to three years, it's probably gonna be five to 10 years. You know, a lot of the predictions we make just, just never, never come true or don't come true for a long time. So we have to take those with a, a real grain of salt.

AI may actually eliminate jobs, but we'll also create a whole bunch of new ones. And, and we gotta, and It's gonna make, and, and it's gonna improve the lives of people. I mean, DevOps engineers don't need to go have to update thousands of workflow files to put a step in it to generate a, an sbo That should be, that should be generated.

Let's, so it's gonna make our lives better. But I'm just saying that he's on CNN talking to the wider public right? About this topic.

And C and CC level execs are gonna listen to that and say, are we going to miss out on being able to cut our labor? That's just how the reality Most. All right, guys, I gotta end this.

But most of that noise to Jack's point, seems to be for the consumption of Wall Street and investors rather than actual, real, meaningful people. But I wouldn't leave you with this thought. There's an old joke that says, how do you know when an IT person disagrees with you?

They look at their shoes, how do you know when they agree with you? They look at your shoes. Hopefully that conversation's getting better.

Hey guys, thanks for spending some time with us and sharing your thoughts and insights. And I wanna thank you all for watching the latest episode of the Textron Gang. Please stay tuned for the rest of the Textron TV lineup.

There's more programming right behind it, and we'll see you guys again tomorrow. Take care. Hey everyone, welcome back here to Techstrong tv.

Our next guest is Jeremy Rossbach. Jeremy is Chief Technology technical evangelist at Broadcom. Jeremy, welcome to Textron tv.

Man, it's good to see you, Alan. Thanks for having me back. Pleasure.

So, well, Jeremy, let's start about you. Right? You know, let's be honest, technical evangelists have have had a bit of a rough ride over the last two, three years, right?

Mm-hmm. There was a time there, Jeremy, where like the pendulum swung where companies were investing more in evangelist, more in people who talk the talk walk, the walk of the developer of the ops person of the IT person than the traditional bad carrying salesman who took you out for martinis, right? That, that was out of style.

Mm-hmm. Over the last couple years, we've, I've seen more companies investing in salespeople mm-hmm. At the, at the, at the, uh, loss of the evangelist crowd.

Mm-hmm. What do you see? You know, it's just a title.

Uh, to be honest, when they, uh, wanted me to be more customer facing the face of the product, I just started Googling terms that would fit that. And to be honest, our main competitor Thousand Eyes was hiring tons of chief technical evangelists. So, uh, that's where I got it.

But to be honest, listen, in today's it age, you want somebody that you can trust and Yep. Is not just blowing fluff, fluff at you, you know? Um, I'm that person because I spent the first half of my career running data centers, being the guy that woke up at 3:00 AM because a router went down racking and stacking one new servers until my hands were bleeding, just because I had to have that cable management looking perfect.

Mm-hmm. No Spaghettis for you. Um, yeah.

So, you know, I I, I know what it's like to drop a router in an entire branch office in the middle of the night because I didn't want to go into the office and do the changes. I did it from home and screwed up. So, yep, I get it.

And, you know, and I believe I'm that, that person. 'cause I can tell those war stories. I've been in the trenches and, you know, it pays off because like if my company sends emails, say directly from Broadcom to our customers, getting them interested in something, the open rates are, are pretty normal for the industry, but they've been starting to send emails to our customers directly from me.

And then when someone sees that has heard from me before or seen me talk somewhere online or, or at a conference, we get 50 more, 50% more open rates and clicks in those emails because they trust the sender. And I just want to be that tr you know, maybe it should be advisor, trusted advisor. Yeah.

Whatever. It's, I I Get it. Yeah.

You know, look, the big thing when you talk to the PR agencies out there, and the marketing pros out there now is, you know, influencer marketing. Mm-hmm. And what you just described actually is influencer marketing, right?

Mm-hmm. Mm-hmm. People say, okay, he, I, I know Jeremy, I, he's not a sales guy.

Yeah, right? We have a real strong anti-marketing sales, of course bias in it. Mm-hmm.

And, um, and influencer marketing is, is the way to go. Yeah. Jeremy, for young people out there who say, you know, that's what I'd like to be, that's what I'd like to do.

Mm-hmm. You talked a little bit about your journey, 15 years in data centers and so forth, but what advice would you give to people out there who maybe want to kind of follow in your footsteps? You know, um, the reason why I love to do it is two reasons.

One, it's very creative and I am a very creative person. I was a music major in college. I drew, I've drawn all my life and made money at sketches and stuff.

So this gives me ability to be creative in a very lucrative field. Like it, um, where I can create videos, right. Uh, speak in front of an audience.

And then the fact that, um, I feel like a trusted advisor. I feel like people trusted me. It's, it's, it's big.

You know, I wanna do an amazing job at what I do, and I want it to have an impact. So if as an influencer, you know, um, I'm, you know, I'm not just trying to, uh, get people to click and buy something. I'm getting people to think differently about their IT strategies.

Be aware down the road what's gonna happen. And to be honest, consider the other fluff that's coming at you before you, you know, pay for something. You know, are you future proofing down the road with the solution?

Are you just putting a bandaid and duct tape on something? Now? I want to be that person that's trusted, that helps someone, uh, down the road and now with an IT issue, and then see that impact along the way and then build a relationship.

Very cool. Yeah. Thanks for that.

Yeah. Alright. Let's, yeah, we kind of get your role at Broadcom.

Mm-hmm. You know, look, today, Broadcom, you know, they still king in the data center space. Mm-hmm.

Whether it's with Broadcom, you know, labeled machine or Broadcom chips within other companies machines. They also have a huge, you know, they've invested heavily in software over the less, let's say five, seven years acquiring some of the leading names out there, including VMware. Mm-hmm.

And ca and, and others. Where do you reside within this whole Broadcom? Yeah.

We were, uh, one of the first big acquisitions ca technology. So I'm former ca Technologies computer associates. Uh, Broadcom bought us in 2018, of course, scared the hell out of all of us.

Um, and, uh, that's where I come from. So I had been pre-sales, I was running data centers for ca at the time and before we started consolidating them. And then went into pre-sales as like that technical sales person doing POCs and demos and, um, and, uh, been still there ever since I moved from server virtualization management software to process automation to now network, uh, observability.

And, uh, it's an exciting time to be in network observability because network monitoring is old and three thir 30, 40 years old and gets kind of crusty. But with the fact that networks are so complex now with cloud and AI and SD this and ESY that, and on and off-prem, it's a really fun time to be in a, in, in this area where we can do a lot of help, you know? Um, and, uh, you know, listen, real quick, I just wanna say, and I say this all the time, you know, there's always so much fun out there about acquisitions and m and a, I just want folks to remember, and it's the reason why I'm still here, is that even though they're always disruptive, um, there is decades and decades of people behind the software that are still here, that are proud of that software that work every day to make it better for our customers.

And even though there's a lot of news and a lot of complaints, I get it. Uh, but the, the, the people behind the software are still proud of what they do and what they're doing for our customers today. You know, maybe I'm jaded 'cause I've been in tech so long, but m and a is a way of life in tech.

Mm-hmm. Little companies get bought by medium-sized companies, medium-sized companies get bought by bigger companies. And, and the fact of the matter is, it, it's almost like an innovation farm system.

Mm-hmm. That's how I look at it. Mm-hmm.

A lot of companies get to a certain size. Look, they've got great pipes into customers. They, you know, they service 490 outta the Fortune 500, but their innovation engine is not as dynamic as you get at these little companies out here in garages and accelerators Yeah.

And whatnot. They move faster than the big ships like Broadcom. Yeah.

Not just Broadcom, all of 'em. Mm-hmm. And so they look for innovation.

Mm-hmm. They look for, and then it, it's not just usually, you know, the size of a company of Broadcom buying that smaller little innovation engine. Mm-hmm.

Generally there's a medium sized company in between that takes those innovations and builds them in more as a product or, or as a feature into a product. Mm-hmm. And then a Broadcom takes those products into a platform.

Mm-hmm. Right? So it's innovation that becomes a feature into a product that becomes part of a platform.

Mm-hmm. That's been my observation on this over the years. And I've, I've also been involved in a lot of startups, venture startups.

com, we actually, I helped a company, we eventually iPod, but we did 30 acquisitions in 36 months. Oh My gosh. Almost one a month.

Wow. That's Crazy. And yeah, you're right.

Sometimes you break a few eggs making omelets. Yeah. Yeah.

Yep, Yep, yep. But overall, you know, it worked. Mm-hmm.

Anyway, Jeremy, let's pivot. Yes. Let's talk about the maturity model framework for successful AI ready networks.

So, uh, thank you. Get Rid of the marketing gobbly g**k. Exactly.

And tell me what we're talking about. Yes, Alan. Thank you so much.

So listen, you know, um, a lot of the conferences I go to now, or that I speak at have an AI theme, uh, these networking conferences. Um, and it's great, you know, uh, AI is a great buzzword. Everyone's launching onto it.

But, uh, I just want people to slow down a bit, the network operations teams to slow down a bit, think about what they have to do to get ready as they're building these AI ready networks. What is an AI ready network? It's either a network that is so resilient that can handle very large AI workloads, um, maybe near perfect networks.

You know, AI workloads today, demand minimal latency, minimal packet loss, huge capacity near perfection. Um, or obviously the results of your AI engine may different than what you expect. And then there's the other ai, uh, ready networks that are using AI to help our network operations teams troubleshoot.

So a, a chat bot or a knock bot or something like that, what I'm talking about today is really getting ready. Our network's resilient enough to handle any AI initiatives. We give it, for example, meta's AI workloads.

We, we can only imagine are a size that we don't even probably have a name for yet. Terabyte or gigabyte, you know, exo byte, their workloads spend 54% of their time on their network. So if their network isn't resilient, if it's dropping packets here and there, you know, the game, they, they can't stand for that.

AI workloads won't stand for that. So what I'm talking about here today, and I know it's a short segment, is if you have a resilient network observability practice that is feeding and analyzing data that is being collected, and then feeding those answers to your AI engine, imagine how fast, how accurate those results are gonna be when you come in in the morning and say, Hey, my knock bot, where should I focus my attention today? And it knows that Seattle's having packet loss above 10%, and three tickets have come in from Seattle, and here's the IP and name of that router that's drop in packets.

I mean, none of that happens without really good data collected. So this maturity model that I'm talking about is, uh, uh, developed by us and my team and backed by industry analysts. You know what, it goes from like one to five.

So at one in our, our network observability ma mature maturity, we're all where we used to be, which is probably not where we are today. Very hyper reactive, tons and tons of little spot tools doing this part of the network and that tar part of the network. Nothing talking, only monitoring traditional S and MP and it's swivel chair monitoring.

And it's, it's where we used to be. Hopefully no one is, uh, nowadays. And then you move to from hyperreactive to more reactive, um, where we have a hundred percent land coverage.

We're looking at all the metrics that we need to look at, like alarms and faults and logs and packet loss and capacity and voice latency and jitter, and you name it, the tons of data that's on a network that's very valuable. We're collecting that now. Um, are we doing anything with it yet?

No, it's still a lot of noise in our dashboards or, or our consoles. Um, we're also starting to, uh, consolidate some tool sets as well. And then in stage three, we talk about going from manual to traditional and then now to more of a modern approach, a more proactive approach.

So all the deli data I just said that we're collecting, we're now doing something with it. We have a solution in place that has some analysis and some, uh, capabilities that can do predict predictive and thresholding and baselining. And, and you get it, you know, 30, 60, 90 days out, this is what it's gonna look like.

Um, we're also now expanding our monitoring and observability from traditional to more software defined. So we're looking inside of sd-wan, uh, or orchestrators. And, um, and other software defined platforms, SD lan, et cetera, like an a CI, uh, or a Vitello for the wan.

Um, so we can understand more about the overlay and the underlay, uh, correlation. So our virtual network running on our physical infrastructure, how does that correlate, you know, if there's a problem in that SD WAN part of the network, is it the, the physical hardware affecting it, or is the virtualized, uh, wan in on top of that? Is that the, um, affected root cause?

And then finally, now that we're, you know, we have a solution that, um, is using analytics, is using proactive predictive behavior, is taking all the data and actually normalizing it and correlating it and surfacing it as one, uh, item to look at across multi-vendor technology. Like focus here in this part of the network, not caring about if it's Cisco or if it's Juniper, et cetera. But correlating that information.

We then move for, uh, to a very predictive type of environment by now moving, um, from LAN and software defined inside the four walls to now moving to stage four, doing all that outside of the four walls of the data center. So these unmanaged networks like ISP and Cloud, because that is the backbone of most of our enterprise networks today, is public networks. And none of us are gods in a public network, but not the network admins there, unless you're for with a Verizon or a CenturyLink.

So how do you gain that visibility? How do you do everything I just said to do inside the four walls of the data center where you are a God in these public networks that are basically delivering your customer's network experiences. So being able to gain visibility into those ISP and cloud provider networks, get enough information like packet loss, latency, jitter, et cetera.

What's my voice traffic look like? What's my data traffic look like to then not fix it because I said, you're not a network God, but to then find meantime to innocence. So to be able to spend minutes understanding by looking at the entire end-to-end network path of your customers and seeing that this packet loss is occurring in a device in CenturyLink or Comcast or whatever, uh, having packet loss with this IP address, I now know this is the root cause I go to that ISPI give them the information and then magically it gets fixed.

Um, again, this is the innocence for NetOps teams is, is available today within minutes instead of hours, uh, if not longer. And then finally stage five, again, moving towards getting our network's AI workload ready is automation. So now we all know automation has hasn't caught on.

Uh, for one, I wouldn't want a piece of software bouncing one of my routers, bounce in one of my switches. 'cause it thinks it should in the middle of the day. But, you know, can we do those mundane lowline tasks of escalating a help desk ticket, closing a ticket, enrichens enriching it with more data that has been collected from your network observability solution.

Uh, those type of roll back a configuration. You know, I've, I have many times fat fingered a config and drop the site. Um, to be able to roll that back and get it up right away is automation.

Um, these are the things that we can do, uh, to take care of those low hanging fruit use cases until we're all ready to really fully implement automation. And I know we'll get there. I mean, we're already talking about ai.

We still haven't even, uh, fully implemented automation. But, um, yeah. So these stages of, you know, moving from hyperreactive all the way to automated network operations and network observability is what is the foundation that's needed for these AI complimentary solutions that are coming out today that are gonna do amazing things.

I've seen, uh, at the last network automation summit I went to in, in November in, in Colorado, watching someone use their own chat bot to, to do exact, you know, a as as we're asking it questions and, and it is telling us about the topology or, or an effective root cause area of the network that is not done with that AI complimentary solution that is based on the data you are collecting, you're feeding that data from your network observability solution. You're the one making it smart. You know, garbage in, garbage out.

Like I always say, if I only have eight out of the 10 ingredients for my kids buffalo chicken dip that she loves every time she comes from from college, it's not gonna taste the same if I had 10 outta the 10 ingredients. Right? So if we're feeding all the ingredients from a very robust and mature network observability solution, you can expect good AI outcomes.

Absolutely. Jeremy, I didn't wanna stop you when you were on a roll there, but you know, so much of it kind of hit home with me. Um, you know, the whole automation thing, I, back in 2001 I had found, co-founded a company.

We were trying to go from IDS to IPS. Simple, right? Mm-hmm.

You see an obvious code red word. Mm-hmm. Or, or, you know, whatever was hot that day, block it.

Yeah. No, no, no. People were real hesitant.

Then we rolled out a vulnerability management tool that had the ability not only to find vulnerabilities, but to put patches on. Mm-hmm. No, not, not on my watch buddy.

Mm-hmm. Mm-hmm. You Know, because if it does something wrong, you're the one to blame.

Exactly. Yep. Yep.

But to me, the lesson I've learned through all of this, and I think it's it's still true today, and it's gonna be true, especially with ai, is it's a, it's a question of trust. I, I need to do it, do some of these low level obvious things and let me build my trust. Mm-hmm.

Yep. Let me, let me get a good feeling about it. I'm confident, and then I'll let it do more.

So it, that, that's just, I think that's just the way it is. I agree. I agree.

Because one router change can bring down the entire east coast of a network. I mean, It doesn't have to bring down the entire East coast if it brings down my CEO's laptop. True.

Very true. I'm in trouble. Very true.

That's what I used to hear. You're right. What I used to hear Uhhuh back in the day.

But Jeremy, let me ask you, is there a place people can go look at and see maybe a diagram or maybe some, uh, you know, information on this maturity model framework? Yes. Um, so, uh, we'll put that link in the show notes, I'm sure.

But yeah, we have a, uh, great landing page about optimizing network operations, uh, with the help of AI has a lot of great white papers, including the white paper. I'm referencing that myself. Uh, and my team wrote, it's also backed by Seamus McGillicutty at EMA, uh, where he says that, uh, many customers should be measuring themselves against our maturity model to find success.

So very happy for that little, um, talk track there. But I wanna let a, a couple more plugs. I'm gonna be in Dallas next week, uh, for o the AI Networking Summit, sponsored by og I'm speaking about this very same topic.

I'm also MCing the event. So, uh, I'm very cool. Yeah.

Sitting there. I believe my friend Charlene O'Hanlon, who used to work here with us is, is, is, uh, is moderating a panel there as well. Wonderful.

Wonderful. Yeah. Um, well, I'm sure I'll be, uh, watching it 'cause I have a lot of free time.

Not only am I MCing, but I'm speaking, but I'm gonna be talking to a lot of folks there as well. So if you're at the event, please look me up. Also, uh, in two weeks, uh, Broadcom is hosting a, uh, network Observability Virtual Summit.

Um, and I am interviewing, uh, a bunch of our customers, one from BT Ireland, an ISP atos is a, uh, managed service provider. Um, and then Deutsche Bond is a German railroad company and we talked to them about this very same topic. What does their maturity look like and how do they expect to get to AI ready networks?

I'm gonna put that link for the registration page in this, uh, show notes as well for you. Alan. Appreciate it.

Jeremy. Hey man, it's been a pleasure having you, ya on come back and visit us again. Will do.

Keep us posted and it's always great to catch up. Great. Thank you Alan.

Jeremy Ross, back Chief technical evangelist, Broadcom here on Textron tv. We're gonna take a break. We'll be back in a bit.

Hey guys, thanks for the throwaway here with Jim Cassens, the CEO for Perforce software. And we're talking about how this company is evolving after multiple acquisitions, including some of the better known companies in the land of DevOps. But, um, there's a strategy and a method behind the madness, and I think Jim knows the answers to all these questions.

So, Jim, welcome to the show. How you doing? Good, Mike, thanks for having me.

Really do appreciate it. And what a great topic. There you go.

So you guys have Puppet and I think BlazeMeter and a couple other folks and other companies that maybe are startups, but, um, how, in your mind, are all these things gonna come together and, and what might the future of DevOps look like in that context? Yeah, it's a great question. Um, I mean, when we look at strategy around our portfolio and the companies that we've acquired, so we've done 13 acquisitions as an organization, we really look at smart integrations between products and how we can drive value within our customers, right?

So you think about Delphix, which is, you know, virtualized data and being able to get data to customers that are testing, uh, efforts much earlier in the development cycle. And then combine it with Blaze meter where Blaze Meter can generate synthetic data that can go with that production data that's masked to be able to fill in the holes during, uh, new application development, right? Those smart acquisition drive value for the organization.

So that's what we're really look at when we look at the portfolio and where we wanna put effort into, um, combining the different solutions that we acquire along the way. And then of course, we're just looking at AI and how can AI really transition, um, disciplines. So when you take a look at our testing applications with, uh, perfecto and what we're doing there, the use of AI is really gonna change the way people look and how they perceive testing and what they can do earlier on that in the development cycle around testing, as we now start to automate the development of scripts around testing as well as maintaining those scripts and the ability that now to be able to really understand what's happening across a larger landscape in testing those applications than ever before.

So we're really excited about being able to really, again, drive value within those organizations, allowing them at speed and scale to operate much quicker in the development cycle than they've ever done before. You mentioned ai and I wonder in the age of AI and especially Gentech ai, are the silos between the different platforms gonna become much, uh, more porous, shall we say, if not just completely disappear? 'cause I I feel as I kinda watch this evolve that, um, I'm gonna have a task to do and I'm not really gonna care exactly what piece of software did it.

Yeah, there is that you, you know, people are worried on, on one hand saying, are we going to lose application developers, right? As, as a agentic AI is now creating code versus having a human create the code. And I look at it slightly differently than that.

It's not that we're going to eliminate a developer position. What we're we're going to do is enhance that position, giving them the ability to work on some of the greater needs. And really true fo truly focus on application development beyond just, Hey, I'm gonna create shells of programs, or I'm gonna write scripts that are gonna help me do certain things that can be done by the AgTech ai for us as a provider of solutions, um, to developers.

We're in a nice position, we're gonna help them be able to manage the code that's being created by Ag Agent AI potentially, right? Because this is gonna be volumes of newly created applications or newly created code that's gonna need to be managed. It's gonna have to be validated, it's gonna have to be tested.

We have the tools to kind of help with that, with that, um, speed that genic AI is gonna add to the velocity in, in your, in your pipelines. The thing that worries me as an employer and, and as we move forward with AI in general, when talent comes out of a university, generally speaking, they're doing the low level work, right? They're creating some of those shell codes, they're checking those shell codes.

If we take that over with Agen ai, how are those individuals that are just getting into the industry going to be able to learn at a higher level, learn how to be potentially an architect, right? How, how are they gonna learn where all of the different pieces of an application reside? 'cause as you and I know, it's not as simple as saying, Hey, a database is just one database.

They might be touching hundreds of databases to pull in the information they need. So I think as an employer, one of the things we're looking at is how are we going to train and develop this talent, the newly acquired talent, and making sure that they're prepared to be successful in an AI world, right? 'cause they're gonna need more advanced skills than they would normally get right out of the university.

And to your point though, how much responsibility should the university assume to make sure that they're turning out people who can operate at a higher level? I think it doesn't come down necessarily to book learning. It comes down to experience, right?

So there's only so much a university can prepare someone for. That's generic across all industries, all developments, you know, look at just the different ides and, and the different programming languages that you can use, right? There's, there's thousands of those.

So there's only so much a university can do. I think it's our, it's our responsibilities as an employer. Once, once someone gets into the organization is to really upskill them and uplevel them to understand what are you gonna be doing here?

Right? How is our environment maybe different than what you, what you you learned at the university? I think the university's gonna help them from, Hey, here's the new technology with ag agentic ai and other things that you can do out there.

And preparing them to be able to hit the ground running. Like right now we're training our staffs in ai, right? Um, so it's just gonna, it'll turn the focus from, I don't have to now train the, the newly acquired employee just outta university and ai, but I'm gonna have to train them across, Hey, how do you become an architect?

How, how are things put together? Those will become meaningful and important moving forward. Are there other areas that you're looking at that you think may make for a natural extension of your portfolio, either inorganic or organic wise, but, um, are there things like maybe, I don't know, more CICD stuff, or that are part of the DevOps platform workflow that you feel you need to maybe own?

Yes. So CICD is absolutely one of the areas that we're looking at. Another one is anything around data.

You know, when you start taking a look at data and how can we help people? Data is, it's the volume of data that's being created and trying people trying to manage this becomes harder and harder, especially as you're trying to deliver this earlier on in the, in the pipeline. We've run into some regulation issues.

You know, Delphix will, will take production data, it'll size it correctly, so it'll reduce the size of what you need for your testing, and then mass that private data so that people can't see what's behind it. And then keep the referential integrity of that data. Well, for some organizations that are in highly regulated areas, they can't use production data.

They've been told and mandated by their organizations that we're not going to use any of our, our, uh, production data in any form from a testing perspective, which really creates the need for synthetic data. And that's what I talked about earlier with BlazeMeter being able to fill some of the gaps from a synthetic perspective. But we don't have an application today that generates volumes of synthetic data.

And I could see us either, either organically or inorganically acquiring the skills around generation for synthetic data, but then put it into the Delphix engine. So you can still mask it if you wanna mask it, you can still get that referential integrity if you're doing that testing, or you can subset the data into a smaller need for a specific use out of the Delphix application. So we look at that as a potential opportunity for us in the future.

I also look across, and everybody's talking about building AI apps, and when I look at those teams, they're data scientists and they're data engineers and they're all engaged in something loosely called machine learning ops, ML ops. And then I look at DevOps and I go, aren't these two things ultimately gonna converge? And is that part of something you're thinking about?

We definitely are keeping an eye on it and, and we're inquisitive in terms of how would this fit into our portfolio? And more importantly, how is it play into the world of application development in the future? And is that what customers are looking for?

They're looking for a single vendor that can provide an integrated solution. Um, so where might it plug in nicely into our applications to provide that true benefit to, to the customers? Um, it's, it's an interesting play and there's a lot of different areas that we could go into.

Um, you know, one of the things you'll never see us do is kind of over overextend our bonds. We'll look at those things that are, that are nice add-ins, as long as that's what the customers are demanding, that's what they're looking for. Um, but you won't see us jump too far afield from the DevOps, uh, solution base.

That's, that's really where we have our expertise. We wanna make sure we, we can maintain that defensible area of the room in, in our applications. You touched on this earlier, but I want to dive a little deeper on this particular point.

Um, we're gonna see a lot more code coming through those DevOps pipelines when I look at them. And I think one of the dirty little secrets of DevOps is the scripts and everything we use to create those pipelines are fairly brittle. And I wonder if, uh, we're gonna be looking at a point soon where they're just, the existing pipelines are just overwhelmed, and we're gonna need to think about that building DevOps pipelines differently.

Yeah, it's a good point. And and I think that's an evolution we're gonna see over the next few years. And, and I think, you know, there's only so much you can absorb as a, as a human in terms of the volumes of data that are being created.

And, you know, as well as I do, it's gonna evolve over time, right? The learning engines of an AI are gonna get better at coding down the road. They might not be there today, although I do hear that they're generating some really high quality code.

But when you take a look at, you know, performance and you take a look at, you know, making sure that it's the code you're creating is optimal, that's gonna develop over time. It's not something that's gonna be there today. And that's what I mentioned earlier, that it's gonna have to be reviewed, it's gonna have to be, you know, how does this fit in?

How does the design look? How does it fit in from, from a more architectural basis across the entire application? Um, certainly it's just like anything else.

There's enough brittle code out there today, uh, in the world. It'll just be pushed harder and harder when it comes to, um, AI generating these things. And there's a lot of great fit.

I was talking to a customer the other day where they no longer felt like they had to write APIs. They could create the specs and give it to an AI agent and boom, they would get the API that they were looking for. Right?

Great use of ai. Let's, let's get the, the engineer really focused on the application at hand, um, versus maybe doing some of the, the, the remedial work that's needed for organizations and applications. What do your sense of what is it gonna be like to be a DevOps engineer in a few years?

Because there are, of course, everybody's kind of looking over their shoulder a little bit and going, well, who's moving my cheese? But there's another aspect to this thing. I think one of the other dirty secrets of software engineering is there's a lot of toil and a lot of stuff that we do over and over and over again that just, you know, it's, it's soul crushing.

So will we get to a point maybe where there's just more joy in software development because we're not gonna spend as much time and all that's got work? I think that's true, not just of, of software development. I think that's true of a lot of positions within the organization.

The person who's producing invoices over and over again, if, if they can just hand that work off to an agent and then focus their time and efforts on things that will change and drive the organization forward. I you're gonna see the same thing in application development. Some of those low level tasks go away.

To your point, the mundane, repetitive kind of grinded out aspects of application development are no longer needed within the organization and those individuals, 'cause now focus on the more meaningful work, the stuff like, you know, really being innovative in what they do and what they're delivering for their application. I also look at it from, you know, from a security perspective, vulnerabilities, being able to have AI do some things from a vulnerability perspective to make sure you're clean before you deliver an application to you and I through an, through an app on your phone. It just means that that app is gonna be a lot more hardened when it comes to the public than what it is today.

And I think that's a good thing. That's a good thing for, for us as consumers as well. Um, even around private data, you know, making sure that all of that stays secure and is, is neatly buttoned up in an application before it's delivered to the end user.

So I think, I think part of, you know, the world world I lived in originally where, you know, you had more bugs than you need, you had to deal with. Maybe a lot of that can be taken away with, with Agen AI and some of the AI modules that are being built. So what is your best advice to the DevOps leaders out there today that are trying to navigate all this?

Um, you know, on the one hand I'll hear people talking about, you know, we're gonna build the next big software factory, but last time I checked, there's not many humans that are anxious to go to work in the software factory. So what, what's the right approach? Yeah, again, I think, uh, um, part of it is change is change, right?

And, and we're humans and we're people. And so there's a change curve we all go through as we are really redefining a role. And that's what we're going through right now is a redefinition of what application development really means, what these engineers are going through.

So for the leaders of those organizations, they're dealing with a lot of fud, fear, uncertainty, and doubt within their organization. Why? Because the developers are worried.

They're worried. Is a agent AI gonna take away my job? And how do I provide my f for my families?

How do I enjoy the life that I've enjoyed? What does it mean for me? How, how am I gonna navigate this change in the entire industry that's going on right now?

Um, so I think the, my advice to leaders is remember your people, right? Remember their humans. Remember, take a look at the change curve, understand where your individuals are in that change cycle and help them get to the other side.

And some of that can be through education. Some of that is making sure you're re reassuring them. Look, this doesn't mean you're out of a job.

It means your job changes and you're gonna need to adapt to the changes that are coming. And then of course, I would encourage them to learn as much about AI as they possibly can. Right?

All of those aspects around ai, from security to what it can generate to the different models that are out there. Learn as much as you possibly can and that will drive security in what you're doing moving forward. Right?

Folks, you heard in here. Hey, despite all the talk about AI and machines, it's still about the people. Hey Jim, thanks for being on the show.

Appreciate it, Mike. Thank you. All right, I'm back to you guys in the studio.

Immersion Cooling requires specialized servers designed to operate submerged in a tank of coolant. But there are many benefits to this approach. In this episode of utilizing Tech sponsored by Soy, we continue our conversation from last week on Immersion Cooling.

This time with, uh, Patrick Sini of hypertech. Welcome to Utilizing Tech, the podcast about emerging technology from Tech Field Day, part of the Futurum Group. This season is presented by soy and focuses on AI at the edge and related technologies.

Technologies. I'm your host, Steven Foskett, organizer of the Tech Field Day events series. And joining me from SOY is my co-host, Janice Roski.

Welcome to the show, Janice. Thank you, Steven. It's always a pleasure to be back.

Well, Janice, uh, in the last episode, you and I spoke to Doug, a company that is company, uh, really pushing forward with immersion cooling. And we talked about many of the benefits of immersion cooling. Uh, I guess catch us up on, on those benefits and then we'll introduce our guest.

Uh, that would be great. Um, you're right there, there, there's a lot about immersion cooling nowadays, right? I think 2025 is, it's a toggle between AI and just what can I liquid?

Cool. How can I do it? Um, and I'm excited to have Hypertech on the show today because we will take a deeper dive into the, the, the value pillars of liquid cool cooling overall, and how does it, how do all the parts and pieces come together to make it a reality and, and, you know, make some real innovation happen all the way from the core to the edge.

So with that, I wanna turn it over to Patrick to introduce himself and, and give us a little more detail on your background. First, uh, guys, thank you so much for having me with you today. Uh, for the folks on the other side of the screen, my name is Patrick Cini.

I'm the global VP of sales at, uh, Hypertech, who is the company for, uh, 20 plus years. So, and, uh, unfortunately or fortunately, veteran of the IT industry. Big background, uh, as you can hear, uh, I'm not from uh, uh, I'm not from Texas.

Uh, I'm a French, uh, guy from the Southa, France, this. And, uh, I've been, uh, in the IT industry for three plus decade, uh, working for GIBM, uh, and, uh, hypertech, like I said for 20 years. And, uh, for the one who, dunno, Hypertech Hypertech is a company who just celebrate his four years.

We have multiple segment in the four years of the company. But if we focus on, the last one said the past 10 years was to grow our business. We are a IT manufacturer, a global, global it, uh, provider.

And, uh, on the manufacturing side, we do our own server storage, PC, laptop, and so on and so forth. Before we go in Immer, I, I think that that's the, the reason that it's interesting to speak with you after speaking with Doug, because where we talked there about a lot of the benefits. I guess many people are unfamiliar with immersion coaling.

I think that's part of what we talked about, um, as the manufacturer of, of, of the servers. I guess maybe first, uh, give us sort of a lay of the land. 'cause I know that you work closely with them and you also work closely with the companies that actually make, uh, like the tanks that things are.

And, and so, so when a customer is approaching you about, uh, implementing advanced AI or some other kind of power and heat hungry, uh, demanding workload that needs immersion cooling, who were the players at that table? So the players are multi-fold because you need to look that as a, as an infrastructure, as a, uh, as a manu, right? You go from the appetizer to, uh, the, uh, the main course to the cheese and, uh, and French, right?

I need to bring cheese into it, to the cheese plate to, uh, the dessert. And uh, usually you have the people who know about immersion cooling and the people who don't, right? Who are interested for what they heard about the benefit to the less space, uh, 50% less power up to 50% or more of cost reduction on building a data center and uh, so on and so forth.

So the 10 vendor are usually the one who promote emerge, right? They are the, the base of the infrastructure of NT cooling and, uh, is usually a tank or, and, uh, uh, what you, what they call a CDU, uh, to cool the tank. And, uh, you don't need much more.

There is no chiller except in a very, uh, specific case. And that's the step one. Step two is imagine the tank as a pot in a kitchen, right?

To make a plate of pasta, you need to fill that up with water. That's the liquid vendor, right? Uh, exon shell, uh, uh, BP Castrol and so on.

And after that you need to put equipment, right? And today, as you just mentioned, Stephen, I don't need to say that as a surprise. AI is taking over the world, right?

Uh, I think you need to live the work to don't know that. And, uh, AI on the training side, on the inference side, uh, on, uh, maybe a, a generative ai, you are to do some other workload, the power consumption of the asic, what we call A-C-P-U-G-P-U, the memory, and now the drive. So we are gonna come as quickly to Janis after that, right?

Uh, have achieved level of temperature that air cool cannot do, right? So now you are left with liquid cooling. And liquid cooling usually is differentiated in two is the direct that is fairly now the immersion cooling.

And there you have the single phase and the dual phase. But we focus today solely on single phase for all the benefit without the problem. And, uh, that's help us cool the system.

So whatever amount, whatever amount of machine you want to, cool, right? And I will give you some example later in a efficient, sustainable way, very important is sustainability aspect. We can maybe dive a bit more after that, uh, and, uh, help the customer realize of the benefit.

I can put more machine in a particular, uh, uh, amount of square foot. I can reduce my power consumption like I just mentioned so and so forth. And, uh, you add some switches the cable and suddenly you have a complete infrastructure.

Theistic I didn't mention, you still need to have a few dry cooler on the top of the roof to evacuate the, some of the, not the heat, but uh, the cooling of the cooling system. You cool the cooling system. And, uh, you have the, in my opinion, the best solution the planet could sustain is not just at the equipment level, is the best system for the planet, for the customer, for on CapEx.

Opex model is more whistle be and whistle. Yeah. No, Patrick, I think you did, um, just curious.

I mean, I agree with you. I think it is one of the most effective ways to cool technology, you know, for the planet. Um, tell us a little bit about where you see your solutions going, right?

Is it, is it just at the edge in, in some of these, you know, smaller containers, um, like the last episode we did with down under geo computing. Um, tell us a little bit more about where you're really deploying this, this hardware for liquid cooling. So I think today the most of the market for immersion cooling is at data center level, right?

Because, uh, most of the customer demand we have require high density, uh, large quantity. So is a pure data center play. But since day one, I believe that edge is the future.

On top of that, there is multiple reports from, uh, the Garner or, or forester or whatever who show ai, the AI usage at the edge will be bigger than the AI usage in the data center by 2030, right? So always keep that in your mind. And the dug, uh, uh, I think interview you did that, uh, I was part of it, but not for this one in particular, but for the system as a whole is a very good example of what a edge system could look like, right?

Is rugged. You can drop that from an helicopter if you want. You bring that from a truck, uh, is very simple to put in place is all in one.

You have the cooling at the back, you have the server at the front, and that's a pure edge play, right? And really in edge play, you have compute, you have storage, and you have ai, right? Because the AI going to pump some data until, uh, we can't take it anymore and we need to store that somewhere.

And, uh, we were the early, the only, today we are the only immersion board that mean the product we do are being designed solely for immersion. That mean you can't take the server and put it in a, in a Iraq, it doesn't work. It works only in a tank with liquid.

And we are the only one with a server, a storage server for immersion cooling. And we choose solid as our partner for the greatness of their product, the capacity of their E one C one L or uh, U three, for example, uh, in some of our product. And we make a great combo to be able to cater to all the needs of a customer.

You, you, you talked there about, uh, a key phrase and that is immersion born. And I think this came out as well in our previous conversation. You can't just take a server and stick it in a, in a coolant and expect it to work.

It you, there's a lot of engineering that has to be done now. It's not impossible engineering, it's something that we've been doing as an industry and that you've been approaching for, for a long time. But it is not the same as just dunking a a, a server in, in coolant.

Uh, talk to us a little bit about what makes a design immersion born. Oh, it says one of my favorite subjects, right? So we started immersion, uh, five years ago already.

So we are not, and we do direct to chip since 2010. So we are, uh, uh, liquid, uh, co ion. The reason we went to, uh, uh, immersion, if for the reason I gave you, and, uh, when you look at the server, our first implementation was with what people call today immersion ready.

That mean, uh, you take a air pool server and there is a bunch of step to do, uh, remove the heat sink, uh, change the thermal Indian fold, take off the fan, modify the bio, make sure that all the label are covered with an AC vehicle. They will fall at the bottom of the tank, use some cable, all the cable from power cable to network cable, uh, need to be emerge, uh, emergent certified, or they gonna call brittle. And, uh, you, you push on them after three months, they break is something crazy.

Uh, and it is, is feasible, right? We have people today, uh, who do that but is not optimal. I, I'm gonna give you another analogy.

I want to race right on, uh, on a Sunday race, Sunday club. And uh, I take my car, right? Let's say I have a Chevy, whatever, and I put bigger, bigger tire and uh, uh, try to crank up a bit the horsepower and uh, make sure the car is cooling properly.

And I go race, I guess what I never win for one good reason. There is a bunch of people on the track who have a poor ER for example, who's been design to do one thing is to race and the forget the price side because it's not the place, right? But the analogy is our system, when they come to the customer and when we manufacture them, they're ready to be in emergent.

That mean the, the label are the, they are labeled who are made to be there from 3M by the way, uh, we are made to be in I stain immersion. All the cable we use are certified for immersion. The power supply we use are immersion, cool certified power supply with all the change we applied to it, the chassis, who as you can imagine, you see a server is like pizza box, right?

You have model board, the CPU, everything. The chassis have been designed thermal term thermally designed to be in immersion, to conduct the flow of liquid in immersion is always from a cold at the bottom and hot at the top. And you recycle right in the perpetual movement.

The chais have been designed to conduct the liquid, the in the most and faster efficient way and so on and so forth. We have oversized people say, oh, immersion cooling is messy. To avoid that we built over size on the server.

When you grab the server, you don't put your own in the oil you start with, right? Or you can still have a crane, you bring everything. But we thought about ease of use, ease of installation, ease of serviceability as a product to one reason to be is being coding.

And that's what make the big difference between the two. I can tell you that, uh, one of the main three vendor, you know, I call them DHL, Dell, HP Lenovo, I like the DHL uh, acronym, one of the three, maybe the first one. Uh, do some, uh, AI server for emergent cooling.

They need to take via one another company who prepared that for them more than 150 step to prepare the system. Think about that for a sec, right? To convert it, right?

150 step. And uh, in our order, there is no step. The productive was designed to be like that, right?

So that's what make it, I hope I answer your question, Stefan. Uh, that's why we make, uh, an immersion what we call immersion born product. And, and that makes sense because I think that you, most it people are familiar with the airflow of a conventional server.

Yes. And the fact that there are baffles and there's front to back or back to front, there's, you know, there you, you need the, everything needs to work right? In order to get that airflow working in order to cool that server.

And I think if you, if you think about it and wait, this server's gonna be in, in a, in a coolant, in a liquid, then all of those things are gonna be different. And you need to be engineered to be able to be effective in a, in a material that behaves differently than air. So for health and safety, the people, I don't know the, the, the regular folks, right?

And my wife, my kids, whatever, I don't think they ever been in a data center. When you go in a data center and you go between the hot hotel in the back, you can't survive because it's too hot, right? The noise, it is at the level that everybody wear the noise canceling, uh, apparatus on their head when I cooling the not a lot of people talk about that's silent.

Think about that for a sec. There is no heat coming out of a tank. Nine zero.

Even if you open the tank, there is still no heat because the power of heat absorption from a liquid right, is not water, right? Uh, it's a liquid is a hundred of times higher, I think is even thousands of times higher than, so you don't have all the, the, the problem that people in the data center had 20 years ago, data center, you have a couple of machine in Iraq, right? It was not like today where you have football field of, uh, rocks and equipment will become unsustainable in term of, uh, uh, environment.

I appreciate the background. Um, I, I always often forget about the sound myself after being in so many data centers. And it is true.

Um, I don't think I've ever been in a data center with all things liquid colts. I probably would've, uh, would've come to mind. But you talk about sustainability, right Patrick?

So, you know, that's obviously top of mind for everyone in the industry is trying to figure out how do we save on energy and power. Um, can you comment on just, just how much, uh, a particular customer maybe use an example of, I don't know, can probably talk about down under geo or even like someone like Suber. Um, how much more efficient are these solutions for those, you know, deploying these systems?

Like how much energy are they saving? Well, it's very simple. Uh, for the folks on the other side of the screen, like I said, we don't know.

The efficiency of a data center is ma by what they call the P oe. Is power usage efficiency that mean for a kilowatt for example of it load that mean I have a server in consumer a thousand watt, right? 6.

That mean if I put a thousand thousand watt server, I need 500 additional wat to prove the server. Is that the infrastructure and everything that mean the power consumption? I'm gonna pay, you know, my electricity bill is on 1500 watts right now.

I multiply that by thousands and you can have the good idea in. And what we did with Doug, right? Uh, the Doug Nomad where you did the previous episode, Doug have also a data center.

Uh, and we made an announcement, we did, uh, an installation, uh, last year for one of the largest, uh, HPC deployment in immersion cooling ever done, right? Thousand and of nodes. 04.

That's mean my electricity bill would be on 1,003 watts or 1,005 watts. So I just saved 500 watts, right? That's why when we talk about sustainability, the first one is I'm gonna reduce my electricity bill.

Electricity bill tremendously. I think sustainability doesn't stop right there. Sustainability is well above and beyond, well, above and beyond the power consumption is what some people don't know that, uh, emergent cooling, despite his name, doesn't use any water, right?

So now there is another lingo, the WE the water efficiency where emergent cooling is king with no one in sight to be able to compete with that, right? Because it's zero water, that's it. There's nothing else.

And how much water does air cooled servers use and where do they use this water? That's a, that's a very interesting, they take the water out of the water supply, right? And the people in California, they are, sorry, desperate with no, but, uh, uh, uh, a mid-sized data center will use in water that completely evaporate of, uh, oil peak swimming pool size every two days.

To give you an idea is thousands of home water usage. You know, and so that's what you think sustainability come to play, right? And the server, when you go a bit further is all about style reduction, right?

I told you is, uh, you can reduce the size we have and the average of data center, not the most advanced whatever is a ratio of 10 x, we, we get 10 x less space. So space today, God doesn't give more land, right? He create all the land he could create, right?

And, uh, you don't use the space, you don't use water, you consume less power. The server we manufacture are what we call vanity free. There is the least amount of component as possible.

We, uh, even if when you do a, a retrofit to be emerge ready, you remove the fan and everything earth, it was designed, there is no fan. The chassis is a barely a skeleton of a chassis. So that's means less metal.

Less metal, meaning less stuff we bring out overseas that means less traffic on the boat or by airplane and you can peel the sunan to the end, right? And that's why is the most sustainable it possible. I just am fascinated by it.

I think, you know, in my head, I can't stop thinking about the 150 steps and you guys have just built like just a seamless, beautiful server. Um, I can't believe I actually look at your servers and think, wow, they're genuinely beautiful, but they work, uh, amazing, right? And, um, you really have put so much thought into all the parts and the components to make it that Ferrari that's gonna win the race, right?

Um, And by the way, is a ferri at the price of a Chevy. I just want to say that because people have a misconception. The cost of a liquid cool server is a few percent less than a cool server.

So on top of that, you even save on, uh, on the, on the, on the pudding, right? On the, on the price, sorry, on the CapEx. Mm-hmm.

Well, I would assume too, um, we didn't talk about this, but because you are dropping everything in the, in the cooling solution, do the parts and things run a lot longer? Do they, do they stay, you know, is it easier to, um, you know, I guess fix parts, if you will, things don't break down as easily. Maybe if they're in the, So that's a, a very interesting point, right?

Uh, first when we develop the server, we start to choose vendor also. And the same way we try to choose vendor who are sustainable at the core, right? So I'm one of them, right?

So, but uh, uh, talking about the lifespan of the system, because there is no moving part because there is no dust and so on and so forth, the system can run much longer than regular system. Liquid cool immersion cooling. There is one customer in particular in the oil and gas.

So that, for example, is 10 years. They do immersion cooling. And if you go to their main data center in Texas in new, they have system who are, uh, 10 years old.

Uh, you know, and they are running fine, except is old generation, right? Is old technology. They still have a few of that.

There is the other company in the gas run in I cooling for 10 plus years. And I was just talking to them, uh, months-ish ago, and we were talking about, uh, failure rate. And they say their failure rate is close to zero.

Think about that for a sec. Close to zero, right? Because the component are not stressed out.

One of the main reason of component dying is the heat. People probably don't mention that as much, but is always a heat component in, in, except the manufacturing defect or whatever, but is the heat. And, uh, in, uh, pooling, they are secure.

They're shielded for that, right? This means they're only in a very stable environment. The temperature is always the same.

And, uh, that's why we have this, uh, long life, uh, long lifespan on the hardware. Don't think that, uh, everybody's gonna run 10 years. Everybody want to change, right?

Every three to five years because technology advance and, uh, is more efficient and everything. But the figure also because it's very, very, very low is interesting for the customer is, uh, less work at the data center. Uh, so when people say, oh, yeah, is a pain, uh, I emergent cooling, I'm gonna put liquid everywhere is never the case, and you're not gonna grow in a tank as much as you grow in your regular rack to start with.

Right? Thank you. I think, as Janice said, I think that's something that people don't think about, but heat is the enemy of reliability.

And, um, you know, systems, the thing that kills them is, you know, when they, when they experience thermal shocks or when the, um, you know, the, the dust gets in there and uh, and coat coats on, on, on the components and the components get too hot. It just doesn't happen. And, and I think that that is really an overlooked aspect of this.

So, you know, in summary, I guess if we want to kind of wrap it up, um, people should be looking at this. It's not more expensive, it's not more exotic, it's, it's, it's just more less familiar. And you said that, uh, you know, it takes up less space, it makes the servers last longer.

Um, why, why is it that more people aren't using immersion cooling? Or is it that it is getting used and we just aren't aware of it? Why people didn't buy the first Tesla when, uh, they came out with the lotus, uh, retrofitted, right?

Because he was new, because he was disruptive. Because people say, I don't know about that, right? I'm used to have my regular car and previous to that is, uh, dude, I'm riding my horse extremely well.

I'm not afraid I'm gonna go in this crazy, uh, machine here, right? And is, is, is an evolution and immersion cooling is extremely disruptive for that because this, uh, an entire parameter change. And I think that's one of the reason the people are do, are not doing it.

One of the other reason I will say is to be successful, you need to have a very strong ecosystem, right? Of people who do I told you right? The tank, the, the server, the liquid, the switches and everything.

And, uh, there is a, you know, you never get to buy a DE and hp, right? And, uh, they are not the most, uh, even if they do so right, as a retrofit on per project basis, need to sign a waiver that they don't give you a warranty or so on and so forth are say, Hey, okay, I need to buy from, uh oh, who do I buy from? Right?

And, uh, we are not, uh, maybe with, with this podcast, we will, right? We're not as well known, uh, as, uh, our famous three colleague. Yeah.

But we, we start, right? I think we are in a very good place when I see all the tech manor, right? Uh, summer, uh, GRC, uh, uh, there is a, a bunch and there is more and more coming, I think IV is, is coming with one.

We want to solve this portion of the equation. You have plenty of 10 vendor or you have plenty of old vendor to choose from. And now is to have is not to our advantage, but it will be to have more and more people as a server vendor doing emergence, right?

Because the people will have the choice, the large choice to choose from. I can tell you that in boff, pure, pure boff, uh, supplier X versus us, we always win when people take the time to test, right? To see others work and, uh, what we do for them.

If you think about, uh, uh, people like Doug who deploy at large and plenty of other, we have larger project that cannot name them. We develop, you know, we talk about sustainability. We develop packaging to ship in bulk.

What's have to do first when we ship on one single ski, we have, uh, a large amount, much larger amount of machine than any other one. The packaging we use is, uh, made out of a hundred percent recycled cardboard. But more importantly, we designed the packaging to be reusable.

Think about that, right? And the way we designed the packaging, because it's identity, we made it where you go phase when you install the system, if it's compute, you install what we call the, the tray after the node, you open separately. And, uh, we cut the amount of time it takes to install thousands of node by days because of that.

And it's all sustainable, right? Because it's more density. You don't need to have, uh, we just, uh, for a large installation, we went from uh, six truck to three.

So now 10 calculated the of 18 wheelers, 53 feet younger truck who don't need to be on the road. That's sustainability. Well, excellent.

And, and I really appreciate you, uh, sharing your passion for, uh, for this technology and, um, and, and helping, uh, you know, I do hope that our listeners will consider immersion cooling, uh, if only for the environmental, uh, sustainability benefits, the power utilization benefits, uh, even setting aside some of the other aspects and, and of course the long longevity as well. I mean, you know, every server every year that you can use a server a little bit longer is a, is another one you don't have to buy. And I think that that's another, a huge benefit here.

So sustainable. It's sustainable. You don't need to take the server and god know what happened to it.

Well, thank you so much for this conversation. Before we go, um, where can people continue the conversation and where will they be able to see Hypertech servers themselves? So, uh, the next few event, we will be, uh, the defined one.

We will be at Supercomputing, uh, 2025, I think is in, uh, St. Luis will Have Love Las Vegas, but now we get St. Louis, I'm joking.

com. No, uh, no h and you will see all the information and, uh, you will see, uh, so d as a vendor or partner vendor for example, uh, because, uh, there is a reason we choose them for our emergent product storage and for other regular product. And, uh, I think the best way to contact us or you just gimme a phone call and Well, thank you so much.

And, and yes, we did speak on the last episode a little bit about the, the ways that soy is, is making their storage, uh, compatible with immersion cooling. Uh, Janice, I know that, uh, sustainability, longevity, these are, uh, hallmarks of, of soy as well. Um, and I really appreciate having you on the podcast this season.

Oh, it's been a pleasure. Yep. Alright.

Um, so thank you all for listening. Um, if you enjoyed this conversation, uh, please do subscribe. You'll find this podcast in your favorite podcast application.

You'll also find us on YouTube and on the new, uh, text Strong TV app on your TV or in your portable device. If you enjoyed this discussion, please do, uh, give us a, a rating, a review, uh, we'd love to hear from you as well. This podcast was brought to you by Soy.

This whole season was as well as, uh, tech Field Day, my own company, part of the Futurum Group. com. We'll include links to the companies that have, we've mentioned here as well, or you could find us on x Twitter, blue Sky, and Mastodon as utilizing tech.

Thanks for listening, and we will see you next week. Hi everyone. Welcome to another episode of the Open Mainframe.

The Open Mainframe is a monthly video series dedicated to the Open Mainframe. Uh, it's about the mainframe, the vulnerable mainframe computer system. Today, it's IBM Z's mainframe for the most part, and, and how it interacts in today's modern world of DevOps and Cloud Native and cloud and everything else that we do.

AI, I guess, is coming too. We're gonna probably do a show on that soon, I bet. AI in the mainframe.

But anyway, we do these shows once a month. Thrilled to do 'em. Uh, you may or may not believe it, but the audience for the mainframe is one of the most passionate audiences I've come across in the tech world.

And, and we're happy to have you joining us here. Uh, we're also happy to have an amazing panel for this month's show. Let me tell you, before I introduce the panel though, let me just give you the title of what we're gonna be talking about today.

Today's Open, this month's Open Mainframe, uh, episode is on how do you foster the stewardship of education resources? You know, one of the main, uh, goals, one of the main, uh, jobs of the Open Mainframe Project is to foster education around mainframe, mainframe systems, mainframe software, hardware, applications and uses. And, and the Open Mainframe Project does a tremendous job with that on a worldwide basis, on everything from university programs to outside of the traditional, you know, schooling to, you know, training code camps, open source materials online and everything else.

We're gonna find out a lot about that today. But before we do dive in, let me introduce you to our Panel of Experts today. I wanna first start off with Paul Newton and Paul, first of all, welcome.

I think this is your first time on the Open Mainframe. Um, why don't you introduce yourself to our audience. Hi everyone.

Paul Newton, uh, from IBM. I've been in the industry for over 40 years, and the last 20 has been with IBM. Um, I did something called the Master the Mainframe, uh, which ran for a long time.

And now I support the backend of what's called Z Explorer. And so I've got a, um, wealth of experience in industry, uh, both technical and applied to different aspects of industry. So I'll just leave it at that.

Fantastic. Okay, Paul, welcome and thanks. Joining Paul with us this week is JJ Lovett.

Jj, welcome. How you doing? Thanks for having me.

Uh, JJ Lovett, I'm the head of, uh, education and customer engagement for the mainframe division of Broadcom. Uh, while not as, uh, tenured as Paul, I've been in and outta the mainframe and working with the mainframe from the, uh, ca days prior to the Broadcom acquisition, going back about 20 years. Um, in terms of, you know, the role that I have with the Mainframe Open Education Project, uh, I'm on the core team and one of the things that that affords us is the ability to extend beyond our regular product in ZOS education.

So it's really a way for us to go out and get into the market in these other areas in an open source fashion. So, uh, I reside on Long Island, outside New York City, so I hope you enjoy your travels this weekend, and, uh, looking forward to the discussion. Yes, I'll be coming home.

I'm a Long Island boy myself, so appreciate that. The old Islandia for ca days, right? Certainly true.

Um, some people called it Hop Hog. But anyway, uh, let me introduce to our next panel member today. She is Vivian Sanchez.

Vivian, welcome. And if you wouldn't mind telling a little bit about yourself. Of course.

Thank you, Alan. Uh, so I'm Vivian, uh, I work for ndl. Uh, I'm based in Brazil, and I have been working with Mainframe.

It's a half half of Paul. Uh, but I have already worked together with Paul in the past. Uh, I am 20 years working as a service provider, uh, for mainframe.

I have started my career as a computer operator, actually as a VM computer operator. And since, even though it seems it's been a while already, I'm pretty sure there's a lot to learn. Uh, I always learn in, in this space.

Uh, now at Kyro in the past 10 years, I'm more focused on the education strategy, putting this, uh, strategy for the global team as we have a lot of employees and we need this, uh, this is a very key topic that we all should care about. So very happy to be here, and thank you, Alan, thank you team to be partnering with us on that. Very happy to have you here.

And I think this is the first time we've had someone from NDL on the Open Mainframe, so thank you. Thanks for coming in. And then, uh, our next panel member is ti Osh.

If I mispronounced that double I, I apologize, but Tio, why don't, why don't you tell them the right way to pronounce your name and maybe a little bit about yourself. Okay, thanks. A, you'd pronounce it as Ti Tso Teso.

Okay. Yes. So, and a Little bit about yourself.

Okay. I'm a graduate intern at IBM South Africa. That's where I'm based.

And I joined the Open Mainframe project as a summer, met the previous year, um, to work on the mainframe open education project. I then went on to join the core team, uh, and also like, uh, joined the whole project as a mentor to the other students that are coming up. So, yeah, that's just a little background about myself.

And I think I've been on the mainframe for close to half a decade now. Uh, the, that's like leading, uh, the skills, uh, while I was at the university, the University of Johannesburg to be exec, yeah, until now where I'm training as a ZOS systems programmer. Excellent, thank you.

And a half a decade's, nothing to sneeze at, but of course Paul probably has logins older than that. Um, but it's a pleasure to have all of you on. Let me introduce our last face on, on, on your screen.

And that is my co-host of the Open Mainframe Project video series, but he's also the ed, the executive director of the Open Mainframe Project for the Linux Foundation, as well as an author, uh, on Linux, on on open source communities. There's, he's always had, he always has his prop nearby. Shameless plug, right?

Shameless plug available on Amazon. Yeah. John, how are you?

If you wouldn't mind maybe expanding from what I said about you. No, I mean, you, you did, you did, you did great. Um, you know, I, I, I have the, the, um, fortune really to work with great communities like this and just such a variety of different projects.

You know, you and Alan, you always think of open source as we're coming together, we're building code, but we're just seeing more and more of just the open source principles being applied to just other sorts of del uh, you know, development collaborations, you know, where you've seen it in Open Hardware, um, in many cases. Um, I've seen, um, you know, domains, I used to dabble in home brewing, and I know there's a whole open source scenes on, you know, collaborating on home brew recipes. Um, and, and I think it's a really interesting one here around education because it's, you know, it's just such a critical part of, of any c ecosystem of having it.

And, and it's really hard to pull this together. Um, so I'm really fascinating to, you know, talk more about this project here. Um, I've worked with a number of these folks as we were getting this off the ground here, and, uh, I think we think we really have a treat ahead of us.

Absolutely. And, you know, look, three of the four people on our panel have been in mainframes 20 years or more, which to me, you know, calls out that, hey, we need an educational foundation, an educational program that gets more people like TSO in right, who's five years in and gets the next generation of mainframe developers or mainframe operators in, in involved in this and, and up to speed. Um, John, if you wouldn't mind, I mean, you are the ED for the project.

Why don't you, if you, if you could take a shot at sort of framing, you know, the, the, uh, the system or the educational program that the Open Mainframe project is put in place. And then I, I'd like to ask our panel members to jump in from there with kind of their thoughts around it. Yeah, no, that, that, that, that would be great.

Um, you know, this project came together a few years ago, and it was a few folks in the mainframe community that reached out and said, you know, we, we have, we have sort of an education challenge. And it was in the realm of that there's a lot of sort of basic fundamental education materials around mainframe that are being put together in all sorts of different places. You know, IBM was putting together a lot of curriculum, Broadcom, BMC, um, a number of other organizations were doing this as well.

And, you know, for any of you in the audience that have ever been in training development, and, and I remember talking with Clyde, uh, siad, who runs, uh, who's the GM of the Linux Foundation training business, you know, you, us to tell me is like, you know, a lot of that training at that level. It's, it's a lot of work to put together. It's, it's really important, but at the same time it's, it's just a heavy lift.

And we often see a lot of repeated pieces that happen at that layer. And, you know, and this idea came together of, you know, Broadcom has some great verses. IBM has some great resources, ndl a number of other organizations, have great resources, pulling it together in one place where this could be iterated built on, made available to anybody, because making that low barrier to entry is really key.

But, you know, at the same time, being a rallying point for this community, um, to, to work on it, it seems like a very no-brainer. Um, you know, it's really interesting 'cause you don't see a ton of these projects out there. I've been starting to see a few more of them emerge, you know, because often technical training content, it's, it's just hard to build.

I mean, if you're, if you're into training, and I know, you know, jj you know, you lead training for Broadcom, so I'm sure you can resonate. And you know, Paul, you've been around, you know, the blog a few times and I know you've done a ton of this as well. It's, it's really hard to put together.

You have to have a very, you have to be an SME in the place, but then you also have to have a special set of skills to understand how learners appreciate that. And, and I think this is sort of a unique thing of this coming together, that both of those were able to, to come together really well. And out of it, we've seen this early work and yeah, I mean, I think, yeah, you're like, Alan, I'd love to hear, you know, some of the panelists sort of talk about it.

'cause you all come with a lot of different experiences and, you know, certainly maybe talk about like what you were hoping to get out of it and, and how it's really worked out and, and what you all have learned. Yeah. If I may share, uh, something that happened just last week with me, I was, uh, in a training for, uh, around mainframe and we were discussing, and there was a very, um, mixed audience between, um, newer and mainframe or the professional high and other seniors professional with a lot of years of experience.

And then someone told me that they, it started with all of this training that we, we know now that we have in the industry, but their thought it was that, okay, I was learning about the ZOS, but then what is a mainframe? And then they said, oh my God, that's the foundation of the train. That's the base, understand the proposed, the business behind that.

So that's exactly, I believe what this project means. And, and that's what we were aiming when we start to discuss that. And, and just to give you a background, uh, John, why you were talking, why we put this together in the project.

You were actually in a education conference when this idea born. So we were there with different companies talking about new content, about everything new we were learning from the industry. And then we said, why not?

Why not to create that? So that's the beauty of mainframe, combining all of this knowledge, all of this resources. And then in the other side, we have the need.

So, uh, we have here example of Teso. Uh, last week I had this example that someone, uh, deep dive on the content, but so deep without the previous knowledge. So connecting this, uh, having this bridge is super important for someone that is starting.

So I just like to give this testimony because it just happened and it's, uh, really ties to what the proposal of this project. Fair. Um, I, I'd like to hear from Teso, if you will, because you know what, teso among the folks on the panel here, you represent the next generation, right?

You, you just, you were just being mentored a year ago, and here you are this year now mentoring others as a member of the Open Mainframe education, TSC, right? What, what do you think when you, about your experience and what this offers, you know, people of your age, of your generation? I mean, um, the being part of the program came with a lot of benefits because, you know, as a student, you don't have a crafted, uh, path that you wanna follow.

You still trying this and that. So, uh, by being part of, by being part of this team, I got to, to work with mentors who were able to guide me on which career path to follow. So also, like they provided me with a lot of content that I should read up on so that I can find the part of the mainframe that I like the most.

You know, by consuming this knowledge that we've put on our GI book, I could identify the areas that, okay, I like this the most. And also, like, I'd also advise them that, you know, I think this, um, part of the content should be made better so that it can be more attractive to a young person like myself. And also I'd, I'd add something to the Git book also that I found to be helpful as I'm doing more research on my own.

So I'd say the, the, the program is quite helpful. You know, I'd advise more students to join in so that they can get to explore with the mainframe with all these different resources that the project provides for them. Interesting, interesting.

Um, you, Paul, I wanted to, you are the dean here, right? Let's talk about how people get trained on the mainframe today versus forget 40 years ago. I don't know if we had formal training 40 years ago, but even 20 years ago or 25 years ago.

What, what's kind of, you know, and you're at IBM there, you're in a catbird seat. What, what's your view on it? Well, starting out early, guess what?

The mainframe and the operating system was much simpler. We grew up with the original DNA, and it is just grown and advanced over time. So when people want to learn the really complex stuff and are overwhelmed, I tell 'em, go back to the original books.

Look at the original DNA. And so what I do, because it is a very feature function rich environment, it's very critical to understand the fundamentals. And as a profession, it's a fascinating profession that people don't understand.

Like, for example, civil engineering is a profession. If you are a really good civil engineer or a principal architect, civil engineer, you've gotta understand material engineering. You've gotta understand structural engineering.

So as Tisa was saying, there's so many different job roles and career paths, but you gotta start out small. You gotta start out simple and get a view of the landscape. And what I've done with people that I've helped over the years is I try to start out, make things seem simple and give them confidence, because that's critical to really being able to embrace all the feature function that's available in this mainframe environment.

Jay, Jay, To, to build on the points, I think it's important to realize, I was bringing in, brought into conversations around attrition in the community 20 years ago, and people were saying, we have to start preparing. We have to start preparing. And there wasn't the mind shift that had happened yet.

And because of that, a lot of the education within our portfolio and what I've seen across the industry is really meant for experienced practitioners. So it's getting back to what Teso was saying about bringing people in, what Paul was saying about the transition, to make it easy, we have to look at more ways to change the way we're teaching the mainframe because the teaching style or learning style of the students has changed and evolved in that timeframe. It's what works back in the day is likely not gonna work today, right?

So we have to figure out how to make that transition and make or more information available. Going back to the basics, like Vivian said, what is a mainframe starting there, but then figuring out how to build in that early education. So it's no longer read the manual or refer to this reference.

There's always gonna be some of that as a part of the OJT and everything else, but how do we start building the knowledge for the people that are coming in today rather than basing it off of the education we've always done? So, so one interesting thing about open source projects are that you, you see companies invest in them because it sort of gives them an idea, an opportunity, what they call leverage development, you know, meaning that there's all of this development that is happening out there and you know, you put resources into it, but you have such a gain back from it, you know, and you often see, you know, nine, 10, you know, 12 XI mean, it just really depends upon the technology and a lot of different factors. But you know what, but you know, the driver of all of this, obviously for this is, you know, if this wasn't a thing, you all would be building these same resources yourselves.

So, and I know this project's kind of a bit early on, they, you know, you've built out some resources. I've looked at them, they're really, really interesting. What has been sort of, as you all have internally been looking to take these and sort of building them into your curriculum, what, what does that look like?

Has, what has the progress been? Um, what has the feedback been? I mean, I'd love to learn a little bit more about that.

You know, I could go ahead and start with that. And there is the, um, I, I believe Greg is an SVP at, at, um, Broadcom. And he actually came out and made a comment not too long ago that I absolutely love, because one thing you were talking about the game, well, all of us need each other for this worldwide economy.

And this mainframe was running the worldwide economy. All mainframe stopped. The worldwide economy stops, and we all need each other.

It's not just IBM, it's not just the banks. It's all of us together, and it's a community and we know that we need to help each other as a community. And that's where the real gain is in working and collaborating with other people.

This is a community, it's not just one company. Mm-hmm. Fair.

Not only is it a community, it's a worldwide community. I mean, just looking at our panel here today, we have someone joining us from, from Brazil, some joining us from South Africa. John of course is in Ohio the most exotic place there is.

You'd be surprised, Alan. But you know, it, it, and, and in past shows when we've talked about education, obviously India, India may have more people taking cobalt or mainframe education than, you know, the rest of the world combined in some ways. So this is a, you know, a global effort that I think talks about the, the global nature of, of the mainframe, right?

Um, you know, the global footprint, presence of the mainframe. Vivian, if you don't mind, I I'd like to ask you, I mean, down in Brazil, you know, what, what's the level there of, or what's the appetite, if you will, for people to be involved, you know, in mainframe education, become mainframe, you know, be mainframe professionals? So, uh, around me in Brazil, we have about a thousand professionals working on mainframe.

Uh, and, but we do have a, a very, uh, a challenge with the universities because universities around us that they do not teach, uh, mainframe. So that's on us to go to the university to advocate about the mainframe. So we really use the z explore for IBM, we go together there, we showcase what we have available for them to use.

So with that they can touch the mainframe and see how cool that could be. We like the approach on having some hackathons, uh, which make the ways of learning different so we can approach different types of learning. And so that, that's one of the ways we, we attract the new talents to join us.

And, and we have this type of a university partnership to make sure we grow. So, uh, we, we cannot be sustainable in terms of growing new resources, and we need to be balanced in terms of that. So attracting resources for mainframe is a key point for us.

Got it. Tisa, you mentioned, you, you, your mainframe training came at at University of Johannesburg. Is that, is that correct?

Yes. It started when I was a student at the University of Johannesburg, but at my university, we do not learn main grade skills. So there was this employee from IBM who came to our university to advocate for learning of Z skills, and I got interested in learning Z systems and eventually joined the IBMZ systems ambassador program.

That's when I started to learn more about the mainframes. And then I did a lot of, uh, hands-on learning from the platforms headed by the likes of Paul Newton, the master at the mainframe 2019 challenge. And then I went on to do the z explore challenges.

And yeah, I grew just like that, uh, trying this and that. And I got other students also to get involved in they, the mainframe skills by, uh, establishing tech community, my university, so that, uh, we could be a group of students learning the skills. Uh, but yeah, that's more like it.

So I guess the, the best way to, to do it in countries like ours is to use, um, resources that are provided in the community because, uh, our universities are not giving us that opportunity to learn the mainframe skills. Fair enough. You know, I, I think we, we have like everything else, uh, it training goes through phases and epics and what's in style, what's not in style.

When I was a much younger person closer to Tessa's age, right? You had kid, you had friends of mine who went to school for computer science, and I'm ashamed to tell you, but I think most of the Audi, most of our panel will remember this. It was punch cards, right?

You'd have this many punch cards and that was nothing. Um, and you came outta school, you might know fortran, you might, you might know cobol, but you, you, you know, that was kind of either that or you studied hardware design, right? That was what you learned in, in school.

Then when you got out of school and into the field, there were courses, you know, not part of a university, part of technical training, vocational training, whatever you want to call it, you know, continuing education training, if you will. And then, you know, when I got into it, let's say in the nineties, you started seeing much more non university training, right? I, I, I got into security.

They didn't really teach security in schools. Any security training you took was, you know, from a place like SANS or CompTIA or ISC squared or, or these kinds of places. So it was, you know, truly in the field, vocational training.

And I think that's the way a lot of the mainframe training was as well, right? Uh, the, the university stopped turning out mainframe, uh, proficient students sometime in the late nineties, early two thousands, I would guess, if not before. And ever since then, we, we've tried to build this private non university channel of mainframe training.

But it sounds, from what I'm hearing from you folks, that we are now going back to the universities. We've done other shows in the past with some of the folks here in the US who are doing COBAL training, for instance, and other mainframe training. Um, it sounds like we're really moving back to a traditional university college level program, John, is that the fact with this program from Open Mainframe?

You know, I think learning has definitely changed. Um, now I, when I went for computer science, I was just past the cut punch card era. Um, although I did, um, sure Rub it in, I, I I, I did however, um, learn on X terminals, um, okay.

Which were a fun experience in and of themselves. But, you know, I think I, I think we have found that learners are taking, you know, learning a lot different, you know, before it was very classroom oriented. We're seeing, you know, massive online courses, digital training, you know, my, my daughter is a senior in coll, a senior in high school, and she's taking college classes and she's doing 'em all out of our home, right?

So we, we've seen a lot of that change over time. So I, I think there's one aspect that is how we learn and how we educate is different. Um, we're seeing a lot, and I think all of you could resonate with this, of people going through career changes or adding additional skills on their already education.

Um, so there's a new learning path that's, that's going towards that. Um, you know, and, and, and, and so I think it's really critical to make these materials relevant and accessible at multiple different levels. And I think, you know, and I know a couple of the extended members of this community and, and they come from those different backgrounds.

I know, I know there's a few folks from academia that participate in, in a number of the conversations. Um, there's from training partners that have participated. So I, I, I think we're, I think it, you know, Al to come best ask your question, I think universities is a large component of it, you know, because, and we've seen that through our mentorship program.

But I think this group is really taking a broad approach of we have to make this relevant for a lot of different audiences, and how can we best do that? And how can we provide those resources so it can be adapted? Um, and I guess panelists, I see, jj, you're off mute.

I have a feeling you have something really cool to say. Yeah, No, it's, the platform's evolved, right, John? So there are people that are working with the mainframe that may not even know it.

So, you know, with the platform going open with hybrid cloud, with cybersecurity, reaching across all of the infrastructure, no matter where it is, either in the cloud or on-prem, however it is, you know, there are people that, you know, with the modern developer experience, they may be developing stuff for the mainframe and not necessarily even know it. Um, there are people that are in cybersecurity, the security analysts that will need to know how to do policy settings and know how security works end to end across the platform. And, you know, there are a bunch of other different examples in terms of, you know, folks that are mainframe adjacent or actually work in the mainframe and aren't even necessarily aware of it, that need to know about these things.

And so with that open attitude in mind, that's the approach of going back to the universities and making it relevant to programs, whether or not they're focused on the mainframe. That's certainly an effort that ourselves and every other vendor are involved in as well as the, uh, uh, mainframe open education. But, you know, it is becoming much more relevant in many other areas than ever before because it's not in the back of the data center anymore, isolated by itself.

It's now being brought to the forefront of today's economy. Yeah, it's Completely relevant. So the one interesting thing about this project I don't think we've mentioned is it's a matured project.

Um, it recently has moved to the active stage of projects, which if, if you look at some of the, the rigor that's put to it, it means there is a diverse community. There's a lot of, um, great infrastructure put in place, you know, especially from a, a project standpoint, because the idea of an active stage project, it's one that people can trust and invest in. I, I would love for you all to sort of talk about, you know, what that transition is like, and then kind of talk about some of the programs and stuff that you've been putting in place to help grow the community.

Right. I, I, I believe that, uh, the, the main thing around the, our platform, which is GitBook, uh, is where we concentrate all the content. So the main goal, and as you rightly said in the beginning, John, that's hard to put together content for all of us in this ecosystem and count on a platform that's open collaborative.

I think that's the key thing for us here, uh, with this education proposed. Uh, so we created this, uh, platform, uh, with a structure of five, uh, existing key talks around mainframe foundation. And we are looking as much as possible for contributors, for any contributors in the community, no matter what your level of expertise on the mainframe.

If you just watch a presentation and you would like to put together some content and then you would like to share, uh, references, ideas, all of this can be added as part of this community. And then, uh, which is a very brand new thing, we just launched the, uh, our series of badges. So if you are a contributor, we do have level one, level two, level three, there are set of criteria you need to meet to, uh, be, uh, a contributor on our project.

But that's a very interesting way to encourage and to make sure we're gonna get more and more contributors, because that's the key thing. We need to make sure we just not, uh, build the platform, but we, uh, keep that current. We maintain that and we keep that attractive no matter the audience, as you said, it could be students, it could be professors in the university, it could be professionals, it could be re retires that, uh, are not more working on the mainframe, but they like to teach mainframe.

So there is many possibilities. We can work on this platform. And, and the badges will be a very interesting way for, for going further on this project now And add recognition, which I think will be really good.

And recognition of contributors. That's a, it's, it's a really smart idea to put that at place. It is.

I mean, people, you know, in some ways it's gamifying it. What what I find interest interesting is that when you look at it, education as a whole, today in some way, in some places there's a move away from traditional certifications, right? Uh, across the board, right?

People want to do self-paced learning. They, you know, they like to do direct to the computer rather than being in a formal classroom. On the other hand though, the, the badging, digital badging and so forth, right, is, is more popular than ever because I think people like the, the simplicity of, if you will, of having that badge near your graphic, right?

Proving that, Hey, you did do something, you have some expertise here. Um, but it's, it's certainly I think, less onerous than going through sort of the traditional, let's go sit two days in a training center somewhere and listen to someone talk to me with some slideshow. Um, I've went through those.

I, i, I wish those days to be gone. So I'm, I'm glad, I'm glad to see something like this fill in the gap. Not to, not to doubt anybody who does that, it's a wonderful profession.

But, you know, I, I think this is really transitioning, like you said, Alan, to the learner to today. Yeah, direct, direct to learner. I, I think that's the model that people, people want to go.

So Paul, I'll come back to you if it's okay. Again, you have more experience here than all than the rest of the team. Is this the path that keeps the mainframe relevant and on top of its game going forward?

What else could we, should we, would we do? It absolutely is relevant because there's a lot of people working past retirement and they grew up with the mainframe. They know it really well.

They make it look really easy, and it's not as, and there is a lot of complication there. And I've been working with Teso recently and we've discovered that. And one thing that, um, there's a lot of hidden talent to start with also.

And it's not just the universities and JJ will be able to talk to this also, but there's apprenticeship programs. But in terms of moving forward, these things have gotta be taken care of. It's just like our structure of society.

The digital infrastructure has gotta be taken care of and the younger people have gotta come in behind us, the ones that have helped put it all in place and take over and continue to advance it. So this is an opportunity to have the community, and the community is a big word here, to come together and work together to build this next generation. And one thing that I did with tso, they asked me when I joined the project, Hey Paul, would you talk about the job roles, the job responsibilities and the careers?

And I looked at this short narrow list. I said, oh, no, no, I don't think you, because we've got it right. You gotta understand how broad this is.

And it's huge. And I try to categorize all the different, um, value add employers out there as part of the community. And so Teso took a look at that and I was really happy Teso looked at it and he was just blown away.

He said, I had no clue. And I don't know if you wanna make any comments about that Teso. 'cause sometimes I would hear from young people, well, what does a mainframe person do day to day?

And it's the old, it depends and it depends big. So I don't know if you wanna make any comments on what we were working on team. So, Yeah.

Um, when we established, uh, the deeper dive role on our deep book, I was working with Paul. So Paul sent me this document that he had prepared. Um, initially he teaches you about, uh, all the different shops that are out there where, uh, you could apply your main skills and you get to understand the type of work that, uh, you do.

Because, you know, like at a shop like mine, where I work with the customer, it's, it's a small shop. So like, uh, I do a lot of, it's not just, um, systems programming work. You also do storage administration and, you know, sometimes attach on security administration.

So like, I think that document that Paul sent you prepared me for this, even though I did not know I'm gonna get into this kind of a role. So when I started there, I already knew that, okay, this is what's gonna be expected of me as time progressed. So it really opened my eyes.

And also, I gotta learn about the other rules that are there in the mainframe space, you know, because you, when you gonna work in the need to know who you work with, those on the other side so that, uh, you can all communicate and get to come together and solve problems. So it has opened my eyes that many. Excellent.

Very good. That's great. It's great to hear from, you know, from your generation about that ti So jj I feel like we haven't gotten enough from you here, right?

You, you're officially on the, uh, on the, on the board. You're helping to run this. Give us, give us kinda your take a bit.

So there, there's been an interesting evolution, as John said. We, we just went from incubator to full project, and, and Paul has done a ton of work in Vivian in terms of getting get book going, the contributor badges and everything else. So then the next question is content, right?

So where do we go from there? What's important? And as Paul mentioned, you know, a lot of the large vendors have their apprentice programs, they have their career tracks, education tracks, certifications, and they're all, you know, aimed sort of in the same direction.

And they apply, you know, basics, intermediate, you know, along the fundamental track. But one of the things that we wanted to know was going out to a multi-generational crowd, uh, at a recent event, share New Orleans, uh, this past August. And we went out and through a working session hosted by, um, Meredith Stoll, uh, VP of ecosystem at IBM and Neil Cash, uh, senior managers in charge of the vitality program at Broadcom in the mainframe division.

And they ran a session where they crowdsource the information around what's important and what do people wanna learn about first. And we're in the, the effort of prioritizing that right now. But it was really interesting because it was, um, such a wide swath in terms of topics and what people want to know about.

And it just goes to show that, you know, we, we do need to sort of go back to the basics, as Vivian said, around what is a mainframe? But then there's all the other different aspects that come up in terms of generational perspectives. And so, you know, we're gonna be working on prioritizing that to get to the next stage in terms of what the next contact project is and who we're gonna be looking for for contributors.

Um, but it, it was just very interesting to see, um, you know, because people have been in the community and in the industry for so long that they have sort of set opinions on what people need to know. And, you know, looking at teso and other folks coming into the world, uh, you know, they want to know potentially other things. And that's generally because experiences will always vary, you know, based on the role work that Paul did.

There's not necessarily, there may be groupings, but there's not necessarily defined roles because, you know, you wind up in a shop and then the, the role definition changes based on the needs of the shop and the business. So it's, it's just pretty interesting trying to get into that. Uh, and I'm looking forward, maybe on a future episode we can sort of disclose where that went and, uh, and what we found out with our findings after we're done with the, uh, the prioritization.

But it's, it's interesting to just sort of marry together what our impressions are of the new ways of learning and consuming education, and then the topics that people feel they need to know from a generational perspective, those that have been in it for a long time, and then those that are coming into it. Absolutely. So topics Is an interesting area because I think, I feel like the one question we didn't ask is like, what topics are covered?

'cause you've, you've built a decent amount of content that's available right now. Um, if someone were to go through it, what would they learn? What's that?

What's those topics where, you know, talk a little bit about that for maybe a minute or two. I'll look at, uh, Vivian and Paul in terms of, they, uh, like I said, I'm relatively new. They were the ones that built it.

You know, it's almost as if Vivian, I don't know if it's absolutely true 'cause I haven't gone out recently, but I think as we got together as a team recently, we went back and looked at all of it and said, well, wait a minute. Maybe we need to start, start from the beginning again. I think we came to that conclusion and um, 'cause we were building content and we got content, but is it really what we want?

And so we're almost questioning ourselves in retrospect and going back and say, well, is this really what we want? And what works? That's the feeling I got Vivian.

I don't know how you think about that. No, yeah, I totally agree. Uh, and, and to the, we do have five, uh, main talks.

So what is the mainframe foundation technology ing frame, which is, uh, the key point that, um, Paul was mentioning on the deep dive in the role choosing. So that's exactly what, uh, Paul did with Teso. Uh, and then the other one is career path opportunities.

So we would like to deep dive in those foundation contents, but the content is, is very broad. So we do have a lot of things as, as Paul said, we will like to start. Uh, we do have this structure.

We have, uh, a very high level content on each of them because we will like to include that just to cause some, uh, interesting to generate in interest from the audience, from the community. And then they can start, okay, that's too low here. I, I can contribute with that so they can come, uh, because if we start so big with something very ready, they might feel uncomfortable to share something.

So that's why we also started with a high level content so the community could support us on growing this content. So I, I really believe, uh, on the badges as helping us on going further on getting more contributors. I'm really excited to run some programs with the, um, the teams, the professionals I know, I'm pretty sure they'll be very excited with those badges.

And, and they're gonna be con very good contributors for us. And I will add that content is probably not gonna be our problem. I have got so much content I can overwhelm people and foundational content.

I know Broadcom does and IBM's willing to contribute this content. And the issue is not so much the content, it's how do we organize it in an easy, digestible format. And it's kind of like coming up with a nightly news.

We can't overwhelm people. We need to really articulate how to get started in a succinct way and build from there. And that's the key.

It's, there was an overwhelming amount of content. Now how do we organize it? Very well said Guys.

We're, we're about outta time, John, for people out here who want to go see what we've got and maybe, you know, dive in. Where, where on the web do they go? They can go to the Open Mainframe project, uh, website.

And we have actually a special, uh, menu in there under our community where you can actually get a number of these resources and other programming like the Cobalt programing course, um, and many of our other resources as well. So I'd encourage, uh, folks to start there. And this project actually also will be presenting, and I think we will, this show will go live before, um, we go to the Open Mainframe Summit in Las Vegas.

Yeah. But they're also gonna be doing a session there as well. Um, so definitely go check it out and you can learn, you, you can meet these folks that you're seeing here, um, and many more members of that community and learn more about it and learn how to get involved.

Well, now that you mentioned it, what's the date? It is September 11th. It's in Las Vegas.

Uh, it's a whole day. It's the first day zero of IBM Tech Exchange. Uh, we'll be right there.

Uh, we have two tracks of some great content, some good speakers, um, at the upfront part. And, you know, it should be a really good time and a really good opportunity to learn a ton about what's going on in open source in the mainframe. Excellent.

All right guys, this has been a tremendous, uh, open Mainframe project video. I want to thank each and every one of you for participating and dialing in from wherever you are in the world. Um, keep up the great work.

'cause it's, it's good work. It's needed work and, uh, you know, it, it's, it's what this industry needs. John Ag as always, thank you for co-hosting.

Thank You for hosting. Yeah, and that's it here for the Open Mainframe Project this month. We'll see you next month with a fresh new show.

Until then, be well September 11th. Check it out if you're going to be there in Vegas. Bye-bye.