The Keystone Technology Podcast
The Keystone Technology Podcast brings together technology, business, and operational leaders to cut through the noise and talk honestly about what works in the real world. Hosted by George Adair, the show explores how strategy, innovation, operations, and execution come together across today’s biggest topics, including cybersecurity, infrastructure, cloud, and AI.
The Keystone Technology Podcast
Major Disruptors: Virtualization then AI now
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Two decades apart, two waves of technology that rewired enterprise IT from the ground up. In this episode, George Adair sits down with Tony Dilorio to hold virtualization and AI side by side and ask a deeply important question: what can the hypervisor era teach us about surviving and leading through today's AI moment? They trace the early skepticism around VMware and virtual infrastructure, the numbers that proved its ROI, and the organizational shift from server rooms to platform strategy. Then they bring that lens forward to AI, covering where it's delivering real ROI today, where it's still just hype, and what separates the leaders who will thrive from those who'll fall behind.
Welcome back to the Keystone Technology Podcast, where strategy meets execution, buzzwords have to earn their keep, and shiny new tech still has to survive contact with reality. I'm your host, George Adair. I've got my trustee resident guest, Tony Diorio, back in the studio. And today we're back in time. We're going to go back in time before we go forward. We are talking about two of the most disruptive forces in the history of enterprise technology. That is virtualization and AI. Tony has spent his career as at the intersection of technology and sales leadership, and he's seen both of these waves up close. Tony, welcome back.
SPEAKER_00As always, George, thanks for having me.
SPEAKER_01Awesome. Well, Tony, listen, here's what I want to do today. I want to hold these two disruptors side by side. You know, if we look at these in sort of a containerization using the word lightly perspective, we we remember how they hit, right? And we're remembering because we're living out the AI portion of this right now. Although, you know, I would say we're in year two, three of that. So we're sort of coming out of that containerization and seeing the fruits of it and how it's really working. But now, you know, organizations go through nearly the same stuff, right, with AI. So the question isn't just what does AI do? It's what can the virtualization era teach us about surviving and leading through a platform shift. What do you think of that, Tony? I'm excited for this one today, Jim. All right, good. Well, good. I'm glad we're having this conversation. Look, it's it's good to talk about some of the big boulders that have led the organiz the world through transformation because ultimately when people look back, they're gonna say, well, that was a big shift in not just how we deliver technology, but how the businesses ran and how they were able to progress and speed up or slow down, you know, just depending on what we were doing. So, yeah, so this is a good one to reflect and remember, but maybe we'll create some tidbits for us to use in our organizations today.
SPEAKER_00Let's do it.
SPEAKER_01All right. Well, before we get started, I'd like to give a little bit about the format of our podcast. So the format is this we're gonna give some topics, we're gonna talk about some key situations that occurred. We might provide some detailed facts that, yes, we did procure from the interweb, but we did it in a very respectful way and got it from reputable sources. Uh so I promise they'll be as accurate as they can be. But then uh we'll go over those and we'll have some dialogue. We'll create some questions and some feedback. We'll see where those questions that dialogue goes. We'll let it happen organically. So we might have a few topics to talk about. And then we're gonna jump right into what we call rapid fire. Uh, our podcast uh guest is going to have a bit of time to uh give us some quick responses about these particular rapid fire questions. It's really fun. Uh, and then we'll go ahead and end. So that's a little bit on the format. All right, everybody, we're gonna get started today. Our first topic is how virtualization rewired the enterprise organizations and nobody saw it coming. Tony, let's start out with the big hitter, VMware. And the early hypervisors, the players that started gaining serious traction in the early, I think it was 2000s. 2004. Was it 2004? Yeah. Yeah. Very exciting time, but also a bit scary.
SPEAKER_00For most people.
SPEAKER_01Yeah. Yeah. I think uh most people weren't ready for it. They didn't understand it. We didn't know how to sell it. Yeah. You didn't know how to sell it. Uh, didn't know how to package it up, didn't know if it was gonna really produce anything, right? No, yeah. So it wasn't just a technology shift, right? It changed how organizations overall thought about hardware, capacity, cost entirely. Looking back, what do you think the industry got right about virtualization adoption early on? And what did they fundamentally get wrong? Let's go with right first.
SPEAKER_00And I think that's an easier question. But the efficiencies. I mean, the efficiencies were where they nailed it. It took a little while for people to adopt and believe and trust and whatever, but the efficiency of the server, right? That's what they got right. And that was day one. And there's been more that had come, you know, a little bit after that as we saw into those efficiencies and continued to develop. But day one, that's what they got right. From a wrong perspective, deployment, that was probably the worst thing that I've ever gone through. My I if you look at it from a sales perspective, I probably had more customers fire me after a VR deployment in what was it, 2007? The virtual pink slip. Yeah, I never heard that, but yes, that that is so spot on. You know, those deployments were were were difficult, but you know, we were kind of just talking about this. You and I see things from an opposite perspective, right? So I see things from a sales and deployment and operations perspective, you see it from a practitioner perspective, right? Both being in leadership and all of that stuff, but just a fundamentally different view or opposite view. And from a sales perspective and a deployment perspective, you guys were never ready for us to come on to deploy it. So we ran into more customer unpreparedness, if that's the word. But we ran into more of that than we ran into anything else. So it was, you know, when then when they're not prepared, my sh my crew has to go home, right? And from a sales operations and leadership perspective, I'm losing money just sending these guys out there. And it wasn't that that the customers were always maliciously trying not to do it. Nobody really knew how to prepare for them. Yeah. So then when it got there to deploy, it was just like, oh, well, here it is. Figure this out. Here it is. And it's not like VMware sat there and and went through countless hours of deployment, you know, exercises with our engineers, right? It was, hey, let us show you. And you get a two-hour demo or demonstration. Maybe you even have to go configure a quick host and then you're done. Like, hey, go do it. Yeah. Um, that's not we all know that's not real world training. So that's the those are the things they got wrong initially.
SPEAKER_01Yeah, let's start with that real quick. I I just want to make a note because just so everybody is aware, uh, as Tony alluded to, Tony's worked in the MSP space for many, many years selling technology. And I have worked in the customer side, really running IT shops and dealing with so just for that context. But yeah, Tony, I think that's you hit it spot on. When we were first presented this idea of virtualization, we looked at it as, and I think the commercials and the marketing and everything else did, hey, you're just gonna get rid of some physical boxes and you're gonna start to compartmentalize your world. Yeah. That's all you need to do. That's it. You know, don't worry about everything else. And so you're right, when we got hit with the reality of the complexity of that world, that downstream effect of, you know, compartmentalizing all that data and and constructing it in a new and entirely different way, we weren't ready for it. That's actually right. Yeah, we weren't ready for it. So that's a good point. I'd also say, you know, as that began to take off so aggressively, you said by 2004 we got started. And then by 2010, there's a report from the IDC. I'm gonna share a little fun fact here. The IDC was reporting that virtualization become the number one technology priority for IT organizations globally by 2010. So that's about six years from probably the first monetization or marketization of VMware and and some of the probably other players that were coming in and spending on the virtual infrastructure exceeded two billion annually within five years. Okay, let's compare just for a second. Just for a second.
SPEAKER_00Where are we at today? Uh it's not comparable. It's it's not comparable, George. So I for those that are in the know, you know, that pay attention to the financial side or the industry side of it, and right now to George's earlier contextual point, right now I'm in a in a in an AI native startup, right? And so when you look at where the investment dollars are and how they're evaluating these companies and blah, blah, blah, blah, blah. What we're seeing and what we're being told with the firms that are investing in us is that there is over $70 billion earmarked in VC funding for AI native companies. Uh, that number goes down by 40% when it's AI, I think they call it AI-enabled companies. How they define AI native is there is no product without the AI piece of it. And that's where we fit, right? But when you look at that, it's $70 billion being earmarked for startups alone. It you can't compare that to a you know two, two to five billion dollar industry.
SPEAKER_01So I I couldn't agree more. Uh, you know, and it and it all does stem with that massive influx of spend that is coming into the market. I think it it probably creates some trepidation, some scare, some, you know, CEOs, COOs, all of them start to see people spend this kind of money and they just get jittery um and they start asking those questions. I'm gonna say that what we did wrong in the beginning was, you know, as we mentioned before, is we didn't understand what happened after you deployed it, after you received the box. And, you know, the the uh the box with the many different pop-up toys that coming out of it, right? Uh you you didn't know what to do with it. So we didn't prepare for that, and we probably had a lot of difficulty in the deployment part of it. So I'll double down on that and agree with you. So let's jump into what we did right. What do you think fundamentally we did right in the beginning?
SPEAKER_00Oh, I mean, I go back to the efficiencies, right? So as a sales guy, you know, VMware hurts your pocketbook. So you when you when you look at the the average utilization was somewhere around 15 to 30 or 40 percent on a on a server that jumped to 60 to 90 percent after virtualization. Obviously, to your earlier point, that consolidation puts money back into the business, back into the budget of IT, blah, blah, blah, blah, blah. So yeah, I mean, the efficiencies truly, and I use it as a blanket term, we can drill down into it, you know, based on how much time we have allotted today, but we we can certainly drill down into what those efficiencies meant. But it's efficiency, that's what it got, that's what it got right. It returned dollars, it returned value, it returned a lot to the IT teams and the businesses.
SPEAKER_01Yeah. Yeah, you're speaking into kind of the next topic, which is what did it really what did it really return? What did we actually find from the estimates, the ROIs that we built out? What what was that and how good did it look for internal organizations? Uh so I'll give you a quick stat here. Before virtualization, average physical server utilization hovered around uh between 5% and 15%. And then virtualization pushed that to almost 60 to 80 percent for organizations.
SPEAKER_00I wasn't that far off.
SPEAKER_01So that's four to five times the improvement in asset efficiency. And so that's that's one huge piece of improvement from the monetization standpoint and how it actually delivered. The other one that I'll give before we move on from this is according to Gartner 2015, more than 75% of all x86 server workloads ran in virtual machines, up from less than 10% in 2005. That was right after the big uh introduction.
SPEAKER_00Well, and if I can even cut in on you right there, we were training our teams back in 2016. We were training our teams to still ask the question of how virtualized are you as they're going in, you know, to to discover on the on the infrastructure side. We were training them to ask that as a as a fundamental discovery question. So that stat is, at least in my experience, seems spot on.
SPEAKER_01Yep. Yeah, I think so. And and uh, you know, I think for me in my early days when I was running virtual platforms and and transforming organizations to be more nimble and scalable, the ROI conversation was there with finance, right? We we came up with some really top topical kind of, you know, pie in the sky almost uh numbers. And then we didn't track them. What I'm seeing today, just to compare, is organizations are saying not only are we spending exuberantly a lot more than we we did back then. So not only that, we need to also make sure our ROIs are as accurate and traceable, trackable with good KPIs as we go through the life cycle of that deployment. But they're they're really pushing in on that this this time around. So I think that's a big difference from virtualization time to today. Now, I know it depends on the organization that you're at, but I do think holistically, organizations are demanding that traceable ROI, uh, making sure we're getting it. Okay. Well, let's move on from that. I think uh that talks a little bit about how it's uh how it transformed organizations and what it looked like from a budgetary standpoint standpoint uh and in the boardrooms, if you will. So the the next one I wanted to bring up is, you know, AI is reshaping enterprises, right? So what's really, what's actually happening right now, you know, as we see these early, let's call it innings of AI uh in the in the ball game, if I could use that analogy. Uh it's it's what's it really interesting about it is uh in some ways it's it's rhymes with the virtualization story a bit, but in other ways it's a completely different kind of disruption, right? Uh so tell me, where do you see AI having the most immediate and measurable impact in enterprise environments?
SPEAKER_00That's a good question. I am seeing the greatest improvement in context and visibility. So what that transpires to is different arms or different business units of any corporation. But where we're seeing the largest return at the enterprise level is within operations. So we're seeing that the that AI still today, at this point, right? And there's some really cool developments coming that I can't speak to. But the AI where we see it today is merely, you use the baseball analogy, I'm gonna use a car analogy, but it's merely a supercharger for the human. So it's not it's not there just yet. And we've got some milestones that we have to overcome before we get into the next phase of it. But right now it's just a human supercharger. So if people adopt it in that fashion, they understand it in that fashion, and they train people to work with it in that fashion. What we find is those organizations that can do that effectively are seeing a 30% return on efficiency from you know any given employee in any given department, as long as the AI was adopted the right way and the governance is being used the right way, because that's extremely important.
SPEAKER_01Yeah. Yeah. I mean, you speak on governance, and certainly I think that's where the change has been with virtualization too, not just with are we tracking what it's returning on investment, but we're also wanting to track how it's affecting the security and pot security posture, but also the risk level of our organization. Whereas before, we might uh use the words superficially by saying, hey, we want to be a level three, you know, you know, NIST optimized level three, but we but we really don't know how to get there. And we're probably gonna just say it and put out some, you know, buzzwords and then maybe some documentation, not follow through. Now they're saying, okay, we got to follow through. I want to know quarterly, where are we at? Have we improved and and what's the transformation there? And that all comes from the governance committee and how they're tracking that. I want to tap into a couple of areas that I was thinking of in it when it comes to that impact. I think the first one, you know, being that more practitioner space, uh, IT operations is a big one, right? I live in it every day. We get calls all the time. Uh, this, that, and the other thing, we need uh, you know, to fix and improve and optimize and so forth. I think those that are using AI today have found some tremendous improvements and impacts in AI ops. You know, whether they're monitoring incidents, whether they're monitoring data traffic across the wire, east and west, or they're just trying to respond uh quickly. And I know this is a space that you operate in, and you sort of alluded to it a second ago, is hey, there's some stuff you can't talk about, but I know there's some stuff you're doing and you have in place today. Talk about that a little bit for us. Get as granular as you want, or just keep in the high level. Either way, what's happening there that's helping I operate or operate IT operations improve through AI ops?
SPEAKER_00So the biggest one that's being worked on right now, at least from our side, that we can dive into is native language integration. So having your LLM that can be in your meetings listening and speaking to the team in normal everyday voices, uh, everyday language for that matter, um, and then take action, right? So that's where Mantix for specifically, right? So you want to look at at where I am today in the cybersecurity realm, that's exactly what our AI, you know, we call them Dennis, but that's exactly what Dennis is growing into right now. So we've released the natural language update that that's there today. Where we're gonna be going with that is you really don't need a SOC team anymore, right? You need an you need an analyst of sorts that interfaces with Dennis, but at the end of the day, Dennis becomes the person that remediates everything. He may do it on a cue from a human. And again, as we develop, that's gonna start to go less and less. But he's gonna, you know, he's gonna take that cue from the human and he's gonna be the one that does all the uh the actual labor execution within your you know your your cybersecurity realm anyway. I I can't speak to it, but I I do know of a couple of firms that are looking at this in the IT operations space. So your normal uh MITs or managed infrastructure providers, uh, they're looking to do the same stuff that we're doing. So those are just a couple of them. The other ones the other ones get a little bit creepy, I'll be honest with you. And that's giving it an objective, right? So you you just you kind of set a parameter, but you give it an objective. The objective should be specific, but the more vague it is, the scarier some of these models become because they will start to predict. And and that's one thing we've run into, and one thing we can discuss even more at length today is you know, AI is still today only about 75% accurate. You get about 25 anomaly within the AI answering code that's not not accurate. So we have to eliminate that if we're gonna put them into a practitioner position and give them some sort of autonomy. And that's the part right now that, you know, is being worked on ever vigilantly.
SPEAKER_01Okay, I think uh you're on to something there. My next stat I was gonna provide was a McKinse stat, where in 2024, this is going back a couple years, the global survey found that AI, that that 65% of organizations were using gen generative AI. Now, I think that's a that's a suspicious number. And that's why I I prefaced in the beginning that, hey, we're only as good as the data we're giving, but I will use the best, most reputable statistics out there. So I do think McKinsey with 65% at uh gener using generative AI in 2024 is probably a bit overblown. Maybe they were testing it, but I would not doubt if today it was closer to that number, because that's certainly what I'm hearing across the board. And people have learned, they have uh piloted, they have put things into that rigorous POC process, and they found that it's very doable and productive. Um and so I would agree with you. It's it is gonna get to that. So I like how you you sort of outlaid three different transformational times with AI. One was just the beginning of general purpose AI, right? You you jumped into Chat GPT and it told you a few things. It's great. Maybe it did your emails. Uh wonderful. Now we're way past that. We we moved into the generative AI, and and that's great. So now we got agentic agents working for us. You gave yours a couple a name, um, as well as most people do. I have yet to give my my anthropic Claude a name, but I I might someday. It just might feel a little weird uh to do that. Oh, it's weird.
SPEAKER_00There's no doubt about it.
SPEAKER_01And then the third stage, which is where the reasoning comes in for you, that is a fascinating stage. It is scary, but it's fascinating. But I do think that is where you we may see some either tremendous gains or tremendous losses. Sure. And maybe both.
SPEAKER_00Well, and from a man, not to cut you off there, George, but from a manatics perspective, we know the trials and tribulations everybody's about to go through. So we, you know, we've been doing this with the military since 2012-ish. So commercially, right, we've seen this at a government or a nation-state level, and we've been seeing this already for over a decade. Now that we've really released into the commercial market, we know exactly where those trial and tribulations are going to come from because the nation states did that 10 years ago.
SPEAKER_01Yeah. Yeah, without a doubt. So just going back to that, you know, looking at and giving us a retrospect of how virtualization transformed the world and now how AI is. So one of the first things, the mistakes we made was with virtualization was treating it as an infrastructure tool instead of an actual platform strategy. As we talked about uh what it meant to not just deploy virtual platforms into a single source, it was far greater than that. And we we missed the boat for quite a while. We finally caught up and now it's it still transforms organizations when they think about it that way. And maybe with the same with AI, completely different technology, I get it. But as we compartmentalize those three major use cases and we watch the AI transform, that might be the same, basically the same outcome. Where we're not looking at it as uh we're looking at it more as an operational efficiency. Instead, we should be looking at it as a strategic direction for the organization. How do you apply that to the organization as you start to transform and create your strategies into the future? It becomes Embedded in it. And now what I would say is it's not just about technology where virtualization was largely about delivering technology. This now becomes about delivering your services holistically and even operationally managing them, right? So I think it's deeper than what virtualization did for us.
SPEAKER_00I can't disagree with that.
SPEAKER_01All right. All right. So the last topic here, and uh we'll close and we'll head into the rapid fire. But uh the topic is all about leading through an AI adoption transformation. All right. We're gonna basically bring it back to the beginning. What do leaders need to do differently than we did, probably with virtualization, is really how the question's gonna be positioned. Uh so a lot of leaders right now are being pressured from the top level to move super fast, move into this AI, this AI that. And and the they're being questioned from below as well, right? By teams who are worried about what it means for them. So there's a there's a a double-edged sword work in here. Uh, then there's a highly comfortable position to lead from, or that's a highly comfortable uh from the ones who are going to find themselves behind the curve, maybe.
SPEAKER_00Well, I think the the best leaders are gonna see this from a very methodical adoption. And what I what I kind of mean by that is a couple of things, but mostly uh to just narrow that down into a quick phrase, it was use where needed and and experiment where able is the easiest thing that I came up with. But what I mean by that is use where needed. And you know, take it from a cybersecurity again. We were talking about Mantix Force, so that's an easy one, right? But but this is where it's needed. You have to adopt AI within your cybersecurity because the offenders that are out there, they're using AI at a much, much more efficiently, efficient rate than you are, and they can go effectively send AI out to find a thousand companies a day to go penetrate. Yeah. You on the defensive side don't have the human ability to stop you know a mass campaign from an AI agent. So you've got to use it in cyber. You have to. It has to be adopted, and people are gonna have to start to realize that that's just gonna have to become a normal thing, is using it where you have to use it, where you have to defend against AI, where you have to go and compete against AI, you're gonna have to have those AI tools. Experiment where able, what I mean by that is when you look at your production environment or you look at rolling it out to an employee base, that's a different type of AI. So I would be experimenting with it. I'd be managing up and managing down, but I'd be equipping everybody because the humans, it's not just fire somebody, we gained efficiency. As we were kind of alluding earlier, the humans will still be around. It's not a cost of jobs, it's a differentiation. So these guys as they understand AI, they become AI governors. AI governors are gonna be where it's out in every company soon enough. So the more you understand about the model, how to deploy a model, how to govern a model, how to set parameters in a model, those are the skills that are gonna be needed as this continues to develop. So IT teams are just gonna see a transformation, much like we did back in 2006, 2007. To take this back to that virtualization comparison, you know, back in 2008, from a sales perspective, we had tons of Georgia Dares calling us saying, hey, I need an ESXi certified engineer to run this environment. And there were none. I mean, excuse the analogy, but I could really walk out of this building, spit off the rooftop, and I'm gonna hit an ESXI engineer. They're everywhere, right? So this is the transformation that we're gonna start to see uh in a different way, and we'll see how that, you know, turns out in the next few years. But that's the transition that we're now on that we're now facing.
SPEAKER_01Yeah, that's a good that's a really good point. When does that, you know, typical technology cycle mature? And does AI change the the innovation cycle or just the maturity cycle than we've seen in the past? I don't know if it will. And here's why I say that, uh, because I still believe that the leaders who are going to survive this and and thrive in this are not going to be the fastest to adopt. No, no. Right? I that's where we totally agree. They're the most, as we kind of have said before, they're the most deliberate and they know exactly what their organization capabilities are. They know where their resources are strained, they know what they do. And if those leaders are uh the ones driving the AI initiatives, then it's gonna take them a while because they're gonna understand the human element of it. And I also believe that the the greatest, the best leaders who are going through this are the ones that see this as a hybrid capability. So we're not replacing humans. But human judgment, let me let me let me get that correct. We certainly are moving humans, uh, but we're not replacing human judgment with AI, but we're teaching our teams how to use it, use it to amplify their judgment. That's true, right? Okay. So I'm glad we agree on that because I think a lot of people spent probably the last two years freaking out, you know?
SPEAKER_00Oh, I think they still are. I think they're they might be.
SPEAKER_01They might be. And you know what it does, and I'm gonna spin off a bit here. Uh this will be fun. It's the it's your big money-making billionaires, okay, who get on these stages, they get asked these really big questions. I mean, think about our political system, right? They get, hey, how are you gonna control poverty in the world? And how are you gonna have peace? You know, and so they throw out these big questions to them. You know, Elon Musk, I'm thinking of. Hey, what do you think of the future? Are humans gonna be needed? You know, that'll be the question, right? Really obscure and obtuse or whatever you want to call it. Scary. Very scary. Like they know what they're doing. They say it really calm and and and stuff, but they know what they're doing. They're they're they can't wait to put this on one of their social feeds so people get all the watches on it. Exactly. Thumbs up. And so here comes Elon, because he is Elon. He's who he is. He's uh pretty far out there, space cadet, if you will, who dreams big. He dreams bigger than anybody that I know. And so he's gonna say something like, Yeah, you don't even need to work in five years.
SPEAKER_00You don't even need to global economy, you're just gonna get a check.
SPEAKER_01You're just gonna get a check. He said that, and I'm like, Okay, guy, okay, guy, thank you. But uh step aside whenever you're done talking here. Because that's the reality, right? You can love these billionaires for what they do, but they have only a small piece in the future. They, you know, if you if you recall who the AI founder was, okay, 1972. Okay. This guy knew what was coming. And there's some videos here, and I wish I could pan to them, but it's astronomical. He knew it was coming and he set it out there. But uh, if we do the numbers, that's 50 some odd years. We're just now being introduced to it, really. Automation has been basically the thing that we've done prior to this. And so I do think there is a future for some change, but it is not going to come in the in the expense of humans uh running on treadmills like in Ready Player One, right?
SPEAKER_00If if you want if Elon wants to send a fleet of robots to Mars to build him some underground thing because he's got the boring company and he's got this and he's got that, right? So he's got the solar power, he can power everybody up there on Mars. All of that is great, and the conspiracy theories can have such a fun time with the different angles of Elon's companies, but the fact is this I'll be dead before we ever get to Mars, which means Elon will likely be dead before we ever get any civilization. That's right.
SPEAKER_01That's right. That's right. Yeah, I, you know, I love the energy and the excitement that it creates, but I don't think it's any different than the first cartoon that was probably published in the 1940s around, you know, Jetson and all that kind of stuff, where people were like, Oh, are we really gonna be able to fly around in space one day?
SPEAKER_00I have this weird dream where Yvonne sitting at a country club with a brandy and a cigar that goes, you know, how are you gonna make it to a trillion dollars? We've got all these different capitalists of industry, right? All these guys that have conquered this, conquered that. I'm gonna take on space. And then he gets all of his billionaire buddies to give him a billion dollars. And now we've got this complete uh it's awesome for the reasons that you said it was, but I like I said, I just have this dream that he was just sitting there and just came up with the idea of having a brandy and all the other rich people around him, and then we funded it.
SPEAKER_01And then we funded it. That's right. And then we funded it for you. That's right. All right, all right. Well, let me uh I'm gonna s uh kind of creep into rapid fire because I got one more question for you that I I really want to throw at you and I think you're gonna enjoy it. Okay. So, Tony, if you had to give one piece of advice to a technology leader who was around for the virtualization era and is now sitting in the middle of the AI moment, right? Drawing on these specifics we just talked about, what first, you know, uh what first bit of knowledge would you impart on them? That's a good one.
SPEAKER_00I'd I'd say build a plan, don't waver, and execute accordingly.
SPEAKER_01Yeah. Love it. Love it. Three key steps. And that taps into kind of what I was saying earlier. The the fastest one to the to the show is not gonna be the one that survives, and is certainly not gonna be the one that shows the way. And I think that's what we're looking for. You know, when you think of strong leaders in our industry, leaders of Titan, they're the ones who showed us the way. Okay, they lasted, they their names are hallowed in in our time because they showed us the way. And the way they did it, ninety-nine percent of the time, was they had a clear vision. Okay. They didn't run to it, but they certainly uh knew what they were going to accomplish when the timing was right. And when they did, they struck. They struck hard and and fast at that point in time. But it was a lead up to that, which could have taken years in some people's cases. And so I couldn't agree more, and that's gonna be the same case here. All right, let's jump in. It's time for the rapid fire. Yeah, so so there's there's there's kind of rapid, and then there's not so rapid on these guys. So I just want to share. We've got some pretty interesting questions, and it may be more than one uh, you know, once one word response from Tony. But if it if he if he somehow comes up with, I'll tell you what, if he comes up with a one-word response on these, we're gonna give him extra points. So he gets extra points for everyone, he can somehow come up with a one-word response. All right. Here we go. First question the biggest myth IT leaders still believe about virtualization.
SPEAKER_00I'm still scratching my head on what one word would describe an answer to this question. But it it's uh frankly, as as consolidated as I can, it's the lift and shift. Most IT leaders think that migration between hypervisors is is a simple task and it and it's not. It requires evaluation, it requires planning, it requires uh many times a very complex migration. And so uh I would that that's what I think still exists today because now that Broadcom has come in and is just, you know, I'm not gonna say anything right, wrong, or indifferent, good, bad, or or or or or or neutral, but they've shifted that industry. And a lot of people in trying to vacate VMware have learned this lesson and this quote unquote myth.
SPEAKER_01Yeah. That was good. That was good. I I I was kind of looking for, you know, going back up to the top. It's an infrastructure tool, not a platform strategy. I think that's the myth.
SPEAKER_00Fair enough.
SPEAKER_01That's well, that's mine anyway. Fair enough. All right. Next one. VMware, Hyper V or something else, right? If you're building from scratch today, what do you pick? Something else.
SPEAKER_00Okay. All right. And personally, I would just build it on KVM.
SPEAKER_01Yeah, KVM. Okay. Because you knew the future. That's it. Love it. Love it. We gotta have a little crystal ball. That's right. All right. AI at the edge or AI in the cloud.
SPEAKER_00Again, man, no one-word answers here. Um so I I like it at the edge. I still think AI at the edge is is the right spot. I that's where we're deploying more and more, and we're seeing more and more adoption of AI at the edge. So I I I just from experience in my day-to-day life right now, I'm gone AI at the edge.
SPEAKER_01Got it. Yeah. Yeah, there was a lot of just difficulty with cost in the cloud.
SPEAKER_00Yeah.
SPEAKER_01And so maybe bringing that closer is is where the differentiator is.
SPEAKER_00And yeah, I think you're right about that.
SPEAKER_01All right. One AI use case that actually deliver that that's actually delivering ROI in the enterprise.
SPEAKER_00Operational X, right? So anything within an ops, sales ops, IT ops, ops, ops, ops, anything within an operational because the parameters are very predict excuse me, predictable, simple to set. We know the expectations. We really understand after so many years, uh, KPIs and measurements that we can we can not only test, but we can confidently deploy and see efficiency.
SPEAKER_01Yeah, that was very good. Very well said. All right, virtualization, this was kind of goes back to what we were just talking about. Virtualization took a decade to hit mainstream. Does AI move faster or does the same inertia apply?
SPEAKER_00What I think it moves faster, but with an asterisk kind of little check or something. I think it moves faster in certain areas and slower in others. And I I think the proof to that is gonna be in the next couple of years. I think there's gonna be some governing bodies or some regulation that comes out. And, you know, frankly, whether you like this comment or not, but we know what is a fact of regulation and that it slows progress.
SPEAKER_01Yep, that's right. And I I I was gonna lean into that too, is it matters what vertical, what industry, because some industries like government are gonna be highly regulated, banking and so forth, healthcare, and those guys will be you know slow-moving shops.
SPEAKER_00Yep. And even states, right? States are creating differential AI policies within the state, so it makes it very hard for AI companies to operate. You have to have, you know, seven different understandings as they adoperate as they adopt their own specific guidelines.
SPEAKER_01Yep, that's right. All right, Tony. Thanks again for another thought-provoking episode on the Techno uh Keystone Technology Podcast. Two disruptors, right? One conversation. And I think what makes this so valuable is that the people listening have actually lived through both of these waves. Yep. So this was what we've experienced right here in this very same room. If this episode meant something to you, share it, forward it to the infrastructure leader who's trying to figure out their AI roadmap, send it to the team that needs to hear that the pattern has played out before and there is a path through it. That's how these conversations get farther. Until next time, keep building with purpose, keep leading with clarity, and keep pushing past the buzzwords toward what actually works.