Techstrong TV July 28, 2025

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You're watching Textron Gang. Hey, everyone. Happy Monday.

Welcome back after I would, I hope is a great summer weekend for you. It's time to shake out those Saturdays and Sundays, though, 'cause it's Monday, it's Monday morning, and we've got our Monday gang crew here to go over stuff with you. Let me introduce you to them quickly, the people of all regular gang members here.

So we've got our Tracy Reagan, Jack Poer, Dr. Stacy Thayer, Mitch Ashley, and of course the dean, Mike Ard. Mike, hope you had a good weekend.

I did. These Yankees are Heartbreakers. All right.

I'm, I'm, I'm making a little tour of New York State starting tomorrow, so, oh, good For you. This be good. Enjoy it.

Enjoy it. All righty. Hey, uh, gang, you know, we're gonna start off with a look at, uh, a midyear look, let's call it at the, uh, CNCF Open Source, 200 some odd products in there.

Uh, projects, not products, excuse me. They're projects, they're open source, but there's 200 of them. They highlighted some of the movers and shakers.

Mike, what do we got? Yeah, if you look at the report that they published, it wouldn't come as much as a surprise that there's a lot of usage and adoption of Kubernetes. It's kind of always been the, the, the lead poster child, but there's also a lot of uptake on open telemetry, which, uh, in my mind is bigger than Cloud native itself.

It goes well beyond that. And then there's all these other projects, some of which are now starting to maybe get enough traction on their own. Things like Argo are starting to, uh, see a lot more usage and adoption.

But Tracy, you live in this space. Is there anything in this report that kinda leapt out at you? Well, first of all, um, it's near and dear to my heart.

The CNCF has been an amazing open source foundation. It really has the growth that it's experienced over the last, I don't know, five years is incredible. Um, you know, I think backstage kind of leaped out at me, uh, and backstage probably as a reflection of platform engineering.

Um, but, you know, when you look at what Backstage is, it's another kind of tool that we, you're doing plugins around. And I believe that that will get disrupted with new ways of doing things, and maybe backstage will be the ones to disrupt it. But when I look at a report like this, uh, if you, and Backstage is unique because it's, I think the company, it started, it was roadie, and they're kind of like us.

They're small, they've pushed it, and they've done an amazing job of building a, a, a platform for platform engineers. But what it makes me think about truly is when you, when you look at those numbers, you have to really see what's inside those numbers in terms of contributors, particularly like Kubernetes, because it's the giants that are contributing. We don't see, you know, when you look at that, you go, there's thousands of, you know, hardworking volunteering contributors, but many of those are contributors are paid by Microsoft, by Apple to do those open source projects.

And that's really what's driving some of these projects. Now, if you look at the broad scope, you know, even Argo and some of the smaller projects that we're seeing in those 200, if you ever take a look at the, the CNCF landscape, it'll make you dizzy. But the smaller projects are truly being driven by small companies who are trying to leverage and make a business out of the CNCF.

But in reality, the contributors are coming from the big companies. So Kubernetes is an important, very important project for every single company that is trying to do digital transformation. And it doesn't surprise me that it's on the top of the list.

So I think that it, we have to look at and, and be realistic about who those contributors are, and, you know, give every contributor a pat on the back who's in one of those smaller projects that are not getting noticed who are doing this because they love open source and they wanna provide tooling that, uh, their, you know, that their community can benefit from. But we're gonna see a lot more. And there is a, there is a danger to what's happening at the CNCF.

It's, it can, I can see it being too big. Uh, the weight of itself is too much to, to carry. And the problem has become is that everybody wants to, every project wants to be part of the CNCF.

The CDF even is losing projects to the CNCF, because that's where all the, that's where Kubernetes is. That's where the money is. That's where the, the attention is.

And so I, it, it gets diluted, right? It gets a little bit diluted. Those 200 projects, I don't believe all of them should be there.

In fact, I would argue that Argo should be in the CD foundation, and there are security tools that should be in the open SSF. So it's a little bit of, I would call it a bloated look at the projects. And if you really look, look at the contributors they are.

There's a lot of big companies who pay contributors to do the work. Yeah. So couple of things.

First of all, for anyone who looks at the article, there's this bubble chart of all the different projects I should mention, that's not just CNCF projects, that's actually all the Linux Foundation projects. And the, and the list of the, the movers and shakers is not just CNCF as well, there are also Linux ones on there. But that being said, when we look at just the CNC Chasey, I agree with you two, 200 plus and growing projects is, is a lot.

I've spoken to the CNCF people about this. And, you know, they feel they'll just keep taking projects as long as, you know, because that's their charter. And it's, it's sort of becomes the, the child who eats the parent almost, right?

Or it's that one big bird in the nest that's taken all the food and all the other little baby birds aren't as big, and they're not as healthy, and that's not healthy for the whole ecosystem at some level. And I, I do don't disagree there. The other thing I would mention though, is this is momentum, not just who has the most developers, but who's adding the most developers, who's adding the most are the key metrics.

And when you look under the covers a little bit, yes, Kubernetes still rules the roofs, but Open Telemetry may have in fact, more velocity than op, which is, they Probably have, I guess they have more end users. They, it definitely is, you know, the end users who are usually actually implementing. Yes.

The velocity is there. I too was happy to see Backstage on there. com site about, I wrote about Backstage specifically, you know, it just, so Backstage was Spotify, if I'm not mistaken, right?

Right. Backstage is Spotify file. And then they, they contributed, they gave it to the CNCF in 2022.

So in just three years, it's shot up into the top 30 there. And it, and it's rising with a bullet. But at the same time, as, as I think you'll see in the, or my article on Platform engineering, you know, Spotify's now come out with a hosted version of Backstage, uh, SaaS version that they charge for.

And there are some other, uh, you know, internal developer platform, uh, competitors out there because, you know, the, the story with Backstage is the same thing that plagues, not plagues, but it's the same story we hear with a lot of open source stuff. It's hard to use it's command line. It's, you know, it's not as polished.

It's not that commercial version. And, and if, if that's the kind of person go, go use the SAS version. But anyway, um, Argo and Flux, the two GI ops players absolutely are, are, uh, you know, hitting, hitting strides.

Look, overall, it's a very healthy market at the top, but I'd like to see the bottom 50 of the 200 and see what kind of numbers they have, because those people may in fact be better served, finding someone who can show 'em a little more love, a little more Time. Yeah. It's hard as an open source, as a open source, you know, running an open source project within the Linux Foundation, when you're one of those small ones, it's hard to find the love.

You really have to make a big, you have to, you have to make a lot of noise. Mitch, I have a conspiracy theory. My conspiracy theory, Monday morning conspiracy theory.

All right. It was Epstein. It was Epstein, wasn't it?

Right. The project teams, because they're funded by vendors, are not exactly all that interested in making everything easier because their vendors are creating the commercial version to make things easy. And there's not enough time and effort in being put into making things easy, because it's just not in anybody's economic interest.

Hold it, hold it. You mean there's gambling going on here? There's financial interest happening.

Oh my gosh. I, my heart's broken for open source. You know, I mean, it's, it, it's, it's real.

I mean, companies are contributing because that's in their financial interest. It isn't pure altruism. Maybe it's not even altruism at all.

Right? I mean, I'd actually be major open source advocates as much as it's important to their business. And if it wasn't for that, maybe they wouldn't be.

I'm not, I'm not pointing any fingers at anybody, but it, it, it's, you have to also look at the other side of it. It's, that also drives a lot of the innovation and moving projects forward. Whether they're new ones that are contributed, like we talked about backstage, where it's accelerated two and three years, or, you know, Kubernetes and Kub Flow and, and Open Telemetry, I think is the kind of poster child for involving a lot of people, uh, a lot of vendors out of an ecosystem, uh, to work together.

So much so that both Kubernetes and I think Open Telemetry too, has kind of passed the cloud native moniker, at least in terms of their use. Alan, you and I were talking about this the other day, is, is, uh, is Kubernetes really a cloud native thing anymore? It's really a workflow engine, workflow system that works on anything.

AI applications or what, it doesn't matter what it is, anything that's containerized, and it could be a cloud native app, quote unquote. So it, you know, I, and I think on the plus side, I'm, I'm super happy that we've got a great ecosystem of open source and with what the CNCF is doing. I don't have an appreciation like you do Tracy, of what it is to be one of the small teams and projects.

And I'm sure that's a struggle. Maybe that's something the CNCF could take on. It's how do we identify projects that have a lot of promise, but need a little bit of help, need that sort of, that startup nudge, you know, get 'em to the next tier to help 'em accomplish that and maybe find a sponsor or two.

And there may be projects like Open Telemetry that are bigger than Cloud Native, and maybe it's time for them to move on and become their own little foundation. I don't know. I'm just putting it out.

I kind Of think so very Possible. The project I thought we would see in that list was open tofu. Mm.

Mm-hmm. So, I, I don't think it's had enough runway Mm-hmm. To really, 'cause that's a hard list to crack, right?

Oh, yeah. You gotta, yes. And so I, I'd say if it's not on by next year, it'll, or maybe by the end of this year, 'cause this was just a mid-year look, look, let's see, end of year look, then I'd say, okay, maybe it's, maybe there was a lot of smoke and not as much fire there.

That's a good one, Tracy. I agree with you. You know, they, they're not abandoning the, uh, Hashi stuff, um, anyway, though, it is a, it, it's a, it's a colossus, right?

This CNCF and the whole open source world around it. I, I was just interviewing the CEO of Root io and they're doing agentic uh, remediation on the fly of a lot of these open source tools and components that we're seeing in there, even the oss and, uh, we're, they donated a project to the CNCF two, uh, slim or something. How about we see an AI agent for all these projects, and that'll make it easier because that one AI agent can figure out how to learn all these things, right?

Or do I need a hundred AI agents because there's a hundred projects? I don't Know. It could be the maintain, maybe subagent.

I wanna be your secret agent, man, right? Let's, let's get that song going. I always knew it's gonna be like Downton Abbey, there's gonna be a head Butler agent, and then there was gonna be all these other agents working downstairs, taking care of stuff, You know?

But I think about that. Are they other agents or are they just alter egos, you know, sub-agents, if you will, of that master agent? Well, you know, it's funny you say that, Mike, because, uh, Walmart just announced they're actually doing exactly that.

They discovered they had way too many agents. And so they're now consolidating and having one agent, one master agent for consumers, another master agent for their vendor suppliers, another master agent for the developers, which will then form out work to all of the sub-agents that you were talking about, right? Yeah.

To lay out of agents. Wow. That's, that's a first lay, Not, and this goes to that.

There's that amazing discussion that we should still be having around how to lay these agents out. And a domain structure will be so critical. And it's not always about LLMs, right?

It's gotta, we have to start ma making a more container, or I shouldn't say use the word container, but more specialized and more domain specific. I'm with you. And We should, we should have that conversation on one of these calls.

We should somebody I, I know someone I, I can make that happen. We'll put it out there. I'll, I, I'll, I will, I will pencil that in for a, a week from today.

All right. All right guys. Hey, um, we're gonna take a break here on text strung.

Bang dang. Let's come back and talk a little digital transformation modernization. I don't know what you call it.

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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 talking about digital transformation. And a funny thing happened on a way too, innovation.

I think we discovered that there was just a lot of stuff that we were working with that was suboptimal and just wasn't working the way it was supposed to in the first place. And a lot of these digital transformation initiatives shifted from being about some great new innovation thing to simple, good old fashioned modernization. Alan, you wrote two stories related to this topic on digital CXO invite you, I'll check those things out, but what's your take on what's happening here?

You know, I've always felt that digital transformation was a loaded word, right? It meant something different to different people. To some people, digital transformation meant transforming from an analog world to a digital world, right?

They went from a brick and mortar store, maybe to actually being able to sell stuff electronically online. COVID was a huge, uh, catalyst for that, right? You, you, you people didn't come to your store shopping.

You had to get it. You had to get your goods to them and be able to sell it to them online. Then for others, digital transformation became code word for moving to the cloud, which to me was really a modernization play.

But for o, for many people, digital transformation was, I'm going to take advantage of the latest digital. I'm already digital to a certain degree, but I'm gonna take advantage of the latest digital platforms. For others.

It's a much broader term, emphasis on transformation, not on digital. And that's the second article I wrote, the Rise of the ct, little RO, the chief Transformation Officer. Their charter is to transform the company or their organization into a modern organization.

And it's not maybe just, it, even, frankly, it's the people process technologies. It's, it's a modern transforming to take advantage of modern, uh, formulas and, and and so forth. So, while we all are, you know, know, and they're, I cited in the first article, there's companies that put out, here's the stats on digital transformation.

It's a gazillion trillion dollar industry. And there's all of this and all of that, and all, all these big numbers, highfaluting numbers that make you say, oh my god, digital transformation is rocking and rolling. It's, it's the major force in the industry.

But when you peel back those numbers, what you really see is a lot of the money is being spent. Mike, as you said, on good old fashioned modernization. We're modernizing.

We're modernizing from waterfall to microservices. Cloud native cloud and cloud native from data center, right? Containerization, we're using AI and machine learning and other, we're mo we're moving from a PM to observability, right?

All of these lit, not little, but all these disparate things that we do that make for the modern enterprise, that make for the modern IT stack. And we could still call that digital transformation. I do believe that still is digital transformation, but I think we've gotta be smarter about what we think of when we talk digital transformation.

You know, Al, if I had to play historian for a moment, I remember the first time I really kind of heard about it. I don't know if it's the OG of Digital transformation, but if you remember Tom Siebel, Siebel Systems, CRE, early CRM player, he wrote a book on it. And he described there were four things.

Um, it was moving to the, this is, this is how he defined his book. This is pre COVID. So it was, uh, moving to the cloud.

It was automation, it was data, and it was ai. And so that was kind of my OG definition of what digital transformation is. And I think as COVID hit and we needed everything to go digital, then the digital become what the trans trans, uh, transformation was about, is to getting things into a digital format.

'cause we can't do manual processes with everybody working from home. And then that started to expand, as you mentioned. We saw a lot more going to the cloud.

One of the characteristics of my, my time of working into in large enterprises is what happens is as soon as projects start getting of a certain type, start getting funded, suddenly your project is now one of those kinds of projects. 'cause that's how you get funding for it. So our, whatever it was, Mitch's, Mitch's favorite project is now a digital transformation project.

Let's go get those funds. So it just o broadens the definition. And that's kind of where we are.

It's a little bit of everything. It's a, you know, it's a rainbow of different kinds of projects and it really is some modernization, some going digital, some going ai. I think it's a move forward taking your, your technology and moving it forward in some way.

Mitch, to some extent, yes. And to some extent, no. That not a new concept going way before siebel's book was, uh, back in the mid nineties, was a book called Re-Engineering the Corporation, which was all about how do we transform the corporation, if you remember that.

Right? I remember re-engineering projects. Yes, I did.

Right. Re-engineering projects. And it was really about, um, uh, arguing against incremental improvements and going for quantum leap type improvements.

And in one sense, a digital transformation, digitizing your analog processes really is that quantum leap forward or quantum leap different. And they argued for which I think now 30 years later, we're seeing the formalization of a leader, in this case, the Chief Transformation officer at CTRO, because you need somebody senior enough, or under the organization who has the authority and the sway to actually evoke change. That these are not things in large corporation that can be done from the bottom or from the middle.

They have to have a mandate from the top. Now, once you have that mandate and you start doing those projects, yes, everybody wants Tolo on because that's where the money is. But I do think that there's a very valid purpose in having a chief transformation officer who has the mandate to change the organization, how it does business, which really means changing the culture of the business as well.

It's, to me, it's much more than just the digital workflow. The, the mandate though, is complicated because it kind of goes like this digital transformation officer launches new project, builds new app, and then needs data to actually feed this thing. And then they gotta call up the CIO and they call up the CIO and they go, I need access to this data and I need these legacy systems of yours, need to have APIs, which is usually followed by a comment from the CIO that's roughly equivalent to what you're talking about, Willis.

And then it just sits. And that has gone on for how many years, right? Since The beginning of time it Was a digital transformation where went from a, a green screen to a screen scraper.

Remember that phase? Mm-hmm. We've gone through, we've gone through, we, we are never, still, we do not float in still water.

Yeah. This industry mm-hmm. Is in a, it is, you know, on a kayak in a, in, at the Colorado River at its most, you know, highest point, we are constantly, uh, changing.

There is never a point in time that we're not transforming. And how long will it be before AI is not the shiny new tool. That's all quantum.

I'm already getting there. And most every, every new term that comes out, I feel like it's just a way to market to investors, right? It's all about getting funding and these small companies coming up.

And now if you don't have AI in your, in your portfolio, then you're not being looked at. How long will it be before it says you've gotta have Quantum. This is just who we are as a community.

We always have been in a constant flux of change. And, you know, I don't know why we now have a digital transformation officer. I think it's kind of a cool term.

I wanna be one, it looks like a really fun job. But you know, they're gonna, what are they gonna transform? Are they gonna ever stop?

So I think we just have to see it in for what it is that we, we're an industry that can't stop. We can't, we have to constantly evolve. There is no other for you.

We're like sharks. We're like sharks. If you're not swimming forward, they drown.

Yeah. If you're not swimming, I think drown. Yeah.

I think Evolve is really, the word that kept coming to my mind was that we have to evolve. It's almost Darwinian, right? If you don't keep evolving, if you don't keep moving into technologies, some companies, people become extinct in their careers or in their trajectory.

So that, that's what came think transformation. But Chief, chief Evolution Officer, well, you know, we'll see if that comes up. But at least it's, well It's, there are people who don't believe in evolution, still don't figure out.

Well, I know, that's why I'm touching that one the US Anyway. Um, but one more thing, the more things change, the more things stay the same. Yeah.

We still have programmers. We still have to sort scan code. We still have to run and build.

We still have to figure out how to deploy things. We still figure have to figure out the dependencies across all of these components. They're just parts, parts or parts.

And, and in that aspect, we haven't changed any. But given the, the role of the transformation officer is to eat the sacred cows, basically. Right?

They make the Tastiest hamburgers to borrow another book's title. Right? And the problem, the problem in many organizations as people are resistant to change, because we've always done it this way.

It works. And yes, it works, but can it be better? And can we do more, quicker, better, faster, more if we change?

And the in organizations, you know, there, there are two opposing forces. And if you don't have something opposing the force that wants to stay static, you will stay static. You know, that, that kind of reminds me of the goal and Phoenix Project and all that stuff, right?

Mm-hmm. Absolutely Change. And, But I, I wish we were a little more deliberate about it.

'cause when you go visit a lot of these enterprises, you discover that there's just stacks and stacks of crap and projects that people built and they're all kinda isolated and nobody actually, you know, integrated much of this with any kinda real plan. And it's different business leaders funded and all this other stuff. And it's just kind of a mess.

It's interesting how it evolves because some things do fall by the wayside. Like you were talking about. AI might not be the shiny object anymore.

I'm having a little bit of PTSD from the re-engineering era of the nineties. Jack, thank you very much. Um, for me, that was all about moving to clients, client server using X Works stations instead of terminals, you know, on on, on, uh, service reps, desks and things like that.

Which is about bringing graphical user interface, putting Unix boxes on people's desks. Well, that kind of would, by the wayside is we replace that with computers and, you know, windows devices. But it was part of the evolution.

It was part of the way there. Yes. The client server stuff stayed and then we evolved that to where we are today.

But it, it's, it's part, I I like your river analogy, Tracy. 'cause it's a constant motion thing and you, you trying to paddle against the tide or against the, the, the river. Good luck.

Right. Especially if you're at the peak of it. Um, so sometimes it carries you if you're not, uh, be careful.

Yeah, for sure. Alright, let's take a break here on Textron Gang. We're gonna come back into our C block, busy B Body.

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Home of security bloggers network. Hey folks, we're back in Amazon acquiring in a little startup called BI. They make this kind of bracelet thing that auto transcribes conversations and summaries and, uh, might even occasionally raise the privacy alarm.

We'll see about who's listening in on this stuff. But, um, Stacy, I would love to get your impression on this because in my mind, the minute I read this, I was like, so let me get this straight. Every, I gotta check to see everybody's wearing a bracelet before I open my mouth to see what the heck I might say or not, notset.

Mm-hmm. Well, this new wave of wearables, I mean everything from the, the Veta glasses recording things to, you know, if there's something on your wrist, what are people wearing and what are the privacy implications of that? Are people sharing if they're wearing something that can be recording for you?

You know, is it one of those, like, when you go to an event, is it recording is in process where people have to, you know, make, make it known? What will, what will happen with this as we, you know, and, and then the question comes, why are they wearing it? What's the purpose for it?

Yeah. I don't, I mean, my memory is bad, don't get me wrong. But, you know, I don't know if I wanna report.

We called speech. So I I had the same thoughts, Stacy. Um, so first of all, look, we all think Amazon's listening in on us through Alexa anyway, right?

I, you know, and so what's different about this? But there's two things here. And they're on both sides.

You know, one's on one side, one's on the other side of this. The first thing is, there are definitely laws, whether it's on a phone conversation or meeting, right? I mean, we're not talking about policemen or law enforcement.

Two Party or one party is what they Usually call, right? That you've gotta be notified that what you're saying is being recorded. Right?

And I don't know mm-hmm. How that applies in this situation and if it would ever be admissible or usable or, or whatever. But I will tell you, you know, God, my brother-in-law's much older than my wife and I, God bless him, he's 81 years old and he's walked around as long as I know him, which is a long time, uh, with like one of those little recorders.

And you'll, he'll, you'll say something to him, he'll say, wait a second, say it again. And then he'll make a mental note on his recorder with it. My brother-in-law, Phil Mitchell, you've met him, I believe.

Mm-hmm. Mike, you probably have too. I've met him.

He always makes his little notes. 'cause then he goes home and he actually takes notes off of what his recorder says, and he has recordings going back years. So it's, I I think in that regard for seniors, for people with memory or, or just people who like to take notes, what, it's a great thing.

It's a great thing. And if you don't, if you don't mind the privacy, it is a great thing. But will everyone else think it's such a great thing?

And that's really the Idea. Well, there's also the unintended consequences of it, right? Like, like, do all of us want cameras in our car to record everything we're doing and how fast we're driving?

And so the insurance company I know, so the insurance company can up our rates. Um, do we want to be wearing body cam someday? Everybody's wearing a body cam and it's it's in their glasses, or it's in their jewelry or whatever.

I mean, that's where we're headed. And that there are the benefits side of it, but there's also the consequences, which is okay, that can be used in a court of law when something happens. You remember it this way, but you're, your, your body camera remembers it differently.

Um, so, so the putting it in digital form can be a, a blessing and a curse at the same time. Well, that, yeah, that's, I think what I was trying to say. There's two sides to this.

So There's, I think there's two things that I wanna talk about. First, I wanna make a disclaimer that I actually use the Applaud Pen, right? Oh, you've been recording us all this time, Jack.

Well, I'm, I'm heard. So, so it just clips on here, right? The difference is, this is not an always on device.

You have to turn it on when you want court. Now, I find it very useful for in meetings, or particularly in, when I do briefings at a conference, it's very useful because then I can be in the moment and have a discussion knowing that it's take, it's, it's recording it. So I have notes afterwards, rather than sitting there having to write stuff down.

That's so, so that's, but it's not an always on device, so it's not recording everything all the time. I think more importantly for this is Amazon's acquisition and what that means. 'cause Amazon has a very mixed record with privacy, right?

So you have the Alexa device. No, Hold that. Jack, let, let's not mince words.

It's not a mixed record. It's a very, very tarnished record. I'm trying to walk a fine line, dad, you know, I Gotcha.

Um, Amazon bought Ring, which is effectively and always on camera for your home. And, uh, once they bought it, there wa uh, people discovered a lot of privacy implications, uh, including that Amazon basically gave access to your local police department to your cameras so that they could use it. Now, people were less upset about the cameras they had pointing outwards than they had of the cameras pointing into their homes.

And why would police want to get access to any of that? So there's, there's a lot of privacy implications just with Amazon acquiring the technology outside of the technology itself because of Amazon's very mixed reputation. Yeah, no, I, I do think that because it's Amazon, people are gonna look a little bit more closely at it be just because what, what's Bezos gonna do with that information already?

What, what do we think he does now? Um, the other thing though, I'll mention, there's a difference between video and, and Jack voice recorder, which is in essence what this is though. This is a voice recorder, the transcription service.

Is the transcription service really good, right? 90% means one out of every 10 words is still wrong. Uh, I can't speak to BI can speak to plot to say, and to, uh, say Zoom, which does, can, will record your Zoom calls, right?

They're actually very good, um, with most modern English. Now, I can't speak to any other language. They often get names wrong because names are, can be very difficult.

But for my purposes, the recording, the transcription, the AI analysis has been very helpful. I am concerned from a privacy, both from an enterprise data privacy, data security point of view, and from just a personal, uh, privacy, security perspective of who has access to the data and what do they do with it. Now, b claims that they re they instantly transcribe.

So they don't actually record the audio. They listen and transcribe. They don't save the audio itself.

They save the transcription and their analysis of it, and they move that to the cloud. Uh, applaud actually records the audio, sends the audio to the cloud, or to your on app on your phone, and does the transcription afterwards. So again, it's who has access to it and what are they gonna do with that data?

And then from an always on nature, if I'm at a place where it's not expected that you're being recorded, like at a conference, everybody knows everything's being recorded. So I don't worry about it, but if I'm having a conversation one-on-one with somebody, I ask them, can I record this? I, I, I think that's the decent thing to do, Right?

I just think it, it's, It's Just weird to me. It's just weird. I don't know why I would wanna record every conversation I had.

I can see wanting to record some conversations. Like, you know, if I'm doing research and I wanna, I interview somebody, and I usually try to do Zoom so I can get the transcription, but just to have it on all the time is odd. That to me, I don't understand the use case at all.

It just Reality TV Stars. Well, do you remember the episode of Black Mirror where they can see everything and then rewind it? It was like in the, and I think that was around the time I stopped watching, because the idea of that was just like, oh God, the trouble that, like, you know, the, what is the reality on the tooth?

And it's, and when I look at this, and I think, okay, so like when I give toxic conferences, they're very human centric. And so burnout things, what, uh, we wouldn't, uh, uh, sometimes allow recordings in there because it's of human nature. I, and so the idea that anybody could be recording you at any time, conversations like, does it, what's the noise?

You know, how, how much sound does a pickup around you? What's the ambient noise factor? I have a lot of questions.

Can you go to Europe with this, for example? You know, I mean, it's just, to me it seems like such a rabbit hole of what can you do with this? Let Me, let me, let me propose something to y'all here.

Howdy, boomer or Xer, is this a generational thing? Will, will my children really give a hoot if everything's recorded? The 20 some, the Gen, I guess Gen Zs and whatever comes after Gen Zs, right?

I hope so. I hope so. Are you asking 'em to clean their rooms?

Because then No, no. I mean, Like a mom, but yeah, I get it. I, I think this was, I think if they record everything that happens after they let leave a bar at 2:00 AM then I think they'll care.

Yeah, exactly. But it kind of, it's kind of authoritarian actually. I mean, if, look at China, China records, Everyth, Everything, everywhere it looks, it's very authoritarian.

Well, London, there's a lot of video in London too, though. You're, London has More cameras than any other city in the West by far there. It's, everything is surveilled in London.

Yep. So, so I'm Gonna, I'm gonna join The Masons gonna have secret hands, please. The secret handshakes the whole thing, right?

You know what we're talking about. Alright. Anyway, hey, this has been recorded, so it's all good.

Let g thanks so much for joining us in the great discussion today. I hope we've got your week off to a good start. A little hopefully light, not too heavy, but there's good stuff there.

As usual, we have a lot of tech junk TV following the gang today, so stay tuned for that. Do check us out on our OTT channel, whether you're on Amazon or Roku or uh, apple TV or iOS, if you're running the new OS 26 or Android as well, or text Drunk tv, text drunk TV on YouTube, wherever you watch us, we hope you enjoy it. Until tomorrow, this is Alan Shimel, we're out.

Hey everyone, welcome back here to Tech Drunk tv. You know, in our lead up to our, uh, special Black Hat coverage this year, uh, coming at you August 6th and seventh, um, we're doing a series of, of interviews with what I consider industry titans, industry luminaries, uh, about important security topics that, you know, we, we talk about a black hat I could think of a better guest to have in that series. And my next guest here, uh, he needs no introduction to those in the cyber world, but I'll introduce him anyway, my friend John Kinderg.

John is the chief evangelist at Illumio. Of course, he's had a distinguished career as an analyst, a practitioner, and really sort of known as the Godfather of Zero Trust, if you will. John, welcome back to Text Drunk tv.

It's great to have you on. Hey, great to see you Shimmy. It's been been a while.

Been too long, man. Yes, it has. Yes, it has my friend, you know, but you're looking good.

Good for you. Thank you. Thank you.

Yeah. Uh, So John, before we jump into Zero, trust the Black Hat and all these things, look, as I said, everyone knows Zero Trust, and, and if they know Zero Trust, they've probably heard of you, but maybe not everyone knows Illumio and, and some of the great things they're doing. If you wouldn't mind, let, let's just spend a quick minute bringing people up to speed on Illumio.

Yeah. Illumio is a company that, uh, started, I, I guess about 10, 11 years ago. Um, and they do microsegmentation, which is a key technological need in Zero Trust to create the micro perimeter that defines what we call the protect surface.

A answering the question, what are we trying to protect? So I've known the company since it was founded. In fact, I did the first ever analyst inter interaction with it when I was at Forrester.

And they've been a big supporter of Zero Trust. And when I had the opportunity to come over here, um, after looking at what they've, uh, built, you know, over the last 10 years, I really wanted to do that because it, it gives me a way to articulate what we need to do to build zero trust environments. And, uh, there's some really cool technology and some innovations here that we're doing around how we build segmentation, how we create policy, how we map out the transactions on the networks so we have complete visibility, and then how we take that visibility and gain what we call insights from that.

So it was a perfect fit for me in this, the, you know, the later, uh, stages of my career as I, as I age out Here. Oh, come on. The best is yet to come, John.

Be Kidding. We'll see. We'll see.

You and I, we go back 20, I was just thinking how long I've known you, man. And it goes back to the, it's Gotta be 25 plus years, dude. Yeah.

It goes back to the still secure days with Raj and everybody. Absolutely. Yeah.

I had, I had black hair, I had hair, period, and it was black and a different world for sure. My friend, um, you know, I remember when Lumio was launched too, John, it was their chief commercial officer. I forget the guy.

He Was Alan Cohen, probably Alan Cohen. Yeah. What a great guy, Alan.

Yeah. And what's interesting though is Lumio was always about that microsegmentation. Yeah, right.

They were really early though. Yeah. You know, and it was almost like a, a, a solution in search of a problem or, you know what I mean?

Like what, getting that market match. Yeah. No, there was a problem and there was a solution.

'cause I had already written about the need in 2010. I wrote the second zero Trust report ever wrote the first one in, uh, September of 2010. And then November, 2010, I wrote Build Security into Your Network's, DNA Second Ever Zero Trust report.

And I, it was mostly about how to segment networks to create what we now call protect surfaces. And I said, uh, new ways of segmenting networks must be created because all modern networks must be segmented by default. And so I've been on, you know, on this train for a long, long time because I don't see any way to provide any level of secure transmission of packets without segmentation technology.

Yep. Uh, uh, I, I don't disagree with you there. You know, John, though, you mentioned you wrote this first one, what was the September of 2010 you said, right?

Yeah. So just coming up here on the 15 year anniversary. Right, right.

Let's talk, if you don't mind, you know, take a walk through memory Lane. This is your life, John. Ve you know, Judd, let, let's take a walk through though, over the last 15 years and, you know, how has Zero Trust evolved since that seminal first and second paper that you wrote back in 2010 to where we are today?

A lot of it is still right there, right? But the world's changed a little bit too. Well, I mean, I think the strategic elements of it are, are the same.

I mean, zero Trust was designed to be strategically relevant, uh, and, and impactful to the highest levels of any organization. So, you know, grand strategic actors, CEOs, presidents of companies, generals, admirals, those kinds of people. But it was designed also to be tactically implementable using commercially available off the shelf technologies at whatever state those technologies were in.

I knew the strategy wouldn't change, but the technologies would, and that's turned out to be true. So the advancements have been that there's more technology focused on achieving the goals outlined in zero trust of stopping data breaches and, and, uh, designing the network from the inside out so that you have complete visibility into, into what's going on. And, and attackers don't have places to hide.

Good. Good. John, when you wrote those papers back then, did you ever think this, the Z zero trust would, would catch on the way it has and become sort of a standard?

Oh, absolutely not. I mean, I'm the most surprised out of everybody because when I first came out, uh, with this stuff, there was, you know, a lot of people would come up to me and tell me that I was completely insane. I was an idiot.

Uh, those were the nice comments, right. And, uh, uh, we've been there. Yeah.

And, uh, but then, you know, uh, some people started to do it, experiment with it, and got a lot of positive feedback. And then some people who were extremely important but are quiet, uh, came out and said, no, this is, this is the wave of the future. And I remember one guy said to me, you realize zero trust is gonna be your life from here on out.

And I said, you're, no, you, no, it's not. I mean, because I was working on, uh, encryption stuff at Forrester. I ran the data security and privacy playbook.

I was working on analytics and what is the future of sim And I created the concept of a virtual SOC and all these things. And so I thought, no, this is just one part of it. But he was right because he had some insight that I didn't have on some of the, some of the, some of the people in organizations who were very into zero trust.

They just weren't publicly yelling it from a mountaintop. Excellent. Excellent.

Um, so John, let's fast forward to the last couple years. You know, you, you can't walk three steps without tripping over some ai generative agent, whatever's next. Right?

Um, how, how is AI helping herding zero trust initiatives across the board? Uh, it, you know, it actually helps a lot. Uh, I, I wrote a, I wrote a, a blog post a while back, why I'm not losing sleep over AI because everybody else is worried about, oh, ai, they're gonna be able to make more sophisticated attacks.

Yeah. But ai, we're gonna be able to have much better visibility and stop those things before they can ever get there. So it's, uh, you know, I was just up in, uh, Bletchley Park giving a speech out outside of London, north of London, and, uh, I've always been inspired by the movie, the Imitation Game, about breaking the nice sign code because in the, in Turing yeah.

In the movie, the ca uh, the guy playing the character of Turing says, uh, what if only a machine can defeat another machine? And that has always been sort of an inspiration. And so that's what we're doing.

We're building the machine to defeat the machine. And Zero Trust is the strategy behind that machine, the idea that you use to, to put all the parts and pieces together in the machine. And that's what I was always focusing on, is how can I eliminate so much of the manual stuff going on and automate this?

And so AI allows us to do that. And so while they may be able to make more sophisticated attacks, there's not gonna be policies in place that allow that attack to be successful in properly, uh, designed and, and deployed and maintained zero trust environments. And so those attackers are gonna move on to somebody else's environment that's more of a low hanging fruit.

And that's the key thing, right? Is that, that, you know, we're not gonna, not everybody is gonna do it. So there's always gonna be some soft targets, and those soft targets are going to be attacked.

As somebody in the federal government said to me, attackers don't def defend attack well defended, uh, environments. They just don't because it's too costly. Right.

They have an ROI, so once they figure out, this is hard, uh, it is gonna be expensive, we're moving on somewhere else. Agreed. John, when you look back at the 15 years, and you, you know, in your role at Illumio, you're, you're talking to organizations every day that are implementing microsegmentation network segmentation, implementing zero trust.

The people out here, let, let's save them some idiot tax. What do you, what do you see as the most common mistake people use, make commit when they, when they try to implement a Zero trust type of initiative? There's two mistakes that are common and typically tied together.

One is they think it's a product, so they become very product focused versus protect surface PO focused. They don't know what they're gonna protect, right? So I buy a product, what do I do with it?

Well, what are you trying to protect? Haven't thought about that yet. Well, you're gonna fail.

The second big problem is they try to do it all at once for everything. And you can't, you have to do it in bite-sized, manageable chunks. There's no way, you know, take your favorites consumption metaphor, the, uh, journey of a thousand miles begins with the first step, or how to eat an elephant or, or a, uh, One spoon at a time.

Yeah. The Whole thing. Yeah.

Yeah. The whole thing, right? So, uh, but people get too, too big and, and, uh, so those are the two main things that cause people problems.

They, they, they start too big try to do everything, and they think they can buy a product instead of, um, you know, develop out a strategy. Yeah. I, I don't disagree.

I mean, John, we've both been in security community a long, long time. I would say what the, the first one that you mentioned there about buying it before they figured out how they're going to use it is, is such a, it, I remember did a survey one time, I forgot, was it 27%? Like some outrageous number of security tool purchases became shelfware, actually not became, shelfware were always shelfware.

They never got unpacked, right? Because someone bought 'em. It was a great magic bullet.

And then they realized before they could unpack it and install it, there was actually some work that had to be done, and it wasn't the magic bullet that they were hoping it was gonna be. And, and, and so it just stayed there. And then you ask them, is that solution?

And he goes, oh, no, that solution was terrible. Well, you never even unpacked it. Right?

And, but this is, that's the wacky world of security cyber that we, we come from, right? So that is, is a big thing. And, and that's why I, I don't call zero trust of product, right?

It's an initiative. It's, it's the whole enchilada. It's people, process and technology.

And, and if you're not gonna put that kind of effort into it, don't waste your time, dude. Right? I, that's, Yeah.

And the effort isn't that hard. Some people think it's no real really hard. It's actually when people get, uh, get, get to a point where they understand it, I, I had one customer call me up and say, wow, we argued about doing zero trust for a lot longer than it took us to deploy our first zero trust environment.

So that, that's the thing that shocked us. We, we just had to, you know, talk about it, talk about it, and talk about it, and talk about it. Instead of doing a small version of a small, a single protect surface, what I call the learning protect surface.

You know, do something that, that has low sensitivity, uh, early on so that you can begin to learn how to do it. So if you, if you mess up, it's like street basketball, no harm, no foul, right? You ain't bleeding.

Mm-hmm. I'm not calling a fly foul. We've Got plenty eyes, right?

We've got plenty eyes. That's Right. That's all.

Yeah. Yeah. I, I, I, I, I'm from New York.

That's time we played there. That's Right. Yep.

But you know, it, it is interesting like that, John, we are coming into summer security camp season, right? Black hat Defcon besides Vegas and a bunch of other things in the next week or two. Um, unfortunately you are not there this year.

We'll miss you. But what should people keep their eye on there? What, what, you know, what, what do you think are the, the themes and things that you, We should watch?

Well, it's, it's gonna be all ai. I mean, the, the nice thing about AI is at least it's, it's kind of taken a little bit of the hype off of zero trust so that pe people can think of zero trust in a, in a more, uh, coherent, uh, less hypey way. So it used to be zero trust with the big hype.

Now it's ai. Uh, so I think, you know, you, you need to look at what you're gonna get out of your AI and what's, what's happening there. And it, and, and, uh, is it really ai?

And there, there's a, you know, my favorite definition of AI comes from a mathematician friend of mine who says, AI is stati statistics plus if statements. And when you boil it down to, to something like that, you can really see, uh, what's happening. But, uh, you, you know, you're gonna see a lot more stuff about how to use AI than protect the stuff that you put into ai.

And that's gonna be the threat to these organizations. Excuse me, the, the threat to these organizations. We're seeing it already where organizations are using AI and then finding their sensitive data, uh, their intellectual property inside these LLMs, because there's no way to govern them yet Now, well, we, we, not only that, we haven't figured out sort of the best practices and processes, like, don't blame Theis for this.

It's people uploading that data to the ai, you know, it's, it's always the same story, John. It's the guy behind the keyboard, Right? Right.

Buts that upload sensitive data. That's where the technology has to evolve to the point where it understands what the, what data is sensitive and says, no, you can't upload that because, well, Yeah. So we're putting guardrails in, we're gonna do ethical AI to make up for dumb people.

Right? Is that, you Know? Yeah.

I mean, yeah, and let's talk about that, because I don't know that the people are dumb. I think their incentives are misaligned, right? So I talk a lot about incentives, and I've written about that, like for Financial Times in London, and I think we have perverse incentives in our, in our industry.

And so a lot of times it's like, just get this thing done and get it done fast and use ai. I hear people are being told, use ai, so they use it and they, you know, they don't, the nuances of how it works, do they understand that when they upload a document to get it analyzed, that it goes into some massive database called the largest language model, that that is somewhere else? And and they've totally lost control over it?

Probably not. They probably don't know how it works. So I think, I think it's not that the people are, are doing bad things or, or, or doing dumb things.

I think that, that they're doing things that they're incentivized to do, do, and the technology isn't there to protect them from getting themselves in trouble. Right? So it's a lot easier to, to create policy to keep people from getting themselves into trouble than it is to educate them on all of the nuances of all these technological innovations that have been coming down the pike so fast for, for such a short period of time.

I, I agree with you, and you know, John, I think I learned in law school a million years ago, generally it takes society three to four years to catch up on technologies. Yeah. Right?

And, you know, we live in a tech bubble. We're both in the tech world. So, you know, of course, AI is, AI is almost old hat to us already, even though it's only been, you know, two years.

But, um, it's gonna take time. It's gonna take time for, I, I learned this when I did the DevOps Institute too, with DevOps. There were no best practices for DevOps.

There were emerging practices for DevOps. And I think we're gonna go through a similar period here with ai, especially ai with technology and security, there'll be emerging practices that'll eventually solidify into, uh, you know, truly best practices. Yeah.

And, and we can learn from that might take 15 years, maybe. Let, I don't know if we'll be here talking about it then, though, John. Probably not.

Who knows? No. Hopefully we're on an island somewhere enjoying it.

Anyway. Hey, let me bring it back to Black Hat. We gotta wrap up.

You won't be there as you mentioned, but the Imi, Ilum, Illumio folks will be there. Yeah. Uh, a little bird is telling me we're at Booth.

Uh, you're at Booth 9 6 9 and you are gonna, speaking of ai, you guys are gonna be showing off your new AI powered CBR solution called Insights, live Demos, learn more about breach containment offerings or Booth, uh, five four. Four five. Is that right?

Yeah. Yeah. I'm doing that.

I, I, I hope we can show more than that. We got some other cool stuff going on. We got a, I I think speaking of ai, we've got a new integration with Nvidia.

So you can buy an Nvidia card with, uh, Illumio, uh, you know, packaged up in it and run it in there. And I think that that's gonna be, it's originally designed for OT environments, so you, it mm-hmm. You can get it on the, uh, the Bluefield smart Nick from Nvidia.

So you can take out a Nick in an OT environment network interface card and replace it with the, the, this, uh, Nvidia card that has Illumio on it, and then segment out all the traffic coming in from layer two for this, uh, OT device. That's probably really, really hard to secure, uh, given how OT generally works. So I think that's another thing that, that is super exciting, really.

And, uh, I would see that proliferating into a lot of hyperscalers who run Nvidia, uh, you know, as their accelerator. I, I will tell you, NVIDIA's done a hell of a job working with the industry companies like Illumio and, and kind of building, integrating these solutions into their, not just their hardware, because everyone, of course thinks of them as a chip hardware company, but fact of the matter is they're making them unbelievable software, which is really building out the ecosystem Yeah. And locking people into the, this hardware.

Um, it is a great example of it, right? It, it's ju it's just in there. Um, John as always, man, it's great having you on here.

It's good seeing you stay well and healthy and out there reaching you too, man. Zero trust. Alrighty.

John Kinder, uh, from Lumio Chief Evangelist won't be a black hat, but Illumio will stop by their booth. Again, that's 9, 6 9, I believe. And, uh, we will see you then.

Until then, though, this is Alan Shimmel. Stay tuned. We have more black hat coverage coming up your way.

Hey guys, thanks for the throw. We're here with Lawrence Bigger and Jeff Hall, who are both aerospace security experts with NCC group, and we're talking about how aerospace security is evolving. 'cause well, it's like every other industry a moving target.

Gentlemen, welcome to Shah. Thank you. Thank you for having us, Michael.

Alright, Lawrence, what's the current state of aerospace cybersecurity? 'cause I don't think we hear about a lot of attacks, but for all I know, maybe they're getting inundated with attacks and they're just getting better at swatting them away. But, um, how much danger are we in and what is the current level of activity?

So, aviation is at a very complicated ecosystem of a number of di daily holders. But an important thing to remember here is that we're dealing with a critical national infrastructure. We're dealing with some fee that is safety critical as well.

So clearly there are risks that need to be appropriately managed to ensure that citizens aren't armed. And there are, there's wide recognition of that and robust measures already in place. But as we know, cybersecurity threat and the landscape is constantly evolving and technologies are changing as well.

So the regulations that are in place need to adapt with time to deal with those immersion threats. In terms of the number of tax, we're seeing that there has been several, um, high profile, uh, attacks in the aviation sector, mostly targeted against airlines. You tend to be the highest profile, uh, target.

And it doesn't take me much time on Google to just quick search for that. And you can identify, uh, quite a large number of aviation organizations that have been victims there. Obviously, you know, one attack's not the same as another attack.

And there's as many different kinds of motivations for attack is to want to attack a particular type of organization. So yes, certainly there are lots of ransomware type attacks and, uh, lots of, um, data breaches as well. But as I said, many stakeholders maybe as a motivations and different types of harms that fifth threat actors want to, to cause.

So maybe the ones that don't get reported, the ones that potentially more interesting True that Jeff, it seems like there's more regulation coming down the pike, but what's going on here in the aerospace sector and how quickly will these things come about? Because last time I checked, it takes, I don't know, three to five years just to design and build an airplane. This is true.

Um, and the every time you do have a new administration change, there's always gonna be new cybersecurity regulations or policy that's gonna come down. So that takes a while to promulgate and get out. But, uh, what I see is the three, or, well, not the three, but bigger, bigger trends are, uh, safety and security, which you basically have two sides of the coin where I, I think, I don't think I know, I've done a bunch of work on this and have a dissertation on safety and security in, in aerospace.

So you can't have safety if you don't have security. And another, uh, area is emerging technologies, you know, AI is coming down the pike. Everybody wants to jump on the AI bandwagon, but they need to really understand what comes with the AI security implications and how it could over affect or affect the overall system.

'cause aircraft and any aerospace system is just a collection of systems of systems which make up the bigger product. And, uh, you have to look at it as a whole. Mm-hmm.

And how many of these attacks are kind of aimed at the retail side of the airline industry and they are being attacked just like anybody else who does any kind of e-commerce activity versus how many of these attacks are aimed at the systems on the plane itself, which is a much bigger safety concern. So I guess, right, right now we see a lot, a lot of ransomware, um, things on the business side where they're just trying to disrupt your operation. Whereas direct attacks on aircraft, you'll never hear about them because if it happens, you know, governments get involved and they don't really do not want to, uh, put the information out until they really know what has happened and how, how we can, uh, mitigate The impacts.

Mm-hmm. Lawrence, coming back to you. Um, what's your best advice, therefore to all the folks who work in this vertical industry about how to approach cybersecurity?

And it would seem to me at least that, at least in that culture, the engineers are a lot more attuned to that notion and issue. But are they, and what, or is there like a engulf that exists between the security folks and the rest of the business as there is in every other sector? So the, I think there is historically has been some challenges for people responsible for security and aviation organizations to get the resources and that the level of buy-in from, from leadership within these businesses to, to manage these risks appropriately and go.

And so responding to that with a whole plethora of regulations, I think it's important that people aren't fixated on compliance. If there's a real risk here that some budgets may be frozen and some of the resources devoted, uh, um, devoted in debt to compliance, and that reduces the amount of budget available for cybersecurity operations. So really the first key step is for security managers are fixed by the regulations to leverage those regulations to their advantage to make the business case for why there needs to be additional resources made available for them to manage the risk.

So they increase the, the resource available for protecting the operation. But, you know, fundamentally, the, the principles that security managers need to follow are well established regulators don't try to reinvent will they're using to best practice. What security managers need to be mindful of are, are really two things.

First of all, yes, you might use a standard framework for compliance, but you also need to demonstrate that compliance. So you don't want that to become a big bird. So what can you do to automate that when you go about doing a security program, making sure that you're not having to divert those valuable funds towards compliance to demonstrate to your regulator you are compliant.

The other key thing I I would focus on is, is you know what you know, right? So, you know, don't be scared. Some of the regulation is complicated.

There are specific requirements of, and reporting and what and whatnot. But fundamentally the principles are the same. Security, um, controls you leader are broadly the same.

So you can lean into that. But the other flip side of, you know, what you know is, you know, be mindful of your limitations. So yes, you are dealing with operational technology, safety, critical technology.

So you need to make sure you involve the right stakeholders and do apply security controls in the right context for the aviation domain. So this would establish regulations, communities for aviation security in that they're, they deal with, you know, terrorism sabotage, criminal organizations and what do they have in place? What management systems do they have?

How do you interact with them? 'cause attackers don't care about whether it's the cyber domain or the physical domain, they'll hop between the two as they need to, to achieve, to achieve their objective. So you don't wanna have an organ organizational silo, which means that actually the IT security team can't work with the aviation security team.

So it's like knocking down those barriers, ensuring that there's a complete appreciation of the full spectrum of attacks that can occur out there. And the same thing for aviation safety, cybersecurity attacks can now fix safety. Yes.

Um, you need to do detailed effect modeling and risk assessments of that and work in collaboration with the, the aviation safety teams to make sure that those risks are appropriately managed. Yeah. Jeff, in your experience, are there specific types of attacks that the aviation industry is more worried about than others?

Or are they all pretty much the same for everybody else? Or, or are there unique attributes here that really, you know, challenge these folks? I think the ones that kind of probably make them stay up at night or really consider hard are things that would definitely affect safety of flight and their aircraft.

But a lot of ground systems out there are very susceptible things were never designed with security in mind. And when they look, let's say they, when airlines or organizations look, look at their whole safety posture, a lot of times things that aren't on the aircraft don't get a lot of, uh, scrutiny. Um, other than things that make them money, you know, PCI compliance, um, just overall ticketing, you know, the whole business side of it.

But, um, you go the aircraft side, it's, there's so many different things you can come and, and either, and it doesn't have to be a catastrophic event, it could be something that just keeps them from leaving the ground and the problem will just compound over time, depending on how many aircraft they are. And that gets into a whole money figure. So search costing the airline a lot, a lot of money on either side, whether it's the, you know, business administrative side or the aircraft side.

Lawrence, I am not an engineer and I do not pretend to be one, but it seems to me if I look at a plane, there must be thousands of subsystems on each of those planes. Then each of them needs to be protected. So is that part of the challenge here to jump's point is just that there's, the attack surface is so broad, even though it's actually on a physical plane.

The, the attack surface is broad. And I guess there are new attack vectors being introduced as technology moves on. And we get in, I guess, increased demand and increased ways of providing connectivity to the aircraft.

Like, you know, many other enterprises, airlines and the manufacturers of aircraft wanna get data back from the aircraft. And that means there's a trade off there between security, um, and getting that, that data back from the aircraft. But, you know, the, the, this associated with aviation and aircraft in particular are widely recognized and, uh, various regulation, uh, standards, specification, uh, requirements which are needed for certification of aircraft that, um, kind of reflect the risks to the aircraft.

And that is, I would say, well in hand and well recognized. What is often less appreciated is that wider infrastructure. So, you know, not only is it safety effects, but it's resiliency effects.

So we're worried about, you know, this is critical of national infrastructure. There are threat actors out there who want to disrupt, um, the way that certain societies work. And one of the ways of doing that is by disrupting air travel.

And, you know, some of these systems are old and fragile and, and readily exposed to the internet and they were designed many, many years ago. So the real challenge actually is how do you not, uh, disrupt standard commercial operations but ensure adequate security, adequate resiliency of commercial, uh, aviation for systems that also, uh, run operated by, by stakeholders who quite frankly are operating on very thin margins. So they don't have the luxury of spending lots and lots of money of putting, you know, expensive technologies to secure them.

You know, what, what is enough? Uh, that that's always the challenge. What is an appropriate standard for that context?

Mm-hmm. Yeah, we've been dealing with a cybersecurity skills shortage in general for a long time now. And it seems to me the number of people who know how to secure embedded systems is even smaller.

And the number of people who know how to secure embedded systems within an airplane is probably a really tiny number. So, uh, where do we get the expertise from to kind of address these issues? Because it is pretty clear that there's just not enough available given the number of airlines.

That's a really good point. Um, I've dealt with this, I worked in the government for 10 years prior to coming to NCC group and it got to the point there where colleges were just starting then to shift their curriculum to include cybersecurity for all engineering disciplines, just so they could be aware of it, not they're gonna be experts in it, they'd be aware of it. And everybody kept coming to me 'cause I ran, I ran a branch for cybersecurity and avionics and I said, what are we gonna do?

And I said, I think we should just grow young engineers that are interested or any, any people that are new into the business that are having an interest and show a little bit of aptitude, we can train them and get them to where we need to be. And, uh, be a whole lot, whole lot easier in trying to go recruit. 'cause you can recruit forever and ever and you may not get really what you're looking for.

Yeah. Lawrence, you cannot walk down the street today without somebody reaching out and saying, here, check out my great new AI thing. Can we apply AI to any of this to kinda help level the playing field?

So, I mean, it's a double-edged sword, isn't it? So certainly on the defensive side, as with any other sector, AI has its part to play, particularly on the monitoring side. I think what's more interesting from the an aviation perspective is the desire for increased levels of automation and how AI can play its part in that.

So obviously there are concerns around security of ai, so it's about ensuring design, uh, secure by design. So how do you ensure that, uh, AI based systems that are used for autonomous operations, particularly safety critical mission critical systems, are adequately secure? How do you get that assurance?

Um, so one area that I'm particularly interested is how do you move towards, uh, a high assurance approach for these systems and how can you use AI to do that? So for example, we, we specialize as a business in producing assurance cases is how can you automate that? And AI, I'm sure has a, a big vault to play in that.

So we can rapidly turn around, generate robust, uh, security cases, safety cases, um, using AI technology. Alright. Jeff, I'm gonna throw the last question to you, but do you think AI benefits the attackers more, the defenders more since that's an ongoing debate out there?

Hmm. That's a tricky question. Right?

Right now, I, I believe it's, uh, in the defender's court, but as with anything with attackers, they find very novel ways to circumvent and come at you from angles that are very rarely ever considered. 'cause there's avionics, there's so many different pathways, you can only think of so many. I think that's where AI might, may really shine where they come in and start looking at a system by system and say, okay, how many different pathways can you find that are viable to get into a system?

And you look at the larger system of systems, and I'm sure it's exponentially, you know, more, but it's, uh, start with small and work big. All right. Well folks, you heard it here.

We have talked in the past about software defined vehicles. And when you think about it, an airplane is just another type of a software defined platform that we need to secure. The good news is they are secure and there's a highly committed bunch of people that are gonna make sure that it's safe to fly.

The trouble is, there's just not enough of them right now. Gentlemen, welcome. Thank you for being on the show.

Thank you. All right. And back to you guys in the studio all Hey everyone, welcome back here to Techstrong tv.

You know, I'm, I'm really happy to have this gentleman return. He has some great news for you out there for the open, legacy open source industry, as well as for him and his company. Let me introduce you to Aaron Frost.

Aaron is the CEO and co-founder of Hero Devs. If you really in the open source world, you've probably heard of them, but if not, we'll not to worry, we'll get you up to speed. Hey Aaron, welcome back to Tech Drunk tv.

It's great to have you back on, man. Yeah, thanks for having me back on. It's good to be here.

Cool. Um, Aaron, let you know, it's probably been, I'm going to guess nine months, maybe close to a year since you've been on here. Mm-hmm.

Um, why don't, you know, um, I don't assume people are gonna remember, you know, from your last time, but give people a sense of how you came to co uh, co-found hero, Deb, the CEO here, kinda what your path has been like. Yeah, so in 2018, I kind did some work in my personal life and realized I should probably start my own thing. And we started as a consulting shop, but, uh, a really popular piece of open source hit an end of life that was brutal for my customers.

So we, we created a fork and we started shipping updates to that fork to our customers who, who needed it. And it, overnight it became obvious, this is a huge problem for a lot of people. And so through 20 21, 20 22, 20 23, we, we leaned into that.

We started shipping, um, to more customers. We started shipping more open source projects that had reached end of life. We started finding more security problems in those projects and fixing that and kind of validating the need for our existence, uh, with the customers.

And, um, the growth has been honestly kind of fantastic, Alan, in, in 2024, we were nominated as the, the, the first, the fastest growing company in Utah at a 4200% in the previous three years. So, um, wow, That's crazy. Kind of extreme growth.

And then, you know, in, in 2025, we, we took a step back and said, Hey, this is something that needs to be here in another a hundred years. Like this is a business that has to survive. Um, let's bring on a partner that's gonna help, um, take this, uh, uh, to the next level with us.

And so we're, we're here today 'cause we're excited to talk about bringing a, a new partner. And we did, we did a, a big round of funding recently. It was a $125 million round Wow.

Invested into the business as a, as a strategic growth round. And we're excited about what it helps us do as a business. And so we're, we're excited to come tell the community, Hey, this is happening and, and this is what it means for you.

So who's the partner? So it's a, it's a growth equity firm at, based out of p uh, Boston called PSG. And, uh, it's led by a guy named Marco Ferrari and Paul Russ.

They're, they're, they're honestly pretty rad, um, uhhuh, and if you, if you check out PSG, you'll see they raised an $8 billion fund in February. They announced it. And they're focused on like, tech enabled services and, and, and, and mostly on tech.

And so it's exciting. They're really, really cool. They, uh, they've been a huge support to the team already.

And they're, they're continue to push us, um, to serve more of the community faster. So it's a good partnership. First of all, man, Aaron, congratulations.

Right? I, I've, I've, I've done four or five startups, venture back startups. I know what it's like to raise money, you know, a really smart man who, who's a mentor to me and, and, uh, mentor to a lot of people.

This guy named, unfortunately, he's not with the same, his name's Len Faser. He used to tell, tell me, he, he used to tell it to everyone. He did about three or four rollups in his life, three or four different rollups, you know, and then iPod and stuff.

So he bought a lot of companies. Um, every single one, he would sell the develop to the, uh, founders. He'd say, look, be really proud.

'cause anytime someone's willing to reach into their pocket and write a check or give you cash for what you've built from scratch, that's something to really, really be proud of. That's kind of the ultimate, right? You can sell customers, you could get customers, you could build a business.

But if you build a business that someone says, Hey, I wanna invest $125 million into that business, think about it. Right? That's, that's the real mark of success right in, in a lot of ways.

So congratulations to you and the whole team there. That's, that's a fantastic thing. You know, Aaron, as we were talking off off camera, a lot of our audience is gonna say, 125 million in this market.

Wow. That's fantastic. What does it mean?

What does it mean for me? What does it mean for the market? What does it mean for the community?

Let's talk about that. It's a good question and it's an important question. And, uh, we're at the, the part, it, it enables a lot of growth.

And let me talk about the one that I think I'm the most excited about. In 2024, we, we donated back to the open source teams whose projects we've worked, we donated back to those teams over $2 million. We have a bit of a rabbit hood play as part of our business model, which is, um, that we work with the teams to coordinate security vulnerability disclosures and make sure that if we fix something, they have it fixed in the, in the, in the still supporter versions before we announce.

That way, their community, the whole community's not not left exposed. And another part of that is, um, we, we done it 10% of our proceeds back into those projects. And so in 2024 we go into over 2 million.

And this round we were able to schedule, um, to set aside $20 million Wow. TEDx growth TEDx back into these projects. And so we announced an open source sustainability fund, Alan, that we are, we're enabled as a CEO.

It's nice to say, I am empowered to give this money out to the communities. And the, the, the idea here is we wanna improve the end of life ex uh, knowledge around each of these products. 'cause everyone announces their end of life differently.

They have a different cadence. The way that they consider what they will and won't fix on end of life versions is different. The length of their long-term support prior to end of life is different.

And so we want people to be a lot more explicit in announcing, Hey, this is what your exposure looks like at the end. And projects who lean in on doing a better job at announcing end of lifes and protecting the community by just virtue of that knowledge being on their websites, they're now eligible for their share of this, you know, $20 million. And there's a lot more information coming out about it very, very soon.

Um, the press release around it officially launching went out a few weeks ago and, and there's a lot more information coming soon, but open source authors can already today go and sign off to be a part of this program. You know, we have a couple teams, big open source projects like the Bootstrap team and, and the View project who were, we're, we're certainly among their top donors based on this, uh, this, this, this relationship we have with them. And we're excited to continue to expand that out into, um, additional projects in, in additional communities.

The dinette community, maybe the Java community, um, database, community, open source, um, operating systems community. So yeah, it's, that's, that's the piece I'm the most excited about. Beautiful.

Yeah. Yeah. I mean certainly we've seen a lot of shaking around in the Linux space, but Aaron, um, you know, to me that sounds almost like I, so I remember when the whole responsible disclosure thing sort of became real in security.

'cause he, when I first got into security, it was the wild west. Mm-hmm. You know, researchers couldn't wait to, to, to, to, uh, you know, announce that they found a a, a bug.

They give you two days. Yeah. I'm renouncing this Thursday.

Today's Tuesday. Yes. Yeah.

And, and a lot of people push back around responsible disclosure and then, you know, but if you think about it, the responsible disclosure kind of protocols gave rise to bug bounties or maybe the bug bounties help solidify responsible disclosure. Mm-hmm. Right.

And, and this is a very, could be a very similar thing. We could, you know, this serves as the bug bounty, if you will, to having people do the right thing about how they end of life open source projects. So it's a little more predictable, a little less uncertainty.

Right. And, and there's a, there's a process that, that we all follow. Aaron, how many different projects is hero dev kind of shepherding these days?

Currently shipping out to our customers, I think we've got about 1,078 different open source projects that we forked. Wow. And we're shipping continual updates down into our customers.

A lot of these are based around the front end and Java technologies like Spring or Struts, Apache or Tom. So a lot of it's very focused. And, um, this round also helps us do some growth as a business.

Once we can do JavaScript or Java, it's very easy to go deep in those technologies and, and pile on a lot more products. Where we, where we struggle is to get breadth of new technologies, new ecosystems, like a database that's a who new, um, game for us. And so this round helps us expand into additional and, and more complex, um, types of, of ecosystems in the open source community where we can now, once we have that muscle, now we can go deep on that muscle and we can continue to expand out.

So, um, yeah, it's, it's quite a, it's quite a lot already. com, where every open source project that's used by enterprises and governments can have a commercial license on end of life versions. That's our goal.

Got it. Hey, let me get real geeky open source licensing on your first second. So when you guys are fork in the end of life, open source is your fork open source, or that's now closed source?

Yeah, so the, the fork continues under the same license as, as it existed Before. Same license as the original. That's beautiful.

Yeah. And do you keep the same name or you gotta change the name of the fork? Well, we, we add the word NES to the end of the name.

Okay. So like, if I had a project called Alan, if I forked it, I would call it Alan NES. So Alan never ending support version.

And then that way everyone knows this is the opus, this is the community. Well, that's IMing. How do you know the difference then?

Yeah. But yeah, I mean, look, you know, I, again, I come from the security world, so I saw it over and over in security, but we're seeing it. Look, look what on went on with Hashi and open Tofu and all these things, right?

Um, really cool stuff. Aaron w what, I know we're kind of running on time, but they got more for you. Ai, everybody's crazy with AI and, and what's happening.

What effect is this happening on open source projects? I think, um, AI is on, on, in different ecosystems is having a lot more impact than others. Like, if you look at a community like Spring, there's a massive push to get the spring AI modules going so that it can be platformed, um, in a more uniform fashion so that not everyone has to reinvent the wheel to, to add AI into those projects.

There's also a lot of inefficiencies that come with adding ai. If you don't know which of your systems calls out, if it calls out too much, and you add AI to that, you're tokenizing, you know, a hundred times more often than you should. And tokenizing sending tokens to AI is a very, very expensive part of your, um, API going forward.

So there's it, it's forcing everyone to rethink about what we've built and to reconfigure it to make it be significantly more efficient as far as it accelerating programming. I mean, if you look at, you know, satalia na or, um, any of the other, you know, serious execs talk about it, it's, it's shown a 20 to 30% in improvement in efficiency among developments. So it's, it's, it's, uh, incontrovertibly having a serious effect.

If it, if you're leaning into, Hey, how's it affecting end of life supporter versions? It, it doesn't have too much of an effect on, um, people who need end of life code to continue to, to survive. And so where we sit in the open source community, they, it's not wildly impacted, but generally it's impacting open source communities in a big way.

Overall, I think we'll see it continue to expand that. You know, one last thought, Eric, you know, putting out security patches for these end of life open source tools, in so many ways, it's, it's about software supply chain security as well, right? Because in my mind, and I don't know your business, like, you know, your business obviously, right?

But there's two sort of use cases here. One is, hey, I already have an app out there, right? I, my app, my company relies on this app.

Our customers rely on this app. This app is, you know, like most apps today, kind of a Frankenstein made up of a lot of open source components with a little code stitching it together. And I gotta make sure that those components I'm using are secure.

And look, if there's a vulnerability found in a, one of the components I used, I gotta up fix that. And if one of the components I used end of life, it's, it's a bit of a pain in the butt to go find a replacement. I'd rather keep using it if I could make sure that the security and functionality's gonna be up kept to, to a minimum of what I need.

So that's one use case, right? Post-deployment. Then there's the other use case, redeployment, which is, you know, developers are very much creatures of habit.

I need this functionality. This is what I use when I want to put this functionality into an app that I'm building. Struts is a great example, right?

Uh, uh, uh, uh, J two four, whatever, uh, you know, we, we use the same things over and over again as we develop new apps. And sometimes I think developers, they don't care if it was end the Life or not. It's still up in the repo.

It's still, I can still pull it down from GitHub or, or Maven or wherever I'm pulling stuff from. And there, I think it's even more important that DevOps engineers somewhat is saying, Hey, wait a second, wait a second. There's an old version known vulnerabilities, there's an updated fork that they're going to continue to, uh, support.

Right? That, that to me is, you know, let's make sure it gets it into the SBO m all these things, right? That, that's a huge thing.

Especially, you know, they say that with AI helping us with code, we're gonna develop x more applications and lines of code than we already have, right? What we already have in the world is gonna be dwarfed by what's gonna be written, let's say in the next five years with AI and stuff, right? So that's a huge mar even bigger than the existing.

Yeah. So cra you know, when you think of that, it's a crazy time we live in. It's a crazy It is, It is.

And whether you're in the first scenario where I have a massive app that it's responsible for most of my revenue as a business and I need to stay alive, or I'm in the second camp where my developers, they know how to use this, my whole system's configured to use this legacy thing, we don't want to reconfigure to launch new things with new stuff. We're, we're, we're kind, which regardless of which camp you're in, um, we're here to be a partner to make sure that it's safe, it it doesn't have security vulnerabilities as you do that. And, and, uh, when you're ready to upgrade, uh, obviously we'll coach you through that as well.

We'll help, you know, the fastest and best way to, to get your app on a modern platform. So, um, at the end of the day, we do think it is the best idea for everyone to upgrade. But if you can't upgrade, that's, that's, that's the reason why we're here to help, is stay safe before you do that.

So, yeah. I love it, Aaron. I gotta wrap it up, man, but hey, congratulations again.

I'm really, really proud and happy to see you guys succeeding like this. Keep doing what you're doing and don't wait nine months or a year to come back on here. Okay?

Yeah, bet. Thank you. I appreciate you.

Alrighty. I appreciate you. Aaron Frost, CEO co-founder of Hero Devs here on Text Drunk tv.

We'll take a break. We'll be right back. ai Leadership Insights series.

I'm your host, Mike Bazar. Today we're with NAAB Iran, who is head of cloud engineering for cruso, and we're talking about AI infrastructure, and a whole lot of things are going on with these folks. They got a $750 million line in credit partnership with a MD, and I think they're expanding into Europe if I read all the announcements correctly.

But naab welcome to show. Thank you. Thank you, Mike.

Happy to be here. Um, it's a funny thing, but I think we've talked in the past and it felt like this was gonna be some sort of interesting little niche, and now I've turned around and it's kind of this billion dollar industry and everybody and his brother's talking about more efficient ways to consume AI processors. Is there some sort of rapid maturation process going on here?

I I, I think there is. I think, I think we actually talked about it last time we talked, uh, a little bit about kinda like the pace of how it happens and, you know, this hockey stick we're in. I'm, I'm not horribly surprised.

Um, maybe just a little bit. Uh, definitely the, the pace is picked up and, and I think the, the market also looks a lot more interesting in terms of like how you see, uh, both the consumption and the supply mm-hmm. Of, uh, compute infrastructure.

Um, uh, that's, that's focused on AI for sure. I feel like it's also changing in terms of who's responsible for what. I think early on it was a data science team, and there was a couple infrastructure people attached to that, and they built one application.

I think we're at some point now where people are trying to figure out how to operationalize AI infrastructure across multiple applications, and that's changing the nature of the conversation. Is that a fair assessment? I, I, I think to some extent it is.

I, I think there is, um, also the, the focus is shifting to some extent from AI as a technology to the applications that are enabled by ai. Um, it's, it's, you know, it's less interesting what the AI is and more interesting what it does for you. And I think we're seeing that more and more as people start to use AI in more and more areas.

We, we do see that, we do hear from our customers more and more. Like, I just want this thing to work. I have a day job to worry about not building AI infrastructure, not figuring out how to optimize the performance, not figuring out how to, uh, you know, find the right device driver for the hardware I'm using.

Um, I have a day job and, and that means building a business. So building an application or something like that. AI is a tool and, and not the, uh, not the objective of the work.

I also feel like there's more separation between the process of training an AI model and the actual running of the AI model. In terms of the inference engine and where that might be, and are, are we seeing some shifts there or, you know, how is that evolving in your mind? There, there, there's definitely a lot of, um, uh, focus, you know, like, like I mentioned as, as people try to use AI more, that means they do more inference.

Um, inference is becoming more and more important. I think last time, um, we spoke, we, we talked about manage inference, you know, that's a, a product that we, uh, we have out there that is seeing more use even within our, uh, straight up infrastructure, uh, users, we are seeing more and more focused on inference. And, and to me that's just natural, again, because people are using AI more than they're focused on making AI better, bigger, et cetera, that's still an important thing.

But as a percentage of the slice of the pie, uh, you know, that's slice that is using AI is the one that's driving the growth. There's also a point where, um, you know, certain things are at a good enough place. Um, and again, there, there's always the, the, the leading edge, the bleeding edge, the place where you are finding new capabilities in, in the technology.

But there's also, you know, to your point about maturation, there are parts of ai, you know, large language models have been with us, um, roughly with the same general direction they, they, they have today for, for a couple of years now. And so at, at that point, you do see like the, the, the core LLM becoming less of a focus and how you use it becoming more important. Mm-hmm.

And to your point, I also feel like l LMS now come in t-shirt sizes, they're small, medium and large, and, um, and people are getting smarter about which to use when, and then what the infrastructure resources are required to support those. Uh, for sure, 100% that, that's actually something that, that has happened. Um, I, I think pretty since, pretty early on, um, you know, the, the full scale, the large LMS are super expensive to, to use, uh, the, they're slow.

Uh, you need, you need a lot of hardware to make them work. And for a lot of stuff you don't need the power that they provide. I think more and more we're gonna see actually systems that, you know, dynamically use, uh, the, the right size model for, um, the, the situation you're in, depending on, you know, your context window length, depending on the prompt you have, et cetera.

All these things to me are, are natural optimizations that are happening as, um, as the technology matures. Um, you know, there, there's, there's a rule that I've learned in, in, in building infrastructure over the last couple decades, which is the, the closer you are to the end user, the bigger the leverage you have over optimizing the problem. So, you know, we started by optimizing these things by looking very close to the hardware.

Um, that's important early on to understand how the hardware works, et cetera. But you really get the leverage where you look at a prompt from a user or you look at an application in general and say like, you know what? I don't need a 400 billion parameter to serve that need.

4 billion is enough. That's a huge optimization you can make. And so I I, I do think we see as the technology matures people more and more looking at that place in the value chain saying like, Hey, you know, I have a 10 x improvement I can do here by just using something else.

Mm-hmm. Now you also partnered with a MD recently. So are people looking at GPUs from different vendors now and mixing and matching, or are they saying I'm either an all Nvidia or all a MD?

Or is it becoming a little more nuanced? That that, that, that's a great question. I think, um, in general, as a, as a cloud service provider, um, and I think it's good for, for the customers as well, the users, the end users of the technology, uh, competition is good.

Um, I think right now we're still at a place where people have a preference for one or the other. Uh, there is like the, the level at which that we work with our customers, they would be aware, Hey, you know, I'm going to get a MD hardware, I'm gonna get Nvidia hardware. To me, the really interesting next phase, again, going back to the point of like the further up the stack, the more leverage you have is actually building services that can mix and match.

Um, we're not quite there yet. Um, but I think that is forthcoming and I'm, I'm, I'm super happy, you know, to have our hands on on those a MD machines. I think just by having two things from two different vendors that are different from each other, we're gonna, we're gonna find the niches, we're gonna find a place where one is really useful, where the other is really useful.

We do see, um, healthy demand for, for both, you know, we also have, uh, B two hundreds that, that we started, um, uh, offering to our customers a few months back. We have the A MD that, that is still a couple months out. Um, both of these we see healthy demand for.

Um, and I, I think this is a recognition in the market that, um, competition is good as well as specialization. You know, uh, this, this workload is with this model on this type of hardware, this workload acts differently and needs different type of hardware, different model. It's all good.

Um, speaking of different classes of processors, are we also gonna see more usage of things that aren't GPUs to run inference engines? And what might that look like Perhaps? I, I, I think, um, I think in general, um, as the market grows, it would make more and more sense to try and hyper optimize for a fraction of the market, right?

So like, if you have a market that's a hundred billion dollars, whatever, just to, just to pick a random number, uh, an easy round number, um, you know, 20% of that market is a 20 billion market. It's worth an investment to, to create hardware that's hyper optimized for that. I do think that hyper optimizing hardware makes the problem simpler and therefore allows you to get, um, uh, more benefit out of it.

And, and as I mentioned before, also from the usage perspective, as your software stack becomes smart and is able to direct the traffic to the harder that's most useful for it, that again, will lower the bar for people to come up with with new types of hardware. I, I would say today we are seeing, um, more and more interest in the asics that the, uh, big hyperscalers have. So like, you know, train and, and infinium with, with, um, AWS TPUs with Google, you see them taking more and more, uh, perhaps of the market share.

Um, we've been, uh, exploring things with, with several startups that, that work on interesting hardware solutions. Again, all these solutions are trying to limit the scope of their problem to be able to do better than the big GPU vendors. You know, if you wanna build something that will accelerate any AI workload under the sun, it's a very tall order.

But if you're just focusing on, let's say, inference for small to medium models and, and you make that simplifying assumption, I think there's a lot to gain there. And as the market in general grows, even optimizing for just such a slice will become lucrative enough that we will see innovation happening in the hardware in that space. Mm-hmm.

Is it me? But I feel like, and maybe it's just part of that whole maturation process, but people are more sensitive to the cost of AI as they try to figure out what to operationalize. 'cause it's one thing to have a thousand experiments, it's another thing to figure out what can I actually afford to run in production.

Yeah. I, I think that's fair. I think, um, the, the way we sometimes refer to it here is there's, there's sewers and there's harvesters and, uh, I, I think we are leaning more towards the harvesters.

And the harvesters do behave somewhat differently. They're not just, um, more, uh, cost sensitive. They're also, um, less tolerant of complexities and, you know, the need to gain expertise and to learn and to experiment.

They want something that just works and hopefully just works and doesn't break the bank. I, I think for ai, this is actually something super important. You know, when, when you think about using a new technology, um, there is, there is, you know, the 5%, 10% kind of improvement in, in cost performance, which, which is good.

That just helps the current players, you know, uh, improve their products by a little bit or make a little bit more money. We, we are still seeing, and there's a bunch of data about this, about, you know, the cost of inference over the last two years, basically going down by orders and orders of magnitude. When something becomes order of magnitude cheaper, that opens up a whole new, you know, universe of possibilities in terms of the kind of products that you can use it in, the kind of business model you can use to monetize it, et cetera, et cetera.

And so as we're seeing those cost improvements, um, you know, compound, um, and, and continue to improve, uh, I'm, I'm super excited because I think those harvesters will have all, all kinds of different things they could harvest all kinds of different areas where AI will all of a sudden be a great solution just because it's now cheap enough to justify its use. Can we simplify the stack? And I'm asking the question because one of the great things about the cloud was it made it relatively easy for developers to stand up infrastructure and build and deploy software.

I look at AI today and I still feel like there's, you know, ML ops people, data engineers, security people throwing a bunch of developers and shake and bacon. Maybe by the time I get the village together, something good happens. Can we streamline that?

I, I, I think we can. I think, again, this is partly the, the transition from the sewers to the harvesters where, like I said, I think people are looking for more simplicity, so there's more and more demand for that. Um, I, I, I will say a lot of the complexity even starts before you get to ai, for example, you know, um, a as you know, we, we at Chris who are building the world's favorite AI cloud, but we're not pretending to the, to be building a general purpose cloud.

So many of our users have a footprint on a general purpose cloud as well, to run the known AI parts of their workloads. And even just that, you know, the, the ability to work together across two different cloud providers, sometimes 3 0 4, um, and, and hide the complexities there. That's, that's a demand that we see coming from our users and that we're working on helping them with.

So the, the complexities start even before you get to the, you know, the science of ai, if you will. Um, so we're trying to fix that. We're definitely seeing that in the, in the AI space itself.

Like I said, we're seeing more and more demand for managed services, for managed inference, for, you know, a a a point and click kind of interface of like, here's my data, um, do something with it. Um, you definitely see much more demand for that. I think, again, that that part of the journey is still in, in its infancy.

We see some first steps in the right direction, but I think it's still a long journey ahead. So what's the plan for the funding? And as part of that, what's coming next for you guys?

Um, so again, we're, we're excited, uh, as, as everybody knows, um, this is, uh, a pretty CapEx intensive, um, uh, industry and, and so like having that funding secured is, is great. Um, I, I'm not here to talk about future financials for sure, financials in general. So I, I, I, I'm not gonna say what's, what's ahead in terms of the funding, but I can say in terms of our expansion, um, as you mentioned, we just announced, um, a new data center in Norway, um, that uses 100% renewable energy.

Um, we, we announced a big deal with, with a MD. Um, there's more things coming, you know, the business is growing for sure. Um, it does require a lot, a lot of capital to feed it, but, but we do see a lot of growth.

Um, I would also point out, we had, um, an announcement, um, a couple weeks back with Redwoods materials, which is a company that, um, uh, recycles EV batteries where we have, uh, a pilot deployment with them, um, that is 100%, uh, powered by solar energy and recycled EV batteries off the grid. Um, so all sorts of interesting things are happening in terms of our, our growth. The, the business is definitely, um, uh, on the up and up.

So what's the thing you see people doing today that kind of just makes you shake your head a little bit and go, folks, we could be a little bit smarter than that. I, I think, um, this is a great question. I, I, I think one of the things, uh, is, is something that we've touched on in terms of like the, the hyper focus on the model versus the application.

Um, you know, like I said, there's, there's a lot of optimization that can be done at the top layers of the application in terms of like, what actually you're gonna use for when, uh, we don't have good infrastructure for that, and therefore a lot of the people in the space, I think, overlook the ability to optimize at that level. Um, I, I also think, um, that there is still, um, maybe I would say focus on expanding the capabilities where the capabilities are good enough. There is a little bit of like chasing like, oh, let's, let's, let's, let's work on transitioning to this new model, uh, where actually the system that you have is good enough.

Um, I, I would say it's in general, in my experience, something that you need to watch out for with software engineers. Software engineers don't like good enough. They like to be the best.

And so a lot of times, you know, there's a point of diminishing returns, those last 2% of squeezing something, um, is oftentimes really, really hard. And, and 98% may be good enough. So I, again, this is part of moving from like expanding the envelope of the technology to just using it.

Um, and I think shifting that focus will allow us to iterate a lot faster, because again, those last 2% take forever. Very hammer folks. There's a thing called AI optimization.

It's coming to a store near you soon. Nadav, thanks for being on the show. My pleasure, Mike.

My pleasure. Thank you. All right.

ai leadership series. You can find this episode another on our website. We invite you, check them all out, so then we'll see you next time.

Uh, welcome to Red Hat Cloud Fridays with AWS Real talk, real solutions. My name is Reese Powell. I am a managed OpenShift black belt, so I'm part of a team, a global team that helps organizations and customers get up to speed with our managed offerings.

In this case, red Hat OpenShift on AWS I'm also here today with po. Hi everyone. Uh, PO is my name.

As Ris said, I'm working together with Greece. Uh, but my role is more as a sales specialist, helping customers identify the, uh, business and use cases, and also seeing what additional business value our services would bring for your organization. In this specific case, it's Rosa, What we're gonna discuss today.

Uh, what is Red Hat OpenShift on AWS value greater than Return on investment, why you would choose Red Hat OpenShift service on AWS, the customers that we know are currently using it and your buying options. What is Red Hat OpenShift service on AWS It is a native AWS owned service. If you go straight to the console, click on containers, this is what you'll see.

And there you can see right at the bottom is the Red Hat OpenShift service on AWS allowing you to click it and consume it. It is available on demand from the console. It integrates with all the tools that you would expect to, that are built within AWS.

It allows you to consume any agreements you've already got with AWS and the discounts and spend programs that you've got there. It is an AWS service that is jointly supported and managed with Red Hat. It allows you to speed up your ability to deliver applications backend supported by Global Site reliability team, people that really enjoy fixing the problems that they, allowing your organization to focus on delivering value through delivering applications to your customers proven value through a reduction in infrastructure management effort, and in a reduction in development time, delivering the applications.

The SRE team, developers and systems engineers who really know how the, how to handle the volume and diversity in clusters. Uh, they spend a lot of time automating and building and living the SRE principles to allow you to not have to think so deeply about this as they will make sure that your clusters are running, as you would expect. And that brings huge benefits to you.

Accelerate your application delivery time through automation. Focus on the layers above the infrastructure, approving your operational efficiency, security, and resiliency, allowing you to focus on innovation and the skillset that your team have. Don't be the hyperscaler.

So value is greater than ROI. Um, I'll take you through a, uh, i'd, a storytelling or a little journey on, on how we look at containers from our side and how we look at this Rosa part, the red hat OpenShift on AWS as a a application platform, and how to actually look at the benefits from a, from a business value perspective. So if, if you, uh, look at containers, you need to understand your, uh, well, we need to understand from your perspective the, the cloud strategies and plans that you have for the future, uh, when you introduce a core container concept to your organization, and also the fundamental roles that they would play in your organization.

Um, so what we can give you, i, i, is the details and benefits of the containerization journey in the modern application world, where, where you would, uh, look at the advantages of specifically containerization, uh, for the modern applications. Um, but at the same time, we will also help you stay away from the potholes and, and, uh, letting us help you with lessons learned and best practices. And those are the things that we will add into the calculation of not only doing a ROI, but also looking at business, business, uh, value benefits.

So we will then cover the architecture, um, looking at how to build this and the key components of a container system, uh, not at all looking at some organizations as they do on the picture here, but, uh, we would then recommend it to leave it to us to, uh, so you can focus on supporting your, your line of business with the container solutions that you have and the application platform. Um, we will also explain the process involved in deploying and managing containers and, and also the risk management. Um, what can go wrong and how would you avoid that.

Uh, further on, we will also discuss the critical security aspects and also best practices for containers and see how you could solve that and how that would apply for your organization and how you would deploy a solution like Rosa into your, uh, organizational. And also to recap, uh, a little bit of what we just talked and what we will talk about moving forward in, in this, uh, session, we covered the essential aspects of containerization. Um, we will then also explore how containers offer benefits, like improved efficiency, scalability, inconsistency for modern applications, and also how they will be modernized over time and when also when done and deployed correctly.

The how to use OpenShift in, in, in this matter. We also mentioned the underlying architecture, which Reese will also go deeper into, uh, moving forward. And then also the deployment process.

Um, and whether you're just getting started with containers or looking to optimize your existing deployments, uh, there is a team here to help you, uh, which is important. And Reese mentioned the SREs already. Uh, RIS and myself are also part of this team.

Um, uh, we can help you remove technical issues. I wouldn't say myself doing that. That's more of a racist's work, uh, because he is the black belt and he can help you remove blockers also for, uh, making sure that you do the right thing.

And I also encourage you to think about how these container technologies can be applied within your own projects and environments. And also what the business value is not only A TCO or an ROI look at it broader. With that, I'll, I'll move into more of the detailed discussion and also see to show you how an a business value assessment can be done.

Looking at a real example also of, of a, a case where a customer has looked at a doing it yourself or actually doing it with a managed, uh, OpenShift platform, uh, on, uh, AWS. So OpenShift as an application platform, they should bring developers and operate, uh, operations teams together. Um, and that's both for containers and virtualization.

This means that this is not to make it more difficult or increase with process or anything. It makes it easy for you as an organization and people in the organization to cooperate, uh, and make it easy for you to also deploy application and workloads into the containers and basically take care of applications, run them supporting your line of business. Um, questions to ask yourself is what should you be spending your time on?

'cause that's also a key part of the business value assessment and also looking at business value in this case, and what does it cost to build, build a platform. Um, basically you should be up apples to apples comparing, uh, a self-managed or a managed platform. Uh, and if you have done this before, uh, some of the indications we get for people that has done before, you better hire somebody that's smart, uh, and, uh, have somebody to, to actually be, uh, on that level of competency so they can lifecycle manages also.

So what is business value? If you look at chat, GPT, um, it's says, so business value refers to the worth or importance of a business to its stakeholders, such as shareholders, customers, employees, and society as a whole. It can be measured in various ways.

So including financial metrics as revenue, profit and return on investment, as well as nonprofit, uh, or sorry, non-financial metrics with customer satisfaction, employee engagement, social and environmental impact. So business value can actually be something broader and wider. It all depends on what type of organization you have, what type of KPIs and measurements you want to measure it against.

Um, business value is important because it helps to determine the success and sustainability of a business over a long time or long period. So the bottom part, the letters there, there is just to, to give you an example of profit, which is normally something you would, uh, use in a calculation for T-C-O-R-I, what would that we have as part of a business value assessment? And that's profit equals revenue minus cost.

If we then look at the, uh, normal approach from AWS in this graph, you could see that within the, uh, box with the, with the checker door, the, the lines, uh, to your left, that's a normal TCO ca type of calculation. So a movement from an on-prem solution, uh, to a cloud-based platform is normally a lifter shift. Um, that also indicates that you still have, which is quite normal on an on-prem solution, where you usually would have about 25 to 30% over, uh, representation on, on performance for peaks.

Um, and those will be lifted into an a cloud platform. Also, normally, if you do one a lift and shift, what we wanna do is also working together with AWS is look at how we can optimize this. And by that, looking at the right hand side of the, the view where you actually will see modernization optimization, and also where you could see all the business benefits come in.

Normally if you do a cost optimization or A TCO, which could be a total cost of ownership or total cost optimization, uh, depending how you see it is measuring cost savings. Uh, with a business value, you go one step or several steps further. You don't only look at the cost savings as it is, but you also look at agility, elasticity, innovation, global footprint.

We look, we, we had had a other index, uh, wordings or measurements that we mentioned before, but we want to see that the biggest portion actually is the business agility part. Uh, staff productivity, those are two of the biggest, covering 75% of, of, uh, business value. Uh, cost savings is, is a big part that is also needed.

But as I mentioned, a uh, business agility part, being able to move faster with market changes and trends or, uh, staff productivity, doing the right things instead of just, uh, updating, patching and, and managing a, a, a server environment or, um, uh, uh, looking after a, a lifecycle, managing a platform, so to say, and keeping focusing on stuff that is productive and also supporting the line of business, those are things that will bring in more value, uh, for the full investment rather than just looking at A TCO or an ROI, therefore, also the business value is greater than the ROI calculation that we showed, uh, for the topic of, of this session. If we then look at, uh, an example of a customer that has done a, uh, red Hat OpenShift on AWS calculation compared to a do it yourself, uh, environment. Um, this is then based on the help that we have given the customer, we do this together, but it's based on customer data.

Um, what, what's happening to them, the fully loaded, uh, cost of an employee, um, how much is it worth to launch several times per year with new products and services, rather than stay with the normal route of, uh, one, two launches, uh, of application, uh, renewals and, uh, equal things like that. Um, this customer specifically ended up with a 2 million potential saving over a three year period. The three year period is then measured also with the reserved instance part.

So you commit to a three year, um, commitment for those, uh, reserved instances on Rosa. Why Rosa then? So all of these platforms that you can see here integrate equally into the AWS ecosystem as you would expect, but do you want to build and then run the platform?

Or do you just want to focus on delivering value for your organization? Yes, the parts are there. You can assemble, uh, a K eight cluster yourself.

There's also the option for a self-managed version of OpenShift that you can run on top of AWS. But the true value and the focus that PO has already spoken about comes from turning on a platform and knowing that there is a team or multiple teams around you that will help you deliver what you need for your business and for your customers, which is actual applications interacting with the people who spend money with you. Red Hat OpenShift is an AWS solution, jointly run by both Red Hat and AWS integrated dev tools and cloud native services, joint support and engineering, the full security and compliance that you would expect from both AWS and Red Hat.

And then that ability to have the single invoice in and utilize the committed spend that you already have with AWS move from 24 7 operations to nine to five innovation. We, we know that running clusters is a very difficult and hard thing to do, but for most of you listening to this, it is not the core part of your business. 95% SLA and the joint 24 7 support that you would normally have to give, brought to you by Red Hat and AWS Automation and Day two operations supported by a global team of SREs who truly understand the platform that you are working with.

Here's a quick example of all of the components that need to be put together just to get a cluster to run correctly in the manner in which you will be able to, uh, deliver the applications out to your customers, be them internal or be them external. As you can see, platform support is the cloud provider and Red Hat. Then you have to do all of the other bits on top.

And with the number of components and moving parts that fit in this, it's very, very difficult often to get the alignment that you need to be able to do things like security updates and just version upgrades. Yet you move to the managed platform and the only thing you have to be concerned about is actually turning the, uh, Rosa platform on platform support included software and security updates managed and included. Then there's the balance of network configuration monitoring and login management and, and cluster creation that is jointly done.

While we will stand the cluster up, you need to tell us to stand the cluster up. There's levels of MO monitoring and logging that is part of the cluster, but then there is also stuff that you need to manage at the application layer. But all of these are providing value to you as opposed to having to run the underlying systems, which will take time and energy away from delivering what you need.

Keeping the platform up to date is even harder than standing at the platforms normally. As you can see, there's a new update. There are multiple components, moving parts within each cluster that you need to make sure can all be upgraded together.

Service mesh is a great example of a, a product that has multiple components that sometimes don't lie when it comes to upgrade, but then you need to resolve any drift, make sure everything is the same across all of your clusters. Prep for account update changes, backing everything up, updating the control planes, updating the add-ons, so on and so on and so on. For you to be able to get to the point where you are happy with an upgrade.

I saw a post where somebody was clapping themselves. 'cause for the first time ever, things worked as expected when doing an upgrade. A very rare occurrence when you manage and build your own platform.

Yet this is there and this is the driving factor behind Red Hat OpenShift service on AWS. We allow you, because of the work that we do in the background and 'cause of all the customers that we work with, and because there is an SRE team there to just rely on us to make sure things work, you push the button and the cluster will go through the upgrade process for you. You just need to make sure that any of the particular APIs that align with your applications that sit on top are going to work with any of the upgrades that come as part of a K eight upgrade.

Being as this is an application platform, as you can imagine that we utilize the platform to deliver more things for you on top of it itself. So we've got things like Red Hat developer hub, trusted application pipeline signer and analyzer, things that make the delivery of your applications more secure. Uh, we've got IDE plugins and dev spaces, so the ability to run a cluster that your developers can log into with an web-based IDE and build their projects on top of that and push it into the cluster to be able to run Red Hat Service interconnect to allow you to be able to connect services across clusters.

But then the two big ones at this moment in time, there's a lot of conversation around both of these. Obviously, OpenShift AI is an AI platform that allows you to deliver AI applications. It implements the ideas that developers have been utilizing and practicing for years for your data science teams and your AI teams to be able to go to be able to pull in data process that data run their Jupyter Notebooks as you would expect to be able to wrap that up, serve models, and to get the application developers running potentially on the same cluster to hook into those models that have been created.

Another great topic of conversation is virtualization. Here. We, you know, we, we give you the opportunity to be able to run virtual machines within, uh, your OpenShift cluster.

And that can be worked in two ways just in your traditional use, standup a virtual machine or to, as part of a migration process where you sit it in and pretend that it is a container while you start to do your cloud native migrations. And then further down the line, we know that there are a lot of organizations that have endpoints that they want to work with. So we have Device Edge that can populate data back into your core cluster, and that cluster can scale up and down as needed based on the workloads that it's getting pushed to it.

So let's dig a little bit deeper into these. So virtualization options, faster adoption. Uh, you can rehost and then refactor, like I mentioned earlier.

You run it as if it is a container within your cluster and you can start to extract your services around that consistent VM deployment and management, whether it's on-prem or in the cloud. A lot of the conversations I have is, what should I run with the great advantage of OpenShift and Red Hat OpenShift on AWS is the consistency of the platform to allow you to run the right workload in the right location. But also we have a lot of rules and regulations around disaster recovery these days.

You can have the ability to have a cluster in two different locations and quickly recover or quickly stand up should you have a problem somewhere. Part of the virtualization, the app modernization journey, PO has already called us out and showed you earlier how you look at getting more savings, but actually it fits into this VM migration and fits very neatly into Rosa, where you have your legacy on-prem applications, you can move those into a cluster and wrap all of the services around it. As you start to, uh, migrate bits and components out, your VMs can start to get less and things just sit more naturally into the cloud native working ways where you've just then driven down your costs with a very small amount of virtual machines still running in your cluster, but being accessed in the same way.

Some of this can come down to sometimes third party suppliers not willing to move to containers and cloud native ways of working, but you shouldn't get stopped by them. This allows you to continue to deliver your cloud native strategy while still delivering or utilizing services that are essential to running the whole application stack. You require OpenShift AI accelerate deployment's, focus, consistency and choice.

We don't tie you into using any particular models in any particular location. What we do is build the platform for you to build your applications for you to use the right things as you want. You can build the train and deploy your, uh, models within the cluster itself and monitor it in the same way that you would any other application data scientists.

It brings in the kind of shadow IT that we used to have with the data science teams where they're kind of looking after their things themselves. They're now part of a core application stack, and they get the support from Central it, it allows them just to focus on doing the AI bits as opposed to having to run the underlying infrastructure as well. And like I said, the consistency and the choice, the ability for you to run models within the cluster, the ability for you to reach out to models within the cloud provider itself and to get the advantages of using both and comparing both.

So you can deploy your AI enabled apps and, you know, model development. You've got OpenShift ai, we, we got Watson, uh, x Amazon SageMaker, and we have various partners that we tie in. Nvidia, elastic and Starburst, all the people that give you your inferencing, uh, servers, the ability for you to store your data or pull your data in.

And all of this runs on top of the application platform, which is Red Hat OpenShift for AWS and the ability to bolt in and add GPUs as you need. We all know that GPUs are expensive. We all know that running instances with GPUs are expensive.

You don't necessarily need all the GPUs all the time, so you can turn off workers as and when you need based on the workload and the time that you are running it, as you would expect from an application platform. We work with many different vendors as well as our own internal things. So we've got multi cluster management, uh, advanced cluster management for Kubernetes, and, and it allows you to stand up and control and get consistency across any number of both OpenShift and non OpenShift K eights platforms that you'd want to use.

We work with the best in the industry for security. We've got global registries that work with us, and a lot of these third party vendors make sure that their operators, as we use to install this, are certified to run on OpenShift because they understand that organizations want something that is enterprise ready, that is secure, stable, and trusted. So they will make sure that their products work on top of Red Hat OpenShift for AWS modernizing commercial software.

So what we are seeing is a lot of organizations who realize the value that they can provide their customers by running on top of Rosa, if their, uh, software is certified to run on top of Rosa, that is removing a level of complexity for most organizations that they can consume without the need for them to necessarily reach out and have a SaaS service. They're almost providing a bespoke SaaS service for their customers by building products that work on Rosa, that then Red Hat and AWS will take the management brunt for. So customers, as you can see, we have a wide range of customers.

Uh, many large organizations reach out to us and, uh, we've had some very successful delivery. They are just, they're, they're globally. This is not just an EMEA kind of deployment, but we do work across the globe.

And there are many organizations across the globe. A lot of brands that you'll see listed there that I'm sure that you have all heard of or are certainly aware of some of them. Um, we'll take ExxonMobil for example, who migrated workloads to, to, to Red Hat Hybrid cloud infrastructure, utilizing the Red Hat OpenShift service on AWS because that provided them with the scalability that they needed the ability to grow up and down as they started to build upon their ERP system that they were implementing.

And Hitachi supporting their agile development. So hosted control planes Rosa of, uh, uh, as it is and as is moving for the future, um, allow them to cut their management costs and unexpected problems with it being owned and managed by AWS and Red Hat that removed a lot of the complicated component stuff underneath that didn't bring them any value. It allowed them to spin up clusters as they needed very quickly, allowed them to dis uh, to, to establish their agile practices so that they could accelerate their delivery out to their customers to allow them to earn more money, create more value for their organizations Buying.

So how do you buy all this, uh, that we talked about today? So Red Hat OpenShift on AWS is basically procured through, uh, well, it is more than basically is procured through AWS the, uh, uh, marketplace or the console. Um, you could buy it in, uh, different ways, which would be the way that you would want to consume, which could be a PayGo model.

Uh, it's either that or you buy it as a one or a three year committed spend, uh, which is called reserved instances. Uh, this maps against AWS models also. So if you have a agreement with AWS already or sitting on that, uh, these services will help you draw down on those committed spends.

So on EDP for my, uh, for AWS as an example, um, that the Rosa services will help you draw down on those commitments as you see in the slide. Also, uh, one year committed, uh, volume is, uh, giving you an additional 33% discount. If you commit to three years, then you will get a 55%, uh, on demand service fee discount, uh, on top.

Um, this basically the way that it is consumed, you order it, you book it through, um, contracts with AWS, um, and you get both OpenShift and the EC2 instances included in there. If we look at it from the perspective of, uh, an example here, um, the suggested EC2 instance, which is, um, M five A A MD, um, X large with four VVC ps. Um, you have a 600 VCP ceiling, uh, on top.

That's your demand that you want to have, uh, which means that you get 150, uh, to build the cluster and that, uh, adds up to, um, 300 k uh, per year and with a commitment of three years. So you actually can start saving money on this. We normally would recommend a mix of both, uh, committed and pay before you know what your spend is or how much you're, you're going to consume.

Also, uh, the examples here is also that you can actually reach this really easily by going into the console. And I think we showed this previously also. Um, you book it, you sign the contract in the system and you're ready to go to start consume, start using Rosa as an application platform.

There is also a different way of doing this, uh, which is the private offer and something that, uh, we also do together with the customer to give a bit more flexibility, uh, into the commercial model, but it's also signed off within the console. So we agree on, on a, on a setup, uh, we being Red Hat and the customer, uh, but it's also gone going through AWS channels. So it's still being, uh, invoiced, run, handled, administrated by AWS.

So you'll see all your invoices coming from one place. So you can do your financial modeling. You can also do the, um, uh, cost invoicing, uh, internally where you can see who's been using what.

Um, and that was basically it when it comes to buying. So we're end of the show today, and I think a hand over to you is. Yep.

So hopefully you know what Red Hat OpenShift service on AWS is. It is an AWS service that is jointly supported between Red Hat and AWS uh, value is greater than return on investment. You need to look at the whole picture of how your organization runs and how it delivers its application.

Hopefully the why is clear. Most organizations are not hyperscalers. You are not infrastructure builders.

What you should be doing is focusing on the ability to be able to deliver the applications that bring benefit to your internal customers and your external customers. And Red Hat OpenShift service on AWS helps you accelerate that by focusing on that layer and not worrying so much about the underlying, uh, infrastructure. Customers.

Customers are worldwide brand names that you will have all heard of buying. It's an AWS service, PayGo the ability to buy it through the marketplace. But there is also the further option of a private offer should you need a little bit more flexibility in the way that you work.

Uh, if you need to understand anymore, there's an ebook of modernizing it with with cloud services. There's a, a brief on how to migrate your virtual machines into Rosa, uh, migrate to Maximo IBM applications Suite. This is something that we've been working with closely with IBM and then a checklist of five ways Red Hat and AWS build AI value for you.

Scan the QR codes and you'll be able to get access to all of these. So breakout sessions. Next ones pick, uh, fast track to IT automation where we talk about Ansible automation platform or maximizing your, your marketplace spend with AWS, uh, the ability to get the more flexible pricing and the consolidated billing that you can through AWS marketplace to buy Red Hat products.

That's all for today. Thank you all very much for your time. Thank you very much.

Hi everyone, and welcome to the six five Summit AI Unleashed. I'm Melody Brew for more insights and strategy. Today I have JJ Davis, senior Vice President of corporate Affairs at Dell Technologies, joining us for this sustainability track opening keynote on efficiency in the AI era.

Ai, thanks for being here, jj. So to set the stage, AI is reshaping the technology landscape from data center architecture to the edge and beyond. And as enterprises accelerate AI adoption, Dell's position as both technology provider and a sustain sustainability leader becomes increasingly significant.

Can you share how Dell defines this new role of, in the AI driven era and how that vision is guiding your strategy? Sure. So, you know, we have been working with almost a hundred percent of the Fortune 500 for a very long time.

We're a big provider to the CSPs as well. As you think about AI and the opportunity for business growth and, and productivity gains in front of us, it's a really exciting time to be in the infrastructure business. And so we launched, um, more than a year ago, the Dell AI factory with Nvidia as a way to help our customers deploy on-premises AI inside their companies.

And so we continue to see great adoption, and what gets exciting from where I sit is the opportunity to think about modern data centers and what that means from an efficiency perspective. So we've long been the leader in the best performance per wat in our servers. Every generation gets better, but now, um, efficiency becomes even more important when you think about the growing cost of energy associated with ai, how we need to make sure that all of the infrastructure is fully utilized, how you think about where the workloads run based on what it is the end user needs to get done.

And so we're factoring all of this in with a lot of product innovations and how we're advocating on behalf of more secure and sustainable AI with governments around the world as we partner to really, um, build and grow the enterprise AI market, both for the public sector and the private sector. So yeah, I mean, I think like with the AI workloads organizations and I guess, you know, beyond governments and, and others that you mentioned are under pressure to optimize for both performance and sustainability. So how are your customers prioritizing evolving in this context?

And really the big question is, is sustainability remaining a top priority to your customers? Well, it absolutely is because our customers, customers care about it, but we really think about what can we do to create technologies that advance human progress. That is the purpose of our company, and it has been for a very long time.

It's why we exist. How do we drive business and societal value at the same time? And so it's always though been about cost reduction.

And ROII would say that, um, you know, when, when we're talking to customers and we get RFPs from them, 95 plus percent of RFPs have sustainability questions in them. They want sustainability criteria to factor into the decisions they make, but it's not the number one thing. It's always been about price for performance or total cost.

And I think the good news with all this focus on efficiency is we can do both. We can drive down the cost of energy to power these modern data centers. At the same time we're thinking about where does the workload run in the data center or on the end device like the A IPC.

How does that help increase energy availability? We have a new, um, rear door heat exchanger that starts to capture heat, um, and, and really drive more energy efficiency in the data center. So the good news is the energy intensity of these data centers and AI servers are forcing even more sustainable innovation, which is good for the environment without compromising the productivity and economic gains.

We really are so excited about in the AI era. Yeah, this, the timing of this conversation is so great because you've just come off of Dell Technologies world and there was so much engagement there directly with customers who are navigating these challenges. I heard so much of what was going on there with some of the other six five videos.

What are some of the other key themes and concerns that you heard regarding AI adoption in terms of efficiency and sustainability? Well, it's a great question. And the number one inquiry we get from customers is around calculating their product carbon footprint, both across their PCs and their data centers, because understanding the individual product carbon footprint of every product in their enterprise helps them then calculate the total emissions of their infrastructure so they can have that baseline and drive those emissions down.

That continues to be a big topic of conversation. As I mentioned, we launched some new liquid cooling innovation, which is core to our server portfolio. Every new generation of server is more efficient than the previous generation.

So as customers are weighing, is it time to modernize and invest in new technology, this certainly, uh, a lot of companies are doing that because of ai. And they can then factor in, uh, what is the balance of liquid cooling versus air cooling? What do they need to be thinking about differently?

And we also launched something at Dell Technologies world called Concept Astro. So it's a pilot project that we did with the Scripts Oceanographic Institute at, um, uc, San Diego, where they have so many coral reef images, huge amounts of data, a lot of data intensity where they're trying to research and study the coral reefs around the world so that we can drive ocean preservation. And so we installed a, a Dell AI factory with NVIDIA in their data center.

We've been piloting to make that data center grid aware because if you can make it grid aware and the customer can know what is my availability of energy? Is it sustainable energy or not? When do I run this workload at a time when more energy is available so it's cheaper, and how do I run less energy intensive workloads at a time when we're at a peak and it's really expensive?

So we're not there yet. That's why we're calling it a concept. But this is really the promise of what we can do to make our data centers smarter across the board so that customers can meet both their p and L requirements, they can uphold their fiduciary responsibility to their investors, but they can also make sure that they're meeting their own sustainability goals as it relates to their it.

Yeah. So a lot of what you said sort of summarizes that, like the promise of AI is so immense, but there's all of these operational and environmental considerations and costs if that's not managed carefully. So Dell helping to kind of leverage this iteration, what innovation, whether that's infrastructure, edge solutions, AI platforms to help customers address this, what, where would you say people need to start kind of on, on addressing that, those efficiency challenges without compromising on performance or sustainability?

Because as you talking, it's like, these are all great solutions, but that's a lot to take on, right? Yeah. And, and customers often ask like, what do I do?

Where do I start? I start, and many of our leaders are like, you just need to start somewhere and you need to start now. And so a big part of what we do with our customers is help them get started.

So we have reference architectures, we have an AI pursuits team that can help our customers just getting started in ai. Um, you know, from our sales team who are really, um, well educated in how to help customers get started. We also have consulting services, and in that same consulting portfolio is a, a sustainability offering.

For example, we have a customer advisory board of chief sustainability officers that get together that we run separately from the CIO customer advisory boards that we run. But we do encourage more direct connection between the CIOs and the CSOs so they can make these decisions together from a cost and an emissions perspective. And so those are some of the things that are happening.

And by factoring in and, and buying the latest technology with the latest innovations like liquid cooling, that's gonna give you a jumpstart on ensuring that your data center running AI is efficient as it can be. I would also say really think about your AI strategy end to end. The emissions coming off of a PC are less than what would happen inside of your data center where you're running, um, rack servers as an example.

So running the right workload in the right place for the right task or use case is critically important. So we can help customers with that as well. And, and, you know, not AI per se, but I I can't not mention circular economy, you can't go deploy a new fleet of a IPCs to your workforce without thinking about how are you gonna retire the now obsolete equipment.

So what we get excited about is environmental sustainability, leadership and expertise from Dell across circular economy, turning trash into treasure, if you will, connected into climate action and how that relates to our overall innovation agenda for our customers. Like we do look at it holistically and each piece plays off of each other. So looking at that holistically, that involves more than than technology, it involves people.

Yeah. So let, shifting a bit to kind of the more people centric side of ai, this rapid evolution of AI means that there is a skills gap that could be widening and organizations are struggling to kind of keep pace with that. What support is GA Dell giving towards workforce readiness, both in internally and for your customers and partners to ensure that industries and communities can fully participate and benefit from this AI economy?

It's A key part of how we drive human progress and think about human capital management and human rights across our value chain. It's a big part of our AI strategy when we think about skilling and ethical ai. Uh, it's got to be factored into the overall innovation agenda coming out of the company.

So, um, I think public-private partnerships is so key. So for example, um, the White House just issued something called the AI Education for American Youth Executive Order. Mm-hmm.

So we just submitted our response for that with guidance to the US government on what we believe needs to be done, and then also made commitments around initiatives that we currently have or we, or that we are growing that can be factored into what Dell is gonna do actively to advance the education of American youth to help them participate fully in this new digital economy. I'll give you a couple of examples. So yes, we have trained with AI Foundations training all of our employees.

We did that more than a year ago, and we're constantly rolling out new training across the board or by functional area based on whatever it is that employee needs to know. Agenda AI is coming fast to the scene. So that's gonna require a new level of training and understanding as we use AI agents in service of us, the humans, to help us reduce low level work, do more high level work, and add real value at greater efficiency.

What of that training do we turn in through our Dell learning, uh, team to offer that to our customers and to governments? And we have active engagements across many governments as we speak from the US to Malaysia, to India, really helping them address their own strategies and what they are gonna do to advance their citizens. We have interview, uh, digital assistance, if you will, where we have engaged, uh, nonprofits.

Hopeworks Outta Philadelphia is a good example where they are starting to do interview skilling with young adults entering the workforce who are practicing interviews with an AI assistant. So when they go in for the real interview, they're ready. So it's a combination of training our own workforce, working through Dell learning to train and certify our customers together with our customers and partners, training the public and the communities where we live and work, and figuring out with our philanthropic approach, both with people dollars and equipment, how do we get this more into the hands of our communities so they can fully participate?

So there's a lot going on in this space, and it's critically important that everyone lean in and make a contribution here if we're gonna ensure all people have access to this AI economy. I love that message, and that's actually something that I've always sort of taken away from my conversations with leaders at Dell that this isn't something that's like, look at what we're doing. It's like, look at what we should all be doing.

So with that, is there something you wanna kind of leave as the big takeaway? What do you want people to know or do next? Well, I think, you know, at Dell we are AI optimists.

I think it's really important to lean in and educate yourself and factor in both the business and societal impacts of ai. How are you reducing risk? How are you building the right, uh, processes and policies so that you can ensure responsible ai?

Do that it's responsible business, but don't let that sh slow down your embracing of AI and how you can drive real innovation and results within your company, but also leave a really positive imprint on the world. That's great. Well, thank you so much for sharing your insights with us today, jj, it really has been such a pleasure speaking with you.

And for everyone watching, thanks for joining us for this sustainability opening keynote at the six five Summit. com/summit. There are more insights coming up next.

Welcome to the six five Summit AI Unleashed. I'm Dave Nicholson with Six Five Media, and I'm joined today by Varun Chara, senior Vice President Product marketing in ISG and Telecom at Dell Technologies for the enterprise AI track opening keynote on the enterprise AI transformation with lessons and guidance from an AI leader. Welcome Varun.

Hi, Dave. Thanks for having me. So let's dive right into this.

Um, we've heard a lot about this thing called the Dell AI factory. What is that? What are you talking about?

Yeah, it's a great question, Dave. So, um, about a year and a half ago as we started to look at what's happening with ai, with our enterprise customers, uh, as well as other segments such as CSPs and, and sovereign entities that are looking to take advantage of ai, the most common feedback we heard was, Hey, I get that AI is important. I know that it's really, really critical for my business and to stay ahead.

What I need help with is how do I get started? Or if I've already got started, how do I scale? There's so many different, uh, corners, you need to look around when it comes to ai simplify all of this for me.

And that's really where the idea of the de AI factory was born. The de ai factory is really an end-to-end solution that incorporates, uh, our infrastructure. So whether it's compute, storage, networking, cyber resilience capabilities with, uh, an ecosystem, uh, whether it's software or hardware integrated with our infrastructure, and then also capabilities and professional services, whether it's consulting, uh, early in the cycle on what your AI strategy should be, how you should do stakeholder alignment, how should, how you should think about your data before you get any AI effort started all the way through to a deployment guidance, uh, as well as support and, and guidance with how to scale once you've gone out of the POCs, uh, area.

And really over the last year, year and a half that we've been talking about Dell AI factory, we've just seen this message really, really resonate with our customers. The as del tech world in Las Vegas that we just concluded a few weeks ago, uh, we actually shared that there are over 3000 customers now for the de AI factory. Uh, and we've had, I think over 130 or 140 new releases within the span of, of a year, year and a half.

So tremendous amount of, uh, innovation and momentum that we are bringing to our customers on behalf of, uh, uh, you know, our, our ecosystem and our partners. And then also a lot of momentum in terms of customer adoption. So what are you hearing from customers in terms of kind of where they are in this process?

Uh, you could, you could say there was a period of time when fear of missing out, uh, sort of ruled the day, uh, have, have people come to a place where they can methodically work with you to move forward. Yeah, I, I think so. But I also think as with, as with all things that happen in, in technology transitions like this one Dave, there is a spectrum of where customers are.

There's really a maturity curve in terms of where, where we find customers. I think we really think of this as, as, as if I think about it in the last, you know, when, when the AI surge really started about a two, two and a half years ago, the early adopters were obviously the cloud service providers as well as large model trainers. And, and those are still a large part of, uh, you know, the infrastructure conversation today is they have very unique requirements.

They have large scale deployments, they're really at the cutting edge of, of deploying at scale. Over the last year, though, we have started to see a large, uh, upswing in adoption from the enterprise, uh, which is really the, the key trend to watch for obviously the CSPN model trainer, uh, uh, momentum has continued and, and if anything, it continues to grow, as I'm sure we're all seeing in the news. But, uh, with enterprises, I think there is a spectrum of where, where customers are.

There's a large number of customers that are still in the POC phase, but every day there are more and more customers that, that we see that are moving from POC to production. And really what, what they they want from us is, whether they're in the POC or they're starting to think about moving from POC to production or they're in the production phases, uh, help us understand this complex, complex dynamic that's happening. How do I think about my data, for example?

Like that's a big bottleneck that we often hear from customers wherever they are. They could be in their POC and they're starting to think about that, or they're moving from POC to, uh, to production is, well, what's, I have so much data that's sitting across so many different locations could be on premises in your data center, in their data centers. It could be with a, a cloud service provider in the public cloud.

It could be in a private cloud environment with a, with a company like Core, weve could be out increasingly at the edge. How do I make sense of all of this data? What is the right amount of data?

What's good data look like? Uh, how do I think about bringing all of this together? How do I manage all of this?

So we've got capabilities not just from a product perspective, but also from a services professional services perspective, to really help people with that. That's one area that we definitely run into a lot. And then, you know, we'll talk a little bit more about our own experiences at Dell and how, uh, you know, implementing AI within our organization, how that's really, really, uh, really helped us be a, uh, I think a strong strategic advisor for our customers as well.

Yeah, I definitely want to hear about how Dell is drinking its own champagne, as we say. Mm-hmm. Mm-hmm.

But, but first you mentioned, uh, you mentioned a term that's one of my favorites, one of my favorite words, ecosystem. Yeah. Uh, how the heck does Dell manage that ecosystem?

Because you have to be Switzerland, uh, for, for your customers. Yeah, yeah. What has that looked like over the last year and a half?

What, what is it like working with partners? I think Dave, uh, it, it's definitely a very dynamic space, but the one thing I I, I always talk about when we, when we're with customers is that Dell is no stranger to being Switzerland, right? We have across different transitions, whether it's in the laptops and, and client space servers, storage, et cetera.

We have a long history of working with, uh, uh, uh, a multi-dimensional ecosystem to really, uh, build an open ecosystem on our customer's behalf. And that's what we're doing with ai. I, I, I, I really love this line because I think it, it goes to the heart of what, uh, we're trying to do here is that we're really trying to build this open ecosystem on our customer's behalf for ai.

And it is a unique challenge as, as you're, as you're implying with AI and what, what customers are seeing is, and what we're seeing is the pace of change is so rapid there. We are very much at the early innings of AI adoption and AI maturity in the industry. So you're seeing all the, the classic signs of, of an early stage disruption where there's a lot of new vendors that are coming into the, the fray, a lot of different partners to work with evolving business models.

And quite frankly, uh, the pace of change with AI is warp speed compared to what we've seen, right? So there's all these dynamics to work with, work through, and work with, that actually is a huge opportunity for us at Dell to serve our customers better and drive more value. So recently at Dell Tech World, uh, as an example, or even even before, um, at at GTC with Nvidia and with, with what we're doing with other vendors as well, uh, you'll, you'll, you'll see that we are, over the last two years, we have been at the forefront, you know, of announcing partnerships with partners up and down the stack of, and, and our key focus has been through all of these things has been how do we make this simple?

How do we make this turnkey? So I'll give you a few examples. You know, we've got a special flavor of our AI factory, which is the de ai factory with Nvidia.

We've got an incredibly strong relationship with Nvidia. We've driven a lot of joint customer value, uh, in the last two years. 5 came out, and we all had that, that massive aha moment that, that we're still digesting.

Uh, we started with Project Helix, and at that point in time, I remember talking to, uh, customers, talking to analysts, talking to the press. There was just a lot of questions of how is this gonna look? You know, is this just a flash in the pan?

Et cetera, et cetera. Helix basically became the AI factory de AI factory with Nvidia. And, you know, it is the, it is the, whatever the opposite of a flash in the pan, uh, it, it has been so widely successful and we've, we've, we've had such a pace of innovation and joint value that we've delivered to customers.

Uh, and we we're doing the same thing with, with, with other silicon providers and GPU providers as well. We've got strong relationships with, with a MD, we've got a strong relationship with Intel. We've been, we are now on our fourth iteration of the AI platform with a MD.

We are, um, we just announced at DTW, the AI platform with Intel, that takes advantage of the, the innovation that Intel is bringing true in, in the GPU space. So very, very excited to work with, uh, all of these partners. And really that's only the beginning of the ecosystem here, right?

We're just scratching the surface, you know, gi these Nvidia, Intel a MD are so important, but what we're seeing, another dimension of the innovation is really in the model space, in the framework space. And there, we've, we've, we, I really think we've been leading the, the industry on integration and partnerships with, with, uh, with folks that are in the software stack, if you will, or in the model or framework space. This, this year at DTW, uh, in May, we just announced partnerships with, with Glean, with Mistral, with Google, with, um, uh, cohere with, uh, you know, uh, we've, we've been, we've been talking about our partnership with Meta for a really long time as well, to bring Lama on premises.

And in many of these cases, uh, whether it's Lama, whether it's Google, whether it's cohere, we have been at the time of announcement, the first on-prem vendor that they've been working with. And the reason why we, I think they like to work with us, these vendors like to work with us, is that we've got a very clear perspective on making things simple, right? We've got a marketplace on hugging face that's specifically for Dell Enterprise customers.

Customers can go and choose the right models that they want for the right Dell hardware all filtered in for them, and then they can take advantage of automated scripting to really deploy these models and run them. Uh, and then we've done the testing and validation in the past, and we're working with folks like Red Hat as well, um, to, to deploy their approaches to, to ai. So I know I've rambled on a little bit, but it's really important to talk about kind of just the, the work that's happening at all aspects of the ecosystem.

And then we continue, we've also announced, uh, with, with GSIs like Accenture, Deloitte, uh, and, and many, many more, the work that we're doing to kind of really bring AI higher up the cycle earlier in the customer conversations with them as well. So, you're right, the ecosystem is, is a really, really vibrant space. There's a lot of different dynamics going on.

Uh, but we're doing a lot in this space. And then, and then one thing I should forget to mention is, uh, you know, obviously this is not just the infrastructure space, this is also in the PC space. We're doing a lot of work with Microsoft, with Nvidia, with Qualcomm, Intel, and a MD to bring the latest and greatest, uh, AI innovation to our AI PCs as well.

Yeah. Yeah. And, and Varun, I wouldn't call it rambling.

I would, I would call it, I would call it a herculean attempt at, Uh, sharing the narrative and trying to impress upon people that this, this balancing act that you're doing at Dell, uh, that includes agility and choice with being prescriptive enough. Because frankly, choice sounds really good until you're completely confused and you're a customer and you have no, no idea what to do. And of course, let's be honest, this stuff is changing so quickly that yes, you know, it's, it, you know, that that agility, you'll, you'll be working with partners a year from now, probably that maybe today we don't think yes or necessarily vital vitally because of the shiftly.

We, we hear about moving from 80% in, uh, training towards, uh, and 20% inference to a complete flip of that, uh, the AI pc, all of these things as you, as you said. So, yeah, don't, don't call it rambling. I, I, I, I, I, I feel for you, you guys are doing a good job at juggling.

Yeah. It's, it's a huge, I think it's a huge part of the AI factory value proposition is really the open ecosystem. And you're absolutely right.

There's usually a trade off between choice and, and simplicity. What we're trying to do is thread that needle and make it easy for all of our customers to adopt these partner driven solutions on top of, uh, Dell infrastructure, on-prem. 'cause we genuinely believe that's the right thing for our customers.

Yeah. Well, let, gimme an example of some of the things that you're doing within Dell. We, we used to say, eat your own dog food, and someone came up with No, no, no, no.

Drink your own champagne. So tell, tell us about some of the champagne you're drinking, some of the things that you are doing that demonstrate to the market that, that not only is Dell willing to sell these solutions, but they're not afraid to implement them. Yeah.

There's definitely an aspect of, of leading into this, to, to eat your own don food or drink your own champagne. I'd say it's even more basic. Uh, you know, it's, the value of this is even more basic.

I cannot tell you the number of conversations I have. I've had over the last year with customers where the conversation is not so much about selling. It's actually about, uh, helping customers understand, Hey, out of the, I don't know, hundreds of use cases that stakeholders within a company are telling, um, you know, IT teams that they want support on, how do you prioritize?

How do you really decide what the right value is, where the right value is? And, um, in internally at Dell, we've been very aggressive, uh, with, with, uh, rolling out AI for a variety of different use cases. And, and I think what, what, what really stands out are a few salient principles that I think customers find very, very helpful.

The first one is, it's a very, very important to, um, to really prioritize your use cases. Like I said, you know, at Dell, and, and Jeff talked, Jeff Clark talked about this on stage as well. Uh, at DDW when we started out at Dell, we had, I, I don't know, I think it was like 700 or 800 different use cases internally that were being considered right at various stages.

Like some were just ideas, some were actually being, uh, trialed out. Some were kind of on the way to, uh, POC, there was a wide variety of, uh, use cases. Well, that's not really a scalable way to really deploy ai, right?

Chances are that if you, if you split your resources and, and approaches across so many different, uh, so many different use cases, you're just not gonna get the right value. So we have our own internal method methodology where we look at what's the, what's the, uh, you know, an access to ROI versus feasibility. We split things up and look at it from that perspective.

And that really helped us be, uh, methodical and um, and really, really thoughtful about what we're gonna focus on. The few projects we're gonna focus on first. Uh, and then also the other thing I talked to, uh, it, IT leaders about is that IT leaders actually have a unique, unique vantage point in ai and a unique, almost a responsibility to actually drive leadership alignment on what these use cases need to be.

Because there's no one else other than it, that is able to see left and write on what are the different use cases that are being proposed. So this is, you know, we always talk about, uh, Dave, and I'm sure you've had these conversations and you continue to have these conversations about how do we go, how does it go from being seen in some companies still as a cost center to a real difference maker? AI is a really, really interesting opportunity to do that is because, because it is being asked to go solve for these things and say, Hey, you know, most of the conversations I have with it, it leaders, the first question I get is like, I've got so many people asking me for help.

How do I, how do I sort this out? And I usually tell them, it's actually not your responsibility to sort this out. What your responsibility is, is to create a forum and bring your leaders together, your C-suite together.

And actually you can step in and, and drive a framework and some strategic thinking around, uh, helping your leaders and the the C-suite actually have that conversation. Creating a forum where you can then actually drive prioritization. 'cause that's when, that's when the real value comes.

So, uh, you know, you, you are probably looking for an answer on how we're using our own technology to drive this. And of course, we are. We've got, uh, you know, most of the partnerships that we announced and the, and the capabilities that we have around that, we are trialing them or actually are in production with many of them.

And it's not just us. Right? At, at at, uh, Del Tech world.

Jensen Wong and Michael Dell were talking and they talked about how our irrespective companies are using the AI factory within Nvidia internally for our various efforts. So it's definitely happening across our partners as well. But the real value actually, that I've found in customer conversations, and then many of our, our, our sellers find when we're talking to our executives, find when we're talking to customers, is sharing our experiences about them that are not about products that are actually about processes and alignment opportunities that you can do before the, before the, uh, uh, step before you start actually deploying technology.

And that's really how we, we wanna show up in Dell as a, a strategic advisor or trusted advisor to our customers. I mean, Jeff said this, uh, in in slightly more colorful language at Dell Tech world, if you take AI and you apply it to a crappy project process, you basically get crappy results at a much faster pace, right? So you, you, so while it's easy for us to go and say, Hey, you should use this, this server or this, this storage for, for the, we, we are very, being very thoughtful about having the right conversation upfront.

'cause that's really gonna drive the right conversation or right outcomes for us and for our customers in the long term. And that's, I think why you see so much momentum for us with, with, with contact customers, is that we're taking a differentiated approach and being thoughtful about asking customers to pause and really think about what, what they're trying to do before they start deploying our technology and our partners technology. Yeah, I think it's a really good point that you raise about the position that it has in this AI revolution.

Mm-hmm. Um, any, any MBA would tell you first figure out what the business problem is that you want to solve or yeah, the innovation you want to create, the disruption you wanna prevent. First figure that out, then talk about technology.

I would argue that, yeah, that doesn't work in AI the way that maybe it did in the past as much you have to have someone like Dell in working with it, because if you've never seen these tools before, your imagination will be, will be stifled. And, uh, that's what I have seen with, uh, Dell working with its partners with it to help business understand what the capabilities are. If you've only seen a shovel and you've never seen a bulldozer, your imagination for what you might do with a plot of land is very, very different.

So it is, it's, it's definitely a, a, a balancing article. So what's the, what's the call to action from your, from your perspective? I mean, what's the best way, um, I'm gonna be giving a talk in a little while about, uh, you know, what a CEO's first move should be in ai.

Mm-hmm. Let's say there's somebody out there hypothetically that, um, has not made that first move at all. Yeah.

And let's say hypothetically, they haven't talked to Dell yet. What's, what's, what's a good first step? Well, they should definitely talk to Dell.

I think that's a great first step. But jokes aside, I think what I encourage customers to do is really, really think about where are they on the use case selection? Do they have a prioritized list?

And then do they have stakeholder alignment for that prioritized list at the highest level? And the step after that is for those, let's just say they land on three or four or five use cases, have, before they even talk about automation or simplification through AI tools or AI infrastructure, have they really standardized and simplified their processes? Right?

Only then do you really start thinking about what's the right tool? What's the right framework? Do I go open source, close source?

Do I build my own model? Do I just take a, an off the shelf model, et cetera, et cetera. Uh, but, but really I think it's, it's thinking about those dimensions of what are the right use cases that I wanna prioritize?

Are my most senior stakeholders in the organization, are they aligned that these are the prioritized use cases that we're gonna devote resources to? And then third, are the processes that accompany these use cases, simple, simplified, and standardized across the company. And then you start looking at technology.

And I think across all of this, what you've gotta ask yourself is, do I have the right partner that's, that's supporting me in this conversation? Or is the vendor I'm talking to just interested in selling me their gear or their, their agent or their software? Right?

And I think it's pretty clear when you start having those conversations, um, you know, who's what that, I think that lens is gonna be really, really helpful. Great advice, Varun. Excellent advice.

Well, we've heard a lot about Dell's AI Factory. I'd like to thank you for joining us for this enterprise AI track opening keynote at the six five Summit. com slash summit.

On behalf of six five Media, I'm Dave Nicholson. Thanks for joining us.