Techstrong TV July 25, 2025

Hey, everyone. The White House wants us to enter a golden age of ai. I feel like we're, it's dying before it's born.

You're watching Text on Gang. Hello everyone. Welcome.

Happy Friday, Friday. I, I feel like I was just on here saying Monday. Welcome back from the weekend.

But we're here on Friday. We've got a, a Great Friday gang line up with some interesting topics to discuss. Let me introduce you to these interesting people.

Uh, we've got, uh, they, if you've watched the gang, they're regulars here. We've got the one and only Ira Winkler. We've got Fred Wilmont, Mr.

Cecil, well actually he's multi seat time cso, but now founder of his own company, Fred. Well, we'll come back to you. We'll give you a chance to plug that.

And our marketing person extraordinarily Lisa Martin, as well as the dean, still licking his wounds over another Yankee, yet another Yankee loss. Mike Ard gang. Welcome.

Um, so Fred, before we go further, you know, we got Black hat coming up, IRA, I, I, I thought you said you may not be making it to Black hat this year, right? Or airplanes changed? Um, not sure.

It's like 50 50 I think at the moment. All right, we're gonna, we're gonna wait. We'll give you more time, but Fred, you are gonna be there and your company is The tech team.

Yeah. So what basically we, uh, what we're gonna be doing there is really focused hard on some of the things we're gonna talk about a little bit today. But what we do is we take and transform the static sort of expert driven behaviors of trying to write detections over a long period of time at large companies and, uh, and small companies, and democratize that and automate that and bring some of the continuous, uh, automation behind a development lifecycle to detection engineering.

So, what used to take, you know, three or four weeks to figure out how to write stuff for this brand new ransomware kind of example, we can generate data that tests whether or not your infrastructure's prepared for that today, and then generate detections and push 'em for you, you know, in 20 minutes. So the intent there is really buying down the risk. Cool.

Hey, I, I like to let people know, you know, I mean, they could go look you up online, but what the heck? Anyway, thank you for that, Mike. Another announcement from the Talking Heads at 1600 Pennsylvania Avenue, they wanna usher in a golden age.

On, on The face of it, you might find that there's a lot to like here. On one hand, they are saying that they're gonna get rid of some of the, uh, regulatory hurdles that prevent us from building the data centers that we need for ai. They are talking about making, uh, investments with strategic AI partners.

So, you know, we're all gonna kinda benefit from that. Maybe there'll be more choices for GPUs and AI accelerators on the face of it, at least. There's a lot of things in here that sound like things that the IT community wants.

I mean, you know, you get this whole, as you read it, you get this whole, you know, U-S-A-U-S-A chan going with it, you know, I got my little flag here, the whole nine yards. Nice. But, but, um, they, They pulled out a prop in the business.

But go ahead. But there are thing, uh, concerning things in here. It does seem to be very much, uh, America First and a little light on security and almost no concern for environmental impact.

But Alan, what's your take on what's going on here and maybe what is the role of the government in all this AI stuff? I could sum it up. What, what is it, what's the song about Fools Rush?

Where Wise Men tread Fair to Tread? It's not a song, but it's a saying, wise men fear to tread Where fools rush in. Who's the Fools?

And I Think that that might be an old Ben Franklin thing. I think. Okay, we'll go with Ben Franklin.

He was a wise man, but clearly, look, there's a lot to like, as a lot not to, like, are we gonna build AI data centers at the expense of our, of clean water, clean air, faulty, you know, well, are we going back to Love Canal? Damn, the torpedoes. Full speed ahead.

We, we've gotta leave our children a world, I hope, number one. Number two, you know what, frankly, I'm so Jaded and, and callous to, to these announcements where everyone's coming up and, and bending me and, and opening a, supposedly opening a checkbook and telling us about how much they're gonna spend on these AI factories. And then the third thing is, you know, the more, the more we play with ai, the more we hear and see things going on, the more convinced I of, uh, than ever I am, and I agree with Ira on this, we gotta build security in here, man.

We, we can't afford to do the usual. We'll worry about security later. 'cause this is going to it, it's gonna wind up not good.

And then the other thing is, you can't make wine before it's time. I, I think the promise of AI is phenomenal, phenomenal. But it, it's going to take time to develop, to evolve, to be used to, to be refined and get to where we want it to be.

Rushing headlong in here, like bulls in a China shop. I, I just don't think I, I'm not sure it's the right thing. I don't think you could have a golden age till it has an each.

Well, Al could I, oh, sorry, go ahead. So here's the problem. So there were a few things I saw in the bill, like, oh, we're gonna rush out data centers in the first place.

There's just so many places you can put data centers. And then you have the Not in my Backyard thing where everybody says we don't want regulation. And okay, so we're gonna take away some of the regulation to put it.

'cause really what these are, are football, you know, it's like the old days of the NSA has football fields worth of supercomputers or something in the, they're Not football fields. They wanna build them the size of Manhattan. Oh no, I, I understand.

But then you have to figure out where can they actually put them. And when you figure where they can put them, you know, frankly, you're talking about places like the middle of nowhere, like in Yeah, fly over stand, fly over stand. And that comes up to issue number one.

Then the people are gonna be like, okay, not in my backyard. 'cause you know, in some places, I'm not gonna argue whether windmills are good or bad, but some people don't want windmills. Some people love them 'cause it makes great use of their, increases their property values in flyover stand.

But then the next thing that's kind of concerning is this rhetoric of, oh, we're not gonna have woke. Ai, you know, I mean, that's AI again, is mathematics. And people lose track of that.

And how the AI handles facts. Sometimes people don't like the facts like, rock was too woke. So they make rock a Nazi.

And you know, you have to start to look at, okay, I think that's a lot of rhetoric where people are just gonna develop the math that they do. And then at the same time, the one thing that keeps coming back to me is we're building these data centers and we we're missing a buzzword. I hate buzzwords, but we have AI as a buzzword.

The one buzzword that we're failing to look at is quantum computing. And the reason I say this is gonna be an issue is, yes, you need lots of computing power to process all the data going into AI algorithms, but once we get quantum computing, which give or take is five, six years away, we're not going to need central parks worth of computing power. We're gonna have much more computing power and a little bit of things.

So we're gonna start to develop these data centers that are gonna be obsolete in a few years, should quantum computing hit its prime. And so I'll, I don't Disagree. Put that.

So, IRA, let me just paraphrase one of the points you made, which is, and it goes to Mike's America first thing in one hand, they're saying you could have all these things, but with it comes handcuffs, with it comes conditions that it's gotta be America first, that it's gotta be us. That it's gotta be under the subject. You're gonna be subject to whether someone wakes up with a feather up their butt and decides you're not allowed to do business with someone because they're prosecuting a former dictator or something.

That's not, we, we don't need those kinds of strings. We don't need, this is America, right? We need a, a private public partnership, but we don't need government controlled ai.

I'm, I'm not really sure about the quantum argument because we are building a massive data center in Poughkeepsie with IBM for quantum computing. It'll take a few years to build, but it's, it's big. It's not just a smaller thing.

Maybe it's smaller than, you know, the size of Manhattan, but still gonna need a lot of data centers for both. Lisa, though, I wanted to get your opinion here because a lot of this is Alan pointed out traces back to this valley vibe, which says, go fast and break things. And in the case of ai, it seems like the risk of breaking things is really high.

And you know, it's not about the fact that I just broke an application and it was unavailable for an hour. So I look at this from a marketing message perspective, and I tried to look at it neutrally without some of the obvious concerns. I was just watching the news a half an hour ago about all the, all the woke ai and doesn't like the term artificial.

And I just thought, that's just nonsense. But if I look at it from a marketing message perspective, what what I heard was, okay, there's an action plan in an effort. Because what the White House wants is the US to, to remain a leader in the AI industry, this golden age.

And I heard investing in AI r and d building an AI talent pipeline. We, we've been talking for weeks about the AI talent wars promoting AI adoption across industries. And I thought, what I didn't hear was collaboration.

Um, I didn't hear security. I, you bring up a great point. I didn't hear Quantum, I wasn't listening for it, but you bring up a great point that we didn't hear about that.

But I just think, what's the alternative? But the message is, okay, the future of AI is now you gotta join the golden age of innovation and growth, and we've gotta invest in r and d. We've gotta develop talent and adopt technologies to stay ahead of the curve.

What's the alternative? I didn't hear that, but I also didn't hear collaboration. And that's gonna be absolutely critical, um, for us to be successful in a safe and secure way.

I I, I, I agree with you. I mean, look, I, I, you know, I'm hearing friends leaving CISO who, who, you know, the security aspect of this is, is just getting scarier. I I'm not saying we're gonna have Skynet that decides to destroy all carbon based life forms, right?

Outta Star Trek or something. Ger. But, um, I mean, the damage it can do, I, I, So to to your point though, I mean, I guess this was positioned as, you know, the US has an industrial policy as it applies to ai, but I don't feel like this is a, a, a policy as much as it is just kind of a, an invitation to not have a policy maybe.

Yeah. It, it's like everything else that this administration does, it's a PR kind of front. And when you go, you know, when you go past that first inch, it's kind all murky TBDs.

That's it. Not a lot of depth. Yeah.

No. Well, they don't call 'em shallow Hal taco for nothing. All right.

Um, I, I don't have anything else to, I I was, I was just wondering if maybe they used AI to write the 90 point plan. What do you think They do? They do do that.

Yeah. It'll be like, I'm sure there are a few hallucinations in there, but I mean, I understand all the concern, but I mean, there's just so much rhetoric. It's just gonna go on for so long to build these data centers anyway, that, you know, I, it's like, you know, the goals in there that sound authoritative or sorry, authoritarian is the word, right?

Um, I just don't, I mean, maybe I'm too naive on this, but it's just, I don't expect much ability for them to follow up on some of those other lofty goals. I mean, it sounds good. They're gonna build lots of data, you know, maybe they'll build the data centers.

You still need to come up with billions of dollars for it. The government's not doing it, so private industry's gonna do it. And, you know, frankly, It sounds like Mexico's building the wall and they didn't Well, Mexico's The wall.

Yeah, that's the point. I still expect Saudi Arabia to put the data centers in their desert, not our desert. Yeah.

And that's, they're what I, and that's what I think would more likely happen with data centers. 'cause the good or bad about AI data centers is you want the results. You don't care where the results in.

Well, theoretically you should care who's making the results. But if the results are coming from Saudi Arabia takes an extra quarter of a second to get here. Yeah.

And so if they're gonna build these large data centers somewhere else, because you can say what you want, but it's expensive to put, you know, building. I know We, we live in, we live in an age of it sovereignty. They, they want everything here and every, and as a result of that, other people are saying if that's where it's gonna be, I want my stuff in my place too.

I mean, globalism, as we knew it is, is dying. Well, there's globalism, but there's business. And the one thing frankly is you could say what you want, but these people are gonna like go for like, you know, I mean, how many hundreds of billions of dollars were supposed to flow into the US that were big campaign statements and ended and I'll leave it and never happened.

And Agreed. It's, it's the usual BS that comes outta there. We got, we're over time on this though guys.

Let's, let's, let's pull Last thing on this one thing is be wary of real estate scams, right? 'cause a lot of this is gonna wind up being a real estate scam. Yeah.

Wouldn't be the first time. Alright, let's take a break here on the gang. We're going to come back and talk about fan experience re-imagined.

That sounds uplifting. You're watching Textron Gang Discover Textron Group, the epicenter of tech innovation. We are your go-to for reaching IT, leaders and practitioners worldwide.

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Let's revolutionize your tech journey. Contact us today and tell your story to the world in the most powerful way with Textron Group. All right, to Alan's point, we are going to shift gears to something that's hopefully more uplifting, but I'm not entirely sure we're seeing the fan experiences changing rapidly because of advances in sports tech, everything from analytics to even ai.

And, um, it's an interesting time because we're heading into the back half of the baseball season, uh, preseason of football start up next month. And before, you know, we'll be playing hockey and basketball again. And every time I turn around, Lisa, somebody is throwing a stat at me.

And, but I'm not clear that this is for better because most of what I get presented with is really arcane, borderline innate. And that it's like on Tuesdays for the last 10 years, after three o'clock, but before five o'clock, the shortstop has gotten ahead for this team, who cares? And so I think we're kind of getting a little too data happy in sports tech, but I'm not sure, what's your take from the marketer?

What do you see here? You know, from a marketing perspective, I had mixed feelings on this. And then I kind of looked at it from, well, there are six generations alive today, probably attending games.

So what these networks are doing is they're trying to go where the fans are. Certain generations will love this, certain won't, like, you watch a broadcast and then you get hit with a QR code, whether you're watching the pre-game or in-game, and it takes you to a different experience. I think what they're doing is modern marketing.

They're trying to meet fans where they are using technologies. Um, fan engagement helps, uh, teams and sponsors, um, build deeper engagement, deeper connections with fans, with brand partners. I think it's smart modern marketing.

Not everyone is gonna want this. Uh, I know that some networks have been doing this. My San Francisco Giants do it frequently where, where they will take games off of the local networks and put 'em on Roku or streaming services.

And my mom hates that because she's silent generation and doesn't necessarily understand technology and just wants to find the game. But I think what they're trying to do is they're not really re alone in redefining fan experience, but the client trying to go where the fans are. I see beneficiaries, fans, broadcasters, sponsors creating this second screen experience that I think you can either lean into or you can ignore.

I like some of the STAs. I think it's cool that it kind of brings in technology to the everyday fan consumer kind of thing. So I'm excited to see where this goes.

Um, we've also seen this in Formula One. I'm a big Formula One fan. Really, really data heavy sport when I think it makes even more sense there.

Uh, we're seeing the rise of smart stadiums where it's really redefining this entire experience, as Alan said. And I think it's, I think it's a positive. And like I said, I think you can lean into it or you can ignore it.

I had a few thoughts on this one if I could. So, Lisa, I I agree with a lot of what you said. I, I, I put them into different buckets.

So there's the second, second screen alternate experience, which to me, the best example is watching the Manning brothers do Monday night football. It, it's a lot more interesting and funny than the, than the, the usual ESPN commentators who are no dandy Don Frank Gifford and Howard Cosell. But Really, I think when we talk about the fan experience and the realtime stats, like Mike, you know, it started with the AWS third and sixth.

There's a 68% chance he's gonna throw long on the left side, you know, but now it's gotten, you know, now Google Cloud is in there too. Yes. If the golfer bangs his putter two times on the ground before he hits the, put the, there's a 60% chance he's gonna miss the putt.

And if he only bangs it once, there's a 58%. If he doesn't bang it at all, who gives a flying cot? Well, some of it, no, but hold, hold on.

I, I, I just want, but you know, what this is really about is about gambling. It's about gambling. Because they're going to get to the point where you watch this on your screen, and when that quarterback walks up under the center, you could say, he's throwing left, he's throwing right?

What the odds are, right? Because we're going to need to know what those stats are to make odds. And, and you got people who will bet on cockroaches climbing up a wall and they're gonna wanna bet on, did he throw 15 yards to the left or 30 yards to the right or over the middle for two and bang my my, and at, at that action, that adrenaline junkie rush, because there are degenerate gamblers out there who love, who love that stuff.

And I think that's who this stuff is aimed at. That's a great point. Yeah, that's a great point.

I, I mean, I tend to think that this has always been a niche. Most people just want to go to a game and enjoy the game, get excited about and things, things like that. They're talking Yeah.

And most, but then there's talking about feeding the data to you in real time and everything like that of what the stats are. And we've had that as you're saying, like, when you watch this, there's like a 58% blah, blah, blah and all that sort of stuff. And it started to seep in.

The thing is, like you're saying, some people are going to like it, some people are gonna be indifferent and some people are gonna love it and figure out how to monetize it. Yeah. And it's like everything else, because, you know, we're getting data into just about everything.

And when people are there, you know, you're talking about a, what is it? They've, the fantasy football leagues and stuff like that, right? These people are gonna love that.

You know, we're talking Moneyball, like, you know, that movie. Yep. And, and some pe uh, you know, I think this is just gonna be a business aspect of where sports are going because there's a, just a, when you hit a wall to what else you can do, you know, it behooves these leagues to start figuring out how can I go through the wall and do something different and make it more exciting?

Now, how many people are gonna make use of it? That's up to them. But I don't know, I just think the people who want it, everybody else is gonna get sick of it if it gets too intrusive.

Right? That's a great point. Alright, I has to balance, I just wanna dial a dial that says I can turn this up or down.

I don't wanna have to go, well, You know, the, the BA has something like that. The NBA on ES, ESP N has like, not on the main ESPN, but if you go to likes ESPN two or e ESPN three, I figure out which one it is. You could just watch the game.

There's no, there's no commentators, there's no nothing. It's, it's a little quiet. All you hear is the crowd.

But you could just watch the game. And, you know, during the Knicks playoff run last year, I, I actually started watching that. It was easy, right?

I just, you could watch the game. I know enough about basketball if it goes in the basket, you know, I know it scores points, what do I need? All the other nonsense.

But anyway, hey, it's gonna be interesting. And as I said, for those gamblers out there, this is all about you. You're watching Text on Gang, let's come back.

We, I want you to save a little time for this next one, talking about ransomware. Is it dying? Is it morphing?

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Home of Security Bloggers Network. Hey folks, we're back. And yes, we're talking about ransomware and there's multiple reports, one in which Alan covered on Security Boulevard talking about, well, ransomware may be dying.

There's fewer instances of it, and yet there's also all Insecurity Boulevard, at least two articles identifying new flavors of ransomware that have emerged. And so it seems like the bad guys keep building this stuff, but maybe they're being less effective with it. Who knows?

But Alan, you've been looking at this space for a long time. What's your take here? So I, you know, we used to, when we said ransomware, we used to sit, think about somehow using phishing or whatever, they would get access to your system.

They'd encrypt your system and hold your data hostage unless you paid, and then they unencrypted it. I think that, let's call that pure old ransomware or ransomware, I think that pure old ransomware as the stats in the article I cited, that is down. And it's down because people are wiser to it.

They have off-prem copies, they, you know, the insurance, the cyber insurance companies got involved. I think we do a little better with it, but ransomware itself is morphed. There's DDoS ransomware.

It's like, it's basically, look, I'm gonna do any of these laundry lists of bad things that are gonna prevent you from doing business unless you pay me. And so there are new variants, new attack vectors, new, you know, it's the story of security. The cat and mouse came, they're always coming up.

Fred Ira, you, you've seen this a lot of time. Well, I wanted to bring Fred in here for a minute. Oh yeah, sure.

I mean, I, I think maybe on the, uh, the net the overall sort of attack surface of what people are, are, are referring to as ransomware the last four months maybe down. Uh, I don't see that at all, actually. Uh, and, and some of these currents that have been around for a while, like Alan said, I mean, there are just different permutations of things happening here with specific ransomware groups.

And one of the things that's intriguing about it is we don't really see a lot of different vectors, right? There are some pretty common things. And you know, to Shimmy's point earlier, I think is some of these vectors are so common, right?

The mutation of the way that ransomware manifests, um, is changing. You're finding groups taking different types of approaches to, you know, stay current. There's been some crackdown on ransomware, but ultimately you've got, you know, groups that are now providing legal advice to other groups in order to make sure that, you know, they can, you know, subvert DOJ and this location or that.

But by and large, you're seeing optimization of things that already exist. So when we talk about whether or not ransomware is dead, absolutely not. Um, and whether or not the ransomware groups that are prolific today, uh, you know, we will talk about, you know, crux and Burt, but, you know, scattered Spider has had a resurgence, like a massive resurgence over the last month, just this last weekend.

We also saw a couple of drops of O days for Microsoft remote, you know, code execution of vulnerabilities that have been widely exploited over a very short period of time. And so, you know, the differences is how fast and how, uh, short a period of time can an organization that produces ransomware as a service get their hooks into something and then, you know, they're there. They may come back later and say, Hey, look, we'll wait until this particular political circumstance goes away or what have you.

And so we're also starting to see something, we found this, uh, this nuanced, uh, behavior, this, uh, last week is, uh, you know, models that are capable of performing ransomware and mutating techniques in the middle of it. So Quin three is a model, is doing this, you know, today. So what what I think we're seeing is instead of a, my, uh, my, my baseball coach in high school would always say, we're not, uh, rebuilding, we're reloading, right?

In the sense this is the same philosophy as playing out here, whether it's, you know, it's the QN, you know, or it's the crux skies, the, uh, you know, the black bite guys, all of which have mutations of what they do into new sets of offerings for others to take advantage of. Yeah. Alan, let me give some historical context.

Back in 1996, almost 30 years ago, I was the director of technology for the National Computer Security Association. We had that little logo, NCSA approved that went on all anti, uh, malware. It was antivirus at the time.

That has evolved to the ICSA, which evolved and was acquired ICSA labs, which is now part of Verizon. That little logo is still there 30 years later. I think the point of the matter is, is that all this malware has been here for 30 years and has found a way to evolve and has found a way to get worse and also is gonna be prolific.

I mean, there's a couple of technologies I see, like armed cyber has like little, you know, kind of like embedding in the kernel type of thing. But until we actually find a way to stop it, and then we need to get it across all computers, because yes, some companies might find a way to be better and be more resilient against it. But you know, these criminals are like Frank, you know, I was a CSO for like a SaaS provider that focused on the, um, sled markets, state local education.

These like school districts. Municipalities are prolific at being hit by these ransomware purveyors because they don't have the budgets to do things properly. And these people are gonna be hit again and again and again.

And even though you might see large companies be less hit by it, there's gonna be more than enough people who are gonna be hit by ransomware, which is a generation or two old, because these computers are still out there. So for somebody to say ransomware is gonna go away, that the criminals have too much money involved in this, and there's just so much cyber poverty out there, it's not going anywhere anytime soon. Yep.

I haven't heard cyber poverty that Wendy Nather. Yeah, neither Wendy. No, no, I, I've heard the term, I haven't heard it in a while.

Wendy Nather used to talk about it a lot. Friend, Wendy. I sure you guys know Wendy.

That's Right, that's right. The, uh, lower, lower the poverty line, right? Yep.

Yeah. So like, am I reading this correctly though? Essentially this stuff comes in waves and every time there's a a downturn, we think that we're doing something magical and the next thing we turn around and we're getting clobbered again, is that how this continues to go?

Well, what happens is, when you look at the delivery mechanisms, like, so a lot, the good part is a lot of more companies are using, are not endorsing these, but they're using Gmail. They're using like, you know, Microsoft 365 and things which are getting better at filtering out attacks. However, as these services get, filter out the attacks, the criminal, it's, it's a, you know, it's an arms race.

When can they start getting things through? Then they can start doing other things to proactively act directly into targets as opposed to just using phishing to get in and so on. So, um, you know, again, mostly it's an arms race, assuming you're not dealing with just a straight out attack against an organization to get a foothold in it.

Yeah. There's, there's a couple of pervasive behaviors that, that we see, right? And, you know, phishing obviously is in the top of that right?

Now you've got some pretty advanced types of phishing. Phishing, you've got deep fakes, all of those types of things. But in addition to that, you've also got some of the standards, uh, old and, and tried and true, right?

VPN accounts and RDP accounts that aren't properly secured. And these types of things are, you know, the binaries that exist successfully on every system we call law bins, right? They're, these are living off of land binaries that exist in the windows boxes that are potentially exploitable and adversaries are using the tools that, that they have, right?

So some of the challenges are when we look at whether or not somebody is hitting, uh, BC edit xe, right? That's a known thing to back up environments. And so how do we get there?

Well, maybe we, uh, drop the no day on, you know, for Fortinet, right? Uh, gotta love those guys. They have the most software vulnerabilities of any software vendor, but they take advantage of those opportunities and then, you know, they get legitimate credentials and they run around and they'll do it over whatever period of time.

But the thing that's interesting to me about this is the timelines for how fast it happens now. And, you know, there used to be a window of time where that could be three days, you know, to breakout, right? CrowdStrike put out a report about this, where the breakout window used to be three days, you know, five years ago.

Now it's 49 minutes. So in breakout time is defined as the moment you pivot off of a particular exploited box into other places, AKA lateral movement. And so when we look at that, if 49 minutes is the average behavioral time for that type of, of, of movement within a, an attack, then we know that, you know, the speed of these things has changed dramatically.

So the arms race, right? Just like us to get to the moon, just like us, to build our AI infrastructure is this arms race to be able to defend ourselves before known tools that we use every day, continue to, you know, become a mature development lifecycle for adversaries. Agreed.

I mean, and you know, and, and don't think AI doesn't play a role in there though, too, Fred, right? Helping these guys get better A hundred percent Hopefully will help the defenders get better too. But, um, to Fred's point, ira, are we mentally prepared for that level of stress?

Because this game is moving into real time and you know, it seems like we were already freaking out as it was. So now if everything's happening, sounds like it'll only be a matter of time before we're measuring this in seconds. Well, I'm kind of stoic about this.

You shouldn't be in this industry if you are not capable of handling the stress of attacks, becoming more prolific and getting quicker. You know, because it's like one of those things. You can't be a police officer and expect everybody to just say hi as you're walking around your beat.

You know, you don't know what you're gonna be hit with at any point in time. Good programs will have automation, unfortunately, programs that are, I'll keep using the word cyber poverty, they're gonna get more difficult. But I ideally, you know, people are gonna get used to this.

There's gonna be better tools in use that hopefully help these people out a little bit more. Um, but yeah, I mean, it's just a fact of life. And if you can't go ahead and deal with like attacks are getting quicker and better, then frankly, this isn't the industry for you.

I mean, you know, the industry for you involves your technology, your interest, but also the level of mental stress you can put up with. And maybe a GRC job would be better than a hands-on operator job, as an example. So, 'cause there's a lot of jobs in cyber security and you know, it's just a fact of life.

I remember when, again, maybe I am dating myself, but in the 1990s, admins were out there with like zero protections that they had and they were getting hit by people just randomly scanning, doing ping scans and everything else, finding vulnerabilities. And that was what they did. They kept repelling, trying to patch close people out.

30 seconds later, the person was back in and they were back to protecting. I hate to say it's just the nature of the job, but that's what we need to accept It, is it, it is. And you know what, things have gotten a lot more complex.

'cause back then you had the moat in the castle, right? You just had to worry about the castle door for them to come in. You know, the, the term a backdoor kind of, uh, attack really meant something.

Today, you're, your attack surface is all over the place. You, you know, you, you don't even know you're full attack surface most people. So it, it is, it's a cat and mouse game.

You are the cat. The mask is always a little bit ahead. They're well financed, they're well funded, and they make a lot of money.

And, and, and Ira, you're right. If you don't have the thick enough skin for that particular role, you shouldn't do it. 'cause you're only gonna get burnt out and depressed.

So, so CISOs should put pictures of Harry Truman up in their office saying, you know, if you can't stand the heat, get out of the kitchen. Is that where We're going? That, but that's always been, but that's been it in security.

If you can't, you'd get out. That's the nature of it. When nothing happens, it's a good day.

Yeah. I mean it's like, I, I gotta admit I'm a little less so I look at stress levels of CISOs, which is serious and yeah, CISOs are under a lot of stress. But I look at the people who work for them.

These people are actually doing day-to-day stressful things, but they don't have the money a lot of CISOs have. And to me, somebody worried about making their rent payment and dealing with, you know, can't deal with childcare and something like that while working a job. 24, you know, a 24 hour shift work is a little bit more stressful than the person at the top.

And so I appreciate what Still no, the front lines of security, yeah, We have to appreciate everybody is in there for sh is gonna experience stress. And you know, sometimes not all jobs are right for everybody. If you want to be a librarian, go be a librarian if that's what your mentality is meant for.

But if you're in cybersecurity, I hate to say it, depending on the role, your job is essentially being a firefighter where you are going in, you know, and that's all there is to it. I think you're right. My last, my last thing on this is that, uh, a lot of people get deputized in an incident and they're not cybersecurity professionals, right?

Well, It used to be that's how people became security people, right? They were network people or whatever, help desk people. And, and when stuff hit the fan, I go look at the logs.

What are you seeing Fred? Right? Exactly.

That's how it was. So, so the best cybersecurity people are adrenaline junkies, right? They're in it for the fight and they enjoy that.

But the truth of the matter is you can only keep that up for so long and they're gonna burn out. Well, It depends on the processes they put in place, because this is the Ira Winkler philosophy of organization of cybersecurity programs. It's like a ship at sea where it's off course 90% of the time.

You put a process in place that keeps it kind of knowing that there's little things here and there that are gonna push it off and you keep it running. That's what makes good cybersecurity programs, good cybersecurity programs, every so often there's a bigger wave, but they know how to deal with it. And if you can't, another thing we haven't spoken about, I mentioned it a little bit, is outsourcing most of your infrastructure.

'cause cloud computing does allow for offloading a significant amount of your work. Like, if you can't maintain an internal infrastructure well and secure, you can outsource it to Office 365 or whatever. You can outsource everything, but you can outsource most of the internal SaaS function or use SaaS, secu SaaS applications to deal with a lot.

And those providers are there and do serve a good purpose in offloading a lot of your security, assuming these are good providers. I'll leave it at that. And we're gonna leave it at that right here too, because that about wraps up our show today on the gang.

I hope you've enjoyed it. As usual, we have tech drunk TV following the gang today. A couple hours worth of more programming.

But until Monday, have a great weekend everyone. Ira, Fred, Lisa, and Mike. Thanks for helping me out and joining us here on the gang.

Uh, thank you for watching. We'll see you Monday. Try to stay, stay, try to stay safe and think maybe a cybersecurity career is for you.

We haven't scared you off yet. Until next time, I'm Alan Shimel. Thanks for joining us.

We're out. Hey everyone, welcome back here to Text Drunk tv. My next guest is Jeff Reed.

Jeff is the Chief Product Officer at Vectra ai. Let's welcome Jeff in. Hey Jeff, how are you man?

I'm well, Alan, how about yourself? Very good, thank you. Appreciate you coming on.

Um, Jeff, we're gonna talk a little, we're gonna talk a lot about Vectra actually, and we're gonna talk about some new solutions you guys have. But before we do that, let's hear a little bit about you, your chief product officer. How, how'd you wind up there?

How long you doing this? Yeah, what's your background about? Yeah, so been here about a year and a half.

Uh, you know, I lead engineering, product management and product marketing for Vector ai. We'll talk more about what we do, but I got here, uh, by way of Google Cloud. Uh, and so I've kind of spent, if you look the, I call myself a plumber.

Yeah. I've been in kind of the infrastructure security land for the last 25 years. You started starting file systems and volume managers at Veritas for folks that remember back in the day.

Um, and then spent a bunch of time at Cisco, I Remember. Yeah, yeah. So uhhuh, we went through there.

Oh, You're at Cisco too, you Really, yeah, yeah. Then went to Cisco. You were Like a master plumber, huh?

Yeah, no, exactly. Yeah. Yeah.

I never had, I mean, we did our own chips, so I could kind of say I got into chips. This was probably, you know, you know, but, uh, but yeah, so did, was it Cisco and, and really, you know, from there spent a bunch of time in networking and then, you know, went into security side of Cisco for a few, for four years or so, and then, you know, look, the whole cloud thing's pretty important. Uh, so I had an opportunity to go over to Google Cloud and that was great, great experience.

And kind of did two things there. One was in kind of the core services, Kubernetes, serverless, you know, that that kind of space. And then, you know, when Google was looking to acquire Mandiant, uh, they wanted to bring in a, you know, security product leader.

And so had the opportunity to come in there. And that was a great role. So kind of had, you know, VP of product for all cloud security at Google Cloud, um, both from the infrastructure, identity, access management, compliance, HSMs, all that side, but also what we're doing in security operations in that world with, at that point Chronicle.

Uh, and then you had the, you know, it was interesting. Hitachi, who's the chattis, the founder, CEO of Vector, reached out and I really was super interested in what Vector was doing. Um, because, you know, the, what we were doing with Chronicle was really interesting at Google.

You like the economies of scale, the speed, all that was amazing. But we were still like, I think in the sim market was still kind of incumbent on the customers to do most of the, the real like threat finding. And so what I, what I loved about Vector was how they were doing things that were really unique in the industry around how to detect attackers using, you know, initially network side, you know, uh, you know, data, but then expanding into other places.

And so that really kind of got me jazzed about coming over to Vectra. Excellent. What a charm course of, uh, the, you know.

Yeah. I've been lucky. Yeah.

Yeah. Career. Well, you know, what, what was it Branch Ricky, the guy from the Dodgers back in the forties or fifties said, luck is 80% or 90% sation and 10th percent inspiration, right?

So luck comes, you know, God helps those who help themselves. Anyway, Jeff, let's turn to Vectra, right? Yeah.

It's a lot of people out here may or may not be that familiar with it. Sure. How would you describe Vectra to them?

Yeah, so it's, there's, you know, Vectra really started this idea of can we apply advanced AI techniques to what initially was network data to find attackers that had bypassed other, other, you know, security controls within an environment. And, you know, kind of came out of, you know, some breaches that you have the, the founding team had seen in customers back in, you know, the 2010s where, you know, nothing else was able to pick up their activities, but they'd left this like trail in terms of the network, uh, activities they were doing. So command and control, reconnaissance activities, lateral movement and that side.

But I think the, the key thing that the, the key part of the premise though was instead of doing, you know, the normal, you know, what's abnormal behavior, the things that have kind of generated so many alerts and so much noise, it's can we take a much more focused approach around what are the durable attacker behaviors? And if you think of that, like what attackers have done over the past, you know, 10 plus years, like the core pieces are the same. I you establish some presence.

I need to have a com control channel. I need to figure out where I, I'm in the, I am in the environment. I'll probably need to move towards different parts.

And so those behaviors, those minor, uh, techniques are the things that we really try to find. But we find them using in very sophisticated, you know, capabilities. So we use long short-term memory, recurrent neural networks for C two channels.

We do some really interesting clustering techniques around, you know, trying to identify, you know, where is privilege within an environment and then, you know, where are places where we see, you know, potential privilege escalation. So things like that I think is really kind of unique to the, the capability with the whole desire being, can we always find the attack behavior, but do so with as much clarity so there's a little noise as possible. I love it.

ai. It is, yes. B-E-C-T-R-A And, And be clear.

So one thing, like, you know, the obviously AI is, you know, very hot topic these days. Um, you Think, You think just, just as, just as skosh, uh, Uh, We've been around for over 10 years, you know, these techniques were started being deployed in, you know, 20 18, 19, so free the big gen ai, you know, wave. Uh, so, so yeah, we, we describe ourselves as the OG of a, of a AI and security.

Fair enough. Very good. I I love the OG thing current here.

Alright, let's, let's pivot into our topic of discuss. It's not really a pivot, it's a continuation of what we're talking about, but Jeff Vectra AI recently announced a, uh, a new generative AI solution, uh, for AWS powered by the, the Amazon Bedrock platform, which, you know, Amazon has really, uh, put a lot into and continues. Yeah, right?

It's a big part of their strategy there. Um, talk to us about this new, this new solution. Yeah.

So this is where your vector AI analysts, and you could take a little bit of a step back, you think you, I talked about the idea of, you know, we wanna drive great clarity. Like, hey, here are the small number of things that you, Mr. Customers should worry about each week within your environment.

You know, we do that in kind of these stages. So we start with the actual detections themselves, and we talked about recurrent networks, you know, then we have actually a triage, a agentic framework that's been around for a couple years now. And it basically is trying to find out like even if there's attacker behavior within your environment, some of those behaviors are, are actually hard to decipher from what normal, like real, you know, legitimate use.

And so it basically tries to kind of like take that and, and reduce the number of, uh, of potential, you know, alerts and detections that we have. Then we go through a prioritization scheme basically says, and what that is, it's a constrained optimization model that's trying to mimic what a, how an analyst, a security analyst soc analyst, would prioritize all the things that hit his or her desks in a day. And that kind of pops out with a score.

And then, and then the last thing that this new, this new analyst agent is really about the next step from that. So once we've prioritize anin entity, a host or an I or prioritize anin entity, a host or an identity, how can we then like make the next steps for that analyst as easy as possible? So think about this as being able to go out and, and do all the, the kind of work around, you know, investigating that, that entity, what are the behaviors potentially reaching out to new data sources that we haven't naturally?

'cause it's nice about these agentic models is, you know, they have the ability to go out and read blogs of the latest attack, you know, attack techniques out there, or, you know, go to find new additional sources of data within that customer's environment and basically come back with a, a more sophisticated assessment of that entity. And do we think that this is actually, uh, uh, likely to be a malicious behavior or not? So that's kind of the, and it does that this, and I think the, the interesting thing here is if you just throw this to a large language model and have it go, it would, uh, sometimes it would be amazing, sometimes it'll be completely wrong.

Uh, and so the, I think the, a lot of the work that we've been doing is how do you not just leverage what the, the gen generative AI capabilities have brought to bear, which is a lot of good, like fuzzy logic and like long tail reasoning, but compliment that with, hey, there's some guidelines or, you know, guardrails in terms of this type of, you know, we've seen these behaviors on this, on this host, what would the normal steps be for an, an investigation? And so this kind of mix of expert system logic plus large language models kind of combined do we think deliver a really interesting approach. Love it.

Jeff, what about for the people out there who say, this is a great AWS solution, but I'm multi-cloud? Well, So Yeah, yeah, yeah. So to be clear, and this one maybe I should have, should have done this earlier.

My bad. Uh, we are leveraging a W S'S bedrock and the infrastructure to deliver this solution. It is by no means, uh, limited in terms of the, oh, Okay.

Purpose area coverage, just AWS environment since You guys are actually sort of hosting it on AWS, but it's available Network or however you wanna call it on ID inter ID M 365, Azure AWS, like everywhere that we have, uh, detection coverage, so kind of our native signal generation, you can apply this analyst. Now, the one thing I, just to be clear, you right now, this is available as part of our managed detection and response service. So you, we Right.

We provide a managed service that'll, you know, basically we're Vector analysts will sit side by side with their SOC counterparts to help kind of make sure. So initially it's powering that service is is where this analyst is coming to bear That that's available. Right now It's available right now it's, it's on, if you go to the, uh, AWS marketplace and their generative AI tool sections around security, it's one of the, you know, one of the solutions as part of that, I gotta ask you a hard question, Uhoh, bring it on.

Alright. You know, look, I spend most of my day on videos like this with people like you and not just vendors, practitioners, analysts, you know, a good mix. Everyone, everyone has an AI story, everyone, as we talked about earlier, right?

How much of this is not, not that I, I'm not saying it's not real. It's obviously real. Yeah, yeah, yeah.

But how much of it is must have today versus, oh, this sounds cool, but you, I could live without it. I think that comes down to the problems we're trying to solve. You know, every year we do a, a big survey of so practitioners and, and kinda ask them a series of questions around, you know, kind of what their, what their day-to-day life is.

And, and some of the findings from the last one we did were, were amazing. You know, like, um, you, 71% of them worry that they're gonna miss a real attack buried in a flood of alerts every week. Uh, so that to me kind of stands out.

Like that's one of just new numerous findings. But I think for the way I think about it is given the scale of people's environments and, and the thing we've seen is just the fact that it used to be simpler. We had a data center, we had a campus, but the firewalls around it, you know, it was like, it was a much simpler environment to protect.

Yeah, no, I get it. You Know, between, you know, everyone still has those, and they have cloud and they have SaaS. And so the, the, the complexity of their environment and, and, and you used to find, I mean, you've been in this world a long time, you know, 25 years ago you would talk to someone that kind of knew everything that was going on within the IT infrastructure.

That's almost impossible to find now. Not Today. You're Right.

You just thought, yeah. And so I just don't, I don't think the tools that aren't leveraging some degree of more sophistication in how they identify, triage, prioritize, I just don't think they're gonna be successful in, in really helping avoid that problem of, I'm flooded with alerts every week. I'm thinking I'm gonna miss some the thing that really matters in that flood of alerts.

And so, so to me, that's the thing. And, and really that's the foundation of why Vectra and that's, we've 10 years ago, that was That's Right. That was your reason for being to begin With.

Yeah. And, and, and so I think that, you know, and we've been, we have, we have more data scientists at Vectra than we're working at insecurity at Google Cloud. So, so I think that just gives you a sense for the scale of investment and the bet we've made that this stuff is really important.

And it's not really, like, the thing I wanna say is like, like generative is another technique that is very useful in some parts of this problem set, but it's not the end all be all to what we've been trying to do and what we think you need in order to be successful. And so, to me it's a great compliment. It's absolutely, I'm really excited about stuff.

You see, you know, some of the stuff we've seen in terms of like, you know, you know, MCP servers and things like that, I think, you know, how the stock operates I think is gonna radically change in the next five years. And, and I think that we can play a key role in that through the, the mix of technologies that we have. Yeah, no, we, we just had this discussion on Textron gang the other day.

I don't even think it's five years. I think it, yeah, I think it might be two to three years at most. I, The, the way, the way thing, right?

Agentic AI things are snowballing so quickly. Yeah. And you get this kind of compounding capability set the things you really interesting.

Um, so yeah, no, I, I, I was, I I'm very confident by five years it will be totally different. Oh, Absolutely. I, I don't disagree.

18 and I think all sort of relative, right? When you look at how this whole AI thing is affecting the speed and velocity that code is being developed. I was about to say That, yeah.

That new apps are being deployed now. You gotta manage those apps and observe 'em and all of that. You gotta secure them.

You know, it's, uh, it's the circle of life here, right? Absolutely. On steroids and, and so, you know, I, I think that that's what we go with.

ai is the website Yeah. For people who are interested in this particular new offering, where is it Right off the front page kind of thing. Right off the front page.

Yeah. Yeah, yeah. Yeah.

And you'll, you'll also see how well we did in the Magic Quadrant. Uh, for first ever MQ for NDR, we were really both access axes. So yeah.

So really happy about that. Oh, Congratulations. Strategy.

Yeah. Thank you. Good for you guys, man.

Hey, Jeff. Come back on and keep us posted here. Right.

World's changing real quick. We got to stay on top of it. Sounds Good, Alan.

Thank you so much. You're welcome. ai here on Textron tv.

Go check out their new, uh, gen AI solutions, powered by Amazon Bed Bedrock. You're watching Textron tv. We'll be right back.

Hey guys, thanks for the throw. We're with Iran Yaha, who's CTO for tab nine, and we're talking about what it takes to do AI coding and on-premises environments where security is of the utmost concern. Iran, welcome to check.

Thank you for having me. Great to be here. Everybody's talking about AI coding and just about everything that we see out there.

Uh, it seems to run the cloud first, and we have all this data that's in an on-premise environment, and a lot of places, security is of such a concern that they're never gonna put that data in the cloud. So how do we bring AI coding to an on-premises environment in a way that, um, is reasonable for someone to actually go build and sustain? Yeah.

So many of our customers run in, uh, self-hosted environments, either, um, private clouds or completely, um, um, air gap in their own hardware, right? Uh, I think the, the first challenge is just securing sufficient GPU resources for running, uh, LLMs. Uh, one of the challenges is really, uh, LLMs are resource incentive and intensive.

You need to allocate GPUs to run large enough models, uh, to get quality results. So that's part of, you know, just the infrastructure challenge there, uh, securing that. But there's obviously the, the entire product that is built on top of it, uh, that is able to utilize these GPUs efficiently and also do the entire management of the system in a way that the customer does not have to worry about that.

Right. So that kind of, uh, self, uh, almost self-maintaining system, so to speak, Are people gonna split the function a little bit? Will they train maybe in the cloud and then deploy in an on-premise to have the security there?

Or does everything need to be in an on-premises environment? Yeah, I think first, first off, uh, training is not happening as often. It's used to be, uh, these days, uh, a lot of work on contextual awareness of, of the products is done using retrieval augmented generation, which is basically done during inference time.

So you don't actually have to train that much. There are still some cases in which you do need to train, but the whole point of this, uh, customer base is that they are not allowed or not interested in sending the DA their data to the cloud. So training also has to happen in-house if training is to happen at all.

Right? So, uh, there are some customers, uh, that are running hybrid configurations where some of the workloads are running locally, uh, on-prem, and some of the workloads are allowed to access to the cloud. For example, if you have, I know your super secret IP code base, which is not allowed to leave the building, but on the other hand, some very generic front end code that sits on top of it, maybe you want the front end team to be able to use some external models, uh, but the super secret IP team, uh, to only use models that are hosted on-prem.

Mm-hmm. Um, as you think all this through for a minute, a lot of the AI models today require a significant amount of infrastructure, but will that always be the case? It seems to me that, uh, in the history of it, at least we get more efficient as we go along and the models get smaller and maybe we get smarter about the data.

So is this gonna become more affordable as we go along as well? Some functionality is always going to require relatively large models. So the natural language, understanding, planning reasoning, this requires significantly, uh, large models and probably going to do that for a while.

Uh, however, as we move to like more agent workloads, a lot of the agent work can be offload to expert models that are specialized for particular tasks. So you can think about a much smaller, um, expert model just for test generation or just for co-generation. And that model does not be need to be generic.

It does not need to, you know, do literature review of Shakespeare, right? It, it is really targeted only for software development tasks, and that can be much smaller. And we're already starting to see this kind of model's popping up.

There's a recent model from MIS trial called Devs trial, which is just around 22 B or 24 BI don't remember, which is tiny, uh, in LM land, uh, these days and is doing relatively well, at least on, on benchmarks for software development. Mm-hmm. I also feel like we're seeing some separation in terms of who manages what these days.

It used to be early on there would be an AI project and there would be some infrastructure of folks attached to that team. But now as we kind of wanna build out more AI models and we need to share the infrastructure more efficiently, so are IT teams taking over more responsibility for the infrastructure for AI and, and just what are the separation of concerns here? Yeah, that's absolutely true.

And we've seen that over the last couple of years that, again, exactly as you said, it started as like an AI group that was owning the GPU machines, and they had all the power on how to allocate that. But as the AI becomes like part of the foundational infrastructure of any company, uh, this has moved really to the general IT team and they're managing, uh, the GP resources as well. Uh, we're seeing more sharing of GP resources across different projects as well, right?

It used to be allocated to the AI project, but now almost any project is an AI project, right? So we see, uh, more GPUs being deployed, um, more diverse kinds of GPUs, and all of those are managed by the central ITT map. Mm-hmm.

As we go along. How much does the developer need to know about the underlying LLMs? Because I feel like today it's requires a fair amount of expertise, but going back to history again, we always had higher level of abstraction.

So eventually, will LLMs just become something I called through an API and as a developer, I may not care exactly how it works Today. You have to know mostly because things change so rapidly and you want to make sure that you're on kind of the latest greatest, and the, the one that is hitting the, the greatest benchmark numbers, et cetera. This is like reminds me of early days of like Intel CPUs or something with, they did not come as rapidly, but you definitely had to change your laptop every, I don't know, six months or a year for sure.

And now I have not switched my Mac for, for a while now, right? And I don't care what, uh, CP or even GPU is in yet. It's just good enough that I don't worry about it anymore.

So same will be for L Lambs, right? At some point, I don't know when, uh, you would not need to care about that. I think for the API based ones, you already don't care as much.

You know, people say, uh, Claude, but Claude is actually, or Claude Sonnet four, uh, changes with subversions, right? Quite rapidly. And you, you are no longer following the nuances of like what version of what timestamp exactly of model you're using unless you really care about the, the evaluations or sensitivity to a specific version, right?

So it's already happening for the API based ones, and I suspect that for the self-hosted ones, uh, this is coming as well. Yeah. So you're right.

So I think everybody is also having this conversation about where, where their cheese might move, as it were. And everybody's a developer and a software engineer, and they're all a little bit concerned that maybe they're gonna get themselves AI out of a job. But as you look at all this, how will the role of the developer and the software engineers evolve in your mind?

So f first of all, you know, software development was never about writing code, right? It is like the, their job definition is solving business problems using software. We just had to write code as, as part of the job, because that was the only way to do it.

If we have to write less code, that's actually great. We still are going to solve business problems using software. And a lot of the job of, of a developer or software engineer is to actually discover the specification, right?

Discover what actually a product or customer wants to be done. We typically get like very high level specifications, which are partial, right? As as developers or software engineers.

And then as we implement things, we have to make many, many decisions that maybe go back to product and say, Hey, we need to decide either this is like a separate window or this is a tab or whatever, right? That is probably under specified. So all these micro decisions are the way of, in which the software engineer participates in discovering the actual justification of the product.

So if you ask me, programming was never about like coding, it's always about discovering the actual full behavior that you are trying to get out of your application. So in that regard, I don't think anyone should be worried for their job, it's just that it's going to use more of the skillset of, um, elaborating the requirements or elaborating the specification and less writing the, the actual nitty gritty details of the code itself, right? And to your point, somebody has to orchestrate all this stuff.

And I feel like, um, you know, we started out talking about on-premise environments, but there'll be use cases for AI models that will be not as securely or won't have the same level of requirements. So I might use the cloud for that. And in other instances, I may move to on-premise because the security concerns.

But is the challenge going forward gonna be trying to figure out which LLM to use for what? Because ultimately any application is gonna mean up of a series of LLMs and AI agents that are performing some task, and then they gotta get integrated with other AI agents performing some task. So is that where the, the value add is gonna be, as it were?

I, I think the, the LMS themselves, the models themselves, as I said earlier, are going to be abstracted way. I think that's a concern for, for the end user at all. Uh, I think combinations of different agents are going to be an interesting challenge.

We already starting to see that where you have, you know, one agent, uh, generating code, another agent reviewing code, another agent generating tests, and exactly how you orchestrate that and what is actually the right order, uh, between them. Because maybe, uh, you actually want to generate the tests before you write the code these days, given that everything is done automatically, maybe that the right way is to actually generate the spec, uh, to a higher degree of kind of fidelity before you generate, uh, the code that that supports it, right? So, uh, I don't think having to worry about LMS is in the future, right?

Future, don't worry about lms. Um, definitely gonna see some hybrid, I'm guessing, between API based model or cloud-based models. And on-prem models or self-hosted models, part of that is probably going to be purely, um, due to cost, uh, or predictability of cost.

Uh, one aspect of running on-premise, you own the GPUs, you can have your agents running at night when your developers are not working on these machines, and you're basically getting agent work for free, so to speak. Whereas if you're using, uh, cloud-based models, you're paying by the token, right? So you pay as you go for, for the consumption.

So especially in an agent world where you can delegate some tasks to background agents, and you don't care that they run for a few hours at night, uh, I think we're gonna see some com interesting combinations between, uh, owned GPUs and rented GPUs, so to speak, right? So I, I think that's part of what the future holds and what exact combinations or how to optimize this is going to be another interesting challenge. Um, so what's your best advice then to developers and DevOps engineers about how to get ready for all of this?

I think many of them are playing around with various tools already, but I don't think a lot of them have a strategy in place yet. But, you know, where should they be and what should they be thinking about long term? So first piece of advice is, you know, in the future, every developer is really, and or every engineer is going to be an engineering manager who is going to delegate tasks to, to agents, or to AI engineers.

And so practicing that part of the job of breaking tasks into smaller subtasks and how you delegate them even to yourself at this point is a, is an important skill, uh, to master. Uh, second thing is to adopt an AI first mindset. I've been using that mindset for a couple of years now, in which I not ask myself, how do I solve a problem?

I ask myself, how can I make top nine solve that problem? Right? And sometimes it requires some gymnastics on my behalf, breaking tasks, not exactly as I would've imagined, or helping the AI in certain ways, providing additional information, additional context, additional examples, all those things.

But adopting the AI first mindset, I think is the right way to practice the skill, because the future is there in, in the future, a lot of these menial tasks where it's not of the future, it's actually the present. Uh, a lot of the kind of mundane mean tasks are going to be completely delegated or floated to, to these AI agents. And unless you start practicing early, you would not know how to really communicate and, and delegate and work with these agents.

So that is my advice. Start that early, start that now, and practice that skill at the end. It's a communication kind of skill.

It's about communication skills, but communication with agents, uh, both in defining what is that you want, and also in being able to consume the results, which are often quite involved. Hey, folks, you heard it here. Engineering managers have always made more than engineers.

So think about it this way. I'm gonna get myself a, a small army of AI agents, and I'm gonna be their manager, and maybe I'm get myself a raise. There you go, Aran, thanks for being on the show.

Thank you. Finally. All right, and back to you guys in the studio.

Hi, good afternoon, everybody. My name is Dave Ksky. I am a Dell Fellow in the office of the CTO for Dell Technologies.

So whether you believe that the current state of AI is an evolution of smart, uh, technologies, or whether you believe it is a technological revolution, the state of the, the matter is that AI is part of all of our everyday lives. So what is the question that's on the table? The question that's on the table is, is AI a threat to my very livelihood, or is AI a tool that I can use to elevate my human experience?

About 85% of surveyed workers said that they expect AI to impact their jobs over the next several years. 30% of those workers said that they are worried that AI is got, AI is going to actually evolve to the point where it takes their jobs, and they are actively interviewing for new roles. And about 75% of those workers said that they believe that AI is gonna be responsible for less jobs in the future.

That's a pretty dire situation. But let's look at the flip side. 75% of workers said that they use AI in their everyday work in their everyday life.

And nine outta 10 of those say that AI reduces their time to complete their tasks. Well, now that's an interesting dichotomy. Now, how does that translate to the consumer?

Well, consumers have been using AI for decades without even knowing it. Majority of people are using Siri or Alexa, or they have a collision detection system in their car, or they have a smart thermostat. They're using AI in their everyday lives.

But what about the overt use of ai? When you actually ask someone, are you willing to use an AI agent as a digital assistant? About 45% of respondents said yes, but if you go to Gen Z, that goes up to 70%.

So then we broke it down a little bit further, and we said, okay, let's, let's get a more, a little bit more granular. How about looking for a job? How about grocery shopping?

How about health and wellness? Would you be willing to use an AI agent to do those tasks for you? And we got about the same re responses.

About 45% said yes, and 70% of Gen Z, they're all in. Whether you're fearful or whether you're embracing ai, you know, the fact is it's a part of all of our everyday lives. And the people who are embracing ai, they may, may be wondering, it's probably most of this room, right?

I would imagine, right? You're all wondering how the heck can such a large percentage be so fearful of AI impacting them negatively? Well, there's a perception that AI just popped outta nowhere.

It's a technological revolution that's gonna evolve to the point where robots are gonna take over our world and they're gonna take over my livelihood. Now, you all in this audience, I'm sure, realize that that's not true. And we all believe that ai, the current state of AI, is a technological evolution that is actually going to positively impact us.

So let's actually see, start, uh, addressing this, uh, question of evolution versus revolution. Where did the current state of AI come from? Now, I'm not gonna do a big history lesson here, because a lot of you already know this, but we've been putting AI based solutions out into the marketplace.

As I said, for decades, we've been using approaches like linear regression, uh, supervised learning, uh, uh, rules-based engines to address things like customer behavior, modeling, recommendation engines, and even hardware and platform optimizations. But then something happened in 2014. So in 2014, we took, uh, adversarial networks, generational adversarial networks, and we applied those to machine learning and Gen AI was born.

Now, of course, gen AI was so different because this AI system actually created new content out of trained data, and then November, 2022 came along. Does anybody know what happened in November, 2022? OpenAI launched chat GPT.

Why was that such a milestone moment? It was a milestone moment because it was the first time that there was a publicly embraced large language model that was paired with a natural language interface. The natural language interface made it approachable for the mainstream, made it very easy to use, made it so easy to use that we quickly realized that the power necessary to scale AI was just not sufficient.

And so we had a problem on our hands. Now, we also quickly realized that the power and cooling that went along with that was also not sufficient. So what does this room do?

When we have a problem, we attack the problem. So while models, and you've heard today throughout the day today, uh, the evolution of models and the progression of models and where we are in models becoming more performant and, uh, um, uh, better fit for, for many different solutions. But we've had to take those models and apply model adaptation.

A lot of you in this room are probably, uh, playing with different levels of model adaptation. Of course, quantization is the big dog in the room. We've been doing quantization many for many years, and we've been having some great successes with quantization.

But you've also seen new model architectures start to be introduced. Uh, deep Seeq was the one that hit the hit the press. But, uh, you know, being able to introduce model architectures that have test time scaling, um, and other, uh, and other approaches has really made a difference.

Now, what is the difference that it's made? The difference is we now have medium and small language models that are just as performant as large language models for the solution to which they are targeting. Now, that may say to everyone, okay, well that's great.

You know, we've, we've relieved our problem with, uh, compute bandwidth that we need to scale ai. Ah, but that's not the only thing we've done. We've now taken performance language models and made them able to be put on different platforms.

You now not have to run language models on Big Iron back in data centers. You can actually run them at the edge or at the far farge. And so now the, uh, in just, uh, this, this last month, we've seen the broad launch of, uh, IPCs, and now we have agentic AI systems running locally on IPCs addressing things like latency and privacy and agentic ai, AI agents.

I don't think I go more than 10 minutes in my day until I hear the word AI agent. Matter of fact, I was just sitting at lunch and I was counting the number of times that I heard AI agent of the people around me. And I think I, I stopped counting at about 20, and I'm, I'm not kidding, right?

So there is a, there is a boom, there is a explosion of AI agentry being used in solutions. And why is that important? Well, if you look around, uh, you know, there's, there are a lot of solutions that are very, um, closed and, and, and they, they maintain their own agent solution within, within the bounds of their application.

But there are a lot of, uh, solutions that are being played with that are open to agentic solutions. And now, the CHA challenge with open ag agentic solutions, it's uncovered a lot of problems that are barriers to us, uh, scaling this quickly. Things like incompatible ai, agentic frameworks, things like distributed data sets, and being able to have access to that without compromising the security models.

Things like silicon diversity. Currently, every AI model has to be tuned for the silicon that it's running on completely untenable if you're gonna have a distributed model. So a lot of these tough problems are being addressed, but the answer is, we know that we are entering an era of AI, hybrid and distributed solutions.

So, Dave, thanks for the history lesson, right? Um, what does that have to do with evolution versus revolution? So, I, I picked up a couple of, uh, uh, definitions that I wanted to share with you so we can make some determinations in the room here.

So, an evolution, an evolution, technology evolution is defined as a gradual continuous development and refinement of technology over time, driven by, by human innovation and the need to solve problems, often building upon existing technologies. So if I look at that, I could say, all right, well, the application of gans to ml, that's an evolution and model adaptation. That's an evolution, right?

So how many in the room believe that the current state of AI is a technological evolution? At least two. Awesome.

Well, but wait, let's look at this. So I mentioned chat, GPT. So chat, GPT is the fastest growing consumer application in history.

Within five days, it reached 1 million users. Within two months, it reached 100 million users. So if we look at the definition of a technology revolution, it's defined as a period of rapid and significant technological advancement that fundamentally alters industries, economies, and societies, often leading to widespread adoption of new technologies and transformative changes in how we live and work.

So, is it a revolution? You know what I posit it doesn't matter. What does matter is that we're now looking at how we build, deploy, and maintain solutions differently to incorporate AI as a base and incorporate agentic ai, which will change the way that we look at the future going forward.

Now, AG agentic AI is in its infancy. Now, a, a speaker, I believe it was, uh, two or three ago, was just talking about, well, we're past ag agentic AI already we're moving into something else. Well, you know, if you really look at how many AI agents that are deployed out there today in, in enterprise environments, scaled enterprise environments, we're, we're still in its infancy.

And enterprises are very measured when they come to introducing new technologies that may be introduced into or affect their very critical, um, business processes, right? So where are we? You know, do we have adoption of AI based solutions and agentic AI in enterprise?

And so I picked just a couple here. So, uh, Dell Technologies has been helping our customers, uh, deploy infrastructure for AI and help them deploy AI solutions for several years now, we just picked these six, I picked these six because it shows a very broad and diverse set of solutions and the impact that they've had. We have everything from, uh, traditional content creation to, uh, a solution that brings communities together so that they can communicate.

And then one of my favorites is improving outcomes for patients through the use of ai. But look at the numbers. You know, look at the numbers below.

This is not a small impact. These are massive impacts. So that tells us that AI-based solutions agent AI is being deployed, but it has to be deployed against the most important problems that an enterprise is having.

If you're just, you know, picking a low hanging fruit and applying AI to that, you're not gonna have a return on investment, and you're not gonna get funding for the next project that you come along because you didn't have return on investment. So picking the right solutions is important, but the impact is incredibly impactful, right? So this can be very scary for many who are fearful of ai.

Like, oh, man, you know, I know AI's gonna evolve. It's gonna come after my livelihood, it's gonna come after my job. And look at this, it's not going away anytime soon.

Now, I look at the introduction of AI very differently. It's a world of opportunity. And why is it a world of opportunity?

We have created a brand new landscape that is allows us to provide value to our customers to innovate, and most importantly, to answer and solve problems that we have never seen before. So I mentioned that there's a lot of work going on to try to figure out how we scale the compute power necessary for ai. That's true.

We're also doing a lot of work in how we get more performant models. That's also true, but right on the heels of that are new memory architectures to be able to handle bandwidth, new storage architectures to be able to, um, remove the burden of, uh, distributed data lakes and new communication architectures to not only connect NPU to N-P-U-G-P-U to GPU, but also to connect agents to agents, have agents be able to discover one another and work in tandem, and to be able to reuse agentry that's been developed. And they're no longer one-offs.

And of course, who can forget security? We are at RSA ai, uh, AI agents has, again, created an entire new landscape for security. Now, you've heard a lot of it today, but I mean, just off the top of my head, there is, uh, identity and access rights for non-humans, for agents that needs to be developed and, and codified.

We have new platform security models that need to be, uh, put in place. We have now distributed data models that can, that changes the entire data model security that we may have in place. Those are just a couple off the top of my head, of course, model adaptation and, and, uh, uh, model progression will continue.

But then the thing that is really gonna open this up and allow the adoption of agentic AI is standardization. The ability, as I said, for models to be able to communicate with one another, discover one another, and be able to enable to reuse of agents and standardization work has already started. So through MCPA two A, several others, we're really seeing some advancements in, in some, uh, progress.

Um, and I loved, and I love seeing the amount of companies who are engaging in the standardization work as well. So what does that mean for the fearful? Well, they should be able to see that there is a technology landscape that's ever, uh, ever evolving and ever growing.

We're gonna need technology professionals to drive the strategy, and most importantly, drive the implementation of this evolving vision that translates to more jobs. A lot may look at a lot, a lot of, uh, um, enterprises and a lot of the fearful may look at this and say, okay, Dave, you just described this world, this wonderful world of opportunity where we have a brand new landscape and we have, uh, a lot of new technologies coming on board. And Jesus, this whole AI agentic thing, I have no history in that.

I have no idea what I'm doing. This might just be a bridge too far for me. Well, that's where the large system integrators, little pitch like Dell Technologies, and many of you in this room can help.

And this is a great week. So if any of you have solutions, if any of you have trough problems, if any of you you have barriers, come talk to us. We can help.

Now, question is, all right, David. So we, we went from MLDL to Gen ai. Now AI agents, what happens when this startup phase is over?

You said that we're generating a lot of jobs, we have a lot of new technologies. We won't that, you know, kind of, you know, kind of enter the, uh, the maintenance mode. And the answer is absolutely not.

We have a long road to go. We are moving from a world of single AI agency into a world of complex reasoning models, where we need orchestrators and planners that are able to see, again, discover what AI agents are out there, what capabilities we have, and maybe even spawn new agents with new capabilities dynamically real time, and then put those agents together in a way to solve complex problems. We see AI agents being combined in fleets, communicating between agents to share context and sensor data so that they can navigate complex environments much more effectively.

Examples of that self-driving cars, you know, the promise of self-driving cars talking to each other has been on the table for about 10 years. We're finally getting to the point where that can be a reality. And of course, uh, delivery drones and, and many other applications like that will benefit from, you know, simplifying their complex environment.

Now, I have drawn here autonomy. You may say, Dave, why do you have autonomy just drawn on the right hand side of this slide? Don't we have autonomy across the entire thing?

And the absolutely the answer is yes. And don't look at this as a timeline. This is not a timeline.

This is a spectrum of solutions. Just because we're starting to enter the world of multi, multi-agent, uh, reasoning models doesn't mean that we're not gonna be continuing to produce, uh, ML based solutions, if that's the thing that solves, it solves the, uh, uh, job. I mean, the, the gentleman from Meta who was up here early, talked to exactly about that, that before you start to think about a small language model or a larger language model, see if you can do it a little bit simpler, right?

Don't overcomplicate your lives. But given that in order to get to autonomy, we have to increase trust. And when I say trust, that's not just trust in that the, the right, uh, inference is gonna come back from the language model that's balancing that with risk, um, and importance of, of the solution.

But as we, as, as, as trust goes up, we're gonna be able to get to autonomy. Now, how, how are we bridging that trust gap today? So we're saying that we are just entering, you know, POCs and, and experimentation for multi-agent, uh, solutions.

Um, what are, what are we doing for the trust gap? In the trust gap? We have humans in the loop clearly, right?

Today, especially, I mean, you look at an IT professional. If an IT professional is using, um, ag agentic technologies to be able to, uh, look at his fleet or her fleet of devices, and it comes back that, Hey, we see this problem and here's a recommended, um, answer. I can guarantee you that it professionals today are not going to let that agent just go ahead and update a hundred thousand endpoints without them knowing what's going on.

They're gonna be in the loop. They're gonna be the one that wants to push the button. Now, they may give that action base back to the, uh, agentic solution, um, but they may wanna do it a different way, but they're gonna be look to see, to make sure that that answer is reasonable and it's in line with what they expected.

Now, as a trust bar goes up, we're gonna go from humans in the loop to humans on the loop that is monitoring, watching, making sure that we're going the right direction. Are we, are we following the strategic plan? Then ultimately, we'll get to the point where we'll have full autonomy and we can have humans outta the loop.

And that's what a lot of the people in this, in this day have been talking about. Now, when you start to reach the point where you have humans on the loop, and when you have humans out of the loop, security becomes even more paramount. Security has to precede trust.

So a lot of, a lot of the, the fearful will look at the evolution of humans outta the loop for these complex reasoning models and think that just proves it. You know, I am, I am fearful for my wellbeing. And, and I say, I don't agree.

AI has been elevating the human experience for decades. From, from Siri to home automation, to collision avoidance systems in your cars. AI has been elevating the human experience.

Um, let's look at some examples. Now, I was, I had a couple of security examples, but I think after all of the talks today, that would be, uh, a little redundant. So, um, I'm gonna let those just sit.

But if we look at, um, if we look at healthcare, if we now have access to robotic surgeries, to accelerated vaccine development into genome sequencing, there are now tools available for the disabled to help them handle their challenges. In the world of education, teachers are now using AI to generate custom learning plans for better outcomes. But we ask about what about the mainstream?

Well, I'll tell you, AI is going to continue to automate the mundane and the repeatable, which allows humans to move to the more creative, the more strategic, and the more complex. And that gives job satisfaction and personal productivity dissatisfaction. AI is not a job destroyer.

It is a catalyst. And as AI continues to evolve, we have to evolve with it. Humans have to keep cognizant of the value chain and apply our uniquely human skills to play the human in the loop.

So now, after all that you may say, Dave, how do you answer those questions that you, that you put out there in the very, very beginning of this, uh, presentation? I would say that the current state of AI is a technological evolution, which has kicked off a societal and a business revolution. We have to embrace the most powerful tool that has been made available to us in the last several decades, and it's that way that we're gonna elevate your experience.

So thank you. Hello everyone. Thanks for joining us today on the inaugural Red Hat Cloud Fridays with AWS session where you get real talk about real solutions.

This is going to be an introductory session where myself and my colleague on video here from AWS Thanos es, did I nearly get it right? Thanos? You can introduce yourself.

Almost. You can just say Thanos, just say Amish. Okay.

So my, my colleague Thanos is here to join us to talk through what we are doing with the Cloud Fridays event and introduce you to why AWS and Red Hat value the partnership between the two organizations. So much the way this session's going to go, we're gonna talk about the event in entirety and the different sessions that you can enjoy today if you choose to. And then go on to the partnership itself.

Highlight some of Red Hat's software, which is available through AWS, and how that partnership and that software can really help you as customers, giving you some real examples of case studies to give you a guide to what you could expect from that benefit. So the itinerary for today is as follows. You'll have this introductory session for about 30 minutes.

Then there'll be a break where an interactive session with a coffee maker will go through some ideas for how you might be able to get your caffeine fixed, uh, for the next events that come along during this crowd Friday. Then afterwards, we're hoping you'll, you will gather with us for the first of our breakout sessions. The breakout sessions are designed to have two sessions running simultaneously.

The first breakout sessions will be in one stream, a session about Red Hat OpenShift service on AWS, where we'll talk about accelerating and innovating to deliver value at speed using Rosa. And then the second session that runs concurrently will be about Red Hat Enterprise Linux, where we will, um, talk about beyond the standard unlocking re's full value on AWS. You can choose to go to either session, and we will of course make the other session available if you are interested in both topics to catch up with later during the second breakout.

You can see there that, uh, we've got a session, um, that's called your Fast Track to it Automation Genius, where we focus on Ansible automation platform and how you can consume it and best use it in an AWS environment. And then at the same time, also we will have a session about maximizing your marketplace spend with AWS. As I said, you will have to choose one or the alert if you stay with the breakouts, but you will be able to catch up with the other one later as a recorded session.

Then we are going to, uh, wrap up the event with a real time, uh, question and answer, um, session where we talk about takeaways, answer any questions that come up, and, um, then move into a very informal possibility to have a networking lounge. The networking lounge, um, will be available for you voluntarily. And there if you want to come in and speak to us as presenters, um, in an informal way, talk about the way you are using software and how you might want to utilize it in different fashions, then we'll be available there to talk to you, um, face-to-face.

So that's what we're doing today, but let's talk about the really essential, uh, partnership that Red Hat and AWS has formed and why we have the day today to talk to you. Well, Thanos is the best person to talk to you about the power of AWS. So over to you Thanos.

Thank you, Simon. Thank you Aaron. Thank you for the introduction.

Um, I'm Tan es and I'm super excited to be joining, uh, this event, the Cloud Fridays from Red Hat. I'm representing AWS I'm the partner development specialist for, uh, red Hat at AWS, and I'm here to provide you with a brief overview of the world's most comprehensive and broadly adopted cloud, and also talk about our partnership with Red Hat as we are jointly addressing customers, including some of the most, uh, the largest enterprises, fast growing startups and leading government agencies. AWS almost 19 years old has experienced a remarkable growth over the past several years.

Yet we estimate that only 15% of IT workloads have moved to the cloud, indicating an enormous potential ahead. Our mission is to help customers like you lower costs become more agile and innovate faster. Lemme explain why AWS gives this transformation through five key differentiators.

First, it's all about functionality. AWS has significantly more services than any other cloud provider. Most, most importantly, we have the deepest functionality within those services.

Whether you are running basic, uh, web applications or complex AI workloads, we have the right tools optimized for both cost and uh, performance. This makes it faster, easier, and more cost effective to move your existing applications to the cloud and build nearly anything you can imagine. We have also built the world's largest, and I may add to the most dynamic cloud community with millions of active customers and over a hundred of thousand partners globally.

That gives us the right to claim experience with every type of customer and use case experience that is always augmented, uh, by a wide selection of system integrators. With deep cloud expertise and a much broader collection of third party software you can use on top of AWS, security remains our number one job, and this is a central pillar around which AWS infrastructure and services are designed and managed. AWS is architect to be the most flexible and secure cloud computing environment available built to satisfy the strictest security requirements for the military, global banks, and other high sensitivity organizations.

As we commonly say at AWS, we've built our services with secure by design principles from day one, and that means including features that are, that are certainly the bar high for our customers, default security posture. Of course, let's not forget our ecosystem of security partners and solutions through AWS marketplace, which enables customers to create a multi-layered defense with security technology and consulting TE services from familiar solution providers they already know and trust. Fourth, innovation, innovation is in our DNA.

We pioneered several as computing with Lambda, we democratized machine learning with Sage. Major AWS is innovating faster than anyone else, especially in the new areas such as artificial intelligence and machine learning, the internet of things and quantum computing. Our pace of innovation is continuously accelerating with our customer obsession to help customers solve problems faster, leverage the latest technologies to experiment and innovate more quickly, differentiate their experience and transform their business.

What is unique about our innovation approach approach is that 90% comes directly from customer feedback. We listen, we learn, and we build exactly what you need. Finally, we have experience with 18 years of operating at global scale, running a wide variety of use cases, allowing you the flexibility of choosing how and where you want us to run your workloads.

With bid infrastructures spanning 114 availability zones with 36. Within 36 regions, we process millions of requests per second, maintain consistent load latency, and have proven that customers can depend upon us for the most important applications now and to the future. And not to forget that we are still expanding.

That's one of the most proven operational expertise out there at greater scaling of any cloud provider. Our unmatched experience, maturity, liability, security, and performance is, uh, something that we can depend upon for most of our applications. These are not just claims, they are backed up by numbers.

And our customers, 90% of our fortune of Fortune 100 companies use AWS. Whether you are looking to reduce costs, accelerate innovation, or transform your business, AWS provides the most comprehensive, secure and proven platform to build your future on and a natural choice for many of our partners to deliver their offerings, solutions, services. Upon.

At AWS, we say there is no compression algorithm for experience. What does it mean? You can't shortcut the lessons learned from running infrastructure at a massive scale for every imaginable use case.

This experience from AWS translate translates directly into reliability performance for customer workloads. And with that word in mind experience, I'm coming into the partnership between Red Hat and a WSA strategic synergy that delivers a trusted enterprise grade platform for accelerating cloud adoption and modernization across hybrid environments. Leveraging Red Hat's exp expertise in open source solutions and AWS comprehensive cloud infrastructure.

Customers can confidently run their mission critical workloads on AWS while maintaining enterprise support and security with support for both traditional and containerized applications. Through Rail and Rosa plus integration with Red Hat products on AWS uh, marketplace, the partnership enables seamless workload mobility to AWS Red Hat and AWS can help your organization run smoothly on AWS cloud, migrate your VMs to a new platform, simplify hybrid cloud management, and adopt a comprehensive AI portfolio to support each, uh, each stage of the AI journey. With Red Hat, we share a common vision and have a longstanding relationship with a partnership going back to 2008, a partnership that has recently deepened and strengthened focusing on hybrid cloud innovation, virtualization and AI deployments.

We have signed, both parties have signed a strategic collaboration agreement to propel virtualization and AI innovation across the hybrid cloud supported by the new marketplace availability and jointly optimized cloud platforms and synergy between our companies that enables you and your organizations to navigate the complexities of digital transformation more efficiently, ensuring you remain agile and competitive in an increasingly technology driven landscape. And with that, I'll pass it back to Simon to explain more on how this, how Red Hat can help you more within this trip. Thank you.

Thank you, Thanos. That's a fantastic introduction to the power of AWS one party in this really powerful partnership. So give me a few moments to explain how powerful Red Hat is too and why a vendor like AWS would really want to partner with us.

Well, of course, red Hat's very proud to say we are the world's leading open source IT solution provider. Obviously a few years ago that was proven out by, uh, IBM's investment into us as an organization. And, um, at the point we were invested into, um, these metrics, um, really, uh, show how powerful Red Hat had become.

So at the time we were a 3 billion open source company from dollar point of view. And of course, over the years since that, um, purchase by IBM took place, we have only grown even greater, um, most organizations globally, um, particularly the large ones utilize our software in some description. Uh, we have nearly 20,000 employees now across most of the globe.

So as a vendor, we're very proud of our position and what we can bring to the AWS partnership. So Thanos has already told you how important AWS sees this partnership, but I always like to show this slide to, um, really call out, um, how powerful senior leaders in AWS understand this power, uh, this relationship to be. Um, the particularly strong part of this side is the quote down at the bottom from Andy Jassy, one of the leaders at um, AWS.

And he calls out the fact that because Red Hat has such a global reach and has revolutionized, um, Linux services within organizations and open source capability, we are obviously an important vendor to them. And you can see there with the metrics on the right hand side that over 60,000 organizations customers have read hat work on the A AWS platform with us in the partnership. And as Thanos did mention, we have been partners from very early on, A quick spot check might be to ask anyone if they know when AWS delivered its first services.

So that was only, I believe around 2006. If Thanos nods, then I think I'm about correct, correct. Almost 19, almost 19 years old now.

Yes. So within a couple of years, red Hat saw that their customers and the wider it market we're seeing great value in working with AWS on their global service delivery. And as you can see here, it's, we've got an example of how much our partnership has innovated since its inception.

So over time we've added more and more of our capability, obviously concentrating initially on our Red Hat at Linux Platform RL, but then bringing in other services as they became more applicable to our customers. In recent years, AWS has worked very closely with Red Hat to bring those services even closer together. So, um, in 2001, I actually, um, joined Red Hats shortly before the release of Red Hat OpenShift service on AWS, the Rosa service that Ana mentioned earlier.

And that's a very special moment in time from a Red Hat point of view, because it was the first service that Red Hat and AWS delivered jointly to our customers to give them the value of Red Hat software, but also supported directly by AWS and Red Hat site reliability Engineering underneath. Over time, we've enhanced that. Um, I've got a call out for the new, very new, in fact only just announced that reinvent in December, um, the new managed service of Ansible automation platform that's now available to customers where, um, using AWS's Cloud Red Hat's able to deliver, um, the managed control planes for customers Ansible, if the customers are finding it difficult to manage those environments themselves.

And recently we have even expanded this relationship to allow distribution partners to help partner organizations deliver value to end customers when they're looking at purchasing with a value add partner. Um, and those distributors are able to help us in that overall process of sale on AWS's marketplace. So that tells us why the partnership is important to the two organizations and who we are, but I'm sure you are asking yourself, what actual software products can Red Fat sell us via AWS?

And this Blue side is actually an AWS slide. I stole it from a deck that AWS presented, uh, last year at the Red Hat Summit Connect events where we go around important cities within our regions and, um, our important, um, partner vendors present their solutions to our customers. And this side points out that actually those two major forms of delivery of Red Hat software to customers via AWS.

There's, um, the original, as I like to think of it, capability where AWS from their console sell on behalf of Red Hat to customers, our software services baked into EC2 instances that they deliver on their cloud. Now you can see there that very heavily concentrates on Red Hat Enterprise Linux. And over time we've expanded the service to include different facets of Red Hat Enterprise Linux to give customers the most powerful capability possible to utilize Red Hat software through AWS console.

On the right hand side, we can also now sell Red Hat software through AWS marketplace. Thanos did call out the fact that AWS have innovated throughout that nearly 19 years of their existence. And one of the fantastic innovations they've brought to market over the last 12 years is their e-commerce marketplace solution.

This drives, um, customers towards the ability to buy independent software vendors such as Red Hat's software packages to package on top of AWS's, um, networked global services. And in this case, red Hat has now been able to, over the last couple of years, roll out more and more of our software to become available via AWS marketplace. It not only includes the, uh, red Hat solutions that are available on console, but but it also includes OpenShift services, Ansible based services, some of our middleware services such as JBoss and um, even extensions such as being able to take, um, a third party, uh, Linux may be based on CentOS, an open source project, which was based on Red Hat Enterprise Linux.

But now that those services no longer get a level of support, we even offer a listing on the marketplace that allows organizations to migrate, um, those CentOS workloads into rail on the AWS marketplace. I'll also call, call out a recent innovation where Red Hat has actually embedded some, um, predictive AI capability such as a large language model in the IBM Granite Model and, um, projects from the open source community such as Instruct Labs, which allow organizations to build prototype test and deploy their first predictive AI workloads to their customers. Um, and that comes in the form of Red Hat Enterprise in server ai and that is one of the listings that's available on AWS today.

You can even buy non-software products there as well, such as Red Hat Learning subscriptions and our support packages, sorry, consultancy packages very quickly. Um, it's very difficult to explain to organizations the difference between AWS console consumption of Red Hat software and AWS marketplace software. So I tried to build a couple of slides to give you an example of how the two things work differently because sometimes you may have a choice of buying the same solution such as Red Hat Enterprise xenex from either capability in the first case, the case number one, let's concentrate on the AWS console offerings.

That's actually Red Hat Software sold by A-W-S-A-W-S. Um, take that product against the defined price and deliver it to customers directly. They sell the product to the customers and they provide support for that software directly to their customers.

With Red Hat acting as a third line backup support agent on AWS's behalf. If you then move to concentrate on how the marketplace works, it is subtly different. The seller of record for a marketplace listing is actually Red Hat themselves in emea.

It would normally be listed as Red Hat Limited in this case because it's Red Hat that's selling you the solution. Even though we're using AWS's marketplace, red Hat has a responsibility of delivering all the support for that software solution to the customer. And importantly, to many organizations, red Hat is able to take into account different pricing models for a customer in the form of what's called a private offer, which will recognize customers commitments to overall usage of Red Hat's software over time, rather than being a standardized pricing through the initial console or PayGo offering, um, that we discussed earlier.

Importantly though, red Hat's trying to make sure that all our software's available to you. So here's a quick table which shows you that you might be able to buy some of these products from the first option or the second option, or actually you've got a choice between either. But importantly, all of them are available today to you via AWS.

I'd like to take a moment though to call out my fantastic new service that I'm very proud of. So I did touch on it when I was describing the large list of software that's available from Red Hat via the marketplace today, but I really want to give a moment to concentrate on that technology. It's a bit of a bit of a pet project for me.

So this solution was released, as I said, announced in December at Reinvent in the us. And what this solution is, is it's run by, uh, red Hat and we're integrating AWS AWS's, um, services to deliver the control plane of Ansible automation platform straight to our customers. It's billed via the AWS marketplace.

Private offers are available for organizations if they want to enter into commitment, um, contracts with us. And importantly, any commitment they make is recognized against, um, PPA private purchase agreements they may already have with AWS, as well as that agreement with Red. And that starts to dig into the customer value that AWS and Red Hat are bringing to you in our partnership.

Essentially through the partnership, we want to be able to deliver a reach to our customers. The benefits of AWS and their powerful, um, cloud Global services cannot be denied. And by being able to deliver Red Hat's technology combined with that capability further extends the ability for customers to enter into an open hybrid cloud approach.

Many customers today are moving to the cloud, but many of them feel they still need to deliver assets in more traditional models, maybe on premises. And when you combine the capability of being able to deliver Red Hat software in both those locations in the same way through the same procurement engines, we are providing reach for organizations to migrate and augment their capability as rapidly as possible. And that's the speed that we think we bring to organizations.

The managed services I've touched on, Rosa managed a p the capability to have a managed advanced cluster security platform, wrapping your Kubernetes resources. All these things are built with speed in mind and as speed to value for customers. What we are trying to do with AWS is squeeze the amount of time it takes for organizations to build their services and accelerate organizations into the situation where they're delivering powerful solutions on top of those services rather than concentrating on engineering the building of them.

But at the same time, we are delivering all the control, visibility, and risk management that Red Hat provides with AWS. We have great visibility tools that we will go into in more detail during the breakout sessions during the Cloud Friday, which help organizations make sure that even though they're traveling at speed, they're not incurring great risk. And finally, here's some of those case studies that I was talking about at the start.

Red Hat's business on AWS is now massive, and we have some of the biggest organizations in the globe working with us in that partnership in Europe today. Some great examples across all the different software products and services that we are delivering. And as you can see here from a Red Hat OpenShift service on AWS point of view, bp, the well-known energy company company, is delivering very powerful business transformation using our ROSA service in their organization.

And we're very proud to say has created a full case study and video to, uh, walk customers through that, um, journey that they took. And if you are interested in that story, the breakout session concentrating on SSO will give you much more details. The second example there, as I said to you earlier, I'm very proud of the managed Ansible Automation platform service, and it's just been released.

And part of why I'm so proud of it is I work to help the Department of Work and Pensions in the uk. As most of you probably know, the UK government is rather large, and the DWP is a very large part of that large government. And they have found delivering automation across their organization and all the, um, advances that brings to them as an IT operation, they have found it very hard to deliver across their whole infrastructure.

And the managed sensible automation service from AWS has given them the opportunity to centralize that delivery and rollout to their whole organization in a standardized fashion. Again, there is a session in the second breakout where myself and a great colleague Fle from Italy discuss the power of this service and Ansible overall. And we will dig into that case study in more detail there.

From a Red Hat Enterprise Linux point of view, we also, um, I'm very happy to say, have another very large UK government body who is the largest pego consumer or console rail consumer for Red Hat, um, on AWS globally. They use it extensively and they find the power of the dynamic capability to turn on rail services on EC2 dynamically whenever they need them, wherever they need them, um, a great value for their organization. And we will be, uh, driving a separate, um, session to talk about, um, the rail services as well during the breakouts.

So with that said, we're coming to wrap up this introductionary session. I'll quickly remind you about the itinerary today and also take opportunity to thank Thanos for joining us. It's very, um, exciting to have AWS to come and speak to our customers at any point in time.

And I think it really helped show customers the value of our partnership together. So what's gonna happen after this? It's, thank you.

So let's remind ourselves. It's gonna be a short, um, breakout kind of session where, um, a coffee expert will talk to you about your caffeine fix. You probably need it after listening to Thanos and I for this long in the introduction.

And then there's going to be the breakout sessions. Importantly, you can, um, during the session being delivered today in Cloud Fridays, you will have to pick one of the sessions in each of the breakouts because each pairing run concurrently. But we do encourage you to come back and watch the other breakout session as well as it will be made available as a recorded resource to you.

But yeah, I think it's those important. Simon. I think it's important for everybody to access all sessions, all sessions.

Thanks. Thank you. And importantly, though, just, um, to underline each one of those sessions covers the different pillars of our software, which is Red Hat's, OpenShift, red Hat's, enterprise, inex, red Hats, uh, automation platform Ansible, and our capability to deliver value through the AWS marketplace.

Then we will have a, um, a wrap up session where we will, um, give, uh, our feedback on the overall day, give opportunity to answer q and a if questions have been asked during the sessions, and allow you to ask questions directly of our expert panel. And then afterwards, we are going to provide what's called a networking lounge, where you can join those experts informally for a short while and talk to them about your, um, requirements, talk to them about your software usage or just come in to say hello and tell us who you are. It would be wonderful if you could join us for all these sessions.

And then, um, just want to leave you with some resources as well, if you follow these QR codes. There's a lot of information there about the ride wider, um, partnership that helps support this introduction session, um, separately from the breakouts, which will carry on for the rest of the event. And that just saves us with a formal thank you.

Thank you to everyone for joining us, and please stick with us for the great sessions. We have to come. Goodbye.

Enjoy, Enjoy. The sixth annual six five summit is on, and we have special guest Michael Dell to kick this off. Daniel, we made it six years here.

Yeah. You remember six five summit one? Yes.

And we were in full lockdown mode. Yes. And I remember you reached out to Michael asking for a little favor.

Can you help us kick off the event? If you remember at the time, I'm pretty sure Michael picked up his phone. I don't remember if it was an iPhone or an Android.

And he recorded us a 92nd message. Yes. Yes.

And you know, another genesis story, pat, for you and me in our relationship with Michael Dell kicking off, now we're back for six and I couldn't be more excited. Yeah. Michael, I just want to thank you again for doing this.

I mean, you kicked us off six years ago and you know, when you start a business, you just quite don't know where it's gonna go and this, that's the truth. No, it is. I mean, I mean, uh, your revenue size going back 40 years, I mean, who knows if that's where you would expect it to be today?

Uh, but you are, and you know, we operate at a slightly different scale, uh, than you do. But we're really, really glad to have you. Great to be with you guys.

Yeah. And congratulations on six years and all the progress. Thank You.

I kind of want to get Michael guys Do a great job covering the industry and, you know, thinking a little more deeply about things. Yeah. We try to do that.

We, we really do. You know, there were a lot of missing holes out there and what was going on with broadcast video, what was going on with, you know, influencers and I, I feel like we found a model. We kind of stumbled, stumbled across it, pandemic hit.

Dan and I were doing a podcast and we just decided, you know, you know, I, I think there's a need for this, but thanks again. Yeah. It's funny.

I kind of want to get Michael queued up to say you're killing it again like you did last year. That was one of my favorite highlights ever, Michael. I, I have to say, over the last year, it's been a lot of fun to sort of watch you become a bit more open with your journey.

Yes. On X you've started sharing some more stories. I still, every six months or so, you'll share that first income statement from when you started the company.

Love that. And you talk about, this is what I sent to my parents to basically get them to let me drop out of college so I could start this company. And, you know, it always, I think for us in entrepreneurial types, I mean, it, it it's heartwarming.

It's exciting. Um, you have a pretty exciting origin story though, and not everybody necessarily is on X all the time, despite what Pat and I think, um, I'd love for you to just share a little bit, open up a little bit about that because it, it's been fun to watch and I think people out there realize that while they're seeing the Dell of today, uh, humbler beginnings, um, exciting growth. Talk a little bit about that origin story for everyone.

Yeah. Well, you know, when I was about six or seven years old, I kind of liked math for some reason. And, uh, it was the beginning of electronic calculators, right.

And I got one of these and I'm like, this is incredible. I could do math with, with this thing. And, uh, then, you know, I'm 11 or 12 years old.

I'm in a advanced math class, you know, in junior high school. And it turns out they had a teletype terminal that they just dropped in the classroom, teacher had no idea what to do with it. And, you know, me and a bunch of friends are playing around with this thing, and like we are writing programs and sending 'em off to some main frame and it comes back.

I'm like, this is amazing. Right? So like, reading Byte Magazine, learning about the microprocessor and, you know, PCs and, you know, dish drives and, you know, super excited and like, it's pretty much all I did when, when, when, when, when I was in, in my teens.

And then, uh, yeah, I go off to, I go off to college and, you know, my parents aren't looking over my shoulder, so I'm like, I'm doing whatever I want to do, right? So, yeah. So I'm, I'm upgrading computers, I'm selling them.

I'm having a great time. Um, my parents, uh, you know, kind of caught wind of that and got very upset, said, you can't, can't do that. You gotta study, gotta get your priorities straight, Michael, you, you're gonna be a doctor and you gotta stop messing around with these computers, and there's no future in that.

Yeah. It's like, you know, you gotta go to college, you gotta go to medical school. I mean, be be a doctor, like your brother, like your cousins.

Yeah. Like your dad, you know? And, uh, and so I, I, you know, a lot of guilt was laid on me, and, and I, I, uh, I said, okay, I'll stop.

You know, and so I I, I could last 10 days, that was the most I could last. And during those 10 days, I really, uh, you know, thought about it and, and I, I kind of concluded that this wasn't like a hobby thing, right. This was like really what I wanted to do.

Yes. And so maybe I would've never started this company had my parents told me I had to stop. So, so I said I sort of set in motion, you know, what became, uh, Dell Computer Corporation.

Right. Which I incorporated one week before my final exams of my freshman year. Um, probably not the best timing, you know, but here we are.

Right. No, that's amazing. And by the way, I loved your book.

Uh, I recommended it to everybody. Thank you. And it just gives insights and also some of the people that, that made an impact on you, your family, and different family members, uh, but also people like Steve Jobs and Larry Ellison and, and folks like that.

I'm really glad you you pulled that in. You made it personal. Yep.

But you also made it, uh, professional too. And a lot of people don't do that. I I really like that.

Thank you. Um, So you've seen a lot of twists and turns in the economies. I mean, probably when you started the economy, the economy wasn't great.

Uh, and there was even a question on what the new growth was gonna be. Right. You had Japan taking over.

Exactly. Yeah. You, you weren't, weren't even supposed to be American computer companies.

Exactly. It Was all gonna be Japan and was Tron, you know, they were buying up everything. And we sort of had this malaise that we had inferior technology, and the Japanese were far more advanced.

That's right. And there's no way we'd ever beat them. Yes.

So, so, you know, like starting a computer company was like, uh, what are you crazy? You know, how are you gonna beat the Japanese? So we're here now, right?

The economy, um, a lot of ways to measure the economy. Uh, we've got a debt ceiling that, uh, is evaporating a lot of debt. So, but a lot of opportunity, you know, we talked about this, uh, during our de tech world interview.

Um, where do you see this disruption going? Where, what's the end state? And I don't, I don't, I don't know if it's five years or 10 years, but where is this going?

You know, I think, uh, I always think of it as a infinite game. It's a race with no finish line. So there is no right time when you're like, you're done.

I'm here, let's pack it up. Well, There is a time, but not, not that kind of time. Yeah.

So, so we just, we just keep going. I think, I think when we think about, uh, the role technology plays in the world, it is the fulcrum that's driving progress in every field. So whether it is medicine or education or manufacturing or finance or retail, and now we see, you know, there was, there was a lot of data that was created over time.

Right? Right. And the timeframe in which the amount of data in the world doubles is like shrinking all the time.

'cause more and more data is being created. But we all remember a time not that long ago where dirty little secret is that nobody really did much with that data. Right?

Right. But that time has changed now, right now we have these incredible models that are bringing that data to life and allowing companies to express their competitive advantage. And it's not like it changes in a light switch.

There's a lot of work that goes into that, but that is just a massive accelerator of progress. I think we will look back on this time as a point where there was just a big speed up in the whole infinite game, right? Sure.

And, um, yeah, that's, that's super exciting. Lots of work involved in, in making that happen. But, uh, you know, the, the cycles are coming faster and faster.

We had, you know, the internet and, you know, the pc you know, before that, uh, now, you know, this is, this is just a huge speed up. And look, maybe it's as big as those, maybe it's bigger, right? You could, it's pretty easy, at least for me to argue that the next multiple decades of global economic leadership sit on sort of being the leaders in ai.

We can debate the politics and how things are being done and how trade is being addressed, and how export controls are being, you know, put into place. But Michael, I mean, and, and I'm sure over the years you've had some conversations with people in, in, in, in DC and, and in policy. But there is no longer like a separation between the economy, between geopolitics, uh, between defense.

Uh, AI seems to be the frontier in which we really are trying to establish the multi-decade global leadership positioning. Um, so I, I think overall there's, I mean, you have to, I, I'd think that you would believe there's quite a bit at stake right now. And then of course, figuring out how this works from a businesses and enterprise, which we like to talk a lot about.

But there's also, you know, like I said, who has access to the keys? It's, it is almost like the debate we had about nuclear at one time. But like, AI is really going to change the sort of entire dynamics of the world.

No, no question. Uh, national security prosperity of nations will hang in the balance with their ability to have access to, and their ability to utilize, you know, technology to propel their economies forward. Us is in a pretty good position there.

But, you know, it's a, it's a fast moving competitive dynamic. And, you know, we, we certainly spend time with the policy makers doing our best to explain, you know, our point of view and what we're, what we're seeing. Um, but we don't control that, you know, we, we, we tend to focus more on the things we, we can control, right?

How do we help our customers utilize these technologies and how do we build a resilient, uh, you know, supply chain and infrastructure that, uh, works in spite of whatever goes on, you know, in the policy world. Right. Yeah.

I know that was a big question. Let's get back to Dell a little bit though. On, on culture.

You know, you've been a driver of a certain culture archetype that's been very successful over the years. We talked over even the last five minutes about how fast that is. You know, how fast that is changing.

Is it changing your approach? Because like I said, the time you have to make decisions, the time you have to take, uh, to decide about making an acquisition, the time, how you handle integration, how you decide on employee scale versus implementing your, a lot of customer zero talk, uh, at Dell, which AI is an efficiency and a productivity driver. Kind of how are you thinking about that, keeping your ethos as it pertains to culture, but really readying yourself for what you're seeing ahead?

Yeah. You know, depending on the nature of your business, there's a degree to which your job essentially is to make decisions with imperfect information. And when you're in a business Yeah.

That is changing rapidly, that has all these variables, that's, you're dealing with a fair bit of imperfect information, that's our job. Right. So, um, that's fine.

Uh, it's kind of what we've been doing for a long time. It is speeding up a bit. And the good news is that we've had this culture of embracing change and most of the time the changes have worked out pretty positively Yeah.

For the company and for our customers. And so our people tend to trust us and follow us and think that, you know, most of the time we're gonna get it right. But we make mistakes and, and you know, the good news is you make a mistake, like, okay, we'll just change that and we'll, we'll go, we'll go, we'll go fix it.

Yeah. Michael, I want to thank you for, uh, having this chat with us, uh, in prior the six five summit in its sixth year. I want to talk for another hour, but I think you have other things that, uh, you need to get to here.

So I really, uh, want to thank you so much for That. You guys are killing it. Thank you.

Thank you. Thank you. Appreciate, appreciate it.

Appreciate thanks a lot. Alright. Thank you so much.

The six five summit is back for its sixth year, and we are talking AI nonstop. Uh, we're building out those hyperscaler data centers all the way to the endpoint and pretty much everything in between. Um, we have had, uh, Jim Anderson on the six, five, multiple times, multiple companies.

This is, uh, this is wonderful, and I'd like to bring him in. He's now CEO of coherent, and we're gonna talk about cloud infrastructure and talk about photons and how they're enabling all of this ai. Goodness.

Jim, great to see you. Great to see you too, pat. We love photons and happy to talk about photons all day long, so thanks for having me.

I appreciate It. Yeah, absolutely. You know, um, I I like to call it, you know, Jim's playbook, right?

You, you come into a company, you run the play, uh, modify, make tweaks, uh, and I've, I've observed it, um, and really enjoyed watching it at three companies. And, uh, you did the same thing at at, at coherent, and at least from an investor, uh, point of view, it's all about talking to that audience. But you also have to bring in things like core value proposition with your customers, where the market is, is, is headed there.

But I think the, the first thing probably in people's minds is, first of all, who is coherent? Why do photons matter to ai? Yeah, absolutely.

Um, so first of all, coherent, the simple way you can think about the company is it's really the global leader in photonics. And what is photonics? Just to make sure everybody knows what photonics is.

So photonics is really about harnessing the power of a photon. And the photon is the basic particle of light, harnessing that power, that photon to do all sorts of amazing things for our customers. But the three basic things that we can do with a photon is we can transmit data with it at very high speed, at high bandwidth.

We can use photons in, for instance, lasers to change the nature of materials. And then the other thing we can do with a photon is we can bounce it off a surface and we can measure or sense things about the nature of the surface. So that's the basic, basic things that we can do with, uh, photons.

But all of those basic things drive an amazing amount of innovation. And, um, in data centers, ai, data centers, photonics, and the photon is becoming an increasingly important part of data center architecture and how data centers are getting designed and built and, uh, coherence really at the center of that. Yeah, it is amazing.

Uh, even though it took a while for photonics to become a reality, the growth curve is, is astonishing. And it's directly related to, uh, the need to move a lot more data quickly and also do it at lower, um, at lower cost, uh, due to power and, and maybe a, you know, simplified simple infrastructure. It's been pretty astonishing.

And I'm not in that copper will die, um, you know, camp, you know, we keep giving life to copper, uh, here, um, even creating new protocols, uh, for it to talk over. But it's great to see, it's great to see how this industry has, has migrated. You know, there, go ahead, Jim.

Yeah, I was just gonna say, um, you know, one of the, one of the things about photonics is, you know, I started my, I started my career in the world of electrons, right? I, I've been in the semiconductor industry for almost my entire career, actually. I started as a microprocessor architect on data center servers, right?

Uh, way back when. So, um, so I've been around data centers my entire career, mostly in the world of electrons. But photons have some pretty amazing properties relative to an electron, relative to the power efficiency and the bandwidth of data that they can, uh, transmit.

And so what we've seen over the course of, um, data center evolution is pat, uh, you'll remember when we first started building data centers, all of the networking, uh, connections between the computing nodes were all electrical when we first built data centers, right? Um, and now if you fast forward to today, the entire scale out part of the AI data center, which is all the connections between the racks, that's all optical today. Because as we increase, as we increase data rate or we increase distance, you're kind of forced to move out of the electrical domain into the photonic domain.

It's really the only way to achieve the bandwidth that you need. And so now today, AI data centers, entire scale out portion of the network is optical. And what we're gonna see over the rest of this decade is the beginning of the last remaining part of the network within the data center that's electrical, which is the scale up.

These are the connections within the rack, those will start to migrate to the more use of optical in the scale up portion of the network as well. So again, photonics and that technology just continues to become more and more important to how data centers are architected and designed. Yeah.

Optical went from, I can't afford it to You can't afford not to do it. That's exactly right. That's well said, right?

Yeah. Yeah. So, hey, let's talk about differentiation.

You know, every company, uh, I talk to says they're the leader in, in AI and, and they're driving, uh, this wave. And, you know, some, some pundits in your space just say, listen, this is a, this is a commodity, Jim, right? And, you know, Asia is just gonna take over, uh, this, this entire market, that's your investor day, right?

You talked about deep customer relationships, right? Yeah. Uh, why do customers keep coming back and buying more?

Yeah. There's really, it's a great question, and there's really, it comes down to two things, right? Number one, it's our technology.

So the breadth and depth of our photonics technology. And then number two, it's about being able to deliver that, right? It's one thing to innovate on technology.

It's, uh, it's an entirely separate thing to be able to scale, to deliver at scale, high volume, high levels of quality. And when you look at coherent within the photonics world, coherent is really the only company that's got the breadth and depth of technology combined with the manufacturing scale. And you really have to have both of those with the customers.

And so on, the first one, on the breadth and depth of technology, if you look at, for instance, we build the transceivers that go into data center, optical networking, just one example, the transceiver is what converts the electrical signal to optical and, and vice versa. And, uh, in that transceiver, we don't just assemble and test the final product. We build all the key photonic ingredients that go into that, right?

Um, we design all of those photonic ingredients. That means all the different types of lasers, whether it's Indian phosphide or galley maride, right? The, the photo detectors that are used at the other end of the wire, um, from the laser, all of the key materials, isolators optics that go into that transceiver.

So we've got the broadest and deepest portfolio of technology. And as you know, uh, pat, that's probably number one most important thing for customers as a partner of a multi-generational basis, is what is the, what is the portfolio and innovation around technology that you can bring to us? But number two, the second part of the discussion is, okay, so you have the right technology solution.

Can you deliver that? Yeah. And that's where, that's where we've got the other part of, uh, the key customer care about, which is, you know, we're able to deliver, um, you know, complex photonic technologies at scale.

So materials, devices, subsystems, even full systems and software, we're able to deliver high volume at scale. So that combination is critically important to our customers. Yeah, I'm always fascinated, uh, when I look at the evolution of industries, they go through different cycles of aggregation and disaggregation, and in your space, aggregation is the name of the game.

Uh, one throat to choke, uh, and also time to market. You know, people could buy this thing from this place, this thing, and then find somebody to, to, to put it together. Uh, maybe they can do it for one cycle, right?

But try to do that for two, try to do that for three, oh, and by the way, uh, try to have enough manufacturing capacity online to, to make that happen in, in various places, uh, uh, around the world. So, you know, it's interesting as a recovering product guy, Jim, uh, you know, I, I, I look very closely at core value propositions, and it, it's, it, it's working. And, you know, probably the, the tip of the spear, at least on the hyperscaler, uh, side with the is, is you actually had an optical switch that, that I believe you started shipping, um, as of, as of late.

I think that's what you announced. And, you know, you're going all the way from, you know, making the tiniest components to actually shipping the optical switch and, and pretty much everything in between. Yeah.

I actually, I'm glad you asked about the optical switch, because that was one of the, i, the products when I first joined Coherent, that I was like, wow, I, I, I can't believe this, um, because I didn't know about this product before I joined. And by the way, I just passed my one year anniversary, just I think, wow, a few days ago. But when I first, within the first week or two, I joined the company, somebody showed me this kind of prototype around the optical switch, and I said, oh my gosh, guys, we gotta get this to, we gotta get this to market as fast as possible.

Because what an optical switch does is, if you think about the data transmission from one computing node to another computing node, um, you know, the data transmission is done optically in the scale out domain. Um, but if you wanna switch from one place to the next, today it's done mostly with electrical switches, right? Well, um, if you replace that electrical switch with an optical switch, you can keep that data signal in the optical domain.

You don't have to convert it to the electrical domain. And that has a tremendous performance and power efficiency benefit. And so, yeah, we, so we've, uh, um, shortly after I started, we really ramped up investment in this product line and accelerated our time to market.

And the team, I'm really proud of the team, just within the last couple weeks, they achieved first revenue on this product line. And we have a very, very differentiated solution. We, uh, inside our optical switch.

So we're providing the whole switch and the software on top, but inside the key technology that allows the, the signal to switch is based on what's called digital liquid crystal technology. It's a non mechanical technology. And the other solutions that are out there are based on mims, which is fundamentally a mechanical technology, and that has reliability issues.

Our, our solution is non mechanical digital liquid crystal, which is borrowed from our telecom business. It's actually been used in undersea applications in our telecom business for years. So it's field proven, uh, technology.

So we brought this now into the data center, tremendous advantage in reliability and a bunch of other characteristics. So yeah, customers are really excited about it. I'm excited about it.

And now we're, now we're shipping revenue, which, uh, always makes me happy. Jim, I learned something new every day about the company I did not know, uh, under C cables. Yeah, you, you, you, you just don't wanna send a sub down there to fix a, a MEMS based, uh, device, do you?

No. I mean, obviously that, you know, we use undersea cable example because that drive, that is the highest level of reliability that you have to achieve, right? Is 'cause nobody wants to take a submarine down to No, exactly.

This is great. Hey, we're talking submarines here, folks. This is great.

Uh, yet another innovation for, uh, coherent. Now, hey, let's dive in. So we talked a little bit around the data center transceiver, essentially everybody out there.

It's the device that you plug into the switch that, uh, makes that optical, uh, uh, connection. And we talked about what are other parts, uh, of the AI value chain, other, other end markets that, that, that you participate in? Yeah, good.

Okay. Good question. I, I would say a couple, right?

So if we move beyond the data center, first example I can give you is, as you know, these, these, a lot of these workloads are so huge that they're starting to span multiple data centers, right? Or, you know, the data centers are constrained by the amount of just physical space or power that they can get into the data center. And so we're seeing these workloads, you know, span multiple data centers where they're running across, um, these, you know, multiple data centers, and what, what, what if you're gonna run it across multiple data centers?

What's key is that you have to have very high speed connections between those data centers and very, very wide high bandwidth pipes. Um, so what that means is you need optical networking. You need very, you know, high bandwidth, um, high speed optical networking links.

So what we're also seeing is a big increase in demand of these data center interconnect, DCI, uh, transceivers, for instance, that we build that enable that high speed networking between, uh, data centers. And so we're seeing tremendous growth there. And here again, we have great technology here because for years we've been providing key optical technology to telecom applications.

And this is kind of like a telecom application. Yeah. This is, this is a telecom, telecom grade technology using used for the connections between data centers.

So that's one that we're really excited about. You know, the other one that you might not think about that's maybe not as obvious is, you know, AI data centers, the demand for AI that's really driving the entire semiconductor, uh, semiconductor technology transitions, right? Yeah.

So it's driving the semiconductor nodes from, you know, a three nanometer to two nanometer and beyond. So it's driving the most advanced semiconductor technology. And you know, what enables that is the wafer fabrication equipment.

The semi cap equipment. And if you look inside that equipment, a lot of that equipment relies on photonics, and that's where coherent comes in. So another big area that we support AI data centers is in the photonics lasers optics that go into semi cap equipment.

When I, you know, when I started my career, we were on the, I think it was the quarter micron technology node Pat, when you started, what were, were you guys like a hundred micron, or I think it was two 50. I was kidding. That was No, no, No, no.

It was, it was 2, 2 50. Yeah. It was, uh, I was on quarter micron when I started, uh, my, um, my career.

And on the quarter micron node, we maybe had two or three optical inspection steps that's now grown to, when you look at three nanometer going to two nanometer, that's grown to almost a hundred optical inspection steps. And that means photonics. So the amount of photonics, lasers, optics has grown over the years, and the complexity of those photonics has grown, which, which drives the amount of content.

So that's another key area where coherence supports the ai, um, especially AI data centers, is in the complex semi cap equipment that drives the advanced technology notes. That is so cool, Jim. It really is bricking laser beams.

Yeah. Um, by the way, I, I, the hybrid multi-cloud is, is just starting and connecting data centers. And people laughed at me 10 years ago when I talked about having an application where the compute is in one data center and the, and the data is in another data center.

Yeah. And what this does, it makes it, it makes it a, a reality. And it's like, well, why would I wanna do this?

Well, let's say I wanted to have an application in the, in the public cloud and then the private cloud or, or on premises and be able to span this. So that's even regardless of ai. And now, uh, even though, uh, the Chinese did this first, uh, we have US companies even like meta, who are spanning, uh, models across data centers.

Okay? And, and a high speed interconnect is the only way you can do that and, and make that that happen. And then when you get into these reasoning models, when it comes to inference, right?

It's not just about training. Uh, and you need a, a planer, a very large, uh, set of planer memory to go across, um, you know, for reasoning models. Uh, I believe that, that we will see those spanning, uh, multiple data centers, uh, as well.

Yeah. And just as you said, look, that, that, that can only happen if we have super high bandwidth, super fast connections between those data centers, and that means optical networking, and that means driving the most advanced speed and the widest bandwidth, uh, optical connections. I mean, You can't do this with copper.

Okay? Sure. I mean, that's just, and they, I don't even think anybody's connecting data centers with copper anymore.

Um, No, this is all optical, right? Yeah. Yeah.

It's just Telecom grade, super high, high, high speed, high bandwidth connection. Yeah. You know, we need a new, uh, we need a new industry terminology, right?

We have scale up networking, scale out networking, and, I don't know, scale out, out networking Scale plus. Um, Is that what it is? Okay.

No, I just made that up. We, we should come up with It. No, we coined it here.

Let's, uh, let's plant that flag, Jim, and, uh, and move that forward as the standard. So, so Jim, um, the innovation engine, I remember talking to you the first time, uh, you came in, I was like, Jim, first of all, embarrassed, I don't know who coherent is. Why would you join them?

Uh, what are they doing? And I think it was a, I don't know, a month or so in it's a pat, like I, I look under the hood at, at all of the innovations, all of the technology and all of the possibility, uh, here. Um, I I also recognize that, you know, when I look at, you know, what your EPS goals were, I map that against your financial goals, um, it's like, okay, Jim, how are you gonna keep the innovation engine chugging along?

Yeah, that's a great, that's a great question. So, you know, I look, I, I'm an engineer, so I love innovation, right? So, um, certainly comes from a place of love, right?

Uh, on, on innovation. I think in my experience, there's really two things that you need to drive an effective innovation engine, right? Number one is there is a culture around innovation.

You have to have a culture, you know, it's interesting, and I think companies that don't have a strong innovation culture, it's really hard to build an innovation culture. The great thing about Coherent is when I came in is that's probably our strongest cultural attribute as a company, is this is an amazingly innovative company. I think it's the most innovative company I've ever worked for, and I've worked for a number of Wow, extremely.

So that's, that, that's big. I mean, you were Broadcom a MD, right? Lattice.

Yeah. And we innovate and we innovate at, we were talking about optical switches, we innovate at the system and software level, but what's really cool about Coherent is we innovate all the way down through the technology stack. We stack, we innovate, we integrate, innovate at the device level all the way down to the material science, the fundamental physical level for photonics, right?

So there's this really strong culture of innovation, and I certainly wanna continue to nurture that. And there's a number of things that we're doing internally to make sure that we continue to nurture that culture. Actually next week we're gonna have an innovation summit within the company where we bring together all of the key technologists across the company, um, here to, um, to the Bay Area, and have a summit and talk about how do we drive better, faster innovation for the company.

So we're really excited about that. That's one example. So that's one thing.

I think it's about building a culture of innovation. But then I think the second piece is, and this is kinda where what you mentioned around the EPS and profitability is, look, it's about investment too. We've gotta invest for innovation.

You've gotta place the right investment. But I also think you gotta be careful, right? This is where I'm a big believer in customer driven, market focused innovation, where let's innovate, but let's innovate for the benefit of our customers, for the benefit of the market, right?

Uh, for the benefit of the industry. Let's know that as we innovate, that customers are gonna see real genuine benefit from that, and that the company will see a return, uh, a good return over time on that, right? Or good return on that investment.

And so I think the second piece about it is making sure that those innovation dollars, those investment dollars, are directed at the right big areas of innovation that we know are gonna drive a great return for the company over the long term. And so that's certainly something I've been focused on over the past year, is making sure that we've got those dollars invested Yeah. The right long term innovation engines.

Yeah. So it's not about technology for technology's sake, it's really innovation for, uh, customer's sake. You got it.

Yep. Hard to do, particularly, you have to make bets and, and guess on where the industry's going three to five years. I mean, sometimes it's easy, it's like, you know, hey, uh, uh, faster, uh, cheaper and more reliable, okay?

That's where we're going. But to be able to intersect that with industry standards and new technology, and what I always like to, uh, I always like to talk about is the art of the possible, right? Uh, your customers will never tell you, uh, what they need, uh, and certainly if, if, if, if, certainly if they do, it's gonna be too late.

If you don't already have, if you don't already have that. And you have to kind of transpose yourself three to five years from now to see to, to see where, uh, this goes. Um, and I, by the way, I did a few projects like that before.

I bet. And, and I really like it. And you get, like, you get the brainiacs in the room technological standpoint, and then the people who really, you know, have a decent idea of even, you know, um, future casting, uh, out there.

It's a lot of fun. Absolutely. And this is exactly one of the themes of I mentioned.

We're gonna do an innovation summit internally next week. Okay? This is exactly one of the themes is, Hey, let's look beyond the horizon, right?

Let's make sure that we're thinking about what's beyond the horizon and what we can, um, what innovation we can, we can drive way ahead of what our customers have even thought about, right? This is one of the key themes that we'll talk about, uh, internally next week. And then let's make sure we invest for that as well.

And then, you know, what I love about Coherent is the, the deep technology innovation that we do at, at such a fundamental physical layer. I think that that really helps us to be able to understand that our possible, when you understand the fundamental device physics behind, uh, photonics, right, the material science, then you're able to understand what truly is the art of the possible, and then drive to that. Hey, Jim, uh, I really appreciate the time, um, and our audience loves hearing what is Jim Anderson up to, uh, what is the big deal, uh, with photons, and hopefully everybody understands, uh, why those are so important to, uh, AI today and in the future.

So I appreciate that. Yeah, thanks Pat. Thanks for giving me a chance to tell you about, uh, why we love photons so much.

So that's great. Thanks for the time. So everybody out there, thanks for joining this cloud infrastructure spotlight at the six five Summit.

We're in our sixth year, and we, we just keep going here and it keeps getting better. Hopefully. Yes, that's what you told us.

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